From f12330810db1dbf7336dda2b6ffc1bc1407371c3 Mon Sep 17 00:00:00 2001 From: Jonathan Taylor Date: Thu, 3 Apr 2025 13:07:36 -0700 Subject: [PATCH] Jupytext sync (#49) * fixes to Rmd from Daniela's errata * synced the notebooks * syncing notebooks * all but Chapter10 * chapter 10 --- Ch02-statlearn-lab.Rmd | 17 +- Ch03-linreg-lab.Rmd | 13 + Ch03-linreg-lab.ipynb | 335 +- Ch04-classification-lab.Rmd | 13 + Ch04-classification-lab.ipynb | 664 +-- Ch05-resample-lab.Rmd | 13 + Ch05-resample-lab.ipynb | 242 +- Ch06-varselect-lab.Rmd | 13 + Ch06-varselect-lab.ipynb | 7873 +++++++++++++++++++++++++++++++-- Ch07-nonlin-lab.Rmd | 13 + Ch07-nonlin-lab.ipynb | 484 +- Ch08-baggboost-lab.Rmd | 13 + Ch08-baggboost-lab.ipynb | 310 +- Ch09-svm-lab.Rmd | 13 + Ch09-svm-lab.ipynb | 316 +- Ch10-deeplearning-lab.Rmd | 13 + Ch10-deeplearning-lab.ipynb | 993 ++--- Ch11-surv-lab.Rmd | 13 + Ch11-surv-lab.ipynb | 328 +- Ch12-unsup-lab.Rmd | 13 + Ch12-unsup-lab.ipynb | 502 +-- Ch13-multiple-lab.Rmd | 13 + Ch13-multiple-lab.ipynb | 264 +- 23 files changed, 9990 insertions(+), 2481 deletions(-) diff --git a/Ch02-statlearn-lab.Rmd b/Ch02-statlearn-lab.Rmd index 3f7804b..5a54c47 100644 --- a/Ch02-statlearn-lab.Rmd +++ b/Ch02-statlearn-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Introduction to Python @@ -70,7 +83,7 @@ print('fit a model with', 11, 'variables') The following command will provide information about the `print()` function. ```{python} -print? +# print? ``` @@ -220,7 +233,7 @@ documentation associated with the function `fun`, if it exists. We can try this for `np.array()`. ```{python} -np.array? +# np.array? ``` This documentation indicates that we could create a floating point array by passing a `dtype` argument into `np.array()`. diff --git a/Ch03-linreg-lab.Rmd b/Ch03-linreg-lab.Rmd index ec0bf89..36b3491 100644 --- a/Ch03-linreg-lab.Rmd +++ b/Ch03-linreg-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Linear Regression diff --git a/Ch03-linreg-lab.ipynb b/Ch03-linreg-lab.ipynb index ce59674..274f7ab 100644 --- a/Ch03-linreg-lab.ipynb +++ b/Ch03-linreg-lab.ipynb @@ -5,7 +5,6 @@ "id": "18156290", "metadata": {}, "source": [ - "\n", "# Linear Regression\n", "\n", "\n", @@ -31,10 +30,10 @@ "id": "c5fcfd8f", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:52.664224Z", - "iopub.status.busy": "2025-04-03T04:43:52.663745Z", - "iopub.status.idle": "2025-04-03T04:43:55.080525Z", - "shell.execute_reply": "2025-04-03T04:43:55.080187Z" + "iopub.execute_input": "2025-04-03T19:32:49.534485Z", + "iopub.status.busy": "2025-04-03T19:32:49.534275Z", + "iopub.status.idle": "2025-04-03T19:32:50.010417Z", + "shell.execute_reply": "2025-04-03T19:32:50.010150Z" }, "lines_to_next_cell": 2 }, @@ -64,10 +63,10 @@ "id": "42cfb62e", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:55.082578Z", - "iopub.status.busy": "2025-04-03T04:43:55.082413Z", - "iopub.status.idle": "2025-04-03T04:43:58.413315Z", - "shell.execute_reply": "2025-04-03T04:43:58.412991Z" + "iopub.execute_input": "2025-04-03T19:32:50.011974Z", + "iopub.status.busy": "2025-04-03T19:32:50.011862Z", + "iopub.status.idle": "2025-04-03T19:32:50.564863Z", + "shell.execute_reply": "2025-04-03T19:32:50.564550Z" }, "lines_to_next_cell": 0 }, @@ -97,10 +96,10 @@ "id": "b387d3ac", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:58.415310Z", - "iopub.status.busy": "2025-04-03T04:43:58.415163Z", - "iopub.status.idle": "2025-04-03T04:43:58.417748Z", - "shell.execute_reply": "2025-04-03T04:43:58.417483Z" + "iopub.execute_input": "2025-04-03T19:32:50.566473Z", + "iopub.status.busy": "2025-04-03T19:32:50.566349Z", + "iopub.status.idle": "2025-04-03T19:32:50.568873Z", + "shell.execute_reply": "2025-04-03T19:32:50.568660Z" } }, "outputs": [], @@ -128,10 +127,10 @@ "id": "307b7e22", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:58.419369Z", - "iopub.status.busy": "2025-04-03T04:43:58.419266Z", - "iopub.status.idle": "2025-04-03T04:43:59.495961Z", - "shell.execute_reply": "2025-04-03T04:43:59.495556Z" + "iopub.execute_input": "2025-04-03T19:32:50.570051Z", + "iopub.status.busy": "2025-04-03T19:32:50.569963Z", + "iopub.status.idle": "2025-04-03T19:32:50.713742Z", + "shell.execute_reply": "2025-04-03T19:32:50.713496Z" } }, "outputs": [], @@ -160,10 +159,10 @@ "id": "79099069", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.498234Z", - "iopub.status.busy": "2025-04-03T04:43:59.497985Z", - "iopub.status.idle": "2025-04-03T04:43:59.501527Z", - "shell.execute_reply": "2025-04-03T04:43:59.501281Z" + "iopub.execute_input": "2025-04-03T19:32:50.715171Z", + "iopub.status.busy": "2025-04-03T19:32:50.715043Z", + "iopub.status.idle": "2025-04-03T19:32:50.717952Z", + "shell.execute_reply": "2025-04-03T19:32:50.717720Z" }, "lines_to_next_cell": 0 }, @@ -241,10 +240,10 @@ "id": "3f99195f", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.503342Z", - "iopub.status.busy": "2025-04-03T04:43:59.503233Z", - "iopub.status.idle": "2025-04-03T04:43:59.507028Z", - "shell.execute_reply": "2025-04-03T04:43:59.506764Z" + "iopub.execute_input": "2025-04-03T19:32:50.719063Z", + "iopub.status.busy": "2025-04-03T19:32:50.718985Z", + "iopub.status.idle": "2025-04-03T19:32:50.721371Z", + "shell.execute_reply": "2025-04-03T19:32:50.721167Z" }, "lines_to_next_cell": 0 }, @@ -445,10 +444,10 @@ "id": "f3f913ca", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.508536Z", - "iopub.status.busy": "2025-04-03T04:43:59.508431Z", - "iopub.status.idle": "2025-04-03T04:43:59.510630Z", - "shell.execute_reply": "2025-04-03T04:43:59.510369Z" + "iopub.execute_input": "2025-04-03T19:32:50.722431Z", + "iopub.status.busy": "2025-04-03T19:32:50.722351Z", + "iopub.status.idle": "2025-04-03T19:32:50.724233Z", + "shell.execute_reply": "2025-04-03T19:32:50.724037Z" }, "lines_to_next_cell": 0 }, @@ -487,7 +486,7 @@ "\n", "We will use the `Boston` housing data set, which is contained in the `ISLP` package. The `Boston` dataset records `medv` (median house value) for $506$ neighborhoods\n", "around Boston. We will build a regression model to predict `medv` using $13$\n", - "predictors such as `rmvar` (average number of rooms per house),\n", + "predictors such as `rm` (average number of rooms per house),\n", " `age` (proportion of owner-occupied units built prior to 1940), and `lstat` (percent of\n", "households with low socioeconomic status). We will use `statsmodels` for this\n", "task, a `Python` package that implements several commonly used\n", @@ -503,10 +502,10 @@ "id": "d720e2c5", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.512240Z", - "iopub.status.busy": "2025-04-03T04:43:59.512127Z", - "iopub.status.idle": "2025-04-03T04:43:59.518900Z", - "shell.execute_reply": "2025-04-03T04:43:59.518650Z" + "iopub.execute_input": "2025-04-03T19:32:50.725318Z", + "iopub.status.busy": "2025-04-03T19:32:50.725239Z", + "iopub.status.idle": "2025-04-03T19:32:50.729860Z", + "shell.execute_reply": "2025-04-03T19:32:50.729670Z" } }, "outputs": [ @@ -547,10 +546,10 @@ "id": "40e9b0d5", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.520460Z", - "iopub.status.busy": "2025-04-03T04:43:59.520346Z", - "iopub.status.idle": "2025-04-03T04:43:59.525618Z", - "shell.execute_reply": "2025-04-03T04:43:59.525347Z" + "iopub.execute_input": "2025-04-03T19:32:50.730951Z", + "iopub.status.busy": "2025-04-03T19:32:50.730869Z", + "iopub.status.idle": "2025-04-03T19:32:50.735144Z", + "shell.execute_reply": "2025-04-03T19:32:50.734950Z" } }, "outputs": [ @@ -637,10 +636,10 @@ "id": "c1d544d6", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.527107Z", - "iopub.status.busy": "2025-04-03T04:43:59.527008Z", - "iopub.status.idle": "2025-04-03T04:43:59.529257Z", - "shell.execute_reply": "2025-04-03T04:43:59.529030Z" + "iopub.execute_input": "2025-04-03T19:32:50.736270Z", + "iopub.status.busy": "2025-04-03T19:32:50.736186Z", + "iopub.status.idle": "2025-04-03T19:32:50.738112Z", + "shell.execute_reply": "2025-04-03T19:32:50.737910Z" }, "lines_to_next_cell": 0 }, @@ -672,10 +671,10 @@ "id": "3d340a07", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.530690Z", - "iopub.status.busy": "2025-04-03T04:43:59.530586Z", - "iopub.status.idle": "2025-04-03T04:43:59.834480Z", - "shell.execute_reply": "2025-04-03T04:43:59.834171Z" + "iopub.execute_input": "2025-04-03T19:32:50.739181Z", + "iopub.status.busy": "2025-04-03T19:32:50.739096Z", + "iopub.status.idle": "2025-04-03T19:32:50.753432Z", + "shell.execute_reply": "2025-04-03T19:32:50.753220Z" }, "lines_to_next_cell": 2 }, @@ -783,10 +782,10 @@ "id": "11b07af0", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.836170Z", - "iopub.status.busy": "2025-04-03T04:43:59.836022Z", - "iopub.status.idle": "2025-04-03T04:43:59.842946Z", - "shell.execute_reply": "2025-04-03T04:43:59.842669Z" + "iopub.execute_input": "2025-04-03T19:32:50.754563Z", + "iopub.status.busy": "2025-04-03T19:32:50.754462Z", + "iopub.status.idle": "2025-04-03T19:32:50.759448Z", + "shell.execute_reply": "2025-04-03T19:32:50.759257Z" }, "lines_to_next_cell": 0 }, @@ -878,10 +877,10 @@ "id": "d5c7b370", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.844509Z", - "iopub.status.busy": "2025-04-03T04:43:59.844405Z", - "iopub.status.idle": "2025-04-03T04:43:59.850962Z", - "shell.execute_reply": "2025-04-03T04:43:59.850679Z" + "iopub.execute_input": "2025-04-03T19:32:50.760748Z", + "iopub.status.busy": "2025-04-03T19:32:50.760686Z", + "iopub.status.idle": "2025-04-03T19:32:50.765340Z", + "shell.execute_reply": "2025-04-03T19:32:50.765092Z" }, "lines_to_next_cell": 0 }, @@ -982,10 +981,10 @@ "id": "5b205c0b", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.852617Z", - "iopub.status.busy": "2025-04-03T04:43:59.852508Z", - "iopub.status.idle": "2025-04-03T04:43:59.862779Z", - "shell.execute_reply": "2025-04-03T04:43:59.862528Z" + "iopub.execute_input": "2025-04-03T19:32:50.766508Z", + "iopub.status.busy": "2025-04-03T19:32:50.766438Z", + "iopub.status.idle": "2025-04-03T19:32:50.773880Z", + "shell.execute_reply": "2025-04-03T19:32:50.773683Z" } }, "outputs": [ @@ -1004,10 +1003,10 @@ " Method: Least Squares F-statistic: 601.6\n", "\n", "\n", - " Date: Wed, 02 Apr 2025 Prob (F-statistic): 5.08e-88\n", + " Date: Thu, 03 Apr 2025 Prob (F-statistic): 5.08e-88\n", "\n", "\n", - " Time: 21:43:59 Log-Likelihood: -1641.5\n", + " Time: 15:32:50 Log-Likelihood: -1641.5\n", "\n", "\n", " No. Observations: 506 AIC: 3287.\n", @@ -1055,8 +1054,8 @@ "\\textbf{Dep. Variable:} & medv & \\textbf{ R-squared: } & 0.544 \\\\\n", "\\textbf{Model:} & OLS & \\textbf{ Adj. R-squared: } & 0.543 \\\\\n", "\\textbf{Method:} & Least Squares & \\textbf{ F-statistic: } & 601.6 \\\\\n", - "\\textbf{Date:} & Wed, 02 Apr 2025 & \\textbf{ Prob (F-statistic):} & 5.08e-88 \\\\\n", - "\\textbf{Time:} & 21:43:59 & \\textbf{ Log-Likelihood: } & -1641.5 \\\\\n", + "\\textbf{Date:} & Thu, 03 Apr 2025 & \\textbf{ Prob (F-statistic):} & 5.08e-88 \\\\\n", + "\\textbf{Time:} & 15:32:50 & \\textbf{ Log-Likelihood: } & -1641.5 \\\\\n", "\\textbf{No. Observations:} & 506 & \\textbf{ AIC: } & 3287. \\\\\n", "\\textbf{Df Residuals:} & 504 & \\textbf{ BIC: } & 3295. \\\\\n", "\\textbf{Df Model:} & 1 & \\textbf{ } & \\\\\n", @@ -1091,8 +1090,8 @@ "Dep. Variable: medv R-squared: 0.544\n", "Model: OLS Adj. R-squared: 0.543\n", "Method: Least Squares F-statistic: 601.6\n", - "Date: Wed, 02 Apr 2025 Prob (F-statistic): 5.08e-88\n", - "Time: 21:43:59 Log-Likelihood: -1641.5\n", + "Date: Thu, 03 Apr 2025 Prob (F-statistic): 5.08e-88\n", + "Time: 15:32:50 Log-Likelihood: -1641.5\n", "No. Observations: 506 AIC: 3287.\n", "Df Residuals: 504 BIC: 3295.\n", "Df Model: 1 \n", @@ -1138,10 +1137,10 @@ "id": "4b024467", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.864311Z", - "iopub.status.busy": "2025-04-03T04:43:59.864203Z", - "iopub.status.idle": "2025-04-03T04:43:59.866715Z", - "shell.execute_reply": "2025-04-03T04:43:59.866467Z" + "iopub.execute_input": "2025-04-03T19:32:50.775002Z", + "iopub.status.busy": "2025-04-03T19:32:50.774938Z", + "iopub.status.idle": "2025-04-03T19:32:50.777080Z", + "shell.execute_reply": "2025-04-03T19:32:50.776870Z" }, "lines_to_next_cell": 2 }, @@ -1181,10 +1180,10 @@ "id": "195e0ace", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.868150Z", - "iopub.status.busy": "2025-04-03T04:43:59.868043Z", - "iopub.status.idle": "2025-04-03T04:43:59.871887Z", - "shell.execute_reply": "2025-04-03T04:43:59.871636Z" + "iopub.execute_input": "2025-04-03T19:32:50.778190Z", + "iopub.status.busy": "2025-04-03T19:32:50.778121Z", + "iopub.status.idle": "2025-04-03T19:32:50.781279Z", + "shell.execute_reply": "2025-04-03T19:32:50.781079Z" } }, "outputs": [ @@ -1265,10 +1264,10 @@ "id": "768374f6", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.873365Z", - "iopub.status.busy": "2025-04-03T04:43:59.873277Z", - "iopub.status.idle": "2025-04-03T04:43:59.875776Z", - "shell.execute_reply": "2025-04-03T04:43:59.875518Z" + "iopub.execute_input": "2025-04-03T19:32:50.782396Z", + "iopub.status.busy": "2025-04-03T19:32:50.782327Z", + "iopub.status.idle": "2025-04-03T19:32:50.784385Z", + "shell.execute_reply": "2025-04-03T19:32:50.784190Z" }, "lines_to_next_cell": 0 }, @@ -1303,10 +1302,10 @@ "id": "1a86b943", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.877236Z", - "iopub.status.busy": "2025-04-03T04:43:59.877150Z", - "iopub.status.idle": "2025-04-03T04:43:59.879505Z", - "shell.execute_reply": "2025-04-03T04:43:59.879266Z" + "iopub.execute_input": "2025-04-03T19:32:50.785520Z", + "iopub.status.busy": "2025-04-03T19:32:50.785454Z", + "iopub.status.idle": "2025-04-03T19:32:50.787492Z", + "shell.execute_reply": "2025-04-03T19:32:50.787305Z" }, "lines_to_next_cell": 0 }, @@ -1333,7 +1332,7 @@ "id": "3595344e", "metadata": {}, "source": [ - "Prediction intervals are computing by setting `obs=True`:" + "Prediction intervals are computed by setting `obs=True`:" ] }, { @@ -1342,10 +1341,10 @@ "id": "ea9727ca", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.880882Z", - "iopub.status.busy": "2025-04-03T04:43:59.880804Z", - "iopub.status.idle": "2025-04-03T04:43:59.883099Z", - "shell.execute_reply": "2025-04-03T04:43:59.882842Z" + "iopub.execute_input": "2025-04-03T19:32:50.788600Z", + "iopub.status.busy": "2025-04-03T19:32:50.788538Z", + "iopub.status.idle": "2025-04-03T19:32:50.790459Z", + "shell.execute_reply": "2025-04-03T19:32:50.790248Z" }, "lines_to_next_cell": 0 }, @@ -1402,10 +1401,10 @@ "id": "796c20f6", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.884628Z", - "iopub.status.busy": "2025-04-03T04:43:59.884549Z", - "iopub.status.idle": "2025-04-03T04:43:59.886382Z", - "shell.execute_reply": "2025-04-03T04:43:59.886149Z" + "iopub.execute_input": "2025-04-03T19:32:50.791606Z", + "iopub.status.busy": "2025-04-03T19:32:50.791541Z", + "iopub.status.idle": "2025-04-03T19:32:50.793223Z", + "shell.execute_reply": "2025-04-03T19:32:50.793036Z" }, "lines_to_next_cell": 0 }, @@ -1425,7 +1424,7 @@ "source": [ " A few things are illustrated above. First we see the syntax for defining a function:\n", "`def funcname(...)`. The function has arguments `ax, b, m`\n", - "where `ax` is an axis object for an exisiting plot, `b` is the intercept and\n", + "where `ax` is an axis object for an existing plot, `b` is the intercept and\n", "`m` is the slope of the desired line. Other plotting options can be passed on to\n", "`ax.plot` by including additional optional arguments as follows:" ] @@ -1436,10 +1435,10 @@ "id": "a258b8c0", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.887825Z", - "iopub.status.busy": "2025-04-03T04:43:59.887719Z", - "iopub.status.idle": "2025-04-03T04:43:59.889620Z", - "shell.execute_reply": "2025-04-03T04:43:59.889379Z" + "iopub.execute_input": "2025-04-03T19:32:50.794312Z", + "iopub.status.busy": "2025-04-03T19:32:50.794246Z", + "iopub.status.idle": "2025-04-03T19:32:50.795895Z", + "shell.execute_reply": "2025-04-03T19:32:50.795688Z" }, "lines_to_next_cell": 0 }, @@ -1458,7 +1457,7 @@ "metadata": {}, "source": [ "The addition of `*args` allows any number of\n", - "non-named arguments to `abline`, while `*kwargs` allows any\n", + "non-named arguments to `abline`, while `**kwargs` allows any\n", "number of named arguments (such as `linewidth=3`) to `abline`.\n", "In our function, we pass\n", "these arguments verbatim to `ax.plot` above. Readers\n", @@ -1476,17 +1475,27 @@ "id": "56b75491", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:43:59.891025Z", - "iopub.status.busy": "2025-04-03T04:43:59.890922Z", - "iopub.status.idle": "2025-04-03T04:44:00.051132Z", - "shell.execute_reply": "2025-04-03T04:44:00.050413Z" + "iopub.execute_input": "2025-04-03T19:32:50.796931Z", + "iopub.status.busy": "2025-04-03T19:32:50.796870Z", + "iopub.status.idle": "2025-04-03T19:32:50.884204Z", + "shell.execute_reply": "2025-04-03T19:32:50.882265Z" }, "lines_to_next_cell": 0 }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70382/1591428221.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " results.params[0],\n", + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70382/1591428221.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " results.params[1],\n" + ] + }, { "data": { - "image/png": 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qd+6GRe+kKq2sQdr8TBSbSey1Z1RESmCRV1COHflFmLlmv8nzZGakmb0ujuYQESmT1P6bgYwC5RWU69WHMWSpc5eq9Epib85x8ZEZscDJEimBhdgxpiydmISBic1MPi814CMiIvcitf9msq8COasCb0igL1Y/1BfL70sSfV7Kcm5DUvZyEjvGFHOJy9pigXUGsbot7SYiIvfEQEaBnF2Bt87CmJ3UwElKYGHqGEPeKhUGJESaHXnilgtERJ6Pyb4KpK0dY+uqJFPq560IgqD7f7kCJzkDCymJy9xygYjI8zGQUSh7ViUZspSTMiAhEqltwpGdX2Rz4JRXUI6zpZfNHqMNoMyZO7IrUtqES3pPRwV8RETkPhjIKFRIoC+WpyfbvCqpPks5KVuOXkBKm6bo1y7C6sBJSuKuYWBhLvi4K7m11MsCIG/AR0RE7oerltyUs+qeWFoBVV9mRhpOFVViz6li9GwdhmsTIi2+RmzVkCHDVUullTVGwYe9S6blCPiIiMh5pPbfHJFxM86oe1I/SLJm9+wpK3fjwL9lktulTdw1xdQ0kZyjTVrxEQxgiIg8EQMZNyNHFVtTxIKk3rFhkl//V70gRkq7LAVJUSGNzAYXDD6IiMgSLr92I46ueyIWJO02sw2BIcPJIUvt4qohIiJyNAYy9lCrgVGjgE8/Berq7D6dI+uemAqS1Daf8SpT7WoT2RhhJqadwgJ9OdpCRER2YyBjj6++AtasAcaPB665BvjuO8CO3GlHjmBYkwtjyNL+kKbalVdQbnKvpuLKGlbWJSIiuzGQsVVNDfDss1f/fOAAMHw40K8fsGmTTafU1j3xVulHDlKq2FpiKUgyF6z0b6epI2Ntu1hZl4iIHI2BjK2++w44csT48W3bgOuuA266Cdi71+rTvjO2B/q1i9B7TI66J+aCpNQ24ejfTn8pdVJsGBaO7YHMjDQsT0/G+//pZXW73DFHJq+gHJm55zkaRETkIVhHxlaCAPz0EzBzJrB/v+nj7roLeOkloF07q07viLonluqzSHlPa9vlLrtPO2NZOxERyUdq/81Axl5qNbByJfDcc0B+vvgxPj5Aejrw/PNATIz8bbCSM4vDOaK4nS3cJaAiIiJpGMhc4bTKvtXVwOLFwIsvAufOiR8TEABMnQo8/TQQJr1+iyW2VAF2VuVg7Xtl5xdBANBH4j5Jcr+/uerFmRlpXEFFRORmWNnX2fz8gEceASZMAN56C3jtNaBMv4AcLl3SPP7995rkYJWF5UAW2DJd4ugplryCcuzILwSgQqfmwViw/rDLR2OkJB0zkCEiUiaOyDhKUZEmaHn7beCywa7P778PPPig3W9hy3SJ1NdYO2JTUlmNRz7fja3HCs0eZ+t0jj0jSByRISJSHo7IuFrTpppAZupUzXTTkiWaonnt2gH33Wf36U3tY1S/2q5h52zpNauyT6KjjaMoU1futRjEWGqfGDlGkLQrtkwFcAxiiIiUi8uvHa1FC+CDD4CDB4ExY4CXXwZ8TXTAeXnAjz9KKqpnS40WS6+ZsWY/hi/cYhTsaPdU0jXTYAmzpc0hpbZPjLm9p6zhqGXtRETkWhyRcZb27YFVq8wf89xzwIoVQP/+wJw5mv+aYEuNFlujVu0oyp+nSoxGa5LiwtA7zvrEZSk1ZGwZdTLFETtqExGR63FExl38+admGTcAbN4MXHstcMstwL59oodLqQJsOHJi775Kz3y732h0JOd4Md7bmCf5HNZUKXZEZeD4iCAMTGzGIIaIyENwRMZdPPOM8ZTSjz9qiu7dfbcmz6ZNG72n3xnbw6hGS792EXh5RBeMX5JtlFfyxND2djXxwL9llg+ywJrpHHesDExERO6FgYw7EARg9GjNkuwTJ4yf+/xz4IsvgAce0Ew/RUcDMD1dol2ZVJ/2z2JJr5Z4q1To2LwJDpy2PpBJbROOmcM6oLCy2qrpnJLKasz+7qDJ9jBJl4iIAE4tuQeVSrODdm6uZrl2ZKTxMbW1wKJFQNu2mtGbkhLdU/VX0GvzSgwDFW1eScYN7Y2SXi3p1y4Cr9zeVfLxE/vGYc7IrsjMSMPKB/qgW6tQDExsBkEQJO9zJJbkW789TNIlIiKAIzLuxd8fmDIFuPde4H//A15/Hbh4Uf+Yykrg1VeB997DpekZmNK0P347efWYLi3M18oprKjWG8UJD/TDfJHl1hlD2xuNoiTFhWHXiWKoLQzmjO8bpzdaYu0SakuroF4Y3pn7IxEREQAWxHNvFy4Ac+cC774LVFWJHnK2cTim3fIEtsV2AwB4qWA20Fh+XxLqBBhN85hbzSMWiIgxLHanLWK36Pej2H2yRHLhvszc85i4NMfk+yydmISBic3MtoWIiJSNBfE8QUQEMH8+8NhjmmTfjz/WbFJZT9PKUpwKjdL9WRvEeEF/lZKXCgj088b4j68GCPVHReIjrgYwhlV0xaZ5vAA0buSDssu1use0Uz5SAh9zS6iZ5EtERFIxkFGCVq2Ajz4CnnhCk+z71Ve6pz7vMQz/hEQZvaRTTLBecq5aAMqr6vSO0RaW046KiAUgSXFhyDlebHR+NYCyy7X4ND0ZtWpBbyRHLNnYFLF9jliJl4iIpGKyr5J06ACsXg3k5KDyukGo8G2Ed1PHiB76zt09kXVfV3SJCYaprSnrj4oAmgTbzQajKDtPGAcx9dWqBb26LKaSjU0xNbrCSrxERCQFR2SUqHdvBG7cgGnz1qKk2Fev/oxu1KKRgNr+SXgsJB6vD7gHhyPjTJ7ueGEFSiurRaeCLMUjhoGIpSJ2Ru00MbqihEq89mxkSURE8mAgo2CzH70JhSIF8d4Z2wN4cx58Cs7j+oLzGHw0G990GYg3+48TnYY6W3oZr/54yKr3NhWIWMpvMWqnBfVzd9yFHBtZEhGRPLhqyQMYjVoUFmqqAJfpF7Cr9vLB5z2GYWHqaFwIsn5/pPrMddzaHBnD/JaesaF4ZGA7xY9gmLo+U6uwXIUjRkSkZFy11IAYjVocPAj4+Rkd56euxcRd32P0vl+xOGkEFiffjov+1ndwc0Z2xdjk1iafN7V1gieMWMi5kaWjcMSIiBoSjsh4qrIy4I03ICxYAFV5ueghRQHBWNTnDnza8xZU+RgHPqZkZqRJ6qydld/izJEHJdS4UcqIERGROVL7b65a8lTBwcDs2VDl5QGPPw5BZISm6aUyPJv5MTI/fACj/1wPb3WdyImusmbnasDxO02XVFZj/JJsDFqQhYlLczBw/kaMX5KN0soah7wf4P41bixtUSFlewgiIiVhIOPpIiOBN9+E6vBhzdYHXsa3PObiBcz75W2sXzIZw/7ebHKpklzLn/MKys3uuWTpeS2xQn3a2jiOoq1x463SX9RubZDnKJZWjR0vZCBDRJ7FpYHMe++9h27duiE4OBjBwcFITU3Fzz//rHv+8uXLmDx5MsLDw9G4cWOMGjUK586dc2GLFSw2Fli6FNi/H7j9dtFD2hb9gwezvzZ6fNr1CcjMSMPy9GS7cizERlDufH+rbgTFmhEWV448mKpx88TQ9pI3xXQUdx8xIiKSm0sDmZYtW2Lu3LnYtWsXdu7ciUGDBmH48OH466+/AADTpk3D999/j9WrVyMrKwunT5/GyJEjXdlk5evUCVizBuUbN+Ngh15GT88bMEGzG3c9t3VvIctIg2YERT9RNud4MdLmZ6K0ssaqERZXjjxoa9xkZqRh6cQkrJ3cDwAwfOEWp01xmeLuI0ZERHJzaSBz66234qabbkJCQgLat2+PV155BY0bN8b27dtRWlqKJUuW4I033sCgQYPQq1cvLF26FFu3bsX27dtNnrOqqgplZWV6Pw2VuSmaxtf1Q6OsTPxn9EvYF90OAPBH7DXYGneN3nFydX5XR1CMnyuurMG4xdutGmFxh5EHbQ7QgvWHZZvikjqtZg6rIhNRQ+I2y6/r6uqwevVqVFRUIDU1Fbt27UJNTQ2GDBmiO6ZDhw5o3bo1tm3bhj59+oieZ86cOXjhhRec1Wy3JHX57YniS9gc3wOb467BsNwtOBEWY3SujBvaa/4nMxP49FOcmPIk8gLDrV4hZGkEpf6+UGIM92Ryl/2Y5FqOLeeSaSVURSYikovLk33379+Pxo0bw9/fHw899BC++eYbdOrUCWfPnoWfnx9CQ0P1jo+KisLZs2dNnm/mzJkoLS3V/Zw6dcrBV+B+pE7R6EY1VCr83KE/Dka1MTpXYUU1IAiofeppYOlSRCd1Q95/HsCoF9daNX0iteKvKWIjLO4w8iDXFJcjEpcdvWqMiMgduHxEJjExEXv37kVpaSm++uorTJgwAVlZWTafz9/fH/7+/jK2UFmsGSFoE9nY5O7WWnHhQcC338Jnp6Z2in9dLdJ3rsWYfeuxOHkknrycjg8nD7TYLinv1bVFMA6evig6wiIIAjJzz+uNLrjDyIMcU1xKKLJHROSuXB7I+Pn5oV07TY5Gr169kJOTg7feegtjxoxBdXU1SkpK9EZlzp07h+joaBe11v1JGSHQdoolldXwEVmOXd+stX/h/XdehmF33bj6Eh7f/DkKd32PwuJnEJ4xFWjUyOy5Fo9PQtr8TBSbGMXZ/28ZwgJ99Z5Pjm+KWrUagxZcDW4Np1xcuR+THFNc1twzIiLS5/KpJUNqtRpVVVXo1asXfH19sWHDBt1zubm5OHnyJFJTU13YQvdmzQjB1JV7kZ1fZPb4LUcv4IE7n8fSXrei2ss47g2/VIbw555GbbsEzfLu2lqT5woJ9MXGjIFIijW9z1PZpVokxYVh6cQkZGakwdfbCzvy9Nto75SLHAm19dk7xeUOictERErl0hGZmTNnYtiwYWjdujUuXryIFStWYOPGjVi3bh1CQkKQnp6O6dOno2nTpggODsaUKVOQmppqMtGXpI8QmJrOMFQnCNh80QebhzyIJUkjMG3z57j9QCa8oL+6yOfff4D77kPdvHnwfuUV5PW/HieKLxlN94QE+mL1w32x6XABxn+cLfp+OceLERceBOHK1IrYMbZMuThqDyJ7p7jcJXGZiEiJXDoic/78eYwfPx6JiYkYPHgwcnJysG7dOlx//fUAgDfffBO33HILRo0ahQEDBiA6Ohpr1qxxZZMV4Ymh7dGxeRO9xwxHCCxNZ4j5JyQKT9w8HTfe9w7WJ4gHk95//w2MGoWya3rjw9mL9Wqq1B8JMVxmbeh4YYVNibTmRlscXQnYnuRad0hcJiJSIm4a6UHERhy6tAjGq7d3RbeWoXrH5hWU6+Wd2KLnv4fwdNYypJw6YPKY6TdPw9qugxEcoJ/70js2DDtPmE78zcxIgyAIZttYf/NKS6Mtlq5X6kaYjsYl00REGtw0sgESG3E4dPoi5q87bHSsqQqw1tjdoiPGjJ2DCXe+gANRbY2eLwgMxS/t+6JOgFGC756TJQgL9BWtQNs7NgzHCyugulKNVkqVWnOjLXkF5fh+32mz1+IuexBxyTQRkXUYyHgIW/YeEpvOCLM2V0SlQlabXrh1wpt49LankB/W/Or5+45BpV+A6MvqBAHFlTXoGRuq93hwgA92nijWlfqvVauRHN9U7xjDKRdL1z5oQRbe/PWI2ctwZEKt3MnFRER0lcuXX5M8bFnCa5ik2jTQDwvWH9abngkL9EVJZQ0M5x99vFQQBEG35YCg8sJPHQfgl/Z9cef+3zDywO9Yec2NJttz/ZHt2NmiIx4ZmIS48CAcL6zAosyj2H2iRO+4HXlF6NcuApkZaSanXGzJ99FyZEKto5KLiYjoKgYyHsKeJbzaOizjl2QbTc+UXaqBt5cKtWr9UEatFhBiUPOlf0IkatVqfOkzzGwQ07L0HN5dOxfV3r6oDXoCYc88DaFpoGixvLp6K5cGJjYTPZ89VYMdmVBrbrpreXqyQ96TiKihYSDjIexdwmu6uiwAkXxwNTR5L5+mJ6NWLehGSkorazBl5R6jUZ3Syhqor/z58c0r4F9XC/+6WmDuy6j+8D0U3zsFfqruqPYRH6kwVxTO1LWbM+36BNzWvQUEQcDuU8WyJ9eyWq/j5RWU40RRJROjiRo4BjIe5J2xPYyCCKkjDrZOz9SqBb2RErGaKk0D/XTtSig4gdv/ytQ7h19RIXq9MRu/BzfDG9eOw7ed0qD28tY7xlIOi9i1m9O7dRhmrf3LYdM+rNbrOJyyI6L6uPzaA9myhNfW5djWLFvOv1CBM/tz0W3RPAR+/SW8TPzq5Ua0xvwB4/FruxR4e3mhX7sIyVMx9a/dMFCpLyzQF2WXakVHr+SY9lHKcm93Jzbqop0CddS9IyL3ILX/5oiMB7Jl7yFzU1PBAT5Gnb4XgE4x1gWG8RFBiB/YE3ldFuORJv2RsWk5hhzLMTou8cJJfLTmZeyOScTP4x7Ho2Ovt+49rlz7E0MTTAYyYvs9yTntw2q99jE16vLE0PacsiMiPVx+TTqmqst+N7m/0eNqAAdOl+lV7pXqRFEl/m4Wj/vvmIU7xr2G7JadRI/reToXz7z+MEJG3grs3m319RRZ0ab65Kopw2q9tjOVKP3MN/vNvs5d6gERkfNwaomMmJqayr9QgSkrd+Pg6TLUX8Rk7bC+0bSLICAtbyeezlqGjgXHTb/wrruAzz4DvL1NH2PufSSSe9qH1XqtY0/VaU7ZEXkOVvYlm8VHBCG2aSCOF1boFXETBAEH/tUPYoCrw/qrsk+KFn0zLAhnVFVYpcLGtkm4aeLbmHprBk6ERos3zMtLchAj+j4WiFUMlgOr9VrHUqJ0lxbBkqo9E1HDwBEZ0mNuRcjuU5qKu5ZojxcgmDwXAJOrjHzrajBm36+YumUlmlVoassIPj5Q/f030NZ4KwRzy3DFloNbarcSVr548tJjSyMy3z3aD/PXHeaqJSIPJ7X/ZiBDesytCJl9WydJQ/7a4wEYncsLmsJ52mmo/AsVmLLiynSVwXkCqi9j4q7v8NCOr5GTeiMGZ36l97w26Nr69xn41tXikl8jkx3apsMFGP9xtsk2f5qejGsTIi1em6s1lKXHUlYmccqOyLNxaomsZmnPIlObOBrSHi92LjWATUcKsO+fEgCaaZfP7++D/iJBxCW/RliUOhrXPrgYT3QbZTRtpU0IHb3/V2z68H78Z/ePyM49gykr94i2yRzDysXuyly1YE8iJVGaU3ZEBDCQoXqkFHET62Bs8d96q0+0RfTmjOwqemxpQBOUBATrrUjRBl2+1Zfw2JaViKwowcu/vod1Hz2EsG+/RP75i3rnsGcLB3dhy8agSqX9ncjMSMPSiUnIzEjD8vRkjxp1IiJ5MJAhHSmdff0OxlTgIcWBf8uMOt4Ug12uxd5fSxt0Tdj9A6LKi3SPx5acxVs/LEDkgD7Ajz/qtlcwlfirpCRRKYGmp+GoCxFZwkCGdKzp7OMjgjA2ubXZ47tYKJhn2PFa8/7aoKvj+XzRczfOPQjccgtw7bXA5s0AlF/XxRNGlYiI5MZAhvRY29mbO/6V282P2Ih1vFLfXxv0PHHbUxgzdg52xXQQf5MtWzTBzC23IOTIQUVPV3jCqBIRkdxsWrX08ssvY9y4cYiPj3dEm2TFVUu2sWZFSF5BOXbkF0EFIKVNuN7x45dkY/PRAqsL6El5f72l1YKAIUezMWvHCrT695j4SVUqYOxY4MUXRZdxK4HYcnJPXLVEROTQ5dfdu3fHgQMHkJKSgv/85z8YPXo0IiLsTwB1BAYyjiNlKfDJwkoMX7hZb2+jsEBffDe5P1qFm58qkUov6AlrBKxYATz/PHD8uOjxgo8PVJMmAc89BzRvLksbnI1Lj4nI0zm8jsxff/2Fzz//HKtWrcI///yD66+/HuPGjcOIESMQGChPByUHBjLyMSzCZqrWR4/WoRjVqwUAFdbs/ge7T5SYrEtjrqibXUXfqqqAjz4CXnoJOH9e9JDdcxchLH08AwEiIjfk1IJ4W7ZswYoVK7B69WpcvnwZZWVl9p5SNg09kJGjAqzYyEtSXBhyjhfL1Uy9kRw5i76VnC/CZ3dNx/gtqxFcfXXVz4Gotrh1wpsQVF6cmiEickNOLYgXFBSEgIAA+Pn5oabGth2HSV4lldUYvyQbgxZkYeLSHJt2qda6f9lObDYo8b/rhHxBDHC1qFteQTn+s2QHNh8tEH3eWvd//TfmJ9+JAQ8txgfJI1HlrQlW5g0YD0HlpTv3/ctz9PaDIiIiZbB5RCY/Px8rVqzAihUrkJubi+uuuw5333037rjjDoSEhMjdTps11BEZKSXeLSmprMak5TtlHXmxlzW7G4vt2RNddgG3HcrCh8kjNcm/IhZnL0XfCSMQeM/dmo0q653PFfsbWfu+nrwPExE1HFL7bx9bTt6nTx/k5OSgW7dumDhxIsaOHYsWLVrY3FiSl7YCrKH6FWCldHBTV+6VfeTFXscLpbUdAHbkFxk9djY4Ah+mjDL5muRTBzAk82sg82vgfwuAOXNQcu1ATF31p8WpLrkDCGun2BrKPkxERPXZFMgMHjwYH3/8MTp16iR3e0gGUirASllSLWXHaGeTUvRNrEOXRBDwVNayq3/euxcYNgxn2/fApeS7gRYddU9pp7qWpyc7LIAwt6+S2KiatccTEXkCm3JkXnnlFQYxbkyOCrCWgiF3JtahS9Hn1H70/veQ0eMdDu/B6s+exEdfv4jEguMA9Ee3HLGRo7X7KjWkfZiIiOqTPCIzffp0ySd94403bGoMyUNbAdZUjoyUaY+mbjoVYTiaZDidY89I0vZWXfHA7c8gY9OnaF940uj5649mY/DRHHzTOQ1v9h+Hf0KjsT2v0OI0niAIVk85WTuqJscoHBGREkkOZPbs0f92uXv3btTW1iIxMREAcPjwYXh7e6NXr17ytpBs8s7YHkYVYK3ZV2jB+iOOappdtKNJpqZzRie1tOp8HaOb4PC5ck3Ap1JhfftUZCak4MmCnXhgwyfASf2AxgsCRv2ViVsP/YEV19yINQH3AfAzef4pK3bjwOmr5QikTjlZO6rGfZhISZiQTnKSHMhkZmbq/v+NN95AkyZNsGzZMoSFhQEAiouLMXHiRFx77bXyt5Kspt2l2toKsJrtBsRHGVwttd72B6amcy7V1Fp1zrAgP/RrF6F3vantozBm1rPIK5qKT++diUe3foHwS/q1kfzUtbh39w+4c/9v+Lj3cHyYMhIX/Y0/34On9V8nNWfF2lE1OUbhiByNCenkCDYtv27RogXWr1+Pzp076z1+4MABDB06FKdPn5atgfZqqMuvrWVzgqwT9W0bjhWT+oguq64vyM8bFdV1ks+bmZEGAEYBX2bueUxcmoOgqkqk71yLSdlr0KT6kug5/gluhrQHPkStt+a7gZcKevtLib2npeCitLIG9y/P0Vv+bu4ffe7DRO5OjrIQ1HA4dPl1WVkZCgqMO7yCggJcvHjRllOSi9maIGtoZI8YrNnjmEB267FC5F+oMBrlMGRNEANoAhixqRntYxX+gXi731h81uMmPLLtS9yz50f41+mP/Gzqe5MuiAGATjHBOPCv6XZaylnRBpb1g5ik2DCTQYl2qP6F4Z1151fisD2nHDyXXGUhiAzZFMjcfvvtmDhxIhYsWIDkZE0UvWPHDjz55JMYOXKkrA0kx5NzqXVSfLjDAhlA00F/svW4rOdclHlUL2DoHRuGm7o2R4CfN5LiwrDreDHUAIoCQ/Dy4En4OGk4Htu8Encc2ABvQY2igGD0XfQqMpsE6wIIQRDMjhpZylm5f9lO7Dao4bP7ZInRtJSpofonhibgeKFmpZIrOgdrAxJOOXg+JqSTo9gUyLz//vvIyMjA3XffrduSwMfHB+np6Xj99ddlbSA5nlxLrdtGBuHjzfmynMuU86WXsVOmIn3eKhWCA3yw+0SJ3uM7TxSbfY/Twc3w9E2P4cPkkcj441Nc6N4b98RrdtGu/w/xgIRI/LU/Dx3PHMXmuGsAlQpeKs1ojSnaaspi7y/2zVVsJG3TkQKXBQS2BiSsgeP5mJBOjmLXppEVFRU4duwYAKBt27YICnK/X0TmyFhmKedEpQLs31rUPl4qICTAF8U27BWlFdzIB2WXr04J9Y4NkyUouqZFCB4b2h7eKqBOgG4UorSyBltuvxc3rV+BLbHdMG/ABPwZk6h7nVgHP35JNjYfLTCbX7N0YhIGJjazeN+0nJmDYEsOhKXrsGZbCnJvzJEhazg0R0brzJkzOHPmDAYMGICAgAAIggCVif1ryH01DfJDWKBxkOAFoFvLUNSq1XpLiF2hsb+PpA0vE5o1xrGCcr1AwAtAr9gwrH64LzYdLsCeU8Xo2ToMtWoBE5fm2N22vf+WGp1nQEIk3h0QiZuyvgYA9DuxD2s/fQK/tE/F/GvvwdGI1th8pADjFm/HUzcmok7Q/IMuZYpP+81V6kias3IQpORAiNXU4ZRDw2FvWQgiMTYFMoWFhRg9ejQyMzOhUqlw5MgRtGnTBunp6QgLC8OCBQvkbic50NSVe1F2yThI8PJSYe8/Jbo/d2mhiYjNJbE6QvtmQTh8Xlpl2iPny40e658QiZdHdMH4Jdl6/4B2MTPFY68tRy9g97JXkVZVpff4jYe34fojO7Cm8yD8r//dOABg/MfSgikvaK5F26lbGqo3JDUgsDXh1lJAYqqmDqccGg5by0IQmWPTFgXTpk2Dr68vTp48icDAq/8IjRkzBr/88otsjSPHu1ra3vi5WoP5jYOny5wexADAkI5RVr/GC5pAJTMjDcvTk/Hstwew2WC0wJGjTHWCgO+btEFtjPFmqt6CGnce+A2/f/QAnv/tQ4RXlEg6Z68rq5a0tLVjvCWOgloKCEoqqzF+STYGLcjCxKU5GDh/I8YvyZY0EgZYDqxM1dQxdR3eKhUG1AvcyHPERwRhYGIz3luShU2BzPr16/Haa6+hZUv9KqoJCQk4ceKELA0j57Am0ddc3oYjdW0ZavVr1LgaqGiDNbW8zbLo666DseXnrSh84VUUBRiP/vjX1eK+Xd8h68NJeHzz52hcJX4vvFRAUpxmaswwYfadsT3Qr12E2XZIDQjs3TPKVEDideWPhp9//SknsevglAMRSWHT1FJFRYXeSIxWUVER/P397W4UOY+10xOusDL7FAYkRFpMghWjXYLsKq1jwhH+/Ew80DQFXVYtRnr2Nwiquax3TOPqS3h8y0qM3/0jFqaOxmc9bkKVz9VtD/q3izTq0OtP/yxPT8amw+ex51QJEqOaYMWOU1bnIMhV40MsB0JqTR1OORCRLWwKZK699losX74cL730EgBApVJBrVZj3rx5GDhwoKwNJMcyVdpeDonNGsPLS4VDZ+0rkrjpSAE6RjW2aUQoLjwIpZXVdr2/LbxVKvRoHaoLpF6/71pMCWiMZT1uxuRtX2Lc3p+Miuo1vVSG535fjPty1qLof+/gQr+BRh262PJmw0TtAQmR+G5yPxRWVksOCORKuBXLgbCmpk58BAMYIrKOTYHMvHnzMHjwYOzcuRPV1dV46qmn8Ndff6GoqAhbtmyRu43kYGLfosMCfVFaWWPXdEyuSOKtGEvl/AHg0Dlp59LyVqnQM1YTSCzKPGrVa+UQHOCDnSeKdauZtImtRcM7496lMfg4aTge37wCt/+VCW9B/1NucbEAZ4LDRIMQsekfw9Vm2uelLmfNKyjH2dLLZo+RknBrmCRsWFPHnfeBYkVhIuWyuY5MSUkJFi5ciD///BPl5eXo2bMnJk+ejObNm8vdRruwjox09b9FNw30MwpuHCUpLkyvsq4cDGvGWGxDbBhKL9XgsEjwFRLgi1KRVV2mdG0RjIOnL5qslfHnqWIMX7gVAJBQcAIZf3yKG45s1x37Q2J/PDpiBgD9WjNS68ZoLb8vGXWCYLJzlrK/lpQaH1KK4P15qgTPfLPfpp3AHYkVhYncl9T+2+ZA5vLly9i3bx/Onz8PtVr/G+Vtt91myykdgoGMfVZln8SMNfsdcm7tcmLtVMSUFbtx8HSZTaNA3iogyN+64MWwLebe99P0ZNSqBaPtDKylLe42fkk2NtdLQO7x7994atMyJJ36C0PTFyEv/GoifWqbcKx8oA8yc89j8gebUOkXYPX7miq+Z2lKUUqnbq7I2dtjrzEKFLq0CMart3dFNxuSuOXGAm1E7suhgcwvv/yCe+65B0VFRTB8uUqlQl2ddZv2ORIDGftYOwqgZSkwADSjIIsnJOk6SbHdm6WSYyrMnLkju+Ku5NYorazBvUuzsedUiU3nmXZ9Am7r3gJNA/2MdraGIKBd4SkcjWht9LrMjDQINTWo69oNR8JbYcGAe3AsvJXk9zXsnC3d17kjuyKlTbjFaRZL50mKC8PuEyVuGSiwojCRe5Paf9u0/HrKlCkYPXo0Tp8+DbVarffjTkEM2c9SjY/MjDQsHNsDSbFhes/3T4hE37bhJs8rtpxYmyiamZGGadcnSGpfY39vLL8vCcUODGIAYMnmfJRW1iAk0BcLRne3+Txv/noEA+dvxJSVezCsc7T+kyqVaBADANvzCtHm5zVIKDyFmw5vxfolkzH357fRvExa0Fd/9RFgObk3KqSRLEnCOceLjUZ8DNviKlISnInI/dkUyJw7dw7Tp09HVJT1hcpIeczV+IiPCMLN3WOw+uG+yMxIw9KJSboidO+N6yUazKS2Ccfi8Ukm3y8+Igi3doux2K7QAF/8PHUAyi7ZNp1kjWMF5bp6KpY6wI7RTSyeb9ORArz44yHJ7+9ddRmYPfvqnwU17tq3Hhs/fADP/L4YYZWlks6j7ZzlqqZrz/J9VwcKrChM5BlsWrV0xx13YOPGjWjbtq3c7SE3ZGpJ7e5TxXqJpIYrVUICfbFiUh/kX6jAjrxCCABahAagThBQVFltNu+iTWRji5s6fjO5H2rUaryx/rBs12qKWoBuFMFSB+iI3cbaFZxATdlFGH5i/nU1mJTzLe76cx0+Sh6JuJeewfRf8kyeR9s5m1p2b+1KInPn6dE61Oz9c3WgINdnQESuZVOOTGVlJe68805ERkaia9eu8PXV/+d16tSpsjXQXsyRkZetqzxsed2Pf57GZDNVZbu0MF9ozRG0O08bJutqWbtaSgofLxVq1QKCL5djUvY3SN/5LQJrqsQPjozEp4PvwastB+CS99XvKWJ5KWI5Sbas2DF3nikr97h1Mq1cnwERyc+hyb5LlizBQw89hEaNGiE8PFxvx2uVSoW8PNPfCJ2NgYy8bF3lYe3r8grKsSO/CDPNrJiSUn/GknHJLfF59j+Sj9cmgJZW1iBtfqbxjuEytMmSyPJiPLptFcbuXQc/tXjQVBDeHHNT7sI3ndOg9vI2WsZdv2aKdqTNW6Uyu1zbErGqvCcLKzB84Ra9zyks0BffTe6PVuHuU1WaFYWJ3I9DA5no6GhMnToVM2bMgJeXTWk2TsNARj62rvKw5nVSapvIxbAirhTattq6mktOrUrOYtrmzzHir43wgvhf4/KERFQ89wKi/jMaJZdqREfFXh7RBc9+e8AhoxJc3kxEtnLoqqXq6mqMGTPG7YMYkpetqzyseZ1Y5VpHsabInZa2rdZstukop0KjMf2WJzDsvnfwazvxoKDxkVxEPT8DqKkxuSnk8IWb7dos0pSrO6u756olR8krKEdm7nmPvT4id2NTJDJhwgR88cUXdr/5nDlzkJSUhCZNmqBZs2YYMWIEcnNz9Y65fPkyJk+ejPDwcDRu3BijRo3CuXPn7H5vsp6tqzykvs5Ux+cotu7dBABNrRypcGTInxsZh0mjnseocfOwo2Vno+ef6TkaW06UmgwqiitrTAYbmw4X2NwpN7TlzSWV1Ri/JBuDFmRh4tIcDJy/EeOXZKPUylE/IrKOTauW6urqMG/ePKxbtw7dunUzSvZ94403JJ0nKysLkydPRlJSEmpra/Hf//4XQ4cOxcGDBxEUpOkwpk2bhh9//BGrV69GSEgIHn30UYwcOZJ7OrmAras8pL7OHUY5TDFs64L1R6x6vakaN439vVFZVSdLDZxdLTthzN1zkZa3C09tWoZO5/NxKDIOK9v2w9rPdtl0zvEfZ+v+39rpJnde3uyIvZVMjXhNWbmH02hEDmRTjoy5Ha5VKhV+//13mxpTUFCAZs2aISsrCwMGDEBpaSkiIyOxYsUK3HHHHQCAv//+Gx07dsS2bdvQp08fo3NUVVWhqurqio6ysjK0atWKOTIysXWVh5TX2Zt30tjfG+VVlgsySqk6bMiePY9cQSWoceuhP1AQFIptseIF/NoXHMe9u37AW/3uwrkmEaLH1OelAnrFhmH1Q30lt0NqjoyzNm101N5KrBJMJD+pOTI2jchkZmba3DBzSks1Rb2aNm0KANi1axdqamowZMgQ3TEdOnRA69atTQYyc+bMwQsvvOCQ9pF4TRkp/0BLeZ2pkRsvaDrQAD8f0SXPWuVVdZKWP4dYmeT7aXoyrk2I1P3ZnUeOtASVF77rdJ3eY7FNA/BP8WXdZ5vxx2cYemQ7Rv71Oz7peQve63MnSgNMF/NTC5pKvXe+t1VvawlzxHZW1xZTBJy/aaOjRk2kTKMxkCFyDLfJ1lWr1Xj88cfRr18/dOnSBQBw9uxZ+Pn5ITQ0VO/YqKgonD17VvQ8M2fORGlpqe7n1KlTjm56gxQfEYSBic2s/sfZ0uvEqgirAeScKEatWo1EC1VzzQUxKlyp8yKxErC3StOp1g9iAPuq2brSzGEddZ9tz38PYeiVHbcb1Vbjoew1+OOD+zF56xcIqL5s9jy7ThRLTgSuv+1E/arP2iDFXGAhN0cmH9syjcakYCJ5uE0gM3nyZBw4cACrVq2y6zz+/v4IDg7W+yHl0HZ8SXFh8DIokbsjrwiNG9k0iAgAEKAJdKQmE6tUKrwyoovR46b2n3Jnjf29kdg8WPPZxoZh6hbjv2fBVRV48o9PsenD+zF+1/fwrRMftVIDVnf82gBWEARd5+3sVU2WRk125BXafG5Le5LVD9yZFEwkL7cIZB599FH88MMPyMzMRMuWLXWPR0dHo7q6GiUlJXrHnzt3DtHRBhvukcfIKyhHzvFio1VFdYKAnOPFSIoNE+0wehtsXGmvWrWA6av3ij4nNnLkDpJiwxAmMiVTXlWHgfM34s73tyLnRDGevOkxfNrjJtR4eRsdG1lRghd/+wAbPnoII/7KhJdaPO9Iu+ooK/c83tpwGH+Yqf0j1nlPXWV+1EXuVU2WRk1mrNlvV0Bhbk+y+pw5CkXUENiU7CsXQRAwZcoUfPPNN9i4cSMSEvR3PNYm+65cuRKjRo0CAOTm5qJDhw4mc2QMsSCe8mTmnsfEpTkmn184tge+2PmPyZL4m48WyFpd11yiZv6FCnz3579481frVjE5Qo+WIfjkvhQIEHDP4h3Yf9p4+wbDysOxxacx/Y/PMfyQ6UTVvyNi8fp147GhbTJQL4D8LD0JU1buFa3aW6NW6yXviiX9WqqC7IgEWbF21CdHsT5zeWBMCiaSzqGVfeXyyCOPYMWKFVi7di0SExN1j4eEhCAgIAAA8PDDD+Onn37CJ598guDgYEyZMgUAsHXrVknvwUBGeaT+Yy/WYYitjrLXtOsTcFv3FiY7GFevYlIB6B13dTWRqX2gzOl0Lg8Zm5ZjUN5Ok8fsbNER866bgF2tu6Jfuwj8+U+JaFFB7d5QWpY2/zRcRebIyr9Sfz8cFVBYCtK1e3kRkYMr+8rlvffeQ2lpKdLS0tC8eXPdT/1ie2+++SZuueUWjBo1CgMGDEB0dDTWrFnjwlaTozUN8hOdHvEC9PINxBKHtTk2PVqFytaeN389YjaPwdU5M9cmRGLx+CQAVxNaLQUxhn/xD0a1wX13zsadd89FTotOoq/p/e8hfLliBv6b9xvuSm5psjJyrcEwy24zQQwAtGoaoPdnsekYuWh/P+aM7Gr2OEcV63Pn2jpESmV75qQMpAwGNWrUCAsXLsTChQud0CJyB1NX7kWZSCcZEugrqYPLKyjHnlMlsrdr85ECk8t0Xx7RBcMXbra4rLtrTDAOnrloc/ViLxXQKSYYT9/YAf8UX4IKQEqbcN0qIKlLw3vFhiFHJMDIadUFd457DYOO5eDJTcvRseC43vOVvv54P7IHfH84JLnNloKqE0WXAGh2M3/19q7o1jJU8rltlRLf1OzzjgoobC0qSUSmuUWyL5HW1ZUsxs8VV9Zg378lFs/hqDovhqt16i+fffbbA2aXdWtXr3x2fx+7koT7t4vEort74aNN+Zi5Zj9mrNmvN1okZWl4WKAvVj/cF5kZaXjn7h5IijNIklap8Hu7ZNx871t47JYncDIkSvfU0l63oaBxU5wuNb9EW4zhKjRDh05fxPx1h60+ry2sWWUkN6lJwUQkjUtzZJyBOTLKYimHALBcMM2WnBUphfS03h3bA18aJBtbYk9l4PbNgjDz5k5mE2fr55VIyZGZM7Ir+rQJx6y1f5lNfgUA37oa3PXnOozf/SNG/ed1lDVqLHpcysn9ONQs3uTzSXFhyDlufpoJcF7Cq61VquVibVFJooZGEcm+zsBARlmkdPJSkkHFO3ugcSNfo9yOkABfXLxUIzk5Nik2DLtPlkieHpo7sivuSm6t+7OUYE0rLNAXGzMGSt7GITMjDU0D/TBu8XYcEFm1ZBdB0Fu1VF/w5XL88X46AOC9Pnfik1634LJvIwD690vKKi9nJ7wyoLCds7aWUAp+HvJy6BYFRI5iKoegvvoF08xVCDYuja/5tl1UWY3teYVQAYgJbYTxH0sLKgCga4tg0dwSc1LahOv9Wep8blJcGBaP198KQEopfEEQMK5Pa8xcc8CqdlpkJpn5wR1fI6RKM+U2I+sTTNz1Hd7uexe+6DYUTZoE6AoLxkcE4bqESLOBjLMTXuMj2OlYy9lbS7g7fh6uxREZcjtSl8hK+eZu6du2taMjL43ogkdXSCtcZjhyJPaPneHxPVuH4pFB7Uy219KITJKJJF5bfJqejFq1gEWZR7H7hOkRqMjyImz6YBICaquMnjse2hz/G/AfFN8yEssmaeo+jV+SbfIzCAv0xZ7nh8rSfnIcqZuBNhT8PBxDEcuvicRol8guv8/8PwBSvrlb2ttJ6r5JXVsGY2PGQHRqLj0YNkzgFKvoWl+P1qG4t2+c2WFpc0mqTfx9ZAlitAmv1yZEYmBiMywen4QerUNNH69W47d24vcqruQM/vfd63j62btxduUa5J2/aDZALa6s4d5DErlqryZnby3h7vh5uB6nlshtDWgf6fClqlKmsrxUQFiAP0ICfRES6Gu2TS8M7yw6AqT9x86Uri2CsfNEsa5wnLlhabFpsyB/b8nJypYYBmAhgb6YPKidyZGrs8ERmDL8aay7aTzu/OY9XJe/2+iYTufzgbtHoaR3H/RKHIldLcVr1QDO2ylaqfkMtkxjyHmt3OlbHz8P12MgQ25NPNdF3qWqYu9Rn1qAXk6OuTaFBPqK/qNl6R+7/f/qJ+Zq994RG5bWjlhpp80W/X7UbOVccywFYFpSRq7+bdMRE0a/iD4n9+GprGXoeTrX6JjQndvx9c7t+K1tEuYPGI+/m8UbHWNupE2ODlnp+Qzm9moy/H1xxLWyqJ8+fh6uxxwZUgRnrCxZlX0SM9bsN/m8YU6ONW3ae7IYIxZJ21ajPktLke3dHsGaTm38kmyL+1glxYVp8mnUalx/dAcyNi1H4oWToseqocLaTtdhxo1TUOXrr2uPWPAmZ4es5HwGa/dqctS1KvkzdAR+Ho7BHBnyKJZyXeSQbGW1V2va9IaNm0qaK5VfUlltcQdpcz5NT8by9GTJgcA7Y3ugU4z5LwMT+sZpir2pVPg1oQ+GTXwHH9w3C+rWsUbHekFAdHkhqnz8AADXtAzFmN4tRXMK5NoxWun5DFKmMbQcea0s6qePn4drcWqJFE+u+X9HlY+3lB9jjrlh6akr9+KgHbViWoZJS3TWCgn0xdt39TA7ItA5JgTL02MMRqtuA6pmAh9+CLz0ElBw9bMof/5FPJfYCb8cOIuc48WYfCUwqb/03NTnJ2UZviGl5zNYM43hyGs1nN609++eUvOVtOT+PMg6DGRIsRwx/++InBxbtkywFDzZExxpSe3I6ncyUoM9o9os/v7AlCnAvfcC//sf8PrrwODBuD59BMYvyTbaWDInvwg3zPkF62beKGuHrPR8BmuCbWdcq701eJSer2SINYlcg4EMKZY1SY9S2fLNytK3SalLvOuzFDzJsZ+UpY7MVCfzyogueObbA3qP94wNxeiklpZHR5o0AZ57Dnj4YeDSJZMB2ZCj2Xh5/UKsyrsf178+Q9J1SPlW7wmbNkoNtpVwrY74O0wNDwMZUiQ5pxvESPlmZenbZP2O1dIS7wEJkci4oT0KK6olBU+2BEf1dYkJtvgepjqZZ749oAv2/vq3FMu2HkfO8WLdPkpJsWFYPCHJ/DfqCE0+wYnc80ZPeanrkLFpOaLLi/Dginmo2b4GTw65F2+GdUctrtbP0XbIYYG+RkX2rF2+rqR8BmuCbXe+Vkf/HaaGg4EMKZI75DqY6ugf+mwXfL299P6R7ts2HMnxTbEtr9DoPEmxYVYPpUupf2POq7d3Nfu81E5m1tq/sMtwWuhEMdLmZ+rtEWWKWEA2/GAWOlw4ofuzb95RTP7wWdzcOhHPp9yNTfE9gStBjLajtuZbvafkM0gJtt35Wt3h7zB5Bq5aIkVyda6DuRUh2/IKsfmofhCwI68Ivt5eSIoNM/pLt/tkidWrbwDxlRKWeKk0oxXdWoWaPU5KJ6P9DMQ22yyurMH9yy1v/dAmsjGS4sL0Hks5Jb5HVNzJXCxfPQt7Ns7B1rRALE9PRmFFlc0rc5yxEs5dSL1WZ1YLdvXfYfIcDGRIkcyV6h+QEOnwzslSR29Ya0XbseacKDbq+G1dDhsS6IvZt5mukCum/5WNMy2x1Mn4eKksfgY5x4slXdPi8UkIqzdyM2PYVNw95mX82TxB9Piw7K2IGTYIGDECBTuMqwjXZ275ekNiKUApqazG+CXZGLQgCxOX5mDg/I0YvyQbpZU1osfLwdV/h8lzMJAhxXJl7QZ7c1TE2NLpypH0K6ZpkJ9ecGHoniXZWJR51OJ5pFxTSKAvNmYMRFLs1ZGZrXHX4OVnl6JixRdAhw7iL1y7Fsm3pWH+j2+iZek50UOkfqt31b5FjiY1QJGrTo+1WH+F5MDKvqR4rpr/F6vm6QWITrVIYamKrxhLlV5VKqD+33Cp1UbHL8nGZhPTRvXPZWmPJ2uuqaSyGpOW79QlDQNXknbv7IqQr1cBs2YBp06Jvrbaywef9xiGd1PHoDAoVPJ1etryX0NSKs5aWy3YEdwxh4dcj5V9qcFwVa6D2LfJ/gmR6Ns2HN4q4+PDAn2vPCffULqp4XntX2zDrylSprHM5b4YnsvSRpWniir1RjrMjXxMWr7TKHF4y9ELmLJ6PzBxInD4MPDGG7oVT/X5qWsxcdf3GH5Q0yFL/VbvqpEIZ5Ba2deaasGO0pDylUh+XLVEBNsqi5paEVJaWYO0+ZkoNhi+L62sgSBoOlkpy2GltklsiW2nmGAcMFP119yKEDmnq8Z/nK37/+BGPnqBj3bkQ4CA+5ftFN340mgp7rRpQHq6JqBZsAAoL9cdW9uiJa5781ncExOO+Igg5BWUY/epYpOfn6cv/5W6KohJt6R0DGSoQZNjasFwGWxhRZVREANoppy25RUiMyMNAEwOpVvbJrGAShAEs9MF5jqnsADHTKkYjt5oRz5q6tQWd+/WC7yCg4HZs4HJk4FXXwUWLQKqq+Hz0ou4rltrXV6Ipc/P05f/Sg1QlFA4j8gcTi1Rg+aIqQWpHaSpoXRb21T/nPasCLF1g0traUc+xGrrGBINvCIjgTff1Ew5zZgB3HMPAPHPr8eyd7AsY4HeXJunj0RY8zvgzKRbT02sJtfhiAw1WI6aWrC1g8wrKMeO/CLZ2mRLVVc59nCSkwrAtWYCr7yCcpy4HIC4J55FvI+PaPvbFp7C1M0r4P2HGlXbVsP/9deAIUMaxEiE1N8BZxTO8/TEanIdBjLUYDlqasHaDlLsH3g52mRL52TpM5k7siuiQhrBW6XSy39xlHbNGosGXqY6xdFJLY2Onf7HZ/AWNKnL/nt3A9dfDwweDMyZ49Yl/OVg7e+AIzc9lGNfJSXskq2ENnoaBjLUYDlyasGaDlLsH3g522Spc6r/D6+lzySlTbjuXAMSIrH5aIFR8T85fTi+t+i39fuX7TTaMXvL0Qu4VKOfh9PhfD5uzt1ifOING4DkZISMHInlL7+M/OGdPXr5r6t3ZbZ39FMJozlKaKOnYo4MNViOrCyq/SacmZGGpROTkJmRhuXpyUb/oJlaImvIEdVOxYqlzf7uIEJNJPuGBfoa5VX0ig0TPVYOfduGi45e3fn+Vuw0USE553gxkmLDdPf078g4PDx8BvKathB/kzVrgC5dEP/UFAwMuOySzr4h5IzYu8RbCcvkldBGT8VAhho0Ryc5WqqPIXWpsxxtMuwwxf7h3XykACWXxMvSF1fW4I963zZDAn2x+qG+ovtH2WtAQiTeG9fL6PGpK/ca1ZoxdG/fuKv3VKXCzx3648U5X6Jy4ftAC5GARq0Gli4FEhI0y7sL7M8RkhKcuGJbAFvZG2zZM/optR6OK1lq46rsk27RTsAzA2dOLVGD5urdgS39Az93ZFe96RxbiA15944NE13ybKkI3j1Lso2GyxdPSDKaRrPVtOsTcFv3FlbVfTHUqUUIlnePEbmnfYGJ4zXLtV99FSgq0n9hdTXwv/8BixcDGRnA9OlAkyZWtd+a6QU5ckYcTa7pEnsSq5WwTN5SG2es2Q/AtVNNnjz1xREZIriusqil6a27klvb3SaxDtMwv8Qa2s5W+82uqLJabxotKS7M6HqkMhXEAJY7Cy9o/mEWBAGZuecBwPieBgQgb/yD2LRuB4qnPwUEibxXebmmTk379kCFdd9apU4vKGGUAZB3usTW0U8lLJOXuveaK6eaPHnqiyMyRC7myJUzpkYxLI28eKmMd/DW0na29Qvuab/ZxUcEoWmgH575Zr/ZysJG7wfN9g7mgjZLnUW3VqGoVatF2xUS6Gv8jdR3AG55MQ0Ljv4I/8UfAjUGUzq33ioe6JhgTUKrO4wyWFpdI3d5AltHP5WwTN5UGw25qmq0p1exZiBD5GKOnN6yOIphELB4q1RIjm8KX28vq6aKthy9gIc/3wUfL/3XdWkRjFdv74rbF25BnZl85k4xwRYDN1OdhZcK6BUbhgBfH7NTNWLfSH86r0Zuuzsx5cu7MHDlIjRZvUpTNK9RI+D55yVfP2D5s845Xqi7r64cZZA6xeCoYMuWFVRKWCYv1kZTnD0d5g6BsyMxkCFyE45YImupw+wVG6a327S2cyisqMKP+09jwXppVX7rBAFbjxUazVUfOn0RL35/0GwQAwDv3N3T7Dy9dvQgY2h7ANDrLPq3i8QTQxMwfOFW0XZtOlKATYfPmxyZOnK+HFPPA4gfh9HP3YRXdn0B344dgJbGNWkAAJcvAzt2ANddp/ewpc/6xe8PIrJJI8SFB6FpkB/CAn2NtrKQMjJlL6m5OU0t5E04c0rH1blsUtRv4/a8Qsy8khcjxtnTYUqYnrMHAxkiD2ZpWN6wcwgL9LUrcVdsSbSlfZQAYNbav0STDk2NHnz3aD8UVlTrOjRtTowpe06VSGr/l1VhOHv7c1h+r/GKKZ1Fi4AnngCGDtUkDffSHNsmsjG6mNmss7yqDhOX5gDQLGUvFVkdFhLo69BRBmumGMwFsXKXApDK1fVwpNC28ef9Z91mOkwJ03P2YLIvkYezlGRZP9HZmuJ8cjJMOtQmEk9atlN09GD+usN6ibyWvnH2aBUquS2bjhQgv/iy+JNlZZrgBQDWrwd690b58JE4tW03MnPPo1uLEEnvUVxZI5qDVFxZg6LKaslttZbUei6WVohl3NBe1nZ5ImfuX6XE9siJIzJEHk7qsLylziu2aSDeubsH5q87bHVF36S4MOw+UWIyEVI7IvDnqRIsWH/YbDvERg8sfeMc0L6ZpGRMLZM5AwsWAIX6m1w2/u4bNPp+Lf7odj1+7zsWCI4wfp0VHJmvIHWKwVLAU1jhuGDLU7jbdJi7tUdOHJEhaiDsLc53oqgSEz7OxisjuqBTTLCk99QuI188Psno26CYZ77dL3lEyLAarKVvnGLPm2IyZ6BNGxSHGJ/DR1Dj7j/XIevDSZiZ+TFCL0lfsSX5vWUgtZq1p+dUOJOrSjuY4m7tkQMDGSICIK0WRnFlDaav3ou375I2HK0NJLTfBpffl2T2+AP/lkkaMQGMO1NL20LUf75LC9OBmLn8j7ybRiE1/X3MSbsXpf7Gx/jX1eDB7DXY9P79eHTrKgRWX5J0LcDVYEJbB8dRtWSkTDE4cvsOIrmpBEHivxoKVVZWhpCQEJSWliI4WNq3SKKGavySbGw+UmCxzkxmRhpmrf1LdCqnZ+tQPDKoncmh6/FLskVf17F5E8m1Z8ICfbHn+aGiz0nZfbi0sgYPf74LW4/pTxOltgnH+//pZXIFVWbueV3CbvDlcjy442tM3PUdAmuqRI+vDA3Hp4P/g//FD8Ql76sz+V7QBFb1Vy31bRsOQQC25V1tkyMrr1qaYiitrDFK/PaUSrCkDFL7bwYy5PakdEwkj9LKGqQvy7G40mjpxCT0bBVmU0dnqoN8Ymh7DF8oslO1CZkZaXq/D7aUYM+/UIEdeYUQAPSRsBVEXkG5XsE9AIgsL8KUrV9g7J+/wFddJ/q6gojmeCrtAWS2TdJrV1FltS6YMBUY9mgdislmAkNH88ScClIGBjJXMJBRLk/eG8Td3frOH9j/r+nRkfpBhK0dndjrxEZrTFk6MQkDE5vp/mxqpEe7zFwuptrYuvgMpm3+HMMPZsELxu3PX7oSx1MH6a63foAuCIJRgCRGyb///EJC1mIgcwUDGeVyVsfUUFjTkZRW1iBtfqZRwTZvFdCvXaTe5y9nByU2WmNK/WBKbKTE1LH2stTG5LJTeHD9xxh8LEf32M6WnfD2rI+x/P4U0QC9S4tgHDATOGop8fefX0jIVlL7by6/Jrfk6XuDOJMtHUlIoC82ZgzE/ctzDCr/RuqSQh3RQRkuEV30+1HsPllisYiXlPoogiDIEnCJLWPVvoe3Chj/MZB9xyz0/ucvPJW1DMn/HMTc6yZg59ELyL9QoZtC0vKtq8FBiblBSvz9V8Iu36RsDGTILXn63iDOZGtHEhLoi9UP9TU5deTIDkpbHVUsD0esiJelFVeLfj+KnHp5P3KMCBhWmTWsMLyzZWeMvvs1XHPmMPbGJAIAfvjztH6ALghY/uXzKAgKw4Jr/4NTYTEWE60B5fz+8wsJOQMDGXJLrGMhDzk6ErGy8M7qoKQW8TJXEC84wAe7T5boHe+oEQGj31uVShfEAMCCXw/rPX1d/m6kntTsyTMsdwt+63MzZl0zCuebhJt9H6X8/vMLCTkD68iQW2IdC3lILUnvLuc1RUoRL7H6KD1jQ1FcWWOUmFs/4NLSbotgT/0WU7+3YlSCGk9lLdP92Vddh2Fbv8P2pQ8hq+gXpEV6K/73n19IyBk4IkNu652xPSRNK9BVhom3jupI3LGDEhu9OV5Yoav7ImZHXiHCAn1lzfUR+70V06boX7QqPWf0uNelS4j96F0sDfkUqwffjVmxg3HJrxEA5f3+e/pmheQeuGqJ3B7rWFhmLvF2yso9Dln9pYRVZZZWMwGa4npll2pQV+9fQjmuY2X2Scxcs9/sMSGXLuLhHV/hvt0/wM9EUb3aZlE49tA0+D34AOJjwmxuj6uwsB7Zisuvr2AgQw2BuaBCbIRAjo5EKR2UNbVpDGVmpNm82slSEKUC0DkmGO/c3RPxVSXASy8BixcDdeJF9dCmjeaYu+4CvFyfFWDtsnt+ISFrMZC5goEMeTqpNVQc1ZHkX6jA9rwLAFSSquM6mzW1aQwZ1nexNlCTsuWDXo2bI0eA558HVq0y/YJu3YBXXwVuugmQkItjyN66P6wLQ87CQOYKBjLk6erv/yPGsAKuIXs6NiV1alKmegx5qQC1HVNOpZU1GLd4u9k9pETvz549wH//C/zyi/iLwsKA48cBK/5Nk+teuWJKkVWBGyap/bfrxyeJyC62Jt6WVFZj/JJsDFqQhYlLczBw/kaMX5KNUoNqvuaYqyXjblLim1r9GrXB1zyx1U7mhAT64m0Lybmi96dHD+Dnn4GNG4HUVOPnZ860KogB5LlX2mX3UlaByUGO31HyfC4NZDZt2oRbb70VMTExUKlU+Pbbb/WeFwQBzz//PJo3b46AgAAMGTIER44ccU1jidyUrUvV7e3YnN2p2cvU5+QFTcKvNaxZXm5XKYHrrgO2bAHWrgW6dNE8FhMDPPqoVe2V6145e9m9kgJlch2XBjIVFRXo3r07Fi5cKPr8vHnz8Pbbb+P999/Hjh07EBQUhBtuuAGXL192ckuJ3JtYDRVzS3Xl6Nic3anJQexz6p8QiY0ZA7H8viTJ57F2ebm190ePSgXcdhuwdy+wfDnw5ptAQIDeIdoaOCcPHAWmTgXOnNF7Xq575cxl90oLlMl1XFpHZtiwYRg2bJjoc4Ig4H//+x+effZZDB8+HACwfPlyREVF4dtvv8Vdd90l+rqqqipUVV1dxlhWJm0PEyIlk1oBV0uOiqu2dGquznUw9znVScgWtLX+ibX3R/zNvYF77tF7yDDv5eV1C/GfvT9DWLIEqsceA556CggNlS0AcWZdGFYFJqncNkcmPz8fZ8+exZAhQ3SPhYSEICUlBdu2bTP5ujlz5iAkJET306pVK2c0l8gtSKmAC8jzzdqaKRN3y3UQ+5wsfSaA/QXpDN/X3mrC9adeYotPY8y+9QAAVWUlMGeOZsn2vHloE+QlW6Vsu0aXrOCORRfJPbltIHP27FkAQFRUlN7jUVFRuufEzJw5E6WlpbqfU6dOObSdREok1xYQUjs1JeQ6mMuh6RITjMyMNCxPT5ZlNZYcgZ3h1MvULSvhqzaoQVNcDDz9NNCuHT6o3IkBcaF6T9sSgGhHlzIz0rB0YpKsn0t93KaEpPK4LQr8/f3h7+/v6mYQuT05toCQMmWipB2QxT6T/g5YTi7HzuGGUy+vD5iAKh8/jN73K3wEg8o1Z84gYOpkLG3XDuefegZ/9b8RcZFN7PrcxTYTlRu3KSEp3DaQiY6OBgCcO3cOzZs31z1+7tw5XHPNNS5qFZHnkCVv4wpznZqSch3k/ExMkSuwM5x6ORscgf/eOAWLk27H9D8+wy25m41fdPQomj0wEc2uuUYz9XTDDTYV1XMWZ9wPUj63nVqKj49HdHQ0NmzYoHusrKwMO3bsQKpYXQUisonUvBpbuTrXwZY8FEd+JnKtIDI19XIiohW+fHIBsHMnMHSo+Iv37gWGDQPS0oCtWyW9nyt5eN1WspNLR2TKy8tx9OhR3Z/z8/Oxd+9eNG3aFK1bt8bjjz+Ol19+GQkJCYiPj8dzzz2HmJgYjBgxwnWNJiKruGoHZHetOixnYGd26iXQF1i3Dvj9d00Bvexs4xNs2gT06wfMnavJpXEz7noPyb24dIuCjRs3YuDAgUaPT5gwAZ988gkEQcCsWbPw4YcfoqSkBP3798eiRYvQvn17ye/BLQqIXM+RG0yaWtLtrFL6tiwpl7tt5qZeSiqrMXXFHgT89B0yNn2KhEKDBRBeXsD+/UCnTjZfj6O4+w7r7vRZeSLutXQFAxki9yFnrsOfp4rxzDcH9PYx0gZHhRVVkjbStIc9owVigV1SXBgm9I1D55gQWTvF+sGAl7oOI//KxLTNn6NF2ZX3vvdeYOlStxv9kLoZqiu422flqRjIXMFAhsiziHUiWtpv6xP7x9m1kaYUcowW5F+owF//lmLZ1uPIOVGse1zO0SqxYMCvtgbj9v6EZw79BJ+tW4DYWNHr6XouDzE9O+ODKYPsaoeptpkbzbB3M1RHcveRIk/BTSOJyCNNXbkXm48aBzHA1ZU/hgmwhuxNMJarfH58RBC+3PkPdp8s0Xtcrho7phKLq318sbT3cGxenw3Exopej19tDRZ98wrmPH07Cl+cA1y6ZHd7AOk1dFydJG4Kt05wPwxkiEgxtJ2I4a7UhuoEwaHF1ORaeeToTtFSMBAbFQJA/HrG/vkLWpWeQ9NLZQif9V+gfXtg8WKgttauNkktjuiuBfGUuMeYp2MgQ0SKYakT0YoLD3JoKX25Rgsc3SlKDQYMryew+hKmbF2lf7J//gEmTdLswv3VV4ANWQnWBm7O2g7BGu46UtSQuW1BPCIiQ5Y6ES9oKvFqO2hHFVOTa0m5nJ2iqZwTKdVxDa8nqPoSclp2xrDDIjVmcnOBO+8EevXSFNUbMkRyUT1riyO6Y0E8V5UTINOY7EtEiiKWaKnlzJUjci0ptzdxVOoKGkvBgNj13Ot9Hs9u+RQ+WZmmGzBokCagSbbcVlesRHLEEmlHlhOgq7hq6QoGMkSeRawT6dIiGK/e3hXdWoY6vbaHvaMF9naKTqlJ89tvwIwZwK5dpl94++3Ayy/r6tEA4kGEPe215t46Y4m0O40UeSIGMlcwkCHyTIadiNJqexh2yrZ0ik4d4RAEYM0a4JlnNNNLYry8gPHjUTLndUz9MU/0XgCwOnCz5d42pCXSnlqYj4HMFQxkiBoGpXRccgZcLqm1UlsLLFsGzJ6tSQA21KkTJjy+GJvzis3eC2sCN2vvrTsX05OT0oJ3a7GODBE1GEqq7SF1+bEULllB4+MDpKcDhw8D8+cDTZvqPX32qWeRdazI4r2QujGnLfe2oSyRlvN3SckYyBCR4iml45I74HJprZWAAOCJJ4C8POC554CgICAlBYdSTFcB9lbXWX0vbLm3DWGJtJKCd0djIENEiqeUjssRAZfLa62EhAAvvggcOwYsXYpYE591tzOHkfXB/eiybo1VRfVsubfuWkxPTkoJ3p2BgQwRKZ5SOi5HBFzaWiuZGWlYOjEJmRlpWJ6e7PwciagooGNHk/fi6axlaFlWgMjHHga6ddMkDl8ZTcgrKEdm7nnRUQRb763LAzwHU0rw7gxM9iUij6CU2h5KSUq2h+G96Hd8Lz7/4lmj42p7J+H16+7FBz6xusfE7pk999aTl0h7+u8SVy1dwUCGqGFx945LKQGXHLT3oufrzyNkyQcmj9sU1wOvDxiP/c0TzHbE7n5vnc3Tf5cYyFzBQIaI3FGD6pQFAVi/Hpg5E9hjekXNj4n9sODae5AX3tJjlkg7g6f+LjGQuYKBDBGRPpcVUFOrgdWrNaucjhwRPaRW5YXVXYcg9u3X0Pe6a5zXNnI7DGSuYCBDRKThNgXUampQ8PZ7qJv9AqLLi0QPUfv7w+vRRzWjOOHhzmsbuQ0WxCMiIj1uU0DN1xeRT0zFM/O+wdyB96GkUWOjQ7yqqoAFCzQ/RGYwkCEiagDcsYDaGxNScXDcgxjw4GK8mzoalb7++geEhAAZGU5vFykLAxkiIhmYq4XiDtyxgJq2Bs7aZ29B56Xv4MKeg8DkyZptEADgqaeQV+fn1p8ruZ6PqxtARKRkbpN3YoE7F1CLj9AmHTcD3n0XmD4dVXNfw+TQfvit3uaPep/rBx8A0dHAbbcBBsXyqGHhiAwRkR3cJu/EAqVUPwYAtGmDSSnpyDylPwqj+1zPndPs8zRiBNC3L5Bleqdr8nwMZIiIbOSOeSfmKKVsv6XPtfTZ2UDFlc92+3YgLQ248UZg926nt5Vcj1NLREQ2kpJ34k4jHdqcFHcvoGbuc40pO48my5YYP7FuneZnzBjgpZeAhAQHtpDcCUdkiIhs5M55J+bERwRhYGIztwxiAPOf65kmEbjw7gdA27biB3zxBdCxI/Dgg8C//zqoheROGMgQEdlIUXknCmLuc722fRSaPTAROHQIeO89TcKvobo64MMPgXbtgKeeAorEi+6RZ2AgQ0RkB6XknSiNxc/V1xd46CHg2DFgzhwgNNT4JJcvA6+/DrRpA7zyytW8GvIo3KKAiEgG7p53olSSP9fiYmDePOCtt4BLl8SPiYoCDhwAIiLEnye3wr2WrmAgQ0TUgJw+rUn2XbwYqK3Vf+6WW4Dvv3dNu8hq3GuJiIganpgYTe7MoUPA2LFXH1epNNNL5HEYyBARkedp1w5YsQLYswcYNgy4+26gWzfxY9VqIDvbue0j2TCQISIiz3XNNcBPPwEff2z6mC++AFJSNFNPf/7ptKaRPBjIEBGR5/PzE3+8pgZ47jnN///4I9CjBzBunGY1FCkCAxkiImq4lizRD1oEQTMl1aED8MgjwJkzrmsbScJAhoiIGq6gIM2ybEO1tZqk4bZtgf/+FygpcXrTSBoGMkRE1HDdcw9w9Cjw8suA2BLfS5c0Bffi44HXXgMqze+vRc7HQIaIiBq2xo2BZ54B8vKAJ58EGjUyPqakBJgxQ7Ma6v33Nbk15BYYyBAREQFAeLimOvCRI8CkSYC3t/ExZ84ADz+s2ZiSxfXcAgMZIiKi+lq21Gw6efAgMHq0+DHHjgFnzzq3XSSKgQwREZGY9u01NWZ27gSGDjV+buJE17SL9DCQISIiMqdXL2DdOuD33zWF8wDNfk4+PuLH19U5r23EQIaIiEiSgQOBbds0Qc0dd5g+7p57ND95ec5rWwPGQIaIiEgqlUozzeRlovvcswdYuRL47DNNUb0pU4Bz55zbxgaGgQwREZFc/vvfq/9fUwO8+y7Qpg3w7LNAaanr2uXBGMgQERHJ4d9/gS1bjB+vrAReeUUT0MyfrymyR7JhIENERCSHFi00eTHTpwP+/sbPFxVpCu4lJAAffaTZBoHsxkCGiIhILhERwIIFwOHDwH33iefS/Psv8MADQOfOwOrVgFrt/HZ6EAYyREREcmvdWrOz9oEDwKhR4sccPqwpuJeUBKxfr9l5m6zGQIaIiMhROnYEvvoKyM4GhgwRP2b3buDWW7m6yUYMZIiIiBwtKQn49VfNT+/exs9PngxERzu/XR6AgQwREZGzDBmiGZ35+mtNnRlAs/v2zJmubZeCKSKQWbhwIeLi4tCoUSOkpKQgOzvb1U0iIiKyjUoFjBwJ7N+vyaOZMweIjBQ/9uJFYMYM4Px557ZRQdw+kPniiy8wffp0zJo1C7t370b37t1xww034DxvKhERKZmPj2Zl06OPmj7mzTeB114D2rYFZs0Cysqc1z6FUAmCe6dJp6SkICkpCe+++y4AQK1Wo1WrVpgyZQpmzJhhdHxVVRWqqqp0fy4rK0OrVq1QWlqK4OBgp7WbiIjILhcuaIroXbx49bHwcE314EceARo1cl3bnKCsrAwhISEW+2+3HpGprq7Grl27MKRepreXlxeGDBmCbdu2ib5mzpw5CAkJ0f20atXKWc0lIiKSz2uv6QcxAFBYCDzxBNC+PfDxxyyqBzcPZC5cuIC6ujpERUXpPR4VFYWzZ8+KvmbmzJkoLS3V/Zw6dcoZTSUiIpLX5MnAhAniRfVOnQLS04GuXTWJw+49ueJQbh3I2MLf3x/BwcF6P0RERIoTFwd88gmwbx8wYoT4MX//DdxxB5CSAmzY4MTGuQ+3DmQiIiLg7e2NcwZFgs6dO4dorrcnIqKGoHNn4JtvgG3bgLQ08WNycjRLu4cM0fx/A+LWgYyfnx969eqFDfWiTLVajQ0bNiA1NdWFLSMiInKyPn2A338H1q0DevYUP2bDBiA5WbPCqYFw60AGAKZPn46PPvoIy5Ytw6FDh/Dwww+joqICEydOdHXTiIiInEulAoYO1Yy6fPGFZidtMQMHOrddLuTj6gZYMmbMGBQUFOD555/H2bNncc011+CXX34xSgAmIiJqMLy8NBtO3n67Jo9m9mzg9GnNczfcYHoKygO5fR0Ze0ldh05ERKRYly4BCxcCc+dqdtI2NfV05IhmT6cmTZzbPht4RB0ZIiIikiAgAMjI0CzLNhXECAIwdqymSvDbbwP1iscqGQMZIiIiTxEQYPq5r78Gdu0CCgqAxx4DEhOBZcuAujrntc8BGMgQERF5utpa4Nln9R87cQK4916gWzfg228VW1SPgQwREZGnKynRjMCIOXhQkzScmgps3OjMVsmCgQwREZGni4gA1q4FtmwBrr1W/JgdOzTLtm+4QTMFpRAMZIiIiBqKvn2BrCzgp5+Aa64RP2b9eqB3b2DMGODwYac2zxYMZIiIiBoSlQoYNkwz6rJypWYVk5gvvwQ6dQIeeAAoKnJuG63AQIaIiKgh8vIC7roLOHQIeO89oHlz42Pq6oDvvwf8/Z3fPokYyBARETVkvr7AQw8BR49qCuqFhuo//9xzQFCQS5omBQMZIiIiAgIDgaefBvLygJkzNTVp2rQB7r/f9GvcYMk2AxkiIiK6KiwMePVV4NgxYMUKwM9P/Lj8fKBjR+Czz1xaVI+BDBERERlr3hxISTH9/KxZQG4uMHGiZmsEF2EgQ0RERNbZv18zEgMADz4IxMW5rCkMZIiIiMg6K1Zo8mMCA423PnAyH5e+OxERESnPq68C/ftrppSio13aFAYyREREZB2VCrj5Zle3AgCnloiIiEjBGMgQERGRYjGQISIiIsViIENERESKxUCGiIiIFIuBDBERESkWAxkiIiJSLAYyREREpFgMZIiIiEixGMgQERGRYjGQISIiIsViIENERESKxUCGiIiIFMvjd78WBAEAUFZW5uKWEBERkVTaflvbj5vi8YHMxYsXAQCtWrVycUuIiIjIWhcvXkRISIjJ51WCpVBH4dRqNU6fPo0mTZpApVLJeu6ysjK0atUKp06dQnBwsKzndheefo2efn0Ar9ETePr1AZ5/jZ5+fYD81ygIAi5evIiYmBh4eZnOhPH4ERkvLy+0bNnSoe8RHBzssb+YWp5+jZ5+fQCv0RN4+vUBnn+Nnn59gLzXaG4kRovJvkRERKRYDGSIiIhIsRjI2MHf3x+zZs2Cv7+/q5viMJ5+jZ5+fQCv0RN4+vUBnn+Nnn59gOuu0eOTfYmIiMhzcUSGiIiIFIuBDBERESkWAxkiIiJSLAYyREREpFgMZOywcOFCxMXFoVGjRkhJSUF2drarmySb2bNnQ6VS6f106NDB1c2y2aZNm3DrrbciJiYGKpUK3377rd7zgiDg+eefR/PmzREQEIAhQ4bgyJEjrmmsjSxd47333mt0T2+88UbXNNYGc+bMQVJSEpo0aYJmzZphxIgRyM3N1Tvm8uXLmDx5MsLDw9G4cWOMGjUK586dc1GLrSPl+tLS0ozu4UMPPeSiFlvvvffeQ7du3XQF01JTU/Hzzz/rnlfy/dOydI1Kv4eG5s6dC5VKhccff1z3mLPvIwMZG33xxReYPn06Zs2ahd27d6N79+644YYbcP78eVc3TTadO3fGmTNndD+bN292dZNsVlFRge7du2PhwoWiz8+bNw9vv/023n//fezYsQNBQUG44YYbcPnyZSe31HaWrhEAbrzxRr17unLlSie20D5ZWVmYPHkytm/fjl9//RU1NTUYOnQoKioqdMdMmzYN33//PVavXo2srCycPn0aI0eOdGGrpZNyfQAwadIkvXs4b948F7XYei1btsTcuXOxa9cu7Ny5E4MGDcLw4cPx119/AVD2/dOydI2Asu9hfTk5Ofjggw/QrVs3vcedfh8FsklycrIwefJk3Z/r6uqEmJgYYc6cOS5slXxmzZoldO/e3dXNcAgAwjfffKP7s1qtFqKjo4XXX39d91hJSYng7+8vrFy50gUttJ/hNQqCIEyYMEEYPny4S9rjCOfPnxcACFlZWYIgaO6Zr6+vsHr1at0xhw4dEgAI27Ztc1UzbWZ4fYIgCNddd53w2GOPua5RDhAWFiYsXrzY4+5ffdprFATPuYcXL14UEhIShF9//VXvmlxxHzkiY4Pq6mrs2rULQ4YM0T3m5eWFIUOGYNu2bS5smbyOHDmCmJgYtGnTBuPGjcPJkydd3SSHyM/Px9mzZ/XuZ0hICFJSUjzqfgLAxo0b0axZMyQmJuLhhx9GYWGhq5tks9LSUgBA06ZNAQC7du1CTU2N3n3s0KEDWrdurcj7aHh9Wp9//jkiIiLQpUsXzJw5E5WVla5ont3q6uqwatUqVFRUIDU11ePuH2B8jVqecA8nT56Mm2++We9+Aa75e+jxm0Y6woULF1BXV4eoqCi9x6OiovD333+7qFXySklJwSeffILExEScOXMGL7zwAq699locOHAATZo0cXXzZHX27FkAEL2f2uc8wY033oiRI0ciPj4ex44dw3//+18MGzYM27Ztg7e3t6ubZxW1Wo3HH38c/fr1Q5cuXQBo7qOfnx9CQ0P1jlXifRS7PgC4++67ERsbi5iYGOzbtw9PP/00cnNzsWbNGhe21jr79+9HamoqLl++jMaNG+Obb75Bp06dsHfvXo+5f6auEfCMe7hq1Srs3r0bOTk5Rs+54u8hAxkSNWzYMN3/d+vWDSkpKYiNjcWXX36J9PR0F7aMbHXXXXfp/r9r167o1q0b2rZti40bN2Lw4MEubJn1Jk+ejAMHDig6b8scU9f3wAMP6P6/a9euaN68OQYPHoxjx46hbdu2zm6mTRITE7F3716Ulpbiq6++woQJE5CVleXqZsnK1DV26tRJ8ffw1KlTeOyxx/Drr7+iUaNGrm4OACb72iQiIgLe3t5GWdjnzp1DdHS0i1rlWKGhoWjfvj2OHj3q6qbITnvPGtL9BIA2bdogIiJCcff00UcfxQ8//IDMzEy0bNlS93h0dDSqq6tRUlKid7zS7qOp6xOTkpICAIq6h35+fmjXrh169eqFOXPmoHv37njrrbc85v4Bpq9RjNLu4a5du3D+/Hn07NkTPj4+8PHxQVZWFt5++234+PggKirK6feRgYwN/Pz80KtXL2zYsEH3mFqtxoYNG/TmQT1JeXk5jh07hubNm7u6KbKLj49HdHS03v0sKyvDjh07PPZ+AsA///yDwsJCxdxTQRDw6KOP4ptvvsHvv/+O+Ph4ved79eoFX19fvfuYm5uLkydPKuI+Wro+MXv37gUAxdxDMWq1GlVVVYq/f+Zor1GM0u7h4MGDsX//fuzdu1f307t3b4wbN073/06/jw5JIW4AVq1aJfj7+wuffPKJcPDgQeGBBx4QQkNDhbNnz7q6abJ44oknhI0bNwr5+fnCli1bhCFDhggRERHC+fPnXd00m1y8eFHYs2ePsGfPHgGA8MYbbwh79uwRTpw4IQiCIMydO1cIDQ0V1q5dK+zbt08YPny4EB8fL1y6dMnFLZfO3DVevHhRyMjIELZt2ybk5+cLv/32m9CzZ08hISFBuHz5squbLsnDDz8shISECBs3bhTOnDmj+6msrNQd89BDDwmtW7cWfv/9d2Hnzp1CamqqkJqa6sJWS2fp+o4ePSq8+OKLws6dO4X8/Hxh7dq1Qps2bYQBAwa4uOXSzZgxQ8jKyhLy8/OFffv2CTNmzBBUKpWwfv16QRCUff+0zF2jJ9xDMYYrsZx9HxnI2OGdd94RWrduLfj5+QnJycnC9u3bXd0k2YwZM0Zo3ry54OfnJ7Ro0UIYM2aMcPToUVc3y2aZmZkCAKOfCRMmCIKgWYL93HPPCVFRUYK/v78wePBgITc317WNtpK5a6ysrBSGDh0qREZGCr6+vkJsbKwwadIkRQXeYtcGQFi6dKnumEuXLgmPPPKIEBYWJgQGBgq33367cObMGdc12gqWru/kyZPCgAEDhKZNmwr+/v5Cu3bthCeffFIoLS11bcOtcN999wmxsbGCn5+fEBkZKQwePFgXxAiCsu+flrlr9IR7KMYwkHH2fVQJgiA4ZqyHiIiIyLGYI0NERESKxUCGiIiIFIuBDBERESkWAxkiIiJSLAYyREREpFgMZIiIiEixGMgQERGRYjGQISIiIsViIENEbiEtLQ2PP/64q5tBRArDQIaIFMXWgOfee+/FiBEjZG8PEbkWAxkiIiJSLAYyROR2Fi1ahISEBDRq1AhRUVG44447AGhGVbKysvDWW29BpVJBpVLh+PHjqKurQ3p6OuLj4xEQEIDExES89dZbuvPNnj0by5Ytw9q1a3Wv27hxo4uujojk5OPqBhAR1bdz505MnToVn376Kfr27YuioiL88ccfAIC33noLhw8fRpcuXfDiiy8CACIjI6FWq9GyZUusXr0a4eHh2Lp1Kx544AE0b94co0ePRkZGBg4dOoSysjIsXboUANC0aVOXXSMRyYeBDBG5lZMnTyIoKAi33HILmjRpgtjYWPTo0QMAEBISAj8/PwQGBiI6Olr3Gm9vb7zwwgu6P8fHx2Pbtm348ssvMXr0aDRu3BgBAQGoqqrSex0RKR+nlojIrVx//fWIjY1FmzZtcM899+Dzzz9HZWWlxdctXLgQvXr1QmRkJBo3bowPP/wQJ0+edEKLiciVGMgQkVtp0qQJdu/ejZUrV6J58+Z4/vnn0b17d5SUlJh8zapVq5CRkYH09HSsX78ee/fuxcSJE1FdXe28hhORSzCQISK34+PjgyFDhmDevHnYt28fjh8/jt9//x0A4Ofnh7q6Or3jt2zZgr59++KRRx5Bjx490K5dOxw7dkzvGLHXEZHyMUeGiNzKDz/8gLy8PAwYMABhYWH46aefoFarkZiYCACIi4vDjh07cPz4cTRu3BhNmzZFQkICli9fjnXr1iE+Ph6ffvopcnJyEB8frztvXFwc1q1bh9zcXISHhyMkJAS+vr6uukwikglHZIjIrYSGhmLNmjUYNGgQOnbsiPfffx8rV65E586dAQAZGRnw9vZGp06dEBkZiZMnT+LBBx/EyJEjMWbMGKSkpKCwsBCPPPKI3nknTZqExMRE9O7dG5GRkdiyZYsrLo+IZKYSBEFwdSOIiIiIbMERGSIiIlIsBjJERESkWAxkiIiISLEYyBAREZFiMZAhIiIixWIgQ0RERIrFQIaIiIgUi4EMERERKRYDGSIiIlIsBjJERESkWAxkiIiISLH+D9tMdOSpUYNXAAAAAElFTkSuQmCC", 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", 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" ] @@ -1540,17 +1549,17 @@ "id": "74be2100", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.056158Z", - "iopub.status.busy": "2025-04-03T04:44:00.055868Z", - "iopub.status.idle": "2025-04-03T04:44:00.204012Z", - "shell.execute_reply": "2025-04-03T04:44:00.203709Z" + "iopub.execute_input": "2025-04-03T19:32:50.888362Z", + "iopub.status.busy": "2025-04-03T19:32:50.887998Z", + "iopub.status.idle": "2025-04-03T19:32:50.983668Z", + "shell.execute_reply": "2025-04-03T19:32:50.983019Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1588,10 +1597,10 @@ "id": "db8bc925", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.205721Z", - "iopub.status.busy": "2025-04-03T04:44:00.205598Z", - "iopub.status.idle": "2025-04-03T04:44:00.291490Z", - "shell.execute_reply": "2025-04-03T04:44:00.291204Z" + "iopub.execute_input": "2025-04-03T19:32:50.987634Z", + "iopub.status.busy": "2025-04-03T19:32:50.987335Z", + "iopub.status.idle": "2025-04-03T19:32:51.093124Z", + "shell.execute_reply": "2025-04-03T19:32:51.092860Z" }, "lines_to_next_cell": 0 }, @@ -1608,7 +1617,7 @@ }, { "data": { - "image/png": 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", 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vBwAASkeD3+E7dC33bCCFyp5DHCftYHfCCSeIZoXceeedRb2XtmJrS7P988knn0Q2nwAAAMhoC3G3bt2kZcuWsnz58ibP6+Pa2lrHv9HnTaa3g2HNG3722Web5I3otIWd9jZt2mRVnnD73OrqausHAADApiPgkaaRfmVtIW7Tpo3sueeeMm3atMbntFOdPh4+fLjj3+jz+dMrrRSRP70dDM+bN0+eeeYZ6dq16e0TnXb16tVW/rJNg2b9bO3kBwAAYNKRb9+bnpXR97wiYyfNsX7rY30e6VL2lAntIHfPPffIfffdZ1V/OOecc6wqEqeffrr1utYQzu90N3bsWKsixC233GLlGY8fP15ef/11Oe+88xqD4e9973vWcw888ICVo6x5wfqjnfjULrvsIqNGjZIzzjjDqnk8ffp06+9POukkowoTAAAg2zToPef+Wc2GjV5Wt856nqA4XcqaMmGXUfvss8/kmmuusYJWLZ+mAa/dcW7RokVW9QfbPvvsIw8++KBcddVVcuWVV0q/fv2sChMDBgywXl+8eLE8+eST1v/1vfI999xzcsABB1j/12BZg+CDDz7Yev/jjz9efvOb35TwmwMAgLSmSWiJN6e6tfqcJkzo61r1gvSJdCh7HeK0og4xAADZNGP+51Z6hB8d2IiqF+mI18qeMgEAAJAm2oEuyulQfgTEAAAAAWg1iSinQ/kREAMAAASgpdV0WGi37GB9Xl/X6ZAOBMQAAAABaEe5cd/tb/2/MCi2H+vrdKhLDwJiAACAgEYN6Cl3njJYamuapkXoY31eX0d6lL3sGgAAQBpp0Kul1RipLv0IiAEAAELS4JfSaulHygQAAAAyjYAYAAAAmUZADAAAgEwjIAYAAECmERADAAAg0wiIAQAAkGkExAAAAMg0AmIAAABkGgExAAAAMo2AGAAAAJlGQAwAAIBMIyAGAABAphEQAwAAINMIiAEAAJBpBMQAAADINAJiAAAAZBoBMQAAADKNgBgAAACZRkAMAACATCMgBgAAQKYREAMAACDTCIgBAACQaQTEAAAAyDQCYgAAAGQaATEAAAAyjYAYAAAAmUZADAAAgEwjIAYAAECmERADAAAg0wiIAQAAkGkExAAAAMg0AmIAAABkGgExAAAAMo2AGAAAAJlGQAwAAIBMIyAGAABAphEQAwAAINMIiAEAAJBpBMQAAADINAJiAAAAZBoBMQAAADKNgBgAAACZRkAMAACATCMgBgAAQKYREAMAACDTCIgBAACQaQTEAAAAyDQCYgAAAGQaATEAAAAyjYAYAAAAmUZADAAAgEwjIAYAAECmERADAAAg0wiIAQAAkGkExAAAAMg0AmIAAABkGgExAAAAMo2AGAAAAJlGQAwAAIBMIyAGAABAprUq9wwAAIDK1LA5J68uWCUrvlgn3Tu2lSF9u0jLFlXlni2gGQJiAAAQuSlzl8q1T70rS+vWNT7Xs6atjPtufxk1oGdZ5w0oRMoEAACIPBg+5/5ZTYJhtaxunfW8vg4kCQExAACINE1CW4ZzDq/Zz+nrOh2QFATEAAAgMpozXNgynE/DYH1dpwOSgoAYAABERjvQRTkdUAoExAAAIDJaTSLK6YBSICAGAACR0dJqWk3CrbiaPq+v63RAUhAQAwCAyGidYS2tpgqDYvuxvk49YiQJATEAAIiU1hm+85TBUlvTNC1CH+vz1CFG0jAwBwAAiJwGvYf0r2WkOqQCATEAAIiFBr/Dd+ha7tkAfJEyAQAAgEwjIAYAAECmERADAAAg0wiIAQAAkGkExAAAAMg0AmIAAABkGgExAAAAMo2AGAAAAJlGQAwAAIBMIyAGAABAphEQAwAAINNCB8QvvviinHLKKTJ8+HBZvHix9dz//u//yksvvRTl/AEAAADJC4j/+te/ymGHHSbt2rWT2bNny/r1663n6+rq5IYbboh6HgEAAIBkBcTXX3+93HXXXXLPPfdI69atG58fMWKEzJo1K8r5AwAAAJIXEH/wwQey3377NXu+pqZGVq9eHcV8AQAAAMkNiGtra+Wjjz5q9rzmD2+//fZRzBcAAACQ3ID4jDPOkLFjx8rMmTOlqqpKlixZIg888ID813/9l5xzzjnRzyUAAAAQk1Zh/ujyyy+XzZs3y8EHHyxffvmllT5RXV1tBcTnn39+9HMJAAAAxKQql8vlwv7xhg0brNSJNWvWSP/+/WWLLbaQrKivr7dyprWyRqdOnco9OwAAAAgZr4VqIba1adPGCoQBAACAtAoVEB977LFW7nAhfa5t27ay4447yg9+8AP51re+FcU8AgAAAMnqVKdNz88++6xVc1iDYP3RATr0uU2bNsnDDz8sAwcOlOnTp0c/xwAAAEASyq5pC/C//vUva9Q6/Zk/f741lPMOO+wg7733npx22mly2WWX+b7XHXfcIX369LFalocOHSqvvvqq5/SPPPKI7Lzzztb0u+22m0yePLnJ648++qgceuih0rVrVytQnzNnTrP3OOCAAxoDefvn7LPPDrEkAAAAkMmA+Pe//71ccMEF0qLFf/5c/68VJu6++24rwDzvvPNk7ty5nu+jLckXXXSRjBs3zmpt1lZlHRJ6xYoVjtO//PLLMnr0aBkzZozVIn3MMcdYP/mfs3btWtl3333lpptu8i0dt3Tp0safm2++OfByAAAAQEYDYk2LeP/995s9r881NDRY/9cWXKc843y33nqrFZiefvrpVuc8HQ66ffv28oc//MFx+ttvv11GjRoll1xyieyyyy5y3XXXyeDBg2XixImN0/y///f/5JprrpGRI0d6frZ+jrZ02z9UigAAAMimUAGxBp3aSvvrX//aGp1Of/T/+typp55qTfP888/Lrrvu6lmy7Y033mgSuGorsz6eMWOG49/o84WBrrYou03vRQcS6datmwwYMECuuOIKq54yAAAAsidUlQkNfnv06GGlGSxfvtx6Th9feOGFjXnDmserrbluVq5cabUm69/l08dOrc9q2bJljtPr80Fo/vN2220nvXr1krfeesua5w8++MDKP3azfv166ye/rh0AAAAyGhC3bNlSfvazn1k/dmBYmHLQu3dvSaozzzyz8f/aMa9nz57WqHvaMVA7BTqZMGGCXHvttSWcy8rXsDknry5YJSu+WCfdO7aVIX27SMsW3mk2AIDk4/iOtClqYA4VNvdW0xU0sLZbmG36WHN6nejzQaY3pdUtlI665xYQa1qFdgC06YXAtttuW9TnZtmUuUvl2qfelaV16xqf61nTVsZ9t7+MGtCzrPMGAAiP4zsyk0Os/vKXv8gJJ5wgw4YNszq25f+YjnK35557yrRp0xqf27x5s/V4+PDhjn+jz+dPr6ZOneo6vSm7NJu2FLuprq62gv/8H4Q/WJ5z/6wmB0u1rG6d9by+DgBIH47vyFRA/Jvf/MaqDKH5u1r+bMiQIVbdX61LfPjhhxu/j7a43nPPPXLfffdZtYvPOeccq2yavrfSDnraMmsbO3asTJkyRW655RYrz3j8+PHy+uuvWyXebKtWrbIC3Hfffdd6rLnB+tjOM9a0CK1OoR36Fi5cKE8++aT1Ofvtt5/svvvuYRYHAt5G05aDnMNr9nP6uk4HAEgPju/IXED8u9/9zqo3/Nvf/tZq6b300kutltqf/vSnUldXZ/w+J554ovzqV7+yyqQNGjTIClw14LU7zi1atMiqEWzbZ5995MEHH7Q+W2sWayv1448/blWKsGmAu8cee8iRRx5pPT7ppJOsx1rSTen8PvPMM1anPx3g4+KLL5bjjz9ennrqqTCLAgFpTllhy0E+PUzq6zodACA9OL4jzapyuVzgSzWt4astulqpoXv37lYwrAHqvHnzrBSKzz//XCqd5hDrENZ6AUD6hLkn5iyWsZOajx5Y6PaTBsnRg7YuyTwBAIrH8R1pjtdCD92sqQl2NYlXXnnF+v+CBQskRHyNDNHexlFOBwBIBtPjdrcO1bHPCxBUqID4oIMOslITlOb7av3hQw45xEqBOPbYY8O8JTJCS+9ob2O34jv6vL6u0wEA0uPfa/9Tq9/LxY+86dq5TvOLZ8z/3Gpt1t/kGyPRKRNaDUJ/WrX6umrbpEmT5OWXX5Z+/frJWWedZeXpVjpSJorvhazyNz47SL7zlMGU5gGAFNHAdd+bnvXMIfY71lOuDeWM1wIHxJs2bZIbbrhBfvSjH8k222wjWUVAXBwOfABQObQ1d/Q9X6dPmtCguLamrbx02UHWgB12Q0lhQEJDCUoVrwUemENbhXXIZi1VBoSlB7ZD+tcykhEAVAA9jgeRX3FCj/1e5dr0rKCv6zmDcwQSNVKdDnP8/PPPS58+faKfI2SGHtiG79C13LMBAChS2I7QGkgHKdfGOQOJCoh18I3LL79c3n77bWu0uQ4dOjR5/aijjopq/gAAQEo6TOuIdLmAgbRp63LQVmgg9oD4Jz/5ifX71ltvbfZaVVWVNDQ0hHlbAACQ0jt+2gdE84A1qSFnmEOsgbTpQB2U40Tiyq7ZVSacfgiGAQDIZt8Q7fymga4XOwtYA2gNpCnHidS2EOdbt26dtG3LVRsAAFlX2GF64cov5aFXF8my+v+kO9QWVBTyal0uDJ6BRNUh1lZgLb121113yfLly+XDDz+U7bffXq6++mqro92YMWOk0lF2DQAAsxrFJhWFKMeJVJVdU7/4xS/kvvvus8qvnXHGGY3PDxgwQG677bZMBMQAACC6ikKU40Q5hQqI//SnP8ndd99tlV87++yzG58fOHCgvP/++1HOHwAAyAjKcSJVneoWL14sO+64Y7PntVPdxo0bo5gvAAAAILkBcf/+/eXFF19s9vxf/vIX2WOPPaKYLwAAACC5KRPXXHONnHbaaVZLsbYKP/roo/LBBx9YqRRPP/109HMJAAAAJKmF+Oijj5annnpKnnnmGWuUOg2Q33vvPeu5Qw45JPq5BAAAAJJUdg2UXQMAAKiUeC1UC/GPf/xj+ec//1nM/AEAAACJECog/uyzz2TUqFGy7bbbyiWXXCJz5syJfs4AAACApAbETzzxhCxdutQame61116TPffcU3bddVdr9LqFCxdGP5cAACAzI9vNmP+5PDFnsfVbHwOpyCH+9NNP5aGHHpI//OEPMm/ePNm0aZNUuqTlEJsOjQkAQFIxfDNSNXRzPh2I4/XXX5eZM2darcM9evQo9i0REAcQAEAlnMvOuX+WFLbSLatbZz1/5ymDOachWSkT6rnnnpMzzjjDCoB/+MMfWlG31iDW1mKU/gCSHwznH0D0dQAAkkzvcmrDjtMta/s5fZ30CcQlVAvx1ltvLatWrbI61t19993y3e9+V6qrq6OfOxR1ANGECX39kP61pE8AABJLU/4KG3YKz2n6uk43fIeuJZ03ZEOogHj8+PHy/e9/Xzp37hz9HMEYBxAAQCXQ/i9RTgeUJGVCUyU0GP7oo4/k//7v/+Srr76ynmeMj9LiAAIAqATaGTzK6YCSBMSff/65HHzwwbLTTjvJEUccYZVgU2PGjJGLL744zFsiBA4gAIBKoJWRtDO4W3KfPq+v63RAYgLiCy+8UFq3bi2LFi2S9u3bNz5/4oknypQpU6KcP3jgAAIAqATaz0UrI6nCc5r9WF+nP0z5NFR4fehQOcT/+Mc/rFSJbbbZpsnz/fr1k48//jiqeYPhAUSrSeghIn/T5AACAEgTLammpdUKy4jWUka07KZkoLxrqIB47dq1TVqGbVp5gmoTpcUBBABQKfScpZWRGGgqOaZkpD50qID429/+tvzpT3+S6667znpcVVUlmzdvlptvvlkOOOCAqOcRPjiAAAAqhZ67qIyUDA0ZKu8aKiDWwFc71ekIdRs2bJBLL71U3nnnHauFePr06dHPJXxxAAGShyHVAaTZqxkq7xoqIB4wYIB8+OGHMnHiROnYsaOsWbNGjjvuODnzzDPl+uuvtwbrAFCZCPLMZCHnDkBlW5Gh8q5VuQiLB7/55psyePBgaWhokEpXX18vNTU1UldXZw1bDWQBQV5xOXf2ZUOl5NwBqGwz5n8uo+95xXe6h84YltgWYtN4LVTZNQDZDfIKb5/ZHSv0dfjn3Cl9vdJKFgGoPFkq70pADMAXQV48OXcAkGQtM1QfmoAYgC+CPHNZyrkDkJ3yrrU1TUe91ceVlP4VqFOddpzzsnr16mLnB0ACEeSZY0h1AJVmVAbKuwYKiDUp2e/1U089tdh5ApAwBHnBc+40t9opgaTqm5aVSsi5A5AdLSu8vGuggPjee++Nb04AJNae220pXTq0kVVrNzi+TpD3HwypDgDpQw4xAE9aPWL/Xz7nGQwrgrzs5dwBQKUINTAHgGzX0y0M8qhDnM2cOwCoFATEAAKXWrN16dBanr/kQGnTiptNWcy5A4BKwVkMQKhSa2rV2o3yxsf/Ltk8AQAQBwJiAI4otQYAyAoCYgCOKLUGAMgKcogBOKKeLoC094OgUytMERADcEQ9XQBprpCjnYLz+0HoBT4VceCGlAkArqinCyCt5SILOwXr3S59Xl8HCtFCDMAT9XQBVEK5SH1Oj1r6uh7TOIYhHwExAF/U0wVQCeUiNSjW13U6jmnIR8oEAACoCJSLRFgExAAAoCJQLhJhERADAICKKhfplh2sz+vrlItEIQJiAABQUeUiVWFQTLlIeCEgBgAAFYNykQiDKhMAAKCiUC4SQREQAwCAikO5SARBygQAAAAyjYAYAAAAmUZADAAAgEwjIAYAAECmERADAAAg0wiIAQAAkGkExAAAAMg0AmIAAABkGgNzAAisYXOOEaAAABWDgBhAIFPmLpVrn3pXltata3yuZ01bGffd/tZwqQCguHBGmhAQAwgUDJ9z/yzJFTy/rG6d9fydpwwmKAbAhTNShxxiAMatPXqCKwyGlf2cvq7TAcgu+8I5PxjOv3DW14GkISAGYERvfRae4PJpGKyv63QAsokLZ6QVATEAI5oHGOV0ACoPF85IKwJiAEa0U0yU0wGoPFw4I60IiAEY0R7i2inGrY+4Pq+v63QAsokLZ6QVATEAI1ouSXuIq8Kg2H6sr1NWCcguLpyRVgTEAIxpuSQtrVZb07R1Rx9Tcg0AF85Iq6pcLkdXzxDq6+ulpqZG6urqpFOnTuWeHaCkKLgPwAt1iJG2eI2AOCQCYgAA3HHhjDTFa4xUBwAAIqfB7/AdupZ7NgAj5BADAAAg02ghBmCE258AgEpFQAzAFx1kAACVjJQJAL7B8Dn3z2o2HOuyunXW8/o6AABpRkCMULfOZ8z/XJ6Ys9j6rY9RmXTdasuw0xq2n9PX2QYAAGlGygQC4dZ5tmjOcGHLcD4Ng/V1nY7e5ACAtKKFGMa4dZ492oEuyukAAEgiAmIY4dZ5Nmk1iSinAwAgiQiIEfmtc1QOLa2mKTFuxdX0eX1dpwMAIK0IiGGEW+fZpHWGNT9cFQbF9mN9nXrEAIA0IyCGEW6dZ5d2lrzzlMFSW9N03epjfZ7OlACAtKPKBALdOtcOdE5ZwlXfBEjcOq9MGvQe0r+WkeoAABWJgBiBbp1rNQkNgfKDYm6dZ4OuW0qrASgXho9HnAiIEfjWeWEdYm0Zpg4xACAu1MBH3KpyuRx1skKor6+Xmpoaqaurk06dOkmWcJUOACh1DfzCYMU+69CXAVHEa7QQIzBunScfFy0AslADX49q+rr2ceAYh2IQEAMVhluLACoFw8ejVCi7BlQQhtcGUEmogY9SISAGKgTDawOoNNTAR6kQEAMVguG1AVQaho9HqRAQAxWCW4sAKg3Dx6NUCIiBCsGtRQCViOHjkYmA+I477pA+ffpI27ZtZejQofLqq696Tv/II4/IzjvvbE2/2267yeTJk5u8/uijj8qhhx4qXbt2laqqKpkzZ06z91i3bp2ce+651jRbbLGFHH/88bJ8+fLIvxtQStxaBFCpNOh96bKD5KEzhsntJw2yfutjgmFURED88MMPy0UXXSTjxo2TWbNmycCBA+Wwww6TFStWOE7/8ssvy+jRo2XMmDEye/ZsOeaYY6yfuXPnNk6zdu1a2XfffeWmm25y/dwLL7xQnnrqKSu4fv7552XJkiVy3HHHxfIdgVLh1iKALNTAP3rQ1tZvjmWomJHqtEV47733lokTJ1qPN2/eLNtuu62cf/75cvnllzeb/sQTT7QC3qeffrrxuWHDhsmgQYPkrrvuajLtwoULpW/fvlbgrK/bdKSSrbbaSh588EH53ve+Zz33/vvvyy677CIzZsyw3s9ElkeqQ7JRhxgAgJSMVLdhwwZ544035Iorrmh8rkWLFjJy5EgrMHWiz2uLcj5tUX788ceNP1c/c+PGjdbn2DQFo3fv3p4B8fr1662f/AUMJJEGvTpqEyPVAQBgpmwB8cqVK6WhoUF69OjR5Hl9rC22TpYtW+Y4vT5vSqdt06aNdO7cOdD7TJgwQa699lrjzwHSPrw2wz8DALKCoZsNaUt2fuu0thBregdQiUi7AABkSdk61XXr1k1atmzZrLqDPq6trXX8G30+yPRu76HpGqtXrw70PtXV1VbuSf4PUIkY/hlAuekdqhnzP5cn5iy2fjPCJio2INa0hT333FOmTZvW+Jx2qtPHw4cPd/wbfT5/ejV16lTX6Z3oZ7Zu3brJ+3zwwQeyaNGiQO8DVCKGfwZQbnrRve9Nz8roe16RsZPmWL/1MRfjqNiUCU1BOO2002SvvfaSIUOGyG233WZVkTj99NOt10899VTZeuutrfxdNXbsWNl///3llltukSOPPFImTZokr7/+utx9992N77lq1SoruNVSanawq7T1V3+0p6GWbdPP7tKli9XSq1UtNBg2rTABVKogwz8Xm6MMAG53qAovue07VAzEgYoMiLWM2meffSbXXHON1aFNy6NNmTKlseOcBrZaecK2zz77WOXSrrrqKrnyyiulX79+VoWJAQMGNE7z5JNPNgbU6qSTTrJ+a63j8ePHW///9a9/bb2vDsihlSO0UsXvfve7En5zIJkY/hlAUu9QaZdefV2r6ATp4EsHYSS+DnGaUYcYlUhz9fT2pB8dJYoWYgBJP/7QQRj1hvFa2YduBpAcDP8MoFLuUNFBGEEQEANoxPDPAMpF0xmimo4OwgiKgBhAE3obUTuu1NY0PenoYzq0AEjDHaogHYSLQXm4ysHAHAA8h39eVveVrFq7QbpsUS017dpYB3xaiAHEdYdK0xn0CJMr4g5VKToIk59cWQiIATjSk07dVxvk5v/7gAM+gJLeoSoMNGsdjjte1SOiTL9wQnm4ykOViZCoMoFK53bAt9tmOOADiItfqTS/1ln9ex3MQwNUpyCn6psg+6XLDgp8x8t+b7eUjGLeG9GjygSA0OiQAqCcNJDU0mpHD9ra+l0YDPtVj4izg3Cp8pNRWgTEAJrhgA8g7RfrcXUQZgCjykQOMYBmOOADqITh5fM7CEc1Ul3c+ckoDwJiAM1wwAdQKRfrdvpF1OXh/PKTGcAoXUiZANAMI9YBSKIkXKwzgFFlIiAG0AwHfABJlJSLdQYwqjyUXQuJsmvIAgrPA0gau8qEuAzeUcqA1K88HNITrxEQh0RAjKzggA8gabhYhykC4pgREAMAUD5crCPKeI0qEwAAIHWirh6BbKNTHQAAADKNgBgAAACZRsoEAACoSOQZwxQBMQAAqDhUokAQpEwAAICSttrOmP+5PDFnsfVbH8dVqzg/GFY63LI+r68D+WghBgAAFdNqqwG2foZTmK3PacKEvn5I/1rSJ9CIFmIAABC7UrXaas5w4WcUBsX6uk4H2AiIAQBArPxabZW+HkX6hHagi3I6ZAMBMQAAiFUpW221mkSU0yEbCIgBAECsStlqq6XVNC/ZLTtYn9fXdTrARkAMAABiVcpWW+0op530VGFQbD/W1+lQh3wExEAFKEUZIwBIS6utVqy485TBUlvTNMDWx/o8dYhRiLJrQMpRfB5A0tmttlpNQoPfXAlabfX4p6XVGKkOJqpyuRxNSSHU19dLTU2N1NXVSadOnco9O8h4GaPCndg+3NMSAiBJuIBHUuM1WoiBlKL4PIC0odUWSUVADGSgjNHwHbqWdN4AwI0GvxyTkDR0qgNSiuLzAABEg4AYSCmKzwMAEA0CYiClKD4PAEA0CIiBlKL4PAAA0SAgBlKM4vMAABSPKhNAylHGCABgWq6Tc4UzAmKgAlDGCADghUFRvJEyAQAAkIFRTQtr1y+rW2c9P2XuUsk6AmIAAICMjmqqrn3qXWu6LCMgBgAAqFBBRjXNMgJiAACACsWopmYIiAEAACoUo5qaISAGAACoUIxqaoaAGAAAFE07Zc2Y/7k8MWex9TvrnbSSglFNzVCHGAAAFIUat+kY1bRwHemopqyjr1Xlcjku4UKor6+Xmpoaqaurk06dOpV7dgAAKGuN28Jgwm5vZBj55MjiSHX1hvEaLcQAACCWGrcaaunrOrx8kgOvrASKjGrqjoAYAADEXuM2qYEY6R5QdKoDAACZrHHLkMawERADAIDM1bhlSGPkIyAGAACZq3HLkMbIR0AMoAlqiQLIQo3btKd7IFp0qgPQiM4lALJS4zbN6R5BZaWKRjEIiAF41hK1O5dQSxSAGz02aGm1NAVddrqHHuPc7oN16dBaltWvs+6WJf37uKGhwwwDc4TEwByoJNp6sO9Nz7rm01V909rz0mUHpfKEAABeDQHKLxhKYxDJoCliHK+RQwyAziURIf8aSGe6h17w+wlbiq1cx4Uoq2g0ZODYRsoEADqXRIDbkkD60z2W1X0l1/3tPVm1dkMkI++V87gQ1aApUzJybKOFGECmOpfEgeL+QGUMaVxb084xGA5zt6zcx4UoGjqmZOjYRkAMINW1RMuN4v5A5YjqblkSjgvFNnQ0JOA7lBIBMYBU1xItN/KvgcoR1d2yJBwXim3oeDUB36GUCIgBeHYu0cdZ6IkcFvnXyKJK7WQV1d2yZ95dVvbjQrENHSsydmyjUx2AVNcSLTfyr5E1ldzJyg4iNT9Wj3q5EHfL9OLgsTmLE3FcKGbQlO4ZO7YREANw7FwSl0obMcmvuL9dw5n8a1QCt7q2Gmydff8sGTOij4zsX5vq/brYkff0+LZq7Ubfz+naoU1JjgthGzqGZOzYRkAMoGQqsWUpihYlIA28OlnZfj99ofWT9v26mLtlpikERw/qVbLjQpiGjpYZO7aRQwygJCq5fA/518gCv05WlbZf20Hk0YO2tn6bBn6mKQQacCfdqAwd22ghBhA7v/I9QYvdJxH516h0QTpPVcp+HYZfqoGkrIzlqIwc2wiIAcQuqhGTsp5/DZRT0M5TlbJfB1WJqQYtM3BsI2UCsajUkjwIJ2vle4BK5FeSrFT7dRrOL1lKNagUtBAjcpXYcQqlaVlauPLL2OcFQPQtn6UqyxX1+SVM1RvTv8lKqkGlqMrlcsm7tEqB+vp6qampkbq6OunUqVO5ZyfxJXns3Z8r42zSE8iIG6fJsvr1ntPpie2lyw7ihAEkeF+e+OxHcu/0BbL6K+/SYnZZrqj26ajPL2GCaxp8KjdeI2UCkcnauOcwpyfD0UN6+05XScOAApVGg8F9b3pWfv3Mh43BcPs2LUsy5HvU55cwVW8quVIOCIgRoayNe45g+nTrYDQdecRA8rgFg19taLB+17RvHWuubJTnlzDBNQ0+lY8cYkSGjlPwUsnDgFba6HtA0LKJbVu1kAd+PFRWrlkfyz4Q5fklTNWbrFTKyTICYkSmkgMeFK9ShwElpxCVziQY1P4BLaqqrEEskn5+CRNc0+BT+UiZQMlK8lSlrBg54umhXop8w1IhpxBZkIRgMMrzS5jgmgafykdAjMhUYsCDaFVSbU5yCpEVSQgG9bxx1MCenqXeTM8vYYJrkxrMXTq0lmX16xJbGxneSJlALAFP4S1kDXi4hYxKqs1ZjpxCcpWR1XQnvdty9wsLXF8/c7++xueXMCPJmdRgXrV2o1z48Bzr/6RNpQ91iEOiDrE3TtyodDpK1thJX5/8vNx+0qBI8irJVUYS0oPEJYCM8w6Pnk+03JvXBWiYGuZR1SF2Qu399MVrtBAjFlkY9xzZVsrbyG4DEti5ypx0Ucl3//zuxkiAuzGFjTXPX3KgvPHxv40bb/LvcC2r+0qu+9t7smrtBtfqG7q8dHoahJKPgBgAEnwb2aTkFSddVHK6U1Sd+rxahIPcxbEbfDRX2CkYtlGKLV3oVAfjk7Lu/HqbmA4DQOk6kTLgDZLEDgY1gNTfpbgIi+JuTBwVYZJQfQPRoYUYvshdBMp3G5mTLrKu2Lsxcd1lSUL1DUSHgBieyF0EynsbmZMusi5MVYhSVIRJQvUNRIeUCbiizioqWdg0IKe/i/M2MgPeAMXVMI/rLgu19ysLLcQpU8pyZozdjkoVNg2oHOlDxbaOAVm/GxPnXRZq71cOAuIUKfXJmNxFZCkNSPers++fJWNG9JGR/WubnWjLmT5UKSddvwt66pcnQ5LXQ5iSnnGnNlTKYENZx8AcKRmYw+1kHGfxb70dPPqeV3yne+iMYbQQIxVMCvw7XWz6/Z19Qg06MEAlBSrFXtDTeTcZKnU9lHNgEaQjXiMgTkFAXK6Tsf25flfVcQcBQKkv8gpPlB3btpaT/2dmSS4OkxL0Rjkffhf0OuyuDstbygt+JKPhpZQqNdiHN0aqqyDlyuUldxGVJkh6j12O6fJH3xbTZoNi04eScsIOOx9OQbTy65x7z4sLPF9n4JH4ZWEAGFIb4IWAOAXKmctbKbmLQJhOMxoIrP5yY2zvn8QSh2Hnwy2IPmnvbX1TVPwKfNB5N35Z6UQdJgc5bXd3EA4BcQqUuw4pV9WoFH6da8IqtlNOUlrnws6HVxD962fmRTJvU99dlupALOnoRC0VcXcH4VGHOAWSUIe0HMN1orKVYzhwr7qhxSomfSgpwzOHmQ+TeuVReGLOEuuzGEY+HgtXrjWajgFgSjMsNEqPFuIUIJcXlaacrSluaUBhdW7fWm48brei5jsprXNh5sMviDZReFxz8vnaDTLx2Y9k0muLaIWLYX/0a8ln1LVk391B8WghzsAoPUCSJKE1RfcXrY6iVSF+NKKP9VzYU9Udo4vf/8qdFlXMfAQJ0p1G89Kfg3beyujvf/3Mh7TCxRTQmaDhJbl3d1A8WohThFxepF2SWlPsNCD90f2oWcfRTtWybtNmqftyo2fZwWER5LXGPXCAyXp5Zf7nMn3+Z7JFdUtZs77BcTqn+TANoi84uJ/86ZWPZdXaDc0659a0ayPT3v8s1LxTiaI4pi38F4zcqdmFH53IknN3B8UjIE6ZOHvIIjqcKNLVk93tYlM7cpUiVamcaVGT31oql/71LVmzfpPndG7zYRLM17RvLZNe+6RJMNylQ2u5+shdGgc+KbazYyVUQCgH00CtT7f2TR7TiSxZd3dQPFImgIjpiUIHNNEBIMZOmmP91sfc0k12a4pTx9FSpiqVIy1qwuR35ScPzvINhr3mw6ujYlVe6bpl9U3X6b/XbpRzH5xt7Rd+72FKL2AQf0CXhLSnpEhCp3dEg5HqUjJ0M9Kh0kd6KtVIcQ+MGSoj+nWTLLb4l+qzJr+1RH7y4Gzf6Tq3ay13nDxYhm3vXV3GqcXQTjtxq+VcONqlVy1jk/JtXTu0kVd/NpK7MRGOSGqvA3sdJWUY8yRhWOhkY6Q6IMP5sWmvA3zxI2/K+KOSc+vVbj22g9Wn31oSW7BamBZllxmLMkDW97zqiblG067+aqO0qKry/UyntJPNuZznkNd2iswfpy+Qbh2rrb95/pID5Y2P/91stLv7ZiyUVWs3+laiIG0ifLqOm6MG9mxc/0lNeyrnhSkDWFWGRATEd9xxh/zyl7+UZcuWycCBA+W3v/2tDBkyxHX6Rx55RK6++mpZuHCh9OvXT2666SY54ogjGl/XRu9x48bJPffcI6tXr5YRI0bInXfeaU1r69Onj3z88cdN3nfChAly+eWXx/QtUemycqIopiXTK1c23/L60o7OZiKOnEm/ZRhXnqZ+pl9wGSaFpTCY11rBJq7723vNvp+mruQ7dtDW8vvpCyObV/yHbktn7tdX/vuFBY6v3/3CAtmj95bWdElOe4pKmP2OTu/pV/Yc4ocfflguuugiK4CdNWuWFRAfdthhsmLFCsfpX375ZRk9erSMGTNGZs+eLcccc4z1M3fuf1o7br75ZvnNb34jd911l8ycOVM6dOhgvee6dU130J///OeydOnSxp/zzz8/9u+LypWFE0UUedJ2a0qPTtVGlQOSMPBCHDmTfstQO7udHVOeZtBtMGyHoDB/5/b9Rvavje0zs073sSff9N6e7H2x0juRFbOvM4BVupU9IL711lvljDPOkNNPP1369+9vBbHt27eXP/zhD47T33777TJq1Ci55JJLZJdddpHrrrtOBg8eLBMnTmxsHb7tttvkqquukqOPPlp23313+dOf/iRLliyRxx9/vMl7dezYUWpraxt/NHBG8qRlZKq0nyhMl3MUwaEGxbecMMhzmqTU7zQZiS1o4O63DH/xt3fkvIecb2Hnvvm5/K9vy/SPVobaH4Jsg8V0CPLrcCQBlimdl5Jxd6uS10Mc+3oppOUcmXRlDYg3bNggb7zxhowcOfI/M9SihfV4xowZjn+jz+dPr7T1155+wYIFVupF/jSaTD106NBm73njjTdK165dZY899rBSNjZt8u9pXU5Z3OjTVLEhzScK0+Uc5Qlj5Zr1qWhRj7rwvt8y1J97XlwofotQc3s1PzfM/mBvqyaKKfcWdqhsp2VqUomCgSPiv7uly/fqI/u7lthL83pI4yAbaTpHJl1ZA+KVK1dKQ0OD9OjRo8nz+liDWif6vNf09m+/9/zpT38qkyZNkueee07OOussueGGG+TSSy91ndf169dbPRXzf0opixt92kr7pPWEHWQ5R3nCSGKLutNFZ9SpMFEMdVzs/mBvq15bYofqlnJXBDncbuXkwixTRuyMR5B9Ubez6/7mPLJduddDsY1GaUt7S9s5MukS0amuHDRv2aZpFW3atLECY+1YV13dPLdRn7/22mslSaW87I2+Ek8Eaa3YoOvhjh8MtnrwO43IlbT1FHQ5R3nCKPfobIW8Sn5FGVREfTINuz+49YzXQPiMffvK+QfvFNm+VdjhaEX9evnF5P90pAuyTOm8FD3TffHfazfIuQ82PxfZ7IFWyiGKDqhJvEhPyzmyoQIGoyprC3G3bt2kZcuWsnz58ibP62PN6XWiz3tNb/8O8p5KUyo0ZUIrVzi54oorrBp29s8nn3wipZDWnKYs3rpSduuJ24hcaV/OUZ4wktSi7tXSovVvO7dvHVkqTBwn07D7g26TWi/2oTOGye0nDbJ+vzXuMLngkG/FVk6uulUL+f1L//Kc1m+Z0nkpWib7oh7D9Njmdaa58rG5ofPak9BSmqa0tySdI6dUyB3ssgbE2iq75557yrRp0xqf27x5s/V4+PDhjn+jz+dPr6ZOndo4fd++fa3AN38aTW/QahNu76nmzJlj5S93797d8XVtNdaCzvk/ad/o48hJjuo903bryuugnD8iV9IEXc5RnzCScAvcpKXFFkXgHqajmakw+0Mpg0t7H1lW754/nuT0okrmty9u2aHaN9WnmLz2JDQaJekiPS3nyCkVlLbRKgmpC6eddprstddeVu1hrRCxdu1aq+qEOvXUU2Xrrbe2UhbU2LFjZf/995dbbrlFjjzySCsP+PXXX5e7777ber2qqkouuOACuf766626wxoga83iXr16WeXZlHau0wD5wAMPtCpN6OMLL7xQTjnlFNlyyy0lSeLa6OOobxrle6bp1lUSb1/FtZy96giHPWGU+xb4K//63PeiU0dau3DkTjLptUVFF943GQghrKTsD0H3kXxJTS/KAq990bSmdKnT+aKu/56WQTaScI5sSOl5L7EB8YknniifffaZXHPNNVant0GDBsmUKVMaO8UtWrTIarm17bPPPvLggw9aZdWuvPJKK+jVcmoDBgxonEY7x2lQfeaZZ1oDc+y7777We7Zt27axtVcD6fHjx1ud5TRo1oA4P684KeLY6OPISY76PZOWX1qpg3KEWc5xnDAKB3QoVW6bbrdavszExoYGx1HUwhzo7WWon62tasVK2v5QTGfCX31vYKKG7c4at30xyDnGKRiKK8c0jkajcl+kp+Uc+WpKz3uJDYjVeeedZ/04+ec//9nsue9///vWjxttJdZBN/THidYtfuWVVyQNot7o47iii+M942iJzMLtq6DCLuc0nDDc7lp0btdaTh/RR/p17+jZQajQxOfmy19nLXYcRS0MXYYd27b2HNo4n+Yxa0t1GvaHYrb9lWvNyvFFpRI6A5Xie5gOu+4UDNV9tSGWERfjbCmN8yI9Ckk4R65I6XkvsQNzoLQ5TXHkJMeV55yE/NI03b4KK+xyNsk9LWftbLfcNm2R1Y5y5z5kHgzblkacFzds+66++cQawD/w46HyxlWHWGXQ0rA/FLPtz1u+pmTbSho6A5nsQ6X4HmFrSk99d1msOaZp6ggXtXKfI7un+LznpCqnQ7shMO2opwN+aMWJUnSwiyo/Vw+qesD0oz3OTVvB4njPoC0f5W7l0c/XE5BfS7726E9q61PUyzCOPPWg6yPKer/5eka4Lt3SjWxn7ddXrjji60AkCdt6XPtIIb9tpdjl4Lbc7XdIwkWGyT5U6u/hNE9eunRo06TqThzHRXsZiEtLqS6DNNzRCqtcx4SGlJz3TOM1AuKUBMRRbfTawqCtB360/JLp7aI43jMtgVfQg3K5T7ClUu5gw3SbLEaU2/OEye/Kf7+wwPG1Kp/llaYA2W0fceK1rZjs817Lxe+CKQkncpN9SIO8cnwPXX6vzP/cSjlyy4HXT9uyQ2tZtXZjSfYlr21CJeEcUYmmpOC8ZxqvJSKHGKXLaYojEd8kt0xr8e65nXkFD9OTfJIGLUlL7+S4lbPnsb3d/L0Et7yjyovTeX7yTe/5dVteSbkYLHYfkQDbisk+r7yWS9I7A5nuQ5qDXo7voetCOz7eePxunsHQsYO2lt9Pd67tH/W+5NavwU7ZSMI5ohKNqqDzHgFxhfELJONIxPd6T5u2Euz/y+eMdhDTk3wSS76kpbNZnMoVbAS9lVusqPLiwi6vJF0Mht1HXpr3mdzxz/nG391kn7/80bel7suNzabR9zn7/lkyZkQfademVaI7A5luE3onpJzfwy8YqmnXxiggjmpfKmw0SuI5ohKNqpDzHgFxCgRpLTUJJKO6oiucLx2yWEcycjuQm5yog5zkk9rKk/TeyXErR89jvzzcqHXt0CayTjphllfaT/Q6T1p54IGZiwJ9d5N9XitxeDEJ0MrdGch838iV/Xt4BUO6nRZ7R9JOz5jxr5XWX+ixVTujmmzXST1HVKKWFXDeIyBOONMgN2hrUbFXdG7z9bPDd5Zrnmo6dLHpiTroSb7SSr5UilL3PN6wabM1ZGxUwXBVlYhfz4rrjh4QWaAZZnml/UQf9ALG/u6l2pfLXdfZdJsYvn03qxRgueu1uwVDxd6RtOqEP/p2k4ucic99ZJUgvPG43XwbbzhHIAjKriWY6ZCIYYeuDDtkq9d8nTdpjmuPYr8SbEHLt1VayZdKUcoySLotDpvwjOc2F9QRA3p6lpXSqg9H7N6zrMurVCf6uIZ3Nxmxzum7l2JfLqaGa1TLy3SbGLZD18QPNRy2NJju25ri4tTir8+dbVCyjXMEgqCFOKFMWkvHP/mO1alixvyVJWstMgm+w56og57kkzBSD8pXMD5oK2O71i3kq42bfaeb/PZSOXO/vlZHt/z9SjuGXn/0ADli915S7uVlegLv1qHaCsyivAtUbEcZ0xHrxOG7Bx0cIoywnYGiXF5Btok0dGoKekdSzzN6fvPjlxbEOQJBEBAnlElr6bL69cajXEV1WyjoycyN0wm92xbVgf42ysArTaWr0iDuk3SQVkbbWfvtILdNm2c0rQbDUQ3THNXyyt9GNdCt7dRWlte7n+hr2reWix95U5bVBw/Q4uywZ3occrotbtKBN4zzDtxB+vXoGHo9x7G8guxDYVLgSn3MC5JjqvOl5zc/bg09+d/tpL17y23PfJjaER5ROgTECRVHTlMUt4WKnS+3K3I9ofi1CDj9bRSBV9pKV2W557F9opv+0WfGF2b6aT06Vcuk1z4xmt6+o6LBcCnzb72Wl+MQ1O1bN94tKjzR/6dz2cbAAVrcHfZMj0N3jB5slfYq5LbP64h+bjVx/YzYcavQ6zrO5RVkHwoScCb9mBfkPFM4rdu+ovLTL5LUgo5kICBOqChzmkxvC5m0GASZL9MrcpNb315X88UEXmktXZXFnsfFlFUbPaS3NVxzEOXoaOO0vNy2US0tJt+0BOef6DX4X7dps2PupUmAFneHPdPb2Jof68Zpn9+cywW6YxbVLfO4l1fUvfeTfMyzz0Hzln9h/Df55ySvfUWfu3BkP+nTrQN3AeGIgDihosqVM70tZNpiYHoyu/rI/s1KsDldkZve+va7mvc6abgF+kFbdkirKF7YZRi2rJqWR/vFsQNk/Sb/3OEkdrQx2UbbtmohD/x4qKxcs94oMPQL0OLusBdVqpNTzdkgx8yobpnHsbyC7CdBp01qub4wF7y6vnXAJ82TX1b3lVz3t/c8v5veJSr3MMJILgLihIoqV87ktlCQFgPTk5lOf9gA/1Zb05zkX31voOPtUz9egb4WjTdt2dGaqUm+xZgGYW/ThskXtjvBzbjiYGnTqoXc/syHxn+XpI42pn0JWlRVWdVilFY4KCZAK0XP/DhyzIMeM6O6ZR718gqynzhNW9up2roj4tQSmtRyfWEveAds3cka8MnkHFLMd6MxJBsIiBMsyDCnhc47cEcZsWM3o44VQVsMTE9mJrf6TFtNVq7172ARNND/0Yg+Ru+jQ3/eO32h68hXd5FWEett2rAdOXXYWM0D1hakh141GwAiaR1twrQ+Fhuglapnfhw55m7HJv0+Vx+5i2zZoTryoCbo8vIKroLsJ67T1q9vkh6UH0wnsS5v2AteNfXdFYH/Juh3S3q+dRo0pOSCgoA44eyTxq+nfmgVJDehO+uFh+wU60g+UZ3MTE/eK79Yb+1Upu9vEug/ZtiS9vicJZ4Hay0cn9QRwZKg2Nu0QU9g+hZa/lVHJNMfbSnWocNNJK2jTZjgttiAtlRl8+zPirolstTDyAZZXl7Blc6z6X6iTIPI/GA6iXV5o6pcZCrId0tyvnVaTEnRBQUDc6SAHki1tddUkJNVMS0GJgN7+BWq9ytAb9PcsH1veta3EHuQQF+DpC4d2ngWv/86mPIe9EE7L0181uxiJYuCDrgS9gR2+ICvA4XCsRBMg2EtvaX5hUk6SIcZtMMO0OzXC6c3OUaEHUwhKcIOOhSWyfLyG2hp4rPzjPeTIEFk/uBMerekVIPmmCrl6INBvlvYAa/iGMym0gcXSwpaiCuok50e8yeODnayirPFwOTKMEjeX5CrctOD7DGDelnpEG4tO3rbXVsZ/dz78gI576AdaSV2UOxtWpMWT62sMHvR6qLmU0tvJW39hW2tNU1r8rqV6dbSqsIO9lHJvFqmTe6S6HHIhL530Bgrv5RgqVr/TSV19MEwd0/T1Boat4YEd+B0Q0CcEiaB48TRewQeUjaufMEgt5pMc6WD7ESmB1l9H/1uboGDdrwzCYi1lbjUHVHSotiLLpOgMExZtSR2oouyA5pf6oDpBWv+Nl3pJ/xicx3dUkBMgivTGsoLPlsrv5++QMLQ76Wt5kka2U6XsdYJdioTGJUw3y3ohXyQc15acmqLkdQOnF4IiFPEq8NIHD2zw7YYhO2op4//OH2BlR5R7E4UJNDXefBq2TEt+F+OurVpEMVFl19QGKasmv3ZSepEF3VerFuAFiY3Mqp8ylIGA0E+q9hg3+uzjEfna9da6r76umZuIX2ndm1aGo+26HXRWeo8a79Oy3EEw5rudvV3drVGdIyzf4tOF+Scp9+3ki8qbUnswOmHgDhlnA5kmhemt8I0ZynMgS3qEkhhrwx1nrt1rI5kJwoa6LsFDvr86SP6GLU+zlu+xrqVnB9MJ+GEU25RXXR5ncR1uZvQnPH8nPCkdaIrRQe0MBesUd3+LGULc9DyZcUE+46jo7VrbR07zjuon3FwdfqIvq7DDOvjLzc0SBhOF51xdGgMyt6uvLRv01K++uZ7B6kvfcOxTYf9jvNC3vScp31NdP1moZNe9wR24PRDQJxC+QcyPRAX1mEMc4KJssWgmCvDKHeiqAJ9PaHd+/JC31YMrQKiP7r8jxrYU558c2nFtwKYimpd5G/7+Rcc3TpUWy1By+u9T17PX3KgdfGY5YuUMBesUdz+jLPHfuHF57/XbpBzHzS/fV1MsO/2vfSukl5I67HjhmMGGAVX2g/hW7VbNNtPNEf+i/WbZO36hoq6C2LSOVAvAi4cuZNMem1Rs+Op03E2qovcIBfypue8e6cvMK4ikvbGlCElKt8YJQLiFIv6BBNVi0ExQW3UO1EUgb5Oe+NxuxkXjteD83+/0DzHrxJbAYKI8qLLsUWufevGE4vbyUsH6Sh3q5iJMHcXTP8mzAVrsbc/4+xg47Qt6FuYflYxwb5JDV29kD73wdly5n595e4XFvgGV1ENS52GuyCm21Wfbu2tCjBO2/elo3aJLXg0vZA3Ped5pd7ltyI7Bf9JXYduSlm+MSoExCmV5B6cxQS1cexEUQT6xQySkpT1Ui5Rp464XQjWfdOCX1PQQSfJAUFUaQVB/ibMBWuxd27i6mDjti14VWGwP0v7K2iKlqY6hQ3eTMuf6WdqS+YdPxhsNKR94THLdPTBfKcO304OH9Az0a2LQbYrr7S2OC9yTS7kTapA1bRrJXVfbfL9vF87jKqZ1saUUS7nzS07tJbrjx6QuO9CQJxSYU4wpcpp9Qtq9fFJe28rT7+1xHE+4hjWNazCZWbfcp/+0Wcy8bn5gd8viT1r4xR1zqjJhWDbVi3kgR8PlZVr1qfudmMpOruZnLw1/1VbJe3BcIq9c7OsPvoONsWMcKa8Ou+aBm9B5lf3gS07tHFt6fSycOVaCUqD4SQeY8KkOpX7trpf0G1SBWpTEfWI09yYMmpAT9m8WeSqJ+Y29t/Q2vC6/7X45o5IUhAQp1SYkjBegUnUwbJbUKutd8ptaNH8vy93L2ivZdavR8ei3lu/U6V3uosjZ9TkQlCHrm1RVWWVlwqrHOumVJ3d9LfmXjql9eTf2tVb9Pn7ZtA7N/Yy1F71j7zxaeQdbEo1wplXUBa0Q5BuT0FbNHU5mg49nqQgMspUpzQcF+1zno5c6tTfJEz+d9SNKeU4rk2Zu9Q4n7/cCIhTKsitJr/ARHPb4ugAVhjULlz5ZaAetl4nDpMdu5g8TD2J/8Gh/rA9rxeM7CfF0GWhI++lPU+s1Ck9YXNZS1l+K+md3fT7aS6ricJ90/TOjdMyjDqIK0W5Jr+gzG45N/2e9nE7yPao0+lFXhTzW06Vnuqk9Hg2/kmtmuHdAdvtzmmc2305jmsNCU7tdEJAnFKmtzC1JJtWofAafjLODmB2UKs7hgaAYXeM/BOIBpPaYpJ/G7Zwx3bb+a8+chfZskO18UAFbvOqn+91q89N1TcH/qSW3omqBSGunNEwuaylLL9VjDg7uy2r+8oqTae/9Val6TZbuG+a3LlxW4ZRB3GlKNfkF5TZt8rPvn+WccAfNDAJEgAlNYis9FSnphcv/utry4Lyj4UXA3GMGluO49qrKRucg4A4pUw7n2m+a5jbin5BatDAqZgdwyRQzd+xldPOr3//kwdnN3nOPhG5/Y3bvGqLjZYCcuoA4SZ/PZXyitl0XUXZghBXUXaT/NeeeS2NQUePKmdrRpyd3TQIzj8BB1HYCc1rGwqT16u1oX9xbPAONqbD2QdN3TzvwB2slCi/45q9X+mgMHos0JJaTlUE8o/HeucpaGBiuo71Yv+HI/omMogsVapTuZkez3Rd1da0a8yjvviRN41alcOkwpTzuLYiZYNzEBCnmMktzDC9k/2C1DCBU9gdw7S1yd6xxz/5jvU/03Ognoi0dcfOYwtCSwFdOLKf8ZDBul60M6HX9FFfMZuuq6hbEOIqyp5/IehG82PtgVGCnAjK3ZoRpuOaSVCowgbDbp3Q3Pb3MHm9Vx25S2yjbOpw9vYdoRc/XCl/meWfyzxix60cy6sV1jluVi2iU7V8p19PeWneyiaBsX081u0szF0y0+0iqcFwGgOjsEyPZxoM29uYdefGoFU5FzIVxvS49sr8z61OblHmF6dtcA4C4pTzu4UZxYaWf5AKGziF2TGCtjbZrQxB2O8dZujQIMtWW50uPORbVmWNUp0YTNdVHC0IcRZl13nWvHe3TmGaH7tH7y2lpl2bQAFuuU/aYUoOmvRuj4Pb/h5m2WhwEPYOh2les+4LfsGw2zZpmg+9vH69/O0tLa32nyC8cDTFsCN4xlXPtVSdrNIWGIUV5rhnus/8aESfUBeOpu9/7oOzmlzIRZFfnLbBOQiIK4BX5zPTFiTTjiBhA6cwO0apepEHlT+vOo8mtNVJl0mpTgxB1lUcLaNxn8S1E6gX/W6Xjto50Akj7EVbKaqzeOWGuv1Nlw6trfJGcbDXp96Ryd/fg263Oo/aOpY/5HnQOxx+jQImwwOLyzYZJB/a3q+0JV1LqxV+lzAXXPlpGReM3KlZ34licoZL2ckqbYGRKaf93++4p+kS+X/TbYtqo8+yR7ALMi+6DZqW7FtdkO6Tf9Gbv39piod+GZNc77QNzkFAXOGKaUEqPEgVEziF2TGSePuscF6DHujjODE4HQiDrKu4Wkbjqidt+t1WrTG7W2AHcUHXTZiAwiSADlNy0P4bve05418rv0kbyskdIWplB6F3ZHRkrbHfVF0JegGuAfuFD89x7RhrejfKq1HA9MJaA8789RYmH7rwGJi/vld+EWx7dNq+NC1D07T6dOtQ9EiPpexklbbAyITX/u923NN0Lr1gKlynmrKn1TZc64K3b+15TnCbF7/yiiYXeJc/+rZVOcMtrcPvmJekcQX8EBBngNsGae8wdgkmv4NUsSWvgrZyBG1t0rns0UmvtqsCV38wVTivQQ/0UZ8Y3A6ERwzwbk2w2QFXXK3WToGaBgrDtg+fg2u6HWpnrSABrt/Foz7WHPBiBtAwDaDDjL6lHbaKGUlRT7ph0oe0Y+m3ardoDOIPH1BrlSwMegFe2CIVpiaz00VEkOGB8xVzh0o/021IabdOfoWVKJy2L03LuO2ZedYyKqYWbTk6WaUpMPJjsv8XDsCieedO9Xh1nfrtJ7pP6v7ttIzc5kWXcdhg2JZrPB64HxNMLqKSMK6ACQLijPDaIDXf0uQgFVXJK9NWjiCtTfZfjz9qV+t31DmVPxy+nRzmMgxq0AN9VCcGr4Py7x1qKDuxl3+ctzMLA7WJz31U1K3ZIB1Xgl58+A3RrR0i9YJu3abNgQIKvxOoW96pqaClzvJTFq7+zq5WCcH8O0F2i6bpaG5OrUhVVSK5XNPPOnpgL3nizaWOnfzyl13Htq0D3Y3yutgw3V70+9oj8xV7h8qt5rpXMKzsijdxBqzl7DyalsAoqguK/FFiR9zo3qFSeZ2v3NZ5saM1RiFnuE3GPcR2FAiIM8RtgzQ9SIW5pVxMK0eQdI/CYNIrqAnjybeWyrAdurru7CZ5jPmv6bTFnBj8DsqqxTcBid+6ivN2Zhy3Zk3LbWmLzBG7h8vJ1XWjqQBOZfX8Om463Tb3W1fnPTS7SbAU5IIhzEnRXpM3HLtbs8/IP4n/z0sLjPYhp1Yk+/uMGdFHRvavbUzlufflj32XneYUB2mJ9bvYMLmw1uBfv6+93MPm8Xdp39q6aPL6rMKW4vztMWznO9PUnCR0Hk16YOS1LHVI86DrZ+Kz83wrSXhtL27rPCn9bHJ5pRmTXO3EDwExjA9SQQKnqG7LubamdqqW0UN6u7Yy+wU1tg5tWsraDf5DamqLll8A57YM4+i8YnIgtE+4JkFuHLcz47o1a1J6Tb+73p68s8XX6yvMxcek18yHy/UKKIKsqzAXDGFOiibr1XTQCS+6hP/29lI5aJceVoWVecvXGP6lWXj/r8/Wyp9f/8RzG9NA9+oj+1vbg9+FdWHaRpgOydt1bS+zP6nzXd//b1hvGbxdl8bW+WICVpOBi+xBieYt/yITFR9iG2q63depRabrR9/DtDSn6Xu6PS636wouKtOGgBiBmAZOUd6WK+Y2m19Q06ZVC6OA2BY0gIur84rpgfCgnbeS95Z+YZzKEeXtzDhvzeq8astfYcuq1/oK8hlRtLzYAUWYk1aQCwbT9zcdcKJQh+qWsna9+T5S+D20Rf3k/5kZ6O+G9ukqD7RfJP/2yWf+31cWelbSsLcxHRnM5K6Rvdy1eoambYTJh/YLhv8z74vkmfdWNLv7EjQ1zaQsnNOgRG7SWvEham7HbqfBV9zWT5AKJ6bv6fU4CZb5nNtKVeovDAJiuHLbcE0Cp6hvy7kFNF47l0lQoydc7Xylt9f9TnhBA7g4O6+YHgifff8z+Z1Lfqrbsovqdmbct2b1O3kFw8UE3MW2vGi+rA6bXsxJy2v+w1QvcBpwIo685GJUfTOM7SV/fcs3GFamZeV0OekIaLqv6W1dr9xopyC+MB86Krp+tQVe+1Scd1C/wJVrol5Haa34ELVicnMLy3JGkdLgdpFi2s8mbH+aDm1aypcbGgJXWnE7t5Wy1F8YBMQVJMorL78N1y9wKkW9XbcOe3Yqhent2WMG9ZJ7DTuh5QdLurydqieYBuTFBGz2gdDkYHvlY3PljpMHy3d279Wkk1fcB6a4t4E4A+5iW140UNv/l881jlBWTC1wp9Ebw1YvMFWOzjpVhr3aw7DXp27/Ovx0UPayPXjnrWTa+59J1L7urPmJjD/q6/3PJDVNRb2OwqRIBTnvuE1b7lbDoHnCphcUUaQ0eF2k6GNNB/rJg83TmuwpdRAjrdse5vu0btVCZEND4IDa6dxW6lJ/YRAQVwi/4DDIQSaKDTfuygWu81i/PnC+lgYsOh9XPva2UYuTLkv9fO1Zn1+iSqsnaOmqG4/7uqNSsQGb10kiSH6n3uLT1i474FWlODDFvQ3EGXCbzLu2ZLZt1dK1s0z+8ixmNLnCqi1O686kekGQAMO0ZSvKSi5aMlGrdwQt++Z1h8dpGwt7saPv9e7SL+T0ffrIvS+bXUAHWT66HeXvf36paX6d74I478AdZcSO3UJVNzG9sPaqlVsYsJWy1bCYPGGdzmmYbnu+o0hp8LpI0XnXIcT9/u7SUbt45pi7Wf3lRrlw5E5W6mGYbS2/8agcpf6CIiCuAKbBod7GPXbQ1o09vp02vKg23LCVC0xaCqJqvSqstHDQzj1k2IRpjiWh8g+AM//1udw2bZ7rAUSD1LtOGVxUwGY6Qpf24DctsaYBms6bBu25AKOPJbUYf5wBt8m864WP1zaTv79oTVKnACdIy67Jdu9VvSAI04u5YvfB/LxmbZULkmtsLx+3DnNu21jY0TvtVq9ttvQeatoWNpCwj69+qWlRdqjq12OLwHepgjScBK2VW6pWw2LzhO/4wWBp8U1LsNP5qpiRYmvatrLSaPSOhg5Dn18S0GvebdqJ0m3gmvMO2lF+PfVDqxHHpD73S3k1lXWkuneX1ssvJr9nfG4rZ6m/IFqU7ZMRiSDBobZ+avA0+p5XZN+bnrV2qEJBNlw/diuHnrTy6WO3gQt0vnT+xk6a4zqfUeRlOZ0stYPdDccOsF5zC9H0QOkWDOfTdaI5pHowdHsvfb6nQ8BmH+gKv6N9kshfHnpxY8reRvxa4OzRx6IQdBsIE7SqwmVcTMCt+5S2vtkDyfTo5D7vb3z8b88LqPz9RafXE8tDZwyT208aZP2eOHqw4/bmNP+m1Sr0RGi/v35enLWei6V5zZrbqydBHQrWlL18vq6c0EZOH9HH+m2yjXltN0EGfPHbrzXo0OWv82jK3l40FcueV1029jIK0/nORND3MiklqK/rdGFH/Mt/jyTmCes61lKcbuun2G2tbt0mK+jUkRwLz4V+825XV3FbdjpfekfAdNtombcdjujXTX60b99A57Zyl/ozRQtxyoUNDt2uwKPecE0rFwRpbYhipwk6aEZQ+rcaLAVtIQ3aQl9MC4TJ6GOFyydMrl+cxfijLhcXdCCZoPuLU+69loYzmX/Tz9IWJT1xFSOK7SrOVAa3YXDd7oI51QEPu58HHfBF67JqKaogy1JbvPXi3GuwliD9CNyEvYsStOEkzDzG3WoY9txpcrFduL1pS7KmNhSzrvLPhdpiXGyLazF32FoGvPtXij5FUSAgTrmwwaFb+kPYDdcv39XrgBY0CAy705iWncofblhPTKa3z9x6tjudeHt8k9+trZDaGmnPT9BbS0EGLwnKabS1sB3x4izGH1XAHWYgmSgO9KbzX8qTShTbld1ZNUgqgwa0Jnn8J+61rdw+bV6z+fr32o1WmbS985af13ZbeCv44kfedB32vTDFyvRCzKRudiE95hSWSSvc14qtEx0msAs6HHYUjRdxtRqavq9fnnAht+1N7xR0rG4t5zz4hqwJUcbQ3ia1r8tVR3zd6lzMdyw2pW1UgMaIuPuTRIWAOOWKOfk5XUV+/sV6zxJDThtusRULggaBYVuvWrdsaQWoJq2c+lhzw8IGw/nrpjDgsTs15Od328tLA+SgB7qoWrULFQ6NW84ewn7rrNiAO2zufFQHepP5L/VJxW27Mg1a7c6qpq33ugy0ddckJ/6PLy80Wlc6bLhb7mphuTOlVR5MA4QgF2L2stT8fL+RDt047Wv6+3cG9bg7t2slbVu3atKRKmxgF2Q47CguzuJqNTR9X7884Xxex0m9wNH+G2GC4Xy674176h2jabXSUn6DS9R32EYZ7gNx9yeJCgFxSrgFBFHc2rSDqwmT33Xs4FAof8MtNlDS7zX9Iy1b5m9Z3VfWzq3ze9LeveW2Zz4M1Hql03+5YWOzHs1u1TiKaZkozA22Ax5dXjofbsvrgpH9Qh3MTVu1dXm1+6a2pAldBmGDxahKKZWiRFzYTh+lPNBH8VlB14nTCU9z47WknElgru8dpPVeUx1MAmKvC9X8PFy/HNHCcmdBAwSTCxl7mevF7i3fHySvLVxl1A/BdF87YvdeMlGqPEtv3Xj87oHWQ7HDYRdenIU5R8Xdamh6gal5wm6pN4VpOX551UErqLj5Yt0mo+m005z+eB0vi73D1tLgDrC9/WufjMIKF8WMhhq1qlwujnLjla++vl5qamqkrq5OOnXqFOtn+QUE9sFLhVmZ2vnm32vX+45kpPuHdgI6YveejRu6Jvq7BRL2AUVvSzrtXCYjLBXmJOZ3YNKr7SgPMoXLVvO0tDNDGFplwin/1m95+ZWe8lumym17sKfWoNu0NJ1uG8pkOei0+TUnowhi3U7M9neJqmX6iTmLrY6cfrSzmlN+rtP33bJ9Kxm+fVfZfquOzWpUFyPssg3yd36Bs982Fna92PuIZ7m7glvYXilSE5+bb/S5VQXz7FRjfO8+Xax+AUGCBq9SYw+/9mnoO1D5+5rfZ4WpK2xyXLere5hsA2HPUU7H0SgF2Y79lq821oQ9XxTDpFEo6uNlOcrBliJeIyBOeEBsGhAEDS7zD2zPX3KgDJvwjNFt0PwDsekBQP/GHrXHPploZxs9mEax8R0xoIcVdCyt+0r+Omtx0e9nL1ttBdFOO0FaNraobiW/+v7uTQ46dnAx/aPPjE/QXvQWqbYKefE6eGtrwIgbp/neuu35TeD99FtLjINFHfxDK1Rop7xiD8pBL7iKaZEOsi27tYbYn6+36f/8+qeyZn3TVpz2bVrKWftt3+QWfVj2Z+ldE71I7LJFtdR2cv/OfiWa8rcp08AqrpZ7ryBFH39v8NbyF4P9/NhBveSxOUuMPrNwWzIZ/MTvu/odu8cevKPcNi1cNRe3C7Mo7soE2RfqvtpQVB1iL9rg8cZVh8QeLJlsxybnYW0BNTlORq2wociNSWNKlErVmBFlvEbKRIIFuVVdeNtjwWdr5U+vfOy6o+TfYv26dJT5EKhO//eiAcJFf57T7OQS1ZXY5LnLteuTcTF1P/ay/fnT736dmhHg9uZ1R+/qGzQU6+dPv2fltHkdTPxug40/alffzjj27XfTXDvNjfYKtIMWYA+SxhDkxBxXfq5+H50P7dTlRNNUtGX+v1/4lxUYn3PAjoFbHAs/6+b/+8D3O5uUl9IcVL3t3qKF+6Atds6t3bKj6zCO6iFuaQs6EIoyCYaVaTDstC2ZDH7ilRZmcuye9Non1kWMWyc+L277ZBSdV4N0mLOHwzbNo7anNWkc0Ltkcdalzb+V/6vvDbRWyor6dY0XmHbtX2VyHv7V9wdKOWhnPa184rdMS1nrtyElA3EUIiBOsDAVB+w81Ydf/6RJMFzYUS4/b0dvF4c5EC9cudbob5wCBNPSkltUtzTuhFBMB7hCOnsa2AXN9dMDk2mrXFiaf6XBiQ7K4TXIip1jbp+s9Lc9ra53vR1ZONqe2rJ9a5nwzWh7QUZtc8qNNjkoF9uTXS+4tJpBMR3+osrP1U5TfuzAWLet/H1SL+i0nq5JC3KQ3H3T+sWag+o3aItTR9A4WnmcOqKabF/F+tvbS2Ty28uMPsfrxG5y7NbjywUH9wt0jClFb3zT43r+cNimAZY9bbnr0jqOTueQgqfb+El7b2t0Htb/xFEC0+Sck4Rlmi8tA3EUIiBOsDAbuNuJ0j7xOgVRpi2AXTu0aTwQ6+eY5KF6jcRlIklXj0FOVFGNpudFOx/pT9ChUu1p8zvi5edKFua6+gWL9uMg39XeZqPoya7pCVG0RBTb49pKYQhQQaAwWU0v6HSf0mGB7eG/o2h9CXICDJKPH3eFETt4slNnShFk3P/KokDTu53Yn3l3mdHfa7WMoIrtpOnXMUw7PflxGlAoiDiqVJimi7iOTuew7es2btrfYuXa9aFKFZoMA2+yHpJU6/cf7yxLxUAchQiIEyzoBm4ShD02Z7FcdvguoQq8X3f0gMZcTf0cE8UOMlT3lVlv2nJyakGMYjQ9U0GGSnW69a29qHX0obDBoragmJ40bPq5Jj3ZtQOGX5BZmKtbTEtEMT2uozq464nZK9AM2voS1wnQDr61Vbxj29bWSHNxdJIp5b4UVmGjhOlw6kHuamkLpteFkgm/i2TTizpNJStmHes2ot/Hr/OwadBtms8etKEiyOlLt33d35yOk/Z3dbv7pOs1SDqJ7YgBX/+NLqco0r4a8i4qtC63/lHQ/XryW0uML/TKPRBHIQLiBAu6gZucODRXeM/rp8oJe27TpKXYvrJ1OwCc8e0+jdUlSn2C0l7l9V9tLOltqCC+7nG9i5VzpuknupMHvdIvRn6r4EE795DXFqySy//6dqBb318Pges+KpZXsKid7kzZ26xdusurlVM7NAbN4Y4iWA2ah2mfROYt/0KikvNo2Q565yiKEc285lMDqJP/Z2aTOsXXHz3At+OnqaS1Ipk0SsTh3AN2bDaQTxBuF6B2TWZNoTKtg96nW3sphqY5ed2NyAVoCY86fSgs7SjudZzU7+x398mus6+dw01SLwrvEmrlD6/ye17LdIpPfxfTSjZ+1aqc7jgnBQFxggXNazQ9cWgNQ6fb7V6DOzz91jLZc7su1nSlPkHpScYOkpISFOePeqcHwsJhOTUoMDFwmxp589O6oufHbhUcNmGaUY/jfPp3fqNieQWLQa7y7ROddiYzaeV8eb5ZjepSt0TYZbnun7lQXpy3suhi+07cWraD3jnSdaYXPKYnqmLpRbd+1lmfrpYrDEfUSlMrklcwFFfApX1AfjH5vdD52yYto9qfQAehiHudmFw0aIuqBpWm72WaPmSayhKGngMOG/D15zgdJ8MMYmHKvvNn50E7NSpp/wSnZarL0K0yUD7drr3uXAW9GDx6UK/EpUS2KPcMwJsdqOqVZD59XLhhhjlI2a0DeptD30uvMJ3YV9t6BVjqE9Tab26J273Mk2DEjltZPay1R7qWjys8CZpW7YgiGG76ucGCYTf569uLHgQ3b85JhzYtjcvS6UHZ9KLq1YX/lmLpxYm2SEdFl4neZTn59zPl73OXxxIM25yWk3272e87L1n9lfz+xX/JY7MXGwdpun70FBXFaUoH+Zn8lvf2Y8Ju4Y761Nm+tdk2axoM6b4QV2NBYb656f5pMwnUtcX20Tc+tSpfuC3rqgjyh03nRacr9r3y04d0/WjKYFzsz/FiB8p67rA7wjvRc/GZ+/U1/my/gT/s/gmai5+/zej/tTKQXzCc/zmaImVX38gX9GLQ5IKn1GghTgHTK8tiRq0798HZcv6yL6wOSk7yx1GfftnBJe1Na1/pu44nXUL5aSql6DhXLiYd0sKUlNN8X92OS3lRpRcnmp4RRUUE/c5+5eriuhWfX3HBr/ObfueLH3kz8OedsNc2jsMth3X5o2/JyP49pE2rFrHcKQvD3pLP2n/7wLnvbnRZ/Xrqh1aFlqTsn/m1ql/48DOj9/3r7MVWvWynO3L2Y+0zoGlSYfPFo6qGEGSUU7vKjmlDRVhRXRDpd9MRVaOWnx6jwlRBWla/Xn4zbZ4M6dNFps//TJasXidbb9lO5q8wq04iEVxUxYWAOCXc8hoLe9Z+Z/eecs+L/sMvF9Kd4jfP+heJ1wPKkBuekSF9tixpHrHO3+oSd7BzOvnqY739rOtDc/mS3tmnGF4d0oopKafbqg7g4dWpJs4TQdig2LSsWj7dT2YvWi0bA/Yuzb/wiqOWtRsNrnRdO5U8U0HXd/26TVZr+k3H7RYqp9hv2Fc/GtxpMJ6/ndl5m/odtRZwVBf2OkSuSWUdHcFQ1/C/i9z2/fbPsNtM47DuBQdA+w5dsaX3oqiGEPT76XuVItUvqgv9uPvpXPbXt6Rd61aht/vbi+zXoXeik5YuoQiIU6yUJ8rCHORp75u1OKTVhSN3kkmvLXJcttrZSwfGMO2AknaFJ5JiW8b1pOHXqSYoPbR279hGNm72ThvRPMmwxeCDllVTA7aukdcCpn3k9w/Q5RRHLWu/VpvCC/Bv1W5hXQwE/f728SJMTrHbsK9aIWVjw2ajnvj3/L+9rCoqbnfXguZqmvC79vn3l5usQD2u/TOq+uf5pTo7tWvjWAc6TOm9YqshBPl++e9lkoIRVtT1oeMO3rV6UzkrOG3ZoY0kETnEKWUfFNLQQtmhOrqDf7FMw6B+3bfwzac2LWCfdoWtHsW0XuiJUPN5o+yJb6/THwzdzjeHWoNw7UBSqpPU43OWBA5M7P4BGriXIiXHzhn26oGuwc4tJwwqWU6x2/FtuQ6W88w86d+zk2desZ3rqsGwV95m0FzNqDS2wkZAW/BtcaRx/e3tpVbLvFfVGv1Mp7xSrxQYVRWwGkKQ71f4XnYgHpdi60OnrSNpJVaOISBOoTTlrv5oRB85aa9tJQk6tv06N87Ez59+xxq62Yn9HnqS0A4olcqtA03Yg1mVYYUJCRlAal1lE/e+vMD45F3MSUo7tgXp5Kgj1T3w46Hy0mUHNdaELcUFr1MHXSdaj7RYVz8x13fZ+1UOsO/S2BesQYOqUuRqRtLi2KlaenSs9p1W72TZyzSObUbvCnilqeSnbsTRWTxfkO9X+F75gXjUNJ0nysFp4g7ey617QgN+AuIUSkOhepu2cmm94yT4Yl1DpCcBnWb0kN5SibyCijAHM80Xtk9OUbUO7L9TN3nojGGNAaTpfJn2YHc6SWmQYkKX2LGDtjZ+b53+xuN3kxE7dgtcRrEYmg9vL79SnMQ+X7vBd9mbVg7Q265hgqogn1UO9t42/qhd5QdD/Y8v+cFoOVvegn62rh/d9nQfvv2kQU325WI/Q8tiOr2XPtaUm6gVW5e5kB28Jy/LtnhJ7VCnyCFOoaTebvDKqSqmKoW2nAUZ0amU9ED4ux/sIec9NLvoUfnKSeuc5hfx8BquWNfp162f5uvkjtGDG0fDi6p14Oz9d2yS52qVIzPcVsLsQ3qS0iDFr8qEVhqYcNxu1kAtJiOWaYH6Xxw7oNmyLkUrSreO1ca3eYupYhNk2QepQqBpEGFHFgzyWXEq3Gbz9z3Tfgr29yhny1uYzw46CI7pZ2hZTLdt4LyD+slDr34S6eBJcSx3v7EB0mpchKklUSMgTqGwO59f7+c4WxfDlE3SE8UdJ39dHiZ/JKxidOnQJrJavfnDdU6UKtcRgnLf5FGvjbFebb4LDu4nfbfqICu/WG/dWjbx04P6ybDtuxoFFfq8jkRmMtCDfWGkuZxRBlZOrQw6X1p83qSUVth9SE9SWqlCO+cVdgrUWsxn7re9dcK1hzj3+556YTHjioMdy5IFXU5htrEgyyGq8md+nxlm4JEgQVWYz4qTDoahnXSd9r2gyyLqUQl1LnpYd0WqZHl9+CGBoxLF8MRfX9j+pzOlU2k50wo4cX/3/JKr/3hnqdz78seS9s7qoyJMLYkaKRMpFLRQvd1p5oxv9/X9G/t1v8L/XpxuWbrljHnNr30LWQM1v++rt7JNCsprIGfy+abvZx8IdVhrDZQK8770++rzvzx+dykFba2+4JCdrJazH47oa3SLX9f1Tw/uZ1Qw3qYltM4y7IxU2CLg1amm2A5gGox6bbtRDCyg2/IbVx0iD4wZat2aPe/AHa3c37fGHyZjR+7UOF9+nYf054Zjd3Ot0WvS+Uhv/9q3m2dffajxfht2Objtx/peOry7H5PP9Du+RbEOTT8rTvkd/9z2vaDLIuitdp3OreJFfuqGBpD5zxVOU6pWv2I65JnmMOvxWvdvO5VDg7iqMn53+4Jv3FEDrHnz28f1wjyJ7a+1narlvIN2lCSryuUSMNpBCtXX10tNTY3U1dVJp06dSv75di9s5bcC82tFmo5XrkxK29hX1Hpi1k5Nfq2LhXWTnYY9dqpt6fZ97U/Rg5s9z17T2MvAqYUv7Pt5fb/8ZTFh8rtWT/sg3FosClv73eqBmgwkUUxtXq0acNUTcx1b3f1qlPpti21bt5AWVVVNeuSb1D11K8vktd7i5PQ9g9RvDfL3JiWpolgObtu51zZeFeAzTfb3qNah37G0MJ0oCkG+R5hlYVqSUy+gDxvQ06q8cu/0BU1SNwq3sWK34yhFNS9ex+s4Pi8K9lDLhetL7zZpg4/ebfDanjWlSwfgufub/TRnEMiOHtK78fyu5+wrH3c+fzop13E3TLxGQJzSgNhrJ9WOMlt2qHbdyfMPAt06VFtbrPYgL5zW6f0LTw5RHBSiPCiZHrjcDipxnwScAkivVBb7swrzJLV0mVZrMElxcLsAsHNdiz1I5Y+Ipd+ryxZft66b5HL6bYsqTH5okk5gQbbxKP7e9KI3ruXgtI2H+cxSrkOnz9KULU3BOeeAHRv3NS1xVjg4iFPAUHiRb3oBG2T+/N7D3mb0VvtfZi226kF7/a3JNlbsdhylUs9Lkr673/x4bc92SlfY+MH+7Ffmf95kpLp9dugmdV9uNGrgKjUC4gwExKXYSQvfP0ggFoeoD9rlOAl4LVOvC5Ri2AewGf/SoU6/vgWnqShJ7dxQiSewUjK96C3F5xfzmaVch6afFeaYEcVxs5hlkeV9IavKdYHTkMBtjYA4IwExAAAAiovX6FQHAACATCMgBgAAQKYREAMAACDTCIgBAACQaQTEAAAAyDQCYgAAAGQaATEAAAAyjYAYAAAAmUZADAAAgEwjIAYAAECmERADAAAg0wiIAQAAkGkExAAAAMg0AmIAAABkGgExAAAAMo2AGAAAAJlGQAwAAIBMIyAGAABAphEQAwAAINMIiAEAAJBprco9A2mVy+Ws3/X19eWeFQAAADiw4zQ7bnNDQBzSF198Yf3edtttyz0rAAAA8InbampqXF+vyvmFzHC0efNmWbJkiXTs2FGqqqpKcoWjwfcnn3winTp1iv3zEC3WX3qx7tKN9ZderLt0q0/I+tMwV4PhXr16SYsW7pnCtBCHpAt1m222Kfnn6kbFgSG9WH/pxbpLN9ZferHu0q1TAtafV8uwjU51AAAAyDQCYgAAAGQaAXFKVFdXy7hx46zfSB/WX3qx7tKN9ZderLt0q07Z+qNTHQAAADKNFmIAAABkGgExAAAAMo2AGAAAAJlGQAwAAIBMIyBOgTvuuEP69Okjbdu2laFDh8qrr75a7lmCiLzwwgvy3e9+1xr9RkcrfPzxx5u8rv1Vr7nmGunZs6e0a9dORo4cKfPmzWsyzapVq+Tkk0+2ipZ37txZxowZI2vWrCnxN8meCRMmyN57722NNNm9e3c55phj5IMPPmgyzbp16+Tcc8+Vrl27yhZbbCHHH3+8LF++vMk0ixYtkiOPPFLat29vvc8ll1wimzZtKvG3yZ4777xTdt9998aC/8OHD5e///3vja+z7tLjxhtvtI6fF1xwQeNzrL/kGj9+vLW+8n923nnnilh3BMQJ9/DDD8tFF11klS6ZNWuWDBw4UA477DBZsWJFuWct89auXWutD71gcXLzzTfLb37zG7nrrrtk5syZ0qFDB2vd6QHDpsHwO++8I1OnTpWnn37aCrLPPPPMEn6LbHr++eetg/Yrr7xiLfuNGzfKoYceaq1T24UXXihPPfWUPPLII9b0OlT7cccd1/h6Q0ODdVDfsGGDvPzyy3LffffJH//4R+siCPHSUUI1kHrjjTfk9ddfl4MOOkiOPvpoa19SrLt0eO211+S///u/rYubfKy/ZNt1111l6dKljT8vvfRSZaw7LbuG5BoyZEju3HPPbXzc0NCQ69WrV27ChAllnS80pbvSY4891vh48+bNudra2twvf/nLxudWr16dq66uzj300EPW43fffdf6u9dee61xmr///e+5qqqq3OLFi0v8DbJtxYoV1rp4/vnnG9dV69atc4888kjjNO+99541zYwZM6zHkydPzrVo0SK3bNmyxmnuvPPOXKdOnXLr168vw7fIti233DL3P//zP6y7lPjiiy9y/fr1y02dOjW3//7758aOHWs9z/pLtnHjxuUGDhzo+Fra1x0txAmmV1DaAqK32m0tWrSwHs+YMaOs8wZvCxYskGXLljVZdzqWuqa82OtOf2uaxF577dU4jU6v61hblFE6dXV11u8uXbpYv3W/01bj/PWntwV79+7dZP3ttttu0qNHj8Zp9A5AfX19Y0sl4qctTpMmTbJa9zV1gnWXDnqHRlsK89eTYv0l37x586xUwe233966y6kpEJWw7lqV9dPhaeXKldbBPn/DUfr4/fffL9t8wZ8Gw8pp3dmv6W/Nn8rXqlUrKyizp0H8Nm/ebOUvjhgxQgYMGGA9p8u/TZs21gWL1/pzWr/2a4jX22+/bQXAmoKkuYqPPfaY9O/fX+bMmcO6Szi9gNEUQE2ZKMS+l2xDhw61Uhy+9a1vWekS1157rXz729+WuXPnpn7dERADkKy3VOnBPD8PDsmnJ2QNfrV1/y9/+YucdtppVs4iku2TTz6RsWPHWrn72lEc6XL44Yc3/l9zvzVA3m677eTPf/6z1Xk8zUiZSLBu3bpJy5Ytm/XQ1Me1tbVlmy/4s9eP17rT34WdI7WnrVaeYP2WxnnnnWd1Znzuueesjlo2Xf6asrR69WrP9ee0fu3XEC9tidpxxx1lzz33tKqGaAfX22+/nXWXcHpbXY97gwcPtu6I6Y9eyGgHZP2/thay/tKjc+fOstNOO8lHH32U+n2PgDjhB3w92E+bNq3J7V19rLcKkVx9+/a1du78dac5UpobbK87/a0HDj1B2J599llrHetVN+Kj/SA1GNbb7LrMdX3l0/2udevWTdaflmXTXLn89ae37fMvarTVS8uA6a17lJbuN+vXr2fdJdzBBx9sLXtt3bd/tB+F5qLa/2f9pceaNWtk/vz5VnnR1O97Ze3SB1+TJk2yKhP88Y9/tKoSnHnmmbnOnTs36aGJ8vWSnj17tvWju9Ktt95q/f/jjz+2Xr/xxhutdfXEE0/k3nrrrdzRRx+d69u3b+6rr75qfI9Ro0bl9thjj9zMmTNzL730ktXrevTo0WX8Vtlwzjnn5GpqanL//Oc/c0uXLm38+fLLLxunOfvss3O9e/fOPfvss7nXX389N3z4cOvHtmnTptyAAQNyhx56aG7OnDm5KVOm5LbaaqvcFVdcUaZvlR2XX365VRFkwYIF1r6lj7U6yz/+8Q/rddZduuRXmVCsv+S6+OKLreOm7nvTp0/PjRw5MtetWzerUk/a1x0BcQr89re/tTawNm3aWGXYXnnllXLPEnK53HPPPWcFwoU/p512WmPptauvvjrXo0cP66Lm4IMPzn3wwQdN3uPzzz+3AuAtttjCKjtz+umnW4E24uW03vTn3nvvbZxGL1x+8pOfWOW82rdvnzv22GOtoDnfwoULc4cffniuXbt21klBTxYbN24swzfKlh/96Ee57bbbzjom6slU9y07GFasu3QHxKy/5DrxxBNzPXv2tPa9rbfe2nr80UcfVcS6q9J/yttGDQAAAJQPOcQAAADINAJiAAAAZBoBMQAAADKNgBgAAACZRkAMAACATCMgBgAAQKYREAMAACDTCIgBAJaqqip5/PHHyz0bAFByBMQAUAF++MMfyjHHHFPu2QCAVCIgBgAAQKYREANAhTnggAPkpz/9qVx66aXSpUsXqa2tlfHjxzeZZt68ebLffvtJ27ZtpX///jJ16tRm7/PJJ5/ICSecIJ07d7be5+ijj5aFCxdar73//vvSvn17efDBBxun//Of/yzt2rWTd999twTfEgCiQ0AMABXovvvukw4dOsjMmTPl5ptvlp///OeNQe/mzZvluOOOkzZt2liv33XXXXLZZZc1+fuNGzfKYYcdJh07dpQXX3xRpk+fLltssYWMGjVKNmzYIDvvvLP86le/kp/85CeyaNEi+fTTT+Xss8+Wm266yQqwASBNqnK5XK7cMwEAKD6HePXq1VanOG0hbmhosAJZ25AhQ+Sggw6SG2+8Uf7xj3/IkUceKR9//LH06tXLen3KlCly+OGHy2OPPWblIt9///1y/fXXy3vvvWd1tlMaCGtrsX7GoYceaj33ne98R+rr663gumXLltb72NMDQFq0KvcMAACit/vuuzd53LNnT1mxYoX1fw1yt91228ZgWA0fPrzJ9G+++aZ89NFHVgtxvnXr1sn8+fMbH//hD3+QnXbaSVq0aCHvvPMOwTCAVCIgBoAK1Lp16yaPNVDVVAlTa9askT333FMeeOCBZq9ttdVWTQLntWvXWgHx0qVLrcAbANKGgBgAMmaXXXaxOszlB7CvvPJKk2kGDx4sDz/8sHTv3l06derk+D6rVq2yUjV+9rOfWe918skny6xZs6yOdQCQJnSqA4CMGTlypJXmcNppp1ktvJprrEFtPg1uu3XrZlWW0NcXLFgg//znP63qFdqBTmknOk29uOqqq+TWW2+18pb/67/+q0zfCgDCIyAGgIzR9AbtPPfVV19Zne1+/OMfyy9+8Ysm02hJtRdeeEF69+5tVaTQVuUxY8ZYOcTaYvynP/1JJk+eLP/7v/8rrVq1sipaaEe8e+65R/7+97+X7bsBQBhUmQAAAECm0UIMAACATCMgBgAAQKYREAMAACDTCIgBAACQaQTEAAAAyDQCYgAAAGQaATEAAAAyjYAYAAAAmUZADAAAgEwjIAYAAECmERADAAAg0wiIAQAAIFn2/wEn4ZrTixmEEwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -1656,10 +1665,10 @@ "id": "e479373c", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.293264Z", - "iopub.status.busy": "2025-04-03T04:44:00.293146Z", - "iopub.status.idle": "2025-04-03T04:44:00.305835Z", - "shell.execute_reply": "2025-04-03T04:44:00.305546Z" + "iopub.execute_input": "2025-04-03T19:32:51.094541Z", + "iopub.status.busy": "2025-04-03T19:32:51.094455Z", + "iopub.status.idle": "2025-04-03T19:32:51.105229Z", + "shell.execute_reply": "2025-04-03T19:32:51.104994Z" }, "lines_to_next_cell": 0 }, @@ -1754,10 +1763,10 @@ "id": "b903170c", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.308200Z", - "iopub.status.busy": "2025-04-03T04:44:00.308088Z", - "iopub.status.idle": "2025-04-03T04:44:00.310943Z", - "shell.execute_reply": "2025-04-03T04:44:00.310561Z" + "iopub.execute_input": "2025-04-03T19:32:51.106458Z", + "iopub.status.busy": "2025-04-03T19:32:51.106379Z", + "iopub.status.idle": "2025-04-03T19:32:51.108676Z", + "shell.execute_reply": "2025-04-03T19:32:51.108453Z" } }, "outputs": [ @@ -1794,10 +1803,10 @@ "id": "bbe47670", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.312663Z", - "iopub.status.busy": "2025-04-03T04:44:00.312541Z", - "iopub.status.idle": "2025-04-03T04:44:00.332188Z", - "shell.execute_reply": "2025-04-03T04:44:00.331912Z" + "iopub.execute_input": "2025-04-03T19:32:51.109894Z", + "iopub.status.busy": "2025-04-03T19:32:51.109814Z", + "iopub.status.idle": "2025-04-03T19:32:51.124106Z", + "shell.execute_reply": "2025-04-03T19:32:51.123871Z" } }, "outputs": [ @@ -1970,10 +1979,10 @@ "id": "a1484d00", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.333834Z", - "iopub.status.busy": "2025-04-03T04:44:00.333742Z", - "iopub.status.idle": "2025-04-03T04:44:00.353383Z", - "shell.execute_reply": "2025-04-03T04:44:00.353115Z" + "iopub.execute_input": "2025-04-03T19:32:51.125356Z", + "iopub.status.busy": "2025-04-03T19:32:51.125276Z", + "iopub.status.idle": "2025-04-03T19:32:51.138960Z", + "shell.execute_reply": "2025-04-03T19:32:51.138745Z" } }, "outputs": [ @@ -2158,10 +2167,10 @@ "id": "46ac946c", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.354983Z", - "iopub.status.busy": "2025-04-03T04:44:00.354867Z", - "iopub.status.idle": "2025-04-03T04:44:00.361815Z", - "shell.execute_reply": "2025-04-03T04:44:00.361539Z" + "iopub.execute_input": "2025-04-03T19:32:51.140138Z", + "iopub.status.busy": "2025-04-03T19:32:51.140067Z", + "iopub.status.idle": "2025-04-03T19:32:51.146004Z", + "shell.execute_reply": "2025-04-03T19:32:51.145771Z" }, "lines_to_next_cell": 0 }, @@ -2290,10 +2299,10 @@ "id": "b626ecb9", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.363405Z", - "iopub.status.busy": "2025-04-03T04:44:00.363311Z", - "iopub.status.idle": "2025-04-03T04:44:00.368568Z", - "shell.execute_reply": "2025-04-03T04:44:00.368275Z" + "iopub.execute_input": "2025-04-03T19:32:51.147247Z", + "iopub.status.busy": "2025-04-03T19:32:51.147171Z", + "iopub.status.idle": "2025-04-03T19:32:51.151571Z", + "shell.execute_reply": "2025-04-03T19:32:51.151341Z" }, "lines_to_next_cell": 0 }, @@ -2323,10 +2332,10 @@ "id": "8bf77477", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.370113Z", - "iopub.status.busy": "2025-04-03T04:44:00.370024Z", - "iopub.status.idle": "2025-04-03T04:44:00.384235Z", - "shell.execute_reply": "2025-04-03T04:44:00.383955Z" + "iopub.execute_input": "2025-04-03T19:32:51.152822Z", + "iopub.status.busy": "2025-04-03T19:32:51.152716Z", + "iopub.status.idle": "2025-04-03T19:32:51.163070Z", + "shell.execute_reply": "2025-04-03T19:32:51.162825Z" }, "lines_to_next_cell": 2 }, @@ -2431,10 +2440,10 @@ "id": "6289c3cb", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.385906Z", - "iopub.status.busy": "2025-04-03T04:44:00.385783Z", - "iopub.status.idle": "2025-04-03T04:44:00.399387Z", - "shell.execute_reply": "2025-04-03T04:44:00.399084Z" + "iopub.execute_input": "2025-04-03T19:32:51.164397Z", + "iopub.status.busy": "2025-04-03T19:32:51.164312Z", + "iopub.status.idle": "2025-04-03T19:32:51.175546Z", + "shell.execute_reply": "2025-04-03T19:32:51.175305Z" }, "lines_to_next_cell": 0 }, @@ -2549,10 +2558,10 @@ "id": "b6542ac6", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.401031Z", - "iopub.status.busy": "2025-04-03T04:44:00.400918Z", - "iopub.status.idle": "2025-04-03T04:44:00.406569Z", - "shell.execute_reply": "2025-04-03T04:44:00.406300Z" + "iopub.execute_input": "2025-04-03T19:32:51.176770Z", + "iopub.status.busy": "2025-04-03T19:32:51.176688Z", + "iopub.status.idle": "2025-04-03T19:32:51.180893Z", + "shell.execute_reply": "2025-04-03T19:32:51.180653Z" }, "lines_to_next_cell": 0 }, @@ -2649,7 +2658,7 @@ "\n", "The function `anova_lm()` can take more than two nested models\n", "as input, in which case it compares every successive pair of models.\n", - "That also explains why their are `NaN`s in the first row above, since\n", + "That also explains why there are `NaN`s in the first row above, since\n", "there is no previous model with which to compare the first.\n" ] }, @@ -2659,17 +2668,17 @@ "id": "8e4c6004", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.408066Z", - "iopub.status.busy": "2025-04-03T04:44:00.407960Z", - "iopub.status.idle": "2025-04-03T04:44:00.502573Z", - "shell.execute_reply": "2025-04-03T04:44:00.502232Z" + "iopub.execute_input": "2025-04-03T19:32:51.182118Z", + "iopub.status.busy": "2025-04-03T19:32:51.182029Z", + "iopub.status.idle": "2025-04-03T19:32:51.233426Z", + "shell.execute_reply": "2025-04-03T19:32:51.233183Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -2715,10 +2724,10 @@ "id": "a83f4bd2", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.504359Z", - "iopub.status.busy": "2025-04-03T04:44:00.504231Z", - "iopub.status.idle": "2025-04-03T04:44:00.509266Z", - "shell.execute_reply": "2025-04-03T04:44:00.508991Z" + "iopub.execute_input": "2025-04-03T19:32:51.234718Z", + "iopub.status.busy": "2025-04-03T19:32:51.234617Z", + "iopub.status.idle": "2025-04-03T19:32:51.239575Z", + "shell.execute_reply": "2025-04-03T19:32:51.239354Z" }, "lines_to_next_cell": 0 }, @@ -2766,10 +2775,10 @@ "id": "24efc6f1", "metadata": { "execution": { - "iopub.execute_input": "2025-04-03T04:44:00.510850Z", - "iopub.status.busy": "2025-04-03T04:44:00.510746Z", - "iopub.status.idle": "2025-04-03T04:44:00.538766Z", - "shell.execute_reply": "2025-04-03T04:44:00.538446Z" + "iopub.execute_input": "2025-04-03T19:32:51.240897Z", + "iopub.status.busy": "2025-04-03T19:32:51.240800Z", + "iopub.status.idle": "2025-04-03T19:32:51.261250Z", + "shell.execute_reply": "2025-04-03T19:32:51.261014Z" }, "lines_to_next_cell": 0 }, @@ -2961,8 +2970,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -2974,7 +2983,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/Ch04-classification-lab.Rmd b/Ch04-classification-lab.Rmd index 3b9b732..f4b7329 100644 --- a/Ch04-classification-lab.Rmd +++ b/Ch04-classification-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Logistic Regression, LDA, QDA, and KNN diff --git a/Ch04-classification-lab.ipynb b/Ch04-classification-lab.ipynb index ef7e1d1..7b8e5bc 100644 --- a/Ch04-classification-lab.ipynb +++ b/Ch04-classification-lab.ipynb @@ -1,13 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "31b77867", - "metadata": {}, - "source": [ - "\n" - ] - }, { "cell_type": "markdown", "id": "559b7845", @@ -51,10 +43,10 @@ "id": "28bd64cb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:09.508409Z", - "iopub.status.busy": "2024-06-04T23:19:09.508152Z", - "iopub.status.idle": "2024-06-04T23:19:10.300967Z", - "shell.execute_reply": "2024-06-04T23:19:10.300663Z" + "iopub.execute_input": "2025-04-03T19:33:18.698349Z", + "iopub.status.busy": "2025-04-03T19:33:18.698142Z", + "iopub.status.idle": "2025-04-03T19:33:19.540929Z", + "shell.execute_reply": "2025-04-03T19:33:19.540601Z" } }, "outputs": [], @@ -82,10 +74,10 @@ "id": "f0a0173d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.302676Z", - "iopub.status.busy": "2024-06-04T23:19:10.302557Z", - "iopub.status.idle": "2024-06-04T23:19:10.318173Z", - "shell.execute_reply": "2024-06-04T23:19:10.317991Z" + "iopub.execute_input": "2025-04-03T19:33:19.542570Z", + "iopub.status.busy": "2025-04-03T19:33:19.542433Z", + "iopub.status.idle": "2025-04-03T19:33:19.570450Z", + "shell.execute_reply": "2025-04-03T19:33:19.570195Z" }, "lines_to_next_cell": 2 }, @@ -117,10 +109,10 @@ "id": "3640fda5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.319434Z", - "iopub.status.busy": "2024-06-04T23:19:10.319329Z", - "iopub.status.idle": "2024-06-04T23:19:10.328090Z", - "shell.execute_reply": "2024-06-04T23:19:10.327895Z" + "iopub.execute_input": "2025-04-03T19:33:19.571876Z", + "iopub.status.busy": "2025-04-03T19:33:19.571747Z", + "iopub.status.idle": "2025-04-03T19:33:19.581503Z", + "shell.execute_reply": "2025-04-03T19:33:19.581181Z" }, "lines_to_next_cell": 0 }, @@ -337,10 +329,10 @@ "id": "ff825fa5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.329300Z", - "iopub.status.busy": "2024-06-04T23:19:10.329214Z", - "iopub.status.idle": "2024-06-04T23:19:10.331118Z", - "shell.execute_reply": "2024-06-04T23:19:10.330931Z" + "iopub.execute_input": "2025-04-03T19:33:19.582752Z", + "iopub.status.busy": "2025-04-03T19:33:19.582663Z", + "iopub.status.idle": "2025-04-03T19:33:19.584796Z", + "shell.execute_reply": "2025-04-03T19:33:19.584565Z" }, "lines_to_next_cell": 0 }, @@ -381,10 +373,10 @@ "id": "6e10c8fa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.332318Z", - "iopub.status.busy": "2024-06-04T23:19:10.332231Z", - "iopub.status.idle": "2024-06-04T23:19:10.336148Z", - "shell.execute_reply": "2024-06-04T23:19:10.335919Z" + "iopub.execute_input": "2025-04-03T19:33:19.585970Z", + "iopub.status.busy": "2025-04-03T19:33:19.585890Z", + "iopub.status.idle": "2025-04-03T19:33:19.589853Z", + "shell.execute_reply": "2025-04-03T19:33:19.589656Z" }, "lines_to_next_cell": 0 }, @@ -562,17 +554,17 @@ "id": "c30034ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.337474Z", - "iopub.status.busy": "2024-06-04T23:19:10.337394Z", - "iopub.status.idle": "2024-06-04T23:19:10.430280Z", - "shell.execute_reply": "2024-06-04T23:19:10.429786Z" + "iopub.execute_input": "2025-04-03T19:33:19.590933Z", + "iopub.status.busy": "2025-04-03T19:33:19.590859Z", + "iopub.status.idle": "2025-04-03T19:33:19.741595Z", + "shell.execute_reply": "2025-04-03T19:33:19.739146Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -609,10 +601,10 @@ "id": "61a82664", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.434291Z", - "iopub.status.busy": "2024-06-04T23:19:10.433853Z", - "iopub.status.idle": "2024-06-04T23:19:10.501135Z", - "shell.execute_reply": "2024-06-04T23:19:10.483890Z" + "iopub.execute_input": "2025-04-03T19:33:19.745772Z", + "iopub.status.busy": "2025-04-03T19:33:19.744635Z", + "iopub.status.idle": "2025-04-03T19:33:19.781847Z", + "shell.execute_reply": "2025-04-03T19:33:19.778365Z" }, "lines_to_next_cell": 0 }, @@ -749,10 +741,10 @@ "id": "d09a55d9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.504964Z", - "iopub.status.busy": "2024-06-04T23:19:10.504628Z", - "iopub.status.idle": "2024-06-04T23:19:10.523026Z", - "shell.execute_reply": "2024-06-04T23:19:10.521952Z" + "iopub.execute_input": "2025-04-03T19:33:19.786174Z", + "iopub.status.busy": "2025-04-03T19:33:19.785775Z", + "iopub.status.idle": "2025-04-03T19:33:19.792345Z", + "shell.execute_reply": "2025-04-03T19:33:19.791616Z" }, "lines_to_next_cell": 0 }, @@ -794,10 +786,10 @@ "id": "a14e688f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.532342Z", - "iopub.status.busy": "2024-06-04T23:19:10.530799Z", - "iopub.status.idle": "2024-06-04T23:19:10.538953Z", - "shell.execute_reply": "2024-06-04T23:19:10.538379Z" + "iopub.execute_input": "2025-04-03T19:33:19.801853Z", + "iopub.status.busy": "2025-04-03T19:33:19.799854Z", + "iopub.status.idle": "2025-04-03T19:33:19.810009Z", + "shell.execute_reply": "2025-04-03T19:33:19.807541Z" } }, "outputs": [ @@ -845,10 +837,10 @@ "id": "2d1d337a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.545854Z", - "iopub.status.busy": "2024-06-04T23:19:10.545607Z", - "iopub.status.idle": "2024-06-04T23:19:10.550331Z", - "shell.execute_reply": "2024-06-04T23:19:10.549806Z" + "iopub.execute_input": "2025-04-03T19:33:19.813946Z", + "iopub.status.busy": "2025-04-03T19:33:19.813672Z", + "iopub.status.idle": "2025-04-03T19:33:19.821307Z", + "shell.execute_reply": "2025-04-03T19:33:19.819255Z" }, "lines_to_next_cell": 0 }, @@ -889,10 +881,10 @@ "id": "db97e20c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.553209Z", - "iopub.status.busy": "2024-06-04T23:19:10.553027Z", - "iopub.status.idle": "2024-06-04T23:19:10.555510Z", - "shell.execute_reply": "2024-06-04T23:19:10.555019Z" + "iopub.execute_input": "2025-04-03T19:33:19.827261Z", + "iopub.status.busy": "2025-04-03T19:33:19.825242Z", + "iopub.status.idle": "2025-04-03T19:33:19.837412Z", + "shell.execute_reply": "2025-04-03T19:33:19.834835Z" }, "lines_to_next_cell": 0 }, @@ -922,10 +914,10 @@ "id": "5173815c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.558137Z", - "iopub.status.busy": "2024-06-04T23:19:10.557755Z", - "iopub.status.idle": "2024-06-04T23:19:10.568812Z", - "shell.execute_reply": "2024-06-04T23:19:10.568379Z" + "iopub.execute_input": "2025-04-03T19:33:19.842924Z", + "iopub.status.busy": "2025-04-03T19:33:19.842537Z", + "iopub.status.idle": "2025-04-03T19:33:19.858544Z", + "shell.execute_reply": "2025-04-03T19:33:19.856574Z" } }, "outputs": [ @@ -1011,10 +1003,10 @@ "id": "b49ce3a2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.571227Z", - "iopub.status.busy": "2024-06-04T23:19:10.571020Z", - "iopub.status.idle": "2024-06-04T23:19:10.575331Z", - "shell.execute_reply": "2024-06-04T23:19:10.574762Z" + "iopub.execute_input": "2025-04-03T19:33:19.862748Z", + "iopub.status.busy": "2025-04-03T19:33:19.861995Z", + "iopub.status.idle": "2025-04-03T19:33:19.870931Z", + "shell.execute_reply": "2025-04-03T19:33:19.867742Z" }, "lines_to_next_cell": 0 }, @@ -1074,10 +1066,10 @@ "id": "773ab528", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.578285Z", - "iopub.status.busy": "2024-06-04T23:19:10.578069Z", - "iopub.status.idle": "2024-06-04T23:19:10.582814Z", - "shell.execute_reply": "2024-06-04T23:19:10.582379Z" + "iopub.execute_input": "2025-04-03T19:33:19.874767Z", + "iopub.status.busy": "2025-04-03T19:33:19.874637Z", + "iopub.status.idle": "2025-04-03T19:33:19.877345Z", + "shell.execute_reply": "2025-04-03T19:33:19.877115Z" }, "lines_to_next_cell": 2 }, @@ -1140,10 +1132,10 @@ "id": "e8b1b1e8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.585518Z", - "iopub.status.busy": "2024-06-04T23:19:10.585316Z", - "iopub.status.idle": "2024-06-04T23:19:10.592938Z", - "shell.execute_reply": "2024-06-04T23:19:10.592438Z" + "iopub.execute_input": "2025-04-03T19:33:19.878670Z", + "iopub.status.busy": "2025-04-03T19:33:19.878569Z", + "iopub.status.idle": "2025-04-03T19:33:19.882742Z", + "shell.execute_reply": "2025-04-03T19:33:19.882528Z" } }, "outputs": [], @@ -1177,10 +1169,10 @@ "id": "c1993ea5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.595809Z", - "iopub.status.busy": "2024-06-04T23:19:10.595620Z", - "iopub.status.idle": "2024-06-04T23:19:10.599118Z", - "shell.execute_reply": "2024-06-04T23:19:10.598574Z" + "iopub.execute_input": "2025-04-03T19:33:19.883962Z", + "iopub.status.busy": "2025-04-03T19:33:19.883884Z", + "iopub.status.idle": "2025-04-03T19:33:19.885852Z", + "shell.execute_reply": "2025-04-03T19:33:19.885616Z" }, "lines_to_next_cell": 0 }, @@ -1206,10 +1198,10 @@ "id": "8f909de4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.601671Z", - "iopub.status.busy": "2024-06-04T23:19:10.601471Z", - "iopub.status.idle": "2024-06-04T23:19:10.609107Z", - "shell.execute_reply": "2024-06-04T23:19:10.608569Z" + "iopub.execute_input": "2025-04-03T19:33:19.886902Z", + "iopub.status.busy": "2025-04-03T19:33:19.886834Z", + "iopub.status.idle": "2025-04-03T19:33:19.890608Z", + "shell.execute_reply": "2025-04-03T19:33:19.890362Z" }, "lines_to_next_cell": 0 }, @@ -1291,10 +1283,10 @@ "id": "3c479105", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.611827Z", - "iopub.status.busy": "2024-06-04T23:19:10.611594Z", - "iopub.status.idle": "2024-06-04T23:19:10.615795Z", - "shell.execute_reply": "2024-06-04T23:19:10.615260Z" + "iopub.execute_input": "2025-04-03T19:33:19.891741Z", + "iopub.status.busy": "2025-04-03T19:33:19.891675Z", + "iopub.status.idle": "2025-04-03T19:33:19.893860Z", + "shell.execute_reply": "2025-04-03T19:33:19.893667Z" }, "lines_to_next_cell": 2 }, @@ -1349,10 +1341,10 @@ "id": "3f473ec0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.618328Z", - "iopub.status.busy": "2024-06-04T23:19:10.618134Z", - "iopub.status.idle": "2024-06-04T23:19:10.657121Z", - "shell.execute_reply": "2024-06-04T23:19:10.656851Z" + "iopub.execute_input": "2025-04-03T19:33:19.894984Z", + "iopub.status.busy": "2025-04-03T19:33:19.894913Z", + "iopub.status.idle": "2025-04-03T19:33:19.903718Z", + "shell.execute_reply": "2025-04-03T19:33:19.903468Z" }, "lines_to_next_cell": 2 }, @@ -1443,10 +1435,10 @@ "id": "b3cd8b84", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.658566Z", - "iopub.status.busy": "2024-06-04T23:19:10.658490Z", - "iopub.status.idle": "2024-06-04T23:19:10.660432Z", - "shell.execute_reply": "2024-06-04T23:19:10.660219Z" + "iopub.execute_input": "2025-04-03T19:33:19.904860Z", + "iopub.status.busy": "2025-04-03T19:33:19.904789Z", + "iopub.status.idle": "2025-04-03T19:33:19.906865Z", + "shell.execute_reply": "2025-04-03T19:33:19.906646Z" } }, "outputs": [ @@ -1497,10 +1489,10 @@ "id": "d15e7495", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.661646Z", - "iopub.status.busy": "2024-06-04T23:19:10.661582Z", - "iopub.status.idle": "2024-06-04T23:19:10.664591Z", - "shell.execute_reply": "2024-06-04T23:19:10.664373Z" + "iopub.execute_input": "2025-04-03T19:33:19.908065Z", + "iopub.status.busy": "2025-04-03T19:33:19.907996Z", + "iopub.status.idle": "2025-04-03T19:33:19.911278Z", + "shell.execute_reply": "2025-04-03T19:33:19.911069Z" }, "lines_to_next_cell": 2 }, @@ -1549,10 +1541,10 @@ "id": "586b8bc1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.665995Z", - "iopub.status.busy": "2024-06-04T23:19:10.665887Z", - "iopub.status.idle": "2024-06-04T23:19:10.667484Z", - "shell.execute_reply": "2024-06-04T23:19:10.667242Z" + "iopub.execute_input": "2025-04-03T19:33:19.912502Z", + "iopub.status.busy": "2025-04-03T19:33:19.912424Z", + "iopub.status.idle": "2025-04-03T19:33:19.914085Z", + "shell.execute_reply": "2025-04-03T19:33:19.913884Z" } }, "outputs": [], @@ -1577,10 +1569,10 @@ "id": "18fa8ae5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.668696Z", - "iopub.status.busy": "2024-06-04T23:19:10.668623Z", - "iopub.status.idle": "2024-06-04T23:19:10.673662Z", - "shell.execute_reply": "2024-06-04T23:19:10.673442Z" + "iopub.execute_input": "2025-04-03T19:33:19.915337Z", + "iopub.status.busy": "2025-04-03T19:33:19.915270Z", + "iopub.status.idle": "2025-04-03T19:33:19.920614Z", + "shell.execute_reply": "2025-04-03T19:33:19.920393Z" }, "lines_to_next_cell": 0 }, @@ -2037,10 +2029,10 @@ "id": "4c9a8391", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.674879Z", - "iopub.status.busy": "2024-06-04T23:19:10.674804Z", - "iopub.status.idle": "2024-06-04T23:19:10.676917Z", - "shell.execute_reply": "2024-06-04T23:19:10.676691Z" + "iopub.execute_input": "2025-04-03T19:33:19.921735Z", + "iopub.status.busy": "2025-04-03T19:33:19.921653Z", + "iopub.status.idle": "2025-04-03T19:33:19.923541Z", + "shell.execute_reply": "2025-04-03T19:33:19.923340Z" }, "lines_to_next_cell": 2 }, @@ -2078,10 +2070,10 @@ "id": "0b774571", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.678089Z", - "iopub.status.busy": "2024-06-04T23:19:10.678014Z", - "iopub.status.idle": "2024-06-04T23:19:10.679873Z", - "shell.execute_reply": "2024-06-04T23:19:10.679660Z" + "iopub.execute_input": "2025-04-03T19:33:19.924565Z", + "iopub.status.busy": "2025-04-03T19:33:19.924501Z", + "iopub.status.idle": "2025-04-03T19:33:19.926272Z", + "shell.execute_reply": "2025-04-03T19:33:19.926065Z" }, "lines_to_next_cell": 2 }, @@ -2116,10 +2108,10 @@ "id": "148f227f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.680993Z", - "iopub.status.busy": "2024-06-04T23:19:10.680932Z", - "iopub.status.idle": "2024-06-04T23:19:10.682749Z", - "shell.execute_reply": "2024-06-04T23:19:10.682530Z" + "iopub.execute_input": "2025-04-03T19:33:19.927346Z", + "iopub.status.busy": "2025-04-03T19:33:19.927279Z", + "iopub.status.idle": "2025-04-03T19:33:19.929333Z", + "shell.execute_reply": "2025-04-03T19:33:19.929106Z" }, "lines_to_next_cell": 2 }, @@ -2153,10 +2145,10 @@ "id": "73068c40", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.683928Z", - "iopub.status.busy": "2024-06-04T23:19:10.683852Z", - "iopub.status.idle": "2024-06-04T23:19:10.685758Z", - "shell.execute_reply": "2024-06-04T23:19:10.685565Z" + "iopub.execute_input": "2025-04-03T19:33:19.930479Z", + "iopub.status.busy": "2025-04-03T19:33:19.930401Z", + "iopub.status.idle": "2025-04-03T19:33:19.932567Z", + "shell.execute_reply": "2025-04-03T19:33:19.932312Z" } }, "outputs": [ @@ -2191,10 +2183,10 @@ "id": "a8d52279", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.686970Z", - "iopub.status.busy": "2024-06-04T23:19:10.686905Z", - "iopub.status.idle": "2024-06-04T23:19:10.688834Z", - "shell.execute_reply": "2024-06-04T23:19:10.688607Z" + "iopub.execute_input": "2025-04-03T19:33:19.933704Z", + "iopub.status.busy": "2025-04-03T19:33:19.933633Z", + "iopub.status.idle": "2025-04-03T19:33:19.935525Z", + "shell.execute_reply": "2025-04-03T19:33:19.935280Z" } }, "outputs": [], @@ -2218,10 +2210,10 @@ "id": "b83cbac2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.690007Z", - "iopub.status.busy": "2024-06-04T23:19:10.689936Z", - "iopub.status.idle": "2024-06-04T23:19:10.693543Z", - "shell.execute_reply": "2024-06-04T23:19:10.693330Z" + "iopub.execute_input": "2025-04-03T19:33:19.936644Z", + "iopub.status.busy": "2025-04-03T19:33:19.936575Z", + "iopub.status.idle": "2025-04-03T19:33:19.940242Z", + "shell.execute_reply": "2025-04-03T19:33:19.940043Z" }, "lines_to_next_cell": 2 }, @@ -2305,10 +2297,10 @@ "id": "496f213c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.694765Z", - "iopub.status.busy": "2024-06-04T23:19:10.694696Z", - "iopub.status.idle": "2024-06-04T23:19:10.697074Z", - "shell.execute_reply": "2024-06-04T23:19:10.696863Z" + "iopub.execute_input": "2025-04-03T19:33:19.941374Z", + "iopub.status.busy": "2025-04-03T19:33:19.941306Z", + "iopub.status.idle": "2025-04-03T19:33:19.943721Z", + "shell.execute_reply": "2025-04-03T19:33:19.943524Z" }, "lines_to_next_cell": 2 }, @@ -2349,10 +2341,10 @@ "id": "7a306b42", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.698382Z", - "iopub.status.busy": "2024-06-04T23:19:10.698315Z", - "iopub.status.idle": "2024-06-04T23:19:10.700428Z", - "shell.execute_reply": "2024-06-04T23:19:10.700221Z" + "iopub.execute_input": "2025-04-03T19:33:19.944819Z", + "iopub.status.busy": "2025-04-03T19:33:19.944755Z", + "iopub.status.idle": "2025-04-03T19:33:19.946905Z", + "shell.execute_reply": "2025-04-03T19:33:19.946710Z" }, "lines_to_next_cell": 2 }, @@ -2395,10 +2387,10 @@ "id": "f2d7878b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.701614Z", - "iopub.status.busy": "2024-06-04T23:19:10.701548Z", - "iopub.status.idle": "2024-06-04T23:19:10.703307Z", - "shell.execute_reply": "2024-06-04T23:19:10.703099Z" + "iopub.execute_input": "2025-04-03T19:33:19.948164Z", + "iopub.status.busy": "2025-04-03T19:33:19.948083Z", + "iopub.status.idle": "2025-04-03T19:33:19.950352Z", + "shell.execute_reply": "2025-04-03T19:33:19.950123Z" } }, "outputs": [ @@ -2460,10 +2452,10 @@ "id": "6fc87c48", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.704453Z", - "iopub.status.busy": "2024-06-04T23:19:10.704385Z", - "iopub.status.idle": "2024-06-04T23:19:10.707626Z", - "shell.execute_reply": "2024-06-04T23:19:10.707394Z" + "iopub.execute_input": "2025-04-03T19:33:19.951598Z", + "iopub.status.busy": "2025-04-03T19:33:19.951529Z", + "iopub.status.idle": "2025-04-03T19:33:19.954896Z", + "shell.execute_reply": "2025-04-03T19:33:19.954698Z" } }, "outputs": [ @@ -2904,10 +2896,10 @@ "id": "92f4f928", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.708905Z", - "iopub.status.busy": "2024-06-04T23:19:10.708836Z", - "iopub.status.idle": "2024-06-04T23:19:10.710715Z", - "shell.execute_reply": "2024-06-04T23:19:10.710514Z" + "iopub.execute_input": "2025-04-03T19:33:19.956044Z", + "iopub.status.busy": "2025-04-03T19:33:19.955966Z", + "iopub.status.idle": "2025-04-03T19:33:19.957965Z", + "shell.execute_reply": "2025-04-03T19:33:19.957778Z" } }, "outputs": [ @@ -2943,10 +2935,10 @@ "id": "d016f22c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.711933Z", - "iopub.status.busy": "2024-06-04T23:19:10.711857Z", - "iopub.status.idle": "2024-06-04T23:19:10.713787Z", - "shell.execute_reply": "2024-06-04T23:19:10.713572Z" + "iopub.execute_input": "2025-04-03T19:33:19.959056Z", + "iopub.status.busy": "2025-04-03T19:33:19.958988Z", + "iopub.status.idle": "2025-04-03T19:33:19.960957Z", + "shell.execute_reply": "2025-04-03T19:33:19.960753Z" }, "lines_to_next_cell": 0 }, @@ -2985,10 +2977,10 @@ "id": "0c8fa11a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.714998Z", - "iopub.status.busy": "2024-06-04T23:19:10.714933Z", - "iopub.status.idle": "2024-06-04T23:19:10.718857Z", - "shell.execute_reply": "2024-06-04T23:19:10.718646Z" + "iopub.execute_input": "2025-04-03T19:33:19.962266Z", + "iopub.status.busy": "2025-04-03T19:33:19.962197Z", + "iopub.status.idle": "2025-04-03T19:33:19.966113Z", + "shell.execute_reply": "2025-04-03T19:33:19.965924Z" }, "lines_to_next_cell": 0 }, @@ -3070,10 +3062,10 @@ "id": "b010de50", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.720069Z", - "iopub.status.busy": "2024-06-04T23:19:10.720000Z", - "iopub.status.idle": "2024-06-04T23:19:10.721978Z", - "shell.execute_reply": "2024-06-04T23:19:10.721773Z" + "iopub.execute_input": "2025-04-03T19:33:19.967306Z", + "iopub.status.busy": "2025-04-03T19:33:19.967237Z", + "iopub.status.idle": "2025-04-03T19:33:19.969303Z", + "shell.execute_reply": "2025-04-03T19:33:19.969105Z" }, "lines_to_next_cell": 2 }, @@ -3126,10 +3118,10 @@ "id": "78cac089", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.723135Z", - "iopub.status.busy": "2024-06-04T23:19:10.723068Z", - "iopub.status.idle": "2024-06-04T23:19:10.726306Z", - "shell.execute_reply": "2024-06-04T23:19:10.726108Z" + "iopub.execute_input": "2025-04-03T19:33:19.970427Z", + "iopub.status.busy": "2025-04-03T19:33:19.970357Z", + "iopub.status.idle": "2025-04-03T19:33:19.973818Z", + "shell.execute_reply": "2025-04-03T19:33:19.973615Z" }, "lines_to_next_cell": 2 }, @@ -3571,10 +3563,10 @@ "id": "acce3488", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.727548Z", - "iopub.status.busy": "2024-06-04T23:19:10.727484Z", - "iopub.status.idle": "2024-06-04T23:19:10.729522Z", - "shell.execute_reply": "2024-06-04T23:19:10.729308Z" + "iopub.execute_input": "2025-04-03T19:33:19.974888Z", + "iopub.status.busy": "2025-04-03T19:33:19.974817Z", + "iopub.status.idle": "2025-04-03T19:33:19.976636Z", + "shell.execute_reply": "2025-04-03T19:33:19.976441Z" }, "lines_to_next_cell": 2 }, @@ -3608,10 +3600,10 @@ "id": "c176be69", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.730707Z", - "iopub.status.busy": "2024-06-04T23:19:10.730633Z", - "iopub.status.idle": "2024-06-04T23:19:10.732656Z", - "shell.execute_reply": "2024-06-04T23:19:10.732431Z" + "iopub.execute_input": "2025-04-03T19:33:19.977748Z", + "iopub.status.busy": "2025-04-03T19:33:19.977675Z", + "iopub.status.idle": "2025-04-03T19:33:19.979700Z", + "shell.execute_reply": "2025-04-03T19:33:19.979492Z" }, "lines_to_next_cell": 2 }, @@ -3647,10 +3639,10 @@ "id": "bb1b5bed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.733942Z", - "iopub.status.busy": "2024-06-04T23:19:10.733876Z", - "iopub.status.idle": "2024-06-04T23:19:10.735757Z", - "shell.execute_reply": "2024-06-04T23:19:10.735550Z" + "iopub.execute_input": "2025-04-03T19:33:19.980837Z", + "iopub.status.busy": "2025-04-03T19:33:19.980767Z", + "iopub.status.idle": "2025-04-03T19:33:19.982585Z", + "shell.execute_reply": "2025-04-03T19:33:19.982383Z" } }, "outputs": [ @@ -3684,10 +3676,10 @@ "id": "158e9f9f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.736918Z", - "iopub.status.busy": "2024-06-04T23:19:10.736848Z", - "iopub.status.idle": "2024-06-04T23:19:10.738666Z", - "shell.execute_reply": "2024-06-04T23:19:10.738459Z" + "iopub.execute_input": "2025-04-03T19:33:19.983728Z", + "iopub.status.busy": "2025-04-03T19:33:19.983658Z", + "iopub.status.idle": "2025-04-03T19:33:19.985610Z", + "shell.execute_reply": "2025-04-03T19:33:19.985394Z" }, "lines_to_next_cell": 0 }, @@ -3724,10 +3716,10 @@ "id": "eed68a42", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.739817Z", - "iopub.status.busy": "2024-06-04T23:19:10.739748Z", - "iopub.status.idle": "2024-06-04T23:19:10.742249Z", - "shell.execute_reply": "2024-06-04T23:19:10.742035Z" + "iopub.execute_input": "2025-04-03T19:33:19.986718Z", + "iopub.status.busy": "2025-04-03T19:33:19.986651Z", + "iopub.status.idle": "2025-04-03T19:33:19.989221Z", + "shell.execute_reply": "2025-04-03T19:33:19.989016Z" }, "lines_to_next_cell": 2 }, @@ -3763,10 +3755,10 @@ "id": "3e4e63f1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.743382Z", - "iopub.status.busy": "2024-06-04T23:19:10.743316Z", - "iopub.status.idle": "2024-06-04T23:19:10.745780Z", - "shell.execute_reply": "2024-06-04T23:19:10.745564Z" + "iopub.execute_input": "2025-04-03T19:33:19.990328Z", + "iopub.status.busy": "2025-04-03T19:33:19.990264Z", + "iopub.status.idle": "2025-04-03T19:33:19.992928Z", + "shell.execute_reply": "2025-04-03T19:33:19.992722Z" }, "lines_to_next_cell": 0 }, @@ -3803,10 +3795,10 @@ "id": "560b277f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.746988Z", - "iopub.status.busy": "2024-06-04T23:19:10.746915Z", - "iopub.status.idle": "2024-06-04T23:19:10.750768Z", - "shell.execute_reply": "2024-06-04T23:19:10.750556Z" + "iopub.execute_input": "2025-04-03T19:33:19.994124Z", + "iopub.status.busy": "2025-04-03T19:33:19.994054Z", + "iopub.status.idle": "2025-04-03T19:33:19.997995Z", + "shell.execute_reply": "2025-04-03T19:33:19.997792Z" } }, "outputs": [ @@ -3888,10 +3880,10 @@ "id": "1efe1d6a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.751934Z", - "iopub.status.busy": "2024-06-04T23:19:10.751870Z", - "iopub.status.idle": "2024-06-04T23:19:10.754306Z", - "shell.execute_reply": "2024-06-04T23:19:10.754090Z" + "iopub.execute_input": "2025-04-03T19:33:19.999145Z", + "iopub.status.busy": "2025-04-03T19:33:19.999074Z", + "iopub.status.idle": "2025-04-03T19:33:20.001502Z", + "shell.execute_reply": "2025-04-03T19:33:20.001282Z" }, "lines_to_next_cell": 2 }, @@ -3938,10 +3930,10 @@ "id": "b0b8ae27", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.755478Z", - "iopub.status.busy": "2024-06-04T23:19:10.755409Z", - "iopub.status.idle": "2024-06-04T23:19:10.763883Z", - "shell.execute_reply": "2024-06-04T23:19:10.763665Z" + "iopub.execute_input": "2025-04-03T19:33:20.002634Z", + "iopub.status.busy": "2025-04-03T19:33:20.002567Z", + "iopub.status.idle": "2025-04-03T19:33:20.013128Z", + "shell.execute_reply": "2025-04-03T19:33:20.012884Z" } }, "outputs": [ @@ -4026,10 +4018,10 @@ "id": "7c9bdd8e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.765113Z", - "iopub.status.busy": "2024-06-04T23:19:10.765038Z", - "iopub.status.idle": "2024-06-04T23:19:10.767093Z", - "shell.execute_reply": "2024-06-04T23:19:10.766831Z" + "iopub.execute_input": "2025-04-03T19:33:20.014345Z", + "iopub.status.busy": "2025-04-03T19:33:20.014276Z", + "iopub.status.idle": "2025-04-03T19:33:20.016453Z", + "shell.execute_reply": "2025-04-03T19:33:20.016227Z" } }, "outputs": [ @@ -4063,10 +4055,10 @@ "id": "a1750e64", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.768344Z", - "iopub.status.busy": "2024-06-04T23:19:10.768258Z", - "iopub.status.idle": "2024-06-04T23:19:10.774690Z", - "shell.execute_reply": "2024-06-04T23:19:10.774467Z" + "iopub.execute_input": "2025-04-03T19:33:20.017627Z", + "iopub.status.busy": "2025-04-03T19:33:20.017557Z", + "iopub.status.idle": "2025-04-03T19:33:20.024766Z", + "shell.execute_reply": "2025-04-03T19:33:20.024531Z" }, "lines_to_next_cell": 0 }, @@ -4118,10 +4110,10 @@ "id": "2b179be8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.776042Z", - "iopub.status.busy": "2024-06-04T23:19:10.775972Z", - "iopub.status.idle": "2024-06-04T23:19:10.790666Z", - "shell.execute_reply": "2024-06-04T23:19:10.790430Z" + "iopub.execute_input": "2025-04-03T19:33:20.026059Z", + "iopub.status.busy": "2025-04-03T19:33:20.025986Z", + "iopub.status.idle": "2025-04-03T19:33:20.042116Z", + "shell.execute_reply": "2025-04-03T19:33:20.041870Z" }, "lines_to_next_cell": 2 }, @@ -4163,10 +4155,10 @@ "id": "e9de7237", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.791954Z", - "iopub.status.busy": "2024-06-04T23:19:10.791856Z", - "iopub.status.idle": "2024-06-04T23:19:10.793719Z", - "shell.execute_reply": "2024-06-04T23:19:10.793502Z" + "iopub.execute_input": "2025-04-03T19:33:20.043458Z", + "iopub.status.busy": "2025-04-03T19:33:20.043387Z", + "iopub.status.idle": "2025-04-03T19:33:20.045448Z", + "shell.execute_reply": "2025-04-03T19:33:20.045235Z" }, "lines_to_next_cell": 2 }, @@ -4200,10 +4192,10 @@ "id": "f81dcb72", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.794971Z", - "iopub.status.busy": "2024-06-04T23:19:10.794900Z", - "iopub.status.idle": "2024-06-04T23:19:10.796754Z", - "shell.execute_reply": "2024-06-04T23:19:10.796532Z" + "iopub.execute_input": "2025-04-03T19:33:20.046649Z", + "iopub.status.busy": "2025-04-03T19:33:20.046556Z", + "iopub.status.idle": "2025-04-03T19:33:20.048559Z", + "shell.execute_reply": "2025-04-03T19:33:20.048366Z" } }, "outputs": [], @@ -4249,10 +4241,10 @@ "id": "a7102e7d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.797953Z", - "iopub.status.busy": "2024-06-04T23:19:10.797884Z", - "iopub.status.idle": "2024-06-04T23:19:10.799335Z", - "shell.execute_reply": "2024-06-04T23:19:10.799104Z" + "iopub.execute_input": "2025-04-03T19:33:20.049726Z", + "iopub.status.busy": "2025-04-03T19:33:20.049643Z", + "iopub.status.idle": "2025-04-03T19:33:20.051224Z", + "shell.execute_reply": "2025-04-03T19:33:20.050993Z" }, "lines_to_next_cell": 0 }, @@ -4288,10 +4280,10 @@ "id": "c2b6c3fa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.800622Z", - "iopub.status.busy": "2024-06-04T23:19:10.800548Z", - "iopub.status.idle": "2024-06-04T23:19:10.805188Z", - "shell.execute_reply": "2024-06-04T23:19:10.804964Z" + "iopub.execute_input": "2025-04-03T19:33:20.052401Z", + "iopub.status.busy": "2025-04-03T19:33:20.052338Z", + "iopub.status.idle": "2025-04-03T19:33:20.057358Z", + "shell.execute_reply": "2025-04-03T19:33:20.057107Z" }, "lines_to_next_cell": 0 }, @@ -4316,10 +4308,10 @@ "id": "d1e40190", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.806480Z", - "iopub.status.busy": "2024-06-04T23:19:10.806406Z", - "iopub.status.idle": "2024-06-04T23:19:10.810671Z", - "shell.execute_reply": "2024-06-04T23:19:10.810474Z" + "iopub.execute_input": "2025-04-03T19:33:20.058650Z", + "iopub.status.busy": "2025-04-03T19:33:20.058572Z", + "iopub.status.idle": "2025-04-03T19:33:20.063583Z", + "shell.execute_reply": "2025-04-03T19:33:20.063360Z" } }, "outputs": [ @@ -4372,10 +4364,10 @@ "id": "44ff90d4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.811997Z", - "iopub.status.busy": "2024-06-04T23:19:10.811926Z", - "iopub.status.idle": "2024-06-04T23:19:10.814896Z", - "shell.execute_reply": "2024-06-04T23:19:10.814681Z" + "iopub.execute_input": "2025-04-03T19:33:20.064727Z", + "iopub.status.busy": "2025-04-03T19:33:20.064645Z", + "iopub.status.idle": "2025-04-03T19:33:20.067737Z", + "shell.execute_reply": "2025-04-03T19:33:20.067485Z" }, "lines_to_next_cell": 0 }, @@ -4407,10 +4399,10 @@ "id": "f88990de", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.816100Z", - "iopub.status.busy": "2024-06-04T23:19:10.816032Z", - "iopub.status.idle": "2024-06-04T23:19:10.977519Z", - "shell.execute_reply": "2024-06-04T23:19:10.973692Z" + "iopub.execute_input": "2025-04-03T19:33:20.068939Z", + "iopub.status.busy": "2025-04-03T19:33:20.068872Z", + "iopub.status.idle": "2025-04-03T19:33:20.202213Z", + "shell.execute_reply": "2025-04-03T19:33:20.197836Z" }, "lines_to_next_cell": 2 }, @@ -4460,10 +4452,10 @@ "id": "733b69fb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:10.986636Z", - "iopub.status.busy": "2024-06-04T23:19:10.986365Z", - "iopub.status.idle": "2024-06-04T23:19:11.006708Z", - "shell.execute_reply": "2024-06-04T23:19:11.001541Z" + "iopub.execute_input": "2025-04-03T19:33:20.222596Z", + "iopub.status.busy": "2025-04-03T19:33:20.221859Z", + "iopub.status.idle": "2025-04-03T19:33:20.238358Z", + "shell.execute_reply": "2025-04-03T19:33:20.237074Z" } }, "outputs": [ @@ -4545,10 +4537,10 @@ "id": "269d3d95", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.019124Z", - "iopub.status.busy": "2024-06-04T23:19:11.016874Z", - "iopub.status.idle": "2024-06-04T23:19:11.026784Z", - "shell.execute_reply": "2024-06-04T23:19:11.025439Z" + "iopub.execute_input": "2025-04-03T19:33:20.250601Z", + "iopub.status.busy": "2025-04-03T19:33:20.250318Z", + "iopub.status.idle": "2025-04-03T19:33:20.256540Z", + "shell.execute_reply": "2025-04-03T19:33:20.255832Z" }, "lines_to_next_cell": 2 }, @@ -4589,10 +4581,10 @@ "id": "db9963d8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.029587Z", - "iopub.status.busy": "2024-06-04T23:19:11.029376Z", - "iopub.status.idle": "2024-06-04T23:19:11.164487Z", - "shell.execute_reply": "2024-06-04T23:19:11.164119Z" + "iopub.execute_input": "2025-04-03T19:33:20.271197Z", + "iopub.status.busy": "2025-04-03T19:33:20.270174Z", + "iopub.status.idle": "2025-04-03T19:33:20.436472Z", + "shell.execute_reply": "2025-04-03T19:33:20.436095Z" }, "lines_to_next_cell": 0 }, @@ -4656,10 +4648,10 @@ "id": "77f8eb90", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.166394Z", - "iopub.status.busy": "2024-06-04T23:19:11.166286Z", - "iopub.status.idle": "2024-06-04T23:19:11.612761Z", - "shell.execute_reply": "2024-06-04T23:19:11.611650Z" + "iopub.execute_input": "2025-04-03T19:33:20.438083Z", + "iopub.status.busy": "2025-04-03T19:33:20.437974Z", + "iopub.status.idle": "2025-04-03T19:33:20.970277Z", + "shell.execute_reply": "2025-04-03T19:33:20.969638Z" } }, "outputs": [ @@ -4753,10 +4745,10 @@ "id": "907e3299", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.615999Z", - "iopub.status.busy": "2024-06-04T23:19:11.615768Z", - "iopub.status.idle": "2024-06-04T23:19:11.628399Z", - "shell.execute_reply": "2024-06-04T23:19:11.627025Z" + "iopub.execute_input": "2025-04-03T19:33:20.974840Z", + "iopub.status.busy": "2025-04-03T19:33:20.974326Z", + "iopub.status.idle": "2025-04-03T19:33:20.988630Z", + "shell.execute_reply": "2025-04-03T19:33:20.987932Z" } }, "outputs": [ @@ -4828,10 +4820,10 @@ "id": "cb3f2b0e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.631843Z", - "iopub.status.busy": "2024-06-04T23:19:11.631560Z", - "iopub.status.idle": "2024-06-04T23:19:11.636898Z", - "shell.execute_reply": "2024-06-04T23:19:11.636444Z" + "iopub.execute_input": "2025-04-03T19:33:20.991210Z", + "iopub.status.busy": "2025-04-03T19:33:20.990980Z", + "iopub.status.idle": "2025-04-03T19:33:20.995248Z", + "shell.execute_reply": "2025-04-03T19:33:20.994558Z" }, "lines_to_next_cell": 0 }, @@ -4868,10 +4860,10 @@ "id": "23ce05b5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.639885Z", - "iopub.status.busy": "2024-06-04T23:19:11.639635Z", - "iopub.status.idle": "2024-06-04T23:19:11.652008Z", - "shell.execute_reply": "2024-06-04T23:19:11.651483Z" + "iopub.execute_input": "2025-04-03T19:33:20.998328Z", + "iopub.status.busy": "2025-04-03T19:33:20.997827Z", + "iopub.status.idle": "2025-04-03T19:33:21.014088Z", + "shell.execute_reply": "2025-04-03T19:33:21.013466Z" }, "lines_to_next_cell": 0 }, @@ -4894,10 +4886,10 @@ "id": "027a24c4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.655682Z", - "iopub.status.busy": "2024-06-04T23:19:11.655146Z", - "iopub.status.idle": "2024-06-04T23:19:11.661660Z", - "shell.execute_reply": "2024-06-04T23:19:11.658906Z" + "iopub.execute_input": "2025-04-03T19:33:21.017420Z", + "iopub.status.busy": "2025-04-03T19:33:21.017147Z", + "iopub.status.idle": "2025-04-03T19:33:21.023078Z", + "shell.execute_reply": "2025-04-03T19:33:21.021685Z" } }, "outputs": [ @@ -4936,10 +4928,10 @@ "id": "5896ce19", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.664908Z", - "iopub.status.busy": "2024-06-04T23:19:11.664631Z", - "iopub.status.idle": "2024-06-04T23:19:11.731259Z", - "shell.execute_reply": "2024-06-04T23:19:11.730708Z" + "iopub.execute_input": "2025-04-03T19:33:21.027040Z", + "iopub.status.busy": "2025-04-03T19:33:21.026564Z", + "iopub.status.idle": "2025-04-03T19:33:21.248051Z", + "shell.execute_reply": "2025-04-03T19:33:21.245708Z" } }, "outputs": [ @@ -5340,10 +5332,10 @@ "id": "3b8a24b4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.734388Z", - "iopub.status.busy": "2024-06-04T23:19:11.734170Z", - "iopub.status.idle": "2024-06-04T23:19:11.738116Z", - "shell.execute_reply": "2024-06-04T23:19:11.737083Z" + "iopub.execute_input": "2025-04-03T19:33:21.251445Z", + "iopub.status.busy": "2025-04-03T19:33:21.250800Z", + "iopub.status.idle": "2025-04-03T19:33:21.256749Z", + "shell.execute_reply": "2025-04-03T19:33:21.255032Z" }, "lines_to_next_cell": 0 }, @@ -5367,10 +5359,10 @@ "id": "276ec935", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.741132Z", - "iopub.status.busy": "2024-06-04T23:19:11.740955Z", - "iopub.status.idle": "2024-06-04T23:19:11.806258Z", - "shell.execute_reply": "2024-06-04T23:19:11.805740Z" + "iopub.execute_input": "2025-04-03T19:33:21.261815Z", + "iopub.status.busy": "2025-04-03T19:33:21.261210Z", + "iopub.status.idle": "2025-04-03T19:33:21.385431Z", + "shell.execute_reply": "2025-04-03T19:33:21.384744Z" } }, "outputs": [ @@ -5777,10 +5769,10 @@ "id": "c8b43ab6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.812237Z", - "iopub.status.busy": "2024-06-04T23:19:11.811979Z", - "iopub.status.idle": "2024-06-04T23:19:11.819745Z", - "shell.execute_reply": "2024-06-04T23:19:11.819144Z" + "iopub.execute_input": "2025-04-03T19:33:21.392565Z", + "iopub.status.busy": "2025-04-03T19:33:21.391883Z", + "iopub.status.idle": "2025-04-03T19:33:21.410645Z", + "shell.execute_reply": "2025-04-03T19:33:21.407385Z" } }, "outputs": [ @@ -5814,10 +5806,10 @@ "id": "bcc538c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.823477Z", - "iopub.status.busy": "2024-06-04T23:19:11.823254Z", - "iopub.status.idle": "2024-06-04T23:19:11.830397Z", - "shell.execute_reply": "2024-06-04T23:19:11.829852Z" + "iopub.execute_input": "2025-04-03T19:33:21.413730Z", + "iopub.status.busy": "2025-04-03T19:33:21.413066Z", + "iopub.status.idle": "2025-04-03T19:33:21.420050Z", + "shell.execute_reply": "2025-04-03T19:33:21.418033Z" }, "lines_to_next_cell": 2 }, @@ -5857,10 +5849,10 @@ "id": "33a75971", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.833582Z", - "iopub.status.busy": "2024-06-04T23:19:11.833376Z", - "iopub.status.idle": "2024-06-04T23:19:11.840363Z", - "shell.execute_reply": "2024-06-04T23:19:11.839611Z" + "iopub.execute_input": "2025-04-03T19:33:21.424472Z", + "iopub.status.busy": "2025-04-03T19:33:21.422887Z", + "iopub.status.idle": "2025-04-03T19:33:21.433878Z", + "shell.execute_reply": "2025-04-03T19:33:21.430420Z" }, "lines_to_next_cell": 0 }, @@ -5906,10 +5898,10 @@ "id": "eeac39db", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.843459Z", - "iopub.status.busy": "2024-06-04T23:19:11.843230Z", - "iopub.status.idle": "2024-06-04T23:19:11.850225Z", - "shell.execute_reply": "2024-06-04T23:19:11.849688Z" + "iopub.execute_input": "2025-04-03T19:33:21.437344Z", + "iopub.status.busy": "2025-04-03T19:33:21.436963Z", + "iopub.status.idle": "2025-04-03T19:33:21.444237Z", + "shell.execute_reply": "2025-04-03T19:33:21.443169Z" }, "lines_to_next_cell": 0 }, @@ -5963,16 +5955,16 @@ "id": "e86a3652", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.853511Z", - "iopub.status.busy": "2024-06-04T23:19:11.853047Z", - "iopub.status.idle": "2024-06-04T23:19:11.977554Z", - "shell.execute_reply": "2024-06-04T23:19:11.976958Z" + "iopub.execute_input": "2025-04-03T19:33:21.448307Z", + "iopub.status.busy": "2025-04-03T19:33:21.447683Z", + "iopub.status.idle": "2025-04-03T19:33:21.511994Z", + "shell.execute_reply": "2025-04-03T19:33:21.511277Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -6005,10 +5997,10 @@ "id": "5abb32ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.980369Z", - "iopub.status.busy": "2024-06-04T23:19:11.980198Z", - "iopub.status.idle": "2024-06-04T23:19:11.985705Z", - "shell.execute_reply": "2024-06-04T23:19:11.984785Z" + "iopub.execute_input": "2025-04-03T19:33:21.524977Z", + "iopub.status.busy": "2025-04-03T19:33:21.524670Z", + "iopub.status.idle": "2025-04-03T19:33:21.530838Z", + "shell.execute_reply": "2025-04-03T19:33:21.530030Z" } }, "outputs": [], @@ -6034,16 +6026,16 @@ "id": "0f5698be", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:11.989339Z", - "iopub.status.busy": "2024-06-04T23:19:11.988415Z", - "iopub.status.idle": "2024-06-04T23:19:12.111821Z", - "shell.execute_reply": "2024-06-04T23:19:12.111533Z" + "iopub.execute_input": "2025-04-03T19:33:21.546289Z", + "iopub.status.busy": "2025-04-03T19:33:21.546012Z", + "iopub.status.idle": "2025-04-03T19:33:21.606408Z", + "shell.execute_reply": "2025-04-03T19:33:21.605628Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -6080,10 +6072,10 @@ "id": "1262ac4f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:12.113270Z", - "iopub.status.busy": "2024-06-04T23:19:12.113151Z", - "iopub.status.idle": "2024-06-04T23:19:12.187994Z", - "shell.execute_reply": "2024-06-04T23:19:12.187097Z" + "iopub.execute_input": "2025-04-03T19:33:21.619083Z", + "iopub.status.busy": "2025-04-03T19:33:21.618555Z", + "iopub.status.idle": "2025-04-03T19:33:21.886147Z", + "shell.execute_reply": "2025-04-03T19:33:21.883188Z" } }, "outputs": [], @@ -6105,10 +6097,10 @@ "id": "feeea491", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:12.191417Z", - "iopub.status.busy": "2024-06-04T23:19:12.191189Z", - "iopub.status.idle": "2024-06-04T23:19:12.211795Z", - "shell.execute_reply": "2024-06-04T23:19:12.211208Z" + "iopub.execute_input": "2025-04-03T19:33:21.890041Z", + "iopub.status.busy": "2025-04-03T19:33:21.889430Z", + "iopub.status.idle": "2025-04-03T19:33:21.913419Z", + "shell.execute_reply": "2025-04-03T19:33:21.911786Z" }, "lines_to_next_cell": 0 }, @@ -6139,17 +6131,25 @@ "id": "09926adc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:12.215056Z", - "iopub.status.busy": "2024-06-04T23:19:12.214817Z", - "iopub.status.idle": "2024-06-04T23:19:12.405617Z", - "shell.execute_reply": "2024-06-04T23:19:12.405120Z" + "iopub.execute_input": "2025-04-03T19:33:21.916562Z", + "iopub.status.busy": "2025-04-03T19:33:21.916074Z", + "iopub.status.idle": "2025-04-03T19:33:22.052469Z", + "shell.execute_reply": "2025-04-03T19:33:22.051697Z" }, "lines_to_next_cell": 0 }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70549/3779905754.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " ax_hr.set_xticklabels(range(24)[::2], fontsize=20)\n" + ] + }, { "data": { - 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rrSZRqepqxcDCHbZU9fl3kqoOAOgYlBYCbN5fa1vQcf1Z8S4z11QrNu+f0k/u/WCz/Ov7fXLdWfF6ZSdwJgQ4Abic73fl6eBmtzB/WfbgeVJcWXtC4en2/sPbJcxfp0Kom32V547jV3lmFEpWYYXsyinRt7ZSXQNVYW1XmUigY6jg/Jyvd0pNXctC4ifXmrUS9b3/l6sGy5qDBXol9ctL9stvSFUHALQzSgsBNuozzubMIvHx8pCrRsSKK7lyeKz8Z9lBXc7on4v2yuOXDzJ6SHBxLJMA4HJUMWll6vBu4uXlqYtOx0cG6q0RwUC1ylOlp998dg954ecjZcVD50vqw5Pk5ekj5aaxCW16TTVxVqlQqmsg3MsXW7JbHNxsSa1Zq6SqK/9cvFd25RQbPSQAgIW1trSQOkQdr84DrOaDdbbmQhcO7CKdgv3Elaju6Q9fMkDvv706XdKOlBk9JLg4ApwAXEpReY18vyu/6aqdq4oJ9ZeLBneV2yb0dOh11MpUuI/5G7PkD/O36X1VBzbQ10vX1zw5bG9/TNWanZeS3Opas2ZzxfBuMnlAjA78qlT1mrp6o4cEALAoVXNTlxZqaH1pIcBKKmt+/L5WKeCu6Ow+UXr1tOrN8Ldvdhs9HLg4ApwAXMrX2w5LdV299IsJMUXDEbpio6W+2Z4j9324WX9QUit/X7hhhG409ejUgbq27PHUffX46ocnWT64qaiV2X+9arBepbotq1j+/cN+o4cEALBot3TVUKitpYXU+YCV5qaqFFhseICc3TtKXNWsS/rrkk1fbj2sy4UBp8MnawAumZ5+pYvVgDkdumKjJZbtzZffvLtR6uobZNrIOHl86iAd1FMBPdVoStViVeUKOrLWrKvpHOovj18+UO55f7M8t0h1Ve8i/bqEGD0sAICFqKaRx3dLb0tpIVUyCbCC99bY0tN/Nipep4O7qv5dQuWapDj5YF2m/PXLnfLhr8a63TwZLcMKTgAuI7uwQlYfKND7lw/vJmbqit0WVuyKjZ9am1Ygt725Tq9MvnhwF3ly2pCfTCLV94HRtWZdgSpLMXlA56ZU9VpS1QEATlTmYGkgSgvBKlQ9y1UHjup679eOcv1Glvde0E/8fTxlXfox+WZ7rtHDgYsiwAnAZXy2OVtvk3tE6lQJM3XFDlC1FFsYk1KxLXW8Fbti40RbM4vk5rlrpbKmXtcPeu76EeLtxZ/eM3dVHyKh/t6yNatI/r30gNFDAgBYCKWFgBObC6n5aTcTfO7qEuYvt55t633w5IJd1GvHKfEpC4BLNWBx9eZCp6LSjF+anmRrDNPCIKeVu2LDZk9uidz0eqqUVNXqoL36f+7rzZ/dljTwemyqrav6s9/tkd05JUYPCQBgEfbSQq3Nk1DHq/MoLQQrUBkyH663NRe63kWbC53K7ef2lE5BvnLwSJm8tybD6OHABfFJC4BLUEGMXTkl4uPlIZcM6SJmo65+zk1J1l2vT9cV265P5xCXLuQNx6UfLZPp/0nVtb6GxYXJazNG6VW7aJmrR8bKpP62VPUHPiJVHQDgHJQWAkS+350v+SVVEhXsK+f3jxGzCPH3kd9N7qP3n/1ur5RU1hg9JLgYApwAXKq50Hn9Okt4oDmLt6sg55m6Yt89qY8E+HjK7twSeYerjpZ1uKhCfv5qquSVVEm/mBB54+ZkPSFDK7uqX21LVd+SWSSvLCNVHQDgHJQWgrt7f63tc4hqfGm27KLrk7tLz6ggOVpWLf/+gfkhTmSu72YAllRf3yCfbco2Vff007F3xV7ywETZ+MgFsuzB8/RW3b/ngr7y+4v66+Oe+GqnZBVWGD1cONmR0ir5xX9S9f/bHlFB8tatyaYN2LtCqvqj9lT1hXt1yj8AAB1ZWsj+PKWFYBU5RZWyeFee3v+ZidLT7Xy8POX3F9s+T/1n+QH99QB2BDgBuESXaRUQCvHzlvP7dxYrOF1X7JvGJsqohAgpq66Thz/ZKg0NDUYPFU5SVF4jN762Rg7kl+kmWW/fOlo6h/gbPSxTmzYyVv9OUB3oH6CrOtzQ0qVLZerUqdKtWzf9d2T+/PlGDwmwBHtpIX/v05ePUTM3VXpoXkqynNM3ukPHB7SXjzdkSn2DyFmJEdIrOljM6MKBMfrzlGri+cy3u40eDlyI2wQ4X3zxRUlMTBR/f38ZPXq0rFmz5rTHvvrqqzJhwgSJiIjQt8mTJ5/xeACOmd+4evOiwV3E38fadQo9PT3kyWuG6nSQH/bkyycbbKn5MLfSqlqZOW+N7DxcLFHBfjq4qYKccEKq+lVDJMTfWzZnFsmryw4aPSSgQ5WVlcmwYcP0PBaA84Ocj04doPe9VR76SUZ0D5fVD08iuAlLZc3Zu6dfd1Z3MfP88OFLbT+7H23I1PNvwG0CnO+//77ce++98thjj8mGDRv0RHHKlCmSl2dbmn2yJUuWyA033CDff/+9rFq1SuLj4+XCCy+UrCwCEYCzVdfWy1dbD1siPb2l1NVSe4HsP32xQ/JKSK0ws8qaOrntjXWyMaNQd1d9+9ZknZ4O5+gS5i+PXjZQ7/9j4R7ZS6o63MjFF18sf/7zn+Wqq64yeiiAJf2w+4je3n5Oz6bSQi/9YqR+THVqDrT4hXe4l9UHj0r60XKdNWfGpq7HG9k9Qi4d0lVUMtycr3cZPRy4CLcIcP7973+X2267TVJSUmTgwIHy8ssvS2BgoLz++uunPP6dd96RX//61zJ8+HDp37+//Oc//5H6+npZtGhRh48dsLolu/OkqKJGOof4yZiencRd/HJCTxkcG6q/9sc+3W70cNBGNXX1cuc7G2TVgaMS7Octb6QkS/8uoUYPy3KuSYqTif2idar6/R9tIVUdAOCwsqpa+X63bcHLJUO7NpUWumBgjEQG+cqx8hpZfaDA6GECTvP+WtvqzcuHd5NAX28xuwcv6ic+Xh6ydE++LNubb/Rw4AIsH+Csrq6W9evX6zRzO09PT31frc5sifLycqmpqZHIyMjTHlNVVSXFxcUn3AA079PG9PQrhncTr1OkB1mVt5enPDVtmE6J+npbjnzduIoV5lFX3yD3vL9JFu3KEz9vT3ltxigZFh9u9LAsSaUizbm6MVX9UKH8Zzmp6sCpMB8FWm7J7nypqq2XhE6BMrBr6AlztCmDYvT+l8zPYBGF5dX6M4dyvYnT04+X0ClIpo9J0PtzvtqlU/Dh3iwf4Dxy5IjU1dVJTIztj5Sdup+TY/sBb87vf/97Xdz9+CDpyebMmSNhYWFNN5XWDuDMiitrZOHOXL1/xXD3SE8/3sBuofKrc3vp/Uc+3a4nHjAHNYGa9ckW+WLLYX3l+N83JsloN1qBbISuYQHySGOq+t8X7pF9eaSqAydjPgq03FfbDjfVgLc3g7S7ZEhXvf1mew5ZA7CE+RuzdGkwFcxXWWRW8dvz++gL4DsOF8v/NlJS0N1ZPsDpqCeeeELee+89+d///qcbFJ3OrFmzpKioqOl26JBt+TeA01uwLUf/oe3dOVgGdbPOH9rW+M2k3vrrP1JapetxwvWpzvezv9whH6zLFLXo+J/Xj5CJ/TobPSy3cG1SnG4KoX5v3P/hFr2KFsCPmI8CLa+f/f2uxvT0wbZg5vHG9uwkEYE+UlBWLakHSVOH+eeu7zWmp1+fHP+TgL6ZqdISv57YW++rjurqZxvuy/IBzqioKPHy8pLcXNsqMTt1v0uXMxfWffrpp3WA89tvv5WhQ4ee8Vg/Pz8JDQ094QbgzD7dZLvKduXwbpb6Q9saft5e8uS0oaK+fNVR3V4LCq5LNbqZuyJN7//tmmFyceMqD3Rgqrqft2xSqerLDhg9JMClMB8FWuaHPflSXl0nseEBMjQu7CfP29LUbZ8VSVOH2W3JLJJdOSW6pNIVw6yXNZcyPlG6hflLdlGlvL6CMkbuzPIBTl9fX0lKSjqhQZC9YdDYsWNPe95TTz0ls2fPlgULFsioUaM6aLSA+8gtrpSV+4+6bXr68ZISIiRlXA+9/3+fbJWSyhqjh4TT+PcP++Wfi/fp/dlXDJJpSXFGD8ntdAsPkD9cNkDvP6NT1UuNHhLQbkpLS2XTpk36phw8eFDvZ2RkGD00wNTstc8vPkV6+k/S1LflkDEAU7Ov3lTf02GBPmI1/j5ecv+Ufnr/pe/3y9HSKr1qVa3APlRQrrfqPqzP/K2zWuDee++VGTNm6EBlcnKyPPvss1JWVqa7qis33XSTxMbG6rpFypNPPimPPvqovPvuu5KYmNhUqzM4OFjfADju883Zov7OjEqI0B0r3d39U/rKdztzJaOgXJ5csEv+fOUQo4eEk7y1Ol3mfL1L7//+ov5y49hEo4fktn42Kl6+3Jqju2Y+8NFm+ehX49yqSRncx7p16+S88847YU6rqHntvHnzDBwZYF5VtXWyaKctY+biIafP6Bvbq5OEB/rIUZ2mflTG9YrqwFECzlFWVSufNWbNXXeWdesyXzk8Vl5bflC2ZxfLHW9v0Itp0gvKm55PiAyUGeMS9eKEsADrBXnhJis4leuuu06nm6ug5fDhw/WVb7Uy0954SF0FP3z4x9SDl156SXdfv+aaa6Rr165NN/UaAJzDXgT6ihHuvXrTLtDXW5642hbUfHt1hqw+YFvdCtfwyYZMeWT+Nr1/53m95I6JtuZQMIZabaN+XoL9vGVjRqG8Tld1WNTEiRP1qpOTbwQ3gbZbse+IlFTVSkyon4yIjzjtcT5ennLhQNvnxa9IU4dJqRILZdV1ktgpUEb3iBSr8vT0kMuG2lZdr0kr0ItGjqfuz/5ih4yds0iXqIA1uUWAU7nrrrskPT1dqqqqJDU1VUaPHt303JIlS06YKKalpZ1yMvn4448bNHrAWlT3Y3V1zdvTQy6lfmGTcb2j5IZk25XVhz7eIhXVFMl2lWZYD3y0Re/PHJco919oS4GBC6SqX2pLVX/6292yP59UdQBA877aasvOu3hwVx0UORN7mvqCbbmkqcOU3m9MT7/urO6W7nmggpZ/+2Z30/2Tf1obGm8VNXWSMncNQU6LcpsAJwDXMX9jtt6qbsiRQb5GD8elzLpkgHQJ9Ze0o+Xyj+/2GD0ct6cmP7/57wb9oUZ18H70soGWnhyajUq1mtAnSqpq6+WBDzfz4RMAcEY1dfWycEduU/3N5ozvHaXTWY+UVskauqnDZPbmlsj69GO6jM+0JOtmzRVV1Mgdb6//SVDzVFSJNHWcOl6dB2shwAmgQ6nV0J9uJj39dEL9feQvVw3W+6pD9OZDhUYPyW2pDzK3v7VOauoa9ErjJ6YNbXalBwxIVZ82VKeqb8golLl0zgQAnMGq/Ud1UCMq2FdGJTafrkuaOqywenNS/87SOcRfrOrj9Zk6862lfYTUcep4VYIK1kKAE0CH2pBxTA4VVEiQr5dcMMA2YcSJJg2IkSuGdxO1GO3Bj7ZIdW290UNyO1syC+XmeWulsqZezu/fWf5x3XCa2Lio2PAA+b/GVHWVmnSAVHUAwGl8vc0WpJwyqEuL/65f0ljXb8F2uqnDtR3fOfxwUYV83BjAu76xBJZVv+Y3Vqa16dx5K2ylCWEdBDgBGNJcaMrgLhLg62X0cFzWY1MHSacgX9mdWyIvfr/P6OG4ld05JXLT62uktKpWxvSMlH/9YqT4evPn0pVdf1a8nN27MVX9oy18AAUA/ERtXb18sz33hNqaLTG+V5SE+ntLfkmVrEsjTR2uR61KVg0XJ/5tiYycvVAmPPW9jJ2zWI6V10iIn7cMjwsXq1Jfo+qW3tqZnzpenVdYTpq6lfCJDUCH1j36covtyvmVw0lPPxNVm/Txywfp/X8t2Se7coqNHpJbSDtSJtNfS9WTneHx4fKfGWeJvw+BeHOkqtu6qqtaU6SqAwBOVXpGrW6LCPRpVTdpdZHzgoG2ep2kqcMV68WrzuCqQ/jJncOVkqpaOfup7y3bVKesqtah89WCBlgHAU4AHWbpnnx9lS0q2E/G9epk9HBc3mVDu8oFA2N0Dcjff7RFrzxA+8kurJBf/CdVr9Do3yVE3khJ1gEzmENcRKA8fMmPqeoHj5QZPSQAgAv5epute/qFA7uIt1frPgZfOrRL02vUkyUAF6GClqojuOoMbu8SfipW7hwe5OBcnbm+tRDgBNBh5m+ydU+fOqxrqyeW7roq7c9XDpYQf2/ZnFkkr7Mqrd2ooOb0/6RKVmGF9IwKkrduGS1hgT5GDwutdEPycanqdFUHADRSfw9UDU3l4iHNd08/2dm9o/V8LE+lqacfa4cRAm3vHN5cGUkrdw5XK7ITIgOltZXy1fHqvHDm+5ZChAFAh1DL/xfusE0sSU9vuZhQf3nk0oF6/5lv97AqrR0UllfLja+lyoEjZbphzdu3jpboED+jh4U2XhSYc/UQ3cRMfQCd18ai8wAAa1HlS9TFTFVLc1yvqFafb0tTp5s6XAedw3+c+80Yl9imc2eOT9TnwzoIcALoEN9uz9EdqdXquKFxYUYPx1SuHRUnE/rYVqX9/uMtpEY5OfA+Y+5a2ZVTooOa79w6WrqFBxg9LDggPjJQZjWlqu/iogAAoCkoOXlgTJsbB17a2JhIdWJnLgYj0Tn8RNOS4nTz2pbGKj09RB9/9ci49h4aOhgBTgAd2j39iuGxXClrJfXv9derhkigr5cukP/Omgyjh2QJlTV1cusba2XzoUKdnqKCm4lRQUYPC07w8+Tuus6vuqjy4Eeb+SAKAG5M/Q34pjE9/ZLBLe+efrKz+0TpjtS5xVWyIYM0dRiHzuEnCgvwkZemJ+m08+Y+Ztqffnl6kj4P1kKAE0C7yyuplBX7juj9K4Z3M3o4pl2V9uCUfnr/ia926lqRaLvq2npdh2j1gQJdXPzNm5Olb0yI0cOCk3h6esiT04bqiwJr047JG6tIVQcAd7Ups1AOF1Xqv/cqSNlWft5eTWnqX5KmDgPROfynzu0bLXNTkiXAx8sW6DzNcSoAOi8lWc7pG93BI0RHIMAJoN19sfmwqAVUw+PDWSHngJvGJsqohAgpq66Thz/Zarn0ko5sNHDP+5vk+9354u/jKa/PPEuGxoUbPSy0Y6r6kwt2SRqp6gDglr5uDEZOGtBZ/H28HHqtS+xp6lvppg7j0Dn89EHOVbMmyaNTB0r3yMATnlMlqFRquvqx5SfXughwAmh3n26ypadfyepNx1elXTNU1476YU++fLLB9u+KllMfRh76eIteeeHr5Smv3DhKkntEGj0stJNfJHeXsT0bU9WpXwsAbkddDP5qa2P39MGt755+MrUCVAWHcoorZeMh0tRhDDqHn55KO08Z30OWPDBRNj5ygSx78Dy9XfH78/TjypyvduoFD7AeApwA2tWB/FLZnFkkXp4ectkwApyO6hUdLL+b3Efv/+mLHTr9Hy3/kKP+zT5cn6m/H/95wwjSU9zgosBT1wxtql/7JqnqAOBWtmUV67I+Km313L6dHX49tQJ08gDb63y5xRY4BToancObp77GiCBfndGjtur+b87vLaH+3rq56McW6yYPGwKcANrV/E3Zequ6gEcF+xk9HEv45YSeMjg2VIoqauSxT7cbPRyXC2IWlFXLoYJyvT0+jf+Zb/fIvJVpuvbO09cOlYucsJIDJklVv7i/3n9ywW5JP0qqOgC4i6+22dLTz+/fWXdNdoamNHW6qcMFOoe3FJ3DRcIDfeWu83vr/We+3S0V1XVGDwlORoATQLtRwaUf09NjjR6OZXh7ecpT04aJt6eHfL0tp6m2lDtTwd7Xlx+UiX9bIiNnL5QJT32vt+q+evwfC/fIC9/v08fOvmKwXDXCfSd37ugXoxNkTM9Iqaipkwc/sqWqnykYDgAwP/V73T5HuniI8y5qquwPlaauGhepBkaAUanYv5vct0XH2hds0jnc1tMgNjxAcour5LXlB4weDpzMmtVlAbiETYcKJf1ouU4LsnedhHMM7BYqd0zsJc8v3iePfLpdxvbqpK9KuiNVj1R1RD/VVdiMgnKdlm6nVvJNH5PQwSOES6SqTxsmU55dKqkHC+TX726QndnFkl5Q3nSMqkml0r3Uigh3n/wDgBXsPFwiaUfLxc/bU87r53h6+vFp6qph0aebsuWrLYdlZPcIp7020FLq4qy6iK+o0kv21cTHX661J6Krz2IquElpJtvP74MX9ZO739skLy3ZL9ed1V2iQ8gytApWcAJoN2rip1w4KMbhbn/4KZVi0adzsBwprTohiOduwc2UuWv0yjw1oTt5Dd7Jk7z+XUM7eIRwFd07Bco1SbaVuwu25ejg9/HU/dlf7JCxcxbp7ysAgLktaExPV52VnT0P/TFNPYcMAHQ4Fcy894NNutlVz+ggWf7geafsHK7uq8dXPzyJ4OZxpg7tJkNiw6Ssuk6eW7TH6OHAiQhwAmgXNXX18vlmW4CT9PT24eftpbuqq7QT1VH9+9154m5p6Wrlpg5stuSzhYfo49V5cD8qaPlOanrT/VMFw9VNBctV0JwgJwCY21fbck4IRjqTDpr6eukGRipjCehIry47IEt25+vVyS/+fKR0DQ84ZedwdV89HupPZsrJmT0PXzJA7/93zSHZl1dq9JDgJAQ4AbSL5fuOyNGyaukU5Ctn94kyejiWpdKibh7fQ+//3ydbpaTSfYJ3H6/P1GnpLV04oY5Tx39C10S3c3wwvCXfJ+o4guEAYF57c0t00MLHy0POb+x67uw01/MH2MovfUUtdHSg9enH5G/f7Nb7j00dJAOOy046VedwnJoq7zWpf2epq2+QJxfsMno4cBICnADaxacbbc2FLhvaVXy8+FXTnu6/sJ9OQckuqnSbP9AqHeyNlWltOnfeijTSydwMwXAAcC8qdVyZ0Ce63VavXdrYuOirraSpo2MUllfLb/+7UWrrG/RnrBuS440ekqnNuqS/rl+6cEeurDlYYPRw4AREHQA4XXl1rXy7I1fvXzGC9PT2FuDrJU9MG6L3316dIasPHBWrO1ZeoxvEtPbjhDpenVdYzso8d0EwHADcj31V5cWDndc9/WQT+3WWwMY09S2ZRe32PoCi5iMPfLRFf78ldAqUOVcPYYWmg3p3DpHrzrIFif/y1U7mfBZAgBOA06mrYOXVdfqP74j4cKOH4xbG9YqSG5K76/2HPt5yyo7iVlJWVevQ+aUOng/zIBgOAO7lQH6p7MopEW9PD7lgoC2NvD3oNPX+tvR30tTR3uauSNOfsXy9bHU3Q6ir6RS/m9xHX6jYfKhQvtjCz7HZEeAE4HTzG9PTrxjWjSuLHZxm0SXUX9KOlss/vrN2R0BHu6EGO7mbKlwXwXAAcM/0dFVjLzzQt13f69LGBkZfbj3M6i+0my2ZhTLn6516//8uHSCDY8OMHpJldA7xl9vP6aX3n/pml1TVWnuRiNUR4ATgVEdLq2Tp3iN6n/T0jqVqTP316sF6/z/LDugrkVYVEegjnUP8Wn2eCrcnRAZKeCBXvd0FwXAAcC8L2rF7+qnS1AN8vCTzWIVszSJNHc5XXFkjd727UWrqGuSiQV3kprEJRg/Jcm47p4f+XHGooELeWpVu9HDgAAKcAJxKLe1X3eiGxoVJr+hgo4fjds7vHyNXDu8m9Q0iD360Rapr640ektPlFFXqiV5eSVWbzp85PpGVxW5EBcNVULu1/8cJhgOA+RwqKNeBRk8PkQvbMT39+Dro9i7tahUn4ExqVbAqPZVRUC5xEQHy5DVDmcO2g0Bfb7n3gr56//nF+6SI8kSmRYATgFPN39SYnj6c1ZtGeXTqIOkU5Cu7c0vkxe/3iVXU1tXrlamTnlmiP0SoDy+qvlZLp3nqePVB5OqRce08UrgS9UFgxrjENp1LMBwAzOXrbbYg45ienaRTcOszPdriksFdm+pwkqYOZ3o7NUO+2pqj57vP3zBCwgK46Nperh0VL31jgqWookZeXGKdz0/uhgAnAKdJP1omGzMKdSBp6rD2TwvCqUUG+cofrxik91WAc1dOsZjdurQCuez55fLnL3dKWXWdjOweLl/8ZoK8NvMsUfGn5mJQ9udfnp7E5NANTUuK08HtlsYqCYYDgDmpYFB7d08/2Xn9o8Xfx1Ont27LMv+cC65he3aRzP5ih95/6OL+MqJ7hNFDsjQvTw+ZdfEAvT9vRZpeDQ7zIcAJwGk+3ZStt+N7R+mCzTCOKnqvUrNq6xt0qrpa/WhGBWXV8uBHm+Wal1fpjqgqXfjJaUPko1+Nk4HdQuXcvtEyNyVZ179SsauT41f2x9Tz81KS5Zy+0QZ9JTCSCmq/ND3J9v3QwiAnwXAAMJfswgrZdKhQ/56fMqhLh6a3NnVTb1xBip9Sq1vVvE4FjtSW1a5nbnCoyjGpUlOT+neWW87uYfSQ3MLEftEyrlcnqa6rl6e/3W30cNAGBDgBOIWapJCe7jpUWu2frxwsof7esiWzSF5bflDMpL6+Qd5bkyHnP7NEPliXqR+7/qx4WXzfRLnurO7iqZbYNVJBzlWzJsmjUwdK98jAE15H3VePr354EsFNN9eSYLidWrnJ9wsAmLO50FkJkdI5tGMvtNsbGpGm/lMq5ff15Qdl4t+WyMjZC2XCU9/rrbqvHlfPO8pKwVM19v/731Y5eKRMuob5y9PXDqNcTgdR/84PXzKgaeGO6l4Pc6E1KACnUAXdD+SX6RSdKYPav6g7mqcm93+4bKBewfn3hXvkgoEx0tMEjZ9USs4f5m/T5Q6UAV1DdbA2KeH0qTlqpV3K+B4yc1yiFJbX6Cvfqvu1WvHJpBAnB8M/2ZCp04/Sj0s/UsHwkQnh8r+N2foD6v0X9pMuYaxEBwCz1d+8eEjHrd60Uys41Rw4/Wi5bM8ulsGxYR0+Blf0w558uePt9VJRXfeT51TjHJWCrVbKqSwL9Te6tVRw9OP1mfLGyhP/pqsmgar+tipRY7ZsjA/WHdLBNa/GupsRQb5GD8mtqJ/dq0bEyv82Zslfvtwp7/1yDJ8lTIQAJwCnmL/Rlp4+eUCMhPibayJhZdcmxcnnm7Nl2d4j8tDHW/Uf6eNXP7qSksoaHYhVk1TVBT7I10vuvbCfzBibIN5eLUs4UBMQNRFkMoi2BMMV9eF0Q0ahPPH1Tnn2+hFGDxcA0AJ5xZWyLv2Y3r+oA+tvHp+mfl6/zvL1thx9kYwApy24mTJ3jai1lKdaT2l/rKKmTh+nsixaE+Rs7+CpEXbnlMhjn23X+/dd2FdGJUYaPSS3pP7tVUPT1IMFsmhnnkweyOIdsyBFHYDD6uob5PMttgDnlaSnuxQV8PvrVUMk0NdL1qQVyDup6eKKqTifbc6WSc/8IHNX2IKblw3tKovum6hrDrU0uAm0hj0YHh8ZqLfqvro9fvkgXb9t/qZsWZ9eYPQwAQAt8M32HFFZySO6h0vXsABDxkCa+okrK1XwUQc3m/mnUM+rQ9TxLU1XtwdPVXD0VAFU+2P24Kk63pWcKqW+vLpW7nx3g1TW1OsyOb86p5fRw3RbcRGBkjI+Ue/P+XqnaXsZuCM+NQJw2Mr9RyS/pEqvgKJunetRAZzfX9Rf7z/x9S7JPOY6XQEP5JfKja+tkd/+d6PklVRJj6ggeeuWZHnh5yNJD4YhhsaFy8+S4vX+45/t0BdwAADm6J5+yWBbkNEIKk3dz9tT0o6Wy47D7t1NXaWNq5WVLY3zquPU8aqEjNHBU6Pqkd7wymrZl1cqnUP85O8/G+ayGVfu4tcTe0tEoI/szy+T99cdMno4aCECnACclp6uOnf7evNrxRXdOCZBzkqMkLLqOnn4f9sMX1lQWVMnz3y7Wy56dpks33dEfyC474K+suB3E2RCH4LkMNYDF/WTED9vXVv4Qya1AODSjpRWSerBo4alp9sF+XnrLszK140BV3ek5piq3FBbqPrYzc1R2zN42p7UKtKxcxbp1HmVQn88VT90c2aR3r95fA+JCvYzaJQ4vqTRbyf10fv/WLhXlzSC6yMSAcAhasKwoLGouyrIDNekrgI/MW2oDkAv3ZMvH2+wdbw3wuJduXLBP36Q5xfvk+q6ev1hYOE958pvJvURP28vw8YF2KkPFndPtk1q//bNbpdY9QEAOLVvt+fq8jZDYsN01oqRSFMXOVZeowN2rf3q1fHqvD9+vkOe/W6P/PuH/TpQ+v7aDPl0U5YuQ7Bkd578e+n+Vr92S4On7aW5lPrjPfXNLpdLqXdXvxidIImdAvVFlFeWHjB6OGgBmgwBcMh3O3P1qsC4iIAzdrmG8XpFB8s9k/vKkwt26avH5/SNks4hHZcGnlVYIX/6fLt8sz1X3+8a5i+PTR0kUwbF0J0QLuemsYny3zUZOjXpn4v2yiOXDTR6SAAAF+uefrJJA2L0xeQDR8pkV06JDOgaKu6mzMGVbvPauPqzJcFT1VywNY0oVUBUBWzV16RW6KqU5dbOWVuTUm8fqzp+1axJpusAbzXqZ/nBi/rLr9/ZIK8uPSC/GN1dYkIpoeXKCHACcIi6oqpcMbwbQSoTuG1CD72qQKXePjp/u7x8Y1K7v2dNXb28tvygPPfdXn3l2tvTQzcPUmkfarIIuOqk9tGpg2TG62v0CpIbkuOld+cQo4cFADjOsbJqWbnflp5+sYH1N+2CVZp632j5dkeunm+5Y4DT0bmdCiI1NJYzqqqp13PHysZbUUWt7M8vbfNrqzTjlgQ4VVBSpcKrv/8qMGqXEBkoM8YlyrSkuBYHH5tS6qX1KfUp43u08Cy0l4sHd5GR3cNlQ0ah/GPhHplz9RCHg95oP3yyBODQpHLJblsKBd3TzUF1JH9y2lC5/IXlsmB7jp5829Op2kPqgaPyh/nbZG+ebTKanBgps68cLP26ECiC6zu3b7RMHtBZvtuZp1Pm3rw5mUksALiQhTtzdTO4/l1CdKNCV3Dp0K46wPnl1sNy7wV93e7vhgr4qECgqjPZmoRw9a/UPTJQ/nzl4NP+m6mO46opT1t9uz1HLhvW7Yyr8FR6uFpBqYKMJ1Nfk8qCevrb3fLS9CQ9T2jPeqQzxyW63fePq1H//v936QCZ9tIqeX/tIf39cbio0qGgN9oPNTgBtJmauNXWN8jArqHSJ4aAlVkM7BYqv57YS+8/+uk2Hah2NlWr5t4PNsl1r6zWwc1OQb7yzLXD5P3bxxDchKn84dKB4uvlKcv2HtGBTgCA61iwrbF7ejterG1LN3Wdpp5fJrtzS8QdA0Iq4NMWM8efOaBnD562NeQ3+8udMvqvi2Tq88v1arwtmYVSrwq4trBWpv0x9bw6rrlamTlFlQ7VI1Up9TBeaVWdeHl46P8vxwc3jw96qwZS1E41HgFOAA6np185opvRQ0Er3Xl+b+nTOViOlFbrP8rOolZRvLU6Xc5/eol8siFL1BxVpRotuu9cfWWTq9Awm8SoILllgi1FTP2sqBQ5AIDxiitrZNleW0DhEheov2kX4u8j5/Sxrez7yk27qas5n79PyxtHenqIBPh6ydUj49oleKpmn+f1i5bh8eF6bqpKNT23aK9c/sIKGT1nkfz+oy3yv42Z8qu3WlYrUz1vr5Wp0tlVkDT9aJluhKTqdt/5zgaZ9MwSGffEYnEEnbuNZw9615/mm6K1QW+0L1LUAbTJoYJyWZt2TE8SLh9GerrZqG7lT10zVKa9tFI+2ZglU4d1k/P6d3boNdVVcJWOviWzSN8fHBsqf75yiJ5MAmZ213m9dS0sdZVe1ZO987zeRg8JANzeop25UlPXoC/YulqN5EuHdtGNOL/ckq3TjN2tXp+Xp4d0DvWT9KM/1q88Hfs/x8vTk1qU4quCpypFXK+ybGhZ8FQFW5+9foR+/fySKt2NffGuPFm6J1/ff3/dIX1rDfXe5dV1cvFzy6SwvFrvt0dNVxjnhAZRzRyrvxc9aBBlNH5iALTJZ5uz9XZsz07SJYxucmY0onuE3Dy+h/xn+UF5+H9b5dt7ztGrDtryx//pb3bL26np+o97iJ+3PHBRP/nF6AQ9wQXMTn0ofeji/nLP+5vlxe/3ybSRcfzeAwADHN/Vev7G7KYmIK7mrMRIndK6P7/shJqR7lCvTzWXVF2nVXBTzQlr6ut1syA5KUhknyEG+Hjp4OY5zdSztFP/bqr+pVotp17kTEHOUwVPo0P85NpR8fpWVVsnaw8ek+925sjbqzN06a3Wyi6s0FtVlkAF21UpJlUTtl+XUOkXEyw/+/dqvTCkLfVIwwOt+T1iFjSIMh8CnADaNLmcv7ExPZ3mQqZ234X9dCF8tTLtia93yV+uGnLCh4czrTbQ3websuQvX+7Uqe7KlcO7ycOXDpDOIQR/YC3qd91bq9J1F80nvt6pV4IAADrG6bpaK1W19fp5VwkY2pvU1J0i8tbaJjVmo+aGD3+yVa+MVIHLt28drUu9qICPappz/P87FcBTNTdVsDe0lRfY1b/b3JTkE5oBtSV4qjKazu4TpevTz1uZLm318R1jZVhcuG7meTK1grct5aCaq0eK9kWDKHMiwAmg1XYcLtaNY9SVyotcqOYRWk/VO3pi2hD5+aup8k5qhl5x+cPu/BMmoKdabbA3t0Sno6ceLND3e0UH6e7o43pFGfa1AO1JTVIfv3yQXPHiCpm/KVumj0mQUYmRRg8LACzvTF2tlVeWHtD1v10hYGiv13e6FV/2x+31+lSQzugxO9Oz3+2VD9dn6rTwF38xQoY1lilSq9lUwEc1zVF1JVXqtVqd6EgASP27qVRgZwRP1UV9R6gL+6cKbjqSUt9cPVK0L7XY4+SLKa1tEBUR5NsuY8PpEeAE0GqfbrKlBE3q37nVV1zhelRQcmLfaFmyJ1/eXJX+k86Ux682ePa64bLxUKG8uvSATuPx9/GU35zfR26b0FMHvAErGxoXLj9Litd1uh7/fLt8eufZlGEAgA4KGJ4paOgKAcMT6vU1uF+9vvfWZOjGPYrKCDq/f8wJz6tgpgr4ODPoo/7dnBE8VRlL7VUr09GUehjD0aC3+l4kwNnx+DQKoNVdsn/snk56ulU+PCxt7EKqNJyuO2B1nfzyrfXy0pL9Org5eUCMLLznXN1wheAm3IWqL6tqim3LKpYPW9mQAADQfgHD47taG1qvr6H19frM7vtdefJ/87fp/d+e31tuSO7eoe9vD57GRwbqbWtXhqpyTCpjqbWXLNXxCS2olWlPqVcp8+qck9/H/ph6fl5KcovrkaL9tGfQG+2HT6QAWiX1wFHJLa6SUH9vmdiPP75W+vDQHPsxagL23PXD5T8zRumJJOBOooL95O7JffT+377ZbdgHaQCwOjMFDB2t16fON6stmYW6qZBaBHFNUpzcc0FfMRsVEFXlmNqipbUy7Sn1j04dqFPoj6fuq8dXPzyJ4KaLaO+gN9oHYWUAraKayiiXDu2qC3PDvboD2hWU2ZoKAe5IfQj675oM3R33ue/26g8lAAD3bfDhrvX6Mo6Wy83z1uoSARP6RMmcq4eYtrFKR9TKdFZKPTou6E2DKHNhBSeAFqusqZOvt+bo/Svonm567rzaAHCEj5enPDp1kN5/c1Wa7MsrMXpIAGAp9oBhgwMBQ7PV6zMbdbF7xtw1cqS0WgZ2DdV1JtXfR7Oy18rU6eLNxKYcrZXpaEo9Oi7orRqytvR/jwp6q+NpEGUc8/4GAmBIfZ2SqlrpFuYvyXQPNj2zfXgAXIlKNVN1aFU92j9+voOAPwC4ccDQ3er1qeyfW95YKwePlElseIDMSznLdF/DqVArE20NeitqJkiDKGMR4ATQYv/baEtPnzq8m3jSOdj0zPbhAXA1j1w2QHy9PGXZ3iOycEeu0cMBAMswW8DQner1qVqbd7+3UTZmFOpAzhs3nyWdQ/3FKqiVidYGve38vT2lb0xIB48QxzP/ZRYAHaKovEaW7LZ12r6K7umWYLYPD4CrSegUJLdM6CEvLdkvf/5yp/6wo+pxAQCcEzDMaGWmiUdjEKqjA4buUq9PZSs8/tl2+XZHrvh6e+qGk707Wy+gQ61MnCrorRqYqTJdx9fbVb9vbhqXqBcCbcsqkj/M3yqv3jSK7xOD8OkUQIt8te2wVNfVS/8uIdK/S6jRw4EbfngAXNFd5/XWE171c/Ta8oNy53m9jR4SAJieGQOGHdGkpqOCmKqMkcr0URfD1XzR/u/58g8H5K3V6Tpd97nrhstZFi9ZZa+VacYGUOjYoLdqsnXpP5fJdzvz5LPN2fSrMAgp6gBaZH5jejq/rK334aEtzLTaAGhP6sPfQxf31/svfr9PcooqjR4SAFiC2Rp8tLZen7hYvb6iihp5fflBmfi3JTJy9kKZ8NT3eqvuq8ffTU2XJxfs0sc+culAuXhIV6OHDHS40zWIUqnpd53XR++r2uxHS6sMHql7IsAJoFnZhRWSerBA718+vJvRw4Ebf3gAXNGVw2NlZPdwKa+ukye+3mn0cADAEjqyq7UR9fpGdo/Qq75cwQ978mXsnEV6xazKSDieuq8ef/h/2/T92yb0kJvP7mHQSAHXdcfEXjrbsaCsWgc50fEIcAJollpmryT3iNSdEmEdZvzwALgadfX+j5cP1j8j8zdly7o02wUhAIBzA4Zigq7WzTWp+eU5PcXLQ2Rd+jH5YsthcYXgZsrcNbbU+sZO0Mc7/jH1bz2+l2sEZQFXo+rSPjltqF4Qoj4/f0cDyg7n0aCKbMDpiouLJSwsTIqKiiQ0lHqFMLeLnl0qu3JKZM7VQ+SG5O5GDwftNLm94+31UlFdp+8f/4fB/oFCrdxUwU1X+PAAuKLff7RF3l93SAbHhsqnd54tXmqGa3LMZ8yN/3+wCpU+/cj8rfLZ5hMDgqqWuCqbozJSQv1d7+Kr+qh9qnp9/1i4R55btFfXt/z2nnMlOsTPsH9XtXKzpXVD1YU8FUxWAVwudgOnNuernfLvpQckJtRPFt57rkv+brLqfIYVnADOaFdOsQ5u+np5yiWDqbVjVc2tNlCPr354EsFN4AweuKifhPh5y7asYvlw3SGjhwMAlqGCaZ1D/PX+DcnxsuzB82TjIxfIkgcm6sYfrhpAOF29PtWQbmDXUN3MR3VdNmrN0cfrM/XF7Za+vTpOHa+a6wE4td9N7iuJnQIlt7hKBzvRcQhwAjij+Rtt6ekT+0VLGF2z3aI7oPqwoD40mOXDA+AqooL95O7JtgLzT32zW6+MAQA4x968Ur0dEhv+k4ChGVNZn752mHh7esg323ObykF1JBVUfWNlWpvOnbcizbCgLODqVNabSlVX/rvmkKzcd8ToIbkNApwATqu+vkE+22Trnn7lCLqnu4vTrTYA0LwZ4xKlV3SQLjD/3Hd7jR4OAFjGvsYAZ5+YYLGCgd1C5beTbBfFHv10u+QVV3bo+6vVo+kF5T+pudkcdbw6T6XeAzi10T07yfQxttJuD32yVcqra40eklsgwAngtNamFUh2UaVOuTy/f2ejhwMALs/Hy1MemzpI77+5Kk325pYYPSQAML2yqlrJKqzQ+72jrRHgtHddVnWb1Yp/1aW8I1dFqn9TR6i6ogBO7/cX9ZduYf6SUVAuz3y7x+jhuAUCnABOa37j6s2Lh3QRfx8vo4cDAKagatVOHhAjtfUN8qcvdpDGBwAO2p9vW70ZFeyrs0usdFFMpar7eHnIdztzm+beHSHIz9uh81XTJACnF+LvI3+5eojef33FQdmQcczoIVkeAU4Ap1RVWydfbrF1qrxyOOnpANAaj1w2QDdnW7b3iCzckWv0cADA1Pbm2gKcvTtbZ/WmXf8uobopifLYp9slt4NS1VUHd9WFvrWFiNTx6jzVER7AmZ3Xr7NcPSJWN+h68KMt+jM22g8BTgCntGR3vhRX1kpMqJ+uIQIAaLmETkFy64Qeev/PX+6UyhomtADgaIOhPp1DxIpuP6enDI0L03Pvhz/pmK7qqsa6qhvdFjPHJ1KjHWihRy4bqFefqzrCLy7eZ/RwLI0AJ4BT+rQxRebyYd3Ey5MJDAC01p3n9dYXiVTtpdeWHzR6OABgWlZrMHQy78ZUdbXyf9GuPPl4Q8ekql8wMEZaE6dUHwlUh+irR8a157AAS1FlNf54+WC9/68l+2VHdrHRQ7IsApwAfqK4ska+25mn968gPR0A2lzfbNbFA/T+C4v3yeEiW4MMAEDr7MsrsWyKul3fmBD53QW2rup//Hy75BS1b6r6kdIq+eVb66W+cbFoc3FOeyD05elJEhZAejrQGpcM6SJTBtnqs//+4y1SW1dv9JAsiQAngJ9YsDVHqmvr9SRyULdQo4cDAKZ1xfBukpQQIRU1dfLE17uMHg4AmI4q8aFWwls9wKn8ckJPGRYfLiWVtfLQJ1vaLVVd1fm87t+rZOfhYokK9pO/XDlYr8z0OEWg0/5YgI+XzEtJ1o30ALSOKukw+4rBEurvLVuziuQ/ZPa0CwKcAH7C3sHxqhGx1NcBAAeo36GPTx2kV758uilb1qUVGD0kADCVA/llepWhWjUYHewnVqZS1Z+5dqj4envqevgfrst0+ntkHiuXn/17lezPL5OuYf7ywe1j5BdjEmTVrEny6NSB0j0y8ITj1X31+OqHJxHcBBzQOdRf/nDZQL3/j4V75EC+rfQGnMfbia8FwAJUOsyqA0eb6m8CABwzJC5MrhsVL++tPSSPf75dPr3zbGobA0AL7W1MT+/TOdgtLrz37hwi913QV+Z8vUtmf7FDzu4TJd3CA5zy2gePlMkvXl0t2UWVOnD5zq2jJb4xoKkCyCnje8jMcYlSWF4jpVW1Euznrbulu8O/O9ARrk2Kk883Z8uyvUd0qvr7vxwrnswJnYYVnABOoH7hqmyYUQkRTRMeAIBj7p/ST0L8vWVbVrF8sO6Q0cMBANPYb/EGQ6dy64SeMqJ7uJRUqVR153RV35NbolduquBmr+gg+eD2saec66tgpmqKop5TW4KbgPOon6e/XjVEAn29ZG3aMXknNd3oIVkKAU4Ap0xPv2IEzYUAwFlUjbO7J9maR/ztm91SVFFj9JAAwBT2NgY41cpGd6FW+auu6n7enrJ0T768v9axC2Pbsop0zc38kirp3yVE3r99rHQJ83faeAG0nLp48PuL+ut9VZ9dlY1Q1IWMgrJqOVRQrrftVYPXyghwAmiyN7dEtmcXi7enh1w6pKvRwwEAS5kxLlE3yFCT1ue+22v0cADAZAFO91nBqfSKDpYHpvTT+3/+cqdkFVa06XXWpx+TG15dLcfKa2RYXJi898sx+qIbAOPcOCZBZ0yWVdfJgx9tkdeXH5CJf1siI2cvlAlPfa+36v7ryw9yUbwVCHAC+MnqzYn9oiUyyNfo4QCApfh4ecqjjcXl31iVpi8qAQBOr7q2XtKOlDXV4HQ3qiamCoKoepi//6j1XdVX7T8qN76WqruyJydGytu3jpbwQOb4gNFU3c0nrxmqFxat3H9U/vTFTskosK3ktFP3VR3esXMWyQ978g0bq5kQ4ASgqQmT6vCrXDGc9HQAaA+qA+3kATFSV98gf/piB+lHAHAG6UfLpLa+QYJ8vXTHb3ejUtWfumao+Pt4yvJ9R+TdNRktPnfJ7jyZOXeNlFfXyYQ+UTLv5rMkxN+nXccLoOUyj1Xo+aDdyTPChsZbRU2dpMxdQ5CzBQhwAmhKX1G/ZNUEUn34BgC0j0cuGyC+Xp66g+bCHblGDwcAXNY+e3p6TIjbNrvpqVPVbfX6/vrlTl2fr7l6fQu25chtb66Tqtp6mTygs7x60ygJ9PU27GsAcCKVdn7H2+tFWvBrTf1oq59udTzp6mfGbzkAJ6SnTxncRQJ8vYweDgBYVkKnILl1Qg/515L9MvvLHXpVp78Pv3cB4HT1N90xPf14KeMS5ZttObImrUDu+2CTTBnURd5clS7px6W0JkQG6lrPAb6e8of52/XKsEuHdpVnrxuuS6QAcB0fr8+Uiuq6n6zaPFOQUx3/yYZMXboCp8ZvOgC6vtGXWw7r/StJTweAdnfneb0lJtRPDhVUyGvLDxo9HABwSe7aYOhU9fpUqrqPl4esSTsms788db0+Vfpk1ifbdHBz2sg4+ef1IwhuAi5GrbZ+Y2Vam86dtyKN8kZnwG87ALJ0T77urKg6Ko7r1cno4QCA5QX5ecusiwfo/RcW75PDRW3rjgsAVmZvxubuKzgVtVpT1SM9U70+O5X1OnVoV13DE4BrUZ+71c9za8OU6nh1XmE5aeqnQ4ATQFN6+uXDuok3V3kBoENcMbybJCVE6OLxT3y9y+jhAIBLUasQDzR1UA8Rd9ZUr6+lPER+/e4G6vUBLqisqtah80sdPN/KiGQAbk79gvxup63JxZUjuhk9HABwG6phxuNTB4nqm/HppmxZl1Zg9JAAwGWo5jmqjJLqIB4bESDurKleX0Pr6/UBcL0sHkcEO3i+lRHgBNycKlheWVMvPaOCZEhsmNHDAQC3MiQuTK4bFa/3H/vM1hQCAPBj/c1e0cFunWpNvT7AWiICfXRTsNb+VlPHq/PCA33aaWTmR4ATcHP29PQrhsfq1UQAgI51/5R+EuLvLduzi+WDdYeMHg4AuIS9ebb6m+7eYIh6fYC1qM/cM8YltuncmeMT+cx+BgQ4ATeWV1IpK/YdaaoFBwDoeKrB2+8m99X7f/tmNzXTAEBE9uXaVnC6e4Mh6vUB1jMtKU4CfL10maKWUIvY1fFXj4xr76GZGgFOwI19vvmwqGzIEd3DJTEqyOjhAIDbumlsgl6lVFBWLc9+t8fo4QCAy6So93bzBkPU6wOsJyzAR16anqTTzlsa5Hx5epI+D6dHgBNwY582pqdfOTzW6KEAgFvz8fKURy8bqPffXJUue3NtqZkA4I7q6xtkf37jCs4Y917BSb0+wJrO7Rstc1OSJcDHyxboPOn54+/fc0FfOadvdAeP0HwIcAJu6kB+qWzJLNJF2y8d2tXo4QCA21MT1wsGxuhGQ3/8fAeNIQC4reyiCimvrhMfLw8dpHNn1OsDrB3kXDVrkjw6daB0P+l3nbo/ukek3n9ndYYUV1LCqDmsVwfc1PxN2Xo7oU+Urv8GADDeHy4dID/szpfl+47ItztyZcqgLkYPCQAMS0/vERUk3l6syVH1+p7+drdU1NRJS659qXp9/j7U6wPMQKWdp4zvITPHJeqmYKpuriotoVZfV9bUy8XPLZW0o+Xy5y92yFPXDDN6uC6NvxaAG1KrguZvJD0dAFxNQqcguXVCD73/5y93SGVNndFDAgADGwy5d/3NttTrsz9PvT7AXNRq64ggX4mPDNRbdV81FlJBTfVz/cG6TFmyO8/oYbo0ApyAG9p4qFAyCsp1vQ+VDgkAcB13ntdbYkL95FBBhby2/KDRwwGADrc3z1aHWDVfQ8vr9amben5eSjL1+gCLSO4RqVd3KrM+2Uqq+hkQ4ATc0KeNqzenDIpxuDMjAMC51O/lWRcP0PsvLN4nh4sqjB4SAHSofY0p6u7eYKi19frU46sfnkRwE7CYB6b0k4ROgXK4qFL++uVOo4fjsohsAG6mpq5evthyWO9fMYL0dABwRVcM7yZvrU6X9enH5Imvd8lz148wekgA0GGllOw1OElRb129PhoKAdYU6Ostf7tmmFz3yip5b+0huXhIV33BAydiBSfgJhPFgrJqOVRQLl9vOyxHy6qlU5CvTOgdZfTQAACnoD6k/vHyQbrm0qebsmVdWoHRQwKADpFXUiUllbW6UU5ilHt3UG9tvT4A1k5VnzHWlqr+0MdbSFU/BQKcgIUVVdTI68sPysS/LZGRsxfKhKe+l9/+d5N+rmd0kJRV07wCAFzV4NgwuW5UvN5/7LPtUlffgta5AGByexsbDCV2ChI/by+jhwMALuPBi35MVZ/zFanqJyPACVjUD3vyZeycRTL7ix26odDJ1qUd08+r4wAArun+Kf0kxN9btmcXywfrDhk9HABodzQYAoDTp6o/NW2o3v/vmkOylM/yJyDACViQClqmzF0jFTV1otb7nGrNj3pMPa+OI8gJAK4pKthPfje5r97/2ze7pai85oSyI2qr7gOAVdBgCABOb3TPTk1d1VWqegmp6k1oMgRYMC39jrfX2wKbzXzm1c97iD5edWRURcsBAK7lprEJ8t81GfpD/21vrZPcokpJP25lfkJkoMwYlyjTkuL4PQ7A9GgwBADNp6ov3pWnMzX/+tUumXP1EKOH5BJYwQlYzMfrM6Wiuq7Z4KadOk4d/8mGzPYeGgCgDXy8POWqEd30/pqDBT8pO6Luq3IklB0BYKUVnKSoA8AZUtWvsaeqZ8jyvUeMHpJLIMAJWIhKU3xjZVqbzp23Io00RwBwQSpo+cy3e5run/yb2l6KhLIjAMzuaGmVLr2hGoL3iibACQCnM6ZnJ5kxNkHv/55UdY0AJ2Ahx8prdNpia8OU6nh1XmE5vxQBwFXLjjRHXaNSx6nj1XkAYNb09LiIAAnwpYM6AJzJgxf1l/jIAMkqrJA5X+8Sd0eAE7CQsqpah84vdfB8AIBzUXYEgFs2GKL+JgA0K8hPdVUfpvffTSVVnQAnYLFfcI4IdvB8AIDzUHYEgPsGOElPB4CWGNurk25IaU9Vd+dFSwQ4AQuJCPTR3XQ9WnmeOl6dFx5I910AcBWUHQHgbvbmlehtLwKcANBiv7+ovy7toVPVv9op7ooAJ2AhHh4eMmNcYpvOnTk+UZ8PAHANlB0B4G725rKCEwDalKp+ja2r+jupGbJin3umqhPgBCxmWlKcLsre0lilp4fo468eGdfeQwMAtAJlRwC4E9UcLa+kSu/3JsAJAK0yrleU3DjGlqr+4EfumapOgBOwmLAAH3lpepJOO28uyGl//uXpSfo8AIDroOwIAHesv9k1zF9C/Pn9BQCt9dDFP6aqP/G1LVVd1WQvKKuWQwXlemvlGu1c2gcs6Ny+0TI3JVnueHu9lFfX/eR5+4flAB8vHdw8p290h48RANCysiOzv9jR6nMpOwLAbPY11t9k9SYAONJVfaj8/D+p8vbqDPEQD1m6J1/XZrdTF8HV/FJlflptkRMrOAELBzlXzZokiZ0Cf/Jc98hAeXTqQFn98CSCmwDgwig7AsDd6m8S4ASAthvXO0rO72/7jP/W6nTJOC64qaj76uL52DmL5Ic9+WIlbhPgfPHFFyUxMVH8/f1l9OjRsmbNmjMe/+GHH0r//v318UOGDJGvvvqqw8YKOIu/j6ccLqrU+x/fMU6WPXiebHzkAlnywERJGd9DQkn/AQCXRtkRAO5ib2OKep/OIUYPBQBM64c9+bJk94+By5MT0hsabxU1dZIyd42lgpxuEeB8//335d5775XHHntMNmzYIMOGDZMpU6ZIXl7eKY9fuXKl3HDDDXLLLbfIxo0b5corr9S3bdu2dfjYAUdsPlQkVbX1EhXsJyO7h0t8ZKBEBPmStggAJiw7osqK6EDnSc/bH1PPz0tJZmU+AFPX4OwTwwpOAGhrs7Y73l7/k6DmqahSnOo4dbw6zwrcIsD597//XW677TZJSUmRgQMHyssvvyyBgYHy+uuvn/L45557Ti666CJ54IEHZMCAATJ79mwZOXKkvPDCCx0+dsARqQeO6u3onpEENQHAAmVHVHkRVWbkeJQdAWB2ZVW1uimG0juaACcAtMXH6zOlorpOBy9bQh2njv9kQ6ZYgeWbDFVXV8v69etl1qxZTY95enrK5MmTZdWqVac8Rz2uVnweT634nD9/fruPF3Cm1QdtAc4xPSKNHgoAwEEq7VyVF5k5LlEKy2uktKpWgv28dbd0LmIBMLP9+bbVmyrrSGUbAQBap6GhQd5Ymdamc+etSNPzS7PPJy0f4Dxy5IjU1dVJTEzMCY+r+7t27TrlOTk5Oac8Xj1+OlVVVfpmV1xc7PDYAUdU19bL+vRjen90z05GDwcA4CRq8qkCAAQBAFivwVCQ0UMBAFM6Vl5zQrf0llKLPdV56uK52eeWbpGi3hHmzJkjYWFhTbf4+HijhwQ3tzWrUCpr6iUyyFf60I0SAAAALooGQwDgeKkPR6jMILOzfIAzKipKvLy8JDc394TH1f0uXbqc8hz1eGuOV1QKfFFRUdPt0KFDTvoKgLZZfaBAb0f3oP4mAAAAXNe+vBK9pcEQALRNkJ9jCdqq7JHZWT7A6evrK0lJSbJo0aKmx+rr6/X9sWPHnvIc9fjxxysLFy487fGKn5+fhIaGnnADjLTa3mCI+psAAAAwQQf13mQdAUCbRAT6SEJkoLR2aZM6Xp2narqbneUDnIpqGPTqq6/KG2+8ITt37pQ77rhDysrKdFd15aabbjqhCdHdd98tCxYskGeeeUbX6Xz88cdl3bp1ctdddxn4VQAtV1P3Y/3NMb2ovwkAAADXVFlTJxmNdeNIUQeAtvHw8JAZ4xLbdO7M8eZvMKSYfw1qC1x33XWSn58vjz76qG4UNHz4cB3AtDcSysjI0J3V7caNGyfvvvuu/OEPf5CHH35Y+vTpozuoDx482MCvAmi5rVlFUl5dp6/C9GWiCAAAABd1IL9M6htEwgJ8JCrY3A0uAMBI05Li5Olvd0tFTZ00qO5BzfD0EPH38ZKrR8aJFbhFgFNRqy9PtwJzyZIlP3ns2muv1TfAjFKPq7/pqX5rAQAAAC5or73+ZudgS6wgAgCjhAX4yEvTkyRl7hqde36mIKf91+3L05P0eVbgFinqgLtJPWivv0l6OgAAAFy//iYNhgDAcef2jZa5KckS4OOl62ue7rKRv7eXzEtJlnP6RotVEOAELKa2rl7WHmxcwdmTBkMAAAAwQ4MhyioBgLOCnKtmTZJHpw6U7pGBJzzn620LA948PtFSwU23SlEH3MX27GIpq67Ty8wHdAk1ejgAAADAae21r+CkgzoAOE1YgI+kjO8hM8clSmF5jZRW1Uqwn7f8sCdPfvf+ZvloQ6b87oK+4uNlnXWP1vlKAGirD9jS089KpP4mAAAAXFd1bb2kHSnT+70JcAKA03l4eEhEkK/ERwbq7cVDuuqGbrnFVbJwR65YCQFOwGJSG9PTx5CeDgAAABeWfrRMausbJMjXS7qG+Rs9HACwPD9vL7khubvef2NlmlgJAU7AQurqG5rqb47pSYMhAACs4MUXX5TExETx9/eX0aNHy5o1a4weEuDU9PTeMSF0UAeADvLz0d3Fy9NDL47anVMiVkGAE7CQHdnFUlJVKyH+3jKgK/U3AQAwu/fff1/uvfdeeeyxx2TDhg0ybNgwmTJliuTl5Rk9NMB5HdRJTweADtM1LEAuHBij999cZZ1VnAQ4AQtJPfhj/U11RQYAAJjb3//+d7ntttskJSVFBg4cKC+//LIEBgbK66+/bvTQAIfRYAgAjHHj2AS9/d/GLCmurBErIMAJWMjqA9TfBADAKqqrq2X9+vUyefLkpsc8PT31/VWrVv3k+KqqKikuLj7hBriyvbm21EgaDAFAxxrbs5O+uFReXScfr88UKyDACVio/uaaxhWco3tQfxMAALM7cuSI1NXVSUyMLY3MTt3Pycn5yfFz5syRsLCwplt8fHwHjhZo/dz1QGMH9T6dQ4weDgC4FQ8PD7lpXKLef2tVutTXN4jZEeAELGJXTrEUV9ZKsJ+3DOpG/U0AANzNrFmzpKioqOl26NAho4cEnNahgnKprq0Xfx9PiY0IMHo4AOB2rhoRq+MH6mLTiv1HxOwIcAIWkdqYnj4qMUK8vfjRBgDA7KKiosTLy0tyc3NPeFzd79Kly0+O9/Pzk9DQ0BNugKvX3+wVHUzteAAwQLCft1yTFKf331iZLmZHFASwiNUHSE8HAMBKfH19JSkpSRYtWtT0WH19vb4/duxYQ8cGOGpvnq3+Jg2GAMA408fYmg0t2pWrV9abGQFOwAJUvYw1aTQYAgDAau6991559dVX5Y033pCdO3fKHXfcIWVlZbqrOmBm+3JtKzhpMAQAxundOVjO7h0lDQ0i76RmiJl5Gz0AAI7bnVsiheU1EujrJYNjw4weDgAAcJLrrrtO8vPz5dFHH9WNhYYPHy4LFiz4SeMhwKwp6r1pMAQAhrppbIIs33dE3l+bIb+b3Ef8fbzEjFjBCVhAamN6elJChPhQfxMAAEu56667JD09XaqqqiQ1NVVGjx5t9JAAh7OP9ufbApx9YljBCQBGmjQgRmLDA+RYeY18seWwmBWREMACUg/a09OpvwkAAADXll1UIeXVdeLj5SEJkYFGDwcA3JqXp4f8Ykx3vf/GyjRpUPnqJkSAEzA59cvnxwAn9TcBAABgjvT0nlHB4k32EQAY7rpR8eLr5Slbs4pk06FCMSP+mgAWmCAWlFWLv4+nDIkNN3o4AAAAwBnRYAgAXEunYD+5bFhXvf/WqnQxIwKcgEXqb45KiBRfb36kAQAA4Nr25pXoLQFOAHAdN41N1FtVh/NIaZWYDdEQwORWH7Clp4/uQXo6AAAAXN++xhR1GgwBgOsYHh8uw+LCpLquXt5fe0jMhgAnYPr6m7YVnKNpMAQAAAATzF/tNTj7dA4xejgAgFOs4nxndbrU1tWLmRDgBExsf36ZHCmtFj9vTxkWH2b0cAAAAIAzyiupkpLKWt21NzGKDuoA4EouHdpVIoN8JbuoUhbtyhMzIcAJmNjqxvqbI7tHiJ+3l9HDAQAAAM5ob2ODoYTIQOavAOBi/H285Lqz4vX+m6vSxEwIcAImlnrQVn9zDOnpAAAAMAEaDAGAa/vF6O7i6SGyYt9R2df4O9sMCHACJq5fZF/BObonDYYAAADg+mgwBACuLS4iUCYNiNH7b61KF7MgwAmY1MEjZZJfUiW+3p662xkAAADg6mgwBACub0Zjs6GPN2RJaVWtmAEBTsDk6ekj4sN1nQwAAADALCs4SVEHANc1vncn6RkdpIOb/9uQKWZAgBMwqR/T06m/CQAAANd3tLRKCsqqxcNDpFc0AU4AcFUeHh5y05gEvf/GqnRdIs/VEeAETEj9ckk90NhgqAf1NwEAAGCe9PS4iAAJ8CUDCQBc2dVJcRLo66VX3q/ab1tg5coIcAImlFFQLjnFleLr5SkjukcYPRwAAACg5Q2GqL8JAC4v1N9Hrh4Zq/ffNEGzIQKcgInT04fFh3H1GwAAACYLcJKeDgBmcFNjs6Fvd+RIdmGFuDICnIAJ2dPTR/eg/iYAAADMYW9eid7SYAgAzKFvTIiM6Rkp9Q0i76ZmiCsjwAmYsf5mYwf1MTQYAgAAgEnszaWDOgCYzYzGVZz/XZMhVbV14qoIcAImk3msQrIKK8Tb00NGJoQbPRwAAACgWUUVNZJXUqX3CXACgHlcMDBGuoT6y9Gyavlq62FxVQQ4AZPW3xwaFyaBvt5GDwcAAABocf3NrmH+EuLvY/RwAAAt5O3lKb8Y3d3lmw0R4ARMhvR0AAAAmM0+6m8CgGldn9xdfLw8ZGNGoWzNLBJXRIATMOkKztEEOAEAAGCy+pt9OocYPRQAQCtFh/jJJUO66v03V6WJKyLACZhI5rFyXYPTy9NDRiVEGD0cAAAAoEX2Nqaos4ITAMzppsZmQ59uzpZjZdXiaghwAiaSesCWnj4kNkyC/Ki/CQAAAHPV4OwTQ4ATAMxoZPdwGdQtVKpr6+W9tRlSUFYthwrK9bahocHo4QkREsBEUg/a09MjjR4KAAAA0CJlVbWSVVih93tHE+AEADPy8PCQnyXFy2PZ2+Xpb/fIkwt2Nz2XEBkoM8YlyrSkOAkLMKaRHCs4AROhwRAAAADMZn++bfVmVLCfRAT5Gj0cAEAb/LAnX574Zpfer6s/ccVmRkG5zP5ih4yds0gfZwQCnIBJHC6qkPSj5eLpIdTfBAAAgAkbDLF6EwDM6Ic9+ZIyd41U1tSd8vmGxltFTZ0+zoggp8MBzjfffFPfiouLW3xOaWlp03kAWld/c3BsmIT4G7PkGwAAK2EeC3QMGgwBgHkVVdTIHW+vtwUxmym1qZ5Xh6jj1XmmqsE5c+ZMnYc/atQoGThwYIvOyc3N1ed5enrKTTfd5OgQALeqv0l6OgAAzsE8FugY+/JK9JYGQwBgPh+vz5SK6joduGwJFeRUx3+yIVNSxvcQt0hRd4UuS4BZrG5cwTm6Bw2GAAAwGvNYoPUd1FnBCQDmm++8sTKtTefOW5HWofMlQwKcdXW2nH1vb5q4Ay2RV1wpB4+UiYeqv5lIgBMAAKMwjwVaR9VrU80nlD6dQ4weDgCgFY6V10h6QXmLV2/aqePVeYXlNdYOcO7ebWslHxlJoAZoidWN3dMHdQuVsADqbwIAYBTmsUDrHMgvE9VsNzzQR6KC6aAOAGZSVlXr0PmlDp7fGq2+9Lx06dJTPr527Vo5cuTIGc+tqqqS/fv3y9NPP63rHQ0fPry1bw+4pdUHbPU3R/eg/iYAAG3FPBboeHsb62/2jg7WPzsAAPMI8nMsYyXYwfNbo9XvNHHixJ/8YVI59TfffHOLX0Mdr17j9ttvb+3bA24ptSnAyWoRAADainksYFz9TRoMAYD5RAT6SEJkoC410po0dTXb6h4ZqFfvu3SKuprY2W+neqy5W1xcnLz44oty5ZVXOvNrASwpr6RS9ufb6m8mE+AEAMAhzGMBoxoMUX8TAMzGw8NDZoxLbNO5M8cndujK/Vav4Pz++++b9tUk7/zzz9cDfu2116RHj9O3f1fH+Pv7S9euXSU+Pr7tIwbczJrG+pv9u4RKeCB1iwAAaCvmsUDH22tfwUkHdQAwpWlJcfL0t7uloqZOWtIU3dNDxN/HS64eGScdqdUBznPPPfeUjycnJ8vAgQOdMSYAx0k9YAtwjunJ6k0AABzBPBboWNW19ZJ2pEzv9ybACQCmFBbgIy9NT5KUuWt07vmZgpz2BZsvT0/q8AbJDlf7PHjwoN7GxsY6YzwATkKDIQAA2gfzWKB9pR8tk9r6Bgny9ZKuYf5GDwcA0Ebn9o2WuSnJcsfb66Wiuk4/dnyc056IHuDjpYOb5/SNlo7mcIAzISHBOSMB8BNHS6ua0nqovwkAgHMxjwXal30e2zsmhA7qAGCBIOeqWZPkkw2ZMm9FmqQXlDc9pxoKqZqbKp091L9jV27adVy/dgAO1N8Mkcgg6m8CAADAhB3USU8HAEsIC/CRlPE9ZOa4RCksr5HSqloJ9vPW3dKNvpDltABnbW2tfPnll7Js2TI5cOCAlJSUSF2dbdnq6agvftGiRc4aAmDh9HRWbwIA0F6YxwLtgwZDAGBNHh4eEhHkq2+uwikBzuXLl8uNN94oGRkZJ3SmPNM/hHre6Ogu4OpSG1dwju5J/U0AANoD81ig/ezNLdFbGgwBAFw+wLlr1y656KKLpKKiQk/2fH19pU+fPhIZGSmenp7OGSXgho6VVcuuHNukkPqbAAA4H/NYoP3U1tXLgcYO6n06hxg9HACAxTkc4PzrX/8q5eXl4uXlJX/84x/lt7/9rQQHc4UOcNbqTZXSExXsZ/RwAACwHOaxQPs5dKxCqmvrxd/HU2IjAoweDgDA4hwOcC5evFin6Nx9993y8MMPO2dUACT1YGP9zZ6s3gQAoD0wjwXah1oRvTHjmN5PiAwUTyo6AABcPcB55MgRvb3qqqucMR4AjVIP2FZwjqH+JgAA7YJ5LOBcRRU18vH6THljZZqkF5Trx3bnlsrEvy2RGeMSZVpSnO7ACwCAszlcXCg6OlpvAwJIOwCcpai8RnbmFOt96m8CANA+mMcCzvPDnnwZO2eRzP5ih2Q0Bjft1H31uHpeHQcAgMsFOM8++2y93bZtmzPGA0BE1qQViGrg2jM6SDqH+Bs9HAAALIl5LOAcKmiZMneNVNTUSYNKUT/peftj6nl1HEFOAIDLBTjvvfdeXZj9ueeek9raWueMCnBzqw/Y6m+Sng4AQPthHgs4Jy39jrfX24KYJ0c2T6KeV4eo49V5AAC4TIDzrLPOkmeffVY2b94sV199dVMtIwBOaDBEejoAAO2GeSzgOFVzs6K6rtngpp06Th3/yYbM9h4aAMCNONxk6E9/+pPeJicnyxdffCEJCQlywQUXSP/+/SUwMLDZ8x999FFHhwBYirqavSPbVn+TFZwAALQf5rGA493SVUOhtpi3Ik1mjksUDw9arAMAHOfRoP4qOcDT0/OEP0rq5VrzR6qurk6sqLi4WMLCwqSoqEhCQ0ONHg5MZNHOXLnljXXSIypIvr9/otHDAQC4MavPZ6w+j7X6/z8Yr6CsWkbOXtjm8zc+coFEBPk6dUwAAGtp6XzG4RWcyskxUgdjpoBbSz1YoLekpwMA0P6YxwJtV1blWO3a0qpaApwAAKdwOMBZX1/vnJEA0FJpMAQAQIdgHgs4JsjPsY+TwQ6eDwCA05oMAXCeksoa2ZpVpPdH92QFJwAAAFxXRKCPJEQGSmuraKrj1XnhgT7tNDIAgLshwAm4kHXpx6S+QaR7ZKB0DQswejgAAADAaamatTPGJbbp3JnjaTAEAHAeApyAC0k9YKu/OYbVmwAAADCBaUlxEuDrJS2NVXp6iD7+6pFx7T00AIAbcWqAc9GiRXLjjTdK7969JTg4WLy9vWXHjh0nHLN06VL517/+JW+//bYz3xqwhNWN9TdH96D+JgAAHYl5LNA2YQE+8tL0JJ123lyQ0/78y9OT9HkAADiLU6o6l5eXy4wZM+STTz45ofvkqVIOvLy85K677tLPjR49Wvr06eOMIQCW6EJJ/U0AADoW81jAcef2jZa5Kclyx9vrpby67ifP23+aAny8dHDznL7RHT5GAIC1OWUF589+9jM9KVQTwrPOOkvuv//+0x47fvx4GTx4sN7/+OOPnfH2gGXqb9bVN0hcRIDERQQaPRwAANwC81jAeUHOVbMmyaVDuv7kOVVf/tGpA2X1w5MIbgIAXDPAqSZ3X331ld5/5ZVXZPXq1fLUU0+d8Zyrr75aTyJ/+OEHR98esIxU0tMBAOhQzGMB51Jp5wmdbBfqrxsVJ8sePE82PnKBLHlgoqSM7yGh/qSlAwBcNEX9jTfe0Nvp06fLrbfe2qJzkpKS9Hbnzp2Ovj1gGakHbQ2GSE8HAKBjMI8FnC+7sEJve0YHS3wkWUkAAJOs4Fy3bp2uQ3Tddde1+JyuXW1pC/n5+Y6+PWAJ5dW1svlQod4f25MVnAAAdATmsYDzZTUGOGMjAoweCgDAjTgc4Dx61JZW261bt5a/qaftbevr6x19e8ASNqQXSm19g3QL89c1OAEAQPtjHgs4X3Zhpd52C2dOCwAwUYAzLCxMb7Ozs1t8zsGDB/U2KirK0bcHLCH1oO0D1pienU7ZtRUAADgf81jAuWrr6iWn2BbgjCXACQAwU4Czb9++ert58+YWnzN//ny9HTFihKNvD1jCanuDIepvAgDQYZjHAs6VW1IldfUN4uPlIdHBfkYPBwDgRhwOcF566aW6k+Tzzz8vlZW2q3VnsmzZMnnvvff0KrWpU6c6+vaA6VXW1MnmQ0V6nw7qAAB0HOaxQPs0GOoaFiCenmQlAQBMFOC88847JTIyUnJzc+Waa66RggJbJ+iT1dbWyquvviqXXXaZrlkUHx8vM2fOdPTtAdPbkHFMquvqpUuovyR0otMkAAAdhXks0D4Bzm7h/kYPBQDgZrwdfYHQ0FB5//335ZJLLpGvv/5aT/jOPffcpucffPBBqa6u1l0qi4qK9FVyf39/+eCDD8THx8fRtwdMb/WBgqb0dOpvAgDQcZjHAs6Veayxg3o4F+0BACZbwalMmjRJFi9eLN27d5eKigpZsGBBU6BGTRYXLVokhYWFelKoJo7ff/+9JCcnO+OtAdNLtdffJD0dAIAOxzwWcP4KzlhWcAIAzLaC0278+PGyd+9eXZfos88+01e68/LypK6uTjp16qQLsV9++eUyY8YM8fX1ddbbAqavv7nxUKHeH0ODIQAADME8FnB2ijod1AEAJg1w6hfz9pbp06frG4DmbTpUKNW19RId4ic9ooKMHg4AAG6LeSzguCz7Cs4IApwAABOmqANom1R7/c0e1N8EAACAeakyDlmNNThZwQkA6GgEOAEDrW6svzmmJ/U3AQAAYF7FlbVSVl2n97uFEeAEAHQsApyAQapq62RDxjG9T/1NAAAAmJl99WanIF8J8PUyejgAADfT4hqcPXv21FuVRrt///6fPN4WJ78W4E62ZBZJVW29RAX7Sq/oYKOHAwCAZTGPBdofDYYAAKYIcKalpentyXUC7Y+3BTUH4c5W77elp4/u0YmfBQAA2hHzWKD9ZRfZA5z+Rg8FAOCGWhzgnDFjRqseB3BmqQcbGwyRng4AQLtiHgt0XIp6bHig0UMBALihFgc4586d26rHAZxedW29rE+319+kwRAAAO2JeSzQ/rKaUtRZwQkA6Hg0GQIMsDWrUCpq6iQyyFf6dKb+JgAAAKxRgzOWGpwAAAMQ4AQMsPqALT09OTGSGl4AAACwzArO2AgCnACAjkeAEzCw/uYY6m8CAADAAuWX8kqq9D5d1AEApgxwrly5Ury8vCQgIECysrKaPV4d4+/vL97e3rJ+/XpH3x4wnZq6elmXZm8wRP1NAACMwjwWcI6cokppaBDx8/aUTkG+Rg8HAOCGHA5wvvfee9LQ0CCXXXaZxMbGNnu8Ombq1KlSX18v7777rqNvD5jOtqwiKa+uk/BAH+kXE2L0cAAAcFvMYwEnp6eHB1B+CQBgzgDn8uXL9R+xiy++uMXnXHrppXq7dOlSR98eMHX9TU9PJoAAABiFeSzg3AZDpKcDAEwb4Ny/f7/eDhw4sMXn9O/fX2/37dvn6NsDppN68Kjekp4OAICxmMcCzl3B2S3c3+ihAADclMMBzsrKSr1V9Yhays/PT2/LysocfXvAVGp1/c1jen90DxoMAQBgJOaxgHNXcMaGBxo9FACAm3I4wBkZaQvSZGRktPiczMxMvQ0PD3f07QFT2Z5dLKVVtRLq7y0DuoYaPRwAANwa81jAOVjBCQAwfYDTntLz2Weftfic+fPn622/fv0cfXvAlOnpyT0ixYv6mwAAGIp5rONUk6aCsmo5VFCut+o+3LvJEAAApgxwXnLJJXoi8+abb8qyZcuaPV4VZH/rrbd0QXfVsRJwJ6mNDYbGUH8TAADDMY9tu6KKGnl9+UGZ+LclMnL2Qpnw1Pd6q+6rx9XzcA/qZ6gpRT2CACcAwKQBzttvv12ioqKkrq5OTxJfeOGFpnpGx1OP/fOf/9SdJ2trayUiIkLuuOMOR98eMI26+gZZc9AW4BzdgwAnAABGYx7bNj/syZexcxbJ7C92SEZB+QnPqfvqcfW8Og7Wd6y8Ripr6vV+lzBS1AEAxvB29AWCg4Pl3Xff1ZPC8vJyufvuu+Xhhx+WpKQk6dq1qz7m8OHDsm7dOv28usLn7e0t//3vfyU0lBqEcB87DxdLSVWthPh5y8BufO8DAGA05rGtp4KWKXPXiEpEP1Uyuv2xipo6fdzclGQ5t290B48SHSnrmG31ZnSIn/h5exk9HACAm3I4wKlMnjxZvvnmG7nxxhslOztbSktLdQrP8ez1eGJjY3Vqz8SJE53x1oBprD5gq795FvU3AQBwGcxjW06lnd/x9npbcLOZUpv6eQ/Rx6+aNUnCAnw6apjoYNTfBABYIkXd7rzzzpP9+/fLv//9b5k6daqeAPr5+emb2r/88svl1VdflX379nXopLCgoEB+8Ytf6KvsqtvlLbfcoieuZzr+N7/5jS4cHxAQIN27d5ff/va3UlRU1GFjhjWtbqy/ObqHrWMrAABwDa46j3U1H6/PlIrqumaDm3bqOHX8JxtsnedhTU31NwlwAgDMvoLTTk0Cb7vtNn1zFSq4qVKLFi5cKDU1NZKSkiK//OUvdTrSqagr9+r29NNP686a6enp8qtf/Uo/9tFHH3X4+GEN9fUNsjatMcBJgyEAAFyOK85jXYlaxfrGyrQ2nTtvRZrMHJeomzPBuis4u4VTfxMAYJEAp6vZuXOnLFiwQNauXSujRo3Sjz3//PO6zpIKYHbr1u0n5wwePFg+/vjjpvu9evWSv/zlLzJ9+nRdVF7VXQJaa2dOsU7rCvL1ksHU3wQAACZsJJN+UkOhllCLPdV5heU1EhHk2y5jg7FYwQkAsFSKuitatWqVTku3BzftdZY8PT0lNTW1xa+j0tNVivuZgptVVVVSXFx8wg2wS21MTx+VGCneXpb+sQMAABZUVlXr0PmlDp4P1w9wdiPACQAwkKUjLTk5OdK5c+cTHlNBysjISP1cSxw5ckRmz56t09rPZM6cORIWFtZ0i4+Pd2jssJbUg7YGQ6N7Un8TAACYT5CfY1lMwQ6eDzOkqBPgBAAYp8UzjZ49e+qtqp2jirCf/HhbnPxaLfXQQw/Jk08+2Wx6uqPUKsxLL71U1+J8/PHHz3jsrFmz5N577z3hXIKcsNffTD1oW8E5hvqbAAB0OFeax5pVRKCPJEQGSkZBuU47bylVdbN7ZKCEB9JF3Yoqa+rkSGm13o+LIMAJADBBgDMtzVZU/OTi4PbH26Kthcbvu+8+mTlz5hmPURPWLl26SF5e3gmPqzqaqlO6eu5MSkpK5KKLLpKQkBD53//+Jz4+Z56U2TttAifbk1ei604F+nrJkNgwo4cDAIDbcaV5rFmpr3fGuESZ/cWOVp87czwNhqzqcFGl3qp5blgAQWwAgAkCnDfddNMpJyYzZsyQjhYdHa1vzRk7dqwUFhbK+vXrJSkpST+2ePFiqa+vl9GjR5/2PLX6csqUKTpg+dlnn4m/Px0B4Xj9zaSECPGh/iYAAB3OleaxZjYtKU6e/na3VNTUSUMLlnF6eoj4+3jJ1SPjOmJ4MEDWsR/T0wliAwBMEeCcN2/eKR+fO3euuKoBAwboVZi33XabvPzyy1JTUyN33XWXXH/99U0d1LOysmTSpEny5ptvSnJysg5uXnjhhVJeXi5vv/32CQ2DVFDVy8vL4K8KZrP6gK3+JunpAAAYw4zzWFekVui9ND1JUuau0bnnZwpy2mNdL09PYmWfhdFBHQDgKlq8nEytZFS3srIyMZN33nlH+vfvr4OYl1xyiZx99tnyyiuvND2vgp67d+/WAU1lw4YNusP61q1bpXfv3tK1a9em26FDhwz8SmBGDQ0Nsqax/uboHjQYAgDACGadx7qic/tGy9yUZAnw8dL1NU+3Zk89Py8lWc7p23zWFcyLBkMAANOt4Lzyyit12oEK/KmmO3Y333yz3v7lL3/RQUBXozqmv/vuu6d9PjExUQeh7CZOnHjCfcAR+/JK5WhZtfj7eMrQuHCjhwMAgFsy6zzWlYOcq2ZNkk82ZMq8FWmSXmBbKGA3JDZU3rltjIT6s3LTXQKcseGU9AIAmCTAeaaUHzVhvP/++5kYAqdJT1f1N329qb8JAIArYR7bdirtPGV8D5k5LlE3UyytqpX9+aUyc+5a2Z9fJj6ezHvcKkWdDuoAAIO1eOZh7xBeWlranuMBLGV1U3o69TcBADAK89j2owLEEUG+Eh8ZqFd2xkUESHl1nSzalWv00NCBAc5uYQQ4AQAmCXDGxsbq7bJly9pzPIBlqFIH9g7qNBgCAMA4zGM7Ltg5dZitkednm7KNHg7aWX19g2QXVup9anACAEyToq6a9Lz66qvy8MMPy5o1a6Rv377i4/NjXZ1//etf0rlz51YP4NFHH231OYAZqPSsI6VV4uftKcPiw4weDgAAbot5bMe5fFg3eWnJflmyO1+KKmrooG5hR8qqpLquXjw9RLqEUYMTAGAsj4YWdtRRHcRHjhwpR48e1Vdn7eynH/9Ya9TV1YkVFRcXS1hYmBQVFUloaKjRw4EB3klNl//73zYZ0zNS3vvlWKOHAwCA285n3HUea8T/P/VveuE/lsrevFJ56pqh8rNR8R3yvuh4mw4VypUvrpCuYf666RQAAEbOZ1qcoh4fHy8bNmyQW2+9VXceV1e91QTGPiFU+225AVa1mvR0AABcAvPYjqP+TdUqTuXzzaSpW1nWscb6m6SnAwBcgGdrJ4evvPKK7N+/XyorK6W+vr5pcrht2zZ9v7U3wLr1N20d1GkwBACA8ZjHdhx7Hc4V+45IfkmV0cNBe3dQJ8AJADBbgBNAy6QdLZe8kirx9fKUEd3DjR4OAABAh0mMCpJhcWFS3yDy5RZWcVpVlr2DOgFOAICZmgzde++9evvQQw+dUIR97ty5+sp3XFxc+4wQMKHVjas3h3cPF38fL6OHAwCAW2Mea8wqzs2ZRfLZ5myZOb6H0cNBOwY4Y8NpMAQAMFGA89lnn9UTQFW76PiJ4eOPPy6enp4ybtw4UxefB5zJnp4+pkek0UMBAMDtMY81JsD5l692yoaMQjlUUC7xkYFGDwntlaIewQpOAIAFUtTT09MlLS1NqqurnTMiwAr1Nw/aGgyNpsEQAAAui3ls+4kJ9ZcxjXXIPydN3dIBTlLUAQCmCnAGBtquuh45cqQ9xwOYXkZBuRwuqhQfLw8Z2T3C6OEAAOD2mMca4/LhtmZDn20iwGk15dW1cqy8Ru8T4AQAmCrA2bt3b71988039Qo1AKeWesC2enNYXLgE+FJ/EwAAozGPNcbFg7voC767ckpkb26J0cNBO6zeDPH3llB/H6OHAwBAy2twXnXVVbJlyxZdjP3rr7+Wnj17io/Pj3/MUlJSJCgoqFVvrmohLVq0qHUjBlzc6oON9TdJTwcAwCUwjzVGeKCvnNMnWhbtytPNhu67sJ/RQ4KTZBVW6m0sqzcBAGYLcP7+97+Xb7/9VlatWiWHDx/WNzt1JXzt2rWtmhCqc9QWsOoKztE9aTAEAIArYB5rbJq6PcB57wV9+XeziKxj1N8EAJg0wOnv7y8//PCDfPjhh/Ldd99JVlaWVFVV6cfURCUpKanVV74Bq1FdQrMKK8Tb00OSEqi/CQCAK2Aea5zJA2LE38dT0o+Wy5bMIhkWH270kODMDuoEOAEAZgtw6oO9veWGG27QNztPT1sZz3nz5snAgQOdP0LARFYfsKWnD40Lk0DfVv14AQCAdsQ81hhBft46yPnFlsN6FScBTmuggzoAwLRNhgA0L/WgPT2d+psAAADK5cNs3dS/2JItdfU0ebKCzKYAp7/RQwEAQHN4idnBgwf1NjY21tGXAkwvtbHB0Oge1N8EAMDVMY/tGOf2i5ZQf2/JLa6SNQcLZGwvLgRbZQVnXAQrOAEAFlnBmZCQoG8q7QdwZ6r25qGCCvHy9JBRiQQ4AQBwdcxjO4aft5dcNLiL3v9sc5bRw4GD1CrcnCJbF3VS1AEArsKps7mioiL56KOPdIfKnJwcKS8vl7lz5+qJo112drYUFhbqYu89e/Z05tsDhkptrL85ODZMgv34oAQAgJkwj21fVwyPlQ/WZcpXW3Pkj5cPFl9vKmWZVV5JpdTWN+immp1DSFEHALgGp0VhXnjhBfm///s/KS0t1fcbGhp0V8qysrITjluyZIlMnz5dTwwzMzMlMpKVbrCG1AO2+ptjSE8HAMBUXHUe+5e//EW+/PJL2bRpk/j6+urgqlmN6dlJokP8JL+kSpbtzZdJA2KMHhIcTE/vEuavM5cAAHAFTrl0+thjj8ndd98tJSUlevKVlJR02mOvv/566dKli1RVVcnHH3/sjLcHXMLqxvqbagIPAADMwZXnsdXV1XLttdfKHXfcIWanAmGXDumq91U3dZhXViHp6QAACwY4169fL3/+85/1vrqirVJ61qxZc/o39PTUEzV1ZXzhwoWOvj3gElQdovSj5aIuYo9KjDB6OAAAwALz2D/+8Y9yzz33yJAhQ8QKLh9u66a+cEeuVFTXGT0ctFHWMdsKzlgCnAAAKwU4VUqPmuSNHTtW3nzzTQkLC2v2HHWssnXrVkffHnCp7umDuoVJiL+P0cMBAABuOI9VK0uLi4tPuLmSEfHhEh8ZIOXVdfLdzlyjhwMHU9QJcAIALBXgXLp0qa5RdNddd7X4nMTERL3NyqKLIqxhdWODoTE9qb8JAIBZWG0eO2fOHB2ktd/i4+PFlah/66lDbas4SVM3f4CTFHUAgKUCnIcPH9bbfv36tfgcVZjdfpUZsFKDodE9qL8JAIBZGDGPfeihh3Sg70y3Xbt2tem1Z82apbvB22+HDh0SV01T/2F3vhRV1Bg9HLRBVlOAkw7qAAALdVFXxdjVBK81XR1zc20pKeHh4Y6+PWC4vOJKOXCkTDw8RM6igzoAAKZhxDz2vvvuk5kzZ57xmJ49e7bptf38/PTNlfXvEip9Y4JlT26pfLMtR352lmutMkXLA5xxEazgBABYaAVn9+7d9Xbv3r0tPmfx4sWtvloOuKrVB22rNwd2DZWwAOpvAgBgFkbMY6Ojo6V///5nvKnAq5VdPow0dbMqrqyRkspavd81jAAnAMBCAc5Jkybp4uwvv/xyi45X9YpeeeUVnX5z4YUXOvr2gOFSG+tvkp4OAIC5uPo8NiMjQzZt2qS3dXV1el/dSktLxcymNgY4V+4/InkllUYPB22ovxke6CNBfg4nAwIA4DoBTlWU3cfHRzZv3iyzZ88+47G7d++Wiy66SNcECgwMlNtvv93RtwcMR4MhAADMydXnsY8++qiMGDFCHnvsMR3UVPvqtm7dOjGzhE5BMiw+XOobRL7aYquDCnOggzoAwLIBzl69eslf/vIXffX78ccflzFjxshTTz3V9PyHH34of/3rX+XSSy+VwYMHy44dO/RV72effVan6ABmll9SJfvzbfU3k6m/CQCAqbj6PHbevHl6bCffJk6cKFZJU/+UNHVTySq0rbilgzoAwNU4Ja/g/vvv15OtP/zhD7JmzRpZu3atnvwpf/rTn5qOU8d4eXnJ008/Lbfccosz3how1JrG+pv9YkIkPNDa9bIAALAi5rHGmDq0q/z5yx2yMaNQDhWUS3xkoNFDQgtkHWMFJwDAois47R544AFdEyglJUWioqJ+cqU5NDRUbrjhBtm4caPcfffdznpbwEXS06m/CQCAWTGP7XidQ/1lbOP8iWZD5kGKOgDAVTm1MvSAAQPktdde0/uqGHpeXp4uiN6pUyfp2bOneHo6LZ4KuITUg9TfBADACpjHGpOmvnL/Ufl8c7bceV5vo4eDVgQ4SVEHALiadmt91717d30DrOpoaZXsybV1MU2mgzoAAJbBPLZjXDy4qzzy6TbZlVMie3JLpG9MiNFDQjOymgKc/kYPBQCAE3ApGnBC/c3IIOpvAgAAtEZYoI+c29fWrOmzTaSpu7qaunrJLbY1GYqNYAUnAMDiKzjXr18v3333nWzbtk0KCmwBoMjISN15cvLkyZKUlOTstwQMkdoY4BxNejoAAJbAPLbjTR3WTb7bmafrcN53Yd+mBk9wPSq4Wd8g4uvlKVFBfkYPBwCA9glwbt26VX75y1/q7pOn8/DDD8vo0aPl3//+twwZMsRZbw0YggZDAABYA/NY41wwMEYCfLwko6BcNmcWyfD4cKOHhGY6qHcN9xdPTwLRAAALpqirK93Jycl6UmjvNunt7S0xMTH6pvbtj69evVofu2jRIme8NWCIY2XVul6UktyDFZwAAJgV81hjBfp6y+SBMXqfNHXXll1EB3UAgIUDnEeOHJFrr71WqqqqdErJrbfeKqmpqVJWVibZ2dn6Vl5erieNt912m3h5eelj1TlHj9pWwAFmsybNlrbWu3OwRAWTogMAgBkxj3WdburKF1uypU7lQMMlZRfa6m/SQR0AYMkA53PPPSdFRUXi6+srX375pbzyyity1lln6avddmoyOGrUKJ3So47x8fHR56hzATNRqzcKyqpl4Y4cfX90jwijhwQAANqIeaxrOKdvlIT6e0teSZWkHiRw7KoyG1PUCXACACwZ4FQTPXXF+6677pIpU6Y0e/yFF14ov/nNb3SgSJ0LmEFRRY28vvygTPzbEhk5e6F8tD5LP/7N9lz9uHoeAACYC/NY1+Dn7SUXD+6q90lTd13ZhbYAZxwBTgCAFQOcBw8e1NvLL7+8xefYjz1w4ICjbw+0ux/25MvYOYtk9hc7dAH84x0trdaPq+fVcQAAwDyYx7qOK4bb0tS/3pYj1bX1Rg8HZwhwsoITAGDJAGdlpa0WS1BQUIvPsR+rahgBrkwFLVPmrpGKmjpRFaFOrgplf0w9r44jyAkAgHkwj3Udo3t2ks4hfjorZinzKZejVi1nNQY4YyMIcAIALBjg7NKli95u3LixxefYj1WdKQFXpSbYd7y93hbEbKbevXpeHaKOJ10dAABzYB7rOrw8PeTSoY1p6ptJU3c1an5bXl2n97uG+Rs9HAAAnB/gnDBhgr6i98QTT0hxcXGzx5eUlMiTTz6p6x2pcwFX9fH6TKmorms2uGmnjlPHf7Ihs72HBgAAnIB5rGt2U1+4I1fKq2uNHg6OY1+9GRXsK/4+XkYPBwAA5wc4b7/99qYaRuecc46sW7futMeq584991zZv3//CecCrkZ92HljZVqbzp23Ik2fDwAAXBvzWNcyPD5cukcG6tI/3+3MM3o4OE5WYwf1WOpvAgBclLejLzB+/Hj59a9/Lf/6179k69atMnr0aBk0aJDedu7cWV/hzs3NldTUVNm+fXvTeeocdS7gio6V10j6SQ2FWkKFNdV5heU1EhHk2y5jAwAAzsE81rWof++pw7rKi9/vl083ZsnZvaOkrKpWgvy8JSLQRz8PY9BgCABg+QCn8vzzz0tgYKD8/e9/l/r6etm2bdsJk0DFvqLN09NT7r//fp0KBLgqNZl2RGlVLQFOAABMgHmsazm/X4wOcC7alScjZy9sejwhMlBmjEuUaUlxEhbgY+gY3VF2ka0hFwFOAIBlU9QVdTX1qaeekk2bNskdd9whffr00RPB42/qMfWcOsZeuwhwVWqlgCOCHTwfAAB0DOaxruOHPfly4+upp3wuo6BcZn+xQ8bOWaSPQ8ciRR0A4OqcGoUZPHiwvPjii3q/urpajh07pvcjIiLE15fVbDAPlQalVgqoyXRrqmmqjzuqdlR4ICsLAAAwE+axxlJBy5S5a04777I/rupzquPmpiTLuX2jO3CE7s3eZIgVnAAAS6/gPBU1EYyJidE3JoUwG7UyQ6VBtcXM8Yms7AAAwMSYx3asoooauePt9TqI2VyfRvW8OkQdr85Dx9bgZAUnAMAyAc6vv/5aRo4cqW/vvvtuq85Vx9vP/e6771r71kCHUjWeAny9pKWxSk8P0cdfPTKuvYcGAADagHmsa/p4faZUVNc1G9y0U8ep4z/ZkNneQ4OIVNXWSV5Jld6PjSDACQCwQIBT1SC65557ZPPmzRIdHS0///nPW/VmN9xwg0RFRen6Rffdd19rxwp0KFXA/qXpSS061h4EfXl6EoXvAQBwQcxjXff/yxsr09p07rwVaU0NoNB+chobDPn7eOoyTgAAmD7AuXjxYtmzZ4/uIPmPf/yj1W+m0nafffZZ8fLy0h0qf/jhh1a/BtCRRveIlIjjApYnL+b0aLwF+HjJvJRkOYdaUAAAuCTmsa7pWHmNpLey5rmijlfnFZaTpt6R9TcpwwQAsESA8+OPP9bbCy64QAYOHNimN1TnTZkyRe9/9NFHbXoNoKO8m5ohBeU1EhPqJw9f0l83EDqeuv/o1IGy+uFJBDcBAHBhzGNdU1lVrUPnlzp4PppHB3UAgOW6qK9Zs0ZftZs6dapDb3rZZZfJV199JatXr3bodYD2VF5dK/9ask/v3z2pr/x8dHe5bUJPvVJATaaD/bx1t3SuZAMA4PqYx7qmIL9WfRz5CTUfQ/vKLrSlqBPgBAC4slbNCNLT0/W2X79+Dr1p37599TYtrW31doCOMHdFmhwprZaEToFy7Shb4yD1wSgiyFffAACAeTCPdU2qpmNCZKBktDJN3aMxk0ZdbEbHdFBXKeoAAFgiRb2oqEhvIyMjHXpT+/nFxcUOvQ7QXooqauTfP+zX+7+b3Ed8vFr1owIAAFwM81jXpC4ezxiX2KZzZ45PJJOmA2twsoITAODKWhW1CQ0N1dvCwkKH3tR+fkhIiEOvA7SX/yw7IMWVtdKnc7BcPizW6OEAAAAHMY91XdOS4iTA10taGqv09BB9/NUjbRk2aF+s4AQAWC7AGR1ta6KyY8cOh950586detu5c2eHXgdoD0dLq+T15Qf1/n0X9hUvNYsGAACmxjzWdYUF+MhL05N02nlzQU770y9PT9LnoX01NDSwghMAYL0AZ3Jysv4j9/nnnzv0pp9++qlOJznrrLMceh2gPbz8w34pq66TwbGhMmVQF6OHAwAAnIB5rGs7t2+0zE1JlgAfL1ug8zTHqQvP81KS5Zy+toA12tfRsmqpqq3XgecuYf5GDwcAAOcEOC+++GK9/fbbb2X58uXSFkuXLtXnH/96gKvILa6UN1fZmhDcd2E/6joBAGARzGPNEeRcNWuSPDp1oG4gdLzoED+99fXykCGxYQaN0H3T0zuH+ImvNzXpAQCuq1V/paZNmyaJiYn66ve1114re/fubdWb7dmzR372s5/poJF6nWuuuaa14wXa1fOL9+qr1KMSImQiKwMAALAM5rHmoNLOU8b3kCUPTJSNj1wgyx48T29TZ50vA7qGSnlNvcxdYSslhPZH/U0AgCUDnD4+PvL000/r/by8PElKSpLnnntOysrKznheaWmpPPvsszJq1Ch9nvLMM8+It7e3I2MHnOpQQbm8v/aQ3r9/Cqs3AQCwEuax5qLmYRFBvhIfGai3np6e8pvze+vn5q5Mk+LKGqOH6BYyj1F/EwBgDq2emV199dXyxz/+UR577DE9Ibz33nvlkUcekQkTJuiJoiq4HhQUpJ/Lzc2VDRs2yLJly/R9dcVcUedfeeWV7fH1AG323KK9UlPXIGf3jpIxPTsZPRwAAOBkzGPN7aJBXaRP52DZm1cqb65Mk7vO72P0kCwvu7BSbwlwAgBcXZsuPauJYFxcnPzmN7+R8vJyfWV7wYIF+nYq9glhYGCgvPDCCzJz5kzHRg042b68UvlkQ2bT6k0AAGBNzGPNy9PTQ+46v7fc/d4meW35QZ3KHuTHStr2RIo6AMAs2lwpOiUlRdciUle+o6Ki9OTvdDf1/H333aePZ1IIV/SP7/ZIfYPI5AExMjw+3OjhAACAdsQ81rwuHdJVEjsFyrHyGnkn1dYYEu0nqzHAyQpOAICr82iwX5Z20Pbt22Xz5s1y9OhRKSkpkZCQEOnUqZMMGzZMBg0aJO6muLhYwsLCpKioSEJDQ40eDs5gR3axXPLPZaJKbn712wm6gD0AAHCf+YxV57FW/f/3wbpD8uBHWyQq2E+W//488ffxMnpIlpU0e6EcLavWc+SB3azzPQQAsN58xmk5HWryZ+YJINzX3xfu1tvLhnYjuAkAgBtiHmsuV42Ilee+26tXF/53TYZOVYfzVdbU6eCmwgpOAIBlU9QBK9iQcUy+25knnh4iv5tMoXoAAABX5+PlKXdM7KX3//3DAamqrTN6SJZOTw/285bQAGqdAgBcGwFOuLVnvrWt3pw2Mk56RQcbPRwAAAC0wLWj4qRLqL/kFFfKR+ttjSLRXg2G/MVD1XICAMCFEeCE21q5/4is2HdUfLw85LeTWL0JAABgFn7eXnL7uT31/ktL9ktNXb3RQ7IcOqgDAMyEACfckuqt9cy3e/T+DcndJT4y0OghAQAAoBWuP6u7RAX7SuaxCpm/Mcvo4VhO1jE6qAMAzIMAJ9zSkt35sj79mPh5e8qd5/U2ejgAAABopQBfL7ltgm0V57+W7Je6+gajh2QpWYWVessKTgCAGRDghNupr2+Qpxtrb84Ylygxof5GDwkAAABt8IsxCRIe6CMHj5TJF1uyjR6OJVPUWcEJADADApxwOwu258j27GLdEfJX59o6cAIAAMB81HzulvE99P6L3+/TF7Lh3C7qsREEOAEAro8AJ9yKSl36+0Jb7c2bz+4hkUG+Rg8JAAAADpgxPlFC/L1lT26pfLM9x+jhWIIKFB8uoskQAMA8CHDCrXy6KUv25ZVKWICP3DrBdrUfAAAA5hXq7yMzxyXq/ecX79PNJOGYI6VVUlPXIF6eHhIT4mf0cAAAaBYBTriNmrp6efa7vXr/9nN76skwAAAAzO/m8T0kyNdLdhwulsW78owejullNqandwn1F28vPjICAFwff63gNj5Yd0gyCsolKtiv6So/AAAAzC8iyFemj03Q+6zidF6DoW7hNOMEAJgDAU64hcqaOnl+0T69f+d5vSTQ19voIQEAAMCJbj27p/j7eMqmQ4WyfN8Ro4djkQAn9TcBAOZAgBNu4Z3UDMkprpRuYf7y89HdjR4OAAAAnCw6xE9uSLbN8+wXttE2WccaO6gT4AQAmAQBTlheWVWt/Ot72yT3t5P6iJ+3l9FDAgAAQDu4/Zxe4uvlKWvSCiT1wFGjh2NaWYWVessKTgCAWRDghOXNW5kmR8uqJaFToExLijN6OAAAAGgnXcL85dpRcU21OOFYijorOAEAZkGAE5ZWVFEj//5hv96/Z3Jf8aELJAAAgKXdMbGXeHt66DqcGzKOGT0cU8qyBzgjCHACAMyBaA8s7T/LDkhxZa30jQmWqcO6GT0cAAAAtLO4iEC5emSs3n+BVZytVlpVqxcJKF3D6KIOADAHApywrKOlVfL68oN6/94L+omXp4fRQwIAAEAH+PXE3qKmfot35cm2rCKjh2MqhxtXb4b6e0uIv4/RwwEAoEUIcMKyXlqyX8qq62RIbJhMGRRj9HAAAADQQRKjguTyxuwdVnG2TmZTenqg0UMBAKDFCHDCknKKKuXN1el6/74L+4qHB6s3AQAA3Mmd5/UWNQVcsD1HdueUGD0cEzYYIj0dAGAeBDhhSS98v1eqa+vlrMQIObdvtNHDAQAAQAfrExMiFw/uovdf+J5VnK0NcHajgzoAwEQIcMJyDhWUy3trDun9+y/sx+pNAAAAN3XXeX309ost2bI/v9To4ZhC1jH7Ck4CnAAA8yDACct59ru9UlvfIBP6RMnonp2MHg4AAAAMMrBbqEwe0FkaGkT+9f1+o4djCtmFlXrLCk4AgJkQ4ISl7Msrkf9tzNT7913Yz+jhAAAAwGC/Od+2inP+pizJOFomBWXVOuNHbRtU5BMnyCJFHQBgQt5GDwBwpn8s3Cv1DSIXDIyR4fHhRg8HAAAABhsWHy7jenWSlfuPyiX/XC6lVbVNzyVEBsqMcYkyLSlOwgJ8xN3V1tVLTrFtBWdcBAFOAIB5sIITlrE9u0i+3HpYd8tUndMBAACAH/bky/r0Y3r/+OCmklFQLrO/2CFj5yzSx7m7vJIqqatvEB8vD4kO9jN6OAAAtBgBTljG37/do7dTh3aT/l1CjR4OAAAADKaClilz10h1Xf0pn29ovFXU1Onj3D3Iae+g3iXMXzw9adQJADAPApywhA0Zx2TRrjzx8vSQ30221VkCAACA+yqqqJE73l5vC2I2U2pTPa8OUcer89y9/iYd1AEAZkOAE5bw9De79XbayFjpGR1s9HAAAABgsI/XZ0pFdV2zwU07dZw6/pMNtoaV7ogGQwAAsyLACdNbue+ILhqvagX9dhKrNwEAANyd6o7+xsq0Np07b0Wa23ZXt6eos4ITAGA2BDhhamry+fS3ttWbP0/uLnERgUYPCQAAAAY7Vl4j6QXlOu28NdTx6rzCcvdMU886RoATAGBOBDhhat/vzpMNGYXi7+Mpd57X2+jhAAAAwAWUndQtvbVO7rbuLrILK/WWFHUAgNkQ4IRp1dc3yNPf2DqnzxibKJ1D/Y0eEgAAAFxAkJ+3Q+cHO3i+2VPUCXACAMyGACdMa8H2HNlxuFhPQH91bi+jhwMAAAAXERHoIwmRgeLRyvPU8eq88EAfcTeqe3xJ48pVUtQBAGZDgBOmVFffIH9faFu9ecvZPSQiyNfoIQEAAMBFeHh4yIxxiW06d+b4RH2+u67ejAzylQBfL6OHAwBAqxDghCnN35gl+/JK9dX1Wyb0MHo4AAAAcDHTkuJ0oK6lsUpPD9HHXz0yTtw7PZ2yTwAA8yHACdOprq2XZxfZVm/efk4vCfV3vxQiAAAAnFlYgI+8ND1Jp503F+S0P//y9CR9njvKagxwkp4OADAjywc4CwoK5Be/+IWEhoZKeHi43HLLLVJaWtqicxsaGuTiiy/WKSrz589v97GiZT5Yd0gOFVRIVLCfzBiXYPRwAAAA4KLO7Rstc1OSJcDHyxboPM1x6vl5KclyTt9ocVf2ACcNhgAAZmT5AKcKbm7fvl0WLlwoX3zxhSxdulR++ctftujcZ5991i3r77iyypo6eX7xXr1/13m9JNDXPTtcAgAAoOVBzlWzJsmjUwdK98jAnzw/rlcnWf3wJLcObirZhZV6ywpOAIAZWTo6tHPnTlmwYIGsXbtWRo0apR97/vnn5ZJLLpGnn35aunXrdtpzN23aJM8884ysW7dOunbt2oGjxpm8vTpdcour9MTrhtHdjR4OAAAATEClnaeM7yEzxyVKYXmNlFbVyrK9+fLw/7ZJSWUtJY/UCs5j5XpLgBMAYEaWXsG5atUqnZZuD24qkydPFk9PT0lNTT3teeXl5fLzn/9cXnzxRenSpUuL3quqqkqKi4tPuMG5yqpq5aUl+/X+byf1Fj9vujsCAACg5VR2VkSQr8RHBsrkATH6sW3ZRVJUXiPuzr6CkxR1AIAZWTrAmZOTI507dz7hMW9vb4mMjNTPnc4999wj48aNkyuuuKLF7zVnzhwJCwtrusXHxzs0dvzU3BUH5WhZtSR2CnTb7pYAAABwjs6h/tK7c7A0NIisPnhU3FlNXb3klhDgBACYlykDnA899JC++nqm265du9r02p999pksXrxY199sjVmzZklRUVHT7dChQ216f5yauqr+76UH9P49F/QVHy9TfusCAADAhaj6m8rKfUfEneUUVepAr6+3p0QF+xo9HAAA3KMG53333SczZ8484zE9e/bU6eV5eXknPF5bW6s7q58u9VwFN/fv369T2483bdo0mTBhgixZsuSU5/n5+ekb2seryw7o+kj9YkJk6tDT104FAAAAWhPgfHNVuqzc794rOO0d1FX9TZqsAgDMyJQBzujoaH1rztixY6WwsFDWr18vSUlJTQHM+vp6GT169GlXh956660nPDZkyBD5xz/+IVOnTnXSV4DWOFJaJa+vOKj3772wr3h6MukCAACA48b07CQqnrc3r1TySiqlc4i/uKOsY7YAZ7dw9/z6AQDmZ+k83wEDBshFF10kt912m6xZs0ZWrFghd911l1x//fVNHdSzsrKkf//++nlFrewcPHjwCTele/fu0qNHD0O/HnelGguVV9fJ0LgwuXCgrRg8AAAA4KjwQF8Z1C1U769y41Wc2cet4AQAwIwsHeBU3nnnHR3AnDRpklxyySVy9tlnyyuvvNL0fE1NjezevVt3Todr1gN6a3W63r/vwn6kzAAAAMCpxvWK0lu3DnAW2VdwEuAEAJiTKVPUW0N1TH/33XdP+3xiYqI0qIraZ9Dc82g/zy/eK9W19ZKcGCnn9LFNPgEAAABnGdurk7yy9IBb1+HMbEpRJ8AJADAny6/ghHllHC2X99fautHfP4XVmwAAAHC+sxIjxdvTQzIKyuVQQblbp6jHEeAEAJgUAU64rGcX7ZHa+gY5p2+0JPeINHo4AAAAsKBgP28ZFh/utmnqKlstu7BS77OCEwBgVgQ44ZL25ZXI/I1Zev++C/oaPRwAAABY2LhenfR25f4j4m6OlddIRU2d3u8SRhd1AIA5EeCES/rHwr1S3yC6a7r9ijoAAADQXnU4FVWH093q79vT06ND/MTfx8vo4QAA0CYEOOFytmUVyZdbD4squak6pwMAAADtaWT3CPHz9pS8kirZn18m7iSrMcBJejoAwMwIcMLl/H3hHr29fFg36dclxOjhAAAAwOLUysVRiRF6f5WbpalnNXZQjw0nPR0AYF4EOOFS1qcfk8W78sTL00N+N5namwAAAOgY43pFNaWpu2OKeiwrOAEAJkaAEy7lmW936+01I+OkR1SQ0cMBAACAm9XhXHXgqNSrYvBuIruIFHUAgPkR4ITLWLnviL5i7uvlKb+d3Mfo4QAAAMCNDI0Nk2A/byksr5Edh4vF3VLUCXACAMyMACdcgupW+bfG1Zs/H92dFBkAAAB0qP9v707goyqvxo+f7BtJIBAgrIEAArKIuCWoKCgFFOpWq3UDUfvyr2317aK8tWpr3xf7qa1trbW2FrB1qQtWBaWKVrQSBAQERFkS9jUbJCEJWef/OU9yxwlkJzP33pnf9/OZz1xm7k0eMpPkyXmec05kRLicNyjFHK8KgTR1nX8XlVXJ3qKGAGcyNTgBAO5FgBOOoHU3N+w9JrFR4fL/Ls2wezgAAAAIQVkNaerZQdxoqLiiWhZ8vEsu+dUKOfuR5XK0vMo8Pvf59eZxfR4AALeJtHsAgNY4euzd+s7pt2WlS89EVo8BAABgXx3ONbuKpLq2TqIigms/yIfb82Xuc+ukoqq2yVT1R5Z+IY+9u02eunm8TByWassYAQDoiOD6jQ1XpcPsKyo3929vPiRfHiqRxJhI+a+L2b0JAAAAe4zonSTd4qOkrKpWNu0vlmALbs5euEYqqmtFWyid3EbJekyf1/P0fAAA3IIdnAgYTXdZvG6/PJu9W/YUlXsfjwwPM/c3XTBAuiVE2zhCAAAAhLLw8DCzi/PtzYdlVW6BjB/YTYJlHq47N00Qs5UG8eb5MDHnr5o3WZLjogI1TAAAOowdnAgIXQHOnP++SXvZ6xPcVDV19bOsZ7P3sFIMAAAAW2Vm9DD3K3OCp9GQbjLQtPTWgpsWPU/Pf239fn8PDQCATkGAE7anw1hO1JAOAwAAAGc0Glq396icqD61VqUby0NpBlVHLFq521wPAIDTEeCEo9Jh9BQ9n+6NAAAAsMPgHgnSKylGqmrqZP2eo+J2R8urTXmo9oYp9Xy97lg583IAgPMR4IRfkQ4DAAAANwkLC5OshjT17Fz3p6mXVdac1vXHT/N6AAACgQAn/IZ0GAAAALiRNhpS2bkF4nYJMafXV7bLaV4PAEAgEOCE35AOAwAAADfX4dy4v9j1Oxi7xUfJwJR4bYzeLnq+Xtc1ni7qAADnI8AJvyEdBgAAAG7Ur1u8DOweL7V1Hlm7q0jcnnJ/W1Z6h66dNSHdXA8AgNMR4ITfkA4DAAAAt+/iXJnj/jT1a8f3k7joCGlrrDI8TMz515zdz99DAwCgUxDghN+QDgMAAAC3ygyiRkPJcVHy1M3jzTy7tSCn9fyfbh5vrgMAwA0IcMJvSIcBAACAW2UOrt/B+cWhEjlaViVuN3FYqiycfZ7ERUU0uQHBBD9FzPOLZp8nFw9LtWGUAAB0DAFO+BXpMAAAAHCj1MQYGdarizn+ZKf7d3FaQc5V8ybLvOnDT3luQEq8PDhjpHzyP5MJbgIAXIcihwhIOszshWvMkrCnhZbqpMMAAADASbIyesj2I8dNmvq00WkSDHSefeEQDWBulcTYSHn7exdKl5goUx6KDCoAgFuxgxMBS4eJimj67UY6DAAAAJwos6HRUHau+xsN+crJP27uh/VKlP4pCdItIZrgJgDA1djBiYAFObUT5Ypt+ZIUFyklFTWN0mG05qamsyfFsnMTAAAAznDBoO6mhFJufpkcKTkhvZJiJRjk5NUHOIf2rE/BBwDA7QhwIiCKK6olO6e+dtHLd2WayeHxyhrpEhNJOgwAAAAcKTk+Skb1TZZN+4vNLs6rxwVHnficvFJzP4QAJwAgSJCijoB4Z8thqaqtkzN6JcrwtCSTBtM/JZ50GAAAALgjTb1hsT6YdnBmEOAEAAQJApwIiCUbD5r7GWODozg7AAAAQqfRkNJGQ56WOma6RE1tnewqKDPHpKgDAIIFAU74XcHxSjMhVFeO6WP3cAAAAIA2Oze9m0SGh8mBYxWyr6hC3G5PUblU13pMg88+yXF2DwcAgE5BgBN+t2zzIamt88jYfsmS3iPB7uEAAAAAbRYfHSnjBnQNmm7qVnq61t8M1w5KAAAEAQKc8LslGw+Z+xlj2b0JAAAA98n0SVMPpgAnAADBggAn/OpQcYWs2V0k2kfoijHU3wQAAID7ZFmNhoKgDicBTgBAMCLACb9a2rB789z0FEmjxg8AAABcSFPUY6PCTW35HQ0BQrciwAkACEYEOOFXSzZZ3dNJTwcAAIA7xURGmAV7lZ3j3jqcdXUeApwAgKBEgBN+s7ugTDbtL5aI8DCZPqq33cMBAAAAOizTJ03drQ4WV0hFda1ERYTJwJR4u4cDAECnIcAJv1mysX735oQhPaR7lxi7hwMAAOAau3fvljlz5sigQYMkLi5OMjIy5KGHHpKqqiq7hxayshoaDX2ys1Bq69xZh9PavTmoR4JERvCnIAAgeETaPQCEQHo6zYUAAADaZevWrVJXVydPP/20DBkyRD7//HO58847paysTB577DG7hxeSRvVJksSYSCk5USNfHCyR0f2SxW1ITwcABCsCnPCLbYdLZfuR4xIdES5TziQ9HQAAoD2mTp1qbpbBgwfLtm3b5KmnniLAaRPd8Xj+4BR578s8yc4tcHeAM5UAJwAguJCXAL94c+MBc3/JGamSHBdl93AAAABcr7i4WFJS6hvdNKWyslJKSkoa3dC5MhvS1Fe6tA6nN8DZK9HuoQAA0KkIcKLTeTweWbLxkDmmezoAAMDpy8nJkSeeeEK+/e1vN3vO/PnzJTk52Xvr379/QMcYCrIaGg2t3VUkVTV14rY5+g52cAIAghQBTnQ67Zy+t6hc4qIiZPKInnYPBwAAwDHuv/9+CQsLa/Gm9Td9HThwwKSrf+Mb3zB1OJszb948s8vTuu3bty8A/6PQckavROmeEG06kW/cf0zcpOB4lRRXVEtYmMjg1AS7hwMAQKeiBif81j39spG9JD6atxgAAIDlBz/4gcyaNavFc7TepuXgwYNy6aWXSlZWlvz5z39u8bqYmBhzg/+Eh4fJBRnd5a1NhyQ7p1DOTW++ZIBT09MHpMRLbFSE3cMBAKBTEX1Cp6qr88jSTfXp6TNJTwcAAGgkNTXV3NpCd25qcHP8+PGycOFCCQ8n+copaeomwJlbIN+/bKi4RU5eqbknPR0AEIwIcKJTrd1dJIdLTkhibKRcPKy+CDsAAADaR4Obl1xyiQwcONB0Tc/Pz/c+17t3b1vHFuqyGhoNbdh7TCqqaiUuOsJlDYYIcAIAgg8BTnSqJZvq09OnntlbYiLdMdkDAABwmuXLl5vGQnrr16/fKc1iYJ/07vGSlhwrh4pPyLo9R+XCoe5Y1KfBEAAgmJHngk5TXVsnb28+bI5nnkV6OgAAQEdpnU4NZDZ1g720EVRmQzf1lbkF4hbeHZw9CXACAIIPAU50muzcQikqqzKdJTMH10/6AAAAgGBNU9f5rxto9/S80kpzTIATABCMCHCi07unTx+dJpERvLUAAAAQvI2G1Ob9x6TkRLW4Zfdm76RYSYyNsns4AAB0OqJQ6BQnqmvlnc9JTwcAAEDw69M1Tgb1SJA6j8ianUXidLmkpwMAghwBTnSKD7fnS2lljSm4Pn5AN7uHAwAAAPiVVYfTDWnqOfkEOAEAwY0AJzo1Pf3KMWkSHh5m93AAAACAgKSpZ7ug0dCOI6XmngAnACBYEeDEaSurrJH3vjxijmeO7Wv3cAAAAAC/u6ChqebWw6VSeLy+gY9TsYMTABDsCHDitGlw80R1naR3j5dRfZPsHg4AAADgdz26xMjw3onmeNVO56apV1TVyv6jFeZ4KAFOAECQIsCJ07Zk4yFzP2NsHwkLIz0dAAAAocENdThz84+LxyPSLT5KuneJsXs4AAD4BQFOnJbi8mr5cHueOZ45lu7pAAAACB1ZGT3M/SqHBzgV6ekAgGBGgBOn5Z0th6W61mPSc4b2qk/RAQAAAELBeYNSRPtr7iook4PH6tPAnSYnzwpwMlcHAAQvApw4LUs2HfSmpwMAAAChJDkuSkb36+roXZw7jrCDEwAQ/AhwosPySytlZU6BOZ4xhgAnAAAAQk+Ww+twWh3UaTAEAAhmBDjRYcs+PyR1HpGx/bvKgO7xdg8HAAAAsC/AmZMvhccrZV9RuRSVVYlHO/vYrLq2TnYXlJljdnACAIJZpN0DgHst2diQnj4mze6hAAAAALYY1jPR1OE8VFIp43/xnvfxgSnxcltWulw7vp9JZbfDnsIyqanzSEJ0hKQlx9oyBgAAAoEdnOiQA8cqZO3uoxIWJnIl6ekAAAAIQR9uz5dLf73CZDWdbG9RuTyy9AvJnP++Oc/eBkNdJEwn7gAABCkCnOiQtxqaC52XniK9WQ0GAABAiNGg5eyFa6SiurbJ5z0NN31ez7MjyGk1GMogPR0AEOQIcKJDlmw8ZO7png4AAIBQU1xRLXOfW1cfxGyl1KY+r6fo+XqdHQ2GqL8JAAh2BDjRbrsKymTzgWKJCA+T6aOpvwkAAIDQsnjdfqmoqm01uGnR8/T819bvFztS1If2TAzo5wUAINAIcKLDzYUuHNJDUhKi7R4OAAAAEDDaHf3Z7N0dunbRyt0B665eV+eRXHZwAgBCBAFOtItOyN60uqeTng4AAIAQc7S8WvYUlZu08/bQ8/W6Y+XVAWsKeqK6TqIjwqV/t7iAfE4AAOxCgBPtsvVwqUl1iY4Mlyln9rJ7OAAAAEBAlVXWnNb1x0/z+vampw9OTZDICP7sAwAEN37ToUPp6ZeekSpJsVF2DwcAAAAIqISYyNO6vstpXt9WO/JKzT0d1AEAoYAAJ9qVnr5kE+npAAAACF3d4qNkYEq8hLXzOj1fr+saHxXQHZxDUglwAgCCHwFOtNln+47JvqIKiY+OkMnDSU8HAABA6AkLC5PbstI7dO2sCenm+kDYYXVQ70WAEwAQ/Ahwos2WbDxk7i8f2UvioiPsHg4AAABgi2vH9zPz4bbGKsPDxJx/zdn9JFCZV94dnKSoAwBCAAFOtEltnUeWWunpY0hPBwAAQOhKjouSp24eb9LOWwtyWs//6ebx5rpAyC+tlNITNSawOqhHQkA+JwAAdiLAiTZZs6tI8korJSk2Ui4a1sPu4QAAAAC2mjgsVRbOPk/ioiLqA51NnKOP6fOLZp8nFw9LDdjYrPT0gd0TJCaSzCsAQPALTAs/uJ7VXGjaqDQmSQAAAEBDkHPVvMny2vr9smjlbtlTVO59LiYyXO6fNtyksyfFBmbnpsVKT8+gwRAAIEQQ4ESrqmvrZNnm+vqbdE8HAAAAvqJp57MnDJJZWelyrLxaduSVyvVPfyI1tXXyjXP6S5eYwP/JZQU4aTAEAAgVpKijVStzCuRoebX06BItFwxOsXs4AAAAgONod/RuCdFy3qDu0j8lTmo9Imt3FdkyFg2yqiHs4AQAhAgCnGjVmxvr09OvGJ0mkRG8ZQAAAICWZA3u4d0oYIecvDJzTwd1AECoIFqFFp2orpV3txwxx6SnAwAAAK3LGtLd3GfnFgb8cx8rr5KC45XmOIMAJwAgRBDgRItWbMuX45U10ic5Vs4e0M3u4QAAAACOl5lRH+D84lCJHC2rsqX+ps7f7aj/CQCAHQhwokVLGtLTdfdmeHiY3cMBAAAAHK9nYqwMa2jws2pnoT0d1Nm9CQAIIQQ40Szdufn+VtLTAQAAgPbKyqivw5mdW2BPB/WeiQH9vAAA2IkAJ5r1/pdH5ER1nQzqkSBn9kmyezgAAACAa2Q1pKln5wR2B+eOhgAnDYYAAKGEACea9eZnX6Wnh4WRng4AAAC01fmDu4tWeNpZUCaHiisCvoOTACcAIJQQ4ESz3Rc/2pFvjmeMSbN7OAAAAICrJMdFyei+yQHdxVlWWSMHjtUHU4cS4AQAhBACnGjSO1sOS3WtR4b3TpShvajfAwAAALRXprcOZ2ACnDvzy8x994Ro6ZYQHZDPCQCAExDgRJPebOiePvMsmgsBAAAAHTFhSEMdztwC8Xg8fv98Ofml5p70dABAqCHAiVPklZ6QVQ2rzDPGEOAEAAAAOuKcgSkSHREuh4pPyO7Ccr9/vh1HqL8JAAhNBDhximWbD0udR+Ss/l2lf0q83cMBAAAAXCkuOkLGDehqjlfmFPj989FgCAAQqghwovn09LHs3gQAAABOR1ZDHU4rQ8qfcvLrA5xDe1JDHwAQWghwopH9R8tl3Z6jEhYmcgXd0wEAAIDTktVQh3PVzkKp0zQpP6mqqZM9DWnw7OAEAIQaApxo5K1Nh8z9+YNSpFdSrN3DAQAAAFxtbL+uEh8dIUVlVbL1cH0TIH/YXVgmtXUe6RITKb2SYvz2eQAAcCICnGgmPb2v3UMBAAAAXC86MlzOTU/xdlMPRP3NME3HAgAghBDghFdu/nHZcrBEIsPDZOqo3nYPBwAAAAgKExrS1LP9WIeTDuoAgFBGgBNeSzfWp6dfOLSHpCRE2z0cAAAAIKgaDa3eWSjVtXV+bTBEgBMAEIoIcMLweDzy5sYD5njGGLqnAwAAAJ1lZFqSJMdFSVlVrWzaX+yXz7HjSH19z6EEOAEAIYgAJ4wvD5VKbn6ZqRE05cxedg8HAAAACBrh4WGSObihm7of6nBqc6GdBWXmmB2cAIBQRIATxpJN9c2FJp3RUxJjo+weDgAAABCUdThX5nR+Hc79R8ulqqbObFbo1y2+0z8+AABOR4ATJj19SUP39BljSU8HAAAAOltmQx3OdXuPyonqWr80GMpI7SIR4XRQBwCEHgKckA37jsn+oxWSEB0hk4b3tHs4AAAAQNDJSE2QXkkxZqfl+j1HO/Vj02AIABDqCHDCu3vz8pG9JC46wu7hAAAAAEEnLCzM2019ZSfX4czJqw9w0mAIABCqCHCGOC1IvnTTIXNMejoAAADgP1kZ9XU4s3M7tw7njoYAJzs4AQChigBniFu9q1DySyslOS5KLhqaavdwAAAAgKCV2RDg3LS/WEpPVHdaPf1cApwAgBBHgDPELdlYv3tz2qjepusiAAAAAP/QDucDu8ebLKo1u4o65WMeKamU45U1prlQeveETvmYAAC4DRGtEKYFzpd9Tno6AAAAECjeOpw5nZOmviOv1Nxr4JQNCwCAUMVvwBC2MqdAjpVXS48uMXLB4Pp0GQAAAACBqMNZ0KkNhoakkp4OAAhdBDhDmNU9/coxaSalBQAAAEBg6nBuPVwqBccrO6+Dei8CnACA0BX0Ac6ioiK56aabJCkpSbp27Spz5syR48frJwEtWbVqlUyaNEkSEhLMtRdffLFUVFRIsDhRXSvvbDlsjmeMTbN7OAAAAEBI0Oyp4b0TzfEnO08/TZ0O6gAAhECAU4ObW7ZskeXLl8vSpUvlo48+krvuuqvV4ObUqVNlypQpsmbNGlm7dq3cfffdEh4ePF+uD7bmSVlVrfTtGidnD+hm93AAAACAkNGZdTi9HdRT64OmAACEokgJYl9++aX861//MgHKc845xzz2xBNPyPTp0+Wxxx6TPn2abqxz7733yve+9z25//77vY+dccYZEkyWbGpITx+bJmFhpKcDAAAAgazDuWDlLll1mnU4i8qqpLCsyhxn9KSDOgAgdAXPlsRmdmJqWroV3FSXXXaZ2Ym5evXqJq/Jy8szz/Xs2VOysrKkV69eMnHiRPn4449b/FyVlZVSUlLS6OZUxytr5P0v88zxjDF0TwcAAAAC6fzBKaYG/u7CcjlwrOK0629qVlZ8dFDvXQEAIHQDnIcPHzaBSl+RkZGSkpJinmvKzp07zf3DDz8sd955p9kBevbZZ8vkyZNlx44dzX6u+fPnS3JysvfWv39/carlXxyWypo6GZyaIGf2SbJ7OAAAAEBISYyNktF9k81xdk7Hd3HSYAgAABcHODV1XNOqW7pt3bq1Qx+7rq7O3H/729+W2bNny7hx4+Txxx83KeoLFixo9rp58+ZJcXGx97Zv3z5xqiUbD3l3b5KeDgAAAATehCH13dRX5Xa8DueOvFJzPySVACcAILS5Mo/hBz/4gcyaNavFcwYPHiy9e/c2Kee+ampqTGd1fa4paWn1HcVHjhzZ6PERI0bI3r17m/18MTEx5uZ0x8qr5KPt+eZ4xljS0wEAAAC7Gg09+UGurMwtEI/H06GNB9YOTjqoAwBCnSsDnKmpqebWmszMTDl27JisW7dOxo8fbx7797//bXZpnn/++U1ek56ebpoPbdu2rdHj27dvl2nTponbLfv8sNTUeWRkWhITIQAAAMAm4wd2k+jIcDlSUik7C8okowO7MK0O6qSoAwBCnStT1NtKd11OnTrV1NJcs2aNrFy5Uu6++2654YYbvB3UDxw4IMOHDzfPK105/dGPfiS///3v5dVXX5WcnBz56U9/alLe58yZI263ZGN993R2bwIAAAD2iY2KkPEDunW4Dqc2Dj1YfMIcD0lN7PTxAQDgJq7cwdkezz//vAlqapMg7Z5+7bXXmuClpbq62uzWLC8v9z52zz33yIkTJ+Tee+816exjx46V5cuXS0ZGhrhZXskJWbWzvsbPlWPqU/EBAAAA2CMro7uZn2fnFsotmekd2r3Zo0uMJMdH+WmEAAC4Q9AHOLVj+gsvvNDs85qSrjVvmmpkpLdg8tbmQ6L/1bMHdJX+KfF2DwcAAAAIaVlDesivl283Qc66Oo+Eh4e1v4M6ZacAAAjuFHU0Rno6AAAA4Bxj+iVLQnSEHCuvli8OlbTr2h00GAIAwIsAZ4jYV1Qu6/ceE23OeMVo0tMBAAAAu0VFhMv5g7ub41W59aWk2ooO6gAAfIUAZ4hYuumQub9gUHfpmRRr93AAAAAANNThVCtz29doKDefFHUAACwEOEMsPX3mWaSnAwAAAE6RldHD3K/ZVSTVtXVtuuZEda3sKSwzx+zgBACAAGdI0PQVrekTGR4mU8/sbfdwAAAAADQY3jtRUhKipbyqVjbuO9ama3YXlkmdRyQxNlJSE2P8PkYAAJyOAGcI7d68aGgP6ZYQbfdwAAAAADTQzumZDXU4s9tYh3PHka/S08O0yD4AACGOAGeQ83g8smQT6ekAAACAU2VadThz2laHkwZDAAA0RoAzyGlq+s78MomJDJfLRvSyezgAAAAATjJhSH0dzg17j0lFVW2r5+d4Gwwl+n1sAAC4AQHOIPdmQ3r6pOE9JTE2yu7hAAAAADhJevd4SUuOlaraOvl0T1Gr5+c0pKizgxMAgHoEOIM8PX3pxkPmeOZY0tMBAAAAJ9I6mlY39dbqcNbU1smuAjqoAwDgiwBnEFu/95gcOFYhCdERcunwnnYPBwAAAEAzshrqcGa3Uodz39EKs9MzNipc+naNC9DoAABwNgKcIdA9fcqZvSU2KsLu4QAAAABoRtaQ+gDn5gPFUlxR3ex5O46UmvuM1C6mAzsAACDAGbRq6zyydBPp6QAAAIAbpCXHyeAeCVLnEVmzq6jVBkOkpwMA8BUCnEFq9c5CKTheKV3jo7xdGQEAAAA4V2ZDmvrKFtLUc/KsDuoEOAEAsBDgDPLu6dNG9ZboSF5mAAAAwOmsjQmrWmg0ZAU42cEJAMBXiHwFoaqaOln2+WFzPIP0dAAAAMAVLhhcv4Nz25FSyS+tPOV5j8dDgBMAgCYQ4AxCH+fkm8LkqYkxcv6g+kkSAAAAAGdLSYiWkWlJ5njVzlN3cR4sPiHlVbUSGR4mA7sn2DBCAACciQBnEHrzs/r09CtGp0kEnRUBAAAA18hqqMOZ3UQdTmv3ZnqPBImK4E85AAAs/FYMMhVVtbL8iyPmeOZZpKcDAAAAbqzDmd1EHU5venoq6ekAAPgiwBlkPtiWJ2VVtdK3a5yM69/V7uEAAAAAaIdzB6WYFPS9ReWyr6i80XM5eaXmfmgvApwAAPgiwBmk6enaXCgsjPR0AAAAwE26xETK2IaNCid3U6fBEAAATSPAGURKT1TLv7flmeOZdE8HAAAA3F2HM7egUQf1HQQ4AQBoEgHOIKK1N6tq6iQjNUFGpCXaPRwAAAAAHZCVUV+Hc2VuoQlsqsKyKjlWXi2apJVBDU4AABohwBlE3txIejoAAADgduMGdJWYyHDJL62U3PzjjdLT+3WLk9ioCJtHCACAsxDgDBJFZVXy8Y4Cb4ATAAAAgDtpAPOc9G7meGVOYaMA59CeZGoBAHAyApxB4l+fH5aaOo+c2SeJlBUAAAAgSNLUrTqcNBgCAKB5BDiDxBKf9HQAAAAAwdFoSDup19Z5vgpwspkBAIBTRJ76ENxCC44fLa+WPYVlsmpnferKlWPS7B4WAAAAgNM0um+yJMZESsmJGhPk3Hq4xDye0TPB7qEBAOA4BDhdqLiiWhav2y/PZu+WPUXl3se1EPm7W47IteP7SXJclK1jBAAAANBxZVW1ktY1VkqPHJeb/7ra+/g9//hMZk8YxJwfAAAfYR7dBohOV1JSIsnJyVJcXCxJSUmd9nE/3J4vc59bJxVVtebfJ7942js9LjpCnrp5vEwcltppnxcAAIQef81nEBi8fu5lzfnLG+b8J8/3FXN+AEAoKGnjfIYanC6b6MxeuEYqqmtNYLOpyLQ+ps/reXo+AAAAAHfO+Zti/R3AnB8AgK8Q4HRRWrqu4poJTSt7bvV5PUXP1+sAAAAAOB9zfgAAOoYAp0tozU1NS29rQQE9T89/bf1+fw8NAAAAQCdgzg8AQMcQ4HQBLZOqDYU6YtHK3eZ6AAAAAM7FnB8AgI4jwOkCR8urTbf09k5Z9Hy97lg5KSsAAACAkzHnBwCg4whwukBZZc1pXX/8NK8HAAAA4F/M+QEA6DgCnC6QEBN5Wtd3Oc3rAQAAAPgXc34AADqOAKcLdIuPkoEp8RLWzuv0fL2ua3yUn0YGAAAAoDMw5wcAoOMIcLpAWFiY3JaV3qFrZ01IN9cDAAAAcC7m/AAAdBwBTpe4dnw/iYuOkLbOW8LDxJx/zdn9/D00AAAAAJ2AOT8AAB1DgNMlkuOi5Kmbx5sUlNYmPNbzf7p5vLkOAAAAgPMx5wcAoGMIcLrIxGGpsnD2eRIXFVE/6TnpeesxfX7R7PPk4mGpNo0UAAAAQEcw5wcAoP1otefCCc+qeZPltfX7ZdHK3bKnqNz73ICUeFN/R1NbkmJZxQUAAADciDk/AADtE+bxeDztvAZtUFJSIsnJyVJcXCxJSUl++Rz60h0rr5bjlTXSJSbSdE6kuDgAAHDTfAb+w+sXHJjzAwBCWUkb5zPs4HQxndh0S4g2NwAAAADBhzk/AACtowYnAAAAAAAAANciwAkAAAAAAADAtQhwAgAAAAAAAHAtApwAAAAAAAAAXIsAJwAAAAAAAADXIsAJAAAAAAAAwLUIcAIAAAAAAABwLQKcAAAAAAAAAFyLACcAAAAAAAAA1yLACQAAADjQzJkzZcCAARIbGytpaWlyyy23yMGDB+0eFgAAgOMQ4AQAAAAc6NJLL5WXX35Ztm3bJosXL5bc3Fy57rrr7B4WAACA40TaPQAAAAAAp7r33nu9xwMHDpT7779frrrqKqmurpaoqChbxwYAAOAkBDgBAAAAhysqKpLnn39esrKymg1uVlZWmpulpKQkgCMEAACwDynqAAAAgEPdd999kpCQIN27d5e9e/fKG2+80ey58+fPl+TkZO+tf//+AR0rAACAXQhwAgAAAAGiaeZhYWEt3rZu3eo9/0c/+pFs2LBB3n33XYmIiJBbb71VPB5Pkx973rx5Ulxc7L3t27cvgP8zAAAA+4R5mpsh4bRoSpCunOvkMikpye7hAAAAtBvzmc6Xn58vhYWFLZ4zePBgiY6OPuXx/fv3m12Z2dnZkpmZ2ern4vUDAABu19b5DDU4AQAAgABJTU01t46oq6sz9751NgEAAECA02+sjbEUdwcAAG5lzWNI+Am81atXy9q1a+XCCy+Ubt26SW5urvz0pz+VjIyMNu3eVMxHAQBAqMxHCXD6SWlpqbmnuDsAAAiGeY2mBiFw4uPj5bXXXpOHHnpIysrKJC0tTaZOnSoPPPCAxMTEtOljMB8FAAChMh+lBqefaArRwYMHJTEx0RSL92ckWyetWkTeTbWV3DpuN4/dreN289jdOm7F2APPreN289jdOu5Ajl2niTqZ7NOnj4SH05vSbZiPojW8du7G6+devHbuxWsXeG2dj7KD00/0i96vX7+AfT79xnLjN5dbx+3msbt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" ] @@ -6188,16 +6188,16 @@ "id": "bf9e9f1d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:12.409134Z", - "iopub.status.busy": "2024-06-04T23:19:12.408900Z", - "iopub.status.idle": "2024-06-04T23:19:12.505338Z", - "shell.execute_reply": "2024-06-04T23:19:12.505080Z" + "iopub.execute_input": "2025-04-03T19:33:22.055782Z", + "iopub.status.busy": "2025-04-03T19:33:22.055139Z", + "iopub.status.idle": "2025-04-03T19:33:22.142353Z", + "shell.execute_reply": "2025-04-03T19:33:22.139063Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -6241,8 +6241,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -6254,7 +6254,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/Ch05-resample-lab.Rmd b/Ch05-resample-lab.Rmd index 21820c1..56f0ec2 100644 --- a/Ch05-resample-lab.Rmd +++ b/Ch05-resample-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Cross-Validation and the Bootstrap diff --git a/Ch05-resample-lab.ipynb b/Ch05-resample-lab.ipynb index 9fd3ada..496305c 100644 --- a/Ch05-resample-lab.ipynb +++ b/Ch05-resample-lab.ipynb @@ -1,11 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "dc2d635a", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "id": "6dde3cef", @@ -38,10 +32,10 @@ "id": "f1deb5cc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:13.493284Z", - "iopub.status.busy": "2024-06-04T23:19:13.492950Z", - "iopub.status.idle": "2024-06-04T23:19:14.143174Z", - "shell.execute_reply": "2024-06-04T23:19:14.142882Z" + "iopub.execute_input": "2025-04-03T19:33:23.204499Z", + "iopub.status.busy": "2025-04-03T19:33:23.204390Z", + "iopub.status.idle": "2025-04-03T19:33:23.819021Z", + "shell.execute_reply": "2025-04-03T19:33:23.818655Z" }, "lines_to_next_cell": 2 }, @@ -70,10 +64,10 @@ "id": "268c41b3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.144884Z", - "iopub.status.busy": "2024-06-04T23:19:14.144773Z", - "iopub.status.idle": "2024-06-04T23:19:14.146541Z", - "shell.execute_reply": "2024-06-04T23:19:14.146330Z" + "iopub.execute_input": "2025-04-03T19:33:23.820788Z", + "iopub.status.busy": "2025-04-03T19:33:23.820641Z", + "iopub.status.idle": "2025-04-03T19:33:23.822663Z", + "shell.execute_reply": "2025-04-03T19:33:23.822416Z" }, "lines_to_next_cell": 2 }, @@ -114,10 +108,10 @@ "id": "22f44ae0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.147809Z", - "iopub.status.busy": "2024-06-04T23:19:14.147730Z", - "iopub.status.idle": "2024-06-04T23:19:14.152606Z", - "shell.execute_reply": "2024-06-04T23:19:14.152414Z" + "iopub.execute_input": "2025-04-03T19:33:23.823864Z", + "iopub.status.busy": "2025-04-03T19:33:23.823764Z", + "iopub.status.idle": "2025-04-03T19:33:23.829383Z", + "shell.execute_reply": "2025-04-03T19:33:23.829160Z" } }, "outputs": [], @@ -142,10 +136,10 @@ "id": "0c32e917", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.153847Z", - "iopub.status.busy": "2024-06-04T23:19:14.153757Z", - "iopub.status.idle": "2024-06-04T23:19:14.157537Z", - "shell.execute_reply": "2024-06-04T23:19:14.157339Z" + "iopub.execute_input": "2025-04-03T19:33:23.830793Z", + "iopub.status.busy": "2025-04-03T19:33:23.830713Z", + "iopub.status.idle": "2025-04-03T19:33:23.834993Z", + "shell.execute_reply": "2025-04-03T19:33:23.834789Z" } }, "outputs": [], @@ -172,10 +166,10 @@ "id": "86ce4f85", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.158717Z", - "iopub.status.busy": "2024-06-04T23:19:14.158637Z", - "iopub.status.idle": "2024-06-04T23:19:14.162177Z", - "shell.execute_reply": "2024-06-04T23:19:14.161910Z" + "iopub.execute_input": "2025-04-03T19:33:23.836150Z", + "iopub.status.busy": "2025-04-03T19:33:23.836082Z", + "iopub.status.idle": "2025-04-03T19:33:23.839814Z", + "shell.execute_reply": "2025-04-03T19:33:23.839608Z" } }, "outputs": [ @@ -207,7 +201,7 @@ "\n", "We can also estimate the validation error for\n", "higher-degree polynomial regressions. We first provide a function `evalMSE()` that takes a model string as well\n", - "as a training and test set and returns the MSE on the test set." + "as training and test sets and returns the MSE on the test set." ] }, { @@ -216,10 +210,10 @@ "id": "50a66a97", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.163466Z", - "iopub.status.busy": "2024-06-04T23:19:14.163397Z", - "iopub.status.idle": "2024-06-04T23:19:14.165323Z", - "shell.execute_reply": "2024-06-04T23:19:14.165076Z" + "iopub.execute_input": "2025-04-03T19:33:23.841088Z", + "iopub.status.busy": "2025-04-03T19:33:23.841015Z", + "iopub.status.idle": "2025-04-03T19:33:23.843031Z", + "shell.execute_reply": "2025-04-03T19:33:23.842838Z" } }, "outputs": [], @@ -259,10 +253,10 @@ "id": "d49b6999", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.166563Z", - "iopub.status.busy": "2024-06-04T23:19:14.166497Z", - "iopub.status.idle": "2024-06-04T23:19:14.177198Z", - "shell.execute_reply": "2024-06-04T23:19:14.176975Z" + "iopub.execute_input": "2025-04-03T19:33:23.844166Z", + "iopub.status.busy": "2025-04-03T19:33:23.844091Z", + "iopub.status.idle": "2025-04-03T19:33:23.855875Z", + "shell.execute_reply": "2025-04-03T19:33:23.855640Z" } }, "outputs": [ @@ -303,10 +297,10 @@ "id": "dac8bd54", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.178405Z", - "iopub.status.busy": "2024-06-04T23:19:14.178321Z", - "iopub.status.idle": "2024-06-04T23:19:14.188650Z", - "shell.execute_reply": "2024-06-04T23:19:14.188432Z" + "iopub.execute_input": "2025-04-03T19:33:23.857092Z", + "iopub.status.busy": "2025-04-03T19:33:23.857011Z", + "iopub.status.idle": "2025-04-03T19:33:23.868361Z", + "shell.execute_reply": "2025-04-03T19:33:23.868136Z" } }, "outputs": [ @@ -386,10 +380,10 @@ "id": "601ae443", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.189993Z", - "iopub.status.busy": "2024-06-04T23:19:14.189906Z", - "iopub.status.idle": "2024-06-04T23:19:14.876368Z", - "shell.execute_reply": "2024-06-04T23:19:14.876129Z" + "iopub.execute_input": "2025-04-03T19:33:23.869578Z", + "iopub.status.busy": "2025-04-03T19:33:23.869501Z", + "iopub.status.idle": "2025-04-03T19:33:24.584127Z", + "shell.execute_reply": "2025-04-03T19:33:24.583898Z" }, "lines_to_next_cell": 0 }, @@ -427,7 +421,7 @@ "and `score()` methods, an\n", "array of features `X` and a response `Y`. \n", "We also included an additional argument `cv` to `cross_validate()`; specifying an integer\n", - "$K$ results in $K$-fold cross-validation. We have provided a value \n", + "$k$ results in $k$-fold cross-validation. We have provided a value \n", "corresponding to the total number of observations, which results in\n", "leave-one-out cross-validation (LOOCV). The `cross_validate()` function produces a dictionary with several components;\n", "we simply want the cross-validated test score here (MSE), which is estimated to be 24.23." @@ -454,10 +448,10 @@ "id": "11226c85", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:14.877800Z", - "iopub.status.busy": "2024-06-04T23:19:14.877726Z", - "iopub.status.idle": "2024-06-04T23:19:15.384419Z", - "shell.execute_reply": "2024-06-04T23:19:15.384193Z" + "iopub.execute_input": "2025-04-03T19:33:24.585528Z", + "iopub.status.busy": "2025-04-03T19:33:24.585450Z", + "iopub.status.idle": "2025-04-03T19:33:25.124201Z", + "shell.execute_reply": "2025-04-03T19:33:25.123913Z" }, "lines_to_next_cell": 0 }, @@ -511,10 +505,10 @@ "id": "64b64d97", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:15.385768Z", - "iopub.status.busy": "2024-06-04T23:19:15.385690Z", - "iopub.status.idle": "2024-06-04T23:19:15.387686Z", - "shell.execute_reply": "2024-06-04T23:19:15.387484Z" + "iopub.execute_input": "2025-04-03T19:33:25.125598Z", + "iopub.status.busy": "2025-04-03T19:33:25.125513Z", + "iopub.status.idle": "2025-04-03T19:33:25.127886Z", + "shell.execute_reply": "2025-04-03T19:33:25.127667Z" } }, "outputs": [ @@ -542,8 +536,8 @@ "id": "71385c1b", "metadata": {}, "source": [ - "In the CV example above, we used $K=n$, but of course we can also use $K diff --git a/Ch06-varselect-lab.ipynb b/Ch06-varselect-lab.ipynb index 765a7fa..4ddaae0 100644 --- a/Ch06-varselect-lab.ipynb +++ b/Ch06-varselect-lab.ipynb @@ -5,7 +5,6 @@ "id": "e8a7df77", "metadata": {}, "source": [ - "\n", "# Linear Models and Regularization Methods\n", "\n", "\n", @@ -31,10 +30,10 @@ "id": "b59f6bc4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:20.427195Z", - "iopub.status.busy": "2024-06-04T23:19:20.426849Z", - "iopub.status.idle": "2024-06-04T23:19:21.241188Z", - "shell.execute_reply": "2024-06-04T23:19:21.240924Z" + "iopub.execute_input": "2025-04-03T19:33:30.475494Z", + "iopub.status.busy": "2025-04-03T19:33:30.475405Z", + "iopub.status.idle": "2025-04-03T19:33:31.354356Z", + "shell.execute_reply": "2025-04-03T19:33:31.354035Z" } }, "outputs": [], @@ -66,10 +65,10 @@ "id": "8440e64a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:21.242868Z", - "iopub.status.busy": "2024-06-04T23:19:21.242739Z", - "iopub.status.idle": "2024-06-04T23:19:21.482878Z", - "shell.execute_reply": "2024-06-04T23:19:21.482642Z" + "iopub.execute_input": "2025-04-03T19:33:31.356044Z", + "iopub.status.busy": "2025-04-03T19:33:31.355890Z", + "iopub.status.idle": "2025-04-03T19:33:31.614078Z", + "shell.execute_reply": "2025-04-03T19:33:31.613762Z" } }, "outputs": [], @@ -115,10 +114,10 @@ "id": "63d45749", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:21.484407Z", - "iopub.status.busy": "2024-06-04T23:19:21.484321Z", - "iopub.status.idle": "2024-06-04T23:19:21.489374Z", - "shell.execute_reply": "2024-06-04T23:19:21.489194Z" + "iopub.execute_input": "2025-04-03T19:33:31.615776Z", + "iopub.status.busy": "2025-04-03T19:33:31.615659Z", + "iopub.status.idle": "2025-04-03T19:33:31.621269Z", + "shell.execute_reply": "2025-04-03T19:33:31.621028Z" } }, "outputs": [ @@ -154,10 +153,10 @@ "id": "47262cbe", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:21.490561Z", - "iopub.status.busy": "2024-06-04T23:19:21.490484Z", - "iopub.status.idle": "2024-06-04T23:19:21.493093Z", - "shell.execute_reply": "2024-06-04T23:19:21.492903Z" + "iopub.execute_input": "2025-04-03T19:33:31.622460Z", + "iopub.status.busy": "2025-04-03T19:33:31.622370Z", + "iopub.status.idle": "2025-04-03T19:33:31.625183Z", + "shell.execute_reply": "2025-04-03T19:33:31.624905Z" }, "lines_to_next_cell": 2 }, @@ -195,10 +194,10 @@ "id": "096fe127", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:21.494225Z", - "iopub.status.busy": "2024-06-04T23:19:21.494154Z", - "iopub.status.idle": "2024-06-04T23:19:21.495738Z", - "shell.execute_reply": "2024-06-04T23:19:21.495580Z" + "iopub.execute_input": "2025-04-03T19:33:31.626395Z", + "iopub.status.busy": "2025-04-03T19:33:31.626310Z", + "iopub.status.idle": "2025-04-03T19:33:31.628301Z", + "shell.execute_reply": "2025-04-03T19:33:31.628085Z" }, "lines_to_next_cell": 0 }, @@ -227,10 +226,10 @@ "id": "b697d287", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:21.496810Z", - "iopub.status.busy": "2024-06-04T23:19:21.496736Z", - "iopub.status.idle": "2024-06-04T23:19:21.510614Z", - "shell.execute_reply": "2024-06-04T23:19:21.510406Z" + "iopub.execute_input": "2025-04-03T19:33:31.629695Z", + "iopub.status.busy": "2025-04-03T19:33:31.629593Z", + "iopub.status.idle": "2025-04-03T19:33:31.645667Z", + "shell.execute_reply": "2025-04-03T19:33:31.645397Z" } }, "outputs": [], @@ -255,10 +254,10 @@ "id": "2151b133", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:21.511833Z", - "iopub.status.busy": "2024-06-04T23:19:21.511764Z", - "iopub.status.idle": "2024-06-04T23:19:21.513251Z", - "shell.execute_reply": "2024-06-04T23:19:21.513008Z" + "iopub.execute_input": "2025-04-03T19:33:31.647139Z", + "iopub.status.busy": "2025-04-03T19:33:31.647061Z", + "iopub.status.idle": "2025-04-03T19:33:31.648884Z", + "shell.execute_reply": "2025-04-03T19:33:31.648616Z" }, "lines_to_next_cell": 0 }, @@ -293,10 +292,10 @@ "id": "ab28d6fd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:21.514438Z", - "iopub.status.busy": "2024-06-04T23:19:21.514363Z", - "iopub.status.idle": "2024-06-04T23:19:21.515875Z", - "shell.execute_reply": "2024-06-04T23:19:21.515674Z" + "iopub.execute_input": "2025-04-03T19:33:31.650250Z", + "iopub.status.busy": "2025-04-03T19:33:31.650177Z", + "iopub.status.idle": "2025-04-03T19:33:31.651944Z", + "shell.execute_reply": "2025-04-03T19:33:31.651705Z" } }, "outputs": [], @@ -324,10 +323,10 @@ "id": "d8b345ec", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:21.517091Z", - "iopub.status.busy": "2024-06-04T23:19:21.517008Z", - "iopub.status.idle": "2024-06-04T23:19:22.178273Z", - "shell.execute_reply": "2024-06-04T23:19:22.178018Z" + "iopub.execute_input": "2025-04-03T19:33:31.653191Z", + "iopub.status.busy": "2025-04-03T19:33:31.653120Z", + "iopub.status.idle": "2025-04-03T19:33:32.403490Z", + "shell.execute_reply": "2025-04-03T19:33:32.403236Z" } }, "outputs": [ @@ -381,10 +380,10 @@ "id": "915cdff6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:22.179650Z", - "iopub.status.busy": "2024-06-04T23:19:22.179571Z", - "iopub.status.idle": "2024-06-04T23:19:22.573219Z", - "shell.execute_reply": "2024-06-04T23:19:22.572980Z" + "iopub.execute_input": "2025-04-03T19:33:32.404888Z", + "iopub.status.busy": "2025-04-03T19:33:32.404801Z", + "iopub.status.idle": "2025-04-03T19:33:32.916167Z", + "shell.execute_reply": "2025-04-03T19:33:32.915826Z" } }, "outputs": [ @@ -447,10 +446,10 @@ "id": "33ae4aa4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:22.574545Z", - "iopub.status.busy": "2024-06-04T23:19:22.574468Z", - "iopub.status.idle": "2024-06-04T23:19:22.576122Z", - "shell.execute_reply": "2024-06-04T23:19:22.575907Z" + "iopub.execute_input": "2025-04-03T19:33:32.917772Z", + "iopub.status.busy": "2025-04-03T19:33:32.917665Z", + "iopub.status.idle": "2025-04-03T19:33:32.919448Z", + "shell.execute_reply": "2025-04-03T19:33:32.919202Z" } }, "outputs": [], @@ -475,10 +474,10 @@ "id": "f05e1a81", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:22.577302Z", - "iopub.status.busy": "2024-06-04T23:19:22.577233Z", - "iopub.status.idle": "2024-06-04T23:19:22.976283Z", - "shell.execute_reply": "2024-06-04T23:19:22.976065Z" + "iopub.execute_input": "2025-04-03T19:33:32.920703Z", + "iopub.status.busy": "2025-04-03T19:33:32.920631Z", + "iopub.status.idle": "2025-04-03T19:33:33.380565Z", + "shell.execute_reply": "2025-04-03T19:33:33.380305Z" }, "lines_to_next_cell": 2 }, @@ -520,17 +519,17 @@ "id": "0b07ae72", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:22.977573Z", - "iopub.status.busy": "2024-06-04T23:19:22.977503Z", - "iopub.status.idle": "2024-06-04T23:19:23.094004Z", - "shell.execute_reply": "2024-06-04T23:19:23.091687Z" + "iopub.execute_input": "2025-04-03T19:33:33.382092Z", + "iopub.status.busy": "2025-04-03T19:33:33.382007Z", + "iopub.status.idle": "2025-04-03T19:33:33.548626Z", + "shell.execute_reply": "2025-04-03T19:33:33.547870Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -589,10 +588,10 @@ "id": "a1654ad8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:23.097519Z", - "iopub.status.busy": "2024-06-04T23:19:23.097271Z", - "iopub.status.idle": "2024-06-04T23:19:25.139415Z", - "shell.execute_reply": "2024-06-04T23:19:25.139180Z" + "iopub.execute_input": "2025-04-03T19:33:33.552622Z", + "iopub.status.busy": "2025-04-03T19:33:33.552265Z", + "iopub.status.idle": "2025-04-03T19:33:35.915042Z", + "shell.execute_reply": "2025-04-03T19:33:35.914773Z" }, "lines_to_next_cell": 0 }, @@ -643,10 +642,10 @@ "id": "ce919bd3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:25.140773Z", - "iopub.status.busy": "2024-06-04T23:19:25.140698Z", - "iopub.status.idle": "2024-06-04T23:19:25.143330Z", - "shell.execute_reply": "2024-06-04T23:19:25.143113Z" + "iopub.execute_input": "2025-04-03T19:33:35.916384Z", + "iopub.status.busy": "2025-04-03T19:33:35.916302Z", + "iopub.status.idle": "2025-04-03T19:33:35.919340Z", + "shell.execute_reply": "2025-04-03T19:33:35.919102Z" } }, "outputs": [ @@ -685,16 +684,16 @@ "id": "26520d22", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:25.144503Z", - "iopub.status.busy": "2024-06-04T23:19:25.144435Z", - "iopub.status.idle": "2024-06-04T23:19:25.201431Z", - "shell.execute_reply": "2024-06-04T23:19:25.201214Z" + "iopub.execute_input": "2025-04-03T19:33:35.920778Z", + "iopub.status.busy": "2025-04-03T19:33:35.920686Z", + "iopub.status.idle": "2025-04-03T19:33:35.961615Z", + "shell.execute_reply": "2025-04-03T19:33:35.961372Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -731,10 +730,10 @@ "id": "f19c9091", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:25.202701Z", - "iopub.status.busy": "2024-06-04T23:19:25.202634Z", - "iopub.status.idle": "2024-06-04T23:19:25.603532Z", - "shell.execute_reply": "2024-06-04T23:19:25.603301Z" + "iopub.execute_input": "2025-04-03T19:33:35.963125Z", + "iopub.status.busy": "2025-04-03T19:33:35.963008Z", + "iopub.status.idle": "2025-04-03T19:33:36.414362Z", + "shell.execute_reply": "2025-04-03T19:33:36.414111Z" }, "lines_to_next_cell": 0 }, @@ -765,17 +764,17 @@ "id": "1fca6b08", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:25.604861Z", - "iopub.status.busy": "2024-06-04T23:19:25.604789Z", - "iopub.status.idle": "2024-06-04T23:19:25.664722Z", - "shell.execute_reply": "2024-06-04T23:19:25.664495Z" + "iopub.execute_input": "2025-04-03T19:33:36.415824Z", + "iopub.status.busy": "2025-04-03T19:33:36.415741Z", + "iopub.status.idle": "2025-04-03T19:33:36.456597Z", + "shell.execute_reply": "2025-04-03T19:33:36.456334Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -818,10 +817,10 @@ "id": "6e9dd7d4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:25.665992Z", - "iopub.status.busy": "2024-06-04T23:19:25.665901Z", - "iopub.status.idle": "2024-06-04T23:19:25.679390Z", - "shell.execute_reply": "2024-06-04T23:19:25.679171Z" + "iopub.execute_input": "2025-04-03T19:33:36.457976Z", + "iopub.status.busy": "2025-04-03T19:33:36.457874Z", + "iopub.status.idle": "2025-04-03T19:33:36.472264Z", + "shell.execute_reply": "2025-04-03T19:33:36.471998Z" }, "lines_to_next_cell": 0 }, @@ -847,10 +846,10 @@ "id": "caed84ae", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:25.680677Z", - "iopub.status.busy": "2024-06-04T23:19:25.680609Z", - "iopub.status.idle": "2024-06-04T23:19:27.246130Z", - "shell.execute_reply": "2024-06-04T23:19:27.245877Z" + "iopub.execute_input": "2025-04-03T19:33:36.473445Z", + "iopub.status.busy": "2025-04-03T19:33:36.473374Z", + "iopub.status.idle": "2025-04-03T19:33:40.246065Z", + "shell.execute_reply": "2025-04-03T19:33:40.245766Z" } }, "outputs": [ @@ -867,7 +866,13 @@ "output_type": "stream", "text": [ "Iteration: 1. Number of non-zeros: 1\n", - "Iteration: 2. Number of non-zeros: 2\n", + "Iteration: 2. Number of non-zeros: 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Iteration: 3. Number of non-zeros: 2\n" ] }, @@ -875,7 +880,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration: 4. Number of non-zeros: 2\n", + "Iteration: 4. Number of non-zeros: 2\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Iteration: 5. Number of non-zeros: 3\n" ] }, @@ -883,7 +894,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration: 6. Number of non-zeros: 3\n", + "Iteration: 6. Number of non-zeros: 3\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Iteration: 7. Number of non-zeros: 4\n" ] }, @@ -926,10 +943,10 @@ "id": "f2fc2ef8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.247604Z", - "iopub.status.busy": "2024-06-04T23:19:27.247525Z", - "iopub.status.idle": "2024-06-04T23:19:27.249696Z", - "shell.execute_reply": "2024-06-04T23:19:27.249497Z" + "iopub.execute_input": "2025-04-03T19:33:40.247642Z", + "iopub.status.busy": "2025-04-03T19:33:40.247559Z", + "iopub.status.idle": "2025-04-03T19:33:40.250064Z", + "shell.execute_reply": "2025-04-03T19:33:40.249834Z" }, "lines_to_next_cell": 0 }, @@ -961,7 +978,7 @@ "id": "c4a96126", "metadata": {}, "source": [ - "In the example above, we see that at the fourth step in the path, we have two nonzero coefficients in `'B'`, corresponding to the value $0.114$ for the penalty parameter `lambda_0`.\n", + "In the example above, we see that at the fourth step in the path, we have two nonzero coefficients in `'B'`, corresponding to the value $0.0114$ for the penalty parameter `lambda_0`.\n", "We could make predictions using this sequence of fits on a validation set as a function of `lambda_0`, or with more work using cross-validation.\n", "\n", "## Ridge Regression and the Lasso\n", @@ -991,14 +1008,420 @@ "id": "4a5d6fc8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.250946Z", - "iopub.status.busy": "2024-06-04T23:19:27.250878Z", - "iopub.status.idle": "2024-06-04T23:19:27.303537Z", - "shell.execute_reply": "2024-06-04T23:19:27.303273Z" + "iopub.execute_input": "2025-04-03T19:33:40.251487Z", + "iopub.status.busy": "2025-04-03T19:33:40.251378Z", + "iopub.status.idle": "2025-04-03T19:33:40.310093Z", + "shell.execute_reply": "2025-04-03T19:33:40.309814Z" }, "lines_to_next_cell": 0 }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428165.36474803, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64428069.135193564, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427947.709570706, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427794.49147929, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427601.15801401, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427357.208145335, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64427049.39312406, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426660.99818401, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64426170.936871, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64425552.60935727, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64424772.46361481, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64423788.18271286, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64422546.402046196, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64420979.836119056, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64419003.66458898, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64416510.99045885, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64413367.138336174, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64409402.50628651, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64404403.61988451, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64398101.96098537, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64390160.05690916, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64380154.22050254, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64367553.23368757, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64351692.17811265, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64331740.55708714, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64306663.85815487, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64275177.83204634, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64235695.09903011, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64186264.367964305, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64124503.75014188, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 64047531.61120446, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63951901.41718618, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63833551.374737374, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63687785.48493876, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63509309.685659595, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63292354.02159835, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 63030916.89990266, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62719166.29703928, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 62352019.354438685, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61925889.875772476, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 61439539.89859062, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60894903.039219804, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 60297684.607476555, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 59657521.16598571, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58987535.05051082, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 58303257.30893663, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 57621079.35589412, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56956552.362989165, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 56322906.14367991, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55730077.752803415, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 55184365.56435659, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54688640.34364891, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 54242923.97107168, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53845116.92275897, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53491699.68250863, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 53178310.76477921, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52900177.09233121, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52652419.277090184, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52430270.98847021, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52229246.49376922, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 52045276.251295805, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51874817.10761593, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51714935.480955906, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51563358.53546297, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51418487.867063135, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51279371.6204245, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51145634.32609803, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 51017369.002990715, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50895002.06601913, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50779146.50047491, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50670461.07683641, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50569532.273268215, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50476790.981010474, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50392468.80539254, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50316590.69087247, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50248994.15213543, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50189362.60450393, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50137261.69126286, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50092171.83247456, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50053515.0816231, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 50020677.61213055, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49993029.950182974, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49969946.08142715, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49950821.12032734, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49935086.375795275, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49922220.65542218, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49911757.23721766, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49903286.65921827, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49896456.01861009, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49890965.72520982, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49886564.66025478, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49883044.54819732, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49880234.147845834, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49877993.670362815, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49876209.66553557, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49874790.493499264, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49873662.41408341, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872766.272819825, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49872054.73300109, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 49871489.989638604, tolerance: 12885.7065737425\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, { "data": { "text/plain": [ @@ -1052,10 +1475,10 @@ "id": "1bca28f6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.304830Z", - "iopub.status.busy": "2024-06-04T23:19:27.304756Z", - "iopub.status.idle": "2024-06-04T23:19:27.311709Z", - "shell.execute_reply": "2024-06-04T23:19:27.311502Z" + "iopub.execute_input": "2025-04-03T19:33:40.311607Z", + "iopub.status.busy": "2025-04-03T19:33:40.311517Z", + "iopub.status.idle": "2025-04-03T19:33:40.318968Z", + "shell.execute_reply": "2025-04-03T19:33:40.318721Z" } }, "outputs": [ @@ -1474,17 +1897,17 @@ "id": "08bc997b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.312975Z", - "iopub.status.busy": "2024-06-04T23:19:27.312908Z", - "iopub.status.idle": "2024-06-04T23:19:27.473880Z", - "shell.execute_reply": "2024-06-04T23:19:27.473585Z" + "iopub.execute_input": 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", 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", 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" ] @@ -1519,10 +1942,10 @@ "id": "0f7359c7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.475363Z", - "iopub.status.busy": "2024-06-04T23:19:27.475242Z", - "iopub.status.idle": "2024-06-04T23:19:27.477806Z", - "shell.execute_reply": "2024-06-04T23:19:27.477534Z" + "iopub.execute_input": "2025-04-03T19:33:40.465789Z", + "iopub.status.busy": "2025-04-03T19:33:40.465691Z", + "iopub.status.idle": "2025-04-03T19:33:40.468393Z", + "shell.execute_reply": "2025-04-03T19:33:40.468175Z" } }, "outputs": [ @@ -1576,10 +1999,10 @@ "id": "d2984305", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.479160Z", - "iopub.status.busy": "2024-06-04T23:19:27.479064Z", - "iopub.status.idle": "2024-06-04T23:19:27.481153Z", - "shell.execute_reply": "2024-06-04T23:19:27.480921Z" + "iopub.execute_input": "2025-04-03T19:33:40.469573Z", + "iopub.status.busy": "2025-04-03T19:33:40.469491Z", + "iopub.status.idle": "2025-04-03T19:33:40.471847Z", + "shell.execute_reply": "2025-04-03T19:33:40.471586Z" } }, "outputs": [ @@ -1614,10 +2037,10 @@ "id": "9a3424a4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.482508Z", - "iopub.status.busy": "2024-06-04T23:19:27.482402Z", - "iopub.status.idle": "2024-06-04T23:19:27.484554Z", - "shell.execute_reply": "2024-06-04T23:19:27.484320Z" + "iopub.execute_input": "2025-04-03T19:33:40.473082Z", + "iopub.status.busy": "2025-04-03T19:33:40.472995Z", + "iopub.status.idle": "2025-04-03T19:33:40.474973Z", + "shell.execute_reply": "2025-04-03T19:33:40.474756Z" }, "lines_to_next_cell": 0 }, @@ -1655,13 +2078,21 @@ "id": "c864d25f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.485813Z", - "iopub.status.busy": "2024-06-04T23:19:27.485727Z", - "iopub.status.idle": "2024-06-04T23:19:27.495072Z", - "shell.execute_reply": "2024-06-04T23:19:27.494829Z" + "iopub.execute_input": "2025-04-03T19:33:40.476118Z", + "iopub.status.busy": "2025-04-03T19:33:40.476036Z", + "iopub.status.idle": "2025-04-03T19:33:40.486778Z", + "shell.execute_reply": "2025-04-03T19:33:40.486450Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.446e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, { "data": { "text/html": [ @@ -2104,10 +2535,10 @@ "id": "8a07da6e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.496384Z", - "iopub.status.busy": "2024-06-04T23:19:27.496277Z", - "iopub.status.idle": "2024-06-04T23:19:27.498333Z", - "shell.execute_reply": "2024-06-04T23:19:27.498078Z" + "iopub.execute_input": "2025-04-03T19:33:40.488144Z", + "iopub.status.busy": "2025-04-03T19:33:40.488070Z", + "iopub.status.idle": "2025-04-03T19:33:40.490144Z", + "shell.execute_reply": "2025-04-03T19:33:40.489903Z" }, "lines_to_next_cell": 0 }, @@ -2151,13 +2582,21 @@ "id": "c10e3ae5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.499658Z", - "iopub.status.busy": "2024-06-04T23:19:27.499553Z", - "iopub.status.idle": "2024-06-04T23:19:27.505630Z", - "shell.execute_reply": "2024-06-04T23:19:27.505369Z" + "iopub.execute_input": "2025-04-03T19:33:40.491395Z", + "iopub.status.busy": "2025-04-03T19:33:40.491328Z", + "iopub.status.idle": "2025-04-03T19:33:40.497630Z", + "shell.execute_reply": "2025-04-03T19:33:40.497326Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, { "data": { "text/plain": [ @@ -2201,14 +2640,22 @@ "id": "0df2b24d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.507058Z", - "iopub.status.busy": "2024-06-04T23:19:27.506964Z", - "iopub.status.idle": "2024-06-04T23:19:27.512846Z", - "shell.execute_reply": "2024-06-04T23:19:27.512640Z" + "iopub.execute_input": "2025-04-03T19:33:40.499027Z", + "iopub.status.busy": "2025-04-03T19:33:40.498934Z", + "iopub.status.idle": "2025-04-03T19:33:40.505574Z", + "shell.execute_reply": "2025-04-03T19:33:40.505302Z" }, "lines_to_next_cell": 0 }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, { "data": { "text/plain": [ @@ -2250,13 +2697,257 @@ "id": "9b76df85", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.514070Z", - "iopub.status.busy": "2024-06-04T23:19:27.514003Z", - "iopub.status.idle": "2024-06-04T23:19:27.851311Z", - "shell.execute_reply": "2024-06-04T23:19:27.851092Z" + "iopub.execute_input": "2025-04-03T19:33:40.506901Z", + "iopub.status.busy": "2025-04-03T19:33:40.506800Z", + "iopub.status.idle": "2025-04-03T19:33:41.050754Z", + "shell.execute_reply": "2025-04-03T19:33:41.048222Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.134e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.131e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.128e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.127e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.117e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.113e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.107e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.100e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.091e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.081e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.055e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.744e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.494e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.968e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.704e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.448e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.204e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.977e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.769e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.581e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.412e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.261e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.127e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.008e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.900e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.803e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.714e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.632e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.554e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.480e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.409e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.342e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.276e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.214e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.154e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.097e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.043e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.991e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.943e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.898e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.856e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.817e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.780e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.746e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.715e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.687e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.661e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.637e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.616e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.596e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.579e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.563e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.550e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.538e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.528e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.519e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.512e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.506e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.500e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.496e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.493e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.490e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.488e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.486e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.485e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.482e+06, tolerance: 2.272e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.248e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, { "data": { "text/html": [ @@ -2703,14 +3394,1454 @@ "id": "3e4c1b53", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:27.852563Z", - "iopub.status.busy": "2024-06-04T23:19:27.852495Z", - "iopub.status.idle": "2024-06-04T23:19:30.080670Z", - "shell.execute_reply": "2024-06-04T23:19:30.080412Z" + "iopub.execute_input": "2025-04-03T19:33:41.056003Z", + "iopub.status.busy": "2025-04-03T19:33:41.054251Z", + "iopub.status.idle": "2025-04-03T19:33:44.943511Z", + "shell.execute_reply": "2025-04-03T19:33:44.942257Z" }, "lines_to_next_cell": 0 }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, { "data": { "text/html": [ @@ -3157,16 +5288,16 @@ "id": "39ff3251", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:30.082088Z", - "iopub.status.busy": "2024-06-04T23:19:30.082012Z", - "iopub.status.idle": "2024-06-04T23:19:30.194197Z", - "shell.execute_reply": "2024-06-04T23:19:30.193921Z" + "iopub.execute_input": "2025-04-03T19:33:44.949943Z", + "iopub.status.busy": "2025-04-03T19:33:44.949702Z", + "iopub.status.idle": "2025-04-03T19:33:45.033294Z", + "shell.execute_reply": "2025-04-03T19:33:45.032427Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3201,13 +5332,1195 @@ "id": "766ab0b0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:30.195678Z", - "iopub.status.busy": "2024-06-04T23:19:30.195576Z", - "iopub.status.idle": "2024-06-04T23:19:32.425386Z", - "shell.execute_reply": "2024-06-04T23:19:32.425155Z" + "iopub.execute_input": "2025-04-03T19:33:45.039190Z", + "iopub.status.busy": "2025-04-03T19:33:45.038561Z", + "iopub.status.idle": "2025-04-03T19:33:48.083852Z", + "shell.execute_reply": "2025-04-03T19:33:48.083622Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.101e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.233e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.231e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.214e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.225e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.086e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.208e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.197e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.191e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.174e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.184e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.163e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.043e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.091e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.869e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.938e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.826e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.478e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.633e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.713e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.432e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.663e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.403e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.447e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.352e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.305e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.333e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.366e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.262e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.177e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.343e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.331e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.224e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.154e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.278e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.312e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.190e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.255e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.284e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.272e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.159e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.234e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.260e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.132e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.098e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.215e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.237e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.225e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.109e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.083e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.217e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.204e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.182e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.198e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.186e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.069e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.058e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.167e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.181e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.165e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.139e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.138e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.027e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.135e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.012e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.017e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.102e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.097e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.982e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.090e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.804e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.894e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.078e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.084e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.071e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.713e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.808e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.073e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.060e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.627e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.727e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.057e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.048e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.548e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.650e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.047e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.053e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.038e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.579e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.045e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.406e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.514e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.028e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.037e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.343e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.454e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.030e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.286e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.402e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.011e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+07, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.234e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.355e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.019e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.969e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.187e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.314e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.966e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.914e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.279e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.865e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.249e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.843e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.007e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.824e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.075e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.223e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.004e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.790e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.047e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.202e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.743e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.001e+07, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.761e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.184e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.990e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.737e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.000e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.169e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.663e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.717e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.982e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.156e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.630e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.956e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.701e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.966e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.146e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.943e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.688e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.953e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.138e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.575e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.933e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.677e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.942e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.132e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.554e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.924e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.668e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.933e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.126e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.535e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.917e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.926e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.122e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.520e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.911e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.655e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.920e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.119e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.507e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.906e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.651e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.915e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.116e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.496e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.902e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.911e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.114e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.487e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.899e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.644e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.907e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.112e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.480e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.642e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.905e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.111e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.474e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.895e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.640e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.903e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.110e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.469e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.893e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.639e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.901e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.109e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.465e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.892e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.638e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.900e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.462e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.891e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.899e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.108e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.460e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.898e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.458e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.890e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.636e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.107e+06, tolerance: 3.759e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.456e+06, tolerance: 4.201e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.889e+06, tolerance: 4.466e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.635e+06, tolerance: 4.445e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.897e+06, tolerance: 4.437e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, { "data": { "text/html": [ @@ -3687,17 +7000,17 @@ "id": "642f1de3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:32.426644Z", - "iopub.status.busy": "2024-06-04T23:19:32.426570Z", - "iopub.status.idle": "2024-06-04T23:19:32.492503Z", - "shell.execute_reply": "2024-06-04T23:19:32.492264Z" + "iopub.execute_input": "2025-04-03T19:33:48.085154Z", + "iopub.status.busy": "2025-04-03T19:33:48.085082Z", + "iopub.status.idle": "2025-04-03T19:33:48.132789Z", + "shell.execute_reply": "2025-04-03T19:33:48.132537Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", 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OBahN3ZvVkUvaNJBnR3SU/6w9JK/9WNxOUK10p7aOjcKMHiJqEDPDAABZvjNRft2bXOaiBoDVVrizGdElWs8W7zxe0o7t0bmbZOn2BMnJLzBolHA1ZoYBwOKy8wrkhe93GD0MwO1Mu6Wb5OQVyrwNh2XKouJPTn7Ynqi3iCA/GdqJUiJPwMwwAFicWm750MlMaRgWYPRQALejuqnceVFz+9fj+reQqPAAScnKk6/+OGI//s/vd8hv+5Ilr4DVGc2GmWEA8ACq40PsxKV6f8cLw3R/1co4npIlb/+0T+8/MbSdPPX11hodJ2B2Tw5rL8+OiJW1B07K/I1H5L8bj+rjX6w7rLc6wX5yWbuGRg8TVcDMMABY2Ks/7JGsvALp3byuXNO1kdHDAUzBx9tL33D3z+s724/d1DNG6oX4y5nMPPlm0zH78XGfrJeZv+yXXQklXSrgXpgZBgALW7Q1QfcUfv7aTtw0Bzjhxes765vt/jh0WhZuOSaf/R6vj689eEpvjr7ecET6t2kgLRuEGDRaOCIMA4DF3d63mXSOiWBxDcBJvj7eclGr+tK1SYQ9DD9zdQf5ff9J+f3AKf0pjPLct9v1Y4PQAOnZrI799bn5hRLMgo+1jjAMABam7oh/Ymh7o4cBeCx1893fB7aWM5m50v2FH/WxXs3rytajKZKcniM/7Ei0n9v7X8ukfVSYdImJkHbR9DauLYRhALAYx7vdH7qijb5bHkDN8vctuU3rs7v7ireXlw7EqgPF9GXFi3zkFxTJ9mOpenN01fSV0i4qTFo2CLYfy8jJr/SNsqgY/xUBwGJUj1Sbm3s1MXQsgFUF+vlInxb1pFPjcHsY/vHxgbI/KV2H5M2Hz8iqfSf18fhTmXpz1Odfy6VBqL80qxcsTeqWhOR1B0/pWuSo8MBa/o7MizAMABZSVFQks9fElTlbBcBYMXWCpG1kmFzVuVGpdokf39VbDp/Kkh3HU2Xu+sP285PTc/W2Mf6M/dhdn6y376vuFjYTv90m0eFBup94WGBJ/EtOz5FGEdb+e4AwDAAWsjH+tGw9SosnwEzUTXlXdPDVAdkWhtf+3xVyIi1XL5izLylNXj87u9yifrAkpGZLdl6hnMrItb/H/A3F/ZDPNfDlFfoxNKAkEv710/VSJ8hfh+Zgfx/78a/+UH2U/fW5qr2czdEzWVI32F//sm1GhGEAsNhqcwDMLyzQT6LCg852gmloD8OLHhkgQX4+ut/xwZPpcuM7a+z3B6hV806k5UhiarZ9NtnLS31iJJKeU9JNRnW+KMuk/5W9bPuQ134971i/l5ZLgK+3BPj6iJ+Pl1vXOrvXaAAANebo6SxZsi3B6GEAqGGqZ7i6MTbAL9x+7L7LWttDqGMJxpZJQyWvoEivRjnizVX62L9v6qLbvKVm58upjBz5aFVxadXl7RvqGWf1+rTsfDmQnKGPq9Cbk196GWr1fFoZY/Pzcb+SDMIwAFjE52sPSWGRyCWt68vq/cU35gCwNh9vLz3LHOhXElJHdmtcKjjbwvCMO3qWGaj/nDhEAn195HRmrvT65zJ9bNEjl+qOGTl5hXpGeszH6/Rxx1lid0EYBgCL+PpszeCdFzcnDANwKW9vLwlyqC9uUT+kVHC2cceVLt1vrhoAUCNUTWCrhiEyoE0Do4cCAG6DMAwAFvLX/i31DA4AoBhhGAAstPTyTT1ZZAMAHBGGAcAibu3dpFRNHwCAMAwAHm3HsZIFNm7v18zQsQCAOyIMA4AH+2ZTyapTUeGBho4FANwRYRgAPFRBYRGLbADABRCGAcBDrT14UpLTc40eBgC4NcIwAHio7zYfN3oIAOD2CMMA4IHyCgpl8TbCMABcCGEYADzQqn3JciYzT+qH+hs9FABwa4RhAPBA358tkRgWG2X0UADArRGGAcBEMnPzpcXTC/Wm9suSk1cgP2wv7iIxvEujWh4hAJgLYRgAPMzKfcmSlpMvjSICpUfTOkYPBwDcGmEYADzM4q3Fs8LXdG0k3t5eRg8HANwaYRgAPMyK3Sf048hujY0eCgC4PcIwAHiYrLwCaV4/WLrERBg9FABwe4RhAPBAqkTCy4sSCQC4EMIwAHggSiQAoHIIwwDgYVo3DJH2UWFGDwMATIEwDAAe5uoulEgAQGX5VvpMAIDbOpOZa98f3jna0LHAcwX7+0rc1BFGDwNwKcIwAHiANQdO2fdbNAgxdCwwf7itSuh1xXsARiIMA4AH+G1fcq1ej6BjPu7yM3OXcQA2hGEAMLmioqJaD8PlIeigOvj/DYxEGAYAk9ublC6JqTluHVQIO7WH/9ZA1RCGAcDkft1TvPwyrMUKodcK3yOMRxgGAJP7xaRhmKADwB0QhgHAxLLzCmTdwZJOEs4gnLovfjal8d8DrkQYBgATW3vwlOTkF0p0eKAkpGaLJyDoAKhNhGEA8IB64f5t6svXG48aPRw4iV8EnMN/P1QHyzEDgEeE4QZGDwUATImZYQAwqeMpWbqtmreXyMWt64un86RZP0/6Xtwd/61xIcwMA4BJ/bbvpH7s1rSORAT5GT0cADAlZoYBwKRWnV11bmDbhpaeKXPn78edx2Z1/Gxgw8wwAJjU7/uLZ4YHtqtaGAYAlGBmGABMKjU7X8IDfaVbkwjJLSg0ejhi9Vk/ZhoBcyIMA4CJXdq2gfj6eBOGazGwEno9Gz9f6yEMA4CJVbVeGJUPQIQiwBoIwwDgpjJz8yV24lK9v+OFYTqcnYt6YaDm8YuRZ+MGOgAwqVYNQ6RxnSCjhwEApsbMMACY1KWsOgcYihljz8DMMACY1CUWWHUOAGoaM8MAYCJJqdn2/Z7N6l7wfGaugNrHnztzYWYYAExkY/wZ+35oIPMZAOAs/iYFABPZGH/a6CEAqAZmi90XM8MAYCJ/OswMAwCcx8wwAJhEek6+7DyeavQwALgQM8bGIwwDgElsij8jhUVGjwJAbSAk1x7CMACYxPq4U0YPAYCBCMg1gzAMACax4RA3zwE4HyHZOYRhADCB/IJCOkkAcElIJjyXRhgGABPYnZgmmbkFEhboK2nZ+UYPB4CHCbZwcCYMA4CJFtvo3rSOrNybbPRwAFhccBkh2ayBulJ9hvPy8mTjxo2yZcsWKSoq/1Zm9fzs2bNdOT4AgArDZ+uFK7MEMwDAhWH4q6++kkaNGkmfPn2kR48e0qxZM/niiy/KPHfBggUybty4KlweAFCVmeFezesYPRQAsE6ZxLp162T06NHi4+MjQ4YMET8/P1m2bJnceeedsnLlSnn33Xdrb6QAYGEn0nLEz8dLOsdElPm8u38MCQCmDMMvv/yyeHt7y08//ST9+/fXx+Lj43UYfv/99yUrK0s++eQT8fLyqq3xAoBlqSAc6Odj9DAAwDplEr/99ptcf/319iCsqDKJ5cuXy2233abrg8eMGVNhHTEAwDX6tKhn9BAAwFozw6dOnZK2bdue/yJfX/n888912YQKxIWFhfLZZ5/V5DgBwPJ6N+fmOQCo1TAcHR0tJ06cKPM5VRqhSiTUrLAKwioQt2nTxuUDBAAU60UYBoDaDcMdOnSQFStWlPu8CsSffvqp3leBOCwszPUjBABIywYhUj80QDJzWXADAGqtZnj48OGyb98+3TniQoFY1Q6npaW5dHAAgGI9m9FSDQBqfWb41ltvlcTERDl58mSFb2IrmWjRooUcOnTI1WMEAMvrSYkEANR+GG7cuLFMmTKlUm+kAvHzzz/vqnEBgOXl5BXY95kZBgADl2MGANS+7cdS7fvN6gUbOhYA8FSEYQBwU3+eXYJZYXEjAKgZhGEAcFNbj6UYPQQA8HimD8MzZszQN+4FBgZKv379ZN26deWe+9///ld69+4tderUkZCQEOnevTuLhQBwW9uPlpRJAAAMuIGuPElJSTJnzhzZvn275OfnS6tWreS6666Tzp07V/o9Tp8+LYsWLZI77rhDqmvu3Lny+OOPy8yZM3UQnj59ugwbNkx2794tkZGR551fr149+cc//qH7J/v7+8v3338v48aN0+eq1wGAuziVkStHz2QZPQwA8HheRWoJuSpYsGCB7imcmZl53nN/+ctf5L333tOztGXZtWuXfPfddzqErlmzRq9ap8J0dakA3KdPH3n77bf11+r9mjZtKg899JA8/fTTlXqPnj17yogRI+TFF1+s1PmpqakSEREhKSkpEh4eXu2xA0BFftlzQsZ+XPJJ144Xhkmwv69edCN24tJSxwAA1c9rVfpbdM+ePXL77bdLTk5Omc9//vnncvz4cVm6dKn9Zo8tW7bIf/7zH12icODAAfu5KoM7c0NIbm6ubNiwQZ555hn7MW9vbxk8eLAO2heirv/TTz/pWeR///vf5Z6nvlfH71f9xwUAVyor4G49UnLzHACg5lQpDKsyBBUMVYjt1auX3HvvvdK8eXM5ceKEnjGeP3++LF++XK9Id8UVV+gShF9++cX+esdJ6K5du+oZ2epKTk6WgoICiYqKKnVcfa1moMujfkOIiYnR34ePj4+88847MmTIkHLPV32WJ0+eXO1xAkB1bD7CzXMA4HZh+Oeff9ZB+OKLL9ZLNDvO7N52223y9ddfy6hRo+SDDz6QadOmlQqlQUFBcuWVV+oArLYmTZqIEcLCwmTTpk2Snp6ug7uqOVY1z5dddlmZ56uZZ3WO48ywKsUAgJq0tZwwrGaN46ZWfyIBAOBEGD58+LB+fPTRR8sscbjpppvkwQcflDfffNP+fMeOHfVNazfccIMOxK7SoEEDPbOrlot2pL6Ojo4u93WqlKJNmzZ6X3WT2Llzp579LS8MBwQE6A0AaktSarYkpGaLt5dIYZXu6gAA1GhrNdtNc7YwWZZ77rmn1M1pqq5X1Rm7MggrqhuEKtVQs7s26gY69bWaua4s9ZryaqABwAhbjxbPCrdqGGr0UADA4/lWN4iWp127dvZ9VV5QXmcJV1DvP3bsWN07uG/fvrqmOSMjQ9cqK6rrhaoPVjO/inpU57Zu3VoHYNXaTfUZfvfdd2tsjABQVVvOlkh0bhwu+5LSjR4OAHg0l/fkcQzKjsG4Jqj6ZHXz3sSJEyUhIUGXPSxZssR+U118fLwui7BRQfn++++XI0eO6Jlq1W9YdcBQ7wMA7jYz3CkmXL7ZdMzo4QCAR6tWGK5sS7Tg4GCpaapGWW1lWbFiRamv//nPf+oNANyV6rqz5Wxbtc6NI4weDgB4vGqF4f79+0uXLl10ezTbo1p9LjSU+jYAcIa6cS45PVd8vb2kfXSY0cMBAI/nW51ZizNnzsiqVav05jhbrHoOq3Ds2NMXAFB5248WL+zTLipMAv18jB4OAHi8KoVhdaOZ6tGrtq1bt5ZaklmF5IMHD0pcXJy9jOLSSy/V9buqltdxq+laYgAwq23HisNw1yaUSACA24Xhv//976XCr1qe2RaObdu5fX/VjW1qeWa1OdYSqxnkHj16yIwZM1zxfQCAR9h+9ua5LoRhAKgV1e4moWZ/27dvrzfHbgwqDJ8bkPfu3av7+Tp2dfj9999l7dq1hGEAKGtmOKaO0UMBAEtweWs1VRYxbNgwvdlkZWXJli1bSgVkVWahjgMASqRk5Ym/j7e+eS7fYRIBAGCSMFwW1dO3X79+eju3zAIAUFrHRmHi7+st+bmEYQBwq+WYXclWZgEAKI16YQCwQBgGAJSNemEAqD2EYQBwM8wMA0DtIQwDgBsJ8PWWtpGs5gkAtYUwDABupGOjcPH14a9mAKgt/I0LAG6kc0y40UMAAEshDAOAG+ncmHphAKhNhGEAMFhBYZF9vxMzwwDgeYtuAADKdzA5w77fon6IfT/Y31fipo4waFQAYA3MDAOAwXYlpNr3fby9DB0LAFhNpWeG4+Pja2QAzZo1q5H3BQCz2HU8zeghAIBlVToMt2zZskaWZM7Pz3f5+wKAmexOIAwDgNuH4aKikhs8AACus4swDADuH4Y/+eSTCp9/5513ZP369eLn5ydDhw6Vvn37SlRUlH4uMTFRP/fDDz9IXl6e9O7dW+6//37nRw8AJpeUli0nM3KNHgYAWFalw/DYsWPLfe7uu++WP/74Q4fgjz76SGJiYso87+jRozJ+/HhZunSprFy5Uj788MPqjRoAPMSOYyU3zwEATNhNYv78+XrWWM32Lly4sNwgrKjnvvvuO+nVq5d+zbx585y9PACY2k5ungMAc4fh9957T98I9/jjj4uPj88Fz1fnTJgwQdcgv//++85eHgBMbedxZoYBwNRheMuWLfqxXbt2lX6N7dytW7c6e3kAMLUdhGEAMPcKdGlpxR/xJSUlVfo1tnNtrwUAK8jMzZfYiUv1/o4Xhom3l5ccOJFu9LAAwNKcnhlu3ry5fpw9e3alX2M7lwU3AFi9v3BhkUi9EH+jhwIAluV0GL7uuut0/e+cOXPk5ZdfvuD5r7zyinz55Ze6zviGG25w9vIAYPp64Q7RYUYPBQAsy+kyiaefflo+++wzSUhIkGeeeUYHXdWGrU+fPhIZGalDr63PsDpv06ZN+nXR0dHy1FNPueJ7AABT1wu3jw6T1ftPGj0cALAkp8NwnTp1ZNmyZTJs2DA5cuSIvqFOdYsoj5pFbtKkiSxZskS/FgCsiplhAPCAMgmlY8eOsn37dh2CVcBVgbesTT2nWrBt27ZNYmNjXXFpADClwsIie49hwjAAmHhm2CYsLEymTZsmL730kmzYsEG3TTt16pR+rm7dutKlSxe92Ia/PzeKAMDRM1mSnpMv/r7e0qJBiNHDAQDLclkYtvHz85OLLrpIbwCAsu1KKJ4VbhcVKn4+LvmQDgBQDfwNDAAGhuGO0eFGDwUALM3lM8P79++XNWvW6O4SmZmZcv/990uDBg1cfRkAMLVdCcU3z8U2JgwDgEeE4Y0bN8qjjz4qv/32W6njN998c6kwPGPGDJk8ebJERETIjh07dFkFAFhxwQ2lYyPCMACYvkzi+++/l/79++sg7Ng9oixjxoyRrKwsOXDggH4dAFjRsTPZ+pEwDAAmD8PHjx+X0aNHS05Ojm6XtnjxYklLK57xKK/rxLXXXqv31bkAYFUxdYIkIohPxwDA1GH49ddfl4yMDGnevLmsXLlSL74RElJxm6DLLrtMzxyrFmwAYFXUCwOAB4RhtZKcWnLZtuBGZXTo0EE/Hjx40NnLA4BpUSIBAB5wA92hQ4f0Y9++fSv9mvDw4n8A0tPTnb08AJhWbKPileeC/X0lbuoIo4cDAJbk9Mxwfn6+fiwsLKz0a1JSUvRjaGios5cHANOKbRRh9BAAwPKcDsPR0dH6UXWHqKx169bpx2bNmjl7eQAwpZAAH2lSN8joYQCA5TkdhgcMGKBvhvvqq68qdX5ubq689957us5Y3UgHAFbUPipMvL29jB4GAFie02H4rrvu0o//+9//5Mcff7xgEFZ9htUqdSoMjx8/3tnLA4ApdYgurhcGAJg8DKvZ3VGjRunZ4ZEjR8pTTz1lL4NQ4uLiZPXq1TJt2jTp1KmTnkFWQfjee+/VXwOAFXWgkwQAuAWvovKWiqsCteDGTTfdJIsWLdJBtzy2S914440yd+5c8fHxEbNJTU3VS0mrmwBtXTEAoDIyc/MlduJSvT/nb/3kolYlS9UDAIzJay5ZjjkgIEAvraxqgVu1alVqSWbHrUmTJvLOO+/I/PnzTRmEAcAZJ9Nz7PttIymTAACP6DPsSNUAq23Hjh3yxx9/SFJSkhQUFEj9+vWlR48e0rNnzwpnjgHAk+1JLOmtHuTPhAAAeFwYtomNjdUbAKDE3sQ0o4cAAHB1GP7111/1Y58+fSQoqHI9M7Ozs+032Q0cONDZIQCAKexJYtVNAPC4MKy6SXh7e8uWLVsqPRt89OhR++tsK9gBgKfb61AmAQBwDy65ga66DSlc0MgCAEyhsLBI9p0gDAOAR4bhqiosLNSPdJQAYBWHT2dKVm6B0cMAALhDGD506JB+VP3fAMAKdiVw8xwAeETNcHx8fJnHjx8/LqGhoRdcnEMtxfzcc8/pFmusQAfAKnYThgHAM8Jwy5Yty6z9HTp0aJUvPmbMmCq/BgDMaDdt1QDAM8JweTe9VeVmuMDAQHn44Yflr3/9a1UvDwCmxMwwAHhIGP7kk09KfT1u3Dhd8vDiiy9KTExMua9T56gQ3KhRI70a3YVKKgDAU+TkF8jB5AyjhwEAcEUYHjt27HlhWLn++utZdQ4AyrAvKV0KCoskPMhXUrPorQ4AHrXoxs8//1xuLTEAWFFmbr7ETlyq93e8MMxeItEuMkz+OHTa4NEBAFwahgcNGuTsWwCAR7OF4bZRoYRhAHAzhvQZBgArdpJoGxVm9FAAAK6eGT63o8SmTZtk8+bNkpycLFlZWRfsMjFx4kRXDgEA3E5JmQQ3DgOAx4bhWbNmyeTJk+2ry1UWYRiAJ0vJypPjKdn2MgkAgAeG4X/84x8yderUSvUaVi3WqtKTGADMbG9iun6MqRMkYYF+Rg8HAODqmuG1a9fKlClT9P6QIUN0mcTGjRvtwbegoEBOnDghixcvlmuvvVYH4UsvvVQv31xYWOjs5QHAre1NKi6RaB9NvTAAeGQYfvfdd/Vj8+bNZeHChdK1a1fx8yuZ/VCBuH79+jJs2DD55ptvZMaMGbJq1Sq56qqrJDc319nLA4ApZobbcfMcAHhmGF69erUOvGp5ZV/fC1dd3HfffXLTTTfJli1b5J133nH28gDg1vac7STRITpMgv19JW7qCL2pfQCAB4RhVe6gdOrUqeRNvUveNi8v77zX3HnnnbpcYu7cuc5eHgDc2t6k4plhyiQAwEPDsC3sRkZG2o+FhpbcMa3qhc/VpEkT/bhv3z5nLw8Abi0tO198vb2kdUM6SQCAR4bhhg0b6sfU1FT7saioKPHx8dH7O3fuLHc2OS2t+ONDAPBkrRqGiL8vaxwBgDty+m9nW3nErl277Mf8/f3tx8sqhfjss8/0Y+PGjZ29PAC4vfbR4UYPAQBQU2F4wIABuv73559/LnV81KhR+vjHH38skyZNku3bt8u6devk/vvvl3nz5umb7oYPH+7s5QHA7bVnsQ0AcFteRU6ugKFCbpcuXXSd8JEjRyQ8vHgGJDMzUzp37ixxcXE6+DpSl6xXr57uSWyrHzYLVQ4SEREhKSkp9u8VABxl5uZL7MSl9q8/GNNbhsRGGTomALCS1CrkNZeUSahZ4QULFkh+fr79eHBwsD7ev39/HX4dNxWSly9fbrogDADVodqqAQDck0saXQ4aNKjM42ohjpUrV8ru3bv1DLIKy23btpUePXq44rIA4PaC/X30UswAAPdUK13f27dvrzcAsJq2kaHi7V26VAwA4D7o9QMANYhlmAHAvRGGAaAGtYmkkwQAeESZRHx8fI0MoFmzZjXyvgDgDtrSVg0APCMMt2zZ0uUXVy3XHDtQAIAnyMotKFUzDADwgDDsZDtiALCMA8np9v36oQGGjgUA4KIw/Mknn1T4/DvvvCPr168XPz8/GTp0qPTt21eiooqbzCcmJurnfvjhB8nLy5PevXvrlegAwBPtTSwJwwAADwnDY8eOLfe5u+++W/744w8dgj/66COJiYkp87yjR4/K+PHjZenSpbr/8Icffli9UQOAG9ubRBgGAMt0k5g/f76eNVazvQsXLiw3CCvque+++0569eqlXzNv3jxnLw8AboeZYQCwUBh+77339I1wjz/+uPj4+FzwfHXOhAkTdA3y+++/7+zlAcDt7GNmGACsE4a3bNmiH9u1a1fp19jO3bp1q7OXBwC3kpKVJwmp2UYPAwBQW2E4LS1NPyYlJVX6NbZzba8FAE+xN5G/1wDAUmG4efPm+nH27NmVfo3tXBbcAOBpdhOGAcBaYfi6667T9b9z5syRl19++YLnv/LKK/Lll1/qOuMbbrjB2csDgFvZk0AYBgAz8SpycjWNM2fOSKdOnSQhIUF/3bVrV92GrU+fPhIZGalDr63P8GeffSabNm3S4blRo0ayfft2qVOnjphJamqqRERESEpKioSHhxs9HABu5rb318jvB07Zv97xwjAJ9q90F0sAQC3nNaf/hlZhdtmyZTJs2DA5cuSIvqFOdYsojwrCTZo0kSVLlpguCANARdTfb7uZGQYAa5VJKB07dtSzvCoEq4Cr/kEoa1PPqRZs27Ztk9jYWFdcGgDcRnJ6rpzOzBMvL6NHAgCoLJd9dhcWFibTpk2Tl156STZs2KDbpp06VfxRYd26daVLly56sQ1/f39XXRIA3MqeszfPNasXLIdOZho9HABAJbi8kM3Pz08uuugivQGAp8vMzZfYiUv1/jPDO+jHtpGhhGEAsFKZBABAZG9Smj0MAwDMgTAMAC6yN7F4GeY2UWFGDwUA4Ooyifj4ePu+42IZjserg4U3AHiKvUnFYbhrTITETR1h9HAAAK4Mwy1bttSPqm9wfn7+ecer49z3AgAzy8wtED8fL2nRIMTooQAAXB2Gy1ubw8k1OwDAo7RuGCp+PlSgAYDHheFPPvmkSscBwIraUS8MAJ4ZhtUSy1U5XltmzJih+xur5aC7desmb731lvTt27fMcz/44AOZPXu2XvRDUX2PVV/k8s4HgKpqH00YBgAzMfVneXPnztUr2k2aNEk2btyow7BaFjopKanM81esWCGjR4+Wn3/+WdasWSNNmzaVoUOHytGjR2t97AA8EzPDAGAuXkUmLvrt16+f9OnTR95++239dWFhoQ64Dz30kDz99NMXfH1BQYFeHU+9fsyYMZW6ZmpqqkREREhKSoqEh4c7/T0A8JxFN5Rfn7xcmtUPNnRMAGB1qVXIa9VqreZK1W2tlpubq5d9fuaZZ+zHvL29ZfDgwXrWtzIyMzMlLy9P6tWrV60xAICjID8faVI3yOhhAABqsrWaKznTWi05OVnP7EZFRZU6rr7etWtXpd7jqaeeksaNG+sAXZ6cnBy9Of6mAQBlaRMZIt7eXkYPAwBQEzXDqpqiJjajTJ06VebMmSMLFiyQwMDAcs+bMmWKnma3baoMAwDK0iaSemEAsFxrNaM0aNBAfHx8JDExsdRx9XV0dHSFr33llVd0GF62bJl07dq1wnNVGYa6Sc9xZphADKAsbSNDjR4CAKC2W6sZxd/fX7dGW758uVx//fX2G+jU1w8++GC5r3v55ZflX//6lyxdulR69+59wesEBAToDQAupG0UYRgAPDYMuyM1Y6tCugq1qlfw9OnTJSMjQ8aNG6efVx0iYmJidKmD8u9//1smTpwoX3zxhbRo0UL3JlZCQ0P1BgBVlZNXYN9nZhgAzMfUYXjUqFFy4sQJHXBVsO3evbssWbLEflOd6oChOkzYvPvuu7oLxc0331zqfVSf4ueff77Wxw/A/A4kZ9j3G4bxKRIAmI2pw7CiSiLKK4tQi2w4iouLq6VRAbCKvUnppTrkAAAsHIZVd4hNmzbJ5s2bdeuzrKysC3aMULO6AGBWexPTjB4CAMAdwvCsWbNk8uTJcujQoSq9jjAMwMz2JJbMDAMALBqG//GPf+hWZZXpG6w+RjTxCtAAUMpewjAAWGPRjfKsXbvW3q1hyJAhukxi48aN9uCrVolTN7ktXrxYrr32Wh2EL730Ujl+/LhuhQYAZpWSlScJqdlGDwMAYGQYVh0alObNm8vChQv1IhZ+fn7251Ugrl+/vgwbNky++eYbmTFjhqxatUquuuoq3dkBAMxqD/XCAGB6Tofh1atX68D78MMPi6/vhasu7rvvPrnppptky5Yt8s477zh7eQAwzK4EwjAAiNXDsCp3UDp16lTypg69ffPy8s57zZ133qnLJebOnevs5QHAMLsTUo0eAgDA6DBsC7uRkZH2Y46rual64XM1adJEP+7bt8/ZywOAYfYkcPMcAIjVw3DDhg31Y2pqyQyJWgHOx8dH7+/cubPc2eS0ND5iBGBO6tOtXcwMA4DpOR2GbeURu3btsh/z9/e3Hy+rFOKzzz7Tj40bN3b28gBgCNVFIjU7X3y8WXUOACwdhgcMGKBnSH7++edSx0eNGqWPf/zxxzJp0iTZvn27rFu3Tu6//36ZN2+evulu+PDhzl4eAAy9ea55/WCjhwIAcIJXkZMrYKiQ26VLF10nfOTIEQkPD9fHMzMzpXPnzhIXF6eDryN1yXr16umexLb6YbNQ5SARERGSkpJi/14BWM97v+yXKYt3yVWdo2XJtgR9bMcLwyTY36Wr3AMAajivuaRMQs0KL1iwQPLz8+3Hg4OD9fH+/fvr8Ou4qZC8fPly0wVhALDZfXZmuF1kyQ3DAADzcckUxqBBg8o8rhbiWLlypezevVvPIKuw3LZtW+nRo4crLgsAhpdJtIkiDAOAmdXK53nt27fXGwB4gvyCQtl3oritWruoMKOHAwBwAsVtAFAJmbn5Ejtxqd7//qH+kptfKMH+PtKkTpDRQwMAOMHpmuGZM2fKqVOnnH0bADCNPYnFs8Jto8IkNNBP4qaO0Bs3zwGABcOwapXWqFEjufbaa3VP4ezsbNeMDADc1N7E4nrh9tQLA4DpOR2GbUsyL1y4UG6//Xa9+tzYsWPlhx9+kMLCQle8PQC4lb1JxTPD7aNprwgAYvUwvHr1annggQf0ssyqbZpaYvnzzz/XC2rExMTIY489JuvXr3fNaAHAjcokOkRz8xwAiNXD8EUXXSRvvfWWHD16VBYvXix/+ctfJCQkRAfjxMREefPNN/U57dq1kxdeeEH27dvnmpEDgEEOn87Uj+0JwwBgei4pk1B8fHxk2LBhMnv2bElKSpI5c+bIyJEjxc/PTwdjFYInT56sW6z169dPB2h1HgCYjVq3s36IvzQIDTB6KAAAdwnDjgIDA+XWW2+Vb7/9Vo4fPy7vvfeeDBw4UD+ngrEqm3j00UeladOmNXF5AKhxzAoDgGeokTDsqG7dujJ+/HhZsWKFxMfHy7///W+pU6eODsWOyzcDgJkQhgHAM9RaU8xt27bJf/7zH/nyyy8lJSWlti4LADWCm+cAwDPUaBhWM8Eq/H7xxRc6DCtqRlgJCgrSNcUAYEYswwwAnsHlYfj06dMyb948PQus2q6p8GsLwOomuyuuuELuuOMOufHGGyU0lIb1AMyJMAwAnsElYTgrK0vfLKdmgNViG2oRDsUWgnv37q0D8G233aYX5QAAM2taN0hCAlh6GQA8gdN/m9955506CGdkZJQKwK1bt9YBWG1t27Z1fqQA4CbaMisMAB7D6TCsyiFsIiMjZdSoUToA9+3b19m3BgC31DaSEi8A8BROh2G12twNN9ygA/DgwYN1XTAAeLJ2UYRhAPAUTodhtYqc6gwBAJ6ssLC4BEyhTAIAPIfTi24QhAFYwZEzWfb9FvWDDR0LAMAEK9AdO3ZM/vrXv8rdd99dU5cAgFqzOyHNvu/rU+OLdwIAakmN/Y2u+g1/+umnegMAs9vlEIYBAJ6D6Q0AqOLMMADAcxCGAaASCMMA4JkIwwBwAanZeXLU4QY6AIDnIAwDwAXsOs6sMAB4KsIwAFzAzuOpRg8BAOCui26Up27dujJmzBjx8vKqqUsAQK0gDAOA56qxMNy4cWPaqgHwCIRhAPBctVYmkZOTI4mJiVJYWFhblwQApxUUFsnuRGqGAcBTOR2G09PTZdGiRXpT++dKTk6Wm266ScLDw/VssSqfmDBhgg7HAODuDiZnSHZeoQT5+Rg9FACAO5ZJfP311zJu3Dhp0qSJxMXFlXpOzQIPHz5cNm7cKEVFRfpYWlqaTJ8+XZ+rXgsAZiiRaBsVKluOpBg9HACAu80ML126VD/ecMMN4u1d+u3mzp0rGzZs0Ps9e/aUxx57TD+qYPzNN9/IkiVLnL08ANRKGG4fHWb0UAAA7jgzvG3bNt0x4pJLLjnvudmzZ+vHXr16yerVq8XX11fy8vJkwIABsn79epk1a5ZcddVVzg4BAGo8DHcgDAOAR3J6ZjgpKUk/tmzZstRxFXp//fVXHZQfeOABHYQVPz8/uffee/Xs8Lp165y9PAC4VGZuvrR4eqHe1P7OswtutI8iDAOAJ3J6ZvjUqVP60d/fv9RxNfOblZWlw/C5s7/t2rXTjwkJCc5eHgBqzOmMXElIzdb73ZvVlbipI4weEgDA3WaGg4ODS80Q26hZYaVNmzYSFRVV6rmgoCBnLwsANW5XQvGscLN6wRIaUGNt2QEAZg7DrVu31o8rVqwodXzBggV6VnjgwIHnvebEiRP6MTIy0tnLA0CN2X02DHdsRIkEAHgqp8PwkCFDdP3vO++8I4sXL9a9ht966y1dJqGMHDnyvNds2bJFP6q+wwDgrmyLbXRsFG70UAAANcTpz/0eeeQRmTlzpu4ffM0115R6rmPHjmWG4YULF+pZ4x49ejh7eQCo8TIJwjAAeC6nZ4YbNWok3333nURHR+sZYtvWqlUrmT9/vg69jvbv3y8rV67U+4MHD3b28gBQY/afKF5VM5YwDAAeyyV3hKi+wQcPHpTffvtNd4hQAfnSSy+1t1NzdPz4cXnuuef0/tChQ11xeQCoEfkFRRIW4CtN6nLTLwB4KpfdHq1aq11++eUXPE+FZLUBgBl0aBR23idcAADP4XSZBAB4MuqFAcCz1VoYzsnJkcTERCksLKytSwKA0wjDAODZnA7DqpXaokWL9Kb2z5WcnCw33XSThIeH61ZqdevWlQkTJuhwDADujjAMAJ7N6Zrhr7/+WsaNGydNmjSRuLi4Us+pWeDhw4fLxo0bdYcJRbVgmz59uj5XvRYA3JW3l0j7KBbcAABP5vTM8NKlS/XjDTfcIN7epd9u7ty5smHDBr3fs2dPeeyxx/SjCsbffPONLFmyxNnLA0CNaV4/RIL8fYweBgDAnWeGt23bpu+0vuSSS857bvbs2fqxV69esnr1at1qLS8vT7diUyvUzZo1S6666ipnhwAANaJ9NLPCAODpnJ4ZTkpK0o8tW7YsdVyF3l9//VUH5QceeMDec9jPz0/uvfdePTu8bt06Zy8PADWGEgkA8HxOh+FTp07Z+ww7UjO/WVlZev/c2d927drpR7VABwC4q9jGhGEA8HROh+Hg4OBSM8Q2alZYadOmjURFRZV6LiiI1ZwAuKf07Hz7fqfGEYaOBQBggjDcunVr/bhixYpSxxcsWKBLJAYOHHjea06cOKEfIyMjnb08ALjUzuOp9v16IaU/8QIAeB6nw/CQIUN0/e8777wjixcv1r2G33rrLV0moYwcOfK812zZskU/qr7DAOBOdjiEYQCA53O6m8QjjzwiM2fO1P2Dr7nmmlLPdezYscwwvHDhQj1r3KNHD2cvDwAutf0YYRgArMTpmeFGjRrJd999J9HR0XqG2La1atVK5s+fr0Ovo/3798vKlSv1/uDBg529PAC41A7CMABYitMzw4rqG3zw4EH57bffdIcIFZAvvfRSezs1R8ePH5fnnntO7w8dOtQVlwcAl0jPyZeDJzOMHgYAwGxh2NZa7fLLL7/geSokqw0A3HFW+OzK8QAAi3C6TAIAPMXWoylGDwEAYNaZYUeJiYl6mWbbghz16tWTzp07n9dvGADcyTbCMABYjsvCsLpp7v3335e3335bduzYUeY5sbGx8tBDD8n48ePPu7EOAIzGzDAAWI9LyiROnz6tF9e4//77dRB27CrhuKnn7rvvPn3umTNnXHFpAHCJzNx82X8i3ehhAADMNjOsQu51112nO0ko9evXl1tvvVX69eun260pqsPEunXrZN68eZKcnCyrV6/Wr/nll1+c/w4AwIU3z0WGBUhSWo7RwwEAmCUMf/HFF7Jq1Spd9nD77bfrlejCwsLOO2/MmDEydepUeeCBB+Szzz7Tr/nyyy9l9OjRzg4BAKo9Gxw7canef+bqDvoxtlG4JKUVLxkPAPB83q4Iw8qgQYN0yC0rCNuEhobKrFmz9LlqRvnzzz939vIA4NLFNmIbhxs9FACAmWaGN27cqGeFH3zwwUq/Rt1Ep0ok/vzzT2cvDwAuXYa5Z7O6Ejd1hNHDAQCYZWbY1j6tZcuWlX6N7VzbawHAaAfO3jzXpUmE0UMBAJgpDEdEFP/DcezYsUq/Ri3JrISH83EkAPdQWCTSMCxAosIDjR4KAMBMYVgtpqF88sknlX6N7VzbawHAHXSJYVYYAKzG6TB8880365vhFixYIM8//7zer8iLL74oX3/9ta4zvuWWW5y9PAC4TGfCMABYjlfRhdLrBeTl5UnXrl1l9+7dOuB26tRJ7rrrLt1nODIyUh9TyzOvXbtWd5JQyzSrS3bs2FE2b94svr41siJ0jUlNTdWlISkpKZR5AB7UWk35YExvGRLLsvEAYHZVyWtOJ1E/Pz9ZvHixXHnllXLw4EHZvn27PPnkk+Wer4Jwq1at9GvMFoQBeLbOMfyCCwBW45LlmFu0aCFbtmyRCRMm6BRe3nLM6rknnnhCNm3aJM2aNXPFpQHAJeqH+Es0N88BgOW4bGo2JCREpk2bJv/6179kw4YNuhzC1jqtXr16+ma5Xr16ib+/v6suCQAuoxbbUGVdAABrcToMz549Wz+2b99e1wmrsHvxxRfrDQDMohMrzwGAJTldJqFulhs3bpwcOnTINSMCAAOwDDMAWJPLFt1o27atK8YDALUmO6/Avs/MMABYk9Nh2La08unTp10xHgCoNbsT0uz73DwHANbkdBi+4YYbdKeI7777zjUjAoBasu1Yin2fm+cAwJqcDsOPPPKING/eXN59911Zvny5a0YFALVg8+GSMAwAsCanw7Ba1ePHH3+UDh06yFVXXSV/+9vfZMWKFbqtmpOL2wFAjdp8+IzRQwAAmL21mo+Pj31fhd+PPvpIb5WhPpbMz893dggAUGXJ6Tly+HSW0cMAAJg9DJ87+8tsMAAz+DOeWWEAgAvC8KRJk1wzEgCoRX/G0wEHAEAYBmBRGwnDAABX3EAHAGaTX1AoW47QSQIAQBgGYEF7EtMlM7dAQgOc/nAMAGC1MLx48WLp2bOn3r744osqvVadb3vtsmXLqnppAHBpiUSXmOLl5AEA1lWlMKw6RTz22GOyefNmadiwodx+++1Vutjo0aOlQYMGsmnTJpkwYUJVxwoALu0k0a0pYRgArK5KYfinn36SPXv2iLe3t7z++utVvpjqKzx9+nTdm3jbtm3yyy+/VPk9AKCqMnPzpcXTC/Wm9m2dJLo1rWP00AAAZgrDX3/9tX4cMmSIxMbGVuuC6nXDhg3T+/PnzxdnzZgxQ1q0aCGBgYHSr18/WbduXbnnbt++XW666SZ9vi2YA7CWM5m5ciA5Q+93a8LMMABYXZXCsAqaKkSOHDnSqYtec801uuTi999/d+p95s6dK48//rhu77Zx40bp1q2bDtpJSUllnp+ZmSmtWrWSqVOnSnR0tFPXBmBOm892kWjVIETqBPsbPRwAgJnC8KFDh/Rj+/btnbpou3bt9GNcXJxT7/Paa6/J+PHjZdy4cXrGeebMmRIcHCwff/xxmef36dNHpk2bJrfddpsEBAQ4dW0A5rTlcHG9cPdmlEgAAKq46EZKSvGMSr169Zy6qO31qamp1X6P3Nxc2bBhgzzzzDP2Y6qWefDgwbJmzRpxlZycHL3ZODNmAMbbdLj477GezepKsL+vxE0dYfSQAABmmRkODw/Xj2fOFM+sVJft9WFhYdV+j+TkZCkoKJCoqKhSx9XXCQkJ4ipTpkyRiIgI+9a0aVOXvTeA2rflaPHfPz2YGQYAVDUMq3Zqyo4dO5y66M6dO/VjZGSkuDs186xmxG3b4cOHjR4SACdk5BRIsL+PtI+q/i/jAACLhuG+ffvqG9++++47py767bff6hvxVA1vdal+xapFW2JiYqnj6mtX3hynaovVjLjjBsDcujaJEF8fFuAEAFQxDA8fPlw//vDDD7Jq1apqXfDXX3/Vr3d8v+rw9/eXXr16yfLly+3HCgsL9dcXX3xxtd8XgOfr0ayu0UMAAJgxDNt69KrZ4VtuuUX27t1bpYupBTtuvfVWPSus3ufmm2+u6nhLUW3VPvjgA5k1a5YuvbjvvvskIyNDd5dQxowZU+oGO3XTnVr9Tm1q/+jRo3p/3759To0DgLmom+cAAKhyGPbz85NXXnlF76tevmpm9o033tABtCLp6el6gYvevXvbewC/+uqr4utbpWYW5xk1apQez8SJE6V79+462C5ZssR+U118fLwcP37cfv6xY8ekR48eelPH1WvV/j333OPUOACYCzfPAQBsvIrUNG8Vvfjii3qhCzXDq4SEhMiAAQN0OFY3xamvVUBW9btqMYyVK1fqr22XeuGFF+TZZ58VM1Kt1VRXCXUzHfXDgDmoJZhjJy7V+03rBsnKp64wekgAADfJa9Wamn3uueekSZMm8tBDD+lV3dTMr5qRVVtZbCFYLYjx9ttvy1133VWdywKA07o2ZVYYAFCi2rdTq7pcVQOs6nZVZwcVeMvb1PMTJkzQ5xOEARipW5MIo4cAAHAjThXtNm7cWNfdqm379u2yefNmOXnypKSlpekFNerXry/dunWTTp06uW7EAFBFhYUl1WDdmRkGADhw7g42ByrwEnoBuKN9J9Lt++1YbAMA4ICu8wA83rqDp+z7/r78tQcAKMG/CgA83vq4kjAMAIAjwjAAj68XXh932uhhAADcFGEYgEfbk5QmZzLzjB4GAMBNEYYBeLTf9580eggAADdGGAbg0X4/QL0wAKB8hGEAHrXscounF+pN7at64bUHmRkGAJSPMAzAo+uFT2fmSZCfj9FDAQC4KcIwAI+vF+7ZjFXnAABlIwwD8Ph64T4t6xk9FACAmyIMA/BIql7497P1wn1bEIYBAGUjDAPwSHuT0nV/4WB/H+kUE270cAAAboowDMAjrTu7BHPvFvXEz4e/6gAAZeNfCAAeaf3B4jB8UStKJAAA5fOt4DkAMK31caf140Wt6kuwv6/ETR1h9JAAAG6ImWEAHiklq7heuEtMhNFDAQC4McIwAI9FvTAA4EL4VwKAx6JeGABwIYRhAB5L1QsDAFARwjAAjxREvTAAoBIIwwA8Uq9mdagXBgBcEP9SAPBIfVtSLwwAuDDCMACPkZtfaN+nXhgAUBmEYQAeY8Oh4oU2lNhG4YaOBQBgDoRhAB7j170n7Pve3l6GjgUAYA6EYQAeY+WeZKOHAAAwGV+jBwAA1ZGZmy+xE5fq/R0vDJPktFw5kJxh9LAAACbDzDAAj7BiT5LRQwAAmBBhGIBH+HkXYRgAUHWEYQCml5VbIKv3nzR6GAAAEyIMAzC9dXGnJCe/UBpFBBo9FACAyRCGAZjeyj3FLdUGtmto9FAAACZDGAZger+cbak2sG0Do4cCADAZwjAA0zt6Jkv8fb2lX6t6Rg8FAGAyhGEAHuGiVvUl2J/W6QCAqiEMA/AIl7enXhgAUHWEYQAe4fL2kUYPAQBgQnymCMD0mtcPlhYNQvR+3NQRRg8HAGAizAwDMD26SAAAqoswDMCUioqK7PuD6C8MAKgmwjAAU9qdmGbf792ClmoAgOohDAMwpeU7k+z7qscwAADVwb8gANxaZm6+tHh6od7Uvq1EYvG2BKOHBgDwAIRhAKYskThwIsPoYQAAPABhGIDpfL/5uNFDAAB4CMIwAFNRJRLfbTlm9DAAAB6CMAzAVLYdTZVDJzMlyM/H6KEAADwAYRiAqXx/dlZ4UHt6CwMAnEcYBmCqEonvtxTXCw/vHG30cAAAHoAwDMA0Nh9OkaNnsiTE30cGsAQzAMAFCMMATGPxtuJZ4SGxURJIzTAAwAUIwwBMY+n2RP04sltjo4cCAPAQhGEAppGUliPhgb4yoC03zwEAXIMwDMBUhnWKFn9f/uoCALgG/6IAMJVrKJEAALgQYRiAadQN9pNLWtc3ehgAAA9CGAbgFjJz86XF0wv1pvbLorpI+Pnw1xYAwHV8XfheAOByufmF9n3HhTaC/X0lbuoIg0YFAPAUTLEAcGvLdyXZ93u3qGfoWAAAnocwDMCtzVt/2L7v4+1l6FgAAJ6HMAzAbe1LSpe1B08ZPQwAgAcjDANwW1+uizd6CAAAD0cYBuCWsvMKZP6GI0YPAwDg4QjDANzSwi3HJSUrTxrXCTR6KAAAD0YYBuCW/rP2kH68pVdTo4cCAPBghGEAbmdXQqpsjD8jvt5ecmPPGKOHAwDwYIRhAG5n3vriWuFhnaKlYViA0cMBAHgwwjAAt1t6+X+bj+nHO/o1M2B0AAArIQwDcDuZuQXSqkGIXNy6vtFDAQB4OMIwALd0e79m4uXFinMAgJpFGAbgdvx9veWmnk2MHgYAwAIIwwDczlWdoqVuiL/RwwAAWABhGIBbOHYmy75/Wx96CwMAagdhGIBb+HjVQft+92Z1DB0LAMA6fI0eAAAkpWbL/I1Hy3wu2N9X4qaOqPUxAQCsgZlhAIZ779cDkptfaPQwAAAWRBgGYKiT6Tnyn7WHjB4GAMCiCMMADFtpTvl0dZxk5xVKl5jwWh8fAACEYQCG+mLdYf1432WtjR4KAMCCCMMADJWVWyCdGofLoHYNjR4KAMCCCMMADPfQFW1YehkAYAjCMABDtY0MlaGx0UYPAwBgUYRhALUuPbvkZrp7B7USb29mhQEAxiAMA6h1s9bE2feHdmJWGABgHMIwgFppoWZz+FSmfLiyZOllH2aFAQAGIgwDqFX/XLhDclhtDgDgJgjDAGrNL3tOyNLticwGAwDcBmEYQK3IzS+U5/+3Xe//pV8zo4cDAIDmW/wAADVr1uo4OZicIQ1CA+SBy9vIrDWHSj0f7O8rcVNHGDY+AIA1MTMMoFbM/OWAfvy/qztIaCC/hwMA3ANhGECNdI04V1ZegfRuXldu6BFTY+MDAKCqCMMAaoW6Z27ydZ1YdhkA4FYIwwBq9KY5m9v6NJVOjSMMHQ8AAOciDAOoMW8u32vff+jKtoaOBQCAshCGAdRYT+GPfytZdjkiyM/Q8QAAUBbCMACX3yyXlJYtE+ZtqrGxAQDgKoRhAC5VWFgkE+ZtluT0XGkbGWr0cAAAqBBhGIBLfbI6TlbuTZZAP2955dZuRg8HAADPDsMzZsyQFi1aSGBgoPTr10/WrVtX4flfffWVdOjQQZ/fpUsXWbRoUa2NFbCCN5YV3zQ38ZpOzAwDANyeqcPw3Llz5fHHH5dJkybJxo0bpVu3bjJs2DBJSkoq8/zVq1fL6NGj5e6775Y///xTrr/+er1t27at1scOeEJtcFnyC4vk6i7RMrpvU5eMDwCAmmTqMPzaa6/J+PHjZdy4cRIbGyszZ86U4OBg+fjjj8s8/4033pCrrrpKnnzySenYsaO8+OKL0rNnT3n77bdrfeyAp9UJ2zSKCJQpN3StcHGNYH9fiZs6Qm9qHwAAo5g2DOfm5sqGDRtk8ODB9mPe3t766zVr1pT5GnXc8XxFzSSXd76Sk5MjqamppTYApb324x77/iu3dJWIYNqoAQDMwbRhODk5WQoKCiQqKqrUcfV1QkJCma9Rx6tyvjJlyhSJiIiwb02b8tEv4OjDlQdK9RPu0ayuoeMBAMASYbi2PPPMM5KSkmLfDh8+bPSQALeoDVb+t/mY/HPhTpe8FwAARjBtGG7QoIH4+PhIYmJiqePq6+jo6DJfo45X5XwlICBAwsPDS20ARNbsP2lfWOOOfs2MHg4AANYKw/7+/tKrVy9Zvny5/VhhYaH++uKLLy7zNeq44/nKjz/+WO75gFnVxCzwuR6e86fkFRTJiK6N5JnhHWrkGgAA1DRT38at2qqNHTtWevfuLX379pXp06dLRkaG7i6hjBkzRmJiYnTdr/LII4/IoEGD5NVXX5URI0bInDlz5I8//pD333/f4O8EMJ+MnAK5qFU9ee3WblLg0E0CAAAzMXUYHjVqlJw4cUImTpyob4Lr3r27LFmyxH6TXHx8vO4wYXPJJZfIF198Ic8++6z83//9n7Rt21a++eYb6dy5s4HfBVB9atY3duJSvb/jhWE13qZs5/GSbirto0Ll/TG9JcDXp8ZmnwEAqGmmDsPKgw8+qLeyrFix4rxjt9xyi94AVM3qfcky/rM/7F+rIBweSAs1AIC5mbZmGLCS2qgBrsjircdl7CfrdGmETcOwgFofBwAArmb6mWHA09R26UNlPDF/ixQViQztFCU/bC/dkaUitpXmAABwV8wMAxad7a3KEssqCI+5uLm8eks3Q8cEAICrEYYBi4fesiSmZss9s0vqgx8d3FYmX9tJfLy9DB0XAACuZvznr4AHlTK4Y4lDVS3fmShPzt8ipzJy7cf+NrCVeHkRhAEAnoeZYVhuRras41U515P9a+FOuXvWHzoId4gOM3o4AADUOMKwG6tqQHNFyKvt967J90DV/WdtvH68+9KWMudvFxk9HAAAapz5PsMF4DKp2Xny2g977F/XD/GXV27tJpe3j+QXCwCAJRCGAQtSyyfPXR8v05buluT0ktrgBQ9cIs3qhRg6NgAAahNhGLCgUe/9LjvOLq3cskGIHEzO0PsNQqu3kAb9hAEAZkXNMGARGw+dtu+rIBwW4CvPjugo3zxwiaHjAgDASMwMAx7up11J8slvcbLBIQzf3CtGnh7eUc8EUxsMALAywjDggbJyC+z7D37xp3708/GSvILiVeVeuK6zKXsgAwDgavxrCHgItXzy2gMn5euNR2Th1uP246EBvnLHRc1kdJ+mctkrvxg6RgAA3A1hGPAQV72xUo6czjrv+PIJAyUqPIhyCAAAykAYBkwmr6BQVu9Plp92JsnyXUn24yoIq1ngq7tEyzVdG8mYj9fr42GBfgaOFgAA90YYBkxQ/rAvKd3+df+pP0t6zvmzvP++qYtc2y1Ggvx9amwWmBZqAABPQxgG3IxjkH3gPxvlz8Nn5Exmnv2YCsJqpbjLO0RK/zb15bG5m/Xxkd0a6yAMAAAqjzAMGKSoqLizg/L+rwdkb1K67DyWKgdPFi+Aofy8+4R+DPLzkay84g4RX47vJ/1a1hdvb6+zwbk4DAMAgKojDAM1KL+gUI6nlNzU9ubyvXL4dJYcPJFRKvROX7a3zNf/v2Ht5ZI2DaRlg2DpNvlHfaxb0zo6CAMAAOcRhoFqhlybNftPSlp2vpxIy5FjDsF38Gu/SGJqjhQUlswAz/zlQJnvN6JLtHRpUkdiG4VLiwbBMvDlFfr4Xf1b6DpdOkEAAFAzCMOwXGlCbn5JkD1wIl1UVj2ZnmM/Nmf9YcnJK5S07Dw5nZlrP37b+79LalaenMrIldTsknB696w/yrzWsTPZ+tHX20vyzwbiW3s3kXZRYdKyQYg0igiUq99cpY9Pu6WbfREMdwm+3CwHALACwrBJqLCW4VdcM5qVVxKW1GxkkF++qKiV5RCiElKyz+sqcPRMlq49VaWqju8RfypTAn19pLCoyF6XqqgOBoFlnL/jeKo+3/G9N8WfEX9fbx0sHcexel+y+Pp4n/ceS7Yl6OOO587fcES8vbz0TGq2wzhmrtgv4uWlZ2Mdj0/8dpu+Xo7DsXtm/SH5BUWSk19Q6tz+U3+SnPxC/f05lOrKNW/9dt5/6xe+21Hmz2DLkZQyj7duGCLREYHSMDRA6gT7y6er4+y1va0ahkqwv490ef4Hfez5azu5XegFAMDKCMMmMeDsx+bnGjSt7ONXvHr+SmNDXvu1zHOvmr6yzOPXvn1+UFRufnfNecdu/3BtmefeM3tDmccfn3f+TV8Tv91e5rlv/rSvzOPzNxw979jq/SfLPPe0QzcGRxFBfro3b6Cft+w/UVzDe2XHSKkT5C9hgb4S5Oct754tbXhrdA8deusG+0mAr7f9Z/LdQ5eWCri2MKxqeylxAADAvRGGTcjLS33cX7yv7qPyUgfUcVXLevbjeF8fL/EWLymSIskrKD6mAtzZU8VLvOyzwGrmUs3I6qe8RNe/2oKien/1nHIyo7hkIDIsQHzO3sB1PKW4FKBpvSDx8fKyn3sguThYto8K1TPAtuNbjxbPrvZuXlf8fLz1/poDxQH28g4Nxd/HW3y91fEiWbg1QR+/pVcTPUOtvif1fdvC5sNXtpGQsyF0yuJd9l67apEJ9b2qkghbGP/2wf5SN8hfAv291VtL35eWF1/7mSvsgTV24lJ76HUMt7YwrEIys7oAAHgWwrBJ7HhhWKkgZgtu2yaXfXzLpKHnhbw/Jw4p89w/nh1c5nFbUDz3+IonLzvvvZc+OrDMcxc80L/M47Pv7nvee8y4vWepc21hePJ1pUsLbGH43kGt7e9hC8Oq125ZgbVtpCpXIMiWhdpgAICVFU/NAQAAABZEGAYAAIBlUSYBWATlEAAAnI+ZYQAAAFgWYRgAAACWRZkE4IEoiQAAoHKYGQYAAIBlMTMMmBgzwAAAOIeZYQAAAFgWM8OACTADDABAzSAMA26G4AsAQO2hTAIAAACWxcwwYNBMLzPAAAAYjzAMuBABFwAAcyEMA9WYvSX0AgDgGQjDqDVlBUhXhFCCLAAAqC6voqKiomq/2oJSU1MlIiJCUlJSJDw83OjhAAAAwIm8RjcJAAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJbla/QAzKaoqEg/pqamGj0UAAAAlMGW02y5rSKE4SpKS0vTj02bNjV6KAAAALhAbouIiKjoFPEqqkxkhl1hYaEcO3ZMwsLCxMvLy+jheMxvb+qXi8OHD0t4eLjRw0EN4edsHfysrYGfszWkmvTnrOKtCsKNGzcWb++Kq4KZGa4i9R+0SZMmRg/DI6k/ZGb6g4bq4edsHfysrYGfszWEm/DnfKEZYRtuoAMAAIBlEYYBAABgWYRhGC4gIEAmTZqkH+G5+DlbBz9ra+DnbA0BFvg5cwMdAAAALIuZYQAAAFgWYRgAAACWRRgGAACAZRGGAQAAYFmEYRjqX//6l1xyySUSHBwsderUKfOc+Ph4GTFihD4nMjJSnnzyScnPz6/1scK1WrRooVdxdNymTp1q9LDgpBkzZuifbWBgoPTr10/WrVtn9JDgYs8///x5f3Y7dOhg9LDgpF9//VVGjhypV2xTP9Nvvvmm1POq38LEiROlUaNGEhQUJIMHD5a9e/eKJyAMw1C5ublyyy23yH333Vfm8wUFBToIq/NWr14ts2bNkk8//VT/gYT5vfDCC3L8+HH79tBDDxk9JDhh7ty58vjjj+s2TBs3bpRu3brJsGHDJCkpyeihwcU6depU6s/uqlWrjB4SnJSRkaH/zKpfaMvy8ssvy5tvvikzZ86UtWvXSkhIiP7znZ2dLaanWqsBRvvkk0+KIiIizju+aNGiIm9v76KEhAT7sXfffbcoPDy8KCcnp5ZHCVdq3rx50euvv270MOBCffv2LXrggQfsXxcUFBQ1bty4aMqUKYaOC641adKkom7duhk9DNQgESlasGCB/evCwsKi6OjoomnTptmPnTlzpiggIKDoyy+/LDI7Zobh1tasWSNdunSRqKgo+zH1m2hqaqps377d0LHBeaoson79+tKjRw+ZNm0a5S8mpj692bBhg/7o1Mbb21t/rf4cw7Ooj8fVx+mtWrWSO+64Q5ezwXMdPHhQEhISSv35joiI0KVQnvDn29foAQAVUX/4HIOwYvtaPQfzevjhh6Vnz55Sr149XQLzzDPP6I9bX3vtNaOHhmpITk7WZU1l/XndtWuXYeOC66kApMrV2rdvr//MTp48WQYMGCDbtm2TsLAwo4eHGpBw9t/bsv58e8K/xcwMw+Wefvrp826uOHfjH0fPVJWfvaotveyyy6Rr165y7733yquvvipvvfWW5OTkGP1tAKjA8OHD9b0e6s+u+qRu0aJFcubMGZk3b57RQwOqhZlhuNyECRPkrrvuqvAc9dFaZURHR593N3piYqL9OXjOz17NNqkyibi4OD3jBHNp0KCB+Pj42P982qiv+bPq2VQnoHbt2sm+ffuMHgpqSPTZP8Pqz7PqJmGjvu7evbuYHWEYLtewYUO9ucLFF1+s26+pu9FVWzXlxx9/lPDwcImNjXXJNeAeP/tNmzbpGlPbzxnm4u/vL7169ZLly5fL9ddfr48VFhbqrx988EGjh4calJ6eLvv375c777zT6KGghrRs2VIHYvXn2RZ+1b07qqtEed2gzIQwDEOpmy5OnTqlH1W9oQpESps2bSQ0NFSGDh2qQ6/6S1a1dVG1Sc8++6w88MADEhAQYPTwUU3qhgv1l+jll1+uawzV14899pj85S9/kbp16xo9PFSTKn0ZO3as9O7dW/r27SvTp0/X7ZrGjRtn9NDgQk888YTuR9u8eXM5duyYbqWnPhUYPXq00UODk7/U7HOY3Vc3zal/k9V9Hc2aNZNHH31U/vnPf0rbtm11OH7uuef0TZS2X35Nzeh2FrC2sWPH6hYu524///yz/Zy4uLii4cOHFwUFBRU1aNCgaMKECUV5eXmGjhvO2bBhQ1G/fv10O73AwMCijh07Fr300ktF2dnZRg8NTnrrrbeKmjVrVuTv769brf3+++9GDwkuNmrUqKJGjRrpn3FMTIz+et++fUYPC076+eefy/z3WP07bWuv9txzzxVFRUXplmpXXnll0e7du4s8gZf6H6MDOQAAAGAEukkAAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAAAAsCzCMAAAACyLMAwAAADLIgwDAADAsgjDAODmPv30U/Hy8tJbXFycmEFubq60bdtWj3n+/PnlnqcWQQ0PDxdvb2+JioqSUaNGSXx8/AXf/4EHHtDvPXbsWBePHIDVEIYBAC73xhtvyL59+6Rz585y0003lXve/v37JS0tTYfipKQkmTdvnowcOfKC7//UU0+Jv7+/fPbZZ7JhwwYXjx6AlRCGAQAupcLtv//9b73/7LPP6hnc8jRq1Ei2bt0qS5YskVatWuljW7Zskc2bN1d4jWbNmulZYRWin3vuORd/BwCshDAMAHCpd999V06ePKkD6y233FLhuSEhIXr2eNiwYfLiiy/aj2/atOmC15kwYYJ+XLx4MbPDAKqNMAwAcJmCggJ5++239f7o0aN1LXBlXXzxxfb9bdu2XfD89u3bS8+ePfX+W2+9Va3xAgBhGADgMj/++KMcPnxY799xxx1Vem2LFi30THFlw7DjNb766itdngEAVUUYBgAPoLo3vPPOO3L55ZdLw4YN9c1l0dHRcvXVV8vnn38uhYWFF3wPVdrw//7f/9MzrkFBQbq7w5AhQ2TBggWV7mqhboBTVCeJLl26VOl7UO/bunXrKoVh2815mZmZ8u2331bpegCgEIYBwORUMO3WrZtuN7ZixQpJTk6WvLw8SUxM1PW0d955pwwaNEhOnTpV7nuom9g6deok06ZNkz179kh2drbu7rBs2TK58cYb5e9//3ulxvLzzz/rx4suuqjK38eaNWv0OJQjR45ISkrKBV/TvHlzHfoV9b0CQFURhgHAxNLT0+XKK6+UXbt26a+vv/56+d///id//PGHLh1QIVhZtWqVblmmanrPdebMGbnqqqt0eFZUeFbBUr3HnDlzdC3v+++/LzNnzqxwLCrA2maM+/TpU6XvIz8/X+69917dHcJm+/btlXpt37599eMvv/xSpWsCgEIYBgATmzx5shw4cMDexkyVNKjQ26tXL7n55pv1TK2trnb16tU61Jb1HseOHdP706dPl9mzZ+twrN5DLYKxcuVKue6662Tt2rUVjkW9v02PHj2q9H28/vrruqWao8qWSqhxKkePHrUHegCoLMIwAJhUTk6OfPjhh3pflTg8//zzZdbhqlri+vXr669tnR4c30PVAttmcx955JHz3sPHx0fee+89CQwMvODMsE1kZGSlv49Dhw7Zx37JJZdUOQw7Xsv2iwEAVBZhGABMSvXWVSUOyl133aVDa1nUcse33nqr3t+xY4ccP37c/pwqhbC9x1/+8pdyr6VuplO9gCty4sQJ+37dunUr/X08+OCD+ga4iIgIXdoRFhZWpTBcr149+35CQkKlrwsACmEYAFzE1mnBmc02S1sZjmGxX79+FZ7r+Lzj6xz3beUG5endu3eFzzveoFfZMPzf//5Xvv/+e70/depUady4sV6E49yxVcTxWhkZGZV6DQDYEIYBwKQcw+eFyhJsHRfOfd3p06ft+6olW0Uu9LxjGUVWVpZciOoL/PDDD9vLI2wdK2wt2dRMs+pocSGO1/Lz87vg+QDgyLfUVwCAatu5c6fT79GoUaNqvU7NKhvNMSyrwG0rdyjPc889p296UwFW3dhn+x4c+xOr2eErrriiwvdxDPd16tRx4jsAYEWEYQBwkQ4dOtTq9RxrZVUXhXbt2pV7rmMtrePrHEsM1ExsRe/hWBN8oTCsZpxVD+DybNy40X4zn1roQ90AaNO1a9cqhWHH2e1mzZpVeC4AnIsyCQAwKVttrXKhtmfr1q0r83WOIVTdkFcRdbNdRRxndNXCHeVRq+GpkgjV81itVKdawpX3PpWpG7ZdKyAgQNq0aXPB8wHAEWEYAExK3fBmKwuYNWtWuUsuq9pc2zLJsbGxpUox1E1xqouDopZtLo+aeV66dGmF41HvZasbXr9+fbnnzZgxwx6s1UIe57ZsU7PVMTExlQ7Dtmup3sbUDAOoKsIwAJiUmgm955577KHxxRdfPO8ctaKbal2mlmhW1L4jFUTHjBljD5VvvPFGuTO5aonmivj7+9u7VjjORDtSi3vYZoLHjh1bbgmEbXb4QqvQqT7JtsU6hg4dWuG5AFAWwjAAmNjEiROlVatWel8tXKFWnVu4cKGuyf3666912FQryilqWeW//e1v572Hep2t28Sjjz6qw7GaBVbvoWaUBwwYIN9++6192eOKbthTK9XZwrCakT6XWtQjNTVVGjRoIK+++mq535etblidGx8fX+55v/76q+Tl5en9G264odzzAKA8hGEAMDHVsWH58uX2m/dUAL7mmmvsyzGvWLFCH+/fv7/u51vWwhzqhrolS5bYb4D77LPPSi3HrJZZVot62FqfKeWtRqeCtJqxVrPIamloR4sWLZL58+fr/ddee82+Kl5ZKls3/MUXX9hrn7t3717ueQBQHsIwAJhcixYtZPPmzbo7w6BBg3TIVLWzatU4FWpVuFUzqI5dJM7VrVs3vTrdhAkT9E1tKtCq2dvLL79cB85PPvlEz9La2OqMz6WufeONN5YKqrZewLYSjSuvvFLuvPPOCr+nyoRhFbjVoh3K/fffX+H7AUB5vIpUQRkAABeg6pM/+ugjadKkiRw+fLjc81Rni4suukjPQu/fv7/CFmvOUDf8qVCtAnhcXJyEhobWyHUAeDZmhgEAF6RmdlXdsKKCbkXUTXRqdli1TpsyZUqNjEfd1PfSSy/p/SeffJIgDKDaCMMAAD2DW94HhSrU3nffffaOFKoLxIWooOrr66vLK44cOeLy8X711Vd6xT+1yIZtSWcAqA5WoAMA6LZsqgPEbbfdpmd2IyMj9Wywalv2wQcf6M4SyuDBg2XEiBEXfL/27dvLxx9/rEO26gahSitcSQX0SZMm6W4ZQUFBLn1vANZCzTAAQHeLUAt3VER1pFClEhV1gQAAsyEMAwBk9+7dui3bsmXL9M1oJ06c0P17VfBVK8upFmtq1tjbm+o6AJ6FMAwAAADL4ld8AAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJZFGAYAAIBlEYYBAABgWYRhAAAAWBZhGAAAAJZFGAYAAIBY1f8HwANUjdrwdrYAAAAASUVORK5CYII=", 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" ] @@ -3736,13 +7049,2045 @@ "id": "016ffe7c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:32.493853Z", - "iopub.status.busy": "2024-06-04T23:19:32.493772Z", - "iopub.status.idle": "2024-06-04T23:19:32.763066Z", - "shell.execute_reply": "2024-06-04T23:19:32.762828Z" + "iopub.execute_input": "2025-04-03T19:33:48.134035Z", + "iopub.status.busy": "2025-04-03T19:33:48.133940Z", + "iopub.status.idle": "2025-04-03T19:33:48.418974Z", + "shell.execute_reply": "2025-04-03T19:33:48.418704Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795326.355502333, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795268.885511458, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795196.367825005, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18795104.862821113, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794989.399687696, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794843.706650957, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794659.87071198, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794427.908521358, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18794135.22526347, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793765.932449568, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18793299.98803079, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18792712.112872534, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791970.425932087, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18791034.72591697, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18789854.32913581, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18788365.350956466, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18786487.290938053, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18784118.748442672, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18781132.05553399, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18777366.566605024, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18772620.289297033, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18766639.479676694, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18759105.758860495, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18749620.243803147, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18737684.132153213, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18722675.157982755, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18703819.37168406, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18680157.84067929, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18650508.189617783, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18613421.503628485, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18567136.14871325, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18509531.699850053, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18438088.608600505, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18349862.649110064, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18241487.557216965, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18109224.25083878, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17949079.523028806, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17757018.994714484, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17529294.98190815, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17262895.457700975, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16956091.882983487, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16609021.736273043, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16224194.650997939, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15806778.142363887, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15364525.127389485, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14907268.75187378, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14446023.624531085, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13991857.160644894, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13554773.727504015, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13142847.182203237, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12761747.456957739, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12414679.232309299, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12102642.724649917, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11824874.692517474, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11579334.50630629, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11363143.416383019, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11172936.696242273, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11005127.926431675, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10856105.032984463, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10722381.625233045, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10600721.735570516, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10488247.552619573, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10382531.68105097, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10281669.161078628, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10184320.545404715, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089716.55059902, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9997617.850835908, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9908230.155360885, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9822083.085401118, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9739888.930170696, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9662401.666184625, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9590296.226307327, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9524082.854699288, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9464062.902306747, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9410323.196208755, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9362759.024991756, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9321112.753117379, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9285016.290065145, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9254029.627395952, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9227672.214767914, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9205447.27460862, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9186860.578098293, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9171435.130133292, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9158722.527650403, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9148311.191396467, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9139831.50202173, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9132958.012055231, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9127409.145408802, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9122944.972944392, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9119363.705526328, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9116497.490587894, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9114207.980834424, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9112382.00859252, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9110927.575648237, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9109770.269829823, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108850.148759764, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9108119.08491204, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107538.538969103, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9107077.714962069, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9106712.046135923, tolerance: 3759.109166869193\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005651.632865302, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005578.608102243, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005486.463074774, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005370.192059726, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005223.47917251, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21005038.355660334, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004804.76767336, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004510.03120046, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21004138.144828446, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003668.923421204, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21003076.906345215, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21002329.98203154, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21001387.655909717, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21000198.8704182, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20998699.26312138, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20996807.72107362, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20994422.05552329, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20991413.57989597, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20987620.324921425, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20982838.567338496, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20976812.283196613, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20969220.065253027, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20959658.970863715, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20947624.701018073, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20932487.468798272, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20913462.923603535, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20889577.599545892, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20859628.61984418, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20822137.913488373, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20775302.126054227, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20716940.917180095, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20644448.64953633, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20554757.795455974, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20444326.815649558, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20309170.5956441, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20144956.94257016, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19947196.308887925, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19711550.604615457, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19434276.168588594, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19112791.023677077, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18746315.49762964, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18336483.416578818, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17887774.82963546, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17407607.14883928, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16905965.499829993, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16394560.80209675, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15885645.94315279, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15390736.734407002, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14919517.25785277, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14479140.715843389, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14074002.01810337, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13705921.512677444, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13374594.126102015, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13078142.079861483, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12813645.639316088, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12577583.791150972, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12366168.38748323, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12175587.27845306, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12002182.958268248, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11842589.470659975, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11693840.031875866, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11553447.608003614, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11419454.0438313, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11290441.388440857, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11165501.742342338, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11044168.420816425, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10926319.289729377, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10812069.210340973, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10701669.403435929, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10595426.714498514, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10493648.013477515, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10396608.203056702, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10304536.713966034, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10217616.440012157, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10135989.092876725, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10059761.060749074, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9989004.697692012, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9923752.620593691, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9863986.795334544, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9809627.884194935, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9760531.052715844, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9716491.487344079, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9677258.06531545, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9642549.951165989, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9612070.387835179, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9585514.488134576, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9562571.500908706, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9542924.549681034, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9526251.156759001, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9512226.472533092, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9500529.267319635, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9490849.431706948, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9482895.334901826, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9476399.71781569, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471123.439398324, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9466857.004635958, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9463420.20844639, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9460660.409301298, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9458449.957484357, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9456683.22035802, tolerance: 4201.186103419478\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331946.25629055, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331864.018678214, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331760.248581372, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331629.308755428, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331464.086506005, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22331255.607747704, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330992.550247405, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330660.62979839, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22330241.82628314, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329713.40806704, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22329046.702501133, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22328205.546983715, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22327144.338416774, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22325805.578253012, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22324116.784799173, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22321986.613041975, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22319299.9839329, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22315911.97874348, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22311640.198869713, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22306255.226839963, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22299468.750693016, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22290918.833475478, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22280151.72747749, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22266599.559077755, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22249553.162800502, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22228129.35292585, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22201232.036903117, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22167506.872833706, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22125289.76574775, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22072550.542125095, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22006834.845984127, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21925209.906269174, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21824223.56629905, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21699890.94922881, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21547729.124614064, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21362866.213577304, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21140255.446179498, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20875023.13975618, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20562967.32341789, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201195.56502676, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19788844.32939185, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19327763.89751004, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18823001.04313301, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18282896.08461045, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17718660.4886989, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17143422.40324079, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16570887.230051238, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16013892.090309372, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15483171.861886727, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14986579.129588084, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14528848.289413737, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14111836.23977445, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13735069.935277399, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13396407.639332836, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13092660.916831579, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12820093.900344713, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12574781.90922219, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12352853.17507817, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12150651.369793259, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11964850.854771722, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11792543.263225015, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11631302.416094316, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11479227.7561279, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11334963.041737791, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11197685.003164051, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11067056.224580359, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10943139.511030385, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10826278.220752902, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10716956.341549171, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10615659.18706467, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10522756.819315987, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10438426.844454892, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10362623.27115231, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10295087.38179537, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10235388.466414697, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10182978.74114095, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10137247.95260475, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10097567.748922419, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10063321.789749103, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10033922.656392608, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10008819.486834103, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9987500.645290056, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9969494.453323793, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9954369.32479691, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9941733.515465437, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9931234.335989065, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9922556.777457738, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9915421.679110043, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9909583.627876062, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904828.718921196, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9900972.216495434, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9897856.106707212, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9895346.540855931, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9893331.203755047, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9891716.674948305, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9890425.865192825, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9889395.604661888, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9888574.440116653, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887920.67448956, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9887400.660169175, tolerance: 4466.452064951529\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225193.80408011, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225110.813517075, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22225006.093373984, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224873.954836704, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224707.22016197, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224496.83322094, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22224231.36831536, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223896.410779048, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22223473.77603032, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222940.525154293, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22222267.72434169, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22221418.88207672, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22220347.981225494, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22218997.002387535, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22217292.809172478, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22215143.234477364, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22212432.168317866, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22209013.40126823, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22204702.922219783, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22199269.304569546, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22192421.741654057, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22183795.21258787, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22172932.17909693, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159260.14304964, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22142064.35203175, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22120454.95809202, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22093328.06334204, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22059320.403233726, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22016758.03845356, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21963600.508906867, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21897383.65478575, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21815166.968429696, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21713495.123522323, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21588388.30984886, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21435381.888817236, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21249641.65996918, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21026184.505123127, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20760231.655652393, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20447708.31167379, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20085874.01890182, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19674021.850113407, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19214128.3053442, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18711289.424232315, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18173771.014405873, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17612557.62934444, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17040407.03219554, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16470567.131662391, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15915425.819018744, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15385384.27148134, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14888160.528800251, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14428585.410549453, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14008814.608291918, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13628800.43657365, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13286857.361730725, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12980197.224087035, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12705370.4103452, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12458600.903357476, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12236032.779575087, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12033913.49389702, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11848733.596731743, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11677333.403468838, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11516981.070353946, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11365424.704670552, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11220921.203073822, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11082243.920528807, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10948669.457422748, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10819942.016223667, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10696213.317397818, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10577957.546131799, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10465864.125929631, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10360715.842557454, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10263264.873279892, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10174122.558233691, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10093678.084935276, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10022055.90958463, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9959113.332252594, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9904471.381944738, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9857566.988895562, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9817713.479318874, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9784158.875562938, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9756135.4293958, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9732897.547924208, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9713747.93315419, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9698053.28484727, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9685251.610393653, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9674853.346299471, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9666438.328081315, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9659650.291029936, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9654190.159247063, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9649808.977198932, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9646301.012972398, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9643497.331329834, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9641259.984843817, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9639476.879484767, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9638057.315691985, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636928.172691077, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9636030.684258943, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9635317.743812287, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634751.672914786, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9634302.388158688, tolerance: 4445.102149685068\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182535.705905367, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182443.31748153, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182326.738805104, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22182179.636849403, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181994.021044992, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181759.809716668, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181464.28327085, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22181091.39464482, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180620.89990636, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22180027.262331244, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22179278.27131426, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22178333.30250969, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22177141.126954924, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22175637.153777506, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22173739.962460298, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22171346.945451487, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22168328.83898362, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22164522.86814139, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22159724.170510717, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22153675.090679448, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22146051.856030278, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22136448.05522982, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22124354.250566233, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22109132.97552027, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22089988.320511386, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22065929.327634364, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 22035726.555516478, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21997861.52451259, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21950469.43740475, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21891276.769023754, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21817537.260214396, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21725972.80421009, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21612729.92224466, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21473368.08108258, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21302902.69437747, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 21095932.158423126, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20846882.286273133, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20550398.911674757, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 20201904.639180813, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19798303.254432607, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 19338763.60317261, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18825451.629099563, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 18264026.303933263, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17663705.112665202, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 17036766.85903686, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16397496.223623449, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15760744.92149185, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140415.226936534, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14548197.66197113, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13992801.316187331, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13479749.374918321, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13011650.716625076, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12588761.536151327, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12209637.009462353, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11871722.016013274, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11571805.12738007, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11306328.206388844, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11071585.798533637, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10863860.419768564, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10679529.96999051, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10515164.967978276, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10367617.395431116, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10234094.932508666, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10112213.557229094, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10000024.23575536, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9896012.57105465, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9799072.614562154, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9708457.890308099, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9623714.619163413, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9544604.25348744, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9471024.212762302, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9402936.228999598, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9340310.144654753, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9283087.29859646, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9231162.854348717, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9184382.359520858, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9142546.024753645, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9105415.114890344, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9072717.557093464, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9044152.703403218, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9019396.685310023, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8998109.575324507, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8979944.333787149, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8964556.387394711, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8951612.369533507, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8940797.002727017, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8931817.822045382, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8924407.976697778, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8918327.548498938, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8913363.779400796, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8909330.473245148, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8906066.743651448, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8903435.248435475, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8901320.0564094, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8899624.301448012, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8898267.77261921, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8897184.565959448, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8896320.890152888, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895633.08348321, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8895085.869018715, tolerance: 4436.577708196869\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.271e+07, tolerance: 5.332e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, { "data": { "text/html": [ @@ -4235,16 +9580,16 @@ "id": "52f6cf29", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:32.764408Z", - "iopub.status.busy": "2024-06-04T23:19:32.764316Z", - "iopub.status.idle": "2024-06-04T23:19:32.836546Z", - "shell.execute_reply": "2024-06-04T23:19:32.836313Z" + "iopub.execute_input": "2025-04-03T19:33:48.420338Z", + "iopub.status.busy": "2025-04-03T19:33:48.420257Z", + "iopub.status.idle": "2025-04-03T19:33:48.471814Z", + "shell.execute_reply": "2025-04-03T19:33:48.471514Z" } }, "outputs": [ { "data": { - "image/png": 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hQ4Zo5MiRCg4OVmBgoJ555hl16NCBmU4AAABcTIUM3x9//LEkKSoqymb/9OnTNWjQIG3btk0bN26UJN1www02bQ4dOqTIyEh5enpqypQpGjFihAzD0A033GBOG1ioXr16WrRokUaMGKH3339ftWrV0qeffmpOMyhJDzzwgE6dOqWxY8cqMTFRN998sxYvXmzzEOZ7770nNzc39e7dW1lZWYqJidFHH31U2p8WoMJgmAoAwFVVyJ94F08HWJyoqKgrtunevbu6d+9+xXtFRUXpl19+uWybYcOGadiwYZc87uPjoylTpmjKlClXvB8AAAAqrwo55htA5cP4cACAKyB8AwAAAA5SIYedAACAisnf3/+KQ0OByoyebwDlGsNRAACVCeEbAAAAcBDCNwAAcJjMzEz16dNHffr0UWZmprPLARyO8A0AABwmLy9PX331lb766iuWl4dLInwDqJAYCw4AqIgI3wAAAICDEL4BAAAAByF8AwAAAA5C+AYAAAAchPANoFLhQUwAQHnG8vIAAMBh/Pz8lJaWZm4DrobwDQAAHMZiscjf39/ZZQBOw7ATAAAAwEEI3wAAwGGysrI0aNAgDRo0SFlZWc4uB3A4wjeASo+HMIHyIzc3VzNnztTMmTOVm8v/j3A9hG8AAADAQQjfAAAAgIMQvgEAAAAHIXwDAAAADkL4BgAAAByE8A0AAAA4CCtcAnBZ6dm5ajJ2iSRp9/gY+XnxTyJQ1vz8/HTy5ElzG3A1/KQBAAAOY7FYVKNGDWeXATgNw04AAAAAByF8AwAAh8nKytLQoUM1dOhQlpeHSyJ8AwAAh8nNzdVHH32kjz76iOXl4ZII3wAAAICDEL4BAAAAByF8AwAAAA5C+AaAv0jPzlXkmEWKHLNI6dmMSQUAlB7CNwAAAOAghG8AAADAQVjhEgAAOIyvr68OHTpkbgOuhvANAAAcxs3NTZGRkc4uA3Aahp0AAAAADkL4BgAADpOdna1Ro0Zp1KhRys7OdnY5gMMRvgEAgMPk5OTo7bff1ttvv62cnBxnlwM4HOEbAAAAcBDCNwAAAOAghG8AKAFWvQQAlAbCNwAAAOAghG8AAADAQQjfAAAAgIOwwiUAAHAYX19f7dy509wGXA3hGwAAOIybm5uaNm3q7DIAp2HYCQAAAOAg9HwDAACHyc7O1htvvCFJevHFF+Xl5eXkigDHInwDAACHycnJ0auvvipJGjVqFOEbLodhJwAAAICDEL4BAAAAByF8AwAAAA5C+AYAO6Rn5ypyzCJFjlmk9OxcZ5cDACjneOASl2TNylW+UbCdcVGosGblyjBkEzQu3s7IzjO3M3Py5GaxSJKyci7sz87Nl4dbviQpJy/f3J+Xbygv31B+4Y0BAAAqEcI3Lqnt68tLvL/Nv4pv2+q1ZcXuv3l8XLH7m49bWmRfk7FLzO0/c7wk6aZxS2WxSBZZpIv2t35tmdwsstnX/s3lcrdYZLFYLt6tzm+vurD/ogM9P1grD/eLW0oP/N8Gebpb5O5mu//J/2yVl4e7PNxs63h5/k75eLrbtP1wxe/y93KXl4ebLv714odfTyjAx1PeHrZ/jDp8xqpgf2/5eLrLMPiFBACAio7wjQrl4vyZa/aO24bSjIt62AulZhQ/HCApNavY/QdPW4vs+/VYSrFt1+w/Xez+r7cdK7Lv41UHim37z3k7it3f4/21xe7v8vZqVfHxkK/nhbD+4re/KtjPW4G+HvK9KPTHH0lWRFVfVfP3kgcDzQA4mY+PjzZt2mRuA66G8I1L+uXlaPl6FXyLZGTnquWfvdjbXo6Wr6eH0rNz1fpfBfu2vNRVfn+2Tc/ONXvCN/9PV/l6ecgwDGVk5+mWNwr2b3yxi3w8L1y7/ZsrJEnrxnQ2r33b/66UJP30fNSf1yi49h0TV0mSVv7zjj97hAv2R7+7RpK0dERH+Xh4yJqdY4bXhc/cJh9Pd+UbBfe7Z8o6SdJXT3aQl4ebub/fvzdKkmYObitPdzel5+TpsZlbJEkf9W8pdzc35ecbSs/J0z++3C5Jev3eZnK3WJSbbygjJ1evL9orSXq26w2yyKKMnDx9suagJKnfLbWVn18w1CYjJ08/7kyUJLWrF6ycvHxl5RbsP3iqIPxX8fZQRk6e8v4yDCcxNVNKtf16zf/leLFfx4c+3Whue1zUa//3/2xVRJCvqvl7mvv2JqbqhtAABfp4CgDKgru7u9q2bevsMgCnIXzjkrw93c1hE/kXdTn7eLrL18tdxkU9zn5eHmb4vpi/94X9Fw/XCPDxNPd7XjS8o6qfl/y8POR9UY9u9SreFwX7Cz26NQN9bAJ/oVrV/OTn5WGzr36NKsW2bRIRWOz+tvWCi1wjqmGoTdvC8H1vy+ts9heG7yfvuN68RmH4frlnE5u2heF7+uC2NvsLh9ps+p+CX2py8vJ11pqldm8U/JIy78n2ys2TzlqzNHT2L5KkkX9roIycfKVm5OhcerZ++LXg2tdV9VVyeras2XkX/bVA+qmYHvv7PlovSQr08VBEVV9z/1db/1Dj8EBdX6OKfDzpPgcA4FoRvoEKwNPdTQEX9UY3jQgq8svBYx3r2wT4wvAdN7KT/Lw8lJmTpxMpGer89mpJ0mv3NFVyeo6OJWdozuajkqRgfy+dtWYrNTNXqYnnzWuP/W6XuV3V70IdX2/9Qy3rVFOtaheCOgBcTnZ2tt5//31J0nPPPccKl3A5hG/ARfh4uqtm4IXxlb1b1zIDfGH4Xju6syTp2LkM/X4qTU99sU2SdPsN1XXodLqOJWcoOT3HvMbLf4byi/968f324+rUoIZNzzkAFMrJydHzzz8vSXr66acJ33A5hG8ANvy8PNSgZoCuu6g3+5MBbcygvvtEqu7/uGB4Srt6wdpzIlWpmRd64Md8/askqU6wn1rXrebY4gEAKOcI3wBKzM/LQ03CA8330we3la+nu35LOq+YST9JkppFBGr3iVQdOZuuI2fTzbb3fbROMU3D9LcmNVUvxM/htQMAUB4QvgHYxWKxqHbwhTD95ZMdlJdvaMvhc/p5/2l9uvaQJGlv4nntTTyv95fvV3jQheEvzF8OAHAlTFsAoNQF+Hiqc8NQjex2o7nv9XubqVuTmvLxdNOJlExzf+wHa/XxqgM6db74OdcBAKhMKmT4fvPNN9W2bVsFBAQoNDRUvXr10r59+2zaZGZmaujQoapevbqqVKmi3r17KykpyabNkSNHFBsbKz8/P4WGhmrUqFHKzbVdjGXVqlVq1aqVvL29dcMNN2jGjBlF6pkyZYoiIyPl4+Ojdu3amYsHXE0tQGV3b8vr9MmANoof201THmpp7k84k67/XbxXXd5Zbe7Lzct3RokAAJS5Chm+V69eraFDh2rDhg2Ki4tTTk6OunXrJqv1wqqEI0aM0IIFCzRv3jytXr1ax48f13333Wcez8vLU2xsrLKzs7Vu3TrNnDlTM2bM0NixY802hw4dUmxsrDp37qz4+HgNHz5cjz32mJYsubDc+dy5czVy5Ei98sor2rZtm1q0aKGYmBidPHmyxLUArsTH012dG4Wa71/r1VRt6lazWUgo9sO1+u+mI8rKLbpaaUWRnp2ryDGLFDlmkc2UkAAA11Yhx3wvXrzY5v2MGTMUGhqqrVu3qlOnTkpJSdFnn32m2bNnq0uXLpKk6dOnq3HjxtqwYYPat2+vpUuXavfu3Vq2bJlq1qypm2++Wa+99ppGjx6tcePGycvLS1OnTlW9evX0zjvvSJIaN26stWvX6r333lNMTIwk6d1339Xjjz+uwYMHS5KmTp2qRYsWadq0aRozZkyJagFcWe9WtfRI+0jtPJasnh/+LEk6ejZDL3zzq95ftl+Dbq3r5AoBlCYfHx+tXLnS3AZcTYXs+f6rlJQUSVJwcLAkaevWrcrJyVF0dLTZplGjRqpTp47Wry+YIm39+vVq3ry5atasabaJiYlRamqqdu3aZba5+BqFbQqvkZ2dra1bt9q0cXNzU3R0tNmmJLX8VVZWllJTU21eQGVXv0YVc3tM94aqGeitxNRMvbX4wpCyzJyK2xMOoIC7u7uioqIUFRUld3f3K58AVDIVPnzn5+dr+PDhuu2229SsWTNJUmJiory8vFS1alWbtjVr1lRiYqLZ5uLgXXi88Njl2qSmpiojI0OnT59WXl5esW0uvsaVavmrN998U0FBQeardu3aJfxsAJXDgFsjteb5znrzvuaqfdF843d9+LMW70xkhhQAQIVV4cP30KFDtXPnTs2ZM8fZpZSaF154QSkpKebr6NGjzi4JcDhvD3f1u6WOFj17u7nvWHKGnvxiqwZM26RDp62XORtAeZWTk6MpU6ZoypQpysnJufIJQCVTocP3sGHDtHDhQq1cuVK1atUy94eFhSk7O1vJyck27ZOSkhQWFma2+euMI4Xvr9QmMDBQvr6+CgkJkbu7e7FtLr7GlWr5K29vbwUGBtq8AFfl4X7hn6m/d6ovL3c3/bT/tO6Z8rMTqwJwrbKzszVs2DANGzZM2dnZzi4HcLgKGb4Nw9CwYcP07bffasWKFapXr57N8datW8vT01PLly839+3bt09HjhxRhw4dJEkdOnTQr7/+ajMrSVxcnAIDA9WkSROzzcXXKGxTeA0vLy+1bt3apk1+fr6WL19utilJLQBK5rnoBlo6opO6NApVbt6FoSfxR5KdVxQAAFehQobvoUOH6osvvtDs2bMVEBCgxMREJSYmKiMjQ5IUFBSkIUOGaOTIkVq5cqW2bt2qwYMHq0OHDubsIt26dVOTJk30yCOPaPv27VqyZIleeuklDR06VN7e3pKkJ598UgcPHtTzzz+vvXv36qOPPtKXX36pESNGmLWMHDlS//73vzVz5kzt2bNHTz31lKxWqzn7SUlqAVBykSH+mjaorT7qf2Gu8EembdKUlb/bTFcIAEB5VCGnGvz4448lSVFRUTb7p0+frkGDBkmS3nvvPbm5ual3797KyspSTEyMPvroI7Otu7u7Fi5cqKeeekodOnSQv7+/Bg4cqPHjx5tt6tWrp0WLFmnEiBF6//33VatWLX366afmNIOS9MADD+jUqVMaO3asEhMTdfPNN2vx4sU2D2FeqRYAVy+q4YW5wvPyDU1csk8//35ab9zbzIlVAQBweRUyfJdkpgMfHx/zgY5LqVu3rn744YfLXicqKkq//PLLZdsUjl2zpxYA1+71e5vpXwv3aN2BM7r3o3XOLgcAgEuqkMNOAOBi97a8TgufvV1NwgN1Lv3C7AlMSQgAKG8I3wAqhetrVNG3Q2/Vw+3rmPte/m6XcvLynVgVAAC2KuSwEwAojreHu168s7G+2HBEkvTNtmM6Z83WxD43ObkyAIW8vb21cOFCcxtwNYRvAJWWj6ebVu47pcHTtzi7FAB/8vDwUGxsrLPLAJyGYScAKq1pA9uqqp+nfj2W4uxSAACQRPgGUIndXKeqvnryVoUH+Zj7DpxKc1o96dm5ihyzSJFjFik9O9dpdQDOlJOToxkzZmjGjBksLw+XRPgGUKndEFpF/328nfn+8ZlbdTw5w4kVAa4tOztbgwcP1uDBg1leHi6J8A2g0gsNvNDznZiaqUc+26izVn7oAwAcj/ANwKWEBfrowCmrBs/YLGsWQz8AAI5V4vD9wQcf6IMPPtC5c+fsuuHRo0d13333qXfv3nZdBwCuxb8HtlZVP09tP5qs5+bEO7scAICLKfFUg8OHD5fFYlF0dLSqVatW5PiuXbvUvHlzubm5KTf30r1Jqampmj9/viwWy7VVDAB2uL5GFU0f1Fb9P92odQfOOLscAICLKfVhJyznDKC8a1mnmqY+3Foe7nQCAAAcizHfAFxSpxtr6H/va26+//aXY06sBgDgKgjfAFxWj+bh5vZrC3drb2KqE6sBXIO3t7e+/PJLffnllywvD5fE8vIAICkzJ19Pf7FN3z9zu9wYjQKUGQ8PD/Xp08fZZQBOQ883AKhgCsKDp60a8/UOnl0BAJQZwjcASHq3bwt5uFm0cMcJ/XfTUWeXA1Raubm5mjdvnubNm3fZ2dGAyorwDQCSbq5TVWN6NJIkvbV4r5OrASqvrKws9e3bV3379lVWVpazywEcjvANAH8acns9xTStqdw8hp0AAMrGVT9w+d1332nLli1F9h87dmGars8///yS51/cDgDKE4vFogn3t9Du4z/p6LkMZ5cDAKiErjp8v/TSS5c8Vrhq5eDBg6+9IgBwoiBfT737QAv1mbpBkrR8z0nd1SLCyVUBACqLqxp2YhhGqbwAoDxrGhFkbv9r0W6dz8xxYjUAgMqkxD3f06dPL8s6AKBcSkrN0oTF+/TCnY2cXQoAoBIocfgeOHBgWdYBAOXWFxsPK6ZpzTK7fnp2rpqMXSJJ2j0+Rn5erH8GAJUV/8IDwGXc2/I6ffvLMY39fpezSwEqBS8vL/Ov6V5eXk6uBnA8wjcAXMaomBv10/5TOnjK6uxSgErB09NTgwYNcnYZgNOUafjevn27fv/9d1ksFtWvX18333xzWd4OAEpdVT8vvXJXUz3z31+cXQoAoBK4qvD922+/SZKqVq2q0NDQS7ZbsWKFnn76ae3fv99mf926dfXee+/pnnvuuYZSAcA5et4Urq+2/qHVv52SJOXnM2sTcK1yc3O1ZEnBMw4xMTHy8OCP8HAtJZ5qcMeOHWrUqJEaN26sxYsXX7LdkiVL1L17d+3fv7/IFIMJCQnq3bu3Zs+eXSrFA4AjWCwWvdyzsfn+23gWCwOuVVZWlnr27KmePXuyvDxcUonD99KlSyVJQUFB6tevX7Ft0tPT9eijjyo3N1eGYSg4OFiPPPKIRo8era5du0qS8vPzNWzYMJ09e7YUygcAx4io6mtuT17xuzKy85xYDQCgoipx+N60aZMsFotiY2Pl6elZbJvZs2frxIkTslgsatasmXbu3KmZM2fqzTffVFxcnKZNmyZJSklJ0axZs0rnIwAAB0tKzdKMdQnOLgMAUAGVOHzv2bNHktSpU6dLtpk3b565/cEHHygsLMzm+KBBg9SjRw8ZhmH2pANARfTRqt91zprt7DIAABVMicP3H3/8IUlq3Lhxscfz8/O1bt06WSwW1apVS1FRUcW269u3ryRp586dV1kqAJQPDWtW0fnMXH206ndnlwIAqGBKHL7T0tIkSYGBgcUe37Vrl6zWgnlw77jjjktep1GjgiWaz5w5U+IiAaA8GdntRknSzHWHdSw5w8nVAAAqkhKHbx8fH0nS+fPniz2+ceNGc7t169ZXvE5mZmZJbw0A5crtN4To1uurKzsvX5NX0PsNACi5Eofv8PBwSVJ8fHyxx3/66Sdzu3379pe8zrlz5yRJVapUKemtAaBcsVgsGtOj4K94328/7uRqgIrFy8tLkydP1uTJk1leHi6pxOG7TZs2MgxD06dPL3LMarVqwYIFkqSAgAC1adPmktfZt2+fJKlWrVpXWysAlBs31aqqnjeFy2C9HeCqeHp6aujQoRo6dOglZ08DKrMSh+/Cub1/+eUXPf7440pNTZUkJScna9CgQUpOTpbFYtH9998vd3f3S15nzZo1kqSmTZvaUzcAON0/uzWUh5vF2WUAACqQEofvu+66S7fddpsMw9C0adNUo0YNXXfddQoJCdE333wjqeC32dGjR1/yGunp6VqwYIEsFotuu+02+6sHACeKDPFX37a1zfcG3eDAFeXl5WnVqlVatWqV8vJYrAqup8ThW5K+/vprNWvWTIZhKCcnRydOnFB+fr4Mw5Cbm5s++ugjNWjQ4JLnz5w505w1JSYmxr7KAaAceOqO+ub2T/tPO7ESoGLIzMxU586d1blzZyZfgEvyuJrGoaGh2rp1qz755BN9//33OnLkiLy8vNSqVSs9/fTTatu27WXPj4uLU+vWrVWrVq3LhnQAqCiqV/E2tz/96ZC6Nwt3YjUAgPLuqsK3ZPugxNUqHJ4CAJXRlsPntPXwObWuW83ZpQAAyqmrGnYCALi8qasPlNq10rNzFTlmkSLHLFJ6dm6pXRcA4DyEbwAoJRaLFLc7Sb+fLH4xMgAACN8AUEq6NgqVJE1dfdDJlQAAyqsSj/keP358qd987NixpX5NAHCWIbfX07I9JzX/l2N6Kqr+lU8AALicEofvcePGyWIp3cUkCN8AKpMWtauqff1gbTh4Vp+vO+zscoByydPTUxMmTDC3AVdz1bOdlNYiEqUd5AGgPHgq6gZtOLhJX279w9mlAOWSl5eXRo0a5ewyAKe56vDt6+ure+65RwMGDFDjxo3LoiY4kZ+XhxLeii3R/qtpW9bXBsqLTg1C1CQ8ULtPpDq7FABAOVTi8N21a1etXLlSGRkZmjt3rubOnavWrVvrkUce0YMPPqgaNWqUZZ3AZZXVLwfl5RqX24/yxWKx6Mmo6/Xsf39xdilAuZSXl6dt27ZJklq1aiV3d3cnVwQ4VonDd1xcnI4fP65Zs2bpiy++0K+//qotW7Zo69at+uc//6mYmBg9/PDDuueee+Tt7X3lCwKwGwG+fLqzWZgmVPPVH+cynF0KUO5kZmbqlltukSSlpaXJ39/fyRUBjnVVUw1GRERo1KhR2r59u+Lj4zVixAjVrFlTOTk5Wrhwofr166ewsDA98cQTWrNmTVnVDKAUFQbyhLdi5ed11SPRUAwPdzc9eluk+T4/v3SelQEAVHzXPM/3TTfdpHfeeUd//PGHFi9erIceeki+vr5KSUnRZ599ps6dO6tevXoaO3asfvvtt9KsGYADXCqUE9ZL5u6bI8ztdQfOOLESAEB5YvciO25uburWrZu++OILJSUlaebMmercubMsFosOHz6s119/XY0bN1bHjh1Lo14A5RCBvKiLPw9zNh91YiUAgPKkVFe49Pf31yOPPKJly5bpyJEjGj9+vLy9vWUYhrZu3VqatwKACmPVvpM6nsz4bwDANUw1WBLr16/Xf/7zH3355ZfKysoqi1sAqAB4wLNAviHN2XRET0Zd7+xSAABOVmrh+8CBA/riiy/0xRdf6ODBg5IKFuTx8fHR3XffrQEDBpTWrQBUcK4YyudsPqohHes5uwwAgJPZFb7PnTunOXPm6D//+Y82btwoqSBwWywWdezYUY888oj69OmjwMDAUikWACqi6lW8dPJ8llbsPensUgCn8/T01CuvvGJuA67mqsN3Tk6OFixYoP/85z/68ccflZOTYy45f+ONN+qRRx7RI488ojp16pR6sQAqr8rcG35/q1r6vzUHNWcTD14CXl5eGjdunLPLAJymxOF77dq1+uKLLzRv3jwlJyebgbt69ep68MEHNWDAALVt27bMCgWAiqpPm1r6908HtfHQWWeXAgBwshKH706dOsliscgwDHl7e+vuu+/WI488ou7du8vDg6nFAOBSIqr6qkujUC3bw7ATID8/X3v27JEkNW7cWG5upTrxGlDuXXVq9vX1VUxMjPz9/fXNN9/om2++uaYbWywWffbZZ9d0LgDXUVmGo/RvX7fUwnd6dq6ajF0iSdo9Poa51VGhZGRkqFmzZpJYXh6u6ar/xc7MzNR3331XKjcnfANwFXc0qKFa1Xz1xznm+wYAV3ZVf+sxDKNUXwDgKtzcLOrbppazywAAOFmJe77z8/PLsg4AuCoVcTjKfa1q6d24/ZKkncdSdEu96k6uCADgaDzlAAAOEuzvZW5/ve2YEysBADhLhQzfa9as0V133aWIiAhZLBbNnz/f5rjFYin2NXHiRLNNZGRkkeNvvfWWzXV27Nihjh07ysfHR7Vr19aECROK1DJv3jw1atRIPj4+at68uX744Qeb44ZhaOzYsQoPD5evr6+io6O1f//+0vtkAKiQfvz1hLJy85xdBgDAwSpk+LZarWrRooWmTJlS7PETJ07YvKZNmyaLxaLevXvbtBs/frxNu2eeecY8lpqaqm7duqlu3braunWrJk6cqHHjxumTTz4x26xbt079+vXTkCFD9Msvv6hXr17q1auXdu7cabaZMGGCPvjgA02dOlUbN26Uv7+/YmJilJmZWcqfFQAVSWpmrpYz9SAAuJwKOT9Vjx491KNHj0seDwsLs3n/3XffqXPnzqpfv77N/oCAgCJtC82aNUvZ2dmaNm2avLy81LRpU8XHx+vdd9/VE088IUl6//331b17d40aNUqS9NprrykuLk6TJ0/W1KlTZRiGJk2apJdeekn33HOPJOnzzz9XzZo1NX/+fD344IPX/DkAULyKNBb8m21/6M7m4c4uA3AoT09P/fOf/zS3AVdTIXu+r0ZSUpIWLVqkIUOGFDn21ltvqXr16mrZsqUmTpyo3Nxc89j69evVqVMneXldGKMZExOjffv26dy5c2ab6Ohom2vGxMRo/fr1kqRDhw4pMTHRpk1QUJDatWtntilOVlaWUlNTbV4AKp9V+07pdFqWs8sAHMrLy0sTJ07UxIkTbX7GAq6i0ofvmTNnKiAgQPfdd5/N/meffVZz5szRypUr9fe//11vvPGGnn/+efN4YmKiatasaXNO4fvExMTLtrn4+MXnFdemOG+++aaCgoLMV+3ata/mQwZQATS/LlC5+Ya+jz/u7FIAAA5U6cP3tGnT1L9/f/n4+NjsHzlypKKionTTTTfpySef1DvvvKMPP/xQWVnO74V64YUXlJKSYr6OHj3q7JIAlLK7b46QJH3zyx9OrgRwrPz8fCUkJCghIYFpjOGSKuSY75L66aeftG/fPs2dO/eKbdu1a6fc3FwlJCSoYcOGCgsLU1JSkk2bwveF48Qv1ebi44X7wsPDbdrcfPPNl6zF29tb3t7eV/4AAZRIeRwHfmezcE1YvE87j6Vqf9J5Z5cDOExGRobq1asnieXl4Zoqdc/3Z599ptatW6tFixZXbBsfHy83NzeFhoZKkjp06KA1a9YoJyfHbBMXF6eGDRuqWrVqZpvly5fbXCcuLk4dOnSQJNWrV09hYWE2bVJTU7Vx40azDQDXVM3fS50bFvx78x1DTwDAZVTI8J2Wlqb4+HjFx8dLKniwMT4+XkeOHDHbpKamat68eXrssceKnL9+/XpNmjRJ27dv18GDBzVr1iyNGDFCDz/8sBmsH3roIXl5eWnIkCHatWuX5s6dq/fff18jR440r/Pcc89p8eLFeuedd7R3716NGzdOW7Zs0bBhwyQVzDc+fPhw/etf/9L333+vX3/9VQMGDFBERIR69epVdp8gABXCfa0KlptfsOOEkysBADhKhRx2smXLFnXu3Nl8XxiIBw4cqBkzZkiS5syZI8Mw1K9fvyLne3t7a86cORo3bpyysrJUr149jRgxwiZYBwUFaenSpRo6dKhat26tkJAQjR071pxmUJJuvfVWzZ49Wy+99JJefPFFNWjQQPPnz1ezZs3MNs8//7ysVqueeOIJJScn6/bbb9fixYuLjEEH4Hq6NApVVT9PnTrv/GdNAACOUSHDd1RUlAzDuGybJ554wiYoX6xVq1basGHDFe9z00036aeffrpsmz59+qhPnz6XPG6xWDR+/HiNHz/+ivcD4Fq8PNx0d4sIfb7+sLNLAQA4SIUcdgIAlUXh0BMAgGsoUc/3X1eGLA0Wi0UHDhwo9esCQEmVh1lQWtQKUr0Qfx06bXVqHQAAxyhR+E5ISCjRxSwWiyQVGRJS3P7CfQDgyiwWi+65OUKTlu13dimAQ3h4eOjpp582twFXU6Lv+oEDB172eHx8vLZv3y7DMFS1alW1bNnSXNUxKSlJ8fHxOnfunCwWi1q0aFGiqf8AwFX0vCncDN/HkjPUIDTAyRUBZcfb21tTpkxxdhmA05QofE+fPv2Sx6ZNm6bZs2erVq1aeuedd3TvvfcW+U02Ly9P33zzjUaNGqXdu3dr6NChGjJkiH2VA0AlEVHV19xevDNRDboQvgGgsrLrgcstW7boySefVEhIiDZs2KA+ffoU+yckd3d39enTR+vXr1dwcLCefvppbdmyxZ5bA0CltHhn4jWdl56dq8gxixQ5ZpHSs3NLuSqg9BiGoVOnTunUqVNXnLkMqIzsCt/vvfee8vLy9OKLLyoiIuKK7cPDw/Xiiy8qJydH7777rj23BoBKadfxVCXw8CUqsfT0dIWGhio0NFTp6enOLgdwOLvCd+Ec2O3atSvxOe3bt5ckrV271p5bA0CltehXVrwEgMrKrseMT506JUnKyir56myFbQvPBYDyxtlTEC7ccUJDO9/gtPsDAMqOXT3fNWrUkCT9+OOPJT7nhx9+kCSFhITYc2sAqJQ83CzacyJVB06lObsUAEAZsCt8d+nSRYZh6N1339XPP/98xfbr1q3Te++9J4vFoq5du9pzawColDpcX12StHA7Q08AoDKyK3yPGTNG3t7eysrKUteuXTV8+HDFx8fbPL1sGIbi4+M1YsQIdenSRZmZmfLy8tKYMWPsLh4AKpsezcIkSQt3HHdyJQCAsmDXmO9GjRpp5syZevjhh5Wdna0PP/xQH374oby8vBQcHCyLxaIzZ84oOztbUkEQ9/Dw0PTp09WoUaNS+QAAoDLp2jhUXt+7af/JNO1POu/scgAApcyunm9J6tu3r37++We1bt1ahmHIMAxlZWXpxIkTOn78uLKyssz9rVq10tq1a/Xggw+WRu0AUOkE+Hiq041/Pk9zjXN+A+WZh4eHBg4cqIEDB7K8PFxSqXzXt23bVps3b9aWLVu0bNky/frrrzp79qwkqVq1amrevLmio6PVtm3b0rgdAFRqd7UI17I9Sde84A5Qnnl7e2vGjBnOLgNwmlL9lbNNmzZq06ZNaV4SAFxO18Y15e3hpoQzLEACAJUNf+8BgBJw5NzfVbw91LlhqBbvoucblY9hGObKln5+frJYLE6uCHAsu8d8/9Uff/yhLVu2aM2aNcrIyCjtywOAS+jZItzZJQBlIj09XVWqVFGVKlVYXh4uqVTC9/nz5/Xyyy+rdu3aqlu3rtq1a6fOnTvr0KFDNu3mzJmjvn376vHHHy+N2wJApdWlUah8Pd2dXQYAoJTZPexk//79uvPOO3Xw4EGb+b2L+zNS+/bt9fDDD8swDA0cOFC33367vbcHgErJz8tDUQ1rMOMJAFQydvV8Z2ZmKjY2VgcOHJCfn5+ef/55LVy48JLtIyMj1blzZ0nS999/b8+tAaDSK1xwR5JN5wYAoOKyq+f7448/1u+//y5/f3/99NNPuvnmm694To8ePbR8+XKtX7/enlsDQKV3e4MQc3vX8VTdUq+6E6sBAJQGu3q+v/nmG1ksFj333HMlCt6S1KJFC0kFw1UAAJfmc9GY77jdSU6sBABQWuwK33v27JEkdevWrcTnVK9e0HOTnJxsz60BwKXE7U5i6AkAVAJ2DTtJS0uTJFWpUqXE52RlZUmSPD097bk1ALiUhDPp2n8yTTfWDHB2KYBd3N3ddf/995vbgKuxK3xXr15diYmJSkhIUKtWrUp0zq5duyRJYWFhV2gJAOWfIxffWbwz8arDd3p2rpqMXSJJ2j0+Rn5erK0G5/Lx8dG8efOcXQbgNHYNOykM3GvWrCnxOZ9//rksFos6dOhgz60BwOUw7SAAVHx2he/7779fhmHok08+0ZEjR67YftKkSWZQ79evnz23BgCX4u5m0Z4TqTp8xursUgAAdrArfD/yyCO66aablJmZqaioKP34449FFtoxDEObN29W//799Y9//EMWi0UdO3ZUjx497C4eAFxF28hqkqQlu+j9RsVmtVplsVhksVhktfLLJFyPXYP/3Nzc9P333+v2229XQkKCevbsKT8/P3N1y6ioKJ0/f958yNIwDF1//fX68ssv7a8cAFzI35rU1IaDZ/XjzkQ93L6us8sBAFwju3q+JalOnTqKj49Xv3795ObmJqvVKsMwZBiGTp06pczMTLM3vG/fvtq0aZNCQ0PtLhwAXEnXRgX/bv5yJFlJqZlOrgYAcK1K5bH34OBgzZo1S2+88YYWLVqkLVu26OTJk8rLy1P16tXVsmVL3XXXXbrxxhtL43YA4HJCA33Uum41bT18Tsv3nHR2OQCAa1Sqc07VrVtXTz/9dGleEgDwp+5Nw7T18DlWuwSACsyuYSdHjhzRkSNHlJeXV+Jz8vPzzfMAACXXvVnB+gibE846uRIAwLWyK3xHRkaqfv362rdvX4nPOXTokHkeAKDkagf7qWlEoPJZZR4AKiy7h51cPLWgI84DAFfWvWmYdh1PdXYZwDVzd3fXnXfeaW4Drsbh6wwXhm43N7snWgGAcquslp3v3ixM78T9VurXBRzFx8dHixYtcnYZgNM4PAGfOHFCkhQQEODoWwNAhdegZoDqh/g7uwwAwDUqlfBduKjO5eTk5Gjv3r16/fXXJUkNGzYsjVsDgMuJblLT2SUAAK7RVQ07KW5slmEYatas2VXd1GKx6P7777+qcwAABaIbh+qTNQclSZk5efLzcvgIQuCaWa1Wc7G9kydPyt+fv+TAtVxVz3fhypWFr0vtv9KrT58+Gj58eGl/LADgEppGBJrbGw6ecWIlwLVJT09Xenq6s8sAnOKqukteeeUVm/evvvqqLBaLnnzyycsuGW+xWOTj46Pw8HDdeuutuv7666+tWgCAzVC/ZXtO6s7mEU6sBgBwNewO35I0dOhQNWnSpPSqAgCUyMq9J5XHxN8AUGHYNVBw+vTpkqRatWqVSjEAgKtzLj1HWxLOqnmtIGeXAgAoAbvC98CBA0urDgDANVq6O4nwDQAVBCvdAEAFt2RX4lWvGpyenavIMYsUOWaR0rNzy6gyAMBfler8VOfOndP27dt1+vRpZWRkXPGHwYABA0rz9gDgcnw83fTHuQztSzrv7FKAEnFzc9Mdd9xhbgOuplTC96pVq/TKK69o7dq1JT7HYrEQvgHATrdeH6IVe09q2e6Tzi4FKBFfX1+tWrXK2WUATmN3+P7444/1zDPPFJn7GwBgy8/LQwlvxZbqNaMbh2rF3pNavpfwDQAVgV1/79mzZ4+effZZGYah5s2ba/78+Vq0aJGkgp7tAwcOaPPmzfr444/VqlUrSdLtt9+uXbt26eDBg/ZXDwAuLqphDbm7WbQvkWEnAFAR2BW+P/zwQ+Xl5SkkJEQ//fST7r77btWpU8c8Xq9ePbVu3Vp///vftXnzZo0aNUpr167VM888o7p169pdPAC4uqp+XrolMtjZZQAlZrVaVaNGDdWoUUNWq9XZ5QAOZ1f4Xr16tSwWi5599lkFBARctq3FYtH//u//qkuXLlq5cqWmTZtmz60BAH/q1rSms0sArsrp06d1+vRpZ5cBOIVd4fuPP/6QJHNIiWS77HFOTk6Rc5544gkZhqEvvvjCnlsDAP7UrWmYs0sAAJSQXeE7MzNTkhQREWHu8/f3N7fPnTtX5JwbbrhBkrR79257bg0A+NN1VX3VJDzQ2WUAAErArvAdHFwwzvDiMVs1atQwe79/++23IucU/pkpOTnZnlsDAC4S3TjU2SUAAErArvDdqFEjSdL+/fvNfX5+fmrQoIEk6fvvvy9yzrfffiupIKQDAEpH1yYXxn1bs1ixEgDKK7vC9+233y7DMPTTTz/Z7L/vvvtkGIY++OADTZ8+XVarVSdPntSECRP06aefymKxqEuXLnYVDgC44IYaF4b8/bSfB9kAoLyyK3z37NlTkjR//nxz/Lck/eMf/1BwcLBycnL02GOPKTAwUOHh4XrhhReUl5cnHx8fjRkzxr7KAQCmix92X74nyYmVAJfn5uamNm3aqE2bNiwvD5dk1wqX7dq10/Tp05Wbm6tz584pPDxcklS9enUtWbJEffv21aFDh2zOCQ0N1eeff67GjRvbc2sAwCWs+u2UsnLz5O3h7uxSgCJ8fX21efNmZ5cBOI3dy8sPHDiw2P2tW7fW3r17tWLFCu3atUu5ublq0KCBYmJi5OfnZ+9tAQCXYM3K07oDZ9S5IQ9hAkB5Y3f4vhxPT0/FxMQoJiamLG8DAPiLJTsTCd8AUA4x2AoAKqG43UnKyzecXQZQRHp6uiIjIxUZGan09HRnlwM4XJn2fAMArszPy0MJb8WW2vUCfT10xpqtLQln1bxWUKldFygNhmHo8OHD5jbgakoUvj///PMyufmAAQPK5LoA4Mo6NwzVd/HHtWRX0lWF7/TsXDUZu0SStHt8jPy86J8BgNJWon9ZBw0aZDONVWmwWCyEbwAoA9GNa/4ZvhP1j24NnF0OAOAiJe7W4E9DAFAx3HZDdfl6uutYcob2nDjv7HIAABcp0QOXhw4duuRr27Ztatu2rSSpWbNmmjhxolavXq29e/dq7969Wr16td5++201b95cktS2bVtt27ZNBw8evOai16xZo7vuuksRERGyWCyaP3++zfHCnvqLX927d7dpc/bsWfXv31+BgYGqWrWqhgwZorS0NJs2O3bsUMeOHeXj46PatWtrwoQJRWqZN2+eGjVqJB8fHzVv3lw//PCDzXHDMDR27FiFh4fL19dX0dHR2r9//zV/7ABwJT6e7opqWENSwYOXAIDyo0Thu27dusW+wsPD9dhjj2nLli0aP368tm/frn/84x/q2LGjbrzxRt14443q2LGjRo4cqfj4eL322mvavHmzHn/8cXNBnmthtVrVokULTZky5ZJtunfvrhMnTpiv//73vzbH+/fvr127dikuLk4LFy7UmjVr9MQTT5jHU1NT1a1bN9WtW1dbt27VxIkTNW7cOH3yySdmm3Xr1qlfv34aMmSIfvnlF/Xq1Uu9evXSzp07zTYTJkzQBx98oKlTp2rjxo3y9/dXTEyMzYqgAFDaYpqGSZKWsdolAJQrdj1N8+GHH2rbtm3q27evXnrppcu2tVgs+p//+R/9+uuvmjdvnt5//32NGjXqmu7bo0cP9ejR47JtvL29FRYWVuyxPXv2aPHixdq8ebPatGljfix33nmn3n77bUVERGjWrFnKzs7WtGnT5OXlpaZNmyo+Pl7vvvuuGdLff/99de/e3fw4XnvtNcXFxWny5MmaOnWqDMPQpEmT9NJLL+mee+6RVPDwas2aNTV//nw9+OCD1/TxA8CVdG4UKk93iw6csjq7FMCGxWJRkyZNzG3A1dg1z/fs2bNlsVg0aNCgEp8zePBgGYahOXPm2HPrK1q1apVCQ0PVsGFDPfXUUzpz5ox5bP369apataoZvCUpOjpabm5u2rhxo9mmU6dO8vLyMtvExMRo3759OnfunNkmOjra5r4xMTFav369pILhOomJiTZtgoKC1K5dO7NNcbKyspSammrzAoCrEeTrqQ7Xhzi7DKAIPz8/7dq1S7t27WLFa7gku8L3gQMHJEk1a9Ys8TmhoaE255aF7t276/PPP9fy5cv1v//7v1q9erV69OihvLw8SVJiYqJZRyEPDw8FBwcrMTHRbPPXj6vw/ZXaXHz84vOKa1OcN998U0FBQeardu3aV/XxA4AkdW9a/F//AADOY1f4LpwB5WoeICxsW5azpzz44IO6++671bx5c/Xq1UsLFy7U5s2btWrVqjK7Z2l64YUXlJKSYr6OHj3q7JIAVEB/a1JT/FUfAMoXu8J348aNJUmTJk1Sfn7+Fdvn5+frvffesznXEerXr6+QkBD9/vvvkqSwsDCdPHnSpk1ubq7Onj1rjhMPCwtTUpLtg0qF76/U5uLjF59XXJvieHt7KzAw0OYFAFerRoC3Wtau6uwyABvp6elq2rSpmjZtyvLycEl2he8BAwbIMAxt3LhRvXr1uuxQiqSkJN13333auHGjwxfY+eOPP3TmzBlzhpUOHTooOTlZW7duNdusWLFC+fn5ateundlmzZo1ysnJMdvExcWpYcOGqlatmtlm+fLlNveKi4tThw4dJEn16tVTWFiYTZvU1FRt3LjRbAMAZSm6ccmHBQKOYBiGdu/erd27d7OGCFySXbOdPPnkk5ozZ47Wrl2rRYsWqX79+urWrZvatm2r0NBQWSwWJSUlafPmzVq6dKmysrIkSbfddpuefPLJa75vWlqa2YstFTzYGB8fr+DgYAUHB+vVV19V7969FRYWpgMHDuj555/XDTfcoJiYGEkFve7du3fX448/rqlTpyonJ0fDhg3Tgw8+qIiICEnSQw89pFdffVVDhgzR6NGjtXPnTr3//vtmz70kPffcc7rjjjv0zjvvKDY2VnPmzNGWLVvM6QgtFouGDx+uf/3rX2rQoIHq1aunl19+WREREerVq9c1f/wAUFLRTUI1Yck+SdKZtCz5BbNkPAA4k13/Cru5uenHH39U//799f333yszM1MLFizQggULirQt/O32rrvu0qxZs+Tmdu2d7lu2bFHnzp3N9yNHjpQkDRw4UB9//LF27NihmTNnKjk5WREREerWrZtee+01eXt7m+fMmjVLw4YNU9euXeXm5qbevXvrgw8+MI8HBQVp6dKlGjp0qFq3bq2QkBCNHTvWZi7wW2+9VbNnz9ZLL72kF198UQ0aNND8+fPVrFkzs83zzz8vq9WqJ554QsnJybr99tu1ePFi+fj4XPPHDwAlVavahdkk4vac1KO31XNiNQAAu7tA/P39NX/+fC1atEgff/yxVq1aVWQMl6+vr6KiovTUU0+pZ8+e9t5SUVFRl/1T1ZIlS654jeDgYM2ePfuybW666Sb99NNPl23Tp08f9enT55LHLRaLxo8fr/Hjx1+xJgAoS0t2JhK+AcDJSu3vj7GxsYqNjVV+fr4OHDigs2fPSpKqVaum66+/Xu7u7qV1KwBwCX5eHkp4K7bUrrc54axOp2UppIr3lRsDAMpEqQ/+c3NzU4MGDUr7sgAAO+Ub0uKdiXq4fV1nlwIALsuu2U4AABXLoh0nrvqc9OxcRY5ZpMgxi5SenVsGVcGVWCwW1a1bV3Xr1mV5ebgkHnsHABey8dAZnTqfJX9vhgLCOfz8/JSQkODsMgCnKVH4fvTRRyUV/Lb62WefFdl/Lf56LQBA2Wp+XaB+PZaqxbsS1bvVdc4uBwBcUonC94wZM8w/DV0cmC/efzUMwyB8A4CDdWsapl+PpeqHHScI3wDgJCUK33Xq1Ck2ZF9qPwCg/OneNEzvLP1NGw+d0em0LGeXAxeVkZGhTp06SZLWrFkjX19fJ1cEOFaJwvelxmYxZgsAKo7rqvmqRa0gbf8jRct2Jzm7HLio/Px8bdmyxdwGXA2znQCAC4m9KVxSwZSDAADHI3wDgAvp0awgfG85fM7JlQCAayJ8A4ALqR3spxa1qyrfcHYlAOCaCN8A4GJim4c5uwQAcFkleuCyfv36pX5ji8WiAwcOlPp1AQCXd2fzcL3xw15nlwEALsmu2U7swRSFAOActar5qfl1Qfr1WIqzS4GLCgkJcXYJgNOUKHwPHDiwrOsAADhQ92ZhhG84hb+/v06dOuXsMgCnKVH4nj59elnXAQAoIT8vDyW8FWvXNWKbh2nikn2SpKNn09UwLLA0SgMAXAEPXAKACwoN9DG3F2w/fk3XSM/OVeSYRYocs0jp2bmlVRoAVGqEbwBwcd9vPyHDYO5BOEZGRoaioqIUFRWljIwMZ5cDOFyJhp0AACqvI2fT9cvRZDUKC3B2KXAB+fn5Wr16tbkNuJpSD98JCQk6ffq0MjIyrtiT0qlTp9K+PQDgGny77ZheuLORs8sAgEqvVML3vn379MYbb+j7779Xampqic6xWCzKzWWMIACUBwt2HNc/ut3o7DIAoNKzO3zPnz9f/fv3V2ZmJmMGAaACqhHgrVPns7RmP9O/AUBZs+uBy6NHj+rhhx9WRkaGIiIiNGnSJH3yySeSCnq2ly9frnnz5mn06NGKiIiQJN1+++1atmyZVqxYYX/1AAC79bwpXNK1z3oCACg5u3q+P/jgA6WnpysgIEAbN25URESEdu3aZR7v3LmzJKl3794aO3ashgwZorlz5+qzzz7TrFmz7KscAFAq7m4Roek/J2jVPnq+AaCs2dXzvWzZMlksFj399NNmz/al+Pr66osvvlDLli01Z84cff311/bcGgBQShqGBahRWIBy8hg6CMfw8/OTn5+fs8sAnMKu8J2QkCBJuvXWW819FovF3P7rA5Vubm569tlnZRiGpk2bZs+tAQCl6L5W1zm7BLgIf39/Wa1WWa1W+fv7O7scwOHsCt9Wq1WSVLt2bXPfxb/JpqSkFDmnadOmkqTt27fbc2sAQCm65+br5Ga5cjsAgH3sCt9BQUGSpMzMTHNf9erVze0DBw4UOacwkJ8+fdqeWwMASlHNQB+1r1/9yg2vgCXnAeDy7ArfDRs2lCQdPHjQ3BcQEKC6detKkpYuXVrknLi4OElS1apV7bk1AKCU3d3iwrM7TB2LspKZmanY2FjFxsbadN4BrsKu8N2hQwdJ0oYNG2z29+zZU4ZhaOLEiVq5cqW5/8svv9T7778vi8Wi2267zZ5bAwBKWdfGoeb29qNFhw0CpSEvL08//PCDfvjhB+Xl5Tm7HMDh7Arfd955pwzD0DfffGPzP9CoUaPk5+entLQ0RUdHq0aNGgoICFC/fv2UmZkpNzc3jRo1yu7iAQClx9/7wuyzX2456sRKAKDysmue76ioKL3yyivKzc3VsWPHVKdOHUlSnTp1NG/ePPXv31/Jyck6c+aMeY63t7c+/vhjtW/f3r7KAQAmPy8PJbwVW2rX+3Fnol6xZquav1epXRMAYGf4tlgseuWVV4o91qNHD+3fv19fffWVdu3apdzcXDVo0EB9+/bVddcxpRUAlGdZufn6ausferxTfWeXAgCVil3h+0qqV6+uv//972V5CwBAGZm18bCG3F7P2WUAQKVi15hvAEDlVMXbQwln0vXzAaaFBYDSZFf4bt++vSZPnqxTp06VVj0AgHLg7psLph38YsNhJ1cCAJWLXeF706ZNeu6553TdddepR48e+uKLL8xVLwEAFdcDbQtWLo7bnaTEFOZiRunx9/eXYRgyDIPl5eGS7ArfDRo0kGEYys3N1dKlSzVw4EDVrFlTDz30kBYtWsT8nQBQQTUIraJb6gUr35Dmbf3D7uux8iUAFLArfO/bt0+bN2/WiBEjFB4eLsMwlJ6errlz5+ruu+9WeHi4hg0bpnXr1pVWvQAAB3mkfcFqxV+VQvgGABSw+4HL1q1b65133tHRo0e1bNkyPfroowoKCpJhGDp9+rQ+/vhjdezYUfXr19fLL7+sPXv2lEbdAIAyFtM0TCFVvHXqfJazS0ElkpmZqT59+qhPnz4sLw+XVGqznVgsFnXp0kWffvqpEhMT9fXXX6t3797y9vaWYRhKSEjQG2+8oWbNmqlVq1Z69913S+vWAIAy4OXhpgfa1nJ2Gahk8vLy9NVXX+mrr75ieCpcUplMNejl5aV7771X8+bNU1JSkj777DN17dpVbm5uMgxD8fHxLC8PABVAv1vqyGJxdhUAUHmU+TzfAQEBGjx4sJYuXaoZM2aoatWqZX1LAEApqVXNT3c0qOHsMgCg0ijTFS4ladu2bZo9e7bmzJmjEydOlPXtAACl7IFbamvVbwXrOVizcuXnVeY/OgCg0iqTf0EPHjyoWbNmafbs2frtt98kSYZhSCqY37NXr17q379/WdwaAFDKbr8hxNyes/monunSoNSunZ6dqyZjl0iSdo+PIdgDqPRK7V+5U6dOac6cOZo9e7Y2bdok6ULg9vDwULdu3dS/f3/dc8898vPzK63bAgDKmLvbhUHfM9Yl6PGO9eXj6e7EigCg4rIrfFutVn3zzTeaNWuWVqxYYT61XBi6O3TooP79+6tv374KCQm53KUAAGXAz8tDCW/Fltr1zqRl68stRzWgQ2SpXRMAXIld4Ts0NNSco7MwcDdq1Ej9+/fXQw89pHr16tlfIQCgXPm/1Qf1YNs6zi4DFZSfn5/S0tLMbcDV2BW+MzIyJEkRERF68MEH1b9/f7Vs2bJUCgMAlD8hVbx0LDlD8385pp4twp1dDiogi8Uif39/Z5cBOI1dUw0OHjxYy5Yt09GjR/X2228TvAGgkht0a6Qk6ePVB5SXb5TJPdKzcxU5ZpEixyxSenZumdwDAJzFrvD92WefqUuXLrKwAgMAuIQH2tZWVT9PHTpt1ZJdic4uBxVQVlaWBg0apEGDBikrK8vZ5QAOVyaL7CQkJKhLly7q2rVrWVweAOAk/t4eGnxrwfM8n6w56ORqUBHl5uZq5syZmjlzpnJz+csGXE+ZTKhqtVq1atUqesQBoBIadGuk/v3TQf2WlObsUgCgwinz5eUBAJVLkJ+nHulQ1+H3ZSw44Lou9f//1e4vDwjfAICrNuT2evLx5EcIgGtXEYNzaeBfTgDAVQup4q37W9cy3+eX0cwnACqW4oJzZQ/TV4vwDQC4Jo93rG9uL9xxwml18IMdcDz+v7t2hG8AwDWpEeBtbr8T95vSsvgBDFQ2hOzSVyaznYSGhuqVV14pi0sDAMqhU+ezNHnF73q26w3OLsWUnp2rJmOXSJJ2j4+Rn1eZ/MjDVfLz89PJkyfNbZQP/P/iOGXyma1RowbhGwBczLS1h3TPzeV7yXkChvNZLBbVqFHD2WUATsOwEwCA3W6/IUTZefmasHifs0u5Jq466wJcE9/XzlXmv/IvWLBAX375pU6fPq169erpscceU6tWrcr6tgCAy/Dz8lDCW7Gldr0xPRqq15QzWrnvVKlds7y6VO85veolk5WVpZEjR0qS3n33XXl7e1/hDFwrvifLJ7t6vleuXKnQ0FDVqVNHycnJRY6//PLL6tWrl2bPnq2lS5fq//7v/9S+fXv95z//see2AIBypn6NKhp0a6SzyyiX6GW0lZubq48++kgfffQRy8uXEr7HKha7wvcPP/yg06dPq23btqpatarNsR07duiNN96QYRgyDENVq1aVYRjKzc3V3//+dyUkJNhzawBAOfNsdANV9/dydhkVAmEJcF12he+1a9fKYrEoOjq6yLGPP/5YhmGoWrVq2rp1q86cOaNNmzYpODhYWVlZmjp1qj23BgCUM4E+nhr+twbm+9NpWU6spmIilONK+B6p+OwK3ydOFCyq0LRp0yLHFi5cKIvFomHDhqlly5aSpDZt2mjYsGEyDEPLli2z59YAgHLo3puvM7fHzt8lw2Dly9JA4AIqD7vC96lTBQ/W/HXIyYEDB3Ts2DFJ0r333mtzrGPHjmaba7VmzRrdddddioiIkMVi0fz5881jOTk5Gj16tJo3by5/f39FRERowIABOn78uM01IiMjZbFYbF5vvfWWTZsdO3aoY8eO8vHxUe3atTVhwoQitcybN0+NGjWSj4+Pmjdvrh9++MHmuGEYGjt2rMLDw+Xr66vo6Gjt37//mj92ACjP3Nws5vaq305p+s8JzisGqMD4havysit8F/ZopKSk2Oz/6aefJElBQUG6+eabbY5Vr15dkpSenn7N97VarWrRooWmTJlS5Fh6erq2bduml19+Wdu2bdM333yjffv26e677y7Sdvz48Tpx4oT5euaZZ8xjqamp6tatm+rWrautW7dq4sSJGjdunD755BOzzbp169SvXz8NGTJEv/zyi3r16qVevXpp586dZpsJEybogw8+0NSpU7Vx40b5+/srJiZGmZmZ1/zxA0BF8daPe7XzWMqVG+KqEc6AismuOWfCwsJ0+PBh7dmzx+zRlqQlSwqmtbntttuKnGO1WiVJ1apVu+b79ujRQz169Cj2WFBQkOLi4mz2TZ48WbfccouOHDmiOnXqmPsDAgIUFhZW7HVmzZql7OxsTZs2TV5eXmratKni4+P17rvv6oknnpAkvf/+++revbtGjRolSXrttdcUFxenyZMna+rUqTIMQ5MmTdJLL72ke+65R5L0+eefq2bNmpo/f74efPDBa/4cAEB516VRqFbsPaln//uL5v69vbPLcRlML1ex8PVyPXb1fLdv316GYejjjz82e7IPHjyo7777ThaLRX/729+KnPPbb79J0iVDb1lISUmRxWIpMjzmrbfeUvXq1dWyZUtNnDjRZsqj9evXq1OnTvLyuvDkfkxMjPbt26dz586Zbf76sGlMTIzWr18vSTp06JASExNt2gQFBaldu3Zmm+JkZWUpNTXV5gUAFc2/ejVVeJCPDp626vUf9ji7HJQTvr6+OnTokA4dOiRfX19nlwM4nF3h+7HHHpNUMDa6WbNmuv/++9W+fXtlZmbK19dXDz30UJFz1qxZI0m68cYb7bl1iWVmZmr06NHq16+fAgMDzf3PPvus5syZo5UrV+rvf/+73njjDT3//PPm8cTERNWsWdPmWoXvExMTL9vm4uMXn1dcm+K8+eabCgoKMl+1a9e+2g8bAJyuqp+XJj1ws9ws0vxfjl/5BJSp8jJMxc3NTZGRkYqMjJSbm2sttF1evgZwLru+67t06aLnnntOhmEoISFB3377rU6fPi1JmjhxokJCQmzaZ2Zmmr3inTp1sufWJZKTk6O+ffuavfMXGzlypKKionTTTTfpySef1DvvvKMPP/xQWVnOnxrrhRdeUEpKivk6evSos0sCgGvSrn51PdOlwZUbAoCLsHtg0XvvvaeuXbtq3rx5SkxMVHh4uAYMGKAuXboUafv9998rMDBQQUFBuuuuu+y99WUVBu/Dhw9rxYoVNr3exWnXrp1yc3OVkJCghg0bKiwsTElJSTZtCt8XDpm5VJuLjxfuCw8Pt2nz1wdRL+bt7c1yuwAqjWe63KC1v5/W1sMFQ/YysvMY11qOOHrMcXZ2tv7nf/5HkvT666/bDO+sLBjHjcsplb/39OzZUzNnztSSJUs0Y8aMYoO3JPXt21cJCQk6dOiQ6tatWxq3LlZh8N6/f7+WLVtmzrByOfHx8XJzc1NoaKgkqUOHDlqzZo1ycnLMNnFxcWrYsKH5sGiHDh20fPlym+vExcWpQ4cOkqR69eopLCzMpk1qaqo2btxotgGAys7D3U0T7m9uvn9uTryyc/OdWBGupCyHR+Tk5Ojtt9/W22+/bfMztiJiGAmuRYX8VSwtLU2///67+f7QoUOKj49XcHCwwsPDdf/992vbtm1auHCh8vLyzPHVwcHB8vLy0vr167Vx40Z17txZAQEBWr9+vUaMGKGHH37YDNYPPfSQXn31VQ0ZMkSjR4/Wzp079f777+u9994z7/vcc8/pjjvu0DvvvKPY2FjNmTNHW7ZsMacjtFgsGj58uP71r3+pQYMGqlevnl5++WVFRESoV69ejvuEAUAJ+Hl5KOGt2DK5dnjQhQfr1v5+WsPn/qIP+7Uqk3sBQHnmkPB94MABnT59WpGRkUUePrwWW7ZsUefOnc33I0eOlCQNHDhQ48aN0/fffy9JRYZ2rFy5UlFRUfL29tacOXM0btw4ZWVlqV69ehoxYoR5HalgVpKlS5dq6NChat26tUJCQjR27FhzmkFJuvXWWzV79my99NJLevHFF9WgQQPNnz9fzZo1M9s8//zzslqteuKJJ5ScnKzbb79dixcvlo+Pj92fBwCoiDzdLfrh10RV8d6hsT2bOLscXAVXHk7hyh87Spdd3zknT57UV199JUnq37+/goKCbI7//vvveuCBBxQfHy+poCf4nnvu0aeffmrXPN9RUVGXXbL4SssZt2rVShs2bLjifW666SZzwaBL6dOnj/r06XPJ4xaLRePHj9f48eOveD8AcAVv92mhEXPj9eWWP+Tj6e7sclAKKlMwrUwfS0V1cY47di5DknQ2PdvcF7c7SYYhZeXmKTXzwtClD5bv/3N/frkeBmTXd9Q333yjYcOGqUGDBnr66adtjmVlZalHjx46ePCg+Uk0DEPz58/XqVOnzCkHAQCu5W9NamrC/S30z3nb9fn6w84uB2WkvIfY8l5fZXDsXIYyc/KVmJph7pu66oAyc/OVmpFjE6h7frBW6dl5smblynpRcP7be0Xz4nNz4ou939TVB0uv+DJk13fa0qVLZbFYdO+99xY5NmPGDB04cEAWi0V33323unbtqmXLlmnBggX6+eefNXfuXD3wwAP23B4AUEHd37qWrFm5euX7Xea+K/3VEpXDxT2S6dm58vcv+/sRsktHXr6hk6mZOpGSqcNnrOb+l+fv1Ln0HJ1Oy9LJ8xembC4uOH+w4vci+yTp4Glrsfu9Pdzk5+Uub093JaZkSpJa1akqPy8PeXu4ycPdoiW7Cmaee6hdHfl7ucvLw00WSZNXHrjWD7VM2fUduG/fPkkFK13+1ezZsyUVzAU+f/58SdIzzzyjbt26admyZZozZw7hGwBc2MBbI3XGmqUPlhf8MH7+61/19v0tnFwVnKW4kHyp4Hy1+3F1Nhw8o9Pns3X0XLoSLgrZN4+PU15+0V+Sv952rNjreHm4qZqfp6r6empfUpok6f7W1ynY31uBPh7y8XTXvxYVrH47fVAbVa/irSreHnJ3s+iOiaskSb+M/VuR74UvHmtX7Nf8pdjGNvsrZfg+deqUJKlWrVo2+zMyMrRhwwZZLBabBxQl6dFHH9WyZcu0bds2e24NAKgE/t6pvhm+F+04oUOnrJr0IAG8MvP19VX4o1PMbTjeOeuF4R5vL9mno+cydOBUmrnv0Rlbij0vL9+Qm0UKDfBRaKC3dvyRIkl6tssNCq/qq5Aq3qri7a5+/94oSfrl5Wj5e3vaBOTx9zSzCciF4btd/eo2+yszu8J3cnKyJBVZHnbDhg3KycmRm5uboqOjbY7Vq1dPUsHDmgAA12axWMztYH8v7T6Rqj5Tr/xAPCouNzc3edWoa26j7FizLoTY1xft0cFTVu0/eV6n0y6E72k/JxQ5r16Iv2oH+6l2NV/VDPTWu3H7JUkr/3mHalfzk4e7m02gfjLq+mKD88X/f+MCu8J3lSpVlJKSYs6jXWjVqlWSpCZNmhSZ1cTT07Pgxh78KQgAcMG8J9trxNztZm+axDhw4Gr9e81B7T+Zpt3HU3XooiEjszYeKbb9w+3r6MaaAYoI8tFjn2+VJC169nabMF0YvmsG+sjDnV+Y7GVXAm7UqJE2btyoxYsX68477zT3f/3117JYLLrjjjuKnFMY1Etjvm8AQOURHuSrL//eQWO+3qH58cclSYOmb9b4e5qpcXigk6tDacnOzlby2ll/bndmXLYdpv+coN0nUrX9aLK5771l+4ttO/i2SDUJDywI2lV91Pb1gtW3X7yzsTmmGo5h13d8bGysNmzYoE8++USNGzdWx44dNWPGDO3evVsWi0X33XdfkXMKx3pfd9119twaAFAJ+Xi66/V7m5nhe3PCOcV+8JP6t6urp6LqO7k6lIacnByl/PzfP7c/dnI1FcMf59K1+/h5bT18TlsOnzX3T1yyr0jbO5uHqfl1VdU0IlCRIX7qNGGVJGlUTEOXGVNd3tkVvocNG6aPPvpIJ06c0LBhw2yOdejQwWYVykILFiyQxWJR27Zt7bk1AKCSunicaEzTmlqyK0n/2XBY328/7sSqAMfIzcvXnhPn9fPvp8x93d4rfsG/6MahalmnmhqFBWjIzIKHJN/u04KQXc7ZFb6DgoK0bNkyPfLIIzazl3Ts2FH//e9/i7Tfvn27Nm/eLIvFor/97W/23BoA4CB+Xh5KeCvWKfd+74Gbtf1oil5dsEt7E8+b++dsPqq+bWqrijdDFlCxZeXkmduPzdyi7UeTZc3Os2nj4W5Rs4ggtapTTc2uC9TIL7dLkj7o15IhIxWQ3f9qNW7cWFu2bNGhQ4eUmJio8PBwRUZGXrL99OnTJRXM/w0AwJV0uL66Fj5zu2auT9BrCwumJRu/YLfeWbJPvVpep/tb17rCFYDy58Pl+7X1SLLiLxqvve7AGUlSgI+HWtauqjX7T0uSNr3YVcH+3pIKerMLwzcqplLrMqhXr545jeCltGjRQi1aMH8rAODqeLi7qd8tdczwXS/EX4dOWzVr4xGbWRyOnE1XozAezkT5kZ6dq62Hz2njwbNad+C0uf/jYpZC/5/YRrrt+hpqGBagrNw8cyo/H093h9WLssff6wAAFc7CZ27T9j9SNGvDES3ZlajcP1fd6z7pJ9UJ9lPHBiG6pV6wk6uEK0rLyrUZr93+jRXm9+fFYm8K123Xh6hF7SDFfrBWktS/XV1mf3EBpfoVTkpK0qpVq7Rz506dPVvwNG5wcLCaNWumqKgophcEAJQKi8WiW68P0a3Xh+jwGau5FLWHu0VHzqYX6RF/9r+/qEl4oOpW93NSxajsJizep21HzmnnsRRdnLVz8w1dV9VX7eoFq2Xdqnp5/i5J0sT7b2K8tosqlfB94sQJjRw5Ut98841yc4v/JvLw8FDv3r31zjvvKDw8vDRuCwCAagR4m9vrx3TRr8dStOa301rz2ykdPF2wyMiyPSe1bI/tysod/3elQgN9FBrgrWB/L3P/vC1HVc2/YJlsD7cLM68cOZuuQB9P5eXnm/ty8vJlGAYr+V0FHx8fhQ1419yuiL7fflw7/kjRpkMXpv2bsS7B3K5dzVdHz2VIkuJGdFKDmgGSCoagFIZvuC67w/f27dsVHR2ts2fPXnYlspycHM2dO1fLli3T8uXL1bx5c3tvDQCADX9vD3VpVFNdGtW0Wf76hR6NdOi0VXtOpGr7nytonrFm64w1W3tO2F7jle93F3vt7pOKTvfW4tU4SZK7m0XuFwX1jhNWytvdTZ4ebjb7B0/frCreHvL0uLBK4Ltxv6m6v7cCfDzkfdH+w2esuq6anwIq2Ywu7u7u8g6/0dwuzzJz8rT7eKp+OZKszRfNrz3m61+LtO3TppZuv6FguFOQr6f5vXddNV+H1YuKwa7/o61Wq2JjY3XmTMHTudHR0Xr88cfVrl07hYWFSSpY0XLTpk369NNPtXTpUp0+fVqxsbHau3ev/Pz48x8AoOw90qGu+Sf+wlD09VMdlJqZq1OpWfojOV0fLP9dktS5YQ1l5OTJmpWn85k5SjiTLkny83JXTl6+cvKKdjTl5RvKu2iswZm07GLr2HhRT2mhT386VGzbHu8XjAP2dLeoqt+FnvkXv/1VEUG+qubvae5LSs1UnWB/m6CPq5Obl6/9J9O09fA5c98try8vdrz2TbWC1K5esJrXCtKz/42XJL16d1Pm10aJ2BW+J0+erOPHj8vNzU3/93//pyFDhhRpU6dOHdWpU0f333+/pk2bpscff1zHjh3TlClTNGrUKHtuDwDANWscHmgTlgrD95T+rWz2F4b1LS9Fy8/LQ9asHDV9Zakkaf0LXeTl7qbcfEPnM3MU/e4aSdK3T98qdzeLcvLydT4zV4Omb5ZUMM433zCUkpGjN37YK0l6uH0dZWTn63xmjlIycsyA7uvlrozsPOXkGTp1Psuse/4vRRcb6vz2anm4WVQz0Ec1Ay8Mw5mz+ajqh/irVjVfVbsowDtTdna2UjZ+/ee2c5aXT8vK1a9/JJvvH/xkg/YlnldWbr5Nu9x8QyFVvNWqTlU1uy5Q78YVLN0+54n2jNfGNbPrO/67776TxWLRoEGDig3ef/Xoo49q3bp1mjZtmr799lvCNwBUYM5cfMeZLh7fHeTraYbHAJ8LP1IbhgUU2wsae1O4GdoKw/eLdzYuNuxvfSlabhaLzlqzdTw5Q/dPXS9JGh7dQMnpOTqenKGlu5MkFQx7yc03dCw5Q8eSM8z7jV9Q/BCaEXPjVbd6QSivUcW72DZlJScnR8mrpv+5/V6Z3ccwDJ21ZmvviVRz39BZ27T/ZJr+OJdh03bHn0ORqnh7qHF4gDYnFPR+x43spBtqVJHFYlF6dq4ZvgF72BW+f/vtN0nSgw8+WOJz+vXrp2nTppnnAgCA4vl4uiuiqq+q+l0YYvJEp/pFhtDEj/2b0rJydTw5Uwmn0/SPeTskFQyhOZGSqT/OZSgt68IvAUt2JRV7v1teX67rqvoq9KLe86+3/aFa1fwUGuCtQJ/yN/78t6TzOmvN1rHkTB0+YzX3d3hrhVIzbHumV+67MAVgaIC3Tv75F4W3+9ykVnWqKbK6vzIvml/7uqq+PEyLUmfX/0VpaWmSCqYTLKlq1apJKhgvDgAA7OfuZlF4kK/Cg3zVODzADN+FQ2gMw1BiaqY6vLlCkjSme0Mlnc/SsXMZOnI2XXsTz0sqGI6xL+m89iWdN699qdk57pn8s4L9vVTNz8um13/WxsOq6uslT/cLoXX70WRV8faUu5tFqWlWeQRfJ0k6cMqqkxkFY+bTMi8E5R93Jio3L1/J6Tnmvhe/+VUpGTk6Y83W6bQLw3B6TVlXbH2FwTssyEeJKZmSpJdiG6vZdUFqWDNA3p5uZsi+s3k482vDYez6TqtRo4aOHz+uPXv2qFWrViU6Z+/egj+zhYSE2HNrAABQQhaLRUG+F3rPB9waWexQlwXP3Kaz1hwdPmPV2O8KQvftN4TojDVbp85n6ow1W4UTm+0/mVbsvV5ftLfIvn7/3mjz/rrH/0+S9OC0X4q9xj+KWT59fnzRse6SVNXPU9dV9VVEVV/VCPDW7D/nd58/9FY1rBkoQ4b58T3Urg4PRcLp7Arf7du319dff613331XDzzwgDw8Ln+53Nxcvfvuu7JYLGrfvr09twYAAKXs+hpV1Pw6D6VnVzPD9ycDWpuBNSUj25xe8dOBbZSenafk9GydTM3U5JUHJEkxTWsqM6fgAdJtR5IlSbWq+So/31BuvqGcvHydPnNWslgUElxNnu5u8nCzyM3NYo7Fbl23mgJ9POTj6a4fdyZKkkb+rYHCAn1VvYqX/LzczUC/bkwXm0BdGL5vrBkgXy93QjbKHbvC94ABA/T1118rPj5esbGxmj59uiIiIopte/z4cQ0ZMkTbtm0zH9IEAAAVh6f7hXnIb72+uk3oLQzf7z1wc5Ex6UtHdDLbnjqXotDgqpKkbWeTVaNakHmNwvb/GXKLeY3C8P1Yx/r0WqNSsCt833XXXerVq5fmz5+vZcuWqX79+urWrZvatWun0NBQWSwWJSUlaePGjYqLi1N2dsG8p/fee69iY13vCXkAAAC4NrufLvjvf/+rAQMGaN68ecrOztaiRYu0aNGiIu0KV7/s06ePPv/8c3tvCwAAKiAfHx/V7PeGuY3K71LTkl7N/tK4Rnlhd/j29vbW3LlzNWDAAH300UdavXq10tPTbdr4+fnpjjvu0NChQ3XnnXfae0sAQDlVnn/goXxwd3eXT52bzG2Ub5Up9JYXpTavTmxsrGJjY5WXl6eDBw/q7NmCFbqCg4NVv359/gcDAAAoB66mZxmlz67w3aVLF0nSI488osGDB0sq+C22QYMG9lcGAAAqnZycHJ3ftvDP7S4S82vbjV7oisWu7/iffvpJ+fn5evnll0urHgAAUIllZ2frbNzUP7ffkvx9nVxRxUKgrvjsCt+hoaFKTExU1apVS6kcAAAA10GvteuxK3y3aNFCiYmJ+u2339SyZcvSqgkAAKDSIVBDktyu3OTSHnvsMRmGoalTp5ZWPQAAABVaYchOeCvWXBgIKGTXd8R9992nhx9+WF988YUeffRRffjhh/L39y+t2gAAlQQ9fqiM+L7GtbArfH/++efq2rWrduzYoZkzZ+q7777TXXfdpZtuuknVqlW74vSCAwYMsOf2AAAAQIViV/geNGiQLBaL+f7cuXP6z3/+U6JzLRYL4RsAAFQI9HKjtNg9EKlw2fhLvQcAACjk7e2tGve/Ym6XN4RslDW7wvehQ4dKqw4AAOACPDw85Hd9W3PbWQjZcBa7vuvr1q1bWnUAAFwQAQhlje8xlDfMfwMAABwmJydHab8u+3O7dJeXJ2ijIiB8AwAAh8nOztaZHyb9uT3umpaXJ2SjIruqRXZ+/PFHtWrVSq1atdLs2bOv6kazZ882z122bNlVnQsAAFwPi9WgMipx+DYMQyNGjND27dtVo0YNPfTQQ1d1o379+ikkJETx8fH6xz/+cdWFAgBcA4HLNfF1h6socfhesWKFfvvtN7m5uem999676htZLBZNmjRJ7u7u2rlzp1avXn3V1wAAABXDpcL0pbYBV1Hi7/qvv/5akvS3v/1NTZo0uaabNWnSRDExMfrxxx/11Vdf6Y477rim6wAAXA/jfJ3vUl8DvjZAyZU4fG/atEkWi0V33XWXXTfs2bOnfvjhB23YsMGu6wAAgJK7muBMmAbKTonD9+HDhyVJDRs2tOuGN954oyQpISHBrusAACC5RlC8moDsCp8PoCIrcfhOSUmRJAUHB9t1w8LzU1NT7boOAACXU15CKMHZlre3t7788ktzG3A1FsMwjJI0DAkJ0blz57R8+XJFRUVd8w1XrVqlLl26KDg4WKdPn77m67iS1NRUBQUFKSUlRYGBgc4uBwAAAH9R0rxW4tlOatSoIUnavXu3XYXt2bNHkhQaGmrXdQAAAICKpsTh+5ZbbpFhGFqwYIFdN/zuu+9ksVjUtm1bu64DAAAqntzcXM2bN0/z5s1Tbm6us8sBHK7E4btHjx6SpKVLl2rt2rXXdLM1a9Zo6dKlNtcDAACuIysrS3379lXfvn2VlZXl7HIAhytx+O7du7ciIyNlGIb69Omj/fv3X9WNfvvtN/Xt21cWi0WRkZG6//77r7pYAAAAoCIrcfj29PTU22+/LUk6efKkWrdurffff19Wq/Wy56WlpWnSpElq06aNTp48KUl655135OHBqlYAAABwLSWe7aTQa6+9pldeeUUWi0WS5O/vr44dO6p169YKDQ2Vv7+/rFarkpKStG3bNv3000+yWq0qvM348eP10ksvlf5HUokx2wkAoLKwWq2qUqWKpIIOOn9/fydXBJSOkua1qw7fkjR9+nQ988wzSk9PL7jIn0G8OIWX9/Pz0+TJkzVo0KCrvZ3LI3wDACoLwjcqq1KfavBigwcP1m+//aaRI0cqJCREhmFc8hUSEqJ//OMf+u233wjeAAAAcGnX1PP9V7t27dL27dt15swZnT9/XgEBAapevbpatGihpk2blkadLo2ebwBAZUHPNyqrkua1UnnqsWnTpoRsAABwRV5eXpo+fbq5DbgaphwBAAAO4+npyTBUuLRrGvMNAAAA4OrR8w0AABwmNzdXS5YskSTFxMSw7gdcDt/xAADAYbKystSzZ09JBQ9cEr7hahh2AgAAADgI4RsAAABwEMI3AAAA4CAVMnyvWbNGd911lyIiImSxWDR//nyb44ZhaOzYsQoPD5evr6+io6O1f/9+mzZnz55V//79FRgYqKpVq2rIkCFKS0uzabNjxw517NhRPj4+ql27tiZMmFCklnnz5qlRo0by8fFR8+bN9cMPP1x1LQAAAHANFTJ8W61WtWjRQlOmTCn2+IQJE/TBBx9o6tSp2rhxo/z9/RUTE6PMzEyzTf/+/bVr1y7FxcVp4cKFWrNmjZ544gnzeGpqqrp166a6detq69atmjhxosaNG6dPPvnEbLNu3Tr169dPQ4YM0S+//KJevXqpV69e2rlz51XVAgAAABdhVHCSjG+//dZ8n5+fb4SFhRkTJ0409yUnJxve3t7Gf//7X8MwDGP37t2GJGPz5s1mmx9//NGwWCzGsWPHDMMwjI8++sioVq2akZWVZbYZPXq00bBhQ/N93759jdjYWJt62rVrZ/z9738vcS0lkZKSYkgyUlJSSnwOAADlUVpamiHJkGSkpaU5uxyg1JQ0r1XInu/LOXTokBITExUdHW3uCwoKUrt27bR+/XpJ0vr161W1alW1adPGbBMdHS03Nzdt3LjRbNOpUyebpW9jYmK0b98+nTt3zmxz8X0K2xTepyS1FCcrK0upqak2LwAAKgMvLy9NnjxZkydPZnl5uKRKN7lmYmKiJKlmzZo2+2vWrGkeS0xMVGhoqM1xDw8PBQcH27SpV69ekWsUHqtWrZoSExOveJ8r1VKcN998U6+++uqVP1gAACoYT09PDR061NllAE5T6Xq+K4MXXnhBKSkp5uvo0aPOLgkAAACloNKF77CwMElSUlKSzf6kpCTzWFhYmE6ePGlzPDc3V2fPnrVpU9w1Lr7HpdpcfPxKtRTH29tbgYGBNi8AACqDvLw8rVq1SqtWrVJeXp6zywEcrtKF73r16iksLEzLly8396Wmpmrjxo3q0KGDJKlDhw5KTk7W1q1bzTYrVqxQfn6+2rVrZ7ZZs2aNcnJyzDZxcXFq2LChqlWrZra5+D6FbQrvU5JaAABwJZmZmercubM6d+7MzF9wSRUyfKelpSk+Pl7x8fGSCh5sjI+P15EjR2SxWDR8+HD961//0vfff69ff/1VAwYMUEREhHr16iVJaty4sbp3767HH39cmzZt0s8//6xhw4bpwQcfVEREhCTpoYcekpeXl4YMGaJdu3Zp7ty5ev/99zVy5Eizjueee06LFy/WO++8o71792rcuHHasmWLhg0bJkklqgUAAAAuxEGzr5SqlStXmtMUXfwaOHCgYRgFU/y9/PLLRs2aNQ1vb2+ja9euxr59+2yucebMGaNfv35GlSpVjMDAQGPw4MHG+fPnbdps377duP322w1vb2/juuuuM956660itXz55ZfGjTfeaHh5eRlNmzY1Fi1aZHO8JLVcCVMNAgAqC6YaRGVV0rxmMQzDcFryR4mkpqYqKChIKSkpjP8GAFRoVqtVVapUkVTwl2x/f38nVwSUjpLmtQo57AQAAACoiAjfAAAAgIMQvgEAAAAHqXQrXAIAgPLL09NTEyZMMLcBV8MDlxUAD1wCAACUbzxwCQAAAJQzDDsBAAAOk5eXp23btkmSWrVqJXd3dydXBDgW4RsAADhMZmambrnlFknM8w3XxLATAAAAwEEI3wAAAICDEL4BAAAAByF8AwAAAA5C+AYAAAAchPANAAAAOAhTDQIAAIfx9PTUK6+8Ym4Drobl5SsAlpcHAAAo31heHgAAAChnGHYCAAAcJj8/X3v27JEkNW7cWG5u9APCtRC+AQCAw2RkZKhZs2aSWF4erolfNwEAAAAHIXwDAAAADkL4BgAAAByE8A0AAAA4COEbAAAAcBDCNwAAAOAgTDUIAAAcxtPTU//85z/NbcDVsLx8BcDy8gAAAOUby8sDAAAA5QzDTgAAgMPk5+fryJEjkqQ6deqwvDxcDuEbAAA4TEZGhurVqyeJ5eXhmvh1EwAAAHAQwjcAAADgIIRvAAAAwEEI3wAAAICDEL4BAAAAByF8AwAAAA7CVIMAAMBhPDw89PTTT5vbgKvhux4AADiMt7e3pkyZ4uwyAKdh2AkAAADgIPR8AwAAhzEMQ6dPn5YkhYSEyGKxOLkiwLEI3wAAwGHS09MVGhoqieXl4ZoYdgIAAAA4COEbAAAAcBDCNwAAAOAghG8AAADAQQjfAAAAgIMQvgEAAAAHYapBAADgMB4eHho4cKC5DbgavusBAIDDeHt7a8aMGc4uA3Aahp0AAAAADkLPNwAAcBjDMJSeni5J8vPzY3l5uBx6vgEAgMOkp6erSpUqqlKlihnCAVdC+AYAAAAchPANAAAAOAjhGwAAAHAQwjcAAADgIIRvAAAAwEEI3wAAAICDMM83AABwGHd3d91///3mNuBqCN8AAMBhfHx8NG/ePGeXATgNw04AAAAAByF8AwAAAA5C+AYAAA5jtVplsVhksVhktVqdXQ7gcIRvAAAAwEEI3wAAAICDEL4BAAAAB6m04TsyMtIcU3bxa+jQoZKkqKioIseefPJJm2scOXJEsbGx8vPzU2hoqEaNGqXc3FybNqtWrVKrVq3k7e2tG264QTNmzChSy5QpUxQZGSkfHx+1a9dOmzZtKrOPGwAAAOVXpQ3fmzdv1okTJ8xXXFycJKlPnz5mm8cff9ymzYQJE8xjeXl5io2NVXZ2ttatW6eZM2dqxowZGjt2rNnm0KFDio2NVefOnRUfH6/hw4frscce05IlS8w2c+fO1ciRI/XKK69o27ZtatGihWJiYnTy5EkHfBYAAABQnlgMwzCcXYQjDB8+XAsXLtT+/ftlsVgUFRWlm2++WZMmTSq2/Y8//qiePXvq+PHjqlmzpiRp6tSpGj16tE6dOiUvLy+NHj1aixYt0s6dO83zHnzwQSUnJ2vx4sWSpHbt2qlt27aaPHmyJCk/P1+1a9fWM888ozFjxpSo9tTUVAUFBSklJUWBgYF2fBYAAHAuq9WqKlWqSJLS0tLk7+/v5IqA0lHSvFZpe74vlp2drS+++EKPPvqoLBaLuX/WrFkKCQlRs2bN9MILLyg9Pd08tn79ejVv3twM3pIUExOj1NRU7dq1y2wTHR1tc6+YmBitX7/evO/WrVtt2ri5uSk6OtpsU5ysrCylpqbavAAAqAzc3d1155136s4772R5ebgkl1hefv78+UpOTtagQYPMfQ899JDq1q2riIgI7dixQ6NHj9a+ffv0zTffSJISExNtgrck831iYuJl26SmpiojI0Pnzp1TXl5esW327t17yXrffPNNvfrqq9f88QIAUF75+Pho0aJFzi4DcBqXCN+fffaZevTooYiICHPfE088YW43b95c4eHh6tq1qw4cOKDrr7/eGWWaXnjhBY0cOdJ8n5qaqtq1azuxIgAAAJSGSh++Dx8+rGXLlpk92pfSrl07SdLvv/+u66+/XmFhYUVmJUlKSpIkhYWFmf8t3Hdxm8DAQPn6+srd3V3u7u7Ftim8RnG8vb3l7e1dsg8QAAAAFUalH/M9ffp0hYaGKjY29rLt4uPjJUnh4eGSpA4dOujXX3+1mZUkLi5OgYGBatKkidlm+fLlNteJi4tThw4dJEleXl5q3bq1TZv8/HwtX77cbAMAgCuxWq3y9/eXv78/y8vDJVXq8J2fn6/p06dr4MCB8vC40Ml/4MABvfbaa9q6dasSEhL0/fffa8CAAerUqZNuuukmSVK3bt3UpEkTPfLII9q+fbuWLFmil156SUOHDjV7pZ988kkdPHhQzz//vPbu3auPPvpIX375pUaMGGHea+TIkfr3v/+tmTNnas+ePXrqqadktVo1ePBgx34yAAAoJ9LT020mOQBcSaUedrJs2TIdOXJEjz76qM1+Ly8vLVu2TJMmTZLValXt2rXVu3dvvfTSS2Ybd3d3LVy4UE899ZQ6dOggf39/DRw4UOPHjzfb1KtXT4sWLdKIESP0/vvvq1atWvr0008VExNjtnnggQd06tQpjR07VomJibr55pu1ePHiIg9hAgAAoPJzmXm+KzLm+QYAVBbM843Kinm+AQAAgHKG8A0AAAA4COEbAAAAcJBK/cAlAAAoX9zc3HTHHXeY24CrIXwDAACH8fX11apVq5xdBuA0/MoJAAAAOAjhGwAAAHAQwjcAAHAYq9WqGjVqqEaNGiwvD5fEmG8AAOBQp0+fdnYJgNPQ8w0AAAA4COEbAAAAcBDCNwAAAOAghG8AAADAQQjfAAAAgIMw2wkAAHAYNzc3tWnTxtwGXA3hGwAAOIyvr682b97s7DIAp+FXTgAAAMBBCN8AAACAgxC+AQCAw6SnpysyMlKRkZFKT093djmAwzHmGwAAOIxhGDp8+LC5Dbgaer4BAAAAByF8AwAAAA5C+AYAAAAchPANAAAAOAjhGwAAAHAQZjsBAAAOY7FY1KRJE3MbcDWEbwAA4DB+fn7atWuXs8sAnIZhJwAAAICDEL4BAAAAByF8AwAAh0lPT1fTpk3VtGlTlpeHS2LMNwAAcBjDMLR7925zG3A19HwDAAAADkL4BgAAAByE8A0AAAA4COEbAAAAcBDCNwAAAOAgzHYCAAAcxmKxqG7duuY24GoI3wAAwGH8/PyUkJDg7DIAp2HYCQAAAOAghG8AAADAQQjfAADAYTIyMtS2bVu1bdtWGRkZzi4HcDjGfAMAAIfJz8/Xli1bzG3A1dDzDQAAADgI4RsAAABwEMI3AAAA4CCEbwAAAMBBCN8AAACAgzDbCQAAcKiQkBBnlwA4DeEbAAA4jL+/v06dOuXsMgCnYdgJAAAA4CCEbwAAAMBBCN8AAMBhMjIyFBUVpaioKJaXh0tizDcAAHCY/Px8rV692twGXA093wAAAICDEL4BAAAAByF8AwAAAA5C+AYAAAAchPANAAAAOAiznQAAAIfy8/NzdgmA0xC+AQCAw/j7+8tqtTq7DMBpGHYCAAAAOAjhGwAAAHAQwjcAAHCYzMxMxcbGKjY2VpmZmc4uB3A4xnwDAACHycvL0w8//GBuA66Gnm8AAADAQQjfAAAAgINUyvA9btw4WSwWm1ejRo3M45mZmRo6dKiqV6+uKlWqqHfv3kpKSrK5xpEjRxQbGys/Pz+FhoZq1KhRys3NtWmzatUqtWrVSt7e3rrhhhs0Y8aMIrVMmTJFkZGR8vHxUbt27bRp06Yy+ZgBAABQ/lXK8C1JTZs21YkTJ8zX2rVrzWMjRozQggULNG/ePK1evVrHjx/XfffdZx7Py8tTbGyssrOztW7dOs2cOVMzZszQ2LFjzTaHDh1SbGysOnfurPj4eA0fPlyPPfaYlixZYraZO3euRo4cqVdeeUXbtm1TixYtFBMTo5MnTzrmkwAAAIByxWIYhuHsIkrbuHHjNH/+fMXHxxc5lpKSoho1amj27Nm6//77JUl79+5V48aNtX79erVv314//vijevbsqePHj6tmzZqSpKlTp2r06NE6deqUvLy8NHr0aC1atEg7d+40r/3ggw8qOTlZixcvliS1a9dObdu21eTJkyVJ+fn5ql27tp555hmNGTOmxB9PamqqgoKClJKSosDAwGv9tAAA4HRWq1VVqlSRJKWlpcnf39/JFQGlo6R5rdLOdrJ//35FRETIx8dHHTp00Jtvvqk6depo69atysnJUXR0tNm2UaNGqlOnjhm+169fr+bNm5vBW5JiYmL01FNPadeuXWrZsqXWr19vc43CNsOHD5ckZWdna+vWrXrhhRfM425uboqOjtb69esvW3tWVpaysrLM9ykpKZIKvqgAAFRkF69umZqayownqDQKc9qV+rUrZfhu166dZsyYoYYNG+rEiRN69dVX1bFjR+3cuVOJiYny8vJS1apVbc6pWbOmEhMTJUmJiYk2wbvweOGxy7VJTU1VRkaGzp07p7y8vGLb7N2797L1v/nmm3r11VeL7K9du/aVP3gAACqIiIgIZ5cAlLrz588rKCjokscrZfju0aOHuX3TTTepXbt2qlu3rr788kv5+vo6sbKSeeGFFzRy5EjzfX5+vs6ePavq1avLYrE4sbLKIzU1VbVr19bRo0cZylOJ8XV2DXydXQdfa9dQUb/OhmHo/PnzV/ylslKG77+qWrWqbrzxRv3+++/629/+puzsbCUnJ9v0ficlJSksLEySFBYWVmRWksLZUC5u89cZUpKSkhQYGChfX9//b+/OY6K63j6Af4cpDIiCsoMGkYpYUFGhLFrjT6WitcS9SBTB1FQp1gXUtomAS6tW41YVrdoKamwLKqVxjRLUGhALxH1HKaKyuFBABXS47x9k7jsIMwwyzBT4fpJJhnufe85zvbmZZ65nzoFUKoVUKm0wRtGGKjKZDDKZrN45kPaZmZm1qhub3g2vc/vA69x+8Fq3D63xOqt74q3QZmc7UVZRUYHc3FzY29vD09MThoaGSE1NFfffunUL+fn58PPzAwD4+fnhypUrdWYlOXnyJMzMzODm5ibGKLehiFG0YWRkBE9PzzoxNTU1SE1NFWOIiIiIqH1pk8X3woULcebMGeTl5SE9PR3jx4+HVCpFcHAwzM3N8fnnnyMyMhJpaWnIzs7GjBkz4OfnB19fXwDAyJEj4ebmhpCQEFy6dAknTpzAkiVLEBERIT6Rnj17Nu7du4fFixfj5s2biIuLQ2JiIhYsWCDmERkZiZ07dyIhIQE3btxAeHg4Xrx4gRkzZujl34WIiIiI9KtNDjspKChAcHAwnj59Cmtra3z00Uc4f/48rK2tAQAbNmyAgYEBJk6ciKqqKgQEBCAuLk48XiqV4vDhwwgPD4efnx9MTU0RGhqK5cuXizE9evTAkSNHsGDBAmzatAndunXDrl27EBAQIMYEBQWhpKQEMTExKCwsRP/+/XH8+PF6P8Ik3ZPJZIiNja03vIfaFl7n9oHXuf3gtW4f2vp1bpPzfBMRERER/Re1yWEnRERERET/RSy+iYiIiIh0hMU3EREREZGOsPgmIiIiItIRFt/Urnz//fcYNGgQOnTooHLhovz8fIwZMwYdOnSAjY0NFi1ahDdv3ug2UdI6JycnSCSSOq/Vq1frOy3Sgq1bt8LJyQnGxsbw8fGpt0gatX5Lly6td//27t1b32lRM509exaBgYFwcHCARCLBH3/8UWe/IAiIiYmBvb09TExM4O/vjzt37ugnWS1i8U3tSnV1NSZPnozw8PAG98vlcowZMwbV1dVIT09HQkIC4uPjERMTo+NMqSUsX74cjx8/Fl9fffWVvlOiZvr9998RGRmJ2NhY5OTkwMPDAwEBAXUWSaO2wd3dvc79e+7cOX2nRM304sULeHh4YOvWrQ3uX7NmDX788Uds374dmZmZMDU1RUBAACorK3WcqZYJRO3Q7t27BXNz83rbjx49KhgYGAiFhYXitm3btglmZmZCVVWVDjMkbevevbuwYcMGfadBWubt7S1ERESIf8vlcsHBwUFYtWqVHrMibYuNjRU8PDz0nQa1IABCcnKy+HdNTY1gZ2cnrF27VtxWWloqyGQy4ddff9VDhtrDJ99ESjIyMtC3b986CyEFBASgrKwM165d02NmpA2rV6+GpaUlBgwYgLVr13I4UStXXV2N7Oxs+Pv7i9sMDAzg7++PjIwMPWZGLeHOnTtwcHCAs7Mzpk6divz8fH2nRC3o/v37KCwsrHN/m5ubw8fHp9Xf321yhUuid1VYWFhvBVLF34WFhfpIibRk7ty5GDhwICwsLJCeno5vv/0Wjx8/xvr16/WdGr2jJ0+eQC6XN3jP3rx5U09ZUUvw8fFBfHw8XF1d8fjxYyxbtgxDhgzB1atX0alTJ32nRy1A8Znb0P3d2j+P+eSbWr1vvvmm3g9x3n7xg7htasq1j4yMxP/+9z/069cPs2fPxrp167B582ZUVVXp+SyIqDGjR4/G5MmT0a9fPwQEBODo0aMoLS1FYmKivlMjajI++aZWLyoqCmFhYWpjnJ2dNWrLzs6u3kwJRUVF4j76b2nOtffx8cGbN2+Ql5cHV1fXFsiOWpqVlRWkUql4jyoUFRXxfm3jOnfujF69euHu3bv6ToVaiOIeLioqgr29vbi9qKgI/fv311NW2sHim1o9a2trWFtba6UtPz8/fP/99yguLoaNjQ0A4OTJkzAzM4Obm5tW+iDtac61v3jxIgwMDMTrTK2PkZERPD09kZqainHjxgEAampqkJqaijlz5ug3OWpRFRUVyM3NRUhIiL5ToRbSo0cP2NnZITU1VSy2y8rKkJmZqXLGstaCxTe1K/n5+Xj27Bny8/Mhl8tx8eJFAEDPnj3RsWNHjBw5Em5ubggJCcGaNWtQWFiIJUuWICIiAjKZTL/J0zvLyMhAZmYmhg0bhk6dOiEjIwMLFizAtGnT0KVLF32nR80QGRmJ0NBQeHl5wdvbGxs3bsSLFy8wY8YMfadGWrRw4UIEBgaie/fuePToEWJjYyGVShEcHKzv1KgZKioq6vzvxf3793Hx4kVYWFjA0dER8+fPx3fffQcXFxf06NED0dHRcHBwEL9st1r6nm6FSJdCQ0MFAPVeaWlpYkxeXp4wevRowcTERLCyshKioqKE169f6y9parbs7GzBx8dHMDc3F4yNjYUPPvhAWLlypVBZWanv1EgLNm/eLDg6OgpGRkaCt7e3cP78eX2nRFoWFBQk2NvbC0ZGRkLXrl2FoKAg4e7du/pOi5opLS2twc/k0NBQQRBqpxuMjo4WbG1tBZlMJowYMUK4deuWfpPWAokgCIK+Cn8iIiIiovaEs50QEREREekIi28iIiIiIh1h8U1EREREpCMsvomIiIiIdITFNxERERGRjrD4JiIiIiLSERbfREREREQ6wuKbiIiIiEhHWHwTEREREekIi28iIiIiIh1h8U1ERHXEx8dDIpFAIpEgLy9P3+lopLq6Gi4uLpBIJDhw4IDKOEEQYGZmBgMDA9ja2iIoKAj5+fmNth8REQGJRILQ0FBtpk1E7RCLbyIiavU2bdqEu3fvok+fPpg4caLKuNzcXJSXl0MQBBQXFyMxMRGBgYGNtv/111/DyMgIe/fuRXZ2tjZTJ6J2hsU3ERG1auXl5fjhhx8AAEuWLIFEIlEZa29vjytXruD48eNwdnYGAFy+fBmXLl1S24ejoyNCQ0MhCAKio6O1lzwRtTssvomIqFXbtm0bnj59CkdHR0yePFltrKmpKfr06YOAgACsWLFC3H7x4sVG+4mKigIAHDt2jE+/ieidsfgmIqJWSy6XY8uWLQCA4OBgGBho/rHm5+cnvr969Wqj8a6urhg4cCAAYPPmzU3MlIioFotvIiJqtU6ePIkHDx4AAKZOndqkY52cnGBqagpAs+JbuY+kpCSUl5c3qT8iIoDFNxERvYPq6mrExcVh2LBhsLa2hpGREezs7PDJJ59g3759qKmpabSNp0+fYvHixXB1dYWJiQlsbW3x8ccfIzk5GYBms64kJiYCAFxcXNC3b98mnYNEIsH7778PQPPiW/FjzpcvXyIlJaVJ/RERASy+iYioifLy8uDh4YGIiAicPn0aT548wevXr1FUVIRjx44hJCQEQ4cOxbNnz1S2ceXKFbi7u2Pt2rW4ffs2KisrUVxcjFOnTmHChAmYNWuWRrmkpaUBAHx9fZt8HhkZGbhy5QoAoKCgAP/++2+jx3Tv3h12dnYAasd+ExE1FYtvIiLSWEVFBUaMGIGbN28CAMaNG4c///wTWVlZSEpKwtChQwEA586dQ2BgIORyeb02SktLMWrUKBQVFQEAQkJCcOzYMWRlZeG3336Dn58fduzYge3bt6vNpaCgQHwi/uGHHzbpPN68eYPZs2dDEARx27Vr1zQ61tvbGwBw5syZJvVJRASw+CYioiZYtmwZ7t27B6B2Wr/k5GQEBgbC09MTkyZNQlpamjguOj09HTt27GiwjUePHgEANm7ciD179mDUqFHw9PREUFAQ/vrrL4wdOxaZmZlqc0lPTxffDxgwoEnnsWHDBly+fLnONk2Hnnh6egIAHj58KH6BICLSFItvIiLSSFVVFXbt2gUAcHd3x9KlS+vFSCQSxMXFwdLSEgDEmUiU24iPjwdQ+7R63rx59dqQSqX46aefYGxsrDafgoIC8b2NjY3G5/HPP/+IuQ8aNEjcrmnxrdyX4osIEZGmWHwTEZFGsrOzUVpaCgAICwuDVCptMM7MzAyfffYZAOD69et4/PixuC8rK0tsY9q0aSr7srW1RUBAgNp8SkpKxPddunTR5BQAAHPmzMHLly9hbm6OpKQkdOrUCYDmxbeFhYX4vrCwUON+iYgAFt9ERK2WYiaQ5rwUT6E1oVyc+vj4qI1V3q98nPJ7xfANVby8vNTuV/5Bp6bF96FDh3D48GEAwOrVq+Hg4IA+ffrUy00d5b5evHih0TFERAosvomISCPKxW5jwzwUM4K8fdzz58/F99bW1mrbaGy/8rCUV69eqY0Fapehnzt3LoDa4SaKGVUUUxSWlJSguLi40XaU+zI0NGw0nohI2Xv6ToCIiN7NjRs3mt2Gvb39Ox0nkUia3XdzKRfnz549E4ePqBIdHY2HDx/C0NAQO3bsEM9BeX7wq1evYvjw4WrbUf4y0blz53fInIjaMxbfREStVO/evXXan/JY56KiIvTq1UtlrPJYaOXjlIdslJSUqG1DeUx3Q5SL7+fPn6N79+4qY3NycsQffy5evBju7u7ivn79+onvNSm+lZ/eOzo6qo0lInobh50QEZFGFGOjATQ6DeCFCxcaPE656M3OzlbbRlZWltr9yk+sb9++rTKupqYGs2bNglwuh4uLC5YsWaKyHU3GfSv6kslk6NmzZ6PxRETKWHwTEZFGPD09xWEWCQkJKpeQLy8vF5d9d3NzqzO0xcvLC+bm5gCAffv2qeyrqKgIJ06cUJuPl5eXOO7777//Vhm3detWsZDfvn17vSkMu3Tpgq5duwLQrPhW9DVgwACO+SaiJmPxTUREGpHJZJg5cyaA2iJ1xYoV9WIEQcCcOXPw5MkTALXT+ikzNjbG9OnTAdQWsZs2barXhuJJdWVlpdp8jIyMxFlVlJ+0K3v06JH4pDs0NFTlkBLF0+/GVrmsqqoSF+cZOXKk2lgiooaw+CYiIo3FxMTA2dkZALB06VJMmjQJR44cQU5ODg4ePIjhw4djz549AAA/Pz988cUX9dpYunSpOBvK/PnzMX36dJw4cQI5OTlITEzEkCFDkJKSIi7jDqj+gefYsWMB1Bbf5eXl9fbPmzcPZWVlsLKywrp161Sel2Lcd1lZGfLz81XGnT17Fq9fvwYAjB8/XmUcEZEqLL6JiEhjnTp1Qmpqqvhjz4MHD+LTTz8Vl5c/ffo0AGDw4ME4fPhwgwvxWFhY4Pjx4+IPJvfu3Vtnefn09HSEhYWJUwECULna5fTp0yGTyVBZWYnk5OQ6+44ePYoDBw4AANavXy+uutkQTcd979+/H0Dt2PX+/furjCMiUoXFNxERNYmTkxMuXbqELVu2YOjQobC0tIShoSFsbW0xatQo7N27F2fPnq0zy8nbPDw8cP36dURFRcHFxQUymQxWVlYYNmwY9u/fj927d6OsrEyMV4wTf5ulpSUmTJgA4P8LY6B2Lm7FkJcRI0YgJCRE7TlpUnxXVlbi0KFDAIAvv/xSbXtERKpIBEEQ9J0EERHR22bOnImff/4Z3bp1w4MHD1TGZWZmwtfXF1KpFLm5uWqnHGyOffv2ISQkBJaWlsjLy0PHjh1bpB8iatv45JuIiP5zXr16hZSUFACAr6+v2lgfHx9MmDABcrkcq1atapF8ampqsHLlSgDAokWLWHgT0Ttj8U1ERDqXm5sLVf/xKpfLER4eLs6YEhoa2mh7K1euxHvvvYfdu3ejoKBAq7kCQFJSEm7cuAFHR0dxiXoionfBFS6JiEjnVqxYgQsXLmDKlCnw8fGBjY0NXr16hcuXL2Pnzp3IyckBAPj7+2PMmDGNtufq6opffvkFubm5yM/PR7du3bSar1wuR2xsLIYPHw4TExOttk1E7QvHfBMRkc6FhYUhISFBbczgwYORkpKidpYSIqLWhsU3ERHp3K1bt3Dw4EGcOnUKeXl5KCkpwevXr2FpaQkvLy8EBQVhypQpMDDg6EgialtYfBMRERER6QgfKRARERER6QiLbyIiIiIiHWHxTURERESkIyy+iYiIiIh0hMU3EREREZGOsPgmIiIiItIRFt9ERERERDrC4puIiIiISEdYfBMRERER6QiLbyIiIiIiHfk/RVGCj4y0dtMAAAAASUVORK5CYII=", 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", 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" ] @@ -4282,10 +9627,10 @@ "id": "2b49c3d7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:32.838011Z", - "iopub.status.busy": "2024-06-04T23:19:32.837899Z", - "iopub.status.idle": "2024-06-04T23:19:32.840000Z", - "shell.execute_reply": "2024-06-04T23:19:32.839740Z" + "iopub.execute_input": "2025-04-03T19:33:48.473318Z", + "iopub.status.busy": "2025-04-03T19:33:48.473221Z", + "iopub.status.idle": "2025-04-03T19:33:48.475514Z", + "shell.execute_reply": "2025-04-03T19:33:48.475289Z" } }, "outputs": [ @@ -4321,10 +9666,10 @@ "id": "973e7e79", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:32.841440Z", - "iopub.status.busy": "2024-06-04T23:19:32.841345Z", - "iopub.status.idle": "2024-06-04T23:19:32.843427Z", - "shell.execute_reply": "2024-06-04T23:19:32.843185Z" + "iopub.execute_input": "2025-04-03T19:33:48.476697Z", + "iopub.status.busy": "2025-04-03T19:33:48.476608Z", + "iopub.status.idle": "2025-04-03T19:33:48.478652Z", + "shell.execute_reply": "2025-04-03T19:33:48.478434Z" }, "lines_to_next_cell": 0 }, @@ -4386,10 +9731,10 @@ "id": "a9140b3a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:32.844819Z", - "iopub.status.busy": "2024-06-04T23:19:32.844727Z", - "iopub.status.idle": "2024-06-04T23:19:32.846512Z", - "shell.execute_reply": "2024-06-04T23:19:32.846267Z" + "iopub.execute_input": "2025-04-03T19:33:48.479827Z", + "iopub.status.busy": "2025-04-03T19:33:48.479745Z", + "iopub.status.idle": "2025-04-03T19:33:48.481709Z", + "shell.execute_reply": "2025-04-03T19:33:48.481496Z" } }, "outputs": [], @@ -4413,14 +9758,2046 @@ "id": "8fc4aea0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:32.847712Z", - "iopub.status.busy": "2024-06-04T23:19:32.847645Z", - "iopub.status.idle": "2024-06-04T23:19:33.096196Z", - "shell.execute_reply": "2024-06-04T23:19:33.095971Z" + "iopub.execute_input": "2025-04-03T19:33:48.482807Z", + "iopub.status.busy": "2025-04-03T19:33:48.482741Z", + "iopub.status.idle": "2025-04-03T19:33:48.743446Z", + "shell.execute_reply": "2025-04-03T19:33:48.743182Z" }, "lines_to_next_cell": 0 }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002961.893047336, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002909.292721532, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002842.919898538, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002759.16890147, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002653.490324104, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002520.144170538, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002351.888507718, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16002139.586836109, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001871.713040235, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001533.727331886, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16001107.28977405, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000569.269442707, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999890.496647634, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999034.192416634, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997953.993094172, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996591.467783943, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994873.001788342, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15992705.889472542, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15989973.444502639, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15986528.893835295, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15982187.774395373, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976718.499356627, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15969830.707495732, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15961160.960501963, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15950255.320705947, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15936548.344581451, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15919338.096469924, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15897756.97009871, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15870738.473491088, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15836980.785622943, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15794908.961932577, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15742639.305781398, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15677951.783964379, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15598279.520216344, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15500728.213326858, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15382142.225333132, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15239236.776243072, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068814.890988702, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14868080.263148528, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14635039.685599191, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14368959.698660212, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14070805.23862632, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13743554.88143778, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13392276.560592549, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13023877.88091306, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12646520.933576018, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12268792.343592053, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11898803.095559342, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11543417.93091813, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11207766.718773343, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10895093.611569963, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10606899.312997252, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10343266.88124088, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10103247.353431445, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9885208.910573516, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9687100.478192497, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9506625.781409387, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9341352.903950285, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9188793.402093235, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9046478.453631118, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8912045.904589174, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8783339.10743256, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8658509.901020335, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8536113.828113679, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8415183.975072118, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8295269.742745581, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8176429.120013418, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8059168.8293056125, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7944335.999206972, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7832975.645216511, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7726176.614947152, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7624931.461247044, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7530031.627469168, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7442009.746564689, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7361129.146973606, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7287410.635336244, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7220681.095616929, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7160628.395404535, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7106851.483766066, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7058900.769700954, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7016308.880858365, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6978613.911777701, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945376.571027264, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6916191.049528801, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6890688.792446573, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6868535.393319951, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6849422.765039898, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6833060.05095333, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6819166.544534292, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6807468.458908728, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797699.628345769, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6789604.944998693, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6782944.868629455, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6777499.565630652, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6773071.791553852, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6769488.209512218, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6766599.256783063, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6764277.892213011, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6762417.6162482, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6760930.116967933, tolerance: 3200.6325551004925\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173612.82487654, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173560.33151807, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173494.093703294, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173410.51311625, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173305.049649913, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173171.975059805, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15173004.062268812, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172792.193566969, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172524.866617758, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15172187.571748763, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171762.00720005, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15171225.090500388, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15170547.71354342, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15169693.175771877, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15168615.213598879, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15167255.524179863, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15165540.657224856, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15163378.11903821, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15160651.497821936, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15157214.378191706, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15152882.766135195, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15147425.6946986, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15140553.628850497, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15131904.241777299, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15121025.105980713, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15107352.850599289, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15090188.41286841, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15068668.205066573, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15041731.400110113, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15008084.208955988, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14966163.110870235, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14914100.653844737, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14849699.805850953, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14770425.961151276, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14673429.41690654, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14555614.815015966, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14413776.349016687, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14244816.178940995, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14046055.366934752, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13815628.708094303, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13552926.205683708, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13259008.940702371, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12936897.573228309, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12591625.616217315, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12229982.920676824, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11859948.802383406, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11489906.8603167, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11127805.377401602, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10780443.14443526, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10453012.587348029, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10148944.578529166, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9870012.667698368, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9616601.230672905, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9388032.941233683, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9182876.289070565, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8999193.791535858, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8834727.194341874, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8687036.347689679, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553612.383287674, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8431979.280234471, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8319788.946660183, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8214909.054690647, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8115501.1056430675, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8020086.35524954, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7927596.53846852, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7837403.822275469, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7749321.535335509, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7663566.80208477, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7580680.550684168, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7501409.564666606, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7426566.500521793, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7356892.242162683, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7292946.117484922, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7235042.041596897, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7183235.551674369, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7137353.553695727, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7097050.348456673, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7061872.01272655, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7031315.123405474, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7004872.089238734, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6982061.123036068, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6962442.578610088, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6945624.890073966, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6931263.4663270125, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6919055.476661856, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6908732.977539104, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6900056.2920428645, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6892808.858171555, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6886793.977603645, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6881833.233569127, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6877765.97498893, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6874449.207170451, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6871757.386867455, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6869581.8539127605, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6867829.838852356, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6866423.119345377, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6865296.456501478, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6864395.94700243, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863677.402652601, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6863104.834999975, tolerance: 3034.7626598069196\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000126.775776321, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16000067.997791689, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999993.829780785, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999900.242584623, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999782.152469946, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999633.14527111, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999445.128467944, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15999207.892430544, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998908.557207122, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998530.875140417, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15998054.351968959, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15997453.139532348, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15996694.641307216, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15995737.757220387, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15994530.675893765, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15993008.099962447, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15991087.762599917, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15988666.060097354, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15985612.585588472, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15981763.302383827, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976912.042096594, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15970799.954194367, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15963102.47325135, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15953413.314912459, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15941224.973906962, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15925905.198558565, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15906668.990428165, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15882545.878220897, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15852342.621036042, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15814602.219371142, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15767561.301116722, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15709109.781098895, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15636759.341258615, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15547630.840385439, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15438475.105455775, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15305746.07465526, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15145748.542592412, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14954882.27386727, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14729996.36384661, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14468848.510940228, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14170631.317143818, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13836485.361873377, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13469879.089990832, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13076719.754361462, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12665089.79937819, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12244586.676668119, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11825360.36369123, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11417044.801169304, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11027817.645702794, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10663776.910200799, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10328716.2675956, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10024263.64783793, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9750266.819731826, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9505284.688773429, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9287065.61072085, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9092940.776433397, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8920108.266351493, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8765816.866835387, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8627473.905487, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8502702.196109628, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8389365.458262948, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8285575.962199782, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8189695.129107313, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8100335.848355204, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8016371.614804336, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7936951.343980087, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7861511.843847991, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7789775.81877588, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7721724.491724883, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7657540.53569288, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7597526.506006125, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7542012.431576015, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7491270.131106991, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7445449.741931618, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7404547.164364969, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7368402.734578147, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7336724.612267373, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7309126.908337014, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7285172.53934286, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7264413.0266303, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7246420.465473388, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7230809.549602925, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7217249.407961924, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7205466.206412938, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7195238.325930048, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7186386.647782043, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7178762.875061372, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7172238.602284237, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7166697.001612171, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7162027.848205515, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7158125.584421616, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7154889.512672322, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7152225.062096559, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7150045.262096588, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7148271.882784204, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7146836.014641162, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7145678.080780019, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144747.393668608, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7144001.407092072, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7143404.8053056635, tolerance: 3200.070250165819\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766426.844425442, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766379.012219734, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766318.655993313, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766242.496938994, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766146.398082258, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13766025.13980752, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765872.136748439, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765679.08077331, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765435.490848666, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13765128.145612368, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764740.368286435, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13764251.12581003, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13763633.894413952, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13762855.231859002, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13761872.98172164, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13760634.01686267, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13759071.406945651, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13757100.867966294, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13754616.31968939, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13751484.339396805, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13747537.257695232, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13742564.595583746, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13736302.49455343, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13728420.749109622, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13718507.02436845, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13706047.848124275, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13690406.03569032, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13670794.381086988, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13646245.795015218, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13615580.679837886, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13577373.323622873, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13529920.608156208, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13471218.48980598, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13398954.581488008, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13310528.590455977, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13203115.797389356, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13073790.981404455, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12919729.112886174, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12738491.873820404, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12528392.752768412, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12288907.120278118, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12021061.050642934, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11727704.457379244, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11413566.98420337, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11085024.381464427, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10749570.986969216, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10415080.823900381, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10089009.138994666, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9777704.218602655, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9485957.157639354, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9216836.907742979, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8971777.239570614, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8750831.806329573, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8553002.845594909, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8376568.552591967, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8219365.99395707, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8079015.288983279, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7953088.9445123505, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7839237.297915861, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7735280.8174845725, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7639277.052384096, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7549567.214150196, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7464805.436922483, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7383972.203368484, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7306371.938584399, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7231613.9732326735, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7159576.877369866, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7090358.56776332, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7024217.2241215855, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6961509.172985473, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6902628.941757251, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6847954.742128507, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6797801.388530005, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6752382.798167879, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6711786.944628098, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6675965.981966857, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6644742.448857503, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6617829.550839442, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6594860.867273544, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6575423.588385814, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6559089.833983606, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6545442.225937976, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6534091.895329608, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6524688.87351594, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6516926.039701696, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6510538.426567691, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6505299.7780512385, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6501017.943079299, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6497530.176477653, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6494698.902794392, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6492408.111473216, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6490560.3336993465, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6489074.07424213, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6487881.578697735, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486926.855244275, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6486163.908028902, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485555.163897057, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6485070.084972435, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484683.961142942, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6484376.8736711275, tolerance: 2753.321903486231\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123836.286658319, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123762.414447501, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123669.200043006, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123551.579596577, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123403.163871313, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16123215.891543608, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122979.591935372, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122681.433587788, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16122305.228986472, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121830.55809336, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16121231.663752725, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16120476.060052717, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16119522.779778486, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16118320.168518286, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16116803.109996723, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16114889.538918179, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16112476.063036688, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16109432.47434148, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16105594.879294181, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16100757.119470121, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16094660.087017829, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16086978.46580684, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16077304.35332688, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16065127.149018394, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16049809.047450969, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16030555.476241706, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 16006379.911872495, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15976062.758394275, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15938104.483596483, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15890674.11469827, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15831555.686060235, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15758097.525340755, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15667172.578206709, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15555162.420748936, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15417983.020182043, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15251175.908593165, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 15050092.453317674, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14810198.177746587, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14527514.082835246, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 14199187.811678281, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13824146.920817537, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 13403734.027286602, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12942174.869677957, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 12446711.659031235, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11927272.408043081, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 11395650.820912804, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10864314.587176824, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 10345084.699656613, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9847974.664610261, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 9380422.144947704, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8947015.008946367, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8549670.25861056, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 8188124.101396974, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7860558.677097185, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7564216.251072414, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7295907.831051515, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 7052382.339382325, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6830565.9531656, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6627701.871803499, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6441421.548990706, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6269768.629955587, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 6111186.722532658, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5964477.8422521455, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5828739.876908956, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5703294.550898108, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5587617.998651163, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5481282.987854579, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5383916.678079197, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5295172.882818847, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5214714.536832884, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5142200.898831903, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5077274.992035702, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 5019549.576235791, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4968593.444998967, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4923922.319001437, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4884998.717484867, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4851242.93844572, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4822053.96370539, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4796836.339338534, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4775027.808895556, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4756122.72319144, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4739687.533593078, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4725366.49534371, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4712877.711579567, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4702001.540622955, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4692564.7733192, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4684424.413915036, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4677454.213645212, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4671535.66214718, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4666553.581406483, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4662395.341810633, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4658952.253038207, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4656121.776081925, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4653809.600037623, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4651931.081491712, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4650411.90595999, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4649188.052146212, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4648205.237515733, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4647418.038791819, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: UserWarning: Coordinate descent without L1 regularization may lead to unexpected results and is discouraged. Set l1_ratio > 0 to add L1 regularization.\n", + " model = cd_fast.enet_coordinate_descent_gram(\n", + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:683: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4646788.852992944, tolerance: 3224.823681413525\n", + " model = cd_fast.enet_coordinate_descent_gram(\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jonathantaylor/anaconda3/envs/ISLP_test2025/lib/python3.11/site-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.153e+07, tolerance: 3.855e+03 Linear regression models with null weight for the l1 regularization term are more efficiently fitted using one of the solvers implemented in sklearn.linear_model.Ridge/RidgeCV instead.\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + }, { "data": { "text/plain": [ @@ -4471,10 +11848,10 @@ "id": "008ecc7c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.097496Z", - "iopub.status.busy": "2024-06-04T23:19:33.097429Z", - "iopub.status.idle": "2024-06-04T23:19:33.139203Z", - "shell.execute_reply": "2024-06-04T23:19:33.138992Z" + "iopub.execute_input": "2025-04-03T19:33:48.744858Z", + "iopub.status.busy": "2025-04-03T19:33:48.744761Z", + "iopub.status.idle": "2025-04-03T19:33:48.790552Z", + "shell.execute_reply": "2025-04-03T19:33:48.790319Z" }, "lines_to_next_cell": 2 }, @@ -4507,10 +11884,10 @@ "id": "1b909861", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.140345Z", - "iopub.status.busy": "2024-06-04T23:19:33.140279Z", - "iopub.status.idle": "2024-06-04T23:19:33.148367Z", - "shell.execute_reply": "2024-06-04T23:19:33.148166Z" + "iopub.execute_input": "2025-04-03T19:33:48.791792Z", + "iopub.status.busy": "2025-04-03T19:33:48.791716Z", + "iopub.status.idle": "2025-04-03T19:33:48.800833Z", + "shell.execute_reply": "2025-04-03T19:33:48.800607Z" }, "lines_to_next_cell": 0 }, @@ -4541,17 +11918,17 @@ "id": "ff719798", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.149542Z", - "iopub.status.busy": "2024-06-04T23:19:33.149477Z", - "iopub.status.idle": "2024-06-04T23:19:33.268439Z", - "shell.execute_reply": "2024-06-04T23:19:33.268204Z" + "iopub.execute_input": "2025-04-03T19:33:48.802059Z", + "iopub.status.busy": "2025-04-03T19:33:48.801989Z", + "iopub.status.idle": "2025-04-03T19:33:48.900286Z", + "shell.execute_reply": "2025-04-03T19:33:48.899954Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -4584,10 +11961,10 @@ "id": "f21ad8dc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.269744Z", - "iopub.status.busy": "2024-06-04T23:19:33.269654Z", - "iopub.status.idle": "2024-06-04T23:19:33.271587Z", - "shell.execute_reply": "2024-06-04T23:19:33.271375Z" + "iopub.execute_input": "2025-04-03T19:33:48.901749Z", + "iopub.status.busy": "2025-04-03T19:33:48.901632Z", + "iopub.status.idle": "2025-04-03T19:33:48.903962Z", + "shell.execute_reply": "2025-04-03T19:33:48.903682Z" } }, "outputs": [ @@ -4620,16 +11997,16 @@ "id": "7624e483", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.272793Z", - "iopub.status.busy": "2024-06-04T23:19:33.272706Z", - "iopub.status.idle": "2024-06-04T23:19:33.345703Z", - "shell.execute_reply": "2024-06-04T23:19:33.345426Z" + "iopub.execute_input": "2025-04-03T19:33:48.905382Z", + "iopub.status.busy": "2025-04-03T19:33:48.905259Z", + "iopub.status.idle": "2025-04-03T19:33:48.959586Z", + "shell.execute_reply": "2025-04-03T19:33:48.959297Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -4667,10 +12044,10 @@ "id": "b389c7d4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.347189Z", - "iopub.status.busy": "2024-06-04T23:19:33.347081Z", - "iopub.status.idle": "2024-06-04T23:19:33.349101Z", - "shell.execute_reply": "2024-06-04T23:19:33.348883Z" + "iopub.execute_input": "2025-04-03T19:33:48.961021Z", + "iopub.status.busy": "2025-04-03T19:33:48.960915Z", + "iopub.status.idle": "2025-04-03T19:33:48.963358Z", + "shell.execute_reply": "2025-04-03T19:33:48.963067Z" } }, "outputs": [ @@ -4737,10 +12114,10 @@ "id": "cf302d4c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.350411Z", - "iopub.status.busy": "2024-06-04T23:19:33.350323Z", - "iopub.status.idle": "2024-06-04T23:19:33.353307Z", - "shell.execute_reply": "2024-06-04T23:19:33.353084Z" + "iopub.execute_input": "2025-04-03T19:33:48.964798Z", + "iopub.status.busy": "2025-04-03T19:33:48.964696Z", + "iopub.status.idle": "2025-04-03T19:33:48.968238Z", + "shell.execute_reply": "2025-04-03T19:33:48.967949Z" } }, "outputs": [ @@ -4781,10 +12158,10 @@ "id": "af4145b6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.354462Z", - "iopub.status.busy": "2024-06-04T23:19:33.354377Z", - "iopub.status.idle": "2024-06-04T23:19:33.357347Z", - "shell.execute_reply": "2024-06-04T23:19:33.357140Z" + "iopub.execute_input": "2025-04-03T19:33:48.969645Z", + "iopub.status.busy": "2025-04-03T19:33:48.969565Z", + "iopub.status.idle": "2025-04-03T19:33:48.973148Z", + "shell.execute_reply": "2025-04-03T19:33:48.972886Z" } }, "outputs": [ @@ -4824,10 +12201,10 @@ "id": "16e047ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.358664Z", - "iopub.status.busy": "2024-06-04T23:19:33.358558Z", - "iopub.status.idle": "2024-06-04T23:19:33.448710Z", - "shell.execute_reply": "2024-06-04T23:19:33.448481Z" + "iopub.execute_input": "2025-04-03T19:33:48.974549Z", + "iopub.status.busy": "2025-04-03T19:33:48.974466Z", + "iopub.status.idle": "2025-04-03T19:33:49.071307Z", + "shell.execute_reply": "2025-04-03T19:33:49.071074Z" } }, "outputs": [ @@ -5288,16 +12665,16 @@ "id": "7390847f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.450064Z", - "iopub.status.busy": "2024-06-04T23:19:33.449993Z", - "iopub.status.idle": "2024-06-04T23:19:33.518771Z", - "shell.execute_reply": "2024-06-04T23:19:33.518504Z" + "iopub.execute_input": "2025-04-03T19:33:49.072453Z", + "iopub.status.busy": "2025-04-03T19:33:49.072377Z", + "iopub.status.idle": "2025-04-03T19:33:49.122561Z", + "shell.execute_reply": "2025-04-03T19:33:49.122228Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -5343,10 +12720,10 @@ "id": "1e084cbb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.520137Z", - "iopub.status.busy": "2024-06-04T23:19:33.520059Z", - "iopub.status.idle": "2024-06-04T23:19:33.525293Z", - "shell.execute_reply": "2024-06-04T23:19:33.525077Z" + "iopub.execute_input": "2025-04-03T19:33:49.123891Z", + "iopub.status.busy": "2025-04-03T19:33:49.123789Z", + "iopub.status.idle": "2025-04-03T19:33:49.129167Z", + "shell.execute_reply": "2025-04-03T19:33:49.128947Z" }, "lines_to_next_cell": 2 }, @@ -5389,10 +12766,10 @@ "id": "c7616c02", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.526523Z", - "iopub.status.busy": "2024-06-04T23:19:33.526454Z", - "iopub.status.idle": "2024-06-04T23:19:33.528563Z", - "shell.execute_reply": "2024-06-04T23:19:33.528353Z" + "iopub.execute_input": "2025-04-03T19:33:49.130401Z", + "iopub.status.busy": "2025-04-03T19:33:49.130324Z", + "iopub.status.idle": "2025-04-03T19:33:49.132374Z", + "shell.execute_reply": "2025-04-03T19:33:49.132165Z" } }, "outputs": [ @@ -5444,10 +12821,10 @@ "id": "9c69429e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.529851Z", - "iopub.status.busy": "2024-06-04T23:19:33.529780Z", - "iopub.status.idle": "2024-06-04T23:19:33.532907Z", - "shell.execute_reply": "2024-06-04T23:19:33.532670Z" + "iopub.execute_input": "2025-04-03T19:33:49.133585Z", + "iopub.status.busy": "2025-04-03T19:33:49.133511Z", + "iopub.status.idle": "2025-04-03T19:33:49.137003Z", + "shell.execute_reply": "2025-04-03T19:33:49.136729Z" } }, "outputs": [ @@ -5890,10 +13267,10 @@ "id": "043f27fa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.534240Z", - "iopub.status.busy": "2024-06-04T23:19:33.534173Z", - "iopub.status.idle": "2024-06-04T23:19:33.610294Z", - "shell.execute_reply": "2024-06-04T23:19:33.610076Z" + "iopub.execute_input": "2025-04-03T19:33:49.138232Z", + "iopub.status.busy": "2025-04-03T19:33:49.138161Z", + "iopub.status.idle": "2025-04-03T19:33:49.219949Z", + "shell.execute_reply": "2025-04-03T19:33:49.219720Z" } }, "outputs": [ @@ -6347,16 +13724,16 @@ "id": "b702fb04", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:33.611587Z", - "iopub.status.busy": "2024-06-04T23:19:33.611516Z", - "iopub.status.idle": "2024-06-04T23:19:33.678469Z", - "shell.execute_reply": "2024-06-04T23:19:33.678245Z" + "iopub.execute_input": "2025-04-03T19:33:49.221213Z", + "iopub.status.busy": "2025-04-03T19:33:49.221141Z", + "iopub.status.idle": "2025-04-03T19:33:49.270040Z", + "shell.execute_reply": "2025-04-03T19:33:49.269676Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -6383,7 +13760,7 @@ "metadata": {}, "source": [ "CV error is minimized at 12,\n", - "though there is little noticable difference between this point and a much lower number like 2 or 3 components.\n", + "though there is little noticeable difference between this point and a much lower number like 2 or 3 components.\n", "\n" ] } @@ -6391,8 +13768,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -6404,7 +13781,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/Ch07-nonlin-lab.Rmd b/Ch07-nonlin-lab.Rmd index 1337ae2..6af825f 100644 --- a/Ch07-nonlin-lab.Rmd +++ b/Ch07-nonlin-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Non-Linear Modeling diff --git a/Ch07-nonlin-lab.ipynb b/Ch07-nonlin-lab.ipynb index 7534f6d..8a3ac95 100644 --- a/Ch07-nonlin-lab.ipynb +++ b/Ch07-nonlin-lab.ipynb @@ -31,10 +31,10 @@ "id": "4f8339f1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:34.911110Z", - "iopub.status.busy": "2024-06-04T23:19:34.910732Z", - "iopub.status.idle": "2024-06-04T23:19:35.700317Z", - "shell.execute_reply": "2024-06-04T23:19:35.700028Z" + "iopub.execute_input": "2025-04-03T19:33:50.366185Z", + "iopub.status.busy": "2025-04-03T19:33:50.366102Z", + "iopub.status.idle": "2025-04-03T19:33:51.247056Z", + "shell.execute_reply": "2025-04-03T19:33:51.246749Z" } }, "outputs": [], @@ -65,10 +65,10 @@ "id": "816d4b81", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:35.702094Z", - "iopub.status.busy": "2024-06-04T23:19:35.701965Z", - "iopub.status.idle": "2024-06-04T23:19:35.722229Z", - "shell.execute_reply": "2024-06-04T23:19:35.722033Z" + "iopub.execute_input": "2025-04-03T19:33:51.248683Z", + "iopub.status.busy": "2025-04-03T19:33:51.248548Z", + "iopub.status.idle": "2025-04-03T19:33:51.270590Z", + "shell.execute_reply": "2025-04-03T19:33:51.270329Z" } }, "outputs": [], @@ -104,10 +104,10 @@ "id": "bd30b197", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:35.723538Z", - "iopub.status.busy": "2024-06-04T23:19:35.723455Z", - "iopub.status.idle": "2024-06-04T23:19:35.730561Z", - "shell.execute_reply": "2024-06-04T23:19:35.730367Z" + "iopub.execute_input": "2025-04-03T19:33:51.271940Z", + "iopub.status.busy": "2025-04-03T19:33:51.271839Z", + "iopub.status.idle": "2025-04-03T19:33:51.280136Z", + "shell.execute_reply": "2025-04-03T19:33:51.279890Z" } }, "outputs": [], @@ -134,10 +134,10 @@ "id": "fe40e0fb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:35.731830Z", - "iopub.status.busy": "2024-06-04T23:19:35.731745Z", - "iopub.status.idle": "2024-06-04T23:19:35.758719Z", - "shell.execute_reply": "2024-06-04T23:19:35.758161Z" + "iopub.execute_input": "2025-04-03T19:33:51.281339Z", + "iopub.status.busy": "2025-04-03T19:33:51.281249Z", + "iopub.status.idle": "2025-04-03T19:33:51.445879Z", + "shell.execute_reply": "2025-04-03T19:33:51.445039Z" }, "lines_to_next_cell": 2 }, @@ -269,10 +269,10 @@ "id": "23a9ff8e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:35.762173Z", - "iopub.status.busy": "2024-06-04T23:19:35.761857Z", - "iopub.status.idle": "2024-06-04T23:19:35.766441Z", - "shell.execute_reply": "2024-06-04T23:19:35.765894Z" + "iopub.execute_input": "2025-04-03T19:33:51.449802Z", + "iopub.status.busy": "2025-04-03T19:33:51.449381Z", + "iopub.status.idle": "2025-04-03T19:33:51.453378Z", + "shell.execute_reply": "2025-04-03T19:33:51.452750Z" } }, "outputs": [], @@ -304,10 +304,10 @@ "id": "ca7a1d45", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:35.771011Z", - "iopub.status.busy": "2024-06-04T23:19:35.770581Z", - "iopub.status.idle": "2024-06-04T23:19:35.776229Z", - "shell.execute_reply": "2024-06-04T23:19:35.775762Z" + "iopub.execute_input": "2025-04-03T19:33:51.456232Z", + "iopub.status.busy": "2025-04-03T19:33:51.455822Z", + "iopub.status.idle": "2025-04-03T19:33:51.462370Z", + "shell.execute_reply": "2025-04-03T19:33:51.461648Z" }, "lines_to_next_cell": 0 }, @@ -361,17 +361,17 @@ "id": "b05a60ba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:35.779177Z", - "iopub.status.busy": "2024-06-04T23:19:35.778961Z", - "iopub.status.idle": "2024-06-04T23:19:36.009418Z", - "shell.execute_reply": "2024-06-04T23:19:36.008910Z" + "iopub.execute_input": "2025-04-03T19:33:51.465083Z", + "iopub.status.busy": "2025-04-03T19:33:51.464836Z", + "iopub.status.idle": "2025-04-03T19:33:51.558483Z", + "shell.execute_reply": "2025-04-03T19:33:51.555890Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -430,10 +430,10 @@ "id": "c338a92a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.012673Z", - "iopub.status.busy": "2024-06-04T23:19:36.012313Z", - "iopub.status.idle": "2024-06-04T23:19:36.072501Z", - "shell.execute_reply": "2024-06-04T23:19:36.071990Z" + "iopub.execute_input": "2025-04-03T19:33:51.562604Z", + "iopub.status.busy": "2025-04-03T19:33:51.561921Z", + "iopub.status.idle": "2025-04-03T19:33:51.635689Z", + "shell.execute_reply": "2025-04-03T19:33:51.634613Z" } }, "outputs": [ @@ -570,10 +570,10 @@ "id": "a1f74b79", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.075597Z", - "iopub.status.busy": "2024-06-04T23:19:36.075369Z", - "iopub.status.idle": "2024-06-04T23:19:36.090416Z", - "shell.execute_reply": "2024-06-04T23:19:36.089945Z" + "iopub.execute_input": "2025-04-03T19:33:51.639833Z", + "iopub.status.busy": "2025-04-03T19:33:51.639168Z", + "iopub.status.idle": "2025-04-03T19:33:51.663992Z", + "shell.execute_reply": "2025-04-03T19:33:51.661163Z" }, "lines_to_next_cell": 2 }, @@ -679,10 +679,10 @@ "id": "570892d1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.092926Z", - "iopub.status.busy": "2024-06-04T23:19:36.092726Z", - "iopub.status.idle": "2024-06-04T23:19:36.096105Z", - "shell.execute_reply": "2024-06-04T23:19:36.095602Z" + "iopub.execute_input": "2025-04-03T19:33:51.670914Z", + "iopub.status.busy": "2025-04-03T19:33:51.669751Z", + "iopub.status.idle": "2025-04-03T19:33:51.675840Z", + "shell.execute_reply": "2025-04-03T19:33:51.674407Z" }, "lines_to_next_cell": 2 }, @@ -720,10 +720,10 @@ "id": "3ff20916", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.098614Z", - "iopub.status.busy": "2024-06-04T23:19:36.098427Z", - "iopub.status.idle": "2024-06-04T23:19:36.147734Z", - "shell.execute_reply": "2024-06-04T23:19:36.145914Z" + "iopub.execute_input": "2025-04-03T19:33:51.681787Z", + "iopub.status.busy": "2025-04-03T19:33:51.681279Z", + "iopub.status.idle": "2025-04-03T19:33:51.737645Z", + "shell.execute_reply": "2025-04-03T19:33:51.735323Z" }, "lines_to_next_cell": 2 }, @@ -830,10 +830,10 @@ "id": "2952bde3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.154502Z", - "iopub.status.busy": "2024-06-04T23:19:36.154251Z", - "iopub.status.idle": "2024-06-04T23:19:36.179360Z", - "shell.execute_reply": "2024-06-04T23:19:36.177817Z" + "iopub.execute_input": "2025-04-03T19:33:51.742075Z", + "iopub.status.busy": "2025-04-03T19:33:51.741235Z", + "iopub.status.idle": "2025-04-03T19:33:51.786585Z", + "shell.execute_reply": "2025-04-03T19:33:51.784529Z" }, "lines_to_next_cell": 2 }, @@ -943,10 +943,10 @@ "id": "c999b609", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.182205Z", - "iopub.status.busy": "2024-06-04T23:19:36.181928Z", - "iopub.status.idle": "2024-06-04T23:19:36.188474Z", - "shell.execute_reply": "2024-06-04T23:19:36.187832Z" + "iopub.execute_input": "2025-04-03T19:33:51.795007Z", + "iopub.status.busy": "2025-04-03T19:33:51.794240Z", + "iopub.status.idle": "2025-04-03T19:33:51.802485Z", + "shell.execute_reply": "2025-04-03T19:33:51.801339Z" } }, "outputs": [], @@ -970,17 +970,17 @@ "id": "5f18a110", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.191550Z", - "iopub.status.busy": "2024-06-04T23:19:36.191370Z", - "iopub.status.idle": "2024-06-04T23:19:36.311190Z", - "shell.execute_reply": "2024-06-04T23:19:36.310416Z" + "iopub.execute_input": "2025-04-03T19:33:51.815260Z", + "iopub.status.busy": "2025-04-03T19:33:51.814446Z", + "iopub.status.idle": "2025-04-03T19:33:51.889477Z", + "shell.execute_reply": "2025-04-03T19:33:51.888860Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1035,10 +1035,10 @@ "id": "62e053ea", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.315065Z", - "iopub.status.busy": "2024-06-04T23:19:36.314672Z", - "iopub.status.idle": "2024-06-04T23:19:36.333569Z", - "shell.execute_reply": "2024-06-04T23:19:36.333006Z" + "iopub.execute_input": "2025-04-03T19:33:51.903456Z", + "iopub.status.busy": "2025-04-03T19:33:51.902924Z", + "iopub.status.idle": "2025-04-03T19:33:51.931633Z", + "shell.execute_reply": "2025-04-03T19:33:51.931013Z" }, "lines_to_next_cell": 2 }, @@ -1164,10 +1164,10 @@ "id": "f032f577", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.336489Z", - "iopub.status.busy": "2024-06-04T23:19:36.336258Z", - "iopub.status.idle": "2024-06-04T23:19:36.343862Z", - "shell.execute_reply": "2024-06-04T23:19:36.343366Z" + "iopub.execute_input": "2025-04-03T19:33:51.941994Z", + "iopub.status.busy": "2025-04-03T19:33:51.941511Z", + "iopub.status.idle": "2025-04-03T19:33:51.954506Z", + "shell.execute_reply": "2025-04-03T19:33:51.952472Z" }, "lines_to_next_cell": 0 }, @@ -1207,10 +1207,10 @@ "id": "c3c38823", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.346759Z", - "iopub.status.busy": "2024-06-04T23:19:36.346545Z", - "iopub.status.idle": "2024-06-04T23:19:36.380669Z", - "shell.execute_reply": "2024-06-04T23:19:36.380119Z" + "iopub.execute_input": "2025-04-03T19:33:51.964142Z", + "iopub.status.busy": "2025-04-03T19:33:51.963489Z", + "iopub.status.idle": "2025-04-03T19:33:51.997785Z", + "shell.execute_reply": "2025-04-03T19:33:51.995701Z" }, "lines_to_next_cell": 0 }, @@ -1333,10 +1333,10 @@ "id": "d01c2c26", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.384846Z", - "iopub.status.busy": "2024-06-04T23:19:36.384459Z", - "iopub.status.idle": "2024-06-04T23:19:36.418050Z", - "shell.execute_reply": "2024-06-04T23:19:36.415940Z" + "iopub.execute_input": "2025-04-03T19:33:52.016425Z", + "iopub.status.busy": "2025-04-03T19:33:52.015784Z", + "iopub.status.idle": "2025-04-03T19:33:52.050518Z", + "shell.execute_reply": "2025-04-03T19:33:52.049543Z" } }, "outputs": [ @@ -1469,10 +1469,10 @@ "id": "672d44ae", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.421424Z", - "iopub.status.busy": "2024-06-04T23:19:36.421207Z", - "iopub.status.idle": "2024-06-04T23:19:36.429792Z", - "shell.execute_reply": "2024-06-04T23:19:36.429086Z" + "iopub.execute_input": "2025-04-03T19:33:52.061323Z", + "iopub.status.busy": "2025-04-03T19:33:52.060825Z", + "iopub.status.idle": "2025-04-03T19:33:52.068372Z", + "shell.execute_reply": "2025-04-03T19:33:52.067421Z" }, "lines_to_next_cell": 0 }, @@ -1514,10 +1514,10 @@ "id": "4a3f93ee", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.432272Z", - "iopub.status.busy": "2024-06-04T23:19:36.432078Z", - "iopub.status.idle": "2024-06-04T23:19:36.457934Z", - "shell.execute_reply": "2024-06-04T23:19:36.457375Z" + "iopub.execute_input": "2025-04-03T19:33:52.072307Z", + "iopub.status.busy": "2025-04-03T19:33:52.071691Z", + "iopub.status.idle": "2025-04-03T19:33:52.108285Z", + "shell.execute_reply": "2025-04-03T19:33:52.106248Z" }, "lines_to_next_cell": 0 }, @@ -1648,10 +1648,10 @@ "id": "bd2b4215", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.460525Z", - "iopub.status.busy": "2024-06-04T23:19:36.460295Z", - "iopub.status.idle": "2024-06-04T23:19:36.489739Z", - "shell.execute_reply": "2024-06-04T23:19:36.489248Z" + "iopub.execute_input": "2025-04-03T19:33:52.111583Z", + "iopub.status.busy": "2025-04-03T19:33:52.111100Z", + "iopub.status.idle": "2025-04-03T19:33:52.147048Z", + "shell.execute_reply": "2025-04-03T19:33:52.146152Z" }, "lines_to_next_cell": 0 }, @@ -1765,16 +1765,16 @@ "id": "9162a058", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.493122Z", - "iopub.status.busy": "2024-06-04T23:19:36.492786Z", - "iopub.status.idle": "2024-06-04T23:19:36.668184Z", - "shell.execute_reply": "2024-06-04T23:19:36.667651Z" + "iopub.execute_input": "2025-04-03T19:33:52.151839Z", + "iopub.status.busy": "2025-04-03T19:33:52.150012Z", + "iopub.status.idle": "2025-04-03T19:33:52.224198Z", + "shell.execute_reply": "2025-04-03T19:33:52.223407Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1813,10 +1813,10 @@ "id": "86ff9343", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.671284Z", - "iopub.status.busy": "2024-06-04T23:19:36.671050Z", - "iopub.status.idle": "2024-06-04T23:19:36.705757Z", - "shell.execute_reply": "2024-06-04T23:19:36.705247Z" + "iopub.execute_input": "2025-04-03T19:33:52.237478Z", + "iopub.status.busy": "2025-04-03T19:33:52.236869Z", + "iopub.status.idle": "2025-04-03T19:33:52.297555Z", + "shell.execute_reply": "2025-04-03T19:33:52.296760Z" } }, "outputs": [ @@ -1859,16 +1859,16 @@ "id": "44812968", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.708880Z", - "iopub.status.busy": "2024-06-04T23:19:36.708673Z", - "iopub.status.idle": "2024-06-04T23:19:36.976882Z", - "shell.execute_reply": "2024-06-04T23:19:36.976289Z" + "iopub.execute_input": "2025-04-03T19:33:52.309060Z", + "iopub.status.busy": "2025-04-03T19:33:52.308243Z", + "iopub.status.idle": "2025-04-03T19:33:52.649713Z", + "shell.execute_reply": "2025-04-03T19:33:52.647160Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1905,10 +1905,10 @@ "id": "968e1e42", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:36.979615Z", - "iopub.status.busy": "2024-06-04T23:19:36.979395Z", - "iopub.status.idle": "2024-06-04T23:19:37.195601Z", - "shell.execute_reply": "2024-06-04T23:19:37.195005Z" + "iopub.execute_input": "2025-04-03T19:33:52.655836Z", + "iopub.status.busy": "2025-04-03T19:33:52.655411Z", + "iopub.status.idle": "2025-04-03T19:33:53.201562Z", + "shell.execute_reply": "2025-04-03T19:33:53.200247Z" } }, "outputs": [ @@ -1920,6 +1920,22 @@ "\u001b[38;2;255;0;0m 0%\u001b[39m \u001b[38;2;255;0;0m(0 of 11)\u001b[39m | | Elapsed Time: 0:00:00 ETA: --:--:--" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "\u001b[38;2;255;105;0m 18%\u001b[39m \u001b[38;2;255;105;0m(2 of 11)\u001b[39m |#### | Elapsed Time: 0:00:00 ETA: 0:00:00" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "\u001b[38;2;255;156;0m 36%\u001b[39m \u001b[38;2;255;156;0m(4 of 11)\u001b[39m |######### | Elapsed Time: 0:00:00 ETA: 0:00:00" + ] + }, { "name": "stderr", "output_type": "stream", @@ -1928,6 +1944,30 @@ "\u001b[38;2;255;189;0m 45%\u001b[39m \u001b[38;2;255;189;0m(5 of 11)\u001b[39m |########### | Elapsed Time: 0:00:00 ETA: 0:00:00" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "\u001b[38;2;236;255;0m 63%\u001b[39m \u001b[38;2;236;255;0m(7 of 11)\u001b[39m |############### | Elapsed Time: 0:00:00 ETA: 0:00:00" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "\u001b[38;2;185;255;0m 81%\u001b[39m \u001b[38;2;185;255;0m(9 of 11)\u001b[39m |#################### | Elapsed Time: 0:00:00 ETA: 0:00:00" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "\u001b[38;2;160;255;0m 90%\u001b[39m \u001b[38;2;160;255;0m(10 of 11)\u001b[39m |##################### | Elapsed Time: 0:00:00 ETA: 0:00:00" + ] + }, { "name": "stderr", "output_type": "stream", @@ -1945,7 +1985,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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hcDgcDoej5eGk2OFwOBwOh2MSQHjwl770pbZ06dJgpHnhhRcW/ubKK6+0gw46KHg32W233ezcc8+d8Hr+4Ac/CL62CZ+877772kUXXTR6jUiOH/rQh8L5mTNnhra84Q1vsEceecSaDU6KHQ6Hw+FwOCYB69evt/3339++9KUvlUp/77332oknnmhHH3203Xjjjfbe977X3vrWt9oll1yyxXWAZO+0006Z16+99lp77Wtfa295y1vshhtusJNOOil8brnllnB9w4YN9sc//tE++tGPhu8f/ehHdscdd9hf/dVfWbOhbSQZk9JRGoTuxAclPinnzJkz2dVxOBwOh6PlQORAyOLOO+8cJJnDI8O2qm/VpNVnXvc8a28bv8wRSfEFF1wQCGcWkMj+7Gc/GyWk4DWveY2tWrXKLr744nBMNMXPfOYzds4559hjjz1me+yxRyCsr3jFKzJJ8Rvf+Ea77777Uq+/+tWvDuT9pz/96ei55zznOXbAAQfYV7/61dTfXH/99fbsZz/b7r//ftthhx1sMubBlvC1pvRT/JWvfCV8NIB77723fexjH7MXvehFox3zgQ98wM4///zgQP2EE06wL3/5y7b11luP5vHAAw/Y29/+dvvlL39ps2bNspNPPtnOPPNM6+xsyi5xOBwOh8NhFgjxUf9z1KSVf9Wrr7IF0xdMSN7XXXedHXvssWPOwXGQGAtwme9+97uBsO6+++5BReN1r3tdCC5z1FFHbVGZ73//+59WZp6qB+SzGX12NyUD3G677ezTn/50GGwE3d/+9rftZS97WRDrQ5Df9773hZUUOjCsDN75znfay1/+crvmmmvC74eGhsL2w5IlS8K2wKOPPhr0X7q6uuyMM86Y7OY5HI5xgueARxFzOBxTHUh+YwEf4BhJKGHF29vbA4+57LLL7LDDDgvXd9llF7v66qvta1/72haR4scyyuR8GhBMItFG5aLZdtGbkhSjlB7jU5/6VJAc/+Y3vwmE+Rvf+Iadd955dswxx4Tr3/rWt+yZz3xmuI7I/xe/+IXddtttYdIwsGwBfOITnwiD+C//8i82bdq0SWqZw+EYL5YvX2633367rVixwgYHB8NuD6F2MQpBMuJwOBytgrvuuivo+B533HFjzvf399uBBx44eswOuYCgEIHCrOgckuUs1Yg8YHT3qle9Kggq4GXNhqYkxTEYTCTC6LuwKvrDH/4QBiXeXuDliE4LWwCQYr6xkoxXPmwFoE5x6623jpk4MZg0fARWZg6HY3IJ8W9/+9vwEkA6zG4P9z+7P0iODz30UCfGDodjyoAd7scff3zMOY6RyPb09Ni6devCOXbLt9122zHp2EUTMNITeIYiFLzyyitHz8US3qwyOZ9GiNEjvuKKK5pOStzUpPjmm28OJBgxPasblNP32muvMNBIepN6LLGoP2srQNeygJ7Oxz/+8Qlpj8PhGB+QRCAhhhBDfKUuwYOfY0mQkRq7KoXD0TrA0A293sksf6IA74ndoYFLL710VFUCHsQzELupPFUJXLkJDz30UNhh2y06lyzz8ssvH6O3HJcZE+I777wz2GotXLjQmhFNS4r33HPPQICRBv3whz8MhnJXXTWxN8Hpp58+RtkcSfH2228/oWU6HI50cO+jMpGmP8wxUgquk67ZjD0cDseWA88PE2XoVjWQ7KLyIOA9AW6zYMGCsMMN73j44YftO9/5Trh+yimn2Be/+EU77bTT7M1vfnOQyH7/+98PkmEwe/Zs++AHPxhsq/BCceSRR4ZnIDZVPBPhSuPFe97znkCwP/vZzwZ7LJwY/P73vw/eLUSI8WyBOzY8VLCDLwEj7WgmldSmJcV0slY1Bx98cHD/cfbZZwfXIejO4J4kfhHGon6+f/e7343JT1sDye2AGKy+4u0Hh8MxeUCVCR1iVCaynhFr164do/LkcDgcjQTIJT6HBQneIK8E5UAVDKmvgLsxCDCkF86DHdXXv/71oAIqYCPFbhm72/fcc0/gQgT7+PCHP7xFdTz88MODndZHPvKRkAdODvA8sc8++4TrkPb/+7//C/9joxUDqfHzn/98axZMGT/FGNWxqmKSMBn++7//2/7mb/4mXMOJNHrF0in++c9/bi95yUvCZNtqq61CGlY8p556qi1btqw08XU/xQ7H5HmWYOHLA3fGjBmp9yyqVVhj88JxSbHDMXWR55/W0TrobVU/xWwn4JMYEowkiBUMCuJEdKHRRF1htYXYnsa/613vCrovEGJw/PHHB72b17/+9XbWWWcFMT8roHe84x0uCXY4msSzBN98WNzGOsUi0jwEt9lmm/BMcDgcDoejCE1JipHm4leYlyEvvP322y8QYrkg+dznPhd89SEpjoN3CB0dHUHvBW8TkGVidbNV8a//+q+T2CqHwzFezxKQY/4nLQtgVCZQn4IQI0HmuhvZORwOh6Ol1CcmA64+4XBMDHgs4Ww+SwoMCUYKjBEJUmT3U+xwtC5cfcLR0uoTDodjamM8niUgvpBgj2jncDgcjlrgpNjhcDS9ZwkIsBvTORwOh6MWtNf0a4fD4ZgAIO1FDQId4jSgN8x1N4x1OBwOR1VwUuxwOBoOqD9IJSJp9iDPElx3zxIOh8PhqApOih0OR8MBdQgM5fAggVEdBhREZ+KbY/cs4XA4HI6q4aTY4XA0JDCgw+0aXiYIwrFy5crwzTHn3bOEw+FwOKqEG9o5HI6GhXuWcDgcDke94JJih8PR0JBnia233jp8OyF2OBxTBb/61a/spS99qS1dujQ82y688MLC3xDB96CDDgpCgt12283OPffcCa/nD37wg6Cyhv/ffffd1y666KLMtKecckpoy+c//3lrNjgpdjgcDofD4ZgErF+/3vbff3/70pe+VCo9wSlOPPFEO/roo+3GG2+09773vfbWt741RPXdUkCyd9ppp8zr1157rb32ta+1t7zlLXbDDTfYSSedFD633HLL09JecMEF9pvf/CaQ/GaEq084HA6Hw+GYMhgZHrahVasmrfwOdrTay8kcX/SiF4VPWXz1q18NEds++9nPhuNnPvOZIfrn5z73OTvhhBPCOYySP/OZz9g555xjjz32mO2xxx720Y9+1F7xildsUXvOPvtse+ELX2innnpqOP7EJz5hl156qX3xi18M9REefvhhe9e73hUIOsS9GeGk2OFwOBwOx5QBhPjOw4+YtPJ3v/Ya61ywYELyvu666+zYY48dcw4yjMRYOPPMM+273/1uIKy77757UNF43eteF2w0jjrqqC0q8/3vf//TyoxVPSDir3/96wNx3nvvva1Z4aTY4XA4HA6HowmA5Bf7ihgc47sd7zzt7e12xhln2GWXXWaHHXZYuL7LLrsEafLXvva1LSLFj2WUyXkByTQBld797ndbM8NJscPhcDgcDscUwF133WUbNmyw44477mlRQA888MDR41mzZo3+PzQ0FLz7zIrOIVmOVSPy8Ic//CGoWPzxj39sekNoJ8UOh8PhcDgcTYAlS5bY448/PuYcx3PmzLGenh5bt25dOPezn/3Mtt122zHp8FYhYKQn/Pa3v7UPfehDweBOIL+iMjkPfv3rX9uyZctshx12GEO0P/CBDwQPFPfdd581C5wUOxwOh8PhmDLA0A293sksf6KASkTSHRpGb1KV2GuvvQL5feCBB3JVJXDlJjz00ENB9WG36FyyzMsvv3yM3nJcJrrEaXrOnH/Tm95kzQQnxQ6Hw+FwOKYM8PwwUYZuVQPJLioPscs1pLgLFiwIktfTTz89eHX4zne+M+oDGK8Pp512mr35zW+2K664wr7//e8HyTCYPXu2ffCDH7T3ve99wfjtyCOPDMGPrrnmmiD9Pfnkk8ddx/e85z2BYOPxAq8S559/vv3+978P3i3AwoULwydGV1dXkCTvueee1kxwUuxwOBwOh8MxCYBc4nNYkJcHyCtBOR599NEg9RVwxwYBhvSix7vddtvZ17/+9VF3bHKZhqcJvFDcc889IegRwT4+/OEPb1EdDz/8cDvvvPPsIx/5SMgDjxZ4nthnn31sqqFtZGRkZLIr0azA2pOQs6zCYv0bh8PReOBR5+GiHY6ph97e3iBhhTAScc3RmujNmQdl+ZpLih0Ox5TH8uXL7fbbb7cVK1bY4OBg0J9btGhRCFuKRMXhcDgcDifFDodjyhNirKtxU4SkAF23gYGBsC2J1ODQQw91YuxwOBwOKxeH0OFwOJpUZQIJMYQY4ovaBM7t+eaY81x3LTKHw+FwOCl2OBxTFkiCUZlI0x/mGN0yrpPO4XA4HK0NV59wOBxTFhjVoUOMykQapk2bZmvXrg3pGtFgD5dK+BDFbRPRprA0R9Ld6Kiq3m4cWQzvI4ejOjgpdjgcUxaQBIzq0CGOoznFoU+5nnZtsg327rjjDvvd735nTzzxRIgO1dHREXyXPvvZz25o359V1duNI4vhfeRwVAsnxQ6HY8oCqRkkAaM6SEIsQUPChpuebbbZJqRrJIM9iOVll10WpH8zZ84cLYs6cB40IjGuqt5uHFkM7yOHo3o0/j6cw+FwbCEgwUjNZsyYEUgEfizZ2uebY85zvWi7uZ4Ge9QPSSvEEqf7cVkcc57rpGskVFVvN44shveRwzExcFLscDimNCAJSM2QCG/cuNFWrlwZvjkuK02rp8EeurioHiBpTerhcgyR5zrpGglV1duNI4vhfeRwTAxcfcLhcEx5QHxRo9hSg6SJMNjLAsZp6OLmlYUkkHSNZLRVVb3r2dfNivH2kRvjORzl4JJih8PREoAEsI2/9dZbh+/xkILYYC8N4zXYywPeGjBOyyuL66QbDyBGq1atsscffzx8J7fWUSe5+uqr7Ze//KX96le/Ct8cc76e9a5nXzcrxtNHtY6rY2LBmLz0pS+1pUuXhmfShRdeWPibK6+80g466KAwvrvttpude+65E17PH/zgB0HVjPDJ++67r1100UVPS/PnP//Z/uqv/iosutgxOuSQQ+yBBx6wZoKTYofD4ShpsIe0LUkmZbDH9TIGe0XAfRneGtavX/80/VuOkbZynXRlUUSMZLSFkRZqDgsXLgzfHHO+DIGqqt717OtmRdk+ghzXOq6OiQX3y/77729f+tKXSqW/99577cQTT7Sjjz7abrzxRnvve99rb33rW+2SSy7Z4jpAsnfaaafM69dee6299rWvtbe85S12ww032EknnRQ+t9xyy2iau+++24488shAnMnvpptuso9+9KOBRDcTXH3C4XCMC/Xeii3j87ZMnWqptwz2+P2yZcvCg546yGgPqUhssFdLWeSL+zK8NTz55JOhLCSsqCZQFsdcL+v3V4SXly+/5UO9H3nkkVBH8sJrBKRVRIr/5d4L3VSMtvifNmS1rap6x31N3dGPRR2AekH2ksaREz3240WVZWXlVaaP8PKhcY09r8gYT+7cNK5TCSPDI9a7Pl2KXg9Mn9llbe3l+vRFL3pR+JTFV7/6Vdt5553ts5/9bDh+5jOfGRa4n/vc5+yEE04I57i/P/OZz9g555xjjz32mO2xxx6BoL7iFa/YovacffbZ9sIXvtBOPfXUcPyJT3zCLr30UvviF78Y6gP+6Z/+yV784hfbWWedNfq7XXfd1ZoNToodDkfD+kUt4/O2TJ2qqDfpdt9991AfXjRxfQ444IBKy6JtEJ7f/OY34ZuXHGSSRQFbkmXdsclLASSVPOhH5QVxgkT96U9/Cvqn6Kc++OCDgUTFaWKjLbbr89pWVb1lHKmyqB9lYRw53nGt55ytsqyivIr6iPEsa4yHOtFUAoT4m6dePWnlv/nfjrSe2dMmJO/rrrvOjj322DHnIMNIjIUzzzzTvvvd7wbCyjOL3aHXve51Yc4cddRRW1Tm+9///qeVKVUP7vOf/exndtppp4XzSJMh7qeffnqQKDcTnBQ7HA2ORpGETYRf1Lx6xz5vucYHIhr7vIWQFtUJKI3yodzx1pty77zzzvB79P/IQ5JTzlOXuKxa+oiykEij/8yWtwg4pIfzXFc+eX3IeUnZeXFxXdJbzkFYH3744ZAHBBnylUwjSTgLgbvuuquwr6nfkiVLRvtG38l612ocWWY+jmc8ar2Hqrw/yuaV10fojjeqwaIb/m05uA95LsTgmB0CvOpwT59xxhnhGXnYYYeF67vsskuQJn/ta1/bIlL8WEaZnAfc2zwrPv3pT9snP/nJIKW++OKL7eUvf3lQ1dqSMicLToodjgZGo0jCkn5Rq9iKzas3RBCJLA95CBrlimCRjvOQhh133DG3Thh+ACSlkDzcsUl62dPTE17KZeodS1zJh/LT8iFdrX1UVBbkVflItSFr7CG0khJDbFUu6ehXVCogRtJLnT179tPS6Pr9999fqq+TadSmLZkjMo7M6qMtqU/aeBT1Yz3vj/HmldVHVUdzrAoehW9ioYXrcccd97TxPvDAA0ePY4NXnjM8w2ZF55AsSzWiCLIheNnLXmbve9/7wv/snqGLTB5Oih0OR82oWhJWL7+oZbZii9q2/fbbhzQ8rHng8uKUxJF0kjxybquttsqsE/nxsJfxVywF5RxEE0lqUb3zJK7KBwMYBaqopY/KlMX1e+65JxC/vLHnRcg59HnT6qQFBvnnkTXKhlzPnz8/t69Bre2vaj6WrQ/jdtttt9V0D1V5f1SVV5XRHKuCR+GrHezCsAsQg2PmBYtmuTxEnWHbbbcdky5eAGGkJzAmH/rQh4KBnEB+RWVyHjDPeJbstddeY9JI37mZ4KTY4WhAVC0Jq3VrskrfsWXahjoCeUEAKVNp+OYYsseHNHl14jrux8gnS1IK2UOimocyElfKgaTU2kdlyuJ6GSMq3DWJeFB+khgxpuRJOfwvFRORcG1vS+KY1zbGA9Rju77MfCxTH4hhFcZoVd4fVeU1XoPFicZE7DblGbqh1ztZoPyJAioRSXdoGL1JVQJiSp/iCi1PQsuzQWCRzT2+W3QuWebll18+Rm85LpN5hc0A91KMv/zlL2E3r5ngpNjhaEBUKQmrQjJX5VZsmbbhGQFSmCbB1JYxL1nIbl6dSAe543qWpBRyIQKVhbISV/6vtY/KloUqSJ6UnD7mhYR0F8KeRnj5n7TStWZsSKcFCdupXFf/5LWNF2NRmqq268vMxzL1kcS01nuoyvujyrzKGizWA1XvNuUBzw8TZehWNZDsovIgsHOBFBcbhR122CEYq6H3/53vfCdcP+WUU4LXB4za3vzmN9sVV1xh3//+94NkWCpQH/zgB4MaA89Q3KTRp9dcc03o45NPPnncdXzPe94TCDYeL3AHd/7559vvf//74N1CwDPFq1/9anve854X3MWhU/yTn/xkjPS5GeCk2OFoQFQlCatKMlflVmyZtsmoTDqHsRsvHvScJx3GHjzws+qEbjJ9kCcp5bwIVBa4XiRxhcRSXl59yvRRmbLoH/4vGnu+cWHHb6SfLMKLdJh88E9KXhjNoLYigzv6nd9DVKg3IE1e28qkqWK7vux8LKoPJEGeN2q5h6q8P6pWe6g1mmNV8EiF6YBcQiIFeXmAvBKUg3kQB8DAqwMEGNKLqzTu769//euj7tjkMo1xxwsFalYsMgj28eEPf3iL6nj44YfbeeedZx/5yEdCHni0wPPEPvvsM5rmr//6r4P+MGW++93vDp5m/vd//zeQ8maCk2KHowFRlSSsKslclVuxZdomgok0VCoS8gvM7yBzPPQpEzWSPF+t1DlPUsoLo8jBPNeLJK7kQ3l59SnTR2XKknS3aOzJS+NGXtIJhlyRl+oEqCMEmLzlro1j/kc3UGmy2lYmTVXb9WXmY5n64L+VIAO13kNV3h8TofaQZYxXTzSq4d9k4/nPf/7TArDESItWx29we5Y33kh3+ZStw3333Zeb5pWvfGX45AHJNZ9mhpNih6MBUZUkrEpDmqq2Ysu2jQ+6xZSDnq28T2BMwhYhjuFxNQRpyKoT5aCKgYRKUdVi1QC+kbQU9RHXJXHNy6eoPmX6qExZsXS3aOy5Fo+bpMDJOpUZ26rS1HM+5qXR/KhCKlulqkIjqT1UhUY0/HM4knBS7HA0IKqShFVtSFPFVmyZ6HBx2yCCkm7qO44gV1QnlYWBGtKyLYlEF9cZHUCpMUiyRR3L1qcoUl9cVl6d47GXznRSAqwyy9Spnmmq8mdLWewo5EU8LDs/qoieV6Y+ZdEoag9VodEM/xyONLSN5MntHbngRuYhxU0euy9xOKaan+KJQFXR6urZjxi1XH/99YGkiKRDVrC8PuaYY+re9jL5NCvqOferKqtZ78V6YiL6iMUiBmro2xapQjmmLnpz5kFZvuakuAY4KXa0UkS7ifJXmiblrDLSmJCXT5b/VNKrPjxsic6EZIvr0vOVQRzGMpDRIsSR+pD6qiykwtSLEK4itGXqzO/SpO3N7PO1zHiAojTjaX+t86Pq+kxlVP28clLsqIoUu/qEw9HgKGMk0wiGNLX6K9W1shG7xousfMr4TyW4A26SIDkQTm2HQ3wgxPz2N7/5jR188MGBLGcB0opkFzIgtQiVRV4Y13Ed626pUhTVOemWjQd+lT5f642y/myriB5Y1fyYiPpMZTTT88rRWhi/opPD4XDUyV9po9QHt0boiEKAk/qhHHOe6xCfPKBriqpDTKzjfJAocp10zdSHVaKsH2sMturR/karj8PhmDg4KXY4HA3nr5Tr9fJXWqY+pFG46TRwnuts0eUB4ozKRV5ZXFeo1mbpw8nw0S01loluf6PVx+FwTBycFDscjknzV5qGevsrLVMf0iDJhdikgfNcL7ItwBsB6hV5ZXGddLXWuVl9vpZpmwKu1KP9jVYfh8MxcXBS7HA4JsVfKVvJSTtf+Svler38lZapD/6HIaqQGyTCMTjmvNyy5QH3XHiHwDguLR90UrlOumbqwypRpm1Lly4NPm3r0f5Gq4/D4Zg4uKGdY8qg2TwwVIksn7cTkU/Zfi7j8zfLT/F4/ZXWMvZl/KfutddeIWgI3icgrlxXGGptmz/nOc8ZNbLLqg/txEMF3icwqiNvlSVPHFwvGruJ8vlaD08fRSg7P8bjo7sWDy7Jvs7yCT2e+tRap4kYj1Z+fjYrrrzyyuD15sknn8w0XCQi3nvf+97wvGkmPP/5z7cDDjjAPv/5z9e1XCfFjimBVvYPWpWv2ir9Bhel44OHBcojKltcHg/Cqn3MVhFBTHnhZYJFA4RHKhMQYrljK6qP+lJ9DRlW2OrxjFnVUc/q6fO3TNvKzI8y7a/Cv3BcHwzq4vrsv//+447m14w+mB0TA+b3mWeeaT/72c+CQILFyG677Wave93r7OSTTw4LqiwcfvjhowaejmrgpNjR9MjyIcrDAsnHVPYPmuXzlj7hPChDssrkAwEo089lxgMQwhmJFCGLY8kb5ymrzJhVOfZlIohBWnG7BoFAAgghhjhIQly2PowJJKtW6X5VUc+q6scq8ykzP4raX3Yulp2vSH+pTyy5Hk99qqxTVeNRVVmO8QOvNkcccUSQ8p5xxhm27777hjlz88032znnnGPbbrut/dVf/VXqb+UffcmSJdZs6N+sh9+IcFLsaGqU9SE6Ff2DjtfnbS358NLccccdC/uZELdb6tM1y09xPce+jP9UCPDee+9dc33o5x122KFUvWqtcx6q6seJzme8fqyr9C+sNGV8QueNRz19Hpcp689//nM4N9Wen7/42hdsxYP3T1r5i7bf0Y7/h3cXpvvHf/zHIJX//e9/H4QRAjYML3vZy8bop9P/X/7yl+3nP/+5XX755XbqqacGFYOk+gTqEh/72MeC1P+EE06wI488spCgvv/977f//d//DflsvfXWdsopp9jpp58ervP8/+AHP2g//vGPwzviWc96ln3uc58LOyTg7rvvDr9nBw1biWc+85lB8k0QIoGF5Fve8pawgLzwwgvt5S9/eajnNddcY//0T/8U3j9SHTv//PNt/vz5o++m0047zb7+9a8HEk29/uVf/sUmEk6KHU2N8fhrnWrO4sfj8zaPfJXJZ+XKlaE/k6Qg2c/kVcanK2A8ahmzRhv7RqtPvevdjPmUmYtVzdcq61RV+5EGV9W2RgKE+NE777BGBs/UX/ziF0FCHBPiGMkxgRB++tOfDnq2kGkkzTEQXkA+IaUnnXSSXXzxxfbP//zPufX4whe+YP/3f/9n3//+98N74sEHHwwf4ZWvfGWwp4CMM5e+9rWv2Qte8AL7y1/+EnZI2Ol68YtfbJ/61KcCsf3Od75jL33pS8PuY/ze+fd///dA1lUfgiGRz5vf/GY7++yzQ3uw2UA1Sfj2t78dCDftuu666+yNb3xjkKwfd9xxNlFwUuxoapTxIYpu31T0D1rG5y0SoCKft2XyoQ+L/LCShrzK+HQFtY5Zo419o9Wn3vVuxnzKzMWq5muVdapn+xtxzk4F3HXXXUESnFRvQyqPWg54xzveYZ/5zGdGr/3t3/6tvelNbxo9TpJiyOULX/jCIF0Fe+yxh1177bWBHGfhgQceCLuJSJTb2trCjqBw9dVXBykuBq9yJwi5Rdr7wx/+0N72trcFibGkxuATn/iEXXDBBYFov/Od7xw9f8wxx9gHPvCBMW1B6oz0W0juwO23336jJJo6fvGLXwxS8okkxe6SzdHUmMr+WotQlc/bMvnQh2X8sJJXvXy6NtrYN1p96l3vZsynzFys0gdxPX0eu3/l5gQkFCkqBDG5GIFE5gF1GOmJC4cddljub5C+Uh7k/N3vfneQXgt/+tOfgqADtTie7frce++9QW0CcB31CtQm2E3gOvWAbOfVXZLiPECKY2C0CkGfSDgpdjQ1prK/1iJU5fO2TD48FNkKK+pn8qqXT9dGG/tGq0+9692M+ZSZi1X6IK6nz+MyZVGO+1eeHOBhAsksagYx0CfmGioLSWSpWdSCgw46KJBcJLwbN260V73qVfaKV7xilPAyPyCw8Yc6o9MMIMRIhlED+fWvfx2uYzCoXYisuqe1L4nkDgb9lXxHVQ1Xn3A0NSbKX2szoCqft2XyQfoAcWYrNa+fyavMeIzXp2szjH2j1afe9W7WfMrMxTJpyoxrlXWqov1I96pqWyMBQ7dGLx9BA2oAqAS8613vqoTwMp7o38bAAK4IjPurX/3q8IEQo4KBnQmEGZdx7BZgLJcGjOWQNv/1X//1KJG+7777CstECowqxMc//nFrJDgpdjQ9qvbX2kyoyudt2XzK9HPZ8ahizBpt7ButPvWud7PmU6/5WnWdGqmsRkIZzw+NAPRpMRxDtQAjOogigoXrr78+jAfuH8cD1B/ID71fvFdccsklufrE4D/+4z/CWB944IGh7B/84AfBzRuqEHiQQP0Co72zzjor6ChjCIpPZUgw9UbX90c/+lEwrmPx9NGPfrSUNBfvFkiU8cCBVwkWYxjaYdjH7sRkwUmxY0qgKn+tzYiqfN6WyadsP5dJV9WYNdrYN1p9yqLRxqOe+dRzvta7vHq3zVEeu+66q91www1B9QCSyLOXvieCJmoJEMbxgCBC//Vf/xWM0/D0AKn9yEc+ElQjsjB79uxAeHGX1tHRYYcccohddNFFo899/sdtGgZ+7CZAmJ/3vOcF120i1XiQIJAIc+hDH/pQ2GUoAgQb/eUPf/jDQfCCOgWLs9e+9rU2mWgbSSoSOUqDgefBwYOE7QeHw+FwOBz1Bd4a0IvdeeedQ1hwR2uiN2celOVrbmjncDgcDofD4Wh5OCl2OBwOh8PhcLQ8nBQ7HA6Hw+FwOFoeToodDofD4XA4HC0PJ8UOh8PhcDgcjpaHk2KHw+FwOBxND3em1doYqWD8nRQ7HA6Hw+FoWigcMEGHHK2LDZvHPxkeejzw4B0Oh8PhcDiaFgSdIALbsmXLwjHhqT3wSGtJiDds2BDGn3nAfNhSOCl2OBwOh8PR1CDSGhAxdrQe5s2bNzoPthROih0Oh8PhcDQ1kAxvs802ttVWW9nAwMBkV8dRZ6AyUYuEWHBS7HA4HOPcqiNUaF9fn3V3d4fQob5V6xgvfB5NDCBGVZAjR2vCSbHD4XCUxPLly+3222+3FStW2ODgoHV2dtqiRYvsGc94hi1evHiyq+doEvg8cjgaE06KHQ6HoySR+e1vfxsMOpDqsV3HNu2jjz4aJH6HHnqoExpHIXweORyNC3fJ5nA4HCW2upHsQWQgLGx3t7e3h2+OOc9195PqyIPPI4ejseGk2OFwOAqABI+t7jS9T47nzJkTrpPO4ciCzyOHo7HhpNjhcDgKgDEUup9ZTuGnTZsWrpPO4ciCzyOHo7HhpNjhcDgKwPY2xlBZrp76+/vDddI5HFnweeRwNDacFDscDkcB2O7GOwDb2kl9T47XrFkTrpPO4ciCzyOHo7HhpNjhcDgKgL4n7rIIH4v3gN7eXhseHg7fHHOe6+5n1pEHn0cOR2OjbcTNXLcYrOpZ0bPqx0DC4XBMbbh/WUcV8HnkcDQmX3M/xQ6Hw1ESEBZtf3skMseWwueRw9GYcFLscDgc4wDEZd68eZNdDUeTw+eRw9F4cJ1ih8PhcDgcDkfLoylJ8ZlnnmmHHHKIzZ4927baais76aST7I477hiT5vnPf35YicefU045ZUyaBx54wE488cRg3EA+p556atDvcjgcDofD4XC0FppSfeKqq66yd7zjHYEYQ2I//OEP2/HHH2+33XabzZw5czTd3//939u//uu/jh5DfoWhoaFAiJcsWWLXXnttiDv/hje8IThVP+OMM+reJofD4XA4HA7H5GFKeJ/AkhdJL2T5ec973qik+IADDrDPf/7zqb/5+c9/bi95yUvskUcesa233jqc++pXv2of+tCHQn5EFiqCe59wOBwOh8PhaGyU5WtNqT6RhOLEL1iwYMz5733ve8HCd5999rHTTz/dNmzYMHrtuuuus3333XeUEIMTTjghdNytt96aWg5WwlyPPw6Hw+FwOByO5kdTqk/EwPH5e9/7XjviiCMC+RX+9m//1nbccUdbunSp3XTTTUECjN7xj370o3D9scceG0OIgY65lqXL/PGPf3xC2+NwOBwOh8PhqD+anhSjW3zLLbfY1VdfPeb82972ttH/kQhvs8029oIXvMDuvvtu23XXXbeoLKTN73//+0ePkRRvv/32NdTe4XA4HA6Hw9EIaGr1iXe+853205/+1H75y1/adtttl5v20EMPDd933XVX+MbA7vHHHx+TRsdcSwMO1tFFiT8Oh8PhcDgcjuZHU5JibAMhxBdccIFdccUVtvPOOxf+5sYbbwzfSIzBYYcdZjfffLMtW7ZsNM2ll14aiO5ee+01gbV3OBwOh8PhcDQaOptVZeK8886zH//4x8FXsXSAsSzs6ekJKhJcf/GLX2wLFy4MOsXve9/7gmeK/fbbL6TFhRvk9/Wvf72dddZZIY+PfOQjIW8kwg6Hw+FwOByO1kFTumTLig//rW99y974xjfagw8+aK973euCrvH69euD3u9f//VfB9Ibqzzcf//99va3v92uvPLK4N/45JNPtk9/+tPW2VlureAu2RwOh8PhcDgaG2X5WlOS4kaBk2KHw+FwOByOxkZL+Sl2OBwOh8PhcDhqgZNih8PhcDgcDkfLw0mxw+FwOBwOh6Pl4aTY4XA4HA6Hw9HycFLscDgcDofD4Wh5OCl2OBwOh8PhcLQ8nBQ7HA6Hw+FwOFoeToodDofD4XA4HC0PJ8UOh8PhcDgcjpaHk2KHw+FwOBwOR8vDSbHD4XA4HA6Ho+XhpNjhcDgcDofD0fJwUuxwOBwOh8PhaHk4KXY4HA6Hw+FwtDycFDscDofD4XA4Wh5Oih0Oh8PhcDgcLQ8nxQ6Hw+FwOByOloeTYofD4XA4HA5Hy8NJscPhcDgcDoej5eGk2OFwOBwOh8PR8nBS7HA4HA6Hw+FoeTgpdjgcDofD4XC0PJwUOxwOh8PhcDhaHk6KHQ6Hw+FwOBwtDyfFDofD4XA4HI6Wh5Nih8PhcDgcDkfLw0mxw+FwOBwOh6Pl4aTY4XA4HA6Hw9HycFLscDgcDofD4Wh5OCl2OBwOh8PhcLQ8Oie7Ag6Hw+FwTBWMjIzY6tWrra+vz7q7u23u3LnW1tY22dVyOBwl4KTY4XA4HI4KsHz5crv99tttxYoVNjg4aJ2dnbZo0SJ7xjOeYYsXL57s6jkcjgI4KXY4HA6HowJC/Nvf/tY2bNgQpMNdXV02MDBgjz76aJAcH3rooU6MHY4Gh+sUOxwOh8NRo8oEEmIIMcQXtYn29vbwzTHnuU46h8PRuHBS7HA4HA5HDUASjMpEmv4wx3PmzAnXSedwOBoXToodDofD4agBGNWhQ4zKRBqmTZsWrpPO4XA0LpwUOxwOh8NRA1CTwKgOHeI09Pf3h+ukczgcjQsnxQ6Hw+Fw1ADUJvAygXpEUm+Y4zVr1oTrpHM4HI0LJ8UOh8PhcNQA9IZxuzZjxozghaK3t9eGh4fDN8ec57r7K3Y4GhtOih0Oh8PhqBF4mcDt2jbbbGMbN260lStXhm+O3R2bw9EccD/FDofD4XBUAIiv1Cg8op3D0XxwUuxwOBwOR0WAAM+bN2+yq+FwOLYArj7hcDgcDofD4Wh5OCl2OBwOh8PhcLQ8nBQ7HA6Hw+FwOFoeToodDofD4XA4HC0PJ8UOh8PhcDgcjpaHk2KHw+FwOBwOR8vDSbHD4XA4HA6Ho+XhpNjhcDgcDofD0fJwUuxwOBwOh8PhaHk4KXY4HA6Hw+FwtDycFDscDofD4XA4Wh5Oih0Oh8PhcDgcLQ8nxQ6Hw+FwOByOloeTYofD4XA4HA5Hy8NJscPhcDgcDoej5eGk2OFwOBwOh8PR8uic7Ao4HA6Ho3kwMjJiq1evtr6+Puvu7ra5c+daW1vbZFfL4XA4aoaTYofD4XCUwvLly+3222+3FStW2ODgoHV2dtqiRYvsGc94hi1evHiyq+dwOBw1wUmxw+FwOEoR4t/+9re2YcOGIB3u6uqygYEBe/TRR4Pk+NBDD3Vi7HA4mhquU+xwOByOQpUJJMQQYogvahPt7e3hm2POc510DofD0axwUuxwOByOXCAJRmUiTX+Y4zlz5oTrpHM4HI5mhZNih8PhcOQCozp0iFGZSMO0adPCddI5HA5Hs8JJscPhcDhygZoERnXoEKehv78/XCedw+FwNCucFDscDocjF6hN4GUC9Yik3jDHa9asCddJ53A4HM0KJ8UOh8PhyAV6w7hdmzFjRvBC0dvba8PDw+GbY85z3f0VOxyOZoaTYofD4XAUAi8TuF3bZpttbOPGjbZy5crwzbG7Y3M4HFMB7qfY4XA4HKUA8ZUahUe0czgcUw1Oih0Oh8NRGhDgefPmTXY1HA6Ho3K4+oTD4XA4HA6Ho+XhpNjhcDgcDofD0fJwUuxwOBwOh8PhaHk4KXY4HA6Hw+FwtDycFDscDofD4XA4Wh5Oih0Oh8PhcDgcLQ8nxQ6Hw+FwOByOloeTYofD4XA4HA5Hy8NJscPhcDgcDoej5eGk2OFwOBwOh8PR8nBS7HA4HA6Hw+FoeXROdgUcjnpiZGTEVq9ebX19fdbd3W1z5861tra2pi+rnnUu264y6YaHh+2hhx6ydevW2axZs2y77baz9vbGXqtXVeehoSG7/fbbbc2aNTZnzhx7xjOeYR0dHTZRaMb5OJXv6Uab+1N1fjgc44GTYkfLYPny5YGErFixwgYHB62zs9MWLVoUyMjixYubtqx61rlsu8qku+OOO+x3v/udPfHEE4EgQggXLFhgz372s23PPfe0RkRVdSaP3/zmN4EQQY4gQ5dffrk95znPCXlVjWacj1P5nm60uT9V54fDMV40tkgmA2eeeaYdcsghNnv2bNtqq63spJNOCg+ZGL29vfaOd7zDFi5cGFbhf/M3f2OPP/74mDQPPPCAnXjiiTZjxoyQz6mnnhoeCI6pBx76v/3tb+3RRx8N48284JtjznO9GcuqZ53LtqtMOu7Xyy67LPw/ffp0mzdvXvjmmPPJ+7kRUFWdIUO//OUvg4QY8kHf8M0x57leJZpxPk7le7rR5v5UnR8OR8uQ4quuuioQXiQtl156qQ0MDNjxxx9v69evH03zvve9z37yk5/YD37wg5D+kUcesZe//OWj11mdQ4j7+/vt2muvtW9/+9t27rnn2sc+9rFJapVjIrcFkYJs2LAhSD3YGkQyxzfHnOc66ZqprKpQps5//vOfw6eoXUg9y+TFy5ZtWghBnIZjzkMMyatRQF2oU6115rnDc4tnFsRj2rRpIR++OeY810lXBZpxPk7le7qqeVQVpur8cDhaihRffPHF9sY3vtH23ntv23///QOZRer7hz/8IVxHL+ob3/iG/cd//Icdc8wxdvDBB9u3vvWtQH554YBf/OIXdtttt9l3v/tdO+CAA+xFL3qRfeITn7AvfelLgSg7pg6YD2wLpunIcYw+J9dJ10xlVYUydUZqxKeoXehIFuV1//3328qVK23mzJlP06HkGHLItjJ5AV7Iq1atCjs9fKe9oMukKYOsfKgLdSpb5yxAMFCZEBlO5sN5rpOuCjTjfJzK93RV86gqTNX54XC0tE6xblh0sgDkGInLscceO5oG3agddtjBrrvuuqC3x/e+++5rW2+99WiaE044wd7+9rfbrbfeagceeODTymEVz0dgu9PR+GDMUIvp6upKvQ4RWbt27ZixbYayqkKZOmuhWNQuCF2ZvIrSIKEiryr1nIuQlw91QXpbps554JmBFJC808B5+qeqZ0szzsepfE9XNY+qwlSdHw5HS0mKY/CCee9732tHHHGE7bPPPuHcY489Fm5mtqNiQIC5pjQxIdZ1XcvSZWZFrc/2228/Qa1yVAm2AiEbLJTSAAnhOumaqayqUKbO3E98itqF/n6ZvIrSYHjEy7oqPeciFOVDXahTUZ1pfx6QvCERzLJd4DzXSVcFmnE+TuV7mvlRxTyqClN1fjgcLUuK0S2+5ZZb7Pzzz5/wsk4//fQgldbnwQcfnPAyHbWDBQwSP8Ysua3OMVI5rpOumcqqCmXqvM0224RPUbtwK1WU14477hhIJzYASd1JjpGUzZ8/35588slK9JyLVCnK6FWiSkGd8urMThXtzwNSZwgPZCMtH85znXRVoBnn41S+p5kfzJNa51FVmKrzw+FoSVL8zne+0376058Gi+34IbJkyZLwcuFFFgM9Qa4pTdIbhY6VJglekkhw4o+j8YFuHCQDyR8SQTyT8ALim2POc70Kn5z1LKsqndoydX7mM58ZPkXtgkwqr2XLloWXLduvfHOsvA499NBwP1HXOC+OOc+uD7qVVeg5F+lDltGrRAeaOqnOkBrqy7fqjDutIj+zSAFR32K7WnkgpVNenOd6Vf6KJ2s+TjSa9Z5mfjBP8uZ+mXlUz3u/GeeHw9FSOsXc9O9617vsggsusCuvvNJ23nnnMdcxrOPlgt9PXLEB3NxgjHfYYYeFY74/9alPhRc17tgAnix4Ae61116T0CrHRAKJH0RMOqMQNbYFkX5W7YuznmVVpVNbts5l0vC9++67Byt6VJFiP6wYtXJdaeWrFQkZaTgPKUD16e67765Ez7lIH7KsXuW2224bXEFirAuRln9hJLucL+tflvahM3r99deHsnmeQTogRORTtZ/ies/HemE87ao1MEWVfah5kjX3x+OnuJ73vsPRCmgbaUJfK//4j/9o5513nv34xz8e8wDhQdfT0xP+x2DuoosuCp4pILqQaIAHCsCLmhf00qVL7ayzzgov79e//vX21re+1c4444xS9WBriTJ52LrUuDnQrNGvinRhebGSP8QOySPlIuXhZTeel1oVEe3iOnGda/yG9Mk6ZUX1QuLFDhDp0/QZkWRJAizXVmlpNm7caEcfffTT7AtilCmLfPB0g8caJLpqk77xJlC2r9U/5AOR0aIBIjKefMaLqRqxrMx8rCowRSNFtJuMe9/haFaU5WtNKSn+yle+Er6f//znjzmP2zVctYHPfe5z4QGDpJibHM8SX/7yl0fT8hJC9QLyjNSYl9HJJ59s//qv/1rn1jjqCR7yeQSpmcpK6sLqBSZdWJEBCEDZl1uZOuelyaqTriXrxD2KV5gsXUfUI9LykZ4zYEGbl6ZIH7JMWahUQWBoFztLRe3KQtw/teTT6HO/nshrVxZxZKx5OY6XOFbZh1lzv5HvfYdjqqMpSXEZ4TYRgvA5zCcLGPwgTXY4mhHj8TFar5ddVXWSriPpeMHzO6lMQFKlmww4Jk2aVLqMPmSZslCduOmmm2puVyOO2VTFRBDHRoHPI4djYtDUhnYORyujjC4s18fjY7TWIBhV1km6jkh7UV/A2I1vjiXhk/4y+d1333121113hW+OOT9evcqsstjerqJdEzFmUxm1zMepHJjC55HDMTFoSkmxw+EY62M0TRd2vD5Gq9C9rLpOlCuXUWm6jtT5zjvvDCRgp512ClvSsp7nPMZ94yHGWWVByKpoV9X9M5VR63ycyoEpfB45HBMDlxQ7HE2KKn2MVhUEYyL8nkrXkeA6fIsQJ/VzyXP27Nnhm+OyforLlFVVu9wvbDlUMR+ncmAKn0cOx8TASbHD0aSoysdomeAVZcllPf2e1nN7vKp2uV/YYlQ1H6cycfR55HBMDFx9wuFoYlThY7Rqo516+T2t9/a42kUUPSSWClmNW8ep4he2Krdctbj2q6exZjMTx0aeRw5Hs8JJscPR5CjSu50McllrnZpJr3JLXL3Xo3/Gi6r8+ZbJJy8NEs+q5uNUJ46NOI8cjmaGk2KHYwqgFh+jE0UuJ9rvaVlfxlVtj8c+b2mXfN7iJ5myJsLnbb0CKlTlz7dMPiAvDRFF62ms2exw/8IOR3VwUuxwtDjqTS6rQj23xyfD522Vkdjq0bYy+aB6AvLSPPzww8GwrtagLDGcODocjjJwUuxwtDgmilzWQ8pZr+3xegdLqDoSWz3aFucDMPoSmWf8yYf6g9izR1pZ++2332hQlqmmCyx4WOWpCR/X5oaTYofDUTm5rJeUs17b4/U06qu3VLqqtikfqZRQf/SD8Rwht2oQW1BUFsFSprIucD3vD0f94OPa/HBS7HA4KiWX9ZRy1mt7vJ5GffWWSlfVNq5DBB588MFAhjnu6OiwoaEhW7duXZgPM2fODOfLlEXbpqIu8GTcH46Jh4/r1ID7KXY4HIXBK8qiSp/HjYR6+rytdwjfqtoGWadehMfu6ekJ5Jb5wzfHnAdLliwpXVat87HRMFXvj1aHj+vUgZNih8NRGeoZUGOqBkuodyS2qtoGoY0JMAQZEhATZYj+dttt17JBJ6bq/dHq8HGdOnBS7HBMECAEq1atsscffzx8t4KUYLxSznr2UZmy8tJI7xq9VkjeypUrwzfHVW6NTkYktvG0LauPGFNIMaQXnWBIPRIyvjnmPNelLzzR/diIqPcugKM+8HGdOnCdYodjChhcNIrF83j0U+vZR7UGlFCaehj1TVYktjJty+sjjT113WGHHUaJgsZbx1NZX7hZAs44qoWP69SBk2KHo8kNLhrJ4rmsz2NeEr/73e/q0kdVBJSI61MPn7eTFYktr21F/fjsZz97zNhPnz49179wK/oOblaf4I58+LhOHVRKiu+88077zne+Y9ddd11wycOW2CWXXGK77bbbaJpbbrnFHnjggWCFfNRRR1VZvMMx6ai3O61Gs3guI+Xcc8897Y477qhLH5UdD9LVMzBHGVA2bsweeuih4L1BKggY8NQbZfqRMWVs6y3hbiZM1i6AY2Lh4zp1UAkpxlDitNNOs7PPPjv8Lx0zJoD8UgoQ4pe85CVB6nHvvffatttuW0UVHI6GQD3daU1GlLUqpJwQ93r1UZnxeOSRR0oFlKjKBVotOwD333//0yTF9VCdKTuv99133yntX7iZdwEcEwsf16mBSkjxP/zDP9g3v/nN8HCG5B522GH2wx/+MDXti1/8Ytt5553tvvvuC2ne8573VFEFh6MhUM8gD/X2Z1uVfioGWvXqozLjUTagRGwcONEktOwOQL1UZ8Yzr3Gf1or6wuNBPXTTHfWHj2vzo2ZSfPnll9s3vvGNMOgf/vCH7eMf/3hw2J63xffKV77SzjrrLLviiiucFDumFOppcFFPAr4lyNIZrWcflSmLfgKNYhw4HpWPeullj3fMWlFfeLzwPpqa8HFtbtSsnHbOOeeMSoA/+clPBkJcBAwywK233lpr8Q5HQ6Ge7rTq7c+2GfuoTFlLly4NW5xF9aE/kd5COhW2mG+OOQ9ZrQJldgAo68Ybb6xbsIDJcBPncDgcTUeKMarjQf2Wt7yl9G8wFgEY4zkcUwn1DPLQrESlnn1UtqxnPvOZuWmSxoETSULL7ABgxFzPYAH1HDOHw+FoWvWJZcuWhe+ddtqp9G/0sOfB73A0I/L0SmVw8ec//zlIEbVFjzQS8lXVlnajWzyX6aOqjFKqKCtvzMZrHFiL3nEZVQXyogzqxXfSJ/BEqM6oH2+77bZgMK0+wifxXnvt5YZETepb3OFwVEiKca1GVKPxbB3iYggsWLCg1uIdjrqjkfwCN6rFcz0DYdSjrPHob9c6P8r4POU85fE/bUJSjeQW6TWLIUj6RKjOPPHEE8EDBlHo1DbqtGTJEifFTfoMcTgcFZLiXXbZxf74xz8G6cFxxx1X6jc///nPw/fee+9da/EOR10x3kAQSA2VBnUhSEzVvoMbzeI57iPqwgfiNFmBOcoE3YjzSRszpMVlDM3Wr18fnoW1GL+V2QHYf//9w3MXlQ6RX+w5hoaGgk9jBBWofFSpOkNZl112WZhjCEPUNogd5wFlOprLt7jD4aiQFB9//PH2hz/8wb70pS/Zu971rkLH8rwwzj333PDgxzjP4WgWlPEKwPY7qLfv4EaxeFYfPfnkk4GgIVGUBLOnpycQKrUfMlWLtGy8fpqztqvL5PPwww8HwzpIcpb0Fmkpu2BVjH3RDgB5xOUn+6VqMIZ4uqDvmGd6ztM2SB0knOu77777pAQXaRY0qm9xh8NRESl+97vfbV/4whfs7rvvtlNOOcW+/OUvh4d3Gi699FJ705veFIwzeMH8/d//fa3FOxx1QxmvAEh7GjEQRL1AuxSBDSIVSzCRokKYuH7PPfeEBQTnCAfMh/QE0igrLRuPn2YkcVkEvIy+MOQe6SzkN0t6i4/2m266qTK/0Xk7AJBQjO3Q56V8SBZp6N/Zs2eHsrhe1TxjzFCdQEKcJL1S2eA66ahTVZhqereN7Fvc4XBUQIpx1P7Vr37V3vCGNwR/xYR1PvHEE0evE+WOB9s111wzap3NQxRpMWFLHY5mwUQEgmhG5BEVFrxIiSG4ECidh4RCjiHBXGcrXukgU7E+LH1YRlpWVs8X6e5dd92VuV1NGPoy+dCePOktbajab3TWDoDajnCB60lDO8YIIl/VPGORw8Imr230L+mqwlTUu2103+IOR6ujkoh2f/d3fxduciLbPfjgg/a1r31t9GX29a9/fcyWHkT429/+9hji7HA0A+mrOhBEM6KIqNA+2o7kN00SRnokmPLwkJQmQ6okTS6SlpUZD/LFU0LedjXPLNKVGTPqkye9naygJPR3DOpW5TzjuV3UR1yXoKNWCe9U1butZ+Aah8MxSaQYvOpVr7IXvOAFQX3iJz/5SXAsH7tcw6jur/7qr0IEu6222qqqYh2OupE+vou8AiA1BHm6p6RpNN/BVREVFgU6z//J9tOnkF7ILyQqS5oMwUTqXKuXhvnz5wfJW952Ndchc5RZZsyypLdl6lPV2NezLPmWx1sQc4DxjVUoWNho0UG6WiW8U1nvtt7j5nA4xodKLSLYyvvoRz8aDC54oeHDmJsfacHNN99sn/rUp5wQOxqe9GVFLOMlXxTAAC8FRYEgmjHIQZKoZAWv4BgiCrnlHKRIZJhjSRPpE8hVljQZiZlUUWoJKLH99tsXbvtzHT3YWsesEYOSVDXPGGsikTK+WrCoPI45z3VUNmqN+jcevdtmgwdBcThaRFKc9hCNLaQdjkZGWenUkUceWToQRBnfwbwQZZgGWUTS1ojW+zFRAbzEYx1WERVAG7jGB8kXpBMyLN+5ECUWykiTIauoUygvPFRI51KqKHmIA6WgJqHtekiugm5wTZLrpO6ttqvxHEG9ygSmyBuz8QRuqXXss4Jp7LjjjqlBYsqUl5dG7tZEbpkT9J3qsccee9jVV18d7iGFxeZ/SYrlbUQS3qyyYr1b0nD/qG0YESb1butpjFfF/VrPwDUOh8+PBiHFDkczYTzSqTJ+gcukwdiMXRUMzUQc2aJG4tZo/l5FVCAnqIYkg0VQb13nxU6AB/oLMspDmXZzjRc/baNf8DQhYztB+S1duvRperJZoP/uu+++UJ76kTwxAoaoMQ5c1zZ/XG++icbJ2PzlL38pDExR1ZhVlU9WMA3aHpOrMuWVrRP5y0Yk/l/3EGQWPe20gCK6hx5//PHMsqg77SANu40smjSHWDSx28jc4J6qpzFelfdrPQPXOFoXPj/GDyfFDscWWIWX8QuclyYrEAIPsUYMhMBLm/5BSpZmHKegF5yHzEAcSE8agWPO006A7rAIlfwFkzfnQRm9yjIBJSBRuEpTGupIGogk/3MdQlyUDygaM0hSUeAW+qCKsS8bTKPMXFPb2AEQ4WRsIaVp7YecavzpR84fcsghYez4MNbJOULelE9fX3/99Zn1OfbYY0NZLGSYE9x7EGvNDc7zUmeRBUmthzHeRNyvtfoWn6rGiI5q4PNjEiPajRc8DHjwMlA4e3/Oc55jr371qz3ss6MlrMKbMRACUj6IDlK7uM70Cf9TZ7aT+WBoS1okfrRV0kKlo23SLYU0cV6SQKWHRPLN9aztvzL9yEsBSTD1h5xRfz6k5XlD/pB4iFZePr/5zW9CmUVjhrpFUXCXorLKjH3ZObTrrrsWplPbqDfn+NZ46Fhp6DvpiyuNPIrccssto2o1qDkkDShZVFJv0hWNGfcb+Wt+kBffMRGvlzFeI96vU9kY0VE7fH5MIinmAR9DEp80pF3jAfjd737XPvCBDwQjvdNPP73WKjkcDW0VPlmBEGoB7ZfOLwQolgJKcsh1JGpx25Lto22QUH4jneFYfUKBfyiDhzZS3KztP9IU9SO/43/ySdMp5hh9XNLl5cNLhDkB6c8rC1BWlgpOmbLKjH3ZOUQo6DJ9xBgwz7X407Naxo4sUiSt5ROnkYoM5SQ9jiRBWsatqK8B9xrpYxUcLV6Zj/Rl8l6diCAYjXi/ehAQRx58fkwiKT755JPDN9uTN9xwQ3h4YbBywAEHjIrmecjhoo3VPQPCtX322Sc82JAaEA0PCcNHPvKRQEqIkOdwTIZVOA+JrIhlVVmFT0YghFohv7d4c+A+jiOoQRS55yE7Mqwr8vYAsRKBkl4q/+uY/NE5vvfeezO3/yDHRWVJ5UWeLpJ6yiLKZeoMispivuSlKVNWmbGP5xD9pT6VNFX5IMUsKo80LBTkTk9zXJJi+lxeRDhOSyNvIcwFSZ2TCycFFdF4ZNVHXiVYaHHfJdtGHsy1onyqCoLRiPerBwFx5MHnx5aj5r2eb33rW3b00UfbrbfeGlQpfvzjHwdJECGdzzvvvPDhf85deOGFYSuTtEcddZRdcMEFdueddwZpMSFUedh96UtfClt1Dke9IatwJMK8dCF/fHNcpf5VHAghDclACI0ASeh4yCIN23nnnYOXA7455hofFhNl2gbB4aEdb4trm1znIUd5LuBYvJQpSzrEWWlE3orykXpNVhqu87Kpoqyisdccoh/oJwwWIbd8q9+4jhSoTHlCmlQpluZr4ZJMo8UM7UdthvpRJvXgm2Mk6Ehby/Sj0kg9g3wlnS47rrG6E3Wjf3gP8Z21m9ks92us7pVVJw8C0rrw+TGJkmKkw3//938fLLQhs1lu2HihEbzjsMMOs4MPPtje/va323777WfPetazgoEGxgocs013zjnnBD1jh6PeqMoqvKpACI2qXhJLXGP1EqTpbNnntQ3SxHYzkjXpJMdpeGBrqzrWTU1u/2F0xXVITlZZ1BnSnhdMheukl+eErPHgt0VpyAviVWtZRWPPdUgYZUHIJL2VJFb9fNBBBwWVlrzxgDjTh3KRlxZwRRInqZ4k85FhHWUyLuwoMI5KD6mlzbSfPIvaD/LSlBlXqTvVaoHfiPerBwFx5MHnxyRKiv/jP/4jPGjQBS7jl5gBIi0PYH4rsP0KUWbA8HXpcEwWZBXOC57vqg0RygZCaBQju/EEHYCgFbUNqfvhhx8+asSlcM/ya8t5FshFW9b8BjWsorKKgqlwnXR5+bBIL0pDu/FpXGtZZcae8YCoxcaJ8t7BR9fLjAfEmecyaWUoJzLMMXXRYpE02poVGeaY8yKasX41/wOO+Z/+KWo/18ukKRMkh3JrDSbSiPerBwFx5MHnxyRKin/1q1+FbyS+ZYFkGCTJL4ERAKt/R2ugVR2Ly32T/J5quxvy0Yh+iscTdGA8bWN3CYkxhBhSgQQYAop/4V/+8peF3kBIx5wpKquo3qp7mTrXs6wsSO8W9RWkxbEvX154ihxKuqLxoA85xyKE+vLilOoCer1I40lD/qi7MV7J8pBa4+kCFTrGsBH6GpKuYCK1WuA34v1adRAQx9SCz48tQ9vIeJSrUsBDkxcU5PiII44o9ZtrrrnGnvvc54aHEw9X4U9/+pMdeOCBYWuWh06jgy0IXsi8eHgROMYHdyxugYjQB8wl5pCkrVNhIVMm+hfjjroFEjek8kgtFYACQpO1/cfc4eHOQpprZfqxTL3L1LmeZWUBIswzF8knZaH/rrI4R1mce97znhd2PIrKi32aSvosV3mQXl6ugDT8PqlCQH6x3n1V7S+TJqss5hQLK+qf5XmE9w82MWUt8KuMQFmVQKDR8nE0Fnxcx8fXapYUQ2KwEseYriwpvuSSS0Z/myb9aBVC1Mpwx+LpiwKkUI2+KCgbdACykOeiKtl+dFF5cKv9Zb2BlO3HMvUeb50nsqwyhjT0D/0RR4+D6CmkdixlzysvKVXKWqSWSVNl+8ukySpLJFjBU5IR9lg8SP2jLGoZs4kSCNQaBKTq+jgaC1XMj1ZCzaSYVTZ+hj/72c/aS17ykkI1CiIZoUvMQPHbGLh1A0iAHFMX7ljcFwVl21+0/Vd1P+ZJVcZT1kRLZ8iPXTqM6JBSs7sm92f0E1JStvTHY0hTVfjyRovCSMjpoiiMU/3er2peOxxTHTWT4g996EP2P//zP+GGYqvu3e9+t73uda+zvffee/Sm44bEDdv/+3//z/7zP/8z6Kwh9TnttNPG5IWLNn5DPo6pi1Z3LN7qi4LxtD+PhFXdj3nSMr7LlsXv6yl1y3KjtqV51RK+fDKQRfjKRmGsp+rbZNz7Vc3rqfgscjgqJ8WQ32984xv2pje9KZDdf/u3fwsfbiqFbWZ7UVtUPBR4IP3Xf/1XsBwXCOBxzz33hK2pl770pbVWy9HAaHXH4q2+KBhv+7NIWJX9WCQtw9NBmbIINnLbbbeNBq9QwIqqpW7kBdnDV7T8EsfBVKgP16fqHCpD+BjDMlEYUT+pVx/V+97XvEY1id0EPkjNUXkcz7ye6vPI4aiMFAMkw1gdv+Md7wjGcgCCzI2XBK6WCNCR1D/GcpkXiqO1HIvneRaYqo7FJ2tRUG+Di6zyqmp/VfmUkd795S9/GfXjm1UW5Ap1BgJoQLwwdJMOKwSMeoxX6lbUh+jFci5pRBYb301VFC1kdtttt9AfGMPJY0S8cEBow3uqnn1Uz3tf85r5yDykD2Kdap6zZeb1VBZQOBwTQooBfkcJ5IHLGgJxEL6ZmxHMnz8/SJRf8IIXeFAOR8s7Fp+MRUG9DWnyyquq/VXlU0Z6JyPgvLLkFQOpXFKHlXOQETwXlJW6jacPk+GrFZZ7qi4syyxkHnjggdGQ1+xApnmfUNCRqXjvM8/kKSNNp1reRRQZsBUFFA7HhJFiAZ+NfByOIsfiZTwLTEVMxKKgkQxpisrj+VBF+6vqxzLSO/LGXy/1zyqLecxuFwSEiHxKA6mAjECMERQgnWyUPmxWlFnIyG0ausN5URjr2Uf1FAgwzyQlzpqP9JF09ltxHjkcE06KHY4yaGXH4lUvChrJkKaMBA8VAzwj1Nr+qvqxjPQOUkqd0RdetmxZIFhI2hQlCtKx7bbbhu1orqURNcqAgJNfo/Rhs6KsGgISYvqkUfqongIB8mROF81H1EuQqjdKHzkckwknxY5JQzO5d2rURUGRRJGwuvU0pClrSLTvvvtW0n7145///OfQZl7mvNTJByOiMvmUld4RPY6+RUUMv7dsQyNxQzf1gAMOGNP/ki7H+UDiOM+nkfqwGVFWDWHJkiVB77qR+qheAgHmWdF85DrRDym7kfrI4ZhSpPi+++4LN5dCgebB3a+1NhrNvVM9wcuGF/aWRsgqK1GUIQ3pk3qVVRvSjMeQiEhrZdpfxkCQNDKkovzxBOqMpXdZUmCu80wjzDF12GmnnUbdwlEm51mAYD/Bdn2ss6kIcfQ5c13b+GUMEfkd/SWyjwpHsg+rXFg2S3S0eCHDN/2jeS2jR237U24t91mzCgSYZ5qP8oYSe9/gf81HvltVQOFwTAgp5uV7xhln2P/93/+FB1IZcMPxIHM4WhFpag/3339/aenMeAzE0iKfsTWaFvmsXoZEZdpfZCDI9V/84hfB043IMG0nPSTo+OOPL9WXpNl9990zpcCUSdjp5AIEyMDu4YcfDioU9DX6mvSB6gPBhXhAxhivMkZ0hHKGpEu4QD54sUCyB5FR/1a1sKzKGLMeRp1ayNBHlMWcFpjb9FFexMPx3GfNKhBgnjHfaDPzmXkk7xss9Jjfmo/1qI/D0TKk+MILL7S/+7u/C1KV8UhoHI5WRRXGb2WksrwAefnJEr+KyGdVqCJAjiGgee0HRcZm11xzTWgbeau9ECTy5zxE9qSTTiqUeDEeeVJgiFQZlQZINHWTqoTqQ705T/6kK2qXdtyS7YJscx5CV6XxUzwfa/GvXG+jTr1v0nYOJqM+jYR4B4T2IzWO57XrCzscE0CKCaGJn2JWoUhJTj311HCzve1tbws3G+7Z8I/4+9//PkS0Q6Jz5JFH2r/8y7+El7PD0WqoKqpVGaks95jusyojn9ViSAQJZ2eJ9mvrm/8lUYQ0oiMM8vroxhtvtLvuuiv0p1QeAN98WKQTFAgLfAUSGs946FpZP8W0mfrIDZgk15LMsRCh3Twri8aeZya/JS9+TzotcMhH16s0jqzVv3I9o7WpLL6Zb0n1ibJzqNGjtdWqhpLUX1Yfub6wwzFBpPgLX/hCeOig68aKfOnSpSGks3D00UeH77/5m7+xj33sY/aWt7wlhIUmCt73vve9Wot3OJoOVUW1KiOVRTqERLiekc+KDIkglpznm0V1mkoHbQKKaJfWR/xWZCGpH8ox+UtinEeKq/JTLOIvvV8IptoFmaWulAPY3s8qi6198qE+lBerYWghhG4s6iF4V5hIf7bj8a9cz2htcVnUL+mnuewcauRobVWpobSyQbPDUXdSjCSYm+sf//EfAyHOA1KH7373u0Hqcv7559vLX/7yQJYdjlZCVVGtykhlt99+e7v55pvDS5/rSaMtMN7IZxCnIqOlvBcxeqCQLT4KnhAHFUDCK4lfnoGg7BFkyKaPJMXaKoZUVumnOMuwi/6mvKJxlYu3rHYpb/qLPJPkmt/JmG880sSscSvjz7aMf+V6RmuLy8rrR1C2PvWO+JiHqtU+XF/Y4agTKUa/TRHthPhBogeVwEP43e9+t73xjW+0b37zm06KHS2HKqNalZHKso0MaZOkuBZDO7b/0QVm+z42RkMPFrWIMi9iyAjEjHZCNMmHvlCdaAPPDYhbnoEg35LAQmi4LomqSDHXkcpW5af4D3/4Q6phF+NAWGFIJ3Wl/klDO8oQic5rF9fj+sTPTyDPAfRPWWli3rghrCjjz7bIv3I9o7WprLx5Ldd3W2r0OZERH/NQTzUUh8NRMSlGigCQSAk8lAQeWLjDiUHIZ/CnP/2p1uIdjqZD1VGt8qSy5AfpgRSJAMRS2fEY2pEHO0OUgURR0ite0pwHSWKcB+pA+bHXCBFA8qauqBJkGQjuscce4RuiB/i9+pJ06uv42VTLeEBCs0gI5/GJSxok4dSVNsSGTZAcuaHDeC+vXfxGKiaxFB7iJ7JE/5SRJhaN28EHH1yJf+V6Rmsjj6J5TT/SXryJ1Gr0WU9iXE81FIfDMRY1O2rUAy7eWotJMIYuSUg/T/p1DkcrQWoPLB4hJtw78onL8ZZYhUsqC+lK06EESc8w4/EUQ/0gDhAr8pceL98cc57rsQRV2/yQRL5VntQ3IB60WWSYb45FzpRXloGgVBXyiGpSyrql4yHjQM6TFqkwXir45pjzqIXJwl+qHJJg8+E80lnVN6tdfEPE6Fv6La4Px5xHwkt5MUHWeHDMeaSJkMSicSNKH/9DKiXlFhnmOPZnW2s/ToS3g6x5PZ5xLerHenpVKqOGwvWqfIs7HI4KSbEkQ/fcc8/oObZEMewB+BBN4tJLLw3fvsp1tCqk9oCkCkM39Hr55rhKyRQLUPLFIIv7EsLJi55vjjkvQ7s8oBaARBbJW5pRGwSD66QDkA7cof3yl7+0X/3qV+GbY85L35OXPtI+ETBJtTkvcsxzBOlrXG+Oqbf6jIU5JCYmlRwjUYM4qE61jIc8GsSGXdRDXi8oC8861I/Id/SHiAvfHEOipe+d1y7KZXFz7LHHhnqRB2SYb445z/Uy0kR5ssgbNxkHUn5anaR/XEbC22jzmnErO65FUtl6IVZDSUOVaigOh6Ni9YnDDjvMrrvuOvvNb35jf/u3fzt6/iUveYl96Utfsn/7t3+zI444YtQLxfe//307++yzwwOH8w5Hq6IeVuEiZezeSDIYGyRBRMsY2rEljdQxT3oFKSFdkZEQ4ZcBdYCsJQ3k5AWBetJH5JFWb8ie2iYCLH1ZpYkN0opQZBxYJL2Tzi2uKdFjTjNqJMCHyspql8YDgQMBRdKM4+L65EUqhPCWGTepF6AOxzxJi+hXdl422rzOi/pXZlyrjPhYBvVUQ3E4HBWT4he/+MX22c9+1n70ox/Z5z73uVGfqPgr/ta3vhUe5kg22DbkAcsDWP43SeNwVIV6Wo9XVdZEW4XHUqc0ndCk1CmrXRAy7tkin8gQqCIjIbb9SScSDUSKlVa6rXn1jg3SkqSGdqhO1L0sqAfEFeIB0YbMimTH/ch11YF0+h+QhrQiU/xWEtg4DXWWAZt0dpPjIWmlAmrE/SlDMzxDaIHAOZ61zCn+57caN7mo08JB5fE/noN23XXXYJQJGVOdIF8sYiZCp7Zs+O60NEmjvqRqB+nLSFPHaxxY5TMmK68yXmXiRUojec2oElO1XY4pToqf//zn2z//8z+HBzIvE/nN5PsHP/hBiHSHtIZVu8AE/8pXvmLPec5zai3e4Qiop/V4I1mql5U64SVGoV7jwAwQIrb1i0IPI6GEbJEmz/gLElnG5y8veRbJkmQK1AfJHr+X0VpWvXnG8OLEkEqS5dhgj7SQOupeBujesuMlSTW/v/zyy8Nz6pBDDgl9AaGH7Cp6J2VByGi3DORuuumm8MyL20X/Q1T333//kAZDO8pJhnCmzeRTNB58k54AJkkpJvnSb4SnJu0f//jHQHSL+ih+Rk/0/VFrGr7LSlPrkc947/uivIq8yoynH5sRU7VdjilGir/2ta/Z8573vNHtT8BDBFKchhe96EXh4f/DH/4wBPRgcrMd+KpXvSpsMTocVaCeoVybLWws9ydb+RA1hXaFTCF1QrrIApXrRaGHaRfGXXgrgPCRj6RXCg3MdX5TJvQ02/SQWe0aCRA3zkMO8VLD8yOr3nh7gHgSnEPSTz7kIbdlPGeSurRZhBi9Z0lzeQnTDkgR54FCNCfTQEIpb99997Vly5aNRptT//O/otDRl+rvZD4i2+RTJhS0gpekgfNcp+30AV488voIQqzyIO8qj7GgD6oM8wyqSFNGmlpmXleVT9n7vuwzpEgNpdmeRWUxVdvlmIKk+O1vf3u4IblRCdUMQeaDRCJrWwOdr3/4h3+oqr4Ox6SHlm0m/6HUGaLGy17uqkSGkPzyzXUki0Xt4p4H8ncrzwSkkZ9iCHPRdjT5Q8IksYxJMeck/eUlSL0liUaqCsnjmSK9WvRtFT1OH/LkHPmyexWrZqSB3yAhps6QIKUVYaVs7CZkZCdVCIisVEs4xouDiH48/vE2N4FUUFWIQ0Ern/GEgkb6S1l54Dpp6YO8PqIP1c5a5nWZ+6NM6OWy4ZmZj3nSVOqLcWe98ilz34/3GZKlXtWMz6IymKrtckxh9QkmLRPzwgsvDB/Ai4vgHSLJbDWWcYXkcNSKyQot2yz+Q1VntvchrElA6PCaUDYcbp7xV1kjIc5J8iUpqiAJMy9FPNogxYS8JQ2pOEb6CbmmTKWJDe04lkcMqVqkSd14ydIW6THHQUBEHmU0J0liWghnSLoWHCL6MUHmN+RBOtV5S0NB4+oydn+XBq5D5uiDvD6iPAUgqWVel7k/yoReHk94ZurMIiltPjLfy96veVLZ8eRTdN9X9QxpxmdRGUzVdjmaB+Nirueee679+te/Dh906wQm6MUXXxw+AB07Vt4iyXioYOvT4agakxVadqLLqgrUJQ6pHAeL4JwMkopCKsftgnDIdiCJMkZC5AlhUwQ1SYdFQvmfusltm/R2Y8ThkPPSxB4xsozIqJf8CIs0CiK4chunspKL/jjstKLpqT8EzpN3mTkUh4JOGvXJx3Oy34XYpy55yftEVh9pbGsNh1zm/igTenk84ZnTdE9ZLMl39Hju1yypbJX3fVV5NeOzqAymarscU5QUv+ENbwgfwMNIBJkP0en0MmHr76qrrgofwAQ/6KCDRkky21U83B2OWjEZoWXrUVZVUEhl+ZwVeaKekD2FVGbrPi/08HjaVWQkhLoGeStSWlIipJDPIn9ZfZ0Mh5yWhjZSDs8iyo2lq0iZOYc0mjqgzyu3cIJCUKvPisaesrLUNVQ26XhGkldeKGgkytQvaYyHBDmLNCQBYUXSWdRHXKs1HHKZ+6NM6OWy4ZlZOKGykqV7utdee1VyvyY9jyQXjeO576t6hjTjs6gMpmq7HM2DLdZx4MX38pe/PHwAL95rr712lCSjd6god0xkFOf54LeYFwbGJEcddVRw4+ZwbCnqHVq2Wf2H5kXkUrjhvJDKZUNBC3nb0egy49WB/pIHhJg4Sk1hl112CaQwq68JgFEUDpk6oFPLJ9k2nk+clzcLiA7fSX1g8pJOI6oIWWXhNQM9XtqbJNekoUxIFVHvIJd5oaBZjLAbx3np/kq6jycLeYvQuKaNL/k+61nPCmOY5zWEPkLyX2s4ZPTKy9wfoKisojQYWaIykad7ytiiWlFUVtG8jj24qM/iRSPf8uBSr2dIMz+LWrFdjhaKaCcghTr++OPtE5/4hF155ZXhIYk+25lnnhm8UPCS0zYpD3dcCX3hC1+oqnhHi6KeoWXrWVZVgMhwb0IW0kL4cl4BNECaHt+WIiv0NAQPN2eQKupAHUWGOeY81/E+ob6WFFtqGZxH9aEoHDJ5QPZlyKZw0gocwv+QHfnzpV+k5ytVB85DrnCnprIgp5TDt8pCTQxiKL/AkhrzzTHnRRwpV+UoFLQM4CDN+B7mfLxo4JtjzovM5oHrjD1l5vURfUhf1hoOmeuky8uHcorKKpNGHjOKdE9JV+v9Kg8uzD/KlFtAvjnmfJr+90Q+Q5rxWVQGU7VdjuZB20idgrrzwkMn+ayzzrJ77713dDsw1t9rNvAw5KHMi9rVQSYX7qc4HRAfuRTLUo2QDicEh7mclgYQlbJK45Y0v8CQOAgxRA5AtOTtQgZiSJrl7aIoDf//5Cc/CfmmqRxAVmkz6ZHIIn1MqjQglaVvUP2CYBfV+YorrrDrr78+SH6VD7/HABk1MsaDlzxS8GRfQ65oA9ek0pCmYsG8I9gG9YFAx2oh5AWxhsSjqsbvyvRj3rymXOotnfAkaA9qHswR6luVn+KkHjieO6QvTPhweSJJgusQVsaM67Xcr/Q9Ap4iX9/q63o+Q5rpWTQeTNV2ORqfr02YiwheCLw8eHChTsH/SFVAnXi4o4VQj9Cyk1FWlduR22+//ahxmvRWeenwgGCLnW/9nwxPXCYU9HgBITv44IPDy08R5HjpyXMDL0b8FFMPSEccepjzkDrGIs8jBumKILdw5MXWrNrKuEK6IHkQIZ5fkFWIK9v3AnXiPPUlj2OOOSaQMdymsShhIQEZps8VVpg6qj9FUhWwhLw4B+GChCY9VCh8tbaXGV/6RFHrUBEhn1iiVuQ1pIow1zKAygurXKasPOjdMR7dU/q/lvtVHhEY9yxPKOP1iFDVM6SZnkXjwVRtl6PxURkpZvJec801gQBDhP/whz+MGqjoQcYDHV1iVtR8nvvc51ZVvMORaT3eyGVNdCjT2BuECDBEC9LAsbbGMZSFmEK2IE0iapAn6jRRxi0KLsELUFvySX+lya1p2pD0V0p6SBltkJoWUOQ3SKOCVohg8oHsokYByZVrLsrL02ElTySuGjMdx/WhLCLTKY2IPv9DqMiLPpbBI79RhDulF+FLerqQ2zeFZi4rUcvzGpJsr4gzfS1J93gMoGq9P+IADmnBRGL9Zb6Ti72k7mkt9Yk9IhR58BjPPV2mTmXyqudzr56Yqu1yTFFSzMNJRnWQ4FtuueVpRh+8cHl4iQSjcyfJk8Mx1VH0QqvXFmGRNwiFL8aSH6IW7+RA0iB8WPJXbdxStF1f1l8pdc8Lz4yUmbYhAU6qGYi8HnjggUG9IMuNHMSdqIDyn5xUaYA0qz556gNInhW9ToE04iAmfCDppKO+eSG1JemtSqKWp2JBH1VpAFUUerkogIP0l5FgkzY5rixsxqt7mnW/jmdBUM9Q0A6Ho4H8FOM8XoilMkccccSoFFhbhg5Hq6HohVbvUKZ525Hcv5Bf6cAqFLOM36g/16tEUft32223Utv1qCigv5sXnnnnnXcOJEpeIdRmuYTjOiQqb+EQq0jwv6S75EMfIfklf4QFd911V66bMKTWMi4kH+pEPqSDjJIPz06Ifl5IbZHlKiRq9A8hvCkbUq56017OA+040A9IS2N1Fn4Tk9C8BWHR2GNkV2ZBxEIlDqcdY7wqekUkvcyCoMg7x5aGgqb/FOjGQx07HBOLcTHWN7/5zaMvAsDWnaTAfGSw4XC0Mope+hCa2JJ/skOZQryQuGlbOA49rKA7XCcdBlz1COX64IMPjlEhyAoXjfS2KDwzYeiReEKm0AuWFFTeKCB5SELzFg5IbSVplgcL2iGSTX9BqDB8KxpXDOMkYY4Dl3BM/vQzqhHHHntsbkjtuD9rkRRTJuXwe8i1+pG8qBP14frf/d3fBb1k/of8x9Jk+riMEV0ZKTCSf+lHp0HqEdxDtJ1803Tly95DZRaoRQFpkt45armndX8wT+hjzTsZ9jFOHurY4ZgYbJEYlwfPK1/5SnvFK14RyLCvWB2O8oQP/V0kkfUMZZpHVCA9SILRIeV80rCL9Kgm8NsqSHGZUK70D/WJDcrSwkXTz5Jsx1DwD+qNugMks4yRVJbUFYIkTzlJFRPVjfOEzKasrHaRjwhfkhTzkbcJiDDu5IqM46rYYid/ypOLujgACeMP6eM6KnIPPPBA6DdUUmL/yjJ8BHkEE0l50diTDuQtiDQHROKTer5l76Ey9yvXec/l7SSMR92nTChojbl8ZMd+qmkv1z3UscMxyaRY/jN5aJ5//vnhA3hwS2WCbyTIDkcrogzh40XLyzjrhVZ1KNMiSZiCJaiOaSpPIm31DOWKURgEqChcdJaKFucpSyGMyxhJZYE0QCQ2qecLyB9Vgrx2iViLVMf5xAS5jHHceFVwqGcawZZhJZBOdCy9RjrJeEF8SZe2SJH7NFCkC1wkBeZ32J5QlywjOi2cag0HXOZ+FZkt652jTKj0PDCH5GZPCxUgo0yIMdcVHMvhcEwSKWYb59Zbbx01ruObqEFsd/H51re+FdLhkidWq8CIxbd5HK2AMoRPhKPKUKZZW+ixJEwEg/8lUeSFz30NWURaLNWAOF/Ocx3jr/GgVsMljNcos2y46CQ4rwATtfa1DISpv3RpBXm1APRTXllc55rCR8eqGNQXcL3IILnMuMZb7HlGdBBkIAmtVEJUZ6mqMA+ShDgpBQdxoJYtkQJz76COgAejNCM66oC6i1RnahnXsgs0kdmsnQTN6fGESs+6P6g77WKupPWjFnvyLz5VMdGeeRyOStQn2NLjc8opp4RjHJqLIPOBHKNv9sMf/tD+93//N6SRc3tJk/k/KbFxOKYCyhA+JG9FqgFVWfJrW5dv9HTTXtZICtny515GCpUMhUyd2C4XGSjzsqrCcEn51hIuGnJJuGgkebX0NWmkShL7CxahldEc/SipYlpZSH1ltKf8YqmsJLFIcctIOPPGVRJO2p5nRIdfZelgS49bID/qpN/lkUeRtDxpqYKd5PURfU2aLAIkSXIV3jDG62ouC5TDfc3io0yo9Lz7g37SGElyHrdNi8C0heBUgXvecEwWanYNwcuSzxve8IbRyRyTZOlPXnrppaNWzExwpMcQ5H/7t3+rvRUORwMGy8h7WfOCzHMBVtadVBkvDhBdPrxcYv1EyLA8BzzrWc8Kv4OsxduykBgktgTZoD5lI5HVargUtz9LOqdw0XiZkG6xvE9I4ogbSLxL8Ayqpa/JE6k1eSj0M79ReGa5Rttxxx2D94mssvCswE6aSCrnNB4cQ6xizxJZCxCOy4wr0t0iIzqMESlXqhPyiEHbOI7VTvLIo0hakbQUKS+qFkVGa/w2y4hOLtnKzqFa79fxuCNMk+7GKLo/0LtGVZGxkfeJeI7wP2M5VQVL9fbM43DEqNxfGpP1b/7mb8IH8DKKg3r8/ve/Dzc2D2pcKTkpdkwl8AIsQ/iKfAeXDT9bZCSEYRR581JBupbUT+Qa+UDUIH1ZYXXLupEr412gjOFS2ZeeQivLTzH1hoTR73Ho5VrLov64beObnTAZe9E++omFA31SpPJBWSqPvkR1RYQv6VmiSJqo9jKuUscQAdW4kr+M6NKMEUmLfip58z95xN5HJLUkL+n55pFHxr1IWorknvFJzjV+z6IhNlrLM6IjEFSt41r2fi0i1/yeBQiLIv6XH2raT7+RL9fpg6L7A5VE3M3J4JG0cieIBJ1vdhKq9hveCChr+OieNxwThQl3IqztS250tvnYosVCu5ZQz5BryDQ6ZzxUL7jgAjvppJNGr7/xjW+0b3/722N+c8IJJ9jFF188esyL4l3vepf95Cc/CQ8ZSPzZZ589ql/ncGwpyhLeWkOZljESksFOmTyLfBmXeVnts88+lRguVRkuuqhtZSCJIkSUBQTkRkQVqR1lcb1I5UNA9QMCJR1SiBiqFfLgUMaXL4B4qS4i6bHhIUSMNEXuzZgjIoTUWYROHjr4QNR4hueRR/xGa6yTY18W49HzLRNSughVLFBVZyTuyXcbx7SFsSD/MvfHfvvtF/qVnQBJ+LN8Qk8ljMfw0T1vOJqCFPMAuPHGG0fVJ/jwEE2mqQU8KPbff//gN/nlL395apoXvvCFo4Z/ILnlh89NXjCodfCyedOb3mRve9vb7Lzzzqupbg4HqIrw1UoeZNxFurStWI55yUofNEtVoezLSlLNWg2XxgvahK3DRIF6IinGuCupm8vCQ5LkIpWPJOHlN8oH3V/6powfa2w3+B3ESaRXBn8Kaa3w3EX+nrkuvVrGJ5bMxjqsEFB2DvLcksXS0qSEU9LSe++9N0QYlP46hJp68zymPRD+eoaUruJ+VfhuFg20JZaS007aLQPEMvcH/RUTdbU53rmZihiv4aPD0XCkmAcXD3gR4GuvvXbUfVEaAcZdG7rEz3ve88JnS/CiF70ofIoeUmxlpoFtO6TGqG+gSwn+8z//01784hfbv//7v4cHj6N1LYzrVSe5sUrbQi7jSquMkZAMcriORFEuFfkdeovagtXv83RY9bKiPryolQYyoZeVJJVyu8U51YNdoyShIU+ki9QN0pAWBZO8UMGi7tSZqJnJlybkI09SrL5GnUT1RjKb7Ous+tAv6FuTN2kgOiKf1CkOAkIfZNUnlrhzHh/K5AOJQveZ9AgVGGuNDddjgzV5ceA8/cp40Cbp75JGEmhJn2k/fcY5SYH5LfWg/pRPnrRLOwP6jnVY+Sa/tLbJLRnqIywaFOWP+vBMJY30gRWYgnqpPsxt6seuInmgpsK31ESoL2mSer5l5kdV93TWvah5AfllnsdGhjHRpe/iBUjSGDG+P+hr2p/np7rM3K+y/VWhipDatZbV6GjWerccKebmhPjKmA5iGa/akk7t2U6F/IoIxz5RJxJXXnllkMLwkMS6+pOf/OSoSykMS3jgiBADokfxsIHg//Vf/3VqntpGFHgIOaaWhXFVdSoT5vmqq64KRCp2OYUOKOeOOuqokC7PlRYErMhICDLC/0jmIA2xD1pIFR8krDxw8+qslxWkErIS5wOxk04t9xi/QZLJswKSoHTyukG9SXvFFVeMPj+UBqO5Qw45JNyz4Kc//WkgifKjC66++uoQQe0lL3lJOKZ/pFMsYohXCukU065LLrnkafXmmPagWkX/UR8ZpQmcIw/IMf0ivdoYkoRqW5e2Z9WHtpMO4ie/voCxikm3wm1Tx6TBGn0sfVNIggixIMM7+pvfUP+LLrooqK0l3ZtBXg8//PDQR9SBOif9FPOslA5r2nxkEUEZSJKZI3fffXcgyPGY0V6uy083ZTE34vrI9R95ICVF6kreSdUQSUs5LjM/yhqHFqXJuxfVdurJmCX7mXGT+z3yRY1Q+sLx2PKN4XrW/UjExLhORXO/ymdaVajKM02tZTWytL1Z691ypBgSiTeJ+IaPSbC8SogE86kiAtZ4geoEahVIXnhAf/jDHw6SZcgwDzIeWhDmGNSdBxzXsnDmmWfaxz/+8Tq0YOqjES2Mq6pTmTDPvOwhCkmDJEgq59GXZ/7mudICZYyEbr755vBwlZcEPty3pOM8efJdVGfqBlGR9FCSRPLhvKRbfCvf2CME0jLywEAK8sviWuQi1pnkPKAu9JOeMSqP30h3lfuYvJJl0X7Okx4yqHqTRn2tekOi6DvKjQNxyK0YUkjqEnt7iMcs9vbAuGFMnFUf0uKdgvzSQDrqBcnSVnzSswS/hfCqv5L9E+ueSi1G52NwLLJJGfI+IVUMGe9xnuuQ/bz5iGCBtPR3Eor2R3nUjTaIdKve5EX/6h6SEWIsGeVYxoPM16L5wT2reU0b+JAmvqdB0X1PeXltZyGXRvTVz5zX2CbVcORHm3ZJDUf3o1RM+JAPfag6oYaSN/cBz5BGes5W7Zmm1rIakWA2a71bkhTrQSNwo/KylCoE7o+4yScbr3nNa0b/5wWM0QJqG0iPX/CCF2xxvqeffrq9//3vHz3mJt1+++1rrm+roREtjKuqU5l8kGzxQuNaMmIVx5BHpK+8APNcaSElQjc+z0iIhR7heUUcki9szkOaIUF5dUaqyYJR0dxUn5iIcZ2XsrwP8DKLPRnQNggLedE26cIqL+XLCwDpV0zakj6IZbsgjwmSsgERBNoDoVV6iGTS+wZkBZJKHjJIjPWC1TZIDONAnWMfukkvHgookVUfnqFZhFiANEICpIeqfOSLmbGX9wnVU4j/5wVKeuYJ9da2fkyEGRv6mmOpMEgyK0M7bc9rkZA1H8lHATyywFxScJDYB6+OyZ92MbbUg8UB/S8pqNqPgEMLnbz5wZyTqgakU/kwFyirTBQ+0iDZzWs74678Y6Kvb5F5+lQ7AtSJMeZDntyruo6kXUayEPJYmszcYOeH9uXNNfqIvBvlOVtVSO2qPPM0oheLZq13y5JiHpjoa4kEszpuBgfieL9gEvHygxSz1cuDJwYPLR4+WXrIQJIGx9SzMK6qTmXy0ZY4/6elkc9Y0kBkslxpMV/RN0RvNMtIiAiUImxpD1HOi4SzLZ1VZ65L2iZpnCAdYF7AkFDqRb0V1lgvdF741E8uzSQhTrYtLYBEMg35SnLK4jwtDeVL8pgVHQwCE5PUmBTHfUR95As5C5KAKkhFXn2KQLuYB/Rp0jhSYZc1BskIezrmOgsihRWPg0HoGDAejGHemMlYWi7B0uajxjUP8vGcFZo7npOa+8nyKAvyrTbnzQ/aT1lJiTvjQDrIrnY4suY+Kjbc03lu7ZjzWvzFnzgvymXnkryQBqfpFHOsxTB1TNspoEwWnvyfN9e4XnRf1/M5W1VI7ansxaJZ692ypJgVcfIGbAZAHFilS58ZiTZtYasTV07SHeQBpO00x9S0MC5jSFZLncrkI0lcHvTCz8sHAhZLC7O8Rih8cPJFTR1EcCF0eWVJbzWWgolo65j6SCqndEnyI9IFsp4lIjWqcxriOkiXM0nmOA+pTEoSk2XFeaa9iGKpH3Mmy4uHpM1ZhC+PCCbBmKHHC9lKenFAooiKDVC743bEfcd4KOCDxkTXtC2r+ZE3ZtKjLprXaf0qxKop2hFIhrnW7zTXssqK80qD8oRY0mfJHRn6g2u8B1DxK5r7RW7t4j7XHIzbrbJ1PxaVp3dtVr35UCYLpDQoFDSfRvHkUFVI7Ykoq1HQrPWeShgXKW4UQiy9PIGtaLaSeFnwQe8Xv8NIfVmZn3baaSGyFwY1AItz9I7//u//3r761a+Gh/M73/nOoHbhnicmHhNhYVwGZQzJaq1TmXwUaQwiKlIg8CKXwZF0OvNcacmvdpZVfNIKXWUkyUSR2y7Ok1/WA1uEBoKBZK2o3rG6QrJOSZuFpMqHzgMtaESI4+10SaJVnrbt4zwUwY3fi2RnlQUBQcKH2lRacAaIjEhPmkQ5Jo1FUFhkdgGki8ox5YiEq/0iYoJ0xlXnuE1xH8V9L5KaJinW3AdF4xovwPLapnmZVOcAyievrDjiXxp0Xj6C0xY7UjPg/6K5X8atnYizFipql9pKn0LyILSMqXaDklH/JFWWF5G0emvMdD8mF6m6H7VYq+dzthGe+5P1jmnVek8lTHjwjokAhixHH3306LH0fE8++WT7yle+EvS7CN7BSwqSe/zxx9snPvGJMRPpe9/7XiDCqFMoeMcXvvCFSWlPq2EiQqtWYfymOvGdDC1btk5l2sac5OWH4VKa1JF0u++++6jf31jvFshqnfwhv3lW8doW1kszJgsxtFWZ1XZUkEQGk5JukTLahHqVAjxk1ZvFKlvElJNFeOX6TeQiDZJ+Kix1THq1fQ4ppC9QlxIxjuvDh+vSB02rj/KmD6SKkKwvixHsFtRHsSGivhV9LnZZmQVsIZAGy/2bxpUFlTww0Idpuw5qA+kwtEKFQH2dXDiQL2OssMIipLG3B9KwGOAc/ZQ1rnjFYN5rIZNMA8gTP8Z4UdBiRmWJCLJIpZ/z5j73ogwRs8rSojFWG4nHTAaTjH9epD4WJnJDmFUf+keR7JLCI9WR+wMvJtyn3LMiN7FqBGOAnYqiUGbVm3kgOwAtYuK5Kh1y5iz6yfV6zjbKc38y3jFVoFnrPZXQGKLfceL5z3/+mJe8Pueee254CeJ+iZcgLwJ0xs4555xgsBGDByGBOnj48DD75je/6dHs6gRudF58SEZ4+UFqZDHP8XgsjLfEeEFSTxkvKDQtIWgpk7TsRDB3+OZYdS6qU9m24clFun7SReWbY86jr48qj/wLx/lwzHlI75133hms38lbfmT5llU8xKlo254XvaKrZbV9r732CkQNxC67Yt1U3C+SF/XKqzfuosgrJusaJ+WLcSyuqfJAnWPD3iTxB/Q3hjsyrJJhEx/p5+L+Lc+WAHCd+vBSgtRIB5dvjjnPMwbVrHiLm3L0P+epS5FxLteJHke+6OlClmSAxjHnIbFFzysRJ+aESHmSpDM25MVOmnRt+RZJ1TFzlnFj/FDJSH44z5yWAVCszhIvbLjOPcecUTlaQGhnBHJdNPfpZzwdZZWl+5DyRB5F9hXIRj6mi+5XdhbL1Cd5f0hiH98f8c4NeVAXLUJjMk+9ytQbaBy1GJFeN/Xhvq3Xc7aRnvv1fsdUhWat91RCU0qKHc2PKkKrVm28ABHJ01Gsum0s7vKCd8S+SKVbyguR8xBPpMnseORZxaM+VAZpRn9x2yW9hWSQr3RRte1N+drGZXGRV2989bJoZWFKXjFpEomjPki4VHZanSS1hpxKYiaQD4RX0ncRsaQUlPPUAyKKFDRNV4/2cV1eAyQdlNcASBzfXKftkGN54lDdKYvzEGvKoKw0LxS89CCoGEhJOhzrDUsKKGOsPHBdgTmkFiHyKJ1iuQTkmPtDY8t8VBrOkxfjxiIs6ReYPGg3voEh7rjtzBpXiCELLvqDcmOiLr1kFqh4VcmbQ5Sneab6xGVRlxNPPDG42xPhTOpm881OC9J05lDe/Vp0L2pOF90fXGPeIFkmPcdqv9RzaIt8UctDheqthQ715nfkq76Ox0PlUZ96PWcb7blfz7KqRLPWe6qgbaTI4seRCaRDvDAgXbwsHdUZvlUJyBXBZkRektDWqLa201QIuM5DCUlf2fqVaVuZNFkRq9DdveCCCwK5SNMxQ7qgbdg0w7WYsBHAg/RZbYdckZe270UgKZd+pQxe3qg1yTgmS8+Zlzh+VBXQgEWB8qKP5f+V34qYJSO2xUZt1BlQvuotAyTKQqIsopGMjkY7qK+s+pF6ypMA+fAbxkTSN9JmeQ2AKJEnZcrnudqFpFkSXvUj/Y80XhHtIMOqM/VSmWor9ZM+N/8zH9LUYTTGpHnZy14W1CdEnJLuzaSSAeSDOVZ/0OKBdjM2qCClqaFQL8ZefnHlu1l+j2kffYMqAmMfE/Wkpws+BFAibZlobXkR7aQ2JX+/sT9s2h37fC1zL5ad01n3B9JbXNepr+NdHB1TL6Tu8mct3WK5xKMMJMC0i3I1ZrFOscYM//zaKWik6Gj1rE+jtX2q17vZ+ZpLih2TilosjKs0XpC+liSuvEBjbIkrnDJtK0qTZhwIuYIc8GIusoqXVFBEMiZPMcniJY7aRlbbpUur/kkGv5HkNpZekg5ik2dhLWldMi9t29OGNHWN2HuBxjXpI13qKNL5hEwldfE0ruQrXT2+Y8LLNXl7kNFWso+oJ31ImZAW0suISqGV437UYkN5Akg0BF+BQoTYQ4O24pOGgWk6tVyjPJF7Fh/JCIP0C+Mhn7ggGdGOsqTKob5PziP5lsabTyzlShq0KqxznqcLeVXJm/uxtIy82HXJk7qVCadeBmXndNb9QR8ramDs71ptl/tEdhUYs2T7JS2kDzVmyXzkdYPr0revx3N2PKhnfRqt7VO93s0OJ8WOKb9CLWO8AGHhhSQDL15Eaj+/3RJXOFlSpaqMA3k5l7GK11a1tsUlmZOjf1nzS/0hKQUlnUhSFVbR8SKFMpN9TV4iitrGT7ociwOCSF83WW/1G/XPWzjEbUvzGiEiBVRn5oLOs8OgelIHXYslrswxCIoMp/CYE0ejAxBWfivPIyKlsXRX4x1L/pMqJiKtfBSgA4mi9KDVXqAoajLwBHG9Ja3mehwdL4bKou6QOHYdsnzM0sYyc5Y6F0WiG4+EV2OncUq71wiIwe6LxpX7C4lsTJyzyhqv1wD1oVQ70jzF5PnqlYcXLc6S+cglW5HfaIfDMRZOih1TPua6jBfyQoeiE4jXEl42koiJPKDfyfY3L6SyrnDyPEJIF3I8xoFpkY3kY7XIQwW/lU51cmtckm/IEv/TH2luopAqSjWgVqtoLVIILCL9y7ivqQ+6t6i9aA7G5ckoi/P051VXXTUqyY0BYcbaHx3WPLKitlGedDjjyGeMHfWhjujUKly16gwxof9Y9EB2dT3pEUDpgPQEk3565b1AbRTZiT1G6DseyzRvINSbMaFPtXBI8yyhxZAk2knEYbj5XZprOdVX/oyzpFz0EfdBnncS6eXmRaJTVC/mdd7zinKYH+jvajwUcplzRx11VDj3i1/8Yow3D+rPc4BFLd6L4mhiaWXxXcZrAHNQ95F2KOI+1FwUmc3qRy30ZDSalO4DLWgdDscU9z7hmBhIMsmDHTLE9p2iR3FeUa2aEdpG5cXES4QXLd8ccx7XRRAZ3DxJcqqwuhxznutlSB+EOM8jBNerMA6kDRguFXl6kOW8fOjSLr455vzhhx8expkFgaTlHEsiynmOMV6qwipaUllJiEXQJGnlPGSIPEW2JC2VhwHOcx2ylyUN4zzlSNqWJuGErDA30DuWZwmRE77lWYLtcMaQukn/WF5MOOa8pNNaVEiKK5LKee02JIms6qP5JgKb1AUXUVYYbZ1LpgFx5DsFaBFp4ptjheFWmjTELr+yTFAkLS7ydEL7iryTIGlmkUO/Mr7xfOSY85BVyH7e8wrSS4Am8qIMxo+FEt8ccx73nhjjSfIq/WftynCe6+SVVxbjX8ZrAHnLeDKrHxVeOw/kQ57yqBLfQ7FnlaSaj8PhyIdLih0tE3M9bzuSFz+SIRETSbAkxeMFx3VJ4LLAdSTEeR4huI7niDxVirKRjfCYceyxx+Za6Qt5lvNIU9XeGPGxFhZIeCEk2mZmQQGRKbuToPC7akcsCZXxFT6cIR6xfrEg4zD694YbbhjVq0wabUEO0HN91atelbtLQB+xUMnzLIEUmT4SGdVWvDwCqM7JkNGCSJAkzJTF72N1Fkn2ZISV53kjVo1I6hdzPSZWUkEA1JuPiL/mWhxWOw1Z/puTYD4Wocg7CfckRJMysyK6IUVmzPKeV3ilQHIvNZs4H465f/DOIj1rqWjEgS/oG9LQV0XPRoxwi7wGyA2bXM8lDR9lOFdkuM11+kLzUYsb7Vxod8oNwB2O8cFJsaOlYq5nbUfyIuPlqBdj7O9U0i+uS2cyC0iweNHz0k2SXqkjcJ106CxWoaMIycBzQZ6VPmkg4lmW87QNaamibMWuq8iP61yDZOByizz0MuflT7vKkmLqye+1BZy0nKfN1IFxoo8ol49e9PQRH4gR6STVE3EWOaZ/KAeJeh5ZkV4z0uAszxIEmyAfyFGSQIqIaLtfKg4xkZR0UJLuIkjqKnWFeNGgRUTsOSKpzqHfS1pOvbO8PcRSdI1BXI+YcOeB3zGXyhLjrDkb68umPYsUvpvxYMyynlcsYpkfae4GtSggD0neyTO5+OJD33Lf4Jav6NmYt/AGtFXeUfS8kTEjx5znuox+s6B8tCDTzoZIPX1XJh+HwzEWToodAa0ec52Xh1xLxf5l421hzpMuD2U8QuRZ149XR5EXblkr/SLLeSRXMjiMjcgA5OGPf/yjXX/99aMGaeoT0uOOCiDpK9vXWVvtInOSyFLnNDdxEArykeQzaYynMaS8POMv6S7neZaQVFdzI9YFFvGMQxyLUMVGbXGwCuohg6g4H4iR+lbBLOK5KCNEGUpqERFHLlRbJB3VokH5xvNIUlGg+ib1XOUbuQixZLvI+C1vzmqxlBfRTXXNMw5Ni/aXVuc4kmFcVny+qmcjaaij/GLHKk30Efeg1B/K5MPCVtJ2Lci4Z9HbhsxP1ee1wzFRcFLsCGj1mOuSJknHNRl5SueLtiORrI7Xuj7NswSSzSLjQK5DKoryKZLgauzzDO1oO4aI5M05ESkRLX4jl1x56iXqa7nxklRe0G8VVhniQBv5DeXSfo75nxc/ahySmoLYIE0kVcQ+a5egzNyPJbTyG6s85Uc5rn9szKhjtQvSzaIoy4sD1+NIZmmkUC7oZCCZJEWap/yvEM5pIcUleVf9s6TC0oHWtwhw/K1PkbFukVcVXKUV1Zn2cZ75qvYn5yz9A8GEHCaNNamzFiCx5400SKoskp4k4PGzsajtmmuQaHYWYqNOvsk/y+942pyVl4y03Q3NE4fDUR5Oih0BrR5zne1cXii8QCUxFLSFzctK+pBZkrDYul5ES9v+5Btb15fVUczysUo+GAEpT67zv17Esswv0gOn7rz00dGUazby0LhTVyRY5C3JXawHrG19iF6RegmAIGDtL08LMYmWtwXIHFJnpGBZag/89sorrxzV0UyD0ped+2kBPmRoh0RZOsBJUqhdBhmDieQIkraSH/ODY6mpCPSDCK182mouxmVpMcFcY64gdcwKNkMaPtI91k4GZWkBB6mizrQzqT4BqAdEU2XHEuXYkFCLhTxXaoypdIGz5iwGcKhg5NUZbyCcR49bRFDEmfPMWe5V+lK63klyTb0oh/oUkWIWU/JgkSTgfFMf2oKedJ4bOQWaQT+da4yV6sTvOM/c1nM2K3hJ8nkd7240w/O6rBs9h6PecFLsCOCBVEYyOVUfXLxIISvaSufFIxIiCRzX5Xg/LxgAL/6LLrooXE8SI6RXkEb0Jsvob5fRA4c44R0jTVoW64EXvYjkoSDpAkySPJFBfp+me8n1IvUS9QMu7qSXLGIlcsn/XCddno4mY8X/SJNjndj4f66nuRBL9jlzmzFBRSTptoygJuhbQ9YgRvRzctGokLqQI6mYyDBMHiNIs99++4X6EpwBF2DxmCnyHQRVuxUigypHx5qPpM2SpGvhwDd9JZdzyoffUg/mLXXReKSNF+n4De2XO7xYXUMBQtC9zXOlhj46hJ97hv5OEl6N5/777x/qG7tS45s6s0CB8DJW6pcY8TE7F5RBPsmAKIwrCwbc7OUZEuq+pW20gf8VDEd+nqkT5LrIjRyeXljoSQVHOy7ydkNf6jrziN2XWH//8ssvDx5leMY06/N6Krr9dEwdOCl2jKKVY67LWwQSF15+Sb+fvDy5hvHOzTffPBqZTGl4CfISjyNrpW19A1mKF+koUpZCvVI3kVPO8+LDUIkXPR+RMEl45SuXFzhty3sRkS8vc+lmJnVKpeMLyDP2zgGk+8q5MtbuvMjpSySctEWBLiRxhhhyXWQ+S+1BUjbSytAsJsRyf1XGVytEREZXcds4lvs+ubiSsZzqrDpwHb/IkDuRGeVHv0Bm8PTx85//fDSYBsQ2Lovx0SKDfBjHmKQzVkiTGSf+L3u/Jo1G1Uecp55ALu6S4y93aIyLIrLFkfGYY9QVqSt9JRIXS2YlPWdhwe8UtS8mozqnsNq6d5Ljykd1QMotlR8Zh0raLvUIFiJIrzW+CmLC+VgPPMvTB+D+IE8ZxMk7CeMn40Dusby281yRkSltlCeQeO7T11y/9NJLg/eMpP4+bY3196t8XtdDelukOlNG3cvhmEg4KXaMQZH19FSFdPR4WfJS4SWoFyj6jbz05bYOqVpyy5a+4jx+T0WYkUQl1SfoV1yaQfKK9I550eapWMivsPzPxkRFLxuRHiTbWdu6u+66ayD0cicWEydt64r8qW5J9RLpQ5YJTCJ9RwgAdYk9InDMea6XNRKS0VmSXJeNHig3epSJRDTNTRYLIaUlTdq4ypUWZAUJZdq2t1xyMVaxyz4gl33MNamwIIFUUA0RfI0B6YruV81ZvqlDmpoFEk4WBfyWOsl/sSIhUlfuB/xU8/ukZxW5xGORwyIuz5Ua81VzKklCFfyD3zO3KZd8Ymkyc4Pz1Fk+qKlzUqeWvCHBWljSDnTURX5Jj4R45513HlPPpKqKdkTokzzvJNyPlKdFQlrb6UN5UeE5Q1uS3kBUb+ZbGf39qp7X9ZDetoLbT0cxBvuHbPWKjbZ62abPTvsttPlLNnnRaQQ4KXY8DVmSuamMWK9W5DiW9PDSYQudl6zUE5IvPl74vIAhDtJ9TBqd8TsFjJALpzT9bYg4+eWpWPCSpSxJyGLDLhEp6oaf1bxtXXQuZfiV1KeWQZxcW8XBI2LioIh95FM0d3gJ0qcQcUnVpIML+cDtG8SzyEhI0kSpfMT6sCJ/CsGch6QbvSSZZswgC7SLPqM8LS6kcpJ0pUUeSE55wat9WS654oUV56kHaSDI2r1Ii1TI7sV4XC3KyC+GVGykFhC7eRMUMpjzsWQySZ4UWS3PlZokurEnDkEEkbnBgk2qJZL4x1JXGTxqYZlslwzy0haWgN9DwjDUjENpxws+3Sdx5L8s7ySS+saEONl2eYyIDXGTHli0U8N32sJOC8Cy+vvjkd7St7RNuyEs8quU3raK20+H2UDfkK1evtFWL9+wifzy/7IN4Xvdk2OFHd0zOp0UOxyNjCwdRcgPL1iIVtpDHUIjNYQil2y8ZHgBZOkDInVDWpSXj4y+5KorCREJXm6yck/b1tU2dpZ0JjakQmc2qV5CXSBokpoVgf6jnUlJmAgp/cN1eY3IgiLuZfnSlRpJVh+Ox42eyoldYMW+nGMXWHlSN7miK8oHn9LolEKM6SONt6T9SKP5TZGEr4yrRem35rlA4zyfPMkkC8YiV2plAoEwFvRN7FYuKXVlzFSPWhaWlAMJ1OIvqarCR23P29lRWkXFS+42qA/LhLnmOSKPFmmQOhNtrFXCK+ktzy3Kpz9iuwTKqUp62+puP6ca+nsHN5Pdp5Pf9avzBREx+G0jwUmxw7FZilGkoyhilAdJsYpUI1CtQG0hL6AEKg95+fCSksQt7YXFOdLJJ24WwZC7OZHf5Itav5HhG3VE4ixShIEV9ZTkswjolerlqBd5bLSl86RL86ssyJ9ymo9ZGe1xnXQQkSydybJu9OT/Ns8FVqyqkuVurIwrLXSP+X1epMIy+pll3c0hmWMuZLlAiyXWWTtJXC9ypQbRkheTpDs+zQP5fY53Y9KkrhBM6fJu6cKSOnB/M3el/y1SrAUMbWWO064sAi4Cyn0RG36qzgr3Tr0YP8K9Zy14WBChhiUinUSs916rfi7pFNAnuWhWOHOuVyG9bXW3n82I/o2biO8qpLzLnpL2rlq+0TauKU9880C+jQQnxQ5HIoBFlo6iXua8QLP8nkoflJdIliRIW98KI5wWZY785HJJrqtiXVBexBA9pMDaYk6LVibiIUKjEMLkwwtZbuhiqVTSY4b806JTSbuos9xv6aWd5gIqi4TKsIt+1uIjqVMsaWCeWypJeEHawkBtLwqUEkvv1G/x2Mt9GCQWaSjEj3FRu/i92g+RL3I3Rv+Rj9xzxfWN+5H+YeGElwWpUmDIJ2NK6WeSX3KRwjHXjzjiiEJXiyzQ+J+ofTLmisMES7Jdxk2Y3L9prsdScH3TFrkjk1cTeTlR6OxY6prUqRZZZD7SVyxCIMdqP+PE4iNeWGb5F0YqS7/I64zCLIsMq+0sQlig4MVCkmWp+7DYxFsG46RQ8UnvLNSBeaNxBVkLnjjSX+yhQvcm9ZZaUK36udRfUmL5YtYCRvrk9AnpakWru/1sVPSuH8hUddi4Nj/8ey2YMXeazV3cYwu2nWWNBCfFDkeKFCNNR5GXBi9cfIlK/05SFXk/QL2AD66TtmTrG2KirU+50iJNkqhCCHhZc43fpnkNkDRY+s5SoYjzoU2SyPFy5DexZE2GYdQH7wlYxae5LZOEWy+6PBIqyazUDWJVDNVdhmQQhyy3VECEXZK9uG1SqaAP0VPOk6gxJj/5yU/CIiPN+wJpIM5IHiFaMSBktO1Zz3pWcDnGeGe5yJO7MchV1riqH9FvF3ESKeQcdUXnmr5lTJEqJo0MdR2iUcZ1F6DO/CYeD9JBmlWnvPGI3YQxH6VbHZNHrrMw4N5IbpOTRkRcYbwhmUlPH9QZ4s/9x9hyP1Jv9ZGMIZG2My5cz/MvDOH96U9/GsqK5zV9i8cN3Y9IcGm/6iQ9egwQ0zx9xMdJ5IVdB/QnXia0EJf3CalqMIeYF7Xq50oqLx3r5H2txbbUs2rxUCHXh83oRq6ZEXTU1w9ukvZGhFcSYK5NFGbN7w7Ed87iHpu31Yzw/9zN313d+UGeJgtOih0thayHelkpBi8zXiKQmlh6It+7sggXgSja+pZPWqlcxMYtKluqAHpZKb0Mf+KQxnGdOS+Jbmztr9/LqEmk79e//vUY4qAyITMEEoHI0J40t2Wc51MmYtkhhxwyGv0LxMZWMlgiH8gHxCDLLRX9mCTSMSGJ3eVJ8p4V4ESqC2n9yHnqjbcC0qeB8yKnksYnt6JFDEVgQdIntM5Dftli5zeKcCY/wZyHgEMI5elEvosVfRGCyzcfCGJaEBiRXfqa8uSyLBlMhfOMLQsLjYd0aOWbOukmLKss+hwCSJ5poI2QJSShkOd4J0DXGT8RYvpC97L6mrHgPGCRQSTGPP/CkGZJiuNx4FgLKsD4S68+6cVCbg351o5N/GzQ9ZikZoVdVz8CLUAkQZdrP4x+f/WrX9Wsn6tIjdqVSbsXGQvSVeGhopXdfk4kwk7l2s0S3xTy27dhgohv2ybiK8Ibk1/+75rWmMQ3D06KHS2Dood6GSkG6Y466ii77bbbgoRQL31eUgreUSQJ0tY3L1y5VBMx4mXPS0hutESkpCqgQAKcp3xJeUSKBBn6SLokiSqI/ycNdcBnK/lCZiV1o130Dy8x2vK9730v120ZiwBt/eZt6yqCWFIiG5/j+7rrrst1S4XnAG3xpgXoUH+KEGVJbyGaEKMsjwh8iBwYB39I6p4C2oUEU5Hr0ryTSE1BHivIU+MqzxYQSupDXRUFMTb+4hiJtYyiYqM2zQPGHOKXZaylPi7rkg5yxrf8Ucf9LSM1uQkrKitrYSFIYpmnK07bKY+xp81xH9FmzjOXIbB5/oVZeOEPmOvSuY9dspGeech3nhcLubXjGnMg2Yf8drxqCHmu/eTnulb9XKmwxDsugua29PIVibBW/8Kt6vazVgQVvjX9YwzbVun/5RttoPcplZ0q0QbxXTDd5m3VY3MXz7C54XuTxHfOounW2dV8xDcPToodLYGyTuPLSjFifdqs4BBZkiDKw00aZSiUr6R8vHwlsSRfeYZIc0sl11bSO0waLYkUy9VUGpnjPGl44eHeKYvIQ8DltixtuxaCGYdkztvWlQ9a0ojQxlv/tJXyi9xSyY8t31lQeGKIr6Sg0smV9FYScxGqpEs6fsdciNshwhYTKG3Rx0ZkMTECnIOIpbkb4xzpkDzKi4nIjcrQNrYWdlm+mDUfFFVRcz8tCAxztMglXbwzojbF80iePihHuyNpZUFG5Q87C+obuUJLziH6S5Jm9XtM6ETyqLNUUrL8C7OwYh5onsULtdj9GemQduepKmjxldaHMg4scg+YBG1Nc7tWlX6uwolrjsUS7nj3ASEA9a/Kv3CWsWarg7Fbv6p/lOgGaS/kd7PEd7Bvgohve5vNXvCUxDeoOGwmv3MW9lhHVzmf71MBToodUx6xUVLRQ71IihGT67SXfiwxyVLVgMSRjwzgki7JIB+89EWYsrxGxNHl5FZLEOEVQZBuoEiG0suwrCg8c2zURpvk9UHEASkn18lHHiS0vRwTEZET0tJ/seW//PcCpOjJNqWRjJg0JSXlQIZ9CnCSlLrKgFHS9jTSI2IQz6fk/IpBPlrwxHVjHBWpT8dp46r+lX53rCKi+aZx1eIn2XYRbcYDgp039yHhRS7ptLiK1RiSqiqcgzzKdVtaWVL3KIJ2YLSoiVWHpAcbj7mO43PUmXHP8y8sI8+i8OXKJ6t/NBZ57ujk3q0KVKWfy3zTIlEqWYL6hfMsdIoWBe5fuBxGhkds3aq+MaQ3NnQbHMh3WbilaG9vCyoNcxah4gDxfUryO3sh75rWIb55cFLsmPIYr9P4LCnGeMi1dFXTVDV4wSgsc5oUVH5IpUOY55ZKZJMykr6DRSglyZMXjZhgxOGZ0wy7kOyxjYvUGCgISExsyAPSQJnyVkBfyrVdUl1B+p8K9qB6i+woXLUChWS5pZJki7pRTpLQUJ50KuXCTgRJhD2pVpKGtPNZhlTajqZ+aR4IIP1cywvyEBtEpqXRd1ICHRujCeTHAiNv7jNvNReof9KLieZi3F7Nn7j9YXs3RcUgWVYZxGWk5SUJcVISD3RefZGnYiCd+7zw5SLUVbq1qwJV6OfG0RPTwk7rWaR7Jw3uX/jpGIb4PtE7xqBN5HfN8o02NDhBxLezzeYu2qzeIFWHzeQXSXC7E99COCl2NDzKWjxnpRuv0/isfGJyDeJwuKQTuUZahl5oVoQodA5jHdokwYrJTZ7kSQZYiqKWpguKjigkF2lpUhIqfUHqTfo8w65jjjlmVEon8iaIAPIbdKnR3YVgy62TyAF9TJ3wCkA55C1pnOpFn4tY8c1v9PskUSNvSSi1wIiN1vQbkUTVP2nYJumrJOlJ1YDY8DAegzQoEp3UNOL0MhATcdYYxS7AZKAn1RdJ3GOSGBvxSRUhrW6MK+ov6DDnSe41d1nwSF0kjubG/7gTk7sx9WWyPBGnorLKLC5iH9a6prQ6HxO4pC5s0o1glltDQj+zaBBxjcvS+FNnpcvKZ7xu7aoC9wljk+a2L0aWG73YHWHWM4T82dFx/8JjMTw0bGshvqNuzDZJe9HzXbNiow0PFe+IbAk6Ots3G7Q9pdu76bvHZs1n59F1s2uBk2JHQ6OsxXNeuvE4jS+KRibDtVgvVFJQXi5cJ2xyXoQoSW0lmUkGrwAi47ErtzTJEyQUQ6As929IkiAFuM2S3mxMyCEfpMFzQp5hF2UkCYzyEWhjrIaRJeUE9BWkWCQ7rhNto88Jk4wLOFnGx6DP9tlnn2AkJRKXNNzTi13EGwlsMg2SW3kOUVARXYvJHoEg6GP6Mk/qJp1RSRjj/pIeOONGWfGWv8aM/oaoKLQ0bU/mQxrmPoTvD3/4w9MixInY0z/yRc24UPc0yT354Q9Zho3xeEhtAL1j2i/97bSodJIsUhZppXIj93rM53geqz1JcD0OApIcMy0gKEc+jNPaT30YN+5H+StWfWgTC0Zd536Ox0MgHxaxpNOzIcuNXlm3dlUizUUex3KRl5UmdqNXFEzk8MMPD/dqK/oXHoL4ruwdQ3j1/9oV2CNMDPHt7Gp/Sr1hM+EVAZ41rzvoADsmBk6KHU1vHFeUjod+GaMUXgS8QPKikUGaMERLi/7E72T4JQO5tAhRfHipQ5hl6BaTUNLyosWjBUZpspxXIATKkJTnwAMPDMdF7t8gALEfVgC54DzEULqeeYZdnIOsyOVYXGdeprRXRnTUXSQsDuAACaO/OafoZknfqJxXSO08SKKaJEUaV5Ej+ZFOSyPjNgikdG9FzgF1oj5458C1V6xbHUPqGpLIK49Y6ijJP32dRsAA56kH4yYpchophMRT1yySpZ0N+pvxRXKvhV88HymDshSQRv0WzxE+2vVII6FqP/OQPmCnJKliQ/7MT+aF5kBWPlxn/rJLkMxH/ch16frGeuGaj9Jz53+R9NhjhvTXFcBDAWOSZXFe6dKk5JpHgPYVubUbr8uxvF0y7vk8l4VCUZqYPGc9Q1jE1tu/cNkdwlqBKgOS3VFpbxS1DUKMDvBEoLO7IxDdeQnDNojwzHljdwcd9YOTYkdDoqz+LlKcMi7AeLDnPdS5LpdDWflIuscLXoEJgLbuefHyEpauapYhFWWyxYl0VuQwlgLzElawCIgBdYKMxrqw8YuojPs3zpFnWuQzJGWSPmcZdiloAMSIvGSUpPDHCnEsfVry5iWWFhmQetJXktQmjfY4DzGU5wQtFGK1Eo6RkqYRqxjSC80zxlPULhYIimqmtimqGW2TlFjGV7EaBtBiQedivWAdS2UlDyJw6m+Nu75F8vFgIpKcVh+u04/xPZW8xwB5sWCiLOZcUqJKuxgLSfGlhhSrNEiHVxHRuCaPDmozbeLDAlVuypKqGswH5o6i18kQNanOorFhTDRn4j5ShD2CqSjYhjxUqA/ljo17TIs2eSPhWH3P2KMWwe+455LqEywY8c4g1Yk8l4Xcp1keQ5LI27VinJD+5rksvOaaa0bd5WWlkRu9omAi9fYvXIVP5BiDA0O2Znlv5NVhs57v8o1B97eE7ecWoWt6x1MeHRLkd8YcJ76NCCfFjqY2juMhXibdvvvum/tQ50VflA/EAPDiln/U+CXLeW2XKkpcloEc5R577LFBwi3pkh781JOXFIjrrDRpLyLqQJsg3LGkM+5HGdQpHx0rVHSW9wV98zKFNNA2GcEBuZKj/Ui4GZO8yIDqo9gvsCRr2naWBFBEKIYImPwGJw2+4nMxMU3mJbIlfW+2uKUjKuMp+oc6IwGXP2kQS9NFzEXwRCiTBLyIwMeA0MbS9qTKA3NGHjXUh/FcUB8ydxgb1B8gyEnCz2JLEeikW665oQ/jqrDlGs+kdF99zFyTrne82BHBpA4777zzqC9neSshD8qXhJK5TPuTahjotjKXyZd7RXrjadHqINeQ4rzxgMgyT6Vao/HVnNYig3prYZrlZpAxyXNrhxSWeyMrYEeMOLhPml0CpFiu5DSv410JuZLT/3lpmCO4fctyIVlv/8JldwiTGOwfGjVsG/Xlu9nIbd2TfWYTRHyn9XRu9ubwlKqDiPD0WU8JGRzNASfFjoZEWeM4HupljeiQ4GQ91BXRKi8fkTTy4OWkYAySeEKqRJbLuGbiN7z4RRI5x0uJF57Aw5+Xe5YEp0hnkPzlmSJND5q8tUWuuqXVWfqZbI3Lh65Ael7aGNCRBoKQp6pC+6TKIUmWIMliGhlWHkmImKUZpMVb3mmSUtVPJDzZR5Ay6hsbVfKbeFtfhmUi+Wn1jD00lAHzU+GO06Tt6HjGLtLSFgRqF78lD+lwx7qu8jahcYaExfqwmqua40qnBWBs6Ai4J0hPHybDdzPfuD+4f0gXq/NopwG1EOYQwUnkdSR2Jaj+FtFXBMG0kNIKhiKSGveLFkSSaqsPkoFbJP2WZ5Sse0jS47znB79L049PQrs7eXYJPMPKuJLTvZCXRnYAZdQVOJ5It2tFO4TLHltuN1z3Z9tlW1QeesdEbQvEd4LQPbPz6RLfzQR4+kwnvlMJToodDYmyxnGQxPFEdsp6qJcpT35NkYZJ0iZiwAuR87yMy7hm4jrGP6RBuippiKLcxfrSSUkx0i1Jiov0CiHGHGMAlKcHLVUKGVYpjbwkUJZ0fdMIn1yykbbIfyqknu3meItZkPQ5Jid50G+TARxEqOOADFmkWJLWPF1x1UfqA0kCHqsciGjGJESSuVjCngcZYmZJ20UY00h2vBDg/mAhw7yKdwMkySVyHmNPm2KvGXEaCCjjJkM5Sa+lkiBdcXkX0W5FMmQw50XqmS/JSIRSC5HnCcIrx/ONc8wpyoFYQ6ClPsX9oXtdRm3MsXiMYuic7l3Nk1hNR/NJBrF595DuvbznhwwOi0D/ahGcZ5eguRCrzKgfpcZT5G5Ou0VVqytsKWj7ssdW2PSRubbmIbP+tSObPuv432ywd5GtsEG7026tvOyObrP26YPW0TNkXbNGbN7WM23PfXayHfdYGoivozXgpNgxYSgjechKE0dsynKDhBqBXE6VtYwuU15ePpCIpE4x4OUFIYBgElpW+oXxdjXbrzKQQz+ZlylSSCRBqo+OFeY5z/DvWc961qheYSy5k/4y9URnUpHjsvSgqTNSXoI4QDhioz7yZcsal1ToQGdJOjnPVjUhsKV/CCmBkKlteDiQsSKIJY0xeUkjMVmQ9DFte5gPddduQhqks0z9WBTILZUIA+2nj+SlQUZUSRdxmptSE5Aebawrrb4nvyIQLjvPlRi7HpC0PMkz5TFnr7zyyjHS3NiHr+Yo5cg4NJbQq0+ZI+SFpBSvIHxrXCGnzFnqpIVFTKzjsvhI1SBNX5jz6PZTF6k4aX4pP85LjYTfZ7kk070deyaJJddxubGaUHw91pVWFEbOa34oGIskxpBKRaiM+1CST+79IkBiJSWWAWs8H7UgkWvC5O6OdklUDxHneCGntjGfeOZsibpCLejbMGAP3bPcnnx8nfWuHrK+tSNB5eGJx9db3zp5sahe36FnzrRNqg6LntLvHenqs9vvucl6B9T+TQvS1asftzsfXmsLtpth02fWb2HgmFw4KXZMCMpIHorS8M3LN80Nkq6XkUzKIK1MecpHTv1jwzaRWekUJ6XAsnYn3V133fU0N0/8BgIBqYBA8hKCNCa3q0lDHUifZ/h39dVXB8IX+9kVJPHjOi/uMnVWKF+9MOlbrvGikAeCPFAW0m88OSDlg5DHKh2MB3WhfTFiiaLGOU11Ig3kBcmSK7H4pU/d999//0CY4jDNSUjqSnpIWXKuybcw5AGJa5aLOOYS6bToiomYFi6QdPo4zRuGwFxjAYEKAfM1aYzG+DOH6N8sLxaqO7rJWhBkLQxE8OUNRPq1IrOchxRrXvOJdYrpf8YBPXjN06yy+I3IvCTD8dzn/9h7RVoIbwU4oZ9ZyGW5JGNhwdjlhQFnvORqMQ+KLgj5T1MdIp8i94h4cihjZBcHseGeST4fJJGWb2kt1OJFphYLQNEz00JmM6eZZ2UCEo1XRQDiG1yYbY7UJl1fiHDf+okJVwxmzp02Rr1hNIjF4h6bNn0s5aG/eI5CiKtuv6M54aTYUTnKGEqAMmmyHkTx+TKW0WWNN7DARjrL+TiqG+QKYiAn/HJdFLsbk+4p0mTpKMcW7xxznusQUOqUtqWNtI0XGh8F+ki2nRci0uhYtzW5PQokJSuqc6xrGkeQU72k15gHrtM+ykhT6WBcOI+1u/KO3ZdJN7gsIQaMCYSM8RKxkbSc8yKheaA/tMWcnFvqV9UtizxxXr6qmYP6XQz6gzHlG530LCDtlKpOUjdX3k04V2Y8slyfxeC6JI5SnYlVCOT9g4VOcqEnws95yLEkwml1Uz9K717jFM996b9qYQaSuzYygrv22mvtlltuyVQdIi1zPI8Us1Ap0vPVPabxTJJiuf1joYLxbJ5rszKQDre8YiT7iLkKaWNu8xyQV5z4/qUucRjntMWT9PCZr3nPmbwQzr3rB8Z4chjV8V22MVybKMyc3/2UK7PYj+/iGdbVXU71akuinTqmPpwUO+pqKAERRJoIiqQTegFmuUGKV/B5ltFl6qTykDyRPyoQseEO5/faa6/R8jEG4qUlXWNeUPxP2fiz5SUJSQKx3ix5ISXWVnUy1LO2PpE2ibxnRQcTsYijtsXqAyJDMpbCMCmZjwgWhIbf8uKGJKhdkAr5WC0D8i1yFUX/qF5pxk1xcAf1Sdw2/Q8gPBBjSEeaGgoLhzxpKqA/yEceC+QZQ/rB9Ic+eYDoMmb6XTIoi/KkP7Ncu5GG69wjfNMWxkD50BbOMx/LkF3p8BaBfBkP2p90J0a7Y8M4qYzE5XCee0htjOef2hqrKoi8xWoLlK++129j3Wmllds31HOKXJIVLYi4D4v6MfZEkTc/JC3Oc21WBoxZGgGPj6WSwyILAszzUHNfKjdIkbVDQj2SKj+k5zmj3aK050xX1zRbvXKdPXLXk/b44CbfvbEv374NxfrxW4YRa+8etmmz22z6nHbr6Bm0gfYNNnNBlx121LNsm6VbV1LKeKOdOqY+nBQ7KkWZlbdcm8lQLS2NJGnSg00aG6Wt4LOM6MrUKVZXwPAtTqfrSGR4KUK0stxASedPUqsk6ZFKQ9IwLK6PCIWszJF8pbnlkuQ6KbmLjc142GN4RD4sAOJ+lK40L1Z5DWBrOy0SmYhcEUgngpAkAnqRy32brsdSLhkpqW/0MorJk4gBeYm40w+MW3KOsNVfBjJeo5/S3I0pgEqWtJBjxgjimOZKTP3IDkG8g5AMBc15RUXU7+I5Qv8wVnmS5hhFKi8xkm2O26lgKrqepsOqACCxq73kfIx1hLPqEOer7zQ9YMqLdbjjBaHIatGCoIzRo9KRb5o6Q7KMItdmZSBSKn3m+P7gf77lTk/RDZP3Nc8HqVIkFzKaa5uk4GZPLl8b1Bx6Vw/bSG+X2cA0ax8wG+nrspHBBfbLX99rlaPNrGuG2bRZZtNmbzJyQ7/38KOfZd2z2u3Ou/8yRtVt2wkw/BtPtFNHa8BJsaNSlFl5K3hBrWnKruDL1EmeF4q2ESE8isjGS0deAiTVI10cwjYmviILsbQs6X0hafSjLWle/pKgysofaTXlxaoNylP/Q8TwCws5zNK5Jh+M6ORVIH7Jx9H5ykDBD2JiFYPzlC1pGO2JA1RIP5LFDR8F6NCiQqoWpEF3Wd4Kssa1SEocj7H0sqVCoF0C6WfH2/0a3/h/fk/6JGlMSkJJq7olvR1obJhfWXOxzJa/IF+1ReRQuwlpHlM4lu5vli9rkfnYiCutj2Pjr7QQ5+ob3ddaVAhJt3d57sbiNqdJasejpqM6a6Ep8q+FkkKyVwHy0WKIPonvDxlM8uzhWZR3XyMxZnEV5mb/gHW2Tbf2gelm/dPM+rqsZ6Db2ge6be1DPbZ2iP6ZPbZ/KmnNiLVPR+JrtmjpbFu1YXnw6jB9Tpt1zTRr73hqLg0Pd9rKlatt+tz2YLS59TZbTbhP5LIG1lMxhLUjHU6KHZWirGszUGuasiv4MnXSSyePYPGARGdWxJcXol6KbNfLP3H88oofsiIOIJYyJV/oQC9fpY0Rk2Z5PNALO95eFbnhBRO7rkrqXPO/JJtJHUbpaMrCPY9ccR3pldoZ6yYLkpIi2YJoqR9j917y0HHkkUeGNlx//fWj7aMM2nvIIYcELwPSXc4aV9LKD2se1G4QG3/JN2+so53mLk4ES4ZkGj+NoVx3aZFE3eIy9VsRq6KXf9nt+Fj1Igtch2RJ8pgl4VYd0xAbFeaBfqEc+kI+lON6UB51kcGj1E7iNCLm0q/NcjeWbGOtkLqHXLTF8yN+ZtUK5jJ9wEe7REn3dyDpkm7NmrXWPtxlc7q2tvmdS2zVXzZY9+O7WNtAdyDCbcPldW3Hg7b2NpuzcLoNd/XaYPsGm7O4x7pntwUJcNfMNmtrbw/EfcZ8s6G1AzZ9Zm3uMyute1tbaUNtR2vASbFjUlbegK3mrDRs+fN/XprkCr4Wd2tcV5jirAe20kLoJM1K6ufK12ts9JW2hczDFgksL1UZl8WGSLz8FNyDNmlbX66mFGlNklbVX2n4fSxNzAsCgqRZEl5Z48feDmJ92FgaKMQLCozo0Heln2Kd55gMUvfnPve5QTpdFML6mGOOsSOOOMIuvvjiUE8WHy984QtHt5Fjt31JHW/qgJRchC4PMrwUURckqdOLMsvATe0nreaAJOyxsRr1VFTD2AgzlrDKiCpN6iyPDNIRjX3sxuMhUo6BKC7O8qSi8u0LKQAK1S3CznxBx16RFyWtTdY71pHO8uXMRy7c5Hc3JuB8M+cpmwUoc0GGd1IfoA7MZ7mFi1VakveZ5rJIpRD7sS6SGMflQlTTdIpjkF8tOsXx84rfxqHZFyxYaMsffdLm9Sy2h29Zb2sIWrFsnm18rNPWrOyzof5he9yYvw9vap9VQyqR6M5ZNNagbVMUtx6btWC6rV27ybiR8erufnpb5cea/uD+qMV9ZtWodwhrR2PDSbGj7itvfNUCjvNW52XS6CE5HndraXkdcMABgTzkEWc92CU1Teo5a0tcrpLStlP5HfXCXRRW6hAnkQaRCPInLLWIA3lBhESw+IYwUxZ1gDzx4hQxUgQz2sVLiHrkBQHhJaU6JPUwJdGWFI9+SJIIqYlAWPkfd1iXXXZZ6hY/eXEdYlQmhHUyWh/jQ73JA+M60nKM27U013b0tQhvFiT5TdO/pf85T9upE7q8WdJQFkvaLZBhlrbAGQ8tMHi5q81JyNiPsmhXmjoD5eOyjXFj+zzpwUQEkAWBJK95vpG5zthh3JdUleE4NmRkwZNWb8qmzrQr3hFJtg1onmQtiHg+sLtxySWXjKp/JBcE3B/yepH0e62yZBuALQBII7/UmTktDxXJ+z423pMf5aTLvvg+4xki7xOx95rxeJ/APe8O2+xqd//pUXv45geCXi9qD+2DPbZscNjaRhbYEzZk99gdVinaRmxkWn/Q623rHggGbpDeoY6NdtSxR9g2S8e6UtwSgzVFIazVfWbVqFcIa0fjw0mxY9JW3lWlKetujW9e/JyXRJG8eAmLCOcRZwJc4DmhSO1D+oUyjhNiIzkF+IhD3QLaByGA0LANr0hkcZQ5XuD4cpVfXIiLCHUsLdNLWf6Ds/oH8iipY/x7IBUKbbHzf1679DKRZC1JsHW9zIuoKFofkBGgghuIGEnPm7aTryzvk5Aeb5GOLgsTxizpGUPgPAsVLU5i/WeVw//MYRlJJgkoaRSemPlNG5iLLHrULtIg1SVwC+Ba7I4OML7Ma9yDSWKdB9qOP+N44SXIoA1PDwRwYd6lEV7KYR7JIDNtHmmRQPtZFCbvRUWi033N4kBkK17scF79xLcWcuprzVWuc88ydszZJJiz1IN7mnkkqXXcJsiuyk9zEac+Ii3kGz/FsjfQfcYYskgEIsYjwyO2blXfqBeH4M938/9IfwcHKGuRTbNFViVG2obNuvpDxLalOy22rXeYHwJXrBt4wkY6UDcauwBb0dseVBh6ZowVAGypwVpS5aMW95lVox7qGo7Gh5Nix4SgzMq7ijRl3a2RR5k655Fw8kBSWCbqHdf4n3pJ0sFLWNbrbK/ywqW8eHtU4Za5TpuTLqfkwo18ITsy3tKLIyYP2u4nr7z+kTRG6hNqj9Jqu1ikBvIifVDpNEu9gzb95Cc/CelwFUUdlQ6iQDrILm6rRGLTXkSUV+TaDQLCb0mLVDCpqkFZkDiRFkiSdJOlc62oc0VeCEjHAoX2UY7UTWLPKIwb7VXeSfUB+UNWqGjmR1IFh3ZBzqQHnreIAy95yUuCr14kuJpDEMG99947pIHslnFJxwINoLaRrDekmHyQfirsdDJaH+npHyEZ0S4mlVmhjmO9ZO5B+gi98TQXgdRXgWuYZ8n5qIh4SMghYvQB5DiOLslvZRPA3ICsK/Q0+XBOesuAPCTlTs4zrjEOjIE85tCcLpths9tm2/rHB+xX/3O73bWo11Yv7w3Ed2iwGlO2JNo6zGYv7LaF28we9eM7Z9F02zC0yobae8MYS6WDdtzxvd9Y7/K1Nm92esTLWI+5CoM17ZbV4j7Tg2k4JgpOih0ThjIr71rTlHW+TuABpF08bMlLJBJJLQ9sSR+KSHiRGoai3pE/L+GkSoMIFOVSD17Qaa7EFKWN/PWSjo3fpKohIzX5mdU2rwyQqJvKyuof6ZLKG0TsYUBb8SoboqDgAbRXUcmkG832MeRJ26Wx4aFIN9ch6mylZukN8uIrcu0mHWKF+42lfJJeS79XknuNh/RM04zmsqAXtTwPJMM8a7xV37jeSXKo37FQiNNsyZYu+Wh3Itlft956a6m2SXVBRDgG80oqOpQjfXr1n/TpGS/pkSf1nONFDXNCksC0exF/4Lqn1bYYUk+Kg2qk9SP9TDrZALCYS9oAUAfyU4joTb55nwrTrDpqt0FzXzrivRt7bXrHbOtd0WEbVw/Z9OEdrX95tw33dm5ybTayqS9RKkCZ6r5Hyrk2LCPxnTm/y4a6+qyvY8BGZndZb3ebrW4btEHbYNvM7baDD9vJnrHjUmtv13gufFo+9LcWAFkRLxVZL+85PV6DtaxnugfTcEw2nBQ7mhpldNl4KEPWeMHJsb18CXOcDASSR8KLpMlJo56k+gDnZbmeV2fpjvJy4GWVjFjFy0q6kNquVIATSb6Arhe5v5PbsWS4aJWnsnHfBjlKRsZDqklZkoTLMDEZjUtth0Dl6Q1KdznPtVu8tc5LMim9ZAEiF2vx9rqgc+MxgsqDxldjnFRD0DWNjSKexbrA8jxQxr2XtpmRitJWiIf0rrXQG0/Qgax+EMEVCYVAJiMg0iYFrImlrfFCjnFAQotUO08SyL2qRV5W4Brp00udJ82NnNz/5dkAcP8iVacfkXQn9fsZi7332tvu/ctDtmj6/KDi0PcYeitdm1yb9XNvtBmyeGYqM6yyAMbtw0G/d6iz14Y7e21D55At7+iwRzq67c6+dltn02ygbzNx3BDvBmxq53/cdKNN67jJtpk33XZYMMOeu/siO+nAbW2r2U/1g3TdiyJelplHVRiseTANx2TDSbGjqVFGl03bd1xHTzctEMZ4pA95EjwkUzIKy4oOxkuGl21enfWSB+SdFo1K25/ylRxvP8tgT+7misoirfJJkkv1L/+TLisyHv+zNStVBAUfAFoE6GUWSwvT9AbpuyLXbiIwkLSYyEtyLlUFyhR5SwZT0fiUgfwDqz9jVRX+VwRDSe9j4kjfSCLHHKNMvGLwglcarkFCIG/0Xd6igW+uMd/IS76qNacpm+uQHXZIihAT+eTWt/wJc03lxQsM6iZ/0bSN+wOCST+obcx3JLbkIaljliSQ8Qekk+/s5P2qwC1a9CbJHGm0sCjSc6Uv8erQv27Ehjd2bjZqm24dQz3WtWyW/eGO9TYyNH/zr/J1a8eLzu6OTV4cNnt0wLCtb2St/fLaS+xJ1FoGZtmq4S7rHRi0nuENNn9krW03uNb2bVtr3TZoA+xS2NjP4yPz7Z6RpfbIyELrHzK7f+WG8Pn1nSvsMxffYUfvudhecfD2dswzthp9fuZFvOS4bPCKWg3WPJiGY7LhpNjR1CijyybSC3nSA15SpZikjkf6UKT2EZOh+ByQTiMvDkmu4xDW1JkXFESAekstQlA0PcpXCF7pqcaGZmoXREuRr9L6Jy6L/kzTmaSepIPsZEXGQyLERxK6NGMrqVCwVZ4nLYRcU3dJm5JSV/InjcYw2ddyHQbRlE5xlp4vKPJQIdJAMATlJ5UN+RzGEG+TTuQd1j9sNtQx0/rbOm3DULut7zUbauuwrWctsYfXT7NHH1tm1oYO50Lr6jDrahu2jr4Be/DBtbbHzvPsnmVr7P7bb7KB3vRFA3rFcvulhVE8p6kP14877rhRY8Us0G/yHJEWUIP8uc5YyCAxuQMiKTc7CZB9Aquk6QIzZvLgkiUF1sIPvWGdj9vGfYzeNPODeUSZaWHgk64fEeMObDDrX2fWt2bEVi8fss7hefbL6+63vrVLrGdkU/oYVWj9jrQNBUkvxm37P3svm7/1jFECPGPONAK7ma1fbiPL77BH7rnNHr7nVttv3W22xJbbjm2P2+y2jWZb4AK5b6TL7h1ZYveMbBM+1w3vZb8bfqZd9udl4bNg5jQ76YCltv/MBbZ6Vf59PZ7gFbUYrHkwDcdkw0mxY8JQxs9kGZ+eeWliXTZexknjHl6cGHXxguWFH4fN5QXKy1bSOkkkefkiZROhJv+k7mlW2xSNii1tkYJY6qrtcXyxYphDOUkPBEjU5JYKq3VIYlJSTBm4kcPYLA+kx+Idgy0IbdIgiXzispAEaruc9sj9G9uikGv6ihdWUqdU/UTepIOUSK85zotySQtByoseiOST9mFsFwe9ELGWD94bb7xxNO9kuGvNEakoxAFOJCmWDmwRKYbM0Q9yuTYw0GdtI4PWNjJgnSMD1t0+YCtufdCmDa6xnQbXW3dbv023fuuxzd9t/dZmwzbySLsNh//4tNvwSLutt25bZz22bqTH1tkMW31jj11w48W2bGSeretcYBs6NthAxyybN6PTdpg/12avXWu9w09Jiel/Lfjkx1o+szmH+7qrr7461ZUc/XDYYYcFYzV8QTNvYym4pLwYvF111VWpecTnIKjMe+aa8qJv+Z9xlQcX+d7mo3lEvaVHrPuN+sf3UPK+5x7lvn9KXclscAMccpZ1rdnK1j3RZ+vu6rMn1g3Y0MZ2s5H42YIai+pem9FWZ3d7cGO2YXBVUHdA7WFkWp8Nd/TaUFtfCHDxjD33sMOe32VtK/9stuIvNowLvPtvsM5V99r0obWhBttu/lgFGj3dbQP2jLYH7Rn2YDh+p/3Y1oz02FXD+9ulQwfblev3t29e02/TO9vt2G1n2HOGltm0jqc/P2Nd4DLP9FrSjFc3ud4o07Z65uOoHk6KHROCMn4my/j0LJOG/CCSsT9bbaNCCCBfIO2FnkTSLy75XH755aN+cYvaJtJLfSEtSSAhpq6SlIG0FwagfRAVWb/HqgEQWlxkEe1N0p2k6ypJmOkvgE/bpMspiKcMDCErkOekFBACH/tYxaNDVj4QMdpInemfZF5IkOTBIU9aCPmmPyE81Cmp50zbWRzdcMMNo2oZyfGV5FzqDmP0dEk7Mmxd7ZDjYesf3mjD/RtsoI8t/0HrGu4Ln47hAWsfGbYNjw/bbbcMW8fwsM2hj/n9MOPWtolXjbQZTr82jLQFctNmI+G73TqszXqs3aYb9k6dNmydbSPhu6ttxNptxDrbe62rbYMtahu2JW3D1tk+bB1t1I20Tx1vbOu21Q/NtmVtC+weW2TL2hfZk+3zbV37LJvd2W5bdwzatLa+Ua8k0k0mAAoeUVAdSoI+5Dpg/NNULTBQkzpCVrALGXXKhRtzLTn2LL4YN3SKb775ZutHSo8OPPcZi5rly21tR4ftuuOONow/Z7xHPPGEtQ8NW8cI/nnpL7O5s+fY4IY2e2x5h7U9tMHWPoyHk24bNrxazBpllMvsEc1QqwIdnSPW1r7BhtvW20jHBmvv6rNZC7psv4P3tKU7bG3X/va39uQTy23jE/fZzOE1Nmd4nc0fWWOL2p60rTvW2rybV1jbTU8tvKhluh+OicOcto320o7fhM/gSLv9bvgZdv7QMXbR/c+2Xz/UbYdOe8i2b8c49annZ+z6suiZXkWaRg2mUZXv5Hr7YHaMD20jZZiCIxWsXFnhseIrclnTSsjyM0k/sdLngQdpRDKZ9OnJCx1ihI9VoDRphjSkgaxBnPPSERKYbW+5B0umkbswHrpEWUv6xZWxztFHHx38B+e1DeL84x//OLhuywIkg3qzrZumPqGtXzwHxG6ukoDsQzLVrqTRltpFPf/0pz89zZOE1AAOP/zwkN+11147xvWaVA3S0mTlQx+df/75o0Elktuf/A41BCSv/E+/8dLTgofzupcg8yxQpEcowi89Y7boadcYCW+oT7/1Da+xoaH1ZsO91jY0aNbfZx0Dw9Y5MGzT+s26B9qsI5DaqYURVDraO8Ons6Pdlm63nfX2D9jKVatspK09XLf2jvA90t5h7Z2dtudee1l7Z5fddvsdNsR440oMosocGhiybhux7RctsmUPPmgd/QPWMTRonYzX4JB1Dg1aB3Mv/D9kszASxQsFc4Q0fDbnxXcX8wF9cMhwzquHJUXv9Pm2sWcr29Cz2Db2LLYNPVuF7409C22kPd0Qq1Z0DG60GRuXWc/G5eEzY8Pm743LrGtg3fjlyW0QaT6W+B4JrtNG/+d8x6b/23We4w7umU1pidI81NFlAx3TbGR6j82YO8/60QdpH7LOrjbr6IJo99v0oRXW3bbR2jf/nryL8OjIAvt/g8fZeUPH2NzuDnv+nBU2xzaEBTfPWe7Fomc6qCKNiGFVUul6vdPKENqq8nFMHF9zUlwDnBQ/HUwntmqzdMJ4KOA3FMkV5E8+PQWIFNJGyKLSy+VVrKcLiWLL/7Wvfa3993//d0gnA6Z4q1WulhgnGfmkGe5wjCQNCbEMpmKdWLlu2m+//YL0MovM0mban2e8JZUGBblIAikr9YbsF4HtaLb1SZ8MmSsDPQgqL43Y1ZT6WgsAqRek+ZeNjd20QEjLhxfoe9/7Xvvyl79sy1evtzUj023dSLet5TPcbWtGplnvCEFI2m06PoL7egOJ7bKBoDc5u63fZnX025z2Adtpq7nW++Sj1t63zub2dNoMhH1hKNpsYLDfnli/wvrbcKm11oYHBqyjd8Sm9ZlN72u3zqFqvEk4NqF9eCRIyzuGR8IHqW4HC5xwfvO5kac+bRxv/h0S83AuSNM3S9A3fyNdH+zqsYGuWdFntvV3zbaBrpmBxI9F/KranGH8/0h8fiT8PT0N9R60zsH11jm40ToHN4RP1+b/24cGxhDfTfXcNPVG/x+BfG7aLWgPx5vbnuiPsBjAA8vQpv876bfQJ3VGR5u1dY5YR/uQtSPt7hwOhJn/wzH/d0HGR2yos91ua9/Rrm3f26bNnGlLp2+0uVttZYu3385WrF9v85YssZHp08PH2OnZTPR4psf621nP/aI0CAOOPPLIUsS2XhLXMu+0MvWuKh/HxPI1V59wVIoyfibR70WnFClmmg9aSKn0aCWJjMmadB5JQ3hf+cWVO7BYhUBb8TwsIZtZRjkKiCGPDUn1ANKSD6oDSE2yvFiw1V/kzYDr1D0roAh1StvqTgP5oK6QBvmNlcu1tL7WdY1PVghfpcnLZ8XGEfvANy6x65ZvZyuHNy0swBxbZ3u332/PbbvPdm5/zBa2rbGFA2tsYTtby6ttblsi2hjd95jZxuE2u314tt355Gx7tH+mre3tMdsw3bo3dm1WStikFVrV9rgjHcPt6D932OCEdTMDvmbzZ7NPs8r8mj0dZB32Fjo2f8K6lGfVU76OJxIdQ5sIctcQnyHrGhy2zs3/TxsatmmDQ+HTvfmbD2m3mCYNjdjIkAXPFGWwrS23V9qVTzuffMqM8Dzu7rZFRK/s6rThad22O/6ie3rC+ZGe6TbSvYlAT+vssPUILLq7besFC6wT9bTN5HoEF4Wb7RLKegGqZ9S7qnwnuw/m5oCTYkelKONnUqQ0L40ipEFGpRsKRHbJg7LQ24394sYupWK/uOjx8sDhI1dh/F7GG5yTlFlGWILIOR8ZD2V5sUgLJ5uGTcZam6S0SZ1a9U8ZKOyxXGJJ55NFB5JbVCzyfPHGQS+yNo3SgjAI64Y77e7+eXZX/xxbPjzT7IER27ftMXtNx022T/u9tnfbfbZD+6bgIHlYO9xhNwzOszsH5tjyjbNsYEOPTV8/zdojKrCJADsczY2hjvbw6RuHBghS6O6BQZvOc2dgyKbz/8CQ9fQPWE//oPUMDAbyXE/5IhLyNoxpe3tLEYlNlg3ZgDAPTZtmj3/lq/bk/HnWMXOWtc+aZe2zZ1kH37NmbzqeNcvuefgha9+wwZYiucZ4tqfH2qdPt8WLFtnyhN/5WlGV72T3wdwccFLsqBRl/EzGvm+z0sReC9JW1ZIaK+KbJLaxfimEVefZlkKiiiEd0oTYaA8vBrGf3ziiG5C/XKkSgCwvFkkpcXKLTEBagBqG3KvFEmdF6CoTwIEHrDwiKGKbFg7kEUf3SiPGZX30xumDv+XhTvvdxiX2l4FNEo1ntj1gb+z8iZ3Y/lvbqT1bDxoMjZj9eXC23dg/3x7ZOMf618+0ng1P+ZzilTExGqNbBunhGnOO8dz8/+iHjXX+xaME/c+ckT5tpFvbxoKLz8imPicv1JqHw3e7Dbfb5m/fOnWMBXNiY3dX+GQBtRYI8oz+AZvZN2Az+jZ9z9xMnBt9VrXjZxoPMXhXeeCB3LRsfs/JuFcXE7Rn+nS7a9Eim4Z63uzZ1oENQ/iGZM956nsORHv2pu85cwL5bkuQ1qp8J7sP5uaAk2JHpSjjZxJvBvwvH7xJ/VTIHQQWtYikAVkyQhZkV5bv2sYXRJaRmPIgwuodEo0/WUlUkbByHt3cpO5yXG9FZ5MLLxmzJV1FURbW9/Fv00AULTxHyNBQgTOQ8PI/BnuXXHJJYX/THvSJ5Zor7h+k7dRH0vm4nuprLQ5idZEshEAYg8N248Aiu7Fva5tva+w9HT+yl3ZcZ7u1ZxsW9g232+/759vNfQvsiQ2zrWNtz6jeb0edJMAh0lhnl/V1TLd1HTNsdftsG+iYbu2dbcEgbUZ3u01rG7SZ6F52dQYjNMjv8ObfYgAoP8U2NGSze/ts5rq11rN2rU1nt2HdOpuPEdoTK61nY2/NdYUoD7Xrg/Fcmw21tY89ZrHGN5LH9jYbDOefOjfQ0W79HW02GK63hzxLWV05mhLD7e22fvq08EnuzQy3jVhf14D1d/XbEGGh2/ut3Qasc2jA5vR32Zze6bagv9OW9m2wrXtXW9vgiA0PttnIYHv4Dp+Bzd5WGhgsOjuIFLlhgw3y/tiSPHC9CImeA4meEyTTO/X12fqREetesMBGZswIkunhGT023NNja3v7bKsddrCZvDsIlpQSbAi4D+bmgJNixxahFj+TsV9cCB3nlEYhW/GLiosoHiCcSz5AFHAAkhe75Ioln7HUFDKjYBFST6AcyA7k/O677x71r5vmyYFyaAtGbRBpyGvS5RTtUOhZpMZZQF+M35MfeUOi+VAGdaBs1EKKfOfSB4rmRlv4Vl0VjYoxgIDTl1IlifuHslikoCudP95m6+bvbj99qMu6R3rttM7/ttd3XBr87ybRP9xuN/bPsz/1LbQV6+ZY59oea0eUKvXNCcCGjhm2rmuWbeicab2dM6y3s8f6u3qsr7MnGAR1dwzbzPZBm4kxX1u/bWNExHvq94ybVHCGmMd4UlizxmauWWuz1621+b19tuP0btvl3vts2qpVuZ4TagXVwpCtA5F6WKsMBULbO3269XV229D07vAyHpg+3TZA9Kd3Wx9qN9xD07qtv5vvadY9Z46deOKJdtnlP7e1T95pM9ses7ntD9u84Y22fnCxrevfyvr6F9rwwDxr659jHQMzrC2sAphz7JwMmI1AKwbH/M83butC5UavD22+Rgb8r+WEoxHA/dfTPy18Ygy1j9iKWf12x+IBe2JOvz0xp93WzJpt2w932FEbV9izezfa/n39NiMIAsxGhjYR5Ls79rL7F5xgvUPTbSaGwzvsYCMbNth9f77dBlks8nwaGLSRDettaN16mzYwYLO7umwY3+2rV1tbX591lNgJmwyMEKYcwcayZaPnpufEMpRayF2bv9umT99MqpFIz7b2uXxvkkpv39Fp9uQT1ot/9/nzQ5r+ri5bw/NmwQLbc/fd3chukuGk2DFuVOFnUkYQ8kEMYYWgcR4pKZJb1Av4vXR946ALEEasmSF/UjeQazJB6ghA0bSyDOQg7EinIb0Q8dhgD8JOWVyDaEpvOEmKFVSCvqAuWf5cKYfy0HNO0ynmmEUF1x9++OFccg0ppmzKS9ZHahW77rprKA/in5RcIzXHd+y9996bKdVeM9xt1wzsZH0PDts7O39kb+j4hc1oe0rvjZ89MjDDruldbPetn2+2ZqZ1oAuAJNuqA1ujw909NjJ9hnXOmhOMePrbOmyAF017hzHST5n3gV7r7Nw0H1J1tEdGbHpvr81bv97mr19vM1etsp4nV9nstWtsxvoNTyO+I5UH+bXwQuzt6bGNPdOtdzrfPdY3c4YNzJoVfPb2Tef89EByYxbPfGdhqSAZyYrO7Jlj2y/a1Z68d9i6Vu5os1ZtZ+1D023jIGGEn3rsj1mk1KizMtzeZwPTVlrftOXWN22F9U5bYRv47lppQx1rrcPwNIKPZrwxDNi04cFNxyO8iPgesc7htuDHuXt4xKYhmcM7Cd82YtOHR2z6yLD1jIyEz3Qbtp7hEesZHra1bfNshS205fhvblsY/u+av50t3nore+ThR2yd+mi0C9uCCtS2220XjkIQkMcff5qBLfdgN8a4y5db17Qu28AzRs+G4GO6w3pYpAwN2XOPOMKeXLnSbr3lZuvdsD6cYw51d3XaDkuX2taLF9u61avsgXvvtb4N6613/TqzwYGw8xDcBg7XVyc4tHG4zRau6Q6fGGtmDNidc+fYtXP77Mm5fbZk+jo7aGCjHbqx1w7q67M9R26xHdfcYb+fcbR1H/k+2+45m1w2Tt/8bng08W7YNc1P8eOP2/CGDTZtaMgW9vTYTltvbXM6OwNxHsIQb+26zf+vteF164NaxfD6dTa0dp2tW7bMRtavt05CuOcIDiYDIwRIwrf88nRbijTzasVSXGlmT2KALiIdSPXc8N0xd85moj1n0/+j31zblAYpt5Pq2uCk2DEulLX65cPDMM+HJG7JiDaXFq1OBI38RXzjyGginHInpgAXyQcCZUNmkQJCILIM5CDHMsw4+OCDQ1rVG/KB5JZvRcbDgC3N/ZsIeJaursIpE0Esz+CCerBYyIP8+2ZFGZPEGzIMuU7qSpOG8wrtnIZ7BufbjQNL7K2dP7M3dV1ss9o29fMA0uCNC+z6jYttzZq51rXZaqgKBwXUpGPmbBuePsN6Fi62jllzbKBzmg12bDK6ZDxBiL7HCzHHMIX0kJOu9ett7uo1Nmf1apuLS541a2z2mjVBgjVRGOjstPUzZ9qGmTNsw4yZ1jtzhg3OnWfruqfZ2mnTAgEeTJkDjL+8rGSBsZ03e6E98fBGm2eLbHhjh7X1d1v74PTwaRvutBX3mq0wpF0LKpPQd7b12tyOR21ux2M2r5PvRzZ/P2oz2p8cI30PwmKmS23aJGOAB4VlbYtt1Yyd7M6+ufbQ0CJb1rHQhtqe/irr6u21baf3WD/kNAoBLvRvNnTlvuf+ZXGIuzDd9yyE5dlm2YoV1jcwaO1d02x69/SxIcfZWejosrZZc6xv7XrbZs+9nqbu1T1zpu156KGhLLnlYleI/FXe/HnzbMVjj9qCObNtm0UL7ZYbb7CNa9bYcB+Syw023LvRhvju67Uh3BlOIOZs6AqfnR/d5JFj2JAoD9i58/rsc/N6bauetfastjV2WO8vbcGlf7L7Bz9lOx75ylLP/TJpxvMemoOhNKpguNtcsSIsjJ+x/Q42sn6d3XfrrTa0Zm1YRHUimV6/3oYIM97fH9K1bdgQjq2BiPUwAhUWX/bo+H+MjUksoZ6ziSyPkmyRaM4HPeq54Zx0rtsSkVtbEU6KHaXBw50VvtQQ9BDjocaxJAAil4pglgcFc0iCByYqBVxL+haGPCPd5bqiaMUeImLoPMSXdHlhngk+gYs3dIxFaimD8mkfhJ2gHJAyztNuSJdUIDgv3bA8yCCPdLQzTXLNyyLWTU5DGStl6obqCARLRnxJ/e2gJ5vA4Eib/XZgB9th5AG7qPvztm3bStsw2GVXbNzGbl6/2PpWzx6VBtdqFIf0d3jGLBvpmWU2c5YNdfdY57RNfduxWd86SeoYT0nD4yhr0/r6Aumdi3/q1att/pq1NuuJJ6yrpDePcdUbg57Fi2xowUJbR99us8TuWrPGVhHWeOZMG4gIr+acCG+eEWVMiNuGO4KEt2OwZ9Nn6KnvB+7rsk7brfJ2DdmI9XdstO6OFbZ1x/22S+cdtk3nfTa381Gb2f7EWOI7gdhg3fZ42xJ7snNrW9G5xB5vX2KPDc23IeIEtnXYhuENuSqu9CE7Q3lGpiysjjjiiHAvQlB5dkmVi2OFFcbXN+dQs0neQ8xFfsPzj/sJ6XKaD1qejfiglXoZ+cfqZU+uWmWzFyy0A5/97OCjvHOrpbbL3vun5oXUuW/tanvwrjttcMM6612z2oY3bjDr3WgjvRtspL9aDwZ4gZm/blr42EOzobb2cNeQfWVen62f22vbXvUvdthjP7UjX/TvNnPm4sLnfpl3Qx6ethuJVHrmTFu0446hf0cXH3ikWLzYNub4BA7nCFvPAoRnM8IGPuEYyXT0vWbtU99r1oz+z+8bAgMDNrRyZfiMG+zCzpoVSHWQSG/+BN3qzeR5lFDHEurN6dszdKmbDU6KHaVRTz+LUilQ1LakigEPNiSynJcuaJqBXBxwoihOTfL3yfbxkqV8yDFl004ZqtEn1BVJcvybpEqDjpFQox6i9sSSa+lZVwVe1MmXOeCYF3Jy+33V8HS7rX+evb/zu3bE8J/s2jVb2/+s3dGG184IHhbAFssTMGCbPdcGunusY+5865iLDnXnpgAPmxdSiiLIAkYhi59G5tevt4UDA9b10EM278knreexx20OJKZgITFe9E7vtrWzZts6XgxLt7UZu+xsbdtsY0OLF9sIPlk3zxeIES/ahx988Cnd7aT6ReS9ZMzcGGm3jsHNxHdoRviewfFQj7UPT8yLZsSGbaiz14Y6NtpQ50abPqfdFm87xzYMrbYN/atDdLrHh3vsppFn2GMDB9rGNcttt6EHbN+Re2zftntt57bHbJu2lSEMda1YOTLbHh5ZbCttvq1tm2Mb2mbbE13bmM3bwfoHBp9SZbI265reZTOnTRsTQj3vPmP+KDJlmnqRpJV56l78L2NdPYNig1bZLSjEedGzsUi9jLyKnrOPLVu26dnXM8uGu2fYzCXbj4nS2TY8ZDM7222f3Xa1/jWr7ImHH7KVDz1gTzzykA1VJBWdPtBh2y+fYcbHFtifbllhl1/2WutYOst2PfAIO+bQl9kuW+1hE4U8iTNjMZ53FXrAuHSzrbbaoroMb/aagVR6eM3qiFRvPrd2jQ2tfopEh2uQ6kCs14Yoj5MObHLWbloUWI7aXhaCLvWcSLUj7f9YMq3/8RBC3zcInBQ7SqOefhaT7mt4KSXrwnVIpF5Mad4VZCgnX8d6ScYvNfKHhEpCzIspLcDHAw88EF48lJmU8KrOsdpEmjs5vZglCUwSdR2X8QZRFjJMTIPCJwt3Dc63Pftvtff1XWe3r1tgt6499Kn2bEnhHR3WNX+RdcxbaMOz5lrXvAU2NDxsGzEkxLdoynad+hN9ZyIfrn7iiaDzO3fFSpv5+GM28/FlgQB3VvRyH2S3YPZsWzt3rq2dPWvT/7Pn2LrZs2xg2rTR+vASRtc9TeooaZ/68mnjOkwI4R7r6p9p7QObCW+Q/k639qEJMj9sG7GOnmHbOLQqEOBhSHDnxvA93NEXBlTzFaOfWUvmWd/K4RDZgmm7ZFq/7T671xYunGn33ddr9y7vsd8MP9t+MvICW41+8kiHzRxZb9u2rbDt2paH75ltfdZl6AoPWqcN2rS2IeuwIVs30mNP2mzbaNOt36bZQNs0G+bTPt26CXG8eXKNukUc6bCtp3Vbz4yZY7y8yEPMmGbm3Ge696VyEy98yYfnCM8sdqSyCBaqVFrks3sTl891bBXIn3u67LMxj9BJzSsvL34D8ZM9RXL3i+fcBuuwbfc9cDSSHBgeHrLVjz9mKx56wFY+cL89+Jfb7cE7brMRpMwV6Cdv/eR0sycHbfWtV9n/fu9KW7ew3ebvsbMdfMhxdujBx9m0SPWkCmRJnOvtE7gdFT/eWRkBmfIQBDobN24i0qtXbyLSqyHUkGYI9OqnJNMi02ufOo/edSPpUg9GBoplMOfFL7Zt/+Oz1ihwUuwojXr6WSzrvka6xtqWjsmkyK8ejhBfpLNSX5C6AhJehYAOUoMMyQJkmDZC1FSmtu55CXGeOsdSrCzInZxUQ3g4kxcvWMoq0idO1k/9knYu7oM0gsH1oYFBW7t6pe2/4ddma7vtVnu6SkvJylj3wsX/P3vvAWbZVZ1pr8qhcw7q3K3cauUcEQIhA0NywjgQxnj8G9sYGGzGY2PG9mCb3/bvNMZjM8Y4DjJIZGEhhLJACeXQUrc65xyqutL9n/dUf6XVu0+6Xberb1Xt73nuU3XPOXefnfe3117BOuctsIGJU62rsTnx3VtparLJR8tG/fPx3jCGyAoGVISE3bnTzti7zxY/95zZunXWVANJCuoMB6ZOsf1TptqBqVNt/5TJtm/iRDvoyHmoguODxtDeeX0/cVbc02otPZNeU3M4quoA8ZWUvbaoWKW11xraeq2hvdestccGmrussaPfps6eYCtXnmu33/5Q+imI4+2QOlQN6B+SovKXdqJ/Njc32eTGI8lnhe0e2lQivz000GyHKrPtycoCa2xusebGBmtsqFhLE2oOHL0PWGel2yY19lilv28wLPfQaYM2EcdmjTqGgDIe/IaOd3KdUwTp8Pt7aWMXZAWvCds6jWCRF4gnH8ardPmpI41XqcPkBeQJ58a892mezUorGbNHvc4A76tdETh1iqZyDhHwjgm24pIr7PRLr7T569fb5ttuszbIdE9XQrT69u+xvn17rP/g/uMbpkqPF5N3Vqx/5xr7wYN/Yw81ftYaTptqy1ddbJdfcYvNW8EG8+TosI4mn8DJpg71q85Oa5kzp+rfV2h7DBIT0nxUUs3/xxHro9LqY4j1/lMupW6ckh1y+VQgkuKI0hhJP4tlXLtxn4VRvomlVuGDfrAIcJ/nyTeLhxZI/nKf60glucckirFNqOcLceY+75MrM0XP00IkQqF8ZhnbQU4pD/WUpRpynEeBnHpS/Xt4d3SkSXlkjDhEHijfoT1WObTVJuw+aJMTt2knIMlpbbOGqTPstHNX2Vt/6mftsSefTDYICeGmHEc3KtQHGwvqWoaPlV27bNqOHTZ9506btmu3TUP/96gEmKn6RJZMfPLux6PItGlWWbTItne02zYM21BJSVGPgWDR7mlSI/Un6hBDrG3bttvUCTOt92CD9RwwO3Kgkvzt2ttk/d2LbWplidUaUE8ku0h4+5oOJyoPkN7pcyfY7oPbrKV1cPH3bc73Sf1tds4559i99957nAtBDwy+KB+bOdooTEuuAr2LQKWFB4aJjb020XptwVGJLH2Xv6GxWVdXT0IstTHLAmMJt40YiJIWp0QaX/Rh0sJAFzeCoVFrGCDHR88M5wZdp2yAdNKMfv28xzVtjhnnPEce5s+fn6SN+lRWQB76fZm5Ue9jDMleQWlRr9QF9yHncucYthkgf9J1zvIWxH2e7xsYsNYp061h0lRrnLfQ2pnbKgN2aPs2qxzcZw2HD1rvnp1mwzDwQ5JsG/bZ2g3ftbXf+K5V2ptt9lln2KpLbrCl519kU2a/JtEeLsaTT+AGNjRTpyafE5ZSeyn0fve/VD6OkunwGX47XKCjXE+IpDii5kS1Vi5hyrh24zsLkxYqLeLemEnGdfJlrMWCBYdn+Muiwl8Z5oR6vixy/IYFiIlUYZq1EEkywSKO319UMbKA1w1F38tSDRHxzlOj4H3UufeJHOpXQrox/HnuuedeO5ruPmz9+zZa27591jIkJKiuzRomTLLGGbOtcfpsa5o8+I4bbrjBOidPTv7HLzLlIH98qOd9O3faDFzNbdliU595xnqfedY6S4bFzkJPZ6ftnjLZ9kydZnunTrV906Ym6g+4b6Mu6Ct4+lj36KOpUi+egTjSx4ZIcQUDt5ZjDNvaGybbuu802aHdM2xnwsHCtIYv8Rpo6rGBlm5rn9xgk2a22/qtL1tf4+FE5QFVCIG+g4eUJCLiAUjT4LG9IjjK57X6LmQM4pYFCJ28IIQEUycYMtRk7GeB+4w1hTv3kSelwoPOLf0Dw7W88cG4Jx3KqM2cyo46wKWXXpqMG4yp0sg+74U4M0+xyaUeQmJEnTGHQDQxbJN7SB/tEveQ5If5hnR4ztcR+SEN7vPbp59+OjMgT2iAlwWeCceQPOjwDtKiz7Kp1KkXZaF+RaIlhWY+ev755zO9BZEObUIeww0GZWufPNVmLVuRlO/BBx+07oMHrK2n2+zgvoQkI4FUhMZq0dDdZzt++Jzd9cPnku+ds2bYmRdfZUsvvMQWnLPSWlrbRs1aNVpxjJTaqdmUBcFKRJC96seg7rQk0ynqH0f1rQFeMOoJkRRH1JyoVoMs6UxZ9z1M/CtWrEhInwy0Qn/GpMkiwCLKdUmPJMXheS02nvRKyszveL/cwbFA8ZE+s4g5QJJMOkiWJXkLfRSrjlhkeY6/LPy8hwVfk7YCcnhdVSBpNHlTMBEdn+o53qV8JcZqHLfu2WK2d7N1HD4BfeWGBjvtzHNs/rmrrLtjom3bu39IWoY+Josr5eL9EBkWocqePdbwzDM2detWm759h03dtSsJeVw5wVDOXej7zp5tXfPm264pk+3grFm27yhhk+RvyLc0rtgGBoaMFkUSjynSQLO1DEywrc932cDWGTapZ35CgPH20Oh8+QoHDyIlHd4iOtDYY22TGxKp7xE7aEfsgPU3HrZKa491TGhL6g29Zazi7767JSFq/Udea3tFO6S+v/zlLyd9hnJqI0XZZVRJX6bt8zZoAIlrlms/f/ReFBKcfgiRpL4ZG0pT6hhcF9lkjKURbK4zBhjz119/vT377LNJ/jU+qBvKzrjBnaD0+NOk25A83ChKQhr6Omd88C7G31133ZW8g99Kqkw/JsAQIM8i+LxLklu5T/T9nvvMi9Q95WWOIk3uk/8iIubTktRZGx2pelG/OqGSWo/mNOpJxJ65ld/zO37D3MLzfKd+qEPyx/zovfhoQ0O6zFkXXHBB0n64QeN3fe0TrHnuIps1daqdcdo8e271Rnvq0cdtftdma04CulSPwzt22RN3fC35NLa02KJzV9nSCy62ZRdealPnzsusq6y1odZrVcTxIHofetQnpEvNiRHEuHm4/otqi0iKI6pGLfxMgiLpTBn3PfIrzESNuzS/8JEei+LChQuT4BTSxwshMqzFlEUyXECR/Og+RIuFxpMsEVme5x71owh9oRoGeT3//PMTCU4oeYKgICW68sorkwVNUuyQFCsvMiJKi6CXhJzevtV2rn3SWnfvs9YTEOj0d06yeeeusne8/4M2cfqMROpCvlsPD0rvPCnoWb/edt57nzXf8S0795VXkkAYJ4ruCRNs76xZtn/2bNvGEfXUKdZzVP+PdoXs0/d6j24YQgmnwlr3dPfZxtU7rfnIdGs9auAmXd/GyuBkfHgHSiPD85biMdDQm0h3k0/i3eGogVtTF2fIdtNNN9maNTvsAEFijqrZNDY0Dhl9SoJ13nnnJWXwpBDpJxEKdWQOknpwpNBH6MPdHmQoDwpGk+fJIfFoUDC+yT99W6TRGx2KuIuUMS65Tt6Ub9pUJJa5hTH75JNPJs9oDJFXuURk3pBk2qtGkD71yH1tGtLyo2vMQQq2Q7pKR5JXiCDSdvJE2eTeUepTXCefjEHGo4IReSNcxnRZzzzy8pMX3Ic5Su2sd6lc5FHuIXmOv88888wxZeM+6TOeySPpe4k8UDoCczOnYBIsaEO84pxz7Mo3vsl+uGGv/cI//MBs10Z7x5Hv2orutba5a5L1DlRPM9A7f/WHjyWfuz//v23a/AW27KJLE4J82lnnWFNzc2EQqVquVRG1Bz6RT0Tl42QjkuKIE0IeUS0DyCBSGB016liPiU7SGU+Mi8AEDekMpVMywJM+K/DeJ5jcRZQVrc5bl+u3XMeoht/JD7E/Hua9LIpMuNJT5pqkdiIFqGcwSSPZTo6/A+kb37lOPTCB838owZNhH4uUJyxDEvb+Pmvcvc261my2gcN9VWsJ97d3Wt/UmdY3aZpVWlpt7eFe+96DDyVSFznMnzplinXs2mVNTz9jjbf+u21bv952Ha2XqScQ1W33jBmDHwIazJhu/ZMHjS+G/PVSPlfXLHJSFxj07NBpTYE/38S7w8DJMaQZaOgb9OaAju9R0jtv8Qxbt3W1VRpzJGWVwX6WSNtS3HtxXUaWqmuIh8YH/YF+TJRCyq7NkPTqk/o82qdl1Fgk4T0mexneUNLuhZDOr8aTHx8inCKRCk8O6dL44LcaH4899pg9+uijQx4d9AzlufvuuxOdYxHUcKOrMcy7FIxjSAfaqXPwWzahjCWdxvh61AkEaZB38ixVBak2cV0kmd+rXD7P5Efl8nrrWVJO7zlBkn8PqQEof8eMfY2VoycovJv5KitPzOHywpEGzVnUk9QwINPqj1LhYG64YOEs++qvXGe/8I+P2Wc3zLHTp2y0zzT/tc3u2WkbDk219Yem2qauydZfSTd6zMOezRvtMT5fv81aOzpt3lnn2pHOSdY4fZZNnzU7M4iU6mO4LkIjxg8aKkUzXUQmmGx0DMgCFFEOTMr//M//nBA/JqvQjRoLCxPae97znkyrcYHuqwhROiIMjWB4B5IcJs0s5/sscnkSMe7JeEiStVBHEUgdg4WmzLuUjjdI8hbvIkhp76JcWhx5V+XQAevZvtaadu20pio1JJqbK9Y1Za71TpttDR0TjslzQhYaG+265cut5/HHbfrmLda2+iVrPFC9KyByvm/KFNs1ExI803bNmmmHWLCOls9L9IREImcNiQcHiC4uzRK3Zgn5xaVZ+0nx7FCx/iHCi8QXfV8M3SDDDc39Q5oU/lShzHQKqZCajgijlzpCnpDIQTjSjIQYN/yWPi2imdVfV61alZDrkYbIms+PV+/IGx/y6sI489EtlS7PMc6yQqkLIrg8I8NY5UGqAvpfqghhfrTJlgcMzQG+XFJtkEeIvHK9//3vTzazeVJOCB7EXypUIUiLOVI6wMwTYXRNqVdog5CVJ0mJ+e43aH6jxnVOtqjvrP6oQBgJqe/rt9//xvP2hYfWJW75/p+mr9ovN99mLQ39SSTMTYcnJ+HgIck7jgyqnZ0wOGGYd5pNXbLCpixZbm2Tpx6Xn4iIavhalBRHjDhQC4DsMZmnBZRgMeA+z6VFu0s7amQhkf5cGB0O3V4Z1mU53+eayC7XwwXER1FLrLVTggHIDZKk1PqtP9aWBEeqFyIP4ULjj0wlHRXkLivxUEFeD+y27rXrrW1/l1UT6qGlsc8WTjpgz09eZQcmzLGGxtfyDSYcOGCztm6zWVu22Oxt26z9BHx6EuoYye/OmbNs56yZiTQY37/CUD1TlxWzxoE2ayRcsXdpdpT8Nlj1EqZSQSya8OErdYejJLj5cKL/m8a1fR2pDHk6uSHk+i/Uc1afhezI2ApIZ1zH5/RpJMbSIU0jhiI71bj2qxXUpqG3BxFQ+n8YctxLOGX8CnjeP6fxpHGeB6URkl3l0Y+rUNqqZ0Q2vfszP6Y17uUmLa9ctIfcubFRkWcNqXig/sV8hvqYPCdIDcv7TKf/oOcrqbTyIKgfKt8i7Fl5kuqayq9nvcSZvGkuTuuPXjWkrbnJ/sfbVtolS6bbb3zpKfvznnfadwcusD9u+ayd2bjRlkzcm3zAob4WW3t4lj1TOd827+qxypHsSI+pQAVn88bks/HB71n7tOk2ceFS27h/j+0991ybdtSzSEREWURSHDHikBFOnmN1Fr0iXUgAeWRx4SOdTO81gglcBmcivqHzfRm06H1pC4gWeK9nHBJZLaBaGNPCNIfBMkJCrLQAZdDGIZQEDfQcse4ta61p91Zr6alOWXh+xz5bOXWbvdB5ht3T/Gab1tSbcL+2I0ds7rbtNnvr1uQzoaRbOI++CRNs67RptmM2JHhW4hECTxChgdtxYYuP/t9QaTppLs0Uve01XV/0fLursp3LkjyVlRJ7UiUdVW8gJimqTgCy3APKU0SecRwo4zM7qyzVlCmtjD4vfqMp8qgIkX6DKpUYkWH91uctTL8IIr8hSffphptX/U758CoeofGs3/DmlYvvzDEEAZIRLpsW37a0O6plqI7R9kiT07xdcJKARFRzX1pAIi/FzqtrbQzSyq86V59M649IvtNUMP7T+fPtnHmT7Bf/6XF7Zvsy+089v2cfbv6SfbDp69Z01JvKhOZeWzl5s620zbZ7wVn2vda32MSJs+3VF56w/a9urPr8p3vP7uRj9pj96/fvtTMuvyrxybxw5SprqjODroj6RCTFESMOCKoWjyzH6jKmKgIEWioT0vn1kZ2QzChctDxCSMeO+xBipBscVftFLiTA3pjLH6f6hURkW+oRui8oXS1GuuaRRpCPIeFHuqxn6xpr3LHdOqrgwh1NvXbulG22cupW629ts9/r+xnrsDY7a/sGm7N1a/KZFgRDKIOuCRNs55w5tmvuXGs49xybes459v0f/GAodHHLEXR9j5X6ysCt1kCy6wmvN3SzgnDEZQlgEQktC3kz8aSKPiyPJ0DuAelrIpRyD6g+lpcfbdDKwAdz8X3XSyGl312ELMKq6wqkE7o1ZCxr0xgal/pyVQu919e1lxDr5MeTVxFp3QeqGy9R1kmT7BeYz+TvPCyX3ilvO2muH0mb+7gR9PkIJe7kiQ0zv2Xuoj95g0X5VZcBcVaefF14w8Cw3uTJIstdZSIhTpnLV8yeZF/50NX23297xr78xCb7w7532539F9v/2/JZW9Y4aCwoTD/wgr2t4RXrOftj9saf/csklPvqxx+2Rx78tu15/mVrrHLz37V/nz1557eSD3rIGOqdftmVtuSCi621/TUDwogIj0iKI0YcuEiTXp2MPwQZgSAN4bmyyNMdYwLnuJEjZ6/a4KU3sg730lgvLQslRlkEQYujrORDnVEWKa5rgc1aiHhW3jKSdx3cbwO7XrX2vQespQr5CVLhC6ZtsdMn7UykM3fsucSeeXmZvW3HAzZ7xw5rrjKc9OHOTttz2nzbc9pptmveadbdPt3sSKv1Hmq09p2TreHJqTZt+6WJusPJMnBDtQHCSxQ33Jolqg5N3VZpTC/LYBtmpynil0f6vBpLHnmQikAWeEbBVEIiru/cZ1ywgaM/8WzoEUH6yHn6u3wGA44MbvjyQHryPuBd2/E+PoxH3Hd5MhlKk4cMH3MqW2oh0vUPT0BQCWCs+lOWNOllWKe6F0qSfVCdtLpmrKJ/Sh3xnCeg2ljjEYO2kFGjP2nSPMGYZk5jDskqF3VI2SQlzgrPzH0kxNQBz+hkjft85zptwamCPJP4ssl4D+8lXM/LE23B+0TIQ6k070XYQJ9Um6m+tVkjHYh4lp5mZ2uz/fGPn29XLp9hv337U/Z43xn2Iz2fto83/197f/MdxzzbVOm1jvs/bbbm29b5tr+y82+4Ofn09/XZS888Yvfec5vtfPYFa99XHUHu6TpsLzxwT/Jpbmm1RasusNMvu8qWX3yZdUyK9kARryGS4ojjkGUVXSswkaI3h5cJ+ZGVRTUEQL5Yi4zsAL+RL14+4WItgzUmcwIGyNuFJFXeGT7HmtL5S1NxkGRGUhnd8/+zgMjRflpUOspJffKXd3uJlH8nuoS8b8MzT1hl13rrOCRdu4ZSusLnTN5u50/bYtMbu+zQ1jZb98Is27V1ki3p2mxLbLOVRVdbm22Zv9y6Vlxg29tn2ZGGSUPGbg1b2oYIumS/O/d0WdOJRMVzqDT0J4QX371d/XsH1Rxa0PvtTtydoQ6hOupMJGXZah4QGuo3L6IbREUeLbJAu7JJw7VVWpvxPx4RIBgQLL8R8psqJHiAvhESTE+apXfOX0/6pNcuHVZPgEMM6Z2XAPWkCGreS4Gkp7iBg9Q9/vjjx5yi+PLjEQPpdprakACR4zlIWNbYx/2Z3CKq3HnwUlT/vKSmIpVhe8jvL+SRZxSoxBvjkR/u4w3Dk1MvxRVZ5TkCXMhXuU7DNI8yp0liK5/pYXtBNKk/9IkVFdNH9KM9JU3mf9W1VwmR2gT9ENeOefPsFVdckfR7DPukYqF3yQMGRnbkR67wvKSYsnGd30lXPg3k7ccuWWhnTWuw/3rrD+2FvW32P/p+1r7df6l9puWztqhxx7E/2Py42d9cZ3b9x82u/rA1Nbfa2RdcmXzAfT/4tt3xH/9s/Zu22YzdzYYpbln09fbYmsd+kHwaGhtt4bmrEoKMFHnC1GmFfS26dxvbiKQ44hiU8f1YC8jdmvwU60iYd4R+ivMmIpFeJmgIiQ/ewQKAc35F4spzrK9IdAoC4iGVByJ/8Q4WKe8bFfA+LXRaWNIWc66TZ45HCWMrYxkvbZsyebLNauq3tfd9zToOlA+rOqWlyy6cttlOt13Ws63FDj7bYTt3TYFlJvcnWTZZ6W3usK6O2XZg4jzbOXOJ7Z8033racMg+cVDPFyFqr1VlyJeLhoo1tvXZkYaD1td46KiB2+EkfPH8xRiYDbq3MgwOjxw5ztBQQRsgtLR5mu6soqdBjKjfrGAqSFMhu3lGa/Q9CAZ96oknnjhGIsr9Cy+80N785jcnnlB8XxT4He9StEORr7APKeiGyHAYLlzBGUTg+E1a1EPqqKzqBOB9jKu0fMvNIOUDWeWnHiFPjDfv+1jwwTvw1Zw19jXvyCtCWnuo7KHBK/DjnzpnQyMf5j4NxjNzAmORCHkPPfTQMT6R6Tdc57mHH354aLMbqiEoeAYSZ56HGGsTJpsFrktPWO7D1NYqr06Q5D1CAU/CZ6gv5me5hgwl5SL0lJm5O6+u/TxLGRWQiDTJN6RZ+sv4e6dcpKH+J1UN8iid4rz5+rzlC+wL72+1v77zafuXZ7vs+wNn2809f2gfa77V3td0hzW6yI020Gt29++bPfNls7f+f2aLrhi6de1lN9tZSy+y555/zh5Z/6Ct3fCYNW7dY/N3tFlLf3mD3MrAgK1/+ofJ567/89c2/4yzEz3k0y+/yibPHDR0Hem1MeLUIpLiiGMGvXyjpoUE9b4fawEmZIIR5EW0K5qIFLteR7teYiSraUWfK3Ksz3fv19jr83Ld+wtNO44FPAuxSCMFgOssLBAsbwyYSORwZ7R1re1/4lFbf7ivdODgRR177CzbYdO3d9vhZ9ptU/egNDJEf2OLdXXMtMMds+1w52zr6phjhzpn2+EJc6yv+Xj9bb8+nSj6G+XNYfADEW5o77VZp01NPD/sO+pHVmhrgewOSt+oH+o9jEbGPfVNNjOKICivBVJh4Rnqm1MAGSX5aGTchzTx8YZHISTxo+/QVyBZEBLlh+8K4Uu/hPSm+Z8mTxAsXKkpGmF4pE0Z5CorzXes1Isos0hhar0flfhSNhHMLOjYXvXjoTpmjDIH5JWf8StfxWlQABKew5NB1tjnxCZPnYX3UjZ5lskKbsM8wf9sgv0mRGOa64q+t27duiES6vsZ1yW595sMLx1U/0A965FHHjnmxEEEketLly5Nyk+bUO4w9DZlkeGa7B60afbv0imX+rry5dVHJOnFaO+qq64qnGchyARBYq7l3dQtfZm0yQPtQZ3SbmnBRPR/GeJIX/ntn7rRfnTdNvvtb7xkj20w+92+n7Fv9F9un2n5G1veuOXYBt/xvNn/udnsop8zu+l3zDoHPUqQ3nUzr7Pz950/GMCmsdce2Haffe+B22xg9TZbuK3TOnqqMNytVGzzi88ln+994e9s7ooz7IzLr7bTL7/aehubRnRtjDh1iKQ4YmhCZTKTPq+XxPJdk50WmlqBiTnL7Zon6eRDFtV+IoIMcUyoRT+0sOZDOixGkBpN1NIRZcGXE3tIiyI4ed1RhXpmcRBpCaVTskxnsizymsFizMKoEKTbt2yxbS8+bgdWP2fNRwZKkeFGG7DlttuW7txrzawhA0223yZYxRoSPV8R38Odc5L/uzpnW3fbNLOGk+DWrBE93y5rnWS29KzT7NnVT1iPHbBewhc3vLagi4y0NLdYT2/HULQtvzhTv7SH9MlFvvwzCmkLOZAXBoiZXFupzahntbF8unqPJ3znOou0t8IPIUKE+g3tJQNRSZZpb46fBemn+jzznesQE9WFN2JL6tEZuGUFUxC4n+W9xafH5gvJruoihE44qOe0jRy/oXxsLn1ADX4jsq3yE5ZZm440SBefjUHe2Edyq3rJ2nxyn996dScvKeU+6UBqpQvs61rBdigHxJHycZ9+5FUDuO7bOUxHbtYgmvfdd19CBtPA9dtvv90+8IEPDAUJSoOM1qQqoTJ7dQ1FzlMdqk78vCx9YG1Q8uZZQWoyISCBchHHWuCFA+SJvoOUnLGLRLoMcSSv5y6Za7f+4hy79bEN9od3vGiPHxrUNf7V5i/bLzR9bchDxRAe/wezF75h9qZPm533Y4mPYtLxKhs/Mevd9hMr321r9q6x21bfZg888k2bsr7XFm/tsEld1Rn4bn35peRz7z//vXXOmmMts+fbaeddOGRQeLLXxohTg0iKI47x95umI6UjwLJhSmtJ0pGeSCdUhAfiysLCfY70JMnQsaEWUSZlTdper9BLoPSd98gwzrtN4z7fSZd0ssiF8qywtXkgLfQ3zzv3HOvZstrWfOOr1tjdV2owtlb6bMm+vTZnW8X622bZ9o5z7PASpL6S/s6ygcaT4N2hsWJNHf3WXdk3GLyivdesbdDbQ+/A4MK76Kyz7JJrl9jjG/7D+YMeVCMJNyr0oyydUb6zgZEEUGQghMiZJGqhgZjwwx/+MLmH3myasRHRurIIscB9SLEIspe8SlLIsbmelecUL72DLNBn1Z/S3GRlqXBk5SnPGI10ICSoEInQhQaC/I4xD/nLA7+HXJNnNiNKR14MKNsDDzyQK5FWntkYoDecBcaZJ4IhmdXmSkF6FJjC6wJrk+o3Vb6O9Jz6jdyLhYZvjHfqUBvhrHT0rrz2oI7ZGClEc1p7AEhW6Mfcp+nHQ3g65u+TtnxdDwc6AWHMkjfWAukmK7w9p35sLqoVqjQ2NthPXLrI3nTuPPuTO1+0f3x4nf1R30/at/ovs0+3/J2tbHz12Mwc3mn25Z83e+KfzG75Q7PZZ6fmednUZfbRSz9qv3rxr9r9m+63L7/0JbvnuYdtwdY2W7y106YdrE4R7PCObWY7ttm+Z5+wjhmzbNryM23asjMSv8gjvTZGnFxEUhyRwIcWlaTKH5ExCbJAFEmwagUmmCzXRd7YRAuA7ocSI4ViDY3n/AKqBcZLgLXwAulY+sUoTSIgtYtCDPTbQ9/4v3b3n79kDd19pUJStAw028T+RdbUttI2L5hv65d2Ws3RULHWCQ2JxLdtslnrpAZrnVixAz27bcbcKUdd3yHx3J3UaVKHlYZkUdTiy4LvF+80AygZAuXBB1JJg0gkafujZBEpRQ6kX9CX5O85NN4k757MFOVJfSaNzEgiKdWG8LSBMUS+pFurvHv91NJ96Oh75QIsVC+RlwpUESCNSAllvBXq3GepO6SVP4yMBqRexLgUcQyN/7yuK9LbPFIMweC3kHX5Ffd5lvoA7Yq+K3+9SpQ8IbCRprzUkdQJQhUcGcSispAmDGCjDHGWykaow60NTpodQhq+9a1vJe9M81Si+Uubdq8r7POka/JOo74vaK5D6o3xZy0AsdXJFu1DX6fsSIgVhW84QpUpnS32qbetTAjy73z1WfvBq2Zv6/lde2/Tt+0jzbfahIZg3Vl7j9lfX2126QfMbvjEkEpFiObGZrth4Q3JZ+dVO+1rr3zNvrz6y7Zry8aEHPOZta+6UPBdu3Ykn90geXAAAMjtSURBVM0/uN/ap89MyLFNnz1ia2PEyUUkxREJmGCZ5Ji0dJTqHbQzqYkgjwRYqMu4LtJRbEjAshanNEmWP5rUgpLmoN+TgSJpcCoG+q2yd4O179xhR/rKxYxoaJxhze2XWWPrmdZVI9WHgaYeq7QesUrLkaPuzbrt8usutH2Hd1hX92EnCTqS9IVJ0ztt0aKF9vTTTyfklw+LoiR1LL4AEqKjXeoqzVBKJK5IMhv+Lk3yRhoiGDzvCZ/cRclQLS9QTDX+dxX5K+2o2ku+FYLY67CSB65BCqhXGc2p/0n1gP5edoEV8Q8JuCfK8tPtdVRlZIrkDgl4GYRSW4/wdCXU4fUS3CKobiGk9C3qQnlW/xEZpK/KENBv4nmvXNF5QqxyVDN+RfRFWn0dqE8U9WdBJ0lZnnVCXWV/yuL/8j50lDnl8BsrlY2+dvXVV1dlbFmGGNNf0ozopMKWN87KCFXOmT/Z/u8vXGFffXKz/dEdL9rn9v6IfbP/cvudln+wm5sePfbhSr/ZD/632VNfNHvdb5pd8n6zpmxKM7Njpr1v5fvsvee+157c8WRCju949Q5rPNBji44S5Dl72qoKGd+9e6dt2T2oMvONZx61s6+53s644hqbcdrC0mlE1BciKY5IwOTGIsQRmHRoNdkzmbGYckQmw7ZaIctSWVbeea6L+A0SHklMwnCu/og6zUrd50FEOG1xywq0kV8us4OVVuuqtFhPf4PN2r/apuzebM195aSADU1zrLn9cmtsWX5Cemreny/kF1WH5s5+s7ZeYikfzWPFeo4uUotPx0vHslxJ0PPPPz+kTxv2A0UOhKT4yGySvkt3lWdk4V4GXlrmCaZvXx1/e7IiKaz6BP+zMIcES6S6Wvj8hJDk1W/StOHivdQnhqEK1Sxwj/pG3xwvFkUQofdE3RNs7iNJ5dhegUBoH41rpPVcr6Z/ifikkXAR4fBI3286eZY8qU7SDLsg8HL/JV1xX+/y9S2JMeUMQbuyuaAvp216VEcyeFTkNx/CmPdyXX1ZBndpQTBE1IvAfCWVsHAT71W/fD8PvdMIK1euTIhxmtcMdMkxnqu1K7FQhzcUquQFZCorVOEdb7vgNHvTyrn2zw+vt7+6+2X7hUMfsTf0P2qfavm8zW8IQpd37zX71n81e/RzZm/4XbPT35DoG+elf8HsC5LPr1/26/btV79tX1r9Jbtjx1PWcaRxiCDP3dVelau3PZs32oNf/OfkM3PhYjvjymvszCuvtenzy/vbjzj1iKQ44jikkdCTgTxLZXkP0KIXSgp5nus8j99T0mFB45qXHPI79I6RqOShjAQr65n9A222Y2CCHervsP7+idbZ12xLBw7akv4j1nB4q3UfesFsYPBovQgNzQsGyXDzosJ6H7DK0VDFh4d8+RLBLflrRxJRtFwrJZJ/jtWtOVF5UB3yl0VUun9ZkiCe88Y2YXvI2Ia2w5ctkiORNS3WUmuQ9K8MPPFMkxZLApnmgUCW+ZAndJTD4Ay6TxkhqEVQWbzE0R9nS2XB30/DJZdcYitWrEg2Gag3qK5RceC4G1KMgWlRMJEzzjgj2cTKP7evG8rFuKDvYwAGucsKvJBGKtMgTwN8Qikw0IYoi4jyHH2IcmKQJRdg6h933XVX4gIM92WoV6hsfoOuTRZlo11RxZBHDn+yxV+MNbmfB6X17LPPDqlJ+PzybvwPM1flBcFADYW2zAPpveMd77A//dM/TVXD8sRXbu009nRf/ctvIi666KJkzJEP8sd3v0EcCVdioTFe1vxQjVClrbnJ3n/NUvuJSxfa/7l/rf3ve5vt9UdW2n9p/pr9QtPXrb0h6GM7XjD7lx+zyoLLreH1/91s6XWF75jQMsHeefo7k88re19JpMeoWLy4eLu1QZC3d9qSLZ02D4J81LVlGezcsC75QJBnLV6akOMzrrjaps07rXQaEacGkRRHJIAIsXB6HT3vi5JJmPu1MiYocv+GGy3IARO9vE94q3Ad/yLlueaaa5JnkIh5KQ5pol6BNAWfwEXIkvz5+/j8bexrt0N9U+1Q71Sr9E60SQNNdmF/xVorg+QikTr37rKergesMhBINTLQ2LzAmtqvtKaW4NitMmCNvQftQEOP7Wxrst6OXuts2WPtLfts6qwJ1tzYcJxrJw/qSMQ2cVvk6oe2RZrGYiZpXJYkqIyxjRZpThRoC21S5FYNgkObyAVeEeQiTQZhgr5LYumJrm8r9RWIIbrO3oWVNk38D6GRZ5K8vDAOKKvfHHkSRV1Sxrx0qHMFQgDS4ffHzpCWVatWJd4eskBABdxsQcTSjDtJnzEkib/824aGfVwvKymH1EAy/YbEk2PIJW377W9/O3VDwDMQNjxYKFgE5ZZaCXUrLx64B6NcbFa8zjN5pb24T5uh0qOAPJLkMh68rnUeyKcCd4R5lm0FaaFPmxcEg/agrtP8ZQuMK/KmyHdequ7rlPvMwxg2ylVgGEwFKTF/Q8JL/yPPIrwj5Waz7PxwIgKWCW3N9suvP91++orF9tl7X7G/eWiCffHIDfaJln+1tzQ9fHxeNn7f7B/eav2Lr7Wmm37bbOFrEvM8LJuyzH7+9J+3dy94tz2y5xG7Y9Md9lDbQ7Z64UFr7Wm0Rds6bMnWCTZvZ7s1VUGQd6xbm3zu/7cv2KwlyxKCfOYV19jUufOqqoeIkUEkxREJpJPH0WWWjh4Tei2MCcq4f+N4GZdKWthCki5JkAjfG9/4xiTwBiRBuq4sLEje1q5dW1X+Ep2y/mZr6usY/PR3WlNvu1X6JlhLf1typJZu1mE20LfJeg/fa5X+wNdmARlut2nWcXi7dR5+yFqO7LbtbU22ceZUWzNjojW3V2xe4wGb4NybVSCFrVOHnPtnBUuQ3qj0VXlWUi4FPlE9DtfYhvsiEryT+xAavtNGtBtS4rILo9QdQvIlEifJJZARlNetVPQw8kpaIhUiInKr5j1hZIH02KRRz2lSUKmM5EXFA7ybvq0gCAoeQ5oQdwgEx960Bx95TwiNlriHNJ56lo6ySL/0/yGUPMf/jGuIkAi0jMggsfL2kKdnSz2RX6lshME75AM8zG9Yh/hwliQ5y4sHEmRILy7ekKbLQFB+nulrlJt+SFmlCiIVCEglaVE/RbrD3McTjFQhQj1w0iawC3kBWUEwaAPGAb9J26RS/3g/of7l1oyNY6gaQfkYI6RDGSHhUhFSn1WfoX3zXKCRrxPxCHGiKDM/DAfTJrTaJ2452/7LdcvtHx561f77g/PtC11P2W+3/OPxXiqoz3X3mX3uDda15PXW8bqPmS26MlOtIk2a/tMzf9o+9LoP2b277rUvPvdFe3nhbnt54SFr7W20hRDkLZ02f2dHdQT51TXJ5/5//Qebs2xFon985pXX2JTZc4dVNxHjnBTfe++99pnPfCaRpDAB3Hbbbfb2t7996D6TzCc/+Un727/922RSweDgr//6rxPJisDE9su//Mv2ta99LZmA3vWud9mf/dmfDblSGm8IdcK8Hh/wDtqHq6Pm3b8Br8dHWrJURlrGIisdPEmWaC8Iipc8MOFy9Ep707akwQRNevING6JhoMka+9utqa8zIb/NkN+ECLdbQ6W6oTHQv9v6uu6zgd5iiTTo6Ou00/Y325x966zz8KPW0zxg6xYssp2LFtquOTOTAHId7U224sixR7oeLHbyvMFCqUAGCtoASUByhMRQbsckoQQ6jlbgiaQcAwO5Tv6pZxb4rGfUtlyjPRmf6iMsjlq0i1yPSYotkh16sZArLhEhr6KhcojU0CdEDuWCi/+pM/LDglhWoijd3ZCoyqhP37MkpbyHtlAbIXmVjiybONoQF3LUodpVIaEVTpjfstHjPm3HdXSDVc9IxikjaZAm/1NunoNw+Y0DBM4bxmVFkAOUnzzznXGpehQx9VJdGd55FQueId9qm7QTAACZgqDgM5f3pakG8J3yqfwQW4Ugpu+RN78ZzjL8A9SbVDQkMRZJVoAd5ScrCIb6lU6wwkAy2gDwG/lQRo2EjZDaDQm43BVSv5xw8ayEEQpSoz6rzTDXyA+/4V185zf0I64XzbM6/Sszpxc9k6eC5VE0zxSR4w/fdIb9/LXL7F9/sMI+eO8qu+rwd+xXm75sC8Nw0cyjr95l9vd32f5pK23iDb9ijee+w6y5tbQ//FvOucXmdsy1Tc2b7JHuR+xpe9peWXAo+bT2NiRBQk6EIG9b83Lyue9fPp8ECkF6fMaV19rkmbNqqgdeBiP5rnrHqCTFLBIcV73//e+3d77zncfd/6M/+iP78z//c/uHf/iHZGL5rd/6Lbv55psTSaLI3nve856k0995553JJPO+973PPvjBD9q//Mu/2HhENTphw9VRkxSahYNFIdQHRNKjBUR+ZD2Y8Dmq9e+69dZbE6mSyAgSYyKHnXXm2XbOivOt9ch0a+xF6ntU+stnYPieNCoDh6yv6yHr73k6CU1chMmHj9iZW3fZzANddnDiRNu0YIFtWnCl7Zo54xgphgzF8iC3YyyO/tiWuhNBYJGknSSFo669egkkCzKDfupLL700JAnTgk5b+HCwaW0PsVPbq22ZYOlLQ67bGgbVPOhD3j1ZFuTVwhvreUAiJekFkgJ7KZ+Il9z4hWoGXKOPlTH643dSjUgjxVz3YW6z0gDUCXVMf/VAgkx9K3qejsIFeduQmgBEBmJBPXsDUdKhnqXjq2N1ETPVEW1H3TKmvdQ9hJ6VP14FzBBkECfXeNqMhHUkYqy60Mc/o7ak/6TpHfNdoYcpC2Q4lAgjgUUqGwZq8e0QtpFIrK9rH8XOh1ROC4JBXSuQiQ+sofeJ/CmgiOZZBanRc9rQey8uoa9hjSXagv+RZPtxphMAqWiQTt48S7uXmdPLzvtZKlgC0uuieaasWsV/vnaZ/cyVi+0rT5xtv3j/zXb+zq/bLzffZnMbjldjmbznGbPbPmgHv/Gb1nD5B23Clf/ZKh3TCv3hk9/+vn5b1rrMpnRNsQsOX2AvNr1oz7c8b3tb9taEICtQyD3/9H9s3hln2YLzLrTeSdNsf/eRkx5SOoavPhYNlaLzpToHA9BLiikOR1Af/ehH7WMf+1hyjQmWSfLzn/+8/eRP/mSyGCE9I/QmRi/gjjvusB/5kR9JFhm5+SoCEyULCukzkY12+B1zmk4YkleQpqNGHegZDaSs3SdEF3+d/A19EEtFQgYtee6iaMMf+7Efsy9+8VZ74ZnVr6k6iPQm39utoZQn4OrQbrutt/thO9S1miW08PkJ3T12xtbd1m7NtnP5cls7Z7btRYIzzN04m7w8cglZYTHWpO8lnXInxdhgk0nwCbWVbw++33TTTcnCVdT2XCNyl4/qpsVa70OSxW/yIv+RZ57NU0cQEfN60oInfjLEy0JZl1oiOV6FA/gj9zLpFLUZi7GOzLNAPcvHcqjjKzAO3/KWt9h3v/vdpD3kmk7l5R38hudoU/pIloSb+YA8e7d3QqhuUBah9wVPVqX2JENb7+GBMtDXnnrqqVwdXvoHZLFIV7woz+SRk8S0aG8Cc5n6vepZmzLKwP+MM9YpvufNs8xrbOj5P81bA+0AoWT8yMVe+D5JrElbKlNZ8yxklDUxb1xXM+/nAYKJbrb0wMNw7cwz1RBjD/rOQ2t22T/e+6LNe+Vf7RebvmKzGrJ9kPc2tNiueTfYC3a6vdh3mvVVGlLriHwCndgMBYqqDNimyiZ7sfVFe7n5Zeuz1/oZKhaJDvKWCTZ/Z3VGeh6dc+bb9BVn2cRFS+1Qz2DgnFqGlM7SOd9XZbuOBpTla6NSUpwHJlJ2xQwugYqgcXFdAynmL5OBCDHgeQYAHQQL4fGIIp0wdo+4iiqjo8bvs3afkCJJM7Ms4iEG3nI8eddAYyLpTSS+fR224cF++7cXv287Nk2xGZUral4fjdZj05o32dSmLTa1eZNNadxsbXt32PrtvfZc43Trai2OHNfe02fzDx6xI9Nn2RPXnGcHJ0+uKmpZEYoCL4RGRGEUNB17Y/xEe7AgeGkybcJ1xgUeAYraHldQTD5phktA0jSFsM3Lt0ealT79k8lNaguenIogltXzLYNQCh2Wq2ybFrVZUWATwMKl6IpZJB3iJF1gkSttIiRRBN4AM6vcYfCLtGP1E+nTWR5FUAfJi56HSoXvI1n9oygUdhkiLw8VRW2q4CY6tdCpggKe0K48h5pE0TyLtD/v1I45FIk47/ABVdQ3eQ/9no1s3jzL5hNBUN641onGcHWTqRckxPQ3nx/SoZ3ID/dRUTkRN4m8+6rlM5PPul0X2N/d9z5reOIf7Kcq37RFKWoVLZVem7v5Tptrd9pFNtFemXCRbWy+xHY1LTrGHz79jL6nUwAvlZ/ZMtOmHJxiN8+52SZeNjHxXvH87uetp2XAXl5wKPlgpLd424l5sTi8bXPysQcbbNL8hXZw9nx7urXFXveGNw5bvaGMbc8L4zB89ZgjxQqhya7cg++6x9/wSIpBwATiQ3CGkDsioWwkrNGEPJ0w6fEVRS1iY4KqSp5nCVnE65h/aHfefcTaGyfbvo391n5wfiL5be7rsMYMdYddGw9Z4zC6ccUqNtB0xLqajtirDbhz22M/3vZVu6jlMZvYuCtxOHxoW5vtX99um3ZOsYdnz7bdE7PM7F5D00DFOpva7cCKc+yZyZOc94oTIw8nCt4pX7xh0AkRGeodAgV0dKjnRJYgzlyX1CBLP1GGPXkEyxt76ZqQdi3tu6C6lH6q1431Osa1gk/f6+aGRmMjgSwvGIKMxGhDxhrjUD6iaTcIkSK6Sboo6b5/h9e11bvS3le2rvWekEjr99qIyRVjuNkRQfHvy+sfRTreZYDaxg033JCpC+v9qqf5w9baoQ1cke5tkScHiDXeKcIxrXIpeA5InWePHBkKa8+aB0nNmtOZt0HeM2XCHFNvzDOKLukhlQ7u81yaiko1WDxjgn3i7RfboVvOt68+8Wv2hfu+aG888CW7rPHF1Ocn20G78NC9yWdX4wzb0nmObZ6w0l5tWmCHugclxjJU1mmLvic+0gea7U1z32Q/edZP2vO7nk/I8TfWfMMO9B6wntaBxIMFnza8WGzttKVbqvSDXKnYgU3rzTattz0//L5tf+h7tvK6G23FZVda+4QTs4Pytj3DadexhjFHik8mPv3pT9unPvUpG+vI0gnzoaDTwMTNpF1s8fyiNfQ326zOxbZ322E7wj7kSIvZkVazHkLdNiQds5YmjwMNvYkvXwW0wJ8vf480ddsT/fNsY99E+1jzP9pPNX0XnSI7vKPVtm6YbAc2tFtXX4u9NHe6rV86uZS6w8Ck6da1YKkdbB50NZVE8nDH79UcMdcKnjwKXmItUhTqMiuqlnR7pQcuvVIZb0k/kY1TURADX/408luNVMIH5fBkTeSpbLQ6/SaLPOmviFToNSHNAG8kIJ3XsF2BvDIwXmkvHaMLLHYQI0+GQ6KqzZR3e5dXR0Xl5znIj4w8w3cpWI989Ipc+vKqT/o0s77zW7kEDA3tZFhcBqhp5OnCavMvLxhepUcnA3JP6POZRTaKTu2YR2XMqABFKrcM/lS3GF7K24n34IO0mXJJJSUNIuNguNHq2EhI1SMrHfKYp1ZVLdA7fvcVS61y+cftiQ0ftD+5+9u24pUv2M0N37e2hvRN7IyBXTbj4H228uB91m1ttr5lua3rPcM2tiy1vTbZ+o6emqSdAoCzZ5xtvznjN+2jl3zU7lx3Z0KQH902GI3vCAR50cHk036kMQkSgpu3ubuqiKRXqdim555OPnf+7V/ZkvMvtDOvus6WX3y5tXV2lq6bMuv5gRLtOtYw5kgxO2iAbhcTiMD3Cy64YOiZ0Fk/nYPJTr9Pwyc+8Qn7yEc+MvQdAsiEM15QJmqRjvcSneC+ih3c3WPd+wZsoKvJ+g83W/f+6bbzgFmlb8bRXx3r5WI4qFh/ErJ4kPQeJb9HP5XG4yfAXQMddk/PWfaGxkfs863/YhN2d9v29ZPswPoO6+tuSszmNkyfZC/Om2G9zcXhUgemzbLumfNsoGVQIuMXcJGGU0GIh/IXSMVCKV1alC1vMCXvAaFqhPxXs5DrSL8MigzSysCTNS99Elmo5hg2LXqerwv6vIicD44g1QndH2lk9TP+x3sDkj4vvfc65UiJkQipzIpQ5wmwJLX+9yGy9JpDSEeTNBUpTlJ96lRH+9Qjn1BVRbrQHlnqHLqnCJ1hFD6pkpQBv03ThYWccv2qq65KyD79XzrFCubC+BABDqPznagnB212NB+nRdnThpHnkLyGbjb5LpKeN6eXeaZMtDrZCeSlI3/gtQb1ctGiaXbRz/2k7Tn0Trv1B8/Z7h/8X7v60J12cSN2IelotyN2Ru9zyQccaJpq29qW2eaWpbatfbkdbJthR3p6jjkFGPptc7u9dflbk8+6/evsttW32Vde+Yrt7Br00d7dNmAvLj6YfIikt3gLKhYTqgo1PdDfZ2sefyT5NLW02NILLrGzrr7Oll14qbUEHqROZhTCsYQxR4rxNgGxJTqSSDAkDZ3IX/zFX0y+o/eIRAuXbvjDBBijMJnJqCANctcy1pE1Wad5qKgMVKz3sNmR/RXbtaXHmnqnW9e+ftsDCU74QbgLHZ7Rm9QdEklvS5ddd9Pl9p37vjkYya1pMIpbGrSICav7ZtiW3lb73OE/sEUbttne9R2249CgmgPY29lmz5420/Z1liDtEyabLV5hR5pabeKECUNHpaHKglQVvPQlD2GeQ8j4aLjwEtAsSJqIFIf/vV9g8sh1yEJZI9Xw/UKWxC/rGRE2kSl/vZq6UQAJLykVSeTd8uksVRSfBxFkSE8Z37i1gohNVj+DgOGpgblPZfHl0rOSysqIKKzHMMAECJ8BXpdbdeKJukJB44GBeqIuvQs07jE2li1blkgwFaTHt736qdpLm6K0/Cg9ScP8RkYSVbn18+n7ZwR5AsnShSWIiAi43indbRFy+ZCuhcU/ahtIqUknK8qeogdS1/KN7MvGush4lQpFntchUPRMka9zn2cZBvo20+mi98hxMoBLt59+3QVWueF8e2rjx+03bvuqLdhxt7214QFb3Jgf2XJS/16bdPhxW2GPm+0z627stO1N82xnywKbtGGy2cTrzaYtPe5EcfHkxfbhiz9sv3ThL9l9G+9LCPK9m+5NjPVAV9uAvbDkYPLp7G5KCDIqFrP3lhca9ff22suPPJR8mnHxt/Iim3ruZda48Gzb12u251CP7TncYwePDM6LdO8tWyck9Y55QUdzg83qbLA5nY02u8OsZ/9+O21+dVEIxwJGJSlmAfZeCZBe4ZORAcck8OEPf9h+7/d+L1HYl0s2Br88VGDZ/KY3vcl+/ud/3j772c8mk9eHPvShxAjvRBb1sYSsyXrpwhWJfm/b4dl2ZN1he/npbqt0t1jfoUaCrh2FdvjFUtUitHY02UBrtx3s2e3UHZD+duNgOHkG3eS557bZwGMHbCBHAstiRD8gcEDfgNkreybaLRu/Y1dseNZ69zfbLnuNDPc0NdoL82bYxhnF3kQqLa3WsHC59U+daT1H/QPjE5v+iScHqSR412B8x/8yeckzAGMi4hSC8LNZx9VYaROpL086qahveYTXkxl9HyqjCywgApamfybpVVoQkTR41Y208ikYBpK3rGeQzIXBPXy+RV6LDBvp46iAyBtK6KFDx82QYh37h0f65FW6hkXeDopODEhH/TWr7fHXS/nz+hnhkukfOk7Xb30d6Zjdk0w9508PdF9qCOEpg9QRyIOCeyhPSkdBPiBxGNGSFvXuQzjLRRxH+5A55d3nR0EzmO81PtIiwymiH+mQbuh5Q4E+eEb2IWn1TdsX6cKingDhlRQctRSfZ0nCeY/3PpEXZU5Gbj54CeST9UtBQ/Ki7JGOykdaXFOeGDeKMgfIV5b+Mu8resb7jM8SqlAHRXnm/okY2Z0IyNP5C6fZ/Hf/iN334Az7601vsX17t9nyw0/YjY1P2AUNr1hjQ/4Gt33gsC0aeMUW4Z/+znvM7uTiFLNZZ5vNOsNs1llmM88c/H/yAmtpbLEbF92YfLYd2mZffeWrdtvLt9mGAxuG0jzc3m/PLz2QfCZ0NSXSYwjyzH3lBXJ9R47YxsceSj49DS22ZsJSWz1hha3vWGADDeH6jODq+DmrpbHTFq7vtYu3PmVvOX++Xb18hjU3jUzbnEqMSlKMpfzrXve6oe9Safi5n/u5xO3axz/+8WRnj99hBh9hgHG55nfK//zP/5wQ4de//vXJIMTlDr6NxzO2bN5mD33vUTu0u9eaByZa/+Em279/wLbsP2JP9XmfqsVeF8qgsbnBWiZUrKGt1xo7+qx1UsVmzJ9kKy86wxYsmef8D288ToLIRI07NhZFLexZBIL7Hd3ddua6TTbpudX27j2DhiO9rvvzy43TJyWEuEhVooHj5bkLrWvyTBtgsu/pSRZ3jlCZ1AVZWotoaNLHtROLAIthWrhjNiEYgsqXqNwuCXK3xMLI4oOlelbZWcDktipP99Ifm4d6nt7oTt4e+IgwSldSAV7KQMdyocEU74KEUF4WU0hGmvErp0GkAeERKfeqFGllyQpMIcm3yElYfq5TPvILWYUYyMUdv0VFgbYgn3p3GukVuYMwQdKyIuPRtsxv9Ck2+2HbcwKGqzUB15LqZ5JMQohvvPHGxNMO9UC6oS9etZ3aTeUPQ6WLTMpgT66pfJ5oJ0lkvaGZJ8wKCgSRoh/RvyWBlX6u2pS2510YXYWqAUgSSYt5nb+qI7+Bo47e/OY3J95yuKfoit7zBvWLEIR7fEelLuyLjENF61NI7lANQ7qXof6uyLD0d6V3ymanyOKf99xzzz3Jd6/OQ9rUD0Z/cl2WFWVP9xEO8Yz8WauuccMoqXSZSHQ8A0nHB7wIL0IokfQyEvCyeR5J8O5rr7rcZiX5brJ93bPtX/e91T6zr8tO2/94QpAvb3zeZjQMtnEhuveZbXh48ONQaem0hmlLBiXJ05fanOlL7eenLbMPXPdn9tiR7fblNV9PdJCP9L+mw3uoo9+eXbY/+Uw61Jx4sFi6ZaJNP1B+DW6t9NpZB19KPt2NbfZK51JbPfF029g+3yoN2SS3d6DB1uzqsjW7Ntqtj220GRNa7UfOm2dvPX++XbKYID5j0yPFqPdTfCoxGv0Uo+5wYE93YuC2d1vX4N/th23v1sN2YPfJ0YdsaO0za+uxSku3NbT32oQZrfb6W66x+UuIpGaFkXRYzFFvYRJlMmexl54bpPgrX/nKEDH2C2gHPqvXb7DTXnnFZm3bZo0ZXf1Ae6s9c9pM2zPxtWhvWZi6ZIWteP0t9sPnXxjSH2ThY4FFLUekmPxCSLzUkAWCZ5Dy3X333Qkp8NG4WJhxe6bgF8n7pk5NnsmKfMU1+SwNiQqLMSRETvzzjoelDhFeD43MSJO8+mNvkR3a6bzzzkukl0WSaZ6nreUCTgsodcL/XIOQ8D+upzyBJK+QGYXyVejmMN+SVHppp0ivyK7S1TgOXZPpdyzstDG/5Z0+XDbthncbCA959STSv0sSShZhnpVBln+GtLh/yy23JPc4lodkqO0hF9SxP2Kn/GlR3zQ+8J+bJ5lWgAjeQZ7CzQV5on5krCfdVN+vyRvP0T9ECLXp0l+RZ6mhUd+hwaZ3sUdZsiK6kQ82DvyPhw3qiDqlX1FHK1euHCKZkspK9ULS69AnMNch4XoX5Ju8kQZjSIFQwk2D9DK1mdWmINTfJc+o66G2l+eDWESReUG/914jSBfySEAqlSkrOpzKz+ZDRmEKwkJblPErL2BoSFrUhdqMMpMG+anG5+1wItqdLKSVf/uBI/bNp7fYt5/cYHs2Pm+XNLxglzW+kJDktAAhJ4oBa7Adlan2YsMMu2NCpz00+YjtbM92zTj5YLMt3TLBlmyeYtMOnRg5PdzYYS9PWJZIkDe3z6vKZ/78Ke3201cutg9cs9TaStjb1APGrZ/iiEF0H+o9hvAmf5PvXdbfW3uXYBiyIfFF0ttd2W8dU5vsUO9uO9i72/oGBq2j6YhMoomRyAQW43LW+jrmlLN5P3ly3R/ts3+es2mzLXr1VZu7caM15ZCzvsYGe3nONFs7a6pVCiaEgdY265m3xObfcJM9/MQPj7P4ZnGH6AKpT4RH0SxmXOd/6WYjZTymHo/q6PmoVmkEU8eOCussIub1M8PIWuQnlBTqWF2bDC26oYRPhAAJaZgfFkEWWfLM4qbgFFmSWe7TjpRPUmGRKOkWUj88i79aESpfj1zHd6wMydLepXr2ZE+Sb0mWVXZFzcsy2hrUu+tIpGR85zciGNyDwCA1k8eLcIFXvqlHiB3qAyqXl8bzDGWibZGmyRezyByEnPd5gsFvpdrBX59/yIrcc/lNj/+f+7QbxDI0qtNGE7JHX2ADFoZnlrEcusKK2CZPJCKGlEkkClLFPUlt1baS5lIGBXjhr4791Uelw6o6Un9RuUTOVEdeChpKL71PYP4nXT1DvujvtCvvou694R/vUz9nUyT9XfIswysZuinPXsdZagyePHOfTYB8TEMmVdfKE/VEf6MOFXY7zYWZ90NL2ZiXyC/v4Dvv8X5oZQxYTdAN6pTrvIt6L+vzVqdY2iClGW+ONNLKP2dyu73v6qXJZ92mM+y2B0+3f3n1evvt/Q02qW+PrWxYaysb19p5R/+WliYHaLSKzWnYY3Nsj12HC/dDZi+0ttiXJ060b0zstP1H52lh/8Q+e/L0fcln6oEWW7J5oi3dNNWmdJdf2zsHumzVgWeTT3/HZNs39xxb3bnCnuqZbN19+evy5n3d9kd3vGhfemyj/c93nGeXL5PhfDGKNl+nGpEUj2JAbvfteE3au2fbYdt39P+uA+VdUZVGw6CqQ2/jYWto77G+psM20NJtlZYj1tLeYL19vdYxY0bSyRN9s207jpGoyUIfUsTfMsYmSFx1PKxFD/Kp4+EkrdZWm7x+fUKEF6zfYK0lLMp3TWuzxxfOSaIa5YHjpZ5Z86x3+lzrnDgxCYUsLxthdC++Ix0Oj5c98ec66j+o+iDdSwv1q8AySLDQCVV0OIEFkUUYyZp0FKnPUKVBrpYgc2leIUSAFPWORVKEWPkWmWLzQbl8wAhfPnlkoN34IAVK7UJHI6gh7STEunygCpSHslD+b3/720MELc2fLfXCcTXS2bTNFddkhCQPDKFqBKSDxZm+KHLpj6v50PckvQuJrDYWMkIUyQqDk0hFgLLreR3D+42MCBfjgjqUizuvPkCZZPTK2AhDIWNkjIEdpxa0l/pGqHMtSMqviHGhpFySVQUtCH+v79SR9NwVWELgHtJ9jvIhzfKXHG7A5GaM/FMuxkj4DOSS90DU6AMi03qG8tCnRcLK+ATmJEabAt8/+C3qAZBUnkmrH6kj8BxtlpZnys57JFmmn5OfMPSywkXzHv5P099nPFN/zJvhpjrNDy3p54WCLvJDWyboBpt99csin7fMV6MxrHBna5NdNKfZ5g1gSN1j+yqdtr/lOltjb7Zv7DhiL27dZ3Mqu+3sxnW2omGTnd6wyVY0brblDZtsckNxQJ4QZ/X02n/bvcc+umeP3dXZaV+eNNG+33G84d3eSb32wzP32A/P2GPT97fYuVsm27Jtk63hUHmC3NS136avfdgut4ftDbNm28KLr7KW0y+yNQNT7OtPbbEnN6bbv7yy45D9xP9+2H78kgX2iVvOTowYR3tI6UiKRwG6DvbYzo0HE8K7Z0jt4ZAd2MVCUPv3NXeYtU0ya53M3wZrxQ6t7YjtPbzTtm3bOrRwJJKLo9I7DM0AEyQTJYtDlkSNAeClPFnGJhwdS+IqwiDpDNebN26yJevX2fW33W7tBRHSQGPzgLUu7LUHZ821rUeKA3D0TZpqR+YuskrLa5IqFrPQ2h6ISJH3Iuk3+edIm7pKgzyjQKwgF4I33OG6LMcVic6TKumPSufR/96nB6RrL2lt6NpMRlCS7IVGbUrLG/xlHdfrOvnP8n/JdTYOMtpLW2QB9YPkNktVg+uQGUnu0lzScd2rjoCw/HpWfTXUu5VrIxGzvLLzkY6414MX8ee69GhFaHxeyK/XO2UTplML5Zs+qlMLSFOa9whfl/xeeqJpZJfrEN1Q/zt8jvyi3wt5DIOA8J3rlE2qI5JS+02lykIZaDsRay9J5zobD/KkOvJGdNLd5TubLwWdyCN+WZIqrlNuxnVaHaoPcJ+yaWPpy853SX/POOOMhJBC6EPVCIVtZsOr/FC2rLFcJF2j3ZB0S93Bh4KmL3PaopOUPJQJukG+ZeuQBuld0wcwkqesSos2pz29kWG9wauGUEbqck6iGrLLzu/ssk+84XLrnDzNntq4z57bvM9e3rrXvrL7sG0+0GOb9nbb1L5dtrRxqy1q2GaLk8/2of+nNOQbJ7dVzH7k0OHks7G5yb4ycaLdPmmCbQ3tNxrMdk/ptfum7LL7ztxls/a22JUb2m32tknW11teB3n/ju327B23m91xu02bN98+ftV1Nun6S+3eHY321Sc320vbjvcj/cVHN9p3nt9u//3NZ9s7LjwttW9mqdeEBqanGpEUjwK88NBWe/BLr3nbqAVa2pts2pxOmzK706bN7bQpszrs1S0v2Z5DW232vOPd7uzYgZVxx5DvQk8kvL4mixETZJ5EjQkWCWneURsR8SDNfkHn03H4cCIRXrT2VZu6bx+nTPmejhsrNnFet01e1GWvTJ5sX9t5hjUcye/2kOAj8xZZ38TBxbTVGQ8VRRErq6KPhCsPlN8brWkBkYoBoJ5YkADtEraZ6lueALKMv+QaS6RaKhVAv4eASvIsAuclnCLFlCsvKqQIMQtyHvAoo4U/y72XDKiEUIcVSJ86j6gqfKt0fsNACN59GYRGwSe8sRXPSrfb58e3B6B+pIqStrngOnn2EsnQdZUk35Rd+rdemsyH70ha3/CGNwyRHrWdf05BWYrCPKepzYQgnaeeeiqpG/ma9W2m8Mx+HIXtok0vz1EGNmyh3jXX2TRCNPm9PD14FQOIMeNDIY6zIBUD0tdmXRIsxrzC1cuvPcRQ7eA3oLyLPPFbSG2aizTmsxUrVhzXJ8LvlIcPRCFLmi7jvTzIQDAvFLQMCIcbdEMnFUU+iNl8MQZ4PpRcM8fUY1hhr4aSpxqC4eeKyQPWt3mTTW/eaRdN77Pm2c1JO81aeL5Z+yQ70jdg23buspfXrrfH9+xLvk9q7LalnUfs7Blmc5v3W+fhTdZ2cJM1H9xkDfs2mh16LUT1gr5++6W9++y/7N1nD3e0220TJ9h3J3Rab1hfDWY7pvXaV6f1mlUO2NJdLXbN+jbr2DnZevrKE+Q9Wzbbw1/6NzP7N5u5cLH9j6uus4Ebzrc/eGBnsgHw2H2oxz7yxSftrue325/+xAXW2tw4KkNKR1I8CgB5PRE0NDYkZHfq7A6bOqfzmE/n5MEQqse8Z0eTff/72W53GNxIOPKkdzLiyJOokR6TI8d3WUdtqCloMW8+csQWbNxoi1991WZt217KrXnnrCM2eUmXTV7QZYcaW+2L28+wfVun5v4WveLWhcttwunnWK9zbaV6kBeHkYInZH4B9RIWBQZIq0eRIy2mIQnT0S/tJUkadR62mSTk1IckW+GiL0LJZqdI8lQ2YIIkiWlImzjTjvb5P0siL7BAsyjrJEKkTfrPQK7WFBAhhKR5IYkWdC00CkzzCewlyGntqnb0ktvQTzGgzTiZ8cQ07a/fNIR9Se8qu9mjPyoqXdjXFLGsKIANdQnhVVn8JkV9k/u+LfwmTf2d+5A+H8QpL9Stb29BYY6lu5/mXUUnDvQznvcnNoJCGLPwU0foANM+PsqcDA2lG64NnXdtJ2k6uttFoXfZNKk/ajPt60i65jyH+keWrmeZoBs6BlcQn7APUVZOLZA6e+m+l1yH0v16QdlwyGzkEWaEklCEBJQ/iYHQbLZx0/M2q++wrVhwrDHi+r5Om3fxW2xiKC3t7TKDHO9db7Zvg9medda0d71dvXedXb1nne3Ztcm+ObEz0T9+qS1FfaHBbO3M3uTTMHDQLtvWZBdtbLeB3VPsSH95grxzwzrb+X//0cz+0X5s6Qq75bSV9vebp9j2yrH85BtPb7Gu3n77X++5yNpbmkZdSOlIikcBILF5gOCGpBciPWlmuzVV4VewKLSoDES0QIQTn/eHmidRY0ErCi165NAhm7dpky1Z+6rN37Qp12BO2DNlqjUt6rWLlrxiLRNYAMwe2TvP7t2x1Br6C9ysTZ5qzcvOtsZJU6wRH7kpZZOUaiTh1TPCa0BGT9R1KAUUYdZvves6LYr6rgVf+qd+Q+AN1Gj7NF1YtXGtI7p5AunJncpdhqxJL1rEwtejiAYkAwm2VCAEyiuPBmwWkRiKIHvpnYKyKApbaIwGfH3re6i2EboWzCOkRelIlcAT+hC+/fQ9zHdZQuzT1MbC9xEfejgPahMZqYUnUpLm+k14mhvBMhKnMqFuReL9+PJ51TURvax0IEuQI96HgEGbUG9oJzUUecsIx7/ItiIU5pURyav6Zpr/cEnVeS5P17Ns0A102NFxzxKqMMbkPi9Lcs37i6T7I40yfYQyIjDKk4TKfqRqaWlLh9nM0wc/KZjWc9jes2et/dTO1fbclh/YbTsetW8e2WoHUnwsVxrNvj+v374/75C19R+0N21stNM3d9r+fVOtZ6A8Hdy+9mWztS/bT7ARnrbQftCw2F6esNwONw9yle++sN1+/guP2v/+mUuso7VpVIWUjqR4FABy29bZbJNmtB9DepP/Z3daa0ftmjHPKAVJCPfznOEjDWDQS6oQSlbkE1VW+sdIHoiE9eqr1vLgg3bto49aa1cxweqZPNnWLFhg6xYtsNdNvtfOb1yTXN/b027/vu1M23dwcr50uLHJKguW2uQVZyd5UQCK0A2SXFOFBlQnG2mhl0NixcfrlUqiJqt+HT9LMuzTliSK8qpsocsp/mdRY4GTEaGPNCayxTNl/RSXgTceDFVHdORaNlgISCM0vj69Tq2XyHp3aEgNZcGviILUiULpSqKuNPy70lQF8r6XRdapjQ8BnSWdV/uHgVRCNZRqoIAqnqxLil5tubJOQHSKoM2J79faBNFmSF+HG+pWhsFZ6jXAu+HLUx+ALPr3hZJpea2Q7qqM7rwBLb/nlACpa5rXCQEJr+oIpG0Idb9I17NM0A3URvKEKmwoQ/13ge9cJ80i6f5Io0wfkTRc+utpklAZFec9c0LS0tZOsznnWsOcc+3cc99u55rZx/q67TvrvmO3v/hF+/6OJ1J/dqSpwb6yuGK2+JCddmS//ciGRpu7baJt2T/N+irl3ax17Nlg19sGu3b3A7apfX7i4u2VCcvsvtU77X2f/4F97ucuHVUhpSMpHgVA2vuBP752xHRtdJQeggkTN1jS5ZTPUvlqZQHCupwjWwa3BoFIFJOtdslyXwQBH9iyxTp+8AOb9PgT1rLjNf2pLPRgyLdooa1bssSu/eAHbfVXv2g/0fvFJETnQMXsB3tOs/t3LLGGgXwpeWXqDGtadrZZa1uy6GC4Jr+3Ijd+IZPP3yKwaHhL/bBuqyVz+t1Qvh0hZuGTKoOXXkqiIzLmXZ55cihCQTvI3Zhf9CWxor1YEJXvNMk17Y8P5dCjxokC6RQLpEi9Lzfl0wasDMIyhxI4FizVWyh15DtGnwSEoJ7o+7xXIZ9pA8qOOhBqP1mkOLyWpmIQPl8GoVTav2vx4sUJSQnTD+tFEkUvmfbpSEWmCJLkenUdT4zLhiaXdDsrfDeLJ9e9VxC9S5tZnkE6WW3oel83zAP0fd4LqdMG1I8z7tH2QPMe90VkeU7zHuSQPp33PuYHbUS5n3baxlj0+utpoOzkhXfL9iDU8eZdCqJRpC8L5KeYMRD6KS4SqlB/frOZJXGvN5TpI96FZhq0iQB57vhqJS1tb263tyx/S/LZeGCj3f7y7faVV75iWw+l23tsamuyv0XdfcUhu+LAbnvdpmZr3zHF1h+cZv2Il0u6lVvYvSn5XL/rPtvQscBWH1hh7//fXfa3//mawjosEyp8JBBJ8ShBPUwW5IFJXZJiP/Ez0OnQuCVCYvDNb35zyFWWP9KHQCBVmIo04hvftI5HH7Gpm491y5WG/sZG2zpvnr26dIltmT/fBo5KKne88ri9v+8LNrNxv+0+0mFfRjp8aFK+dLil1foXLDObNsuanDU3hF+O+v3Cx2QGYeYYkSOyPHLAQoGFOQuJ99agv9QT9zlGLAJES3qjae+UCyQklTIC8zqikiyJLKdJC2W4I1/HtKt3f8eHzYACa/A+udnzOsVSl0nTJ82qp6JQx7yT93mjNK8zWlby6BfikPDpvhZw0g3LxntohzVr1iRl9MZ9AtcViQ2SAdJUBbjvDdu8hNNLAwH1nAXqW7qsWSoWkrR7HdeQYKoudQScBcqX531C9YguLGVLaxvdzwtv7vu+9G297qlOQOSKTDq2fpOjcSaiIl33rHDJzGnkKS/MMXMaUVG9RxTVvwJTAJ4R+fPqMDzDvEfei94HCZcHkqzTNgXnyYN8XMsbRyjhpo5oD/ptGeklm1SCC1EuH9GO62WEKrQpv5GtSZrEvYx0/1Ste3ltxpyOkWmeJFTjmnSkTx664zsZ0tIFkxbYhy78kP3i+b9oD295OCHId62/y3oH0k+PHp7UZg+fZTbp9P12y75tdtmmNju4e5qtPzTVBqwcQW6yAVvStT759D1wj336+a/bm265wdpbWkqFCj+ViKQ4omqInHiDo5AISbrhf2P9/TZjzRob+MM/st2PPGKnlTC42jlzZiIR3rBoofUEk8W8yma78sm/tHY7Yo/snp/oDluBdLh/+hxrXLzCWo5Kh2XcAVlB19m7VFI55VIJ0lxE5pjUWPyY2OXbU2Cyw78yiywSxTwpJ+RSgQvyQkGzMEqaJH1FBUohr6QhyWZqfRyVIHNfi1QoleU6RoaJG6I5c4Z8/oqAkEe5kcJvsPwnZ4GFWNb1WaGOSYO/kBZJsvwCwkLMpkvtlCWVVxmKIrqJIEqf2ksdpV6gRVH6w/odpANvGgpdrKhkIZRvGZJlSffVZnmkmPfTR8hPGvkmHfoIdS0SKd/A/n30H/qoAq5khblWaOpwXPvnJBWVlDeETouK2oz7kC1JJMPogQrPzH2RLJ8nqU7QbzFywkcvRNU/Q/1z7frrry+0pZCbKEXT8+pICsYiYuifkRpH+IzeFxJ1+TLmWXyUF+nwsknPgwJtkC4uz7wvZ70PFNl3eFdqeneaEVmROy3yK/W7UC9f3l/KlOtUIKvNtLlirDEfFUlCqT8EK5SfMaf1hDpG+o/E/WRJS5sam+zq065OPnu799o31n7Dblt9m724Z9B4PsSBpkb74vQJ9sXpZit6dtnbdm+yM7d02MZ9M23D4SlWKWXybtZc6beZu16yR//ppUQINWXxcuuZc5q1zpprLW1tx42zU41IiiNKg8Et/6hZ7ouYNBTgIpEaIh3btcsWvPyynfbKGmsrcTR0ZPp0O3jxxfb99jY7mCENWTaw2t5t37BDvS1269ZVtuNQwUTS1m4NS8+ylqkzhtwcSQqavPPIkWTxzHMDRcjcIgko95HcQuQgHJLUqo64ziKvxSFLCqrFVe7CQnCdBY+QvjqO9YsMCyDHp6izkG+RDU/2RX4pm1QjpGfoyyOCh/SHyV0ky0t5IBncp+5QoZCLplBS7kMK+2N/f3QqjwPUFx8IUlrYXB0JV6uXmgYZ+oQ6tdo0ABl0ZRkJUTcKY0z7iYTKhRbf6RtsjHCXxm90RC5JPmmvWrUqkTjmgUWUelSEuRAKKCHPAdrQUG9SC1I9aoHPk8p7HfssKGQ3v4G4hmOI8ircsDyepKl9kGckb/KuIgM05YV8ch8/xeQ9jIimPs1GjvkKQhiSEN7PddyoES4579hf8x51xHiTKzy+U07NeyDvmSKXU34jWkaHN8szi8BziiyoNgjdxFFGbSyKXKkN152WL5fGr7fbYMNTplz1iDLSZO7jalDPh78fSUxtn2rvOfs9yef5Xc/bl1d/2b659pu2vyddmPFya6v98dxWa55jdsPhLfaW3Wtt6vZJtnr/LNvUVZ7E96Mzvvp5s9XPW0tHhy254BI7bclCm54ThGakEUlxRGmUcasifeLOrq7Ehdppq1fbpL3Fx6X9nZ12+KILrefKq+zg/Hm2Y+dOO7hm0GguxNkDz9mP2R321N65dtf2ZVYZyDcKmH72edY1Y771VSoJaWFBFFlhoWShZlGQEVkYVMG7gSo6sudZFlokKCIeWmxIg4WYRX/ZsmVDEoJQCsoiilQaaRHpScfQ6yhCFphsWUzIVyhV5HdcVzQ8Gcb5skk/kWs+IlqaDqf0D3kP7cx31SF5Ji+Ui/SoUyTGigAmUA8QZrmnAtJlBZIyKmABzypsrsoqwsl3SB7HzFkeQaQfXeQCTrquykfo3kzH8X7DEOpmS83C6257+O+0PXVF4A0WUNUjJJdIbpCVIi8N3If0Sa0h9PQgiTV9SZ4DREDUh+WVgPFMfiA4jF3aRn2G3yo0exYBV368xN17n5A6ip5TyOEw8iPvY3zSrhCKUA/bb6CkuqBNSqjyogAh9A9+Jz1/32fJgw+XzO8Zn9IRpe7IU5l5zxtRAfIm1Ra5WvMR3dDN1QaBvMj3tKSu0tHltEknJYqICHHU/TxonDLHiBQLcsXGu9E7Z5xlSTgpE3VV1p0WZaHe+Q39Csmvxk1YLh/pMSxXlh542O+y3lUt8t6nwBN5bVZ0AkD90wepb0UzlE0OeZeqy0i7JTt7xtn2mzN+0z526cfsu+u/m6hXPLT5IavY8QKgvgaz70zoTD6zFjbZf9q7wW7Z9bId3jPNXtg/y7Z1l1d96e3qstUP3Zd8Zi5aYj/3mb+0ekAkxRGlIWmdjs1CMjeTgf3oY7b4qSdt1tZthYcrlaYm27NiuW06/XTbuWiRNbS0WGdzk804ulin6aaeP/BDu6nvfvvi1pW28WDB7rK9w2Zfdp1d+vo3JBHSPEkMF2I5oPf6bR7SNS0jKWZh1bF6muN9FuKbbropOXKXL1L9ljpmclUkMknfwiNt6dTef//9mf6TuY70W0TBk0fpHUuS7b0PePUBQTrK/KV84XvkeULBAiTdUtrSlRUZkR6oJ6wiOyKo1AHSQI5tw3qEMOPPFAO4WrjJC6XjYd+T8RRIU2uQeysdN4v0h3Uk9QkWPi3AqnPIGdeK/CoLigoW6kprI8c7eRdk42tf+9px4bBFFjEgRBLImA7Hh/qw/O/mwRvUhWNFZF2nDNpohc9AOCA2IqcKLuI3hFynX/t6CNNRe5F//qfsaeOetJkXVq9enRkuGxJDPmgbyE4YdILjX/VBVDLIu7dJQBedUxtJyHmXgqFoo6E5lHQkdYUgctJzoqSP/Kqvht4/JKXnPnmjz+bpODPOyrjTQjVAhNdvrDzhLVOuMuGAy7yrLPLex1/ulWmzNKhvav1kUw/hTnPHR188VW7J2pra7JaltySfLQe32O2v3G5fefkrtungsfO9sMP67XNTJyWfixo67e0719pP7eqx9ftmJgR555HBQDdlMHPZa0FtTjUiKY4oDR3HQeaYFJIjVYznXn3VZjz1tM1+5RVrKmH8hJ7w7lWrbPvpK6yntTVJp8OFOvXGLEOLXaVil1UesWUHXrDPbb3Y+vrzu27jvEXWuvws65gzb4icSTfRQ+RVRCxLElaNOyn/HkloRVR0pEwdSq/UG9LwneuanEUGPTxxTzP68oDkSKoREj1JNL1Khb8f/u+9T4TgOouF1EekziHpr65DcHVMn1WnckPF5kEqJiJVKjvXIXJFLvLKBgoBEAJJL700HfIkDx5pdaN6U6jnvDqCaLCAQsAUfEIbHNqS8MzymFAEeXZRHpQP6Z1KjcCTbw9twkiDZ/JCimv8lHHF5t3meYi4UJ/0g6E5xEmwyQf/68RJvn+9/rZUPvxmLoQk9t5Hr68j6eBDVCFXkOywPRQuG+kf/YI+p/woz6QN6YUQ6XSGZ/y4V19mI8f/IoNh+bXJ4b6khdKvPhGIYGkzG7aHDOZAnk415UX6WeROi3zTr6XLLL1jCCcqE0BkNa9cZcIBM9dIDaPoXUUoeh86w2XajH6gSK0KBe31rs8555xCd3z14pZs3sR5iWHeL6z6BXt066N228u32Z3r7rQj/emE/fHKYXt8xhTrmNViN1c67B2bn7eF+xrspf2z7MX9M21PT76qW1drR9IO9aBXHElxRFWkQYvjvL4+m/3IIzbz2Wet9UC+ayBwcMIEW7d0SWI0d/CooQyT79SjkcSA3C8pMtQQKhW7sv8h6926x76+/+zc9zRNmGAzLr7aJi9YnEggWBggoAqZmuWaKAzhnKbrWBYiKpKseAknE6IkC3khYZEmyf9lmA5QOmWgdLx3Cv2VzqYnxuG7JH0scn/GQkW7IVGR3+rQL7LIn8hqmtRLR+PUgaJ7peUbDx7Vtk0WSJf+wmIWqobI+4Qka1nqLDptEEI1FNUReoWUn7T1jMgY7y4Kky2kRaHzbSbC88ADDwxJ+sM8cZ3TBh90Rc94giuCWpQfvd+TQk9E+ct45B20qwwbeYbv1DXtzhjIC/Kg8SwCqjJ7abXfUGpT5cslwkoUMqkp+Q2OwkU/8cQTQ67YfJtpvpKKgjYYoXGcTkRoe65L4qh+HQbSoOy1CIKjjbhOBEJdcR+9skinusidFoQf2wV+KzIPSIf6UJhrJMR5km7pbxcFwWCTMdx3lX0fm6YybVYUvIMTBKTEjO96d0smNDY02mXzLks+n7j8E3bH2jsS47xndr12UuPRNdBrt1uv3T5vli1ePNXe3l2xt274odnhdntx/yx7Yd8sO9B37GZgUkuPDUyYHMM8R4w+7F2/3uY+9rid8fjjNnn79lL+hPEasW7p0kQ6bEEACumohoEytDgl0qH+frvk4H32ypZGO9w36As0C20Ll1rj4tNtvzXZwc2bh9zcIFFUFD7v+zQvUt1wyJZ0T0Mpr9dTZRFVaNlw4pbbr9Do60TzJkIuK3MfcIXrMoqS5NaTLG/8U0bPFX1qGVFl5UUk3deVT0OQ/p4n1gIkskhyWQ1EtiDGLHyCCLiM/qgnqfb4Z+RCLs1Hsb6LGNK2nuz5PJxIubz6hAd1CQEXoZd3Ef8+bVL0XflUn/N652WgMnlpu8qvMQ3xoo+HLguljiC1D3n4CNNXX1bZs04cwj6bVUdqX+8PW3nlXQrO4o1mlW++S91A7uLC9uB/eVtAQi6DyrQ+QtqShA8X8g7kx3BaXeg53p+my1rGiIzf0W6MnbQ2kx1BUcCRMvrbzOc8M9x3lX0fZZZhYFab0YcQwGAXkKd3jREtdVbvbsnSMLl1sv34mT+efFbvWZ3oHn99zddtd/exqmLCup699meNZn+xeKFd0jzLXrdpk7330CO2s2tSQpBfPDDTDvW12fzTpljz1GkxzHPE6MBAT48dvOce23f7V+zg975nCwoc+FcaGmzL/HkJEd501J+whz/CRI+MyYtFWUerMgxKwuma2dJtD9oLuzty39mAMdtZF5hNmW6twdGWPArwkY9Y75qIxYmP9HKzyF8ZUnhMPaQ4o5cKgyRVeTp6koB5t12+DovcjIVYunTpkD6klx4hmWDC57qOB9PKTl7L6LrJRZgigIV6nHwkBdPGJHynr2sfBMKTp1pGzgMsUJSPOggjiPFe6glyBGkL1WzkKkxlFYkPn/FkMSv/WmCVRlob593zoJ5YZLyKjid9QxtPp+ojpPXdssgaK0rfqzL5TaFUIqQu49vd5znMW1Ed+LTCfOq6Tmu8CpFcM3p9VY0f5YH+oQAYwPd7v7HgI+M7Havz23BzpU26J7RFxmZZ4HnGuCKMpvkF5n6Z4/oit3VyVVgU5roo4EiZcMBeIj+cd5V9nzY1eW2mzVCR3jWb7jLu/8piOP1jODh92un2Xy/9r/bhiz5s9268N1GvuG/TfTZQOX7cD9iA/aBvm/1gTrN9pu8Mu/BAh31o8qv2wd41tunwFFtzwcetP4Z5jqhnMNC6n346IcL7v/EN6y/hbL934UI7cPHFtnbBabZu375BqROLdgopYvGR1CUNyWLUfcimvHi/bTnymi/kNMw5Z5UdmD7X+qzBJrqdvI62JAXTouQDJ/jJQ3qEWsTCo99qoMUWpP1WEqgiN0g6ss0iqmUleHK3xYSLREJSOBFBJlU2Bln6ySJxZSYsr+rhf58meVZfCKVz/phbbRWS61qDo1b0SkMDOYDkAgkPuorkSYZVIktSm/DGimkLkz/Kl4QxhBZY1aP0YkOCpetFUIAHT9SENJ3fUFJcbf9X2/lIi17tQRtC6k/t63/rwyZLNzqE6kf90Y83lcv3KY3tNGNV1UuW7rnqWJsKxlEYYU66oN7A0I9/9V+ex8iQj/Tz08LJy7VhGWOzPJCGPIrI446vIyStpBfqtmYhT8VCG7miOa0o4EiZcMCqs+G+q5r30SZsirPajLm0yLWd9IVJK6seq8Fw+0ct0NLUYq9f/Prks+PwDvvamq8l6hWv7n819fmB5m57bFq3vW/aFJvQtcQuPdBudniBvTmGeY6oR/Ru3Wr7vvJV2/eVr1hPhjs0j/7Jk6378sus+4orrHf+/GSQLpo717Y/+ujQka3fOWvxUThgdAdD/6GQtt3PPWmV9S/mulrrb2q0yoLT7eqf/Xn79re/bZWc40apDHC8BbzEhMmKD8dePKede6iLyKQjX7NFyJOqSpogn79Zzvn9pMnilabDyvUiQzsZnJAWE6XXV/P6gOi5ibyEpKgaKSH+Y9Gd8yoASkvpKPAEdakFWWUDUmPgd/7oXxBRpvxFhnZlQPqQ4SyvD1xXBELViScS6itck54m8O0qcqWyk++0EMb0RalwcFri3+nTQjdREu0skPb555+f6GCmEWi/QVEf17u8qo82RSGpTqtH8i7JpMaddFt5B2WXtDVN51ynDPRT5pM0kE+IpQz70jaxyidpiYynzUXqQ3nlIp9IiRlDUpMJxyvzB/NLWn/VSQ/zHj6MJZ0kDzIclI41ZeEEjbZFLzbP2KyI+PA70pL0XUakMnYkj9yvRoc1S8WCdOT6bzgBR6oJuw0ZHM67yr6PTTBYt25dZpsR6Y/ny+oLZ9VjWZQxRhxpo7VZnbPs/Svfb+8793325I4nE/WKb639lh3uS7dFOdSxx77XYVbp/z1btu0jdsmyhXWhTz36vGRH1BQDhw8nJHj9+99vL7/uRtvxp3+aT4g55rjwAlv70z9tGz/527b/He+wgzNmJINUOlEMfi1OkhZ5yQ0kTJPGMUe0R7qt79nHbODVl6wykL1r7p0w0bqXrbSBaTOTSYGFmMWP/xWmWF4AuC5S6SVf4TEyuPLKK4ck2J4cSKqH66qinSzPScoLJNnT4q8jedyJkRaEi8VKgQ34znXua7IT0dJCLskUi2yRdIH3rVixImkb2kjurfirNpP7Nxl7yTJaUhSuy5dvUdmRmMgQLU1ixof2oq7lt1P1rWN1rl911VXH9CNJ9LzUFGNNhfvNgvJTlG8kLr7OQl1v7lMnOppN62e8yy9E3k+vQJmuvvrqpIxqd9pU7c916kbunSTh1EfSPt5Vpu2V56LyK7pZWpsBPGIwbvNAnmkP+qVOOaRry3eu0/bqR1nvUsALjSO+U7/SMfZuFLWZ9un44CQKX5z2Lq6T36wjb4H8nnvuuUPj1Y8hjVdct4XzXjjPUH8K88x7GXvUOz69+auxyCbWG23JEFZGW1xXEKU8SBeYNKkv3oMaFX/5znWvw0p6lAcvG/yt9oQAV2h5dVQmMIfynDdf4Q0C0jfcd1XzPj55bUYaPJOXTq30hUPjwBPtHycLDQ0NdsHsC+x3rvodu/vH77bfvfp37eI5F2c+P7ey3FZMn1w3+tRRUjwOUWE3/cijtu/22+3At7+dEONCnHOOzX33T9rkN73JdkMIpBN1NDywd9+jqGZpPoHxtsAAZufsnZgf3rLBel98CjFgZhbgyT2zF1r/jLnWfDSYAkRCu3UkazJQk9EO0gv5VuY5eRbweeI5CA5lYPFDKugNXXiG6zpiJ+0063DKrQVfwQLC8suIkHcpspn0PqkXSAPXkXLg4odFnXoMj4cV4IDnqYMsFQvIE+Uu0gcEkPAsnUlFaxMZDCEC4jcVaZOyyC8SZfL9yCOPDLkL455CYUMMcAVW1I+04cjSvZWkUrqVIeRdIk2vVt9FxqkbypjVz0TggPKsOlCeFY0NiWJe2ZHuZi0QXJc1fB54P6cxZRYaxi2LfFa+KaP0pukvYX9kMVYYYwg0fTq0FaAvUm6+q6+FoX4ZQyK2SN+oa+8XmD6IVFYbVUmes9Ihz0gWkaCF/Zq+pc2GjHDDcunUiPzjWzwrmAZ15IPWpL2L+zqx8eNRR99+Di0y/iprlFT0Lm3ianEUX4uAI2l5TtO7VZ6G+66y7wNl6rGW+sLDMQ6sF6O1zpZOe/uKtyefdfvX2b8+9a/29Ve/bvv6X1PJfNeCG+yKi0Zesp2FSIrHEXrWrbO9t99uu798m1W2bSt8vnvqFNt61tn26pLFNjBrlt20apVNmzTJZk2alKkTxaIqkia3av5onP9ZGFkoGASTJk6w9Q98zw4/92R+3tvbbGDhmVZp7ySRY5yns/Bhkcz7Wby0GLGwsqjyHhFhBdDwIC0WMcJTc0SWdqTPddLRkTDlhyhq0Vc0Irm5ol5kpOHVHmSsQR2RNxZ4yITSIW9c53gcqYcIa2g5rmdEAkLdYxnHiagpIAjeCEiXyZKj3NCwK0tnkrQgO7jTAqGeK++AxLCZgDhL+uY3IKp37hOUg4WM3+goWeWXW7KifkT/4xkZJnoiorIrL3zoG2HAGW+wmEfkRdSk0iDfz1I34TqEiLIQjZByYf2u8cEmRyo8CnzDplCGfdrgcJ3NgNqdPPsAKPKFTNmLJEE6thWBVBl8u2mTQprkm2h7tLFcz1EmnlHAFgVewIuCnuEkgrxRNsoAGaCvKGIZ5aeP850+y33KQ98PdbPVPuQRosmHBV7pSILONfoF9cC1MBAC5aYvq+9T//Lbq6iIlIuNp+qCsqSFd9e4ouyUFdKjqHeQHX4rf9oQcOos7V0+MEOefq7SKhMsQ+2cp5+a965aH8UPN+BI2TzX8l1l31erZ4aLMsaB9WK05rF48mL7jWt+wz56xUft9ie/ands/Jat637VPvD6D1hrc/5p1kgikuIxjv79+23/t+5IpMJdTzxR+HxvS4vtPvts23XeSjuAThYSrIGB43w/ZulEyeiIxSQ83mfyZrAyoBN92B3bbNM9/2Fdu9J1B4VDM2abzV4E00sIMfCS1TxDIhEtT/RCqaOOdyFqImJhBDWu44eTMjPhKYSuJIl8l0RMYXo9IdQ7VXeQeEn7QqJGfhTcI83vqQggC7OkdyFBEgGSBJK2CyN28Z1j30suueQYfcA0nUkme1Q6WChlXOKPpfkNR7M8K1ULros0enUU8k5d83/owohnyQf1I3/GWf1IGxBv3KR68AE/6CMKr6v78k4iqaIPbhH2Ia+fStv6oBnqQ9osAdpFfURHqBBdSBTPUDaID2XwnlDID2WVSgXPijQqz2q7skFJJCmnPRSq2reHSKDCGZN3be4UJlmuowD59tJb6o1xQTtSj0jBCV+Nb18/hthUSmKmqH1yxyiPDdqcyWhJ+fHjQ2HGKcuFF16Y+Fimv3jVKJ1qsOnDn62Mn8ijB/UstQzvolDpqI55hnZJk6ZC9ClTaLCV9q7QkChrDi1j/KW0ykp4s94VHsWn+det1n/scAKOeJTRu63Vu8q+r1bPDAfV9I96w46j/al5Z6O9qe9msxaz7z/0/RE1DixCJMVjEBWMKh56yPbddrsduOsuqxTsGHGj1nvuufbC3Dm2/+yzrcX5aT0R348gbwJNiNL2TfbKw/daJceCvqelwQ7PO8taJg3GU9cCK4i8QS7l3zUkNNJb9S6W/BG5pHD8nnS0kPtn5MKMhQe9MX+cHhpcINmS8VtWHfA+HyY7LUISOoXe1Y8vl/KnyG9Zx+gi5k899VRqBDVF7AIcOeJdAbKiEM2kLX1Z7kOGkDbrGFwSPkin1FcoP+UWcQ6NzUSYIQlet9y3mQKAyEVWFrjnNx/acOhdQFIvT4h9PXKdBUzqEGnPKG1tqML6Fjknz6QFAdSi5NuVukU6DEH20dr0DP3JuwwUKQv7azWAiCoaWXhCwjskcZL/19DwlXoh30jl6Av0S5FsuV5jwwj5ZGFTkAqNNZ9vrnNfxl/acGkM0Y/4Sx3pXZLYapNFn1V+SAcprCRn6i88y3Wk1dR7kdGWJP3aIHtJMXUgvc08aSrjo4zBVhlDorLGX7UwxhtNR/ER1fWPejBaq3fjwDREUjyG0P3SS4Nu1L72NevLsNz2ODRjhu1cudJeWXCaNc2alSxu0zKMkkLfj0zI3/3ud5OFDinjjTfeOESkpOfKJzwab8XK/cWnbPOWwWP4LBycMsGall1mrb2DxNEbxEkCKam0fAzLIj88jmURlWRTUkXBS828NXt4BCeVCB2Fki5E2B+hQyY5OmUnLCllKJmTBJdJQP5tJcVkkpCupSziISiqQ3/sLbUHHwhD6ft6gmARUEMRu0hLJETBUyDMv/RLv5T8hv+1CErqhDQZskvkLyRgXA+Ph0mf31H+NBdYQN+lBiDJZXj0rX6ksksFwPcjBT/gnm/LcOPEb6RLnObKjTyRBuXM8nYAdPzP86pHr9JAGfi9Ti5E1tVuKrtUMBStTVJ/GcrQXvRplSXsh+rDqsci7xPkh4WGhSfLvRn3Q+Idnj7wv8oPvC60+jl9gkWP39NPNP6pH8YKfQoS98Y3vnFI51hu+bRZRSILuUbNR2POexhRfQPGGX0IiXCohkJeINXyvKITDm+EB+Ej9C6bMzzYaG5Te8njDIQX1Sp5hdFmUeODfu/fRfsqOItOCih/aEhE+mnqTGWCZXhjvKw8eQlvlorFqTqKL1L5GK3vGgmU6R/1YrR2Mk8kThYiKR7l6Nuzx/Z/7euJekT3c88VPt/T3m6b0Btcsdz2z5xpLUclh1p4yvh+vPXWWxPpk180WeyQor7hDW9IFoDEtdru3cc809R10Do3v2qVI9kRuwYaB2zf3IX21g98zB6+65vWZYPeF0IJHpO4yKmOmnlnqFcqgiYSLSmch8orEhISES1uCoKACokMPLxjf9xfyfOCSGsouea+0iKvchMHyBsLkCSM0vcMg2D49NKO/D1JlpRUx/MheA/XkyOtIOKZ0gBaPEUwvPET32V5r1C42pyEpE26m7wr7XhcUQgVQU6W8CGQWNMXQylJWl2ImGUZUqnPk6Y2WOG7yBf54DehCy+lQ5lpPwghxEgW/Gp3rquPUlapmvi6kWs0TxY92VffDNWA0qD2gPQpqEqar17ptCvfIuU+35Asld+rbohAMtYYD+q39AmfPy3W0jMmTfR5vToP7Yles05tpCaSpr9OPmXcqQ2FNkwyMCXPqPwA3hXWNd5kJJmSu8VQB17eMuT+C7WmtP6qd2lu4CTIzw3+XQCBQmhoyakNhpYIGIqMv2SMR13k5UkbbuZr2kQbB9Jhvj4VR/Ej6V+3Hnz5ngyUNQ6sF+wbRScSkRSPQuCT94CizN1zDyKH/Oebm20bRlKnr0j+NrW1DS4wR/30apFDSiO3UFm+H5EkMsEe945KJZEk8pdnj5G64Zt311Zr3b7R8kyDGjq7bPfsVXbGJa+3yU2DEs0sCYVXW6AskjqFPoghb0ht5PYqDdIt9JLkkGxJWi2LfhmWeWkQ11nUmXwllQvT4TpEw+uKppVNxmE++psgkkye8yQ4aeoAIURcMZySYRhEQEdbTFSoVeAijb4BSRUJUtkp09q1axNpMpAEOCtP5Jtnio7rpXedBq6H7ZRXD+rjXmKv63xXH4DAiIhJV1hRtEJSGaZPfShEL/3QuwOTD27/3jSptvqZdIvTwgqTJ/pQll9lny+lL08NXsdb5eG9Ckyg4BH+feRbfdwbJYblV4CKMFy4yq8NH1JXqQexSPpACBBF3sX8oY1XaGSqkLo6GfKqSCKF8jpz33332ZNPPjmUN58fvMxoA0fe5ErO1w/X+b3qhzRDtRhJgskH44j7zA16F+VibiBPzKEQ4gcffHCoj/k5hOtAxLjIoFnS+Lw8Pf3000MeQ1Q26o80rr/++hE9ih/JI/TRclx/ohgJo77xaBwYSfEoQXIU/syziUQ4iTJXsCCC9pUrbcrb3277V51n3//mN5NrIn9A0lR1xJUrVyYLQJZeKcd7X/rSl3LfCWH2R7GNfb3WtmmNNR167Zg7DXNmbrPnpt1o513xRms+vCvRy8wisQL51kKfBx0T5kH6tvyVpM4voPyvKFPkiwXZW9frO7qVynfajlhkvUjKJ4mr0kojmWVDLxeBtEWIJXkDIq/0BxZWGZnJawOQZJ0+wgIq/Vwdh4eSSd7Foi0CnSWZ9p4esiDJZVHZfNrKt98AifzxTohR6H1D+sZFLtC4LxUcNhYKWyzpuCSwXoUnJCEiLdQjdZ/lkoy006Lveagc/N67P/SSWUU1lCcVud0TGSffCvrgjUU9PMFWHXl1J6/OQ74hiOpr/gRGRF8kVCc8aUamSot+Kz116R3TXpSTtsSgU/dDwkv6SHWpA22wlR+9lzZDB1wbYm/06aNmki5zltQZtMAzp3h1BsoM8Q8NaPU+ysb96667bmgzlGfQLFeTXg3H9zXmY/If6riTN66jvsG8PhJH8SN5hD6ajuuHg5Nt1Fcr+BMJCRm8ylw9GQdGUjwKsOfWW233P/yD9bz8SuGzzbNn25T/9NaEDLetWDF4cffuoePdLCkoHRX9OiaMLN+PHEEWSR6BSGTjwX3WtvEVa+zPlmT3tfTbjfNesq92vNPOvfRmmz+x0bqbJydHgkXvIt8yapIbtdBfqcLFliE0kpSHQSK0+DGBMqghGY8++uhxqhocU8sAzdevEBqBFYG8yI1Ymr/jWu6qWRQh9mmqI3JIr4U8S3rJX/qIiCATXKhTrUAk1JMnat7YioVZ0aOKUKY/Fj3v20XE3xtbVQNJXiGzoQcT6VGr3lTXXjVCkmIkPtQJG9U0fdmyoXkpD/mgbZWmX4x4N+RIEk35zBbIkyT5voxFyDqlkPRRKlbhGKKM5EF1540nlR+RffUjGdP6dOQjnHoTGfbtofrWhoA+l5Uf+md4ehZChpb8lg2kz49XQXnggQdSjVBVNrlAhKwy5xZBknwZ3EpNTJt52lZEOY04s4lHYjrW/OuOpuP68WQc+Oqrrw4ZFau/0hcVFbAejAMjKR4F6Hn11VxC3IDLoJtuSojwhKuutIbAoIiJkKM7+fv0R9+yStexcZ7vR46iSqFSsdYdm6x5x2bL24P3Tz5kPz/7Gfuzxp+xFZe8yU6bNLhISPe3DJjUpFuqXagWI/56FYQiSKdSEeM86YNAEMADV1PSz/TSZEmtfPS0tA1INeAdTNh8FKBAEjXAglcrqK7SIKIClJe0AB+QeBnoqU7C8kt6zD0WpiyixuRZC0gPVSQhLLOPOChXaB7kk3opK8HQMbj3Ta10VEYfKCKUgkpnVv55IcDUE5sj0uA7m5Sy+ZEERoacUu+QzrZIoxYqL8HWhln+iL0O+3CgyIXSvfdjyLu70xwV1pG+e0m4vHjoO894I1RPhpWOIJWDrBDwQLYCOjXzEldJwdiAaFPhvYrwG1SBRC7DdvdQWxSpxqgNtYnz40xzvDzy0F+yCD/36WfM+WPJv+6JvIu6GQ2qCKMRDUfdb+IJifpVfAGdeMmNYT3UdyTFowBT3/522/25/3Pc9Y6LL7Ypb39bEmWu6ajbsjTIiIhOGB7H0jnlY1QLbZbvR1nW56Ght8faN76SGNVlYcAqNmP+Fnvv5Ffs9wbea4svebMtnvwakWeglF30tctnUUJHLpTQICEru5AT0WvVqlVDknJJPCUpZ9FAv1bHvX5hk1Gcf3/oHUCLdtn86DhVoao9IF5ljK3Kwr8ri1gp31kBPqSPKldWsvZPM7KTqopCSnvo2LlW0EmCdEI9MRDZERlLm5Tlw7YMRKJC/XSlq7LJ80daJDb+py+in54lvaOvl/E+wdjmfejxQvzC8cGYxnWZNiFeD1gSVlBmY6nf5ZFnr5KUpcolXds0Aqsxpw299+AhdQupVKnN0iTOPl2NXRF/nx8RbjYmikiXdrLBd063yLtPR6dMpIMkGV/e6htpxFgnBUXSSx/oJEudRUZ8eYTf1/PJPoofSaO+ao/rx6pBXr2gctTtIWNFdijavOqEkvv4OT/VxDiS4lGAttNPt/Zzz7XuZ5+1ltNOsylve1tChltLOi33fg2zjmO5X3R0cc011yRHg1loOrDX2jevtYYcdYmutj67fsGLdm3rbvu9vp+xuRe91ZZNaTrueBVJGfktAuSdCU9GW5B/gQlRvnP5myWh0UIEEWHRypKUE+JZxCntSM4vdnqXpJRe8iZPCEUg39RFluEj0n3IUS1AvRUZWZJ/+koofZEEjWfQT+ToV4aWWYFAaDfynmXcQzuwQBXBE7A0qbxUEfTe0LWbIp9JP1ek1LdZSO7z4HVSQ5UDqVZQ1/Qt8hMa9SkMsPpclvQujeSl1Q19WtLA0E+1gofwDvVrtZc/5fC+u/P6rTZOCpwSpuMl83kGrdocSH3L9zfVoTYzGl9huZVXtWHWqY1sBUQSvXqLVA24z1hDKovLQS/1pz6pW4QLMuZLg0g6/V6u99L0peWqj3FUaGNydKOikyNfRumFq/2yCL+8Y4w1/7rVHNePdYO8esC+o+os2qSHmxS+14s6S/XxECNOCWZ99CM2/X/9lU38p3+0lp/9GWtZuPC4Z5hYmLjlxspP/Ox4mbghNdI/88exZYwp2NGlHkdVBqx12wbr2LA6lxAfmHnAPrDksYQQ/0H/T9ukc95kU/t2D7nmkrEH+YEQFulzhqoRWpD1UfkZhKQJvHGVN55CEqvFRTpnlJe/qhdIhCe/YTq6J0M076tXhj3aGXvyngYmDzYhTBhImKgf3s9fvnMd8p7me9cjzcVcCCYmXEbpXZBj6pW/ehcLA/6KeRaLdtqJvsNfvnMdabrXk/USc09OgPqjDBeldqD2hxQXSYt9FLm8Z1Q2+TRWO/Od60hk5Z4sNCbz0fnKoIy+LWVTxDYRYvmuVqCUojaT2kMepDOLpwfeIU8tIosKOMOGEqhf63+VR/26qK79iYZva/8/m0wWPfl2lsoEf6W2BJmBfEqKFxrxybsDz+oIXO2nMsvYUyHCJZ0W8fSnHnzkeUJ5ESHmOmMVMku/1EZN84kIMeNVGy3ppGtekIqaTlRwu6ZAIdoU6X+uc1+bE35P++DZh7+qA23ipIqj9gZebUB1ojlfeVL/8dL6kw2/DmWNe78OZa1nZd8lF4jUlU5G+Mt3rivqoDfIoz5UL3znOvdroTZ0qjCceqwVvDqLNqLMBRJCMNfLh/6pRpQUjwIkRzv9/bYTA5T770892ik6/uHDJBCG+qVjQnbK7IQ3bdo0FFBhiHD3HLH2TahLvOZgP0R/Q8WaFm+x32x/xVoazD7T9xN27ls+ZGdMa0xUFeTTNPT3y7vSfN0CSXAAUgAWWsiZNyaUbiaLDWXnfug7GPAuJGryLpHl01NW6mk6qiIMkgRKIhLq3mKMxyJMGb/+9a9n+sX90R/90aRN0BvGGl2+cUmHCZv8rFixIpFecy/teF+6sFJlyKpHlZ//6R/Si1T/YJFG15y6gSTI6l2gr3GdtpMXCwUh8eoBcgsmV3t5xj0sRv4oP4Q/nhaZ8c9J6sk7KRt1mmVAyv/oirMoyxDQ51n6h2r7LH/HPooez/g6ktEevyM/1E9eXReN6ZAUhflRf0RtgmdpH4UzDtVetNlT/tL6tVSJZIwaqjSIJIpQpC1uOtJmrNIPGI/eTy/vZ5xqfPAOFvLQYJExjZqTDAS1CQ77B/klPYg66XgyrOiTIs1sVBVQRHMRZdV9xi3PZPVX+rqk8D6CovqQ+gdtjLs1+iAu4fz8Jt/J3AdsZtL8oWvTJGNWkfiwz5Inys4z8ucethl1LZWfevKvO1x1hrLH9TKAHKsGefWiFtI2ikJTR1Jc5yhztAOKnmFihVxx3fsGZZfOda5pMc6CdNOYmJPFY9d2a9+0xhoGso+WD3b02qolL9s7bFDC8se9P2Y7pl1qb5liuf5+L7nkklyvEbpHXiCWMn7TTl8SIRZDJgFIkXcT5qWYfLjPwLznnnsyfXpee+21yaImV0XhkSX1zyS6bNmyZMKlDN5tm6KikR+khTfffHNCjFjctehBmLRJYVGEoEv31Rv+cZ1nFT1QhCbUYWVR1PG/yIV/RoZfpEGeFXrZqw9wnQ9qEbLUD5/hOpHzkPDxLhYQnpMeL++hziS1oC8hfctSD6APAEm8QiMhfwqgjYKIv4iZLPH58K4stRj6j4w+0vSl6eteyspvw4h29FveIylcuMBKj1PtqAWZ+vZ9mutSPckb04qsKGIU5lkeVNT2ktCEoZ7JszaYlCGrX6v88uccPqPFTqHHNbb0jOqF+7yL8cS7vHqRpOXUCeMDUhpuUOnzLOi+HFmbJo1r/pKeSDj55B2MRcgYv+delmqEjvRJM6u/ai6i7bgmiaxOSbxajNqX/ijjTqmMAO6TN+wXeA91r/bnHtevvPLKpC9xTcQvdCVHeuRFutBZhH+kiUiRf91aqDOUPa5nbh8t/nOrRT2phUwZRaGpIymuY5TxtahAGkXPoFsl36Bet1UTJhIJJum8I1kZ2h06eMCat6yz1m0bc/O/dc5B+7mZz9vKgUEVhz/rfaetbV9lZ7UP6gArz1qIyDOTNZMVvkq99CfUvQMybIPUhAYuqj/uy++yjkRFpEQw5WYNUpfn0xPJDoSVyFPk3etnSh+R+yzALEosbN7vJ991REgZIX46vvdl4zrtRJuE/ly16FEmNjPckz9fERwd2QLu68hXRNG/S1JBRcWCbKXpnFN2+hC/F0Hy9czCwX0WfepNZNi7ivLHyOpLecY9kmRKd1TQ8T/1TX+h7bRoeZJGXUOKRDYk+aEe/LEx72djxiaEzQHPy1hQEnqk8pSJZ2TdT1lk0ERauJjjNMV7PfB1pDJBzElL/oZDEqbj2rwxrQ2gdKRDbyl6nvLrqD5PQkOfpI0VKlh9X4ThwgsvTE4lVJZQ99anp3KGhqjahCt/IvR+nPkxn0eeILF+UxL2a2182Qzhq1jSctqf634sAhZl6lVGenJPlxaeOQ38BgkuhFVqMKp35Z37pKM5PbS2J19p83Wa3/Bnn302KRtzBf1f97wXETbnCuBRRPhHGlnjvlb+hdOO69PILu87FRLMk+3polb1OB5DU0dSXMfwvhaBd6pP56ZjyRhNUag8RAIgoKQjvcpQokbnZJFgsWaizRqwWKk39fVa/wtPWmOOd4n+xortXL7NfrPlFZt6lMD+ed877IWWVba0rdsaGiYkRJaJirzJt6/IEu+TU35/5CuJkox1uI5fXJFdr+OrupA+HcRRuqRe6ijppY5idazoj+clLZRPTyA1FOnmUc8QYvkWzTsiZCK6//77kzrQ0Z7aQ+Rdx6ZqszR3SqQtqXPoeUN6juoHaWWX+zpJS8kDxCfNxyr9DILodax9P5PfUxFpng+Nm0QeqQfIcx4kCQU60vfpqR8gnYP0SHLrTwAYL5BCGdPkHSNefPHFSf4VKcwf19PvuQ9oK6nFCGpbnqE/56l88Du1a1Z4XrnayxvT5JH7MnwM25XvMnyEyFP+PCNKwrNzPww9TD5R6UCdgfrTZsJvduRGLxn7R71KpKlhSPWE/pzno5r+V3RcrY0HRDdPXQFSqNDLaWpakpTxDFI16l79gz7owzMX9SFO2sj3Qw89lBDXNLUYrhcd12u+Ju+hkEL9hD6ECgn9QPq56nc8g0SdkypAfZMeaSuCn3ShQx3eU+2SrFb+hcse19N+Iy3BHAmVhnr00zxrlISmjqS4jqHdbl4oU0lm8o5/pCcpC2u/YEsiyEIBKcgbsFuef8b6n3rYGnPcU+3r7LUpp79qn+7Zak1HX/Onve+y1S2rbG4DE+6gpJHJnAWdMvn8sCByD3Bd0s/weFiqFtIPlVQ41K307sEgSFosfVqUU35Bs4iISB/1AvGFAFFPUiegfrw0M0/Kxbt0jC+Jnw/RSr4k/VR7pflz1ZE0pA3iyiSj9oQseD1wBW+R6oGOfEUgZMznI22JNEHeaLMiAw2pZGijkaXD7DcdeWnJwE0kVxD59fqbaQZiOgGgT0OKio4RzzvvvKEQ5er79Bmua8JG5UVGT6prNpIEvlGgDOBdXakvSzIvn89Z4XklyVNbhUe/knLhSQYSK7UHrxLFd/qpDCCRYGZFquS+xiJlCccH19ksQO4kPfcGpKQlAkd9a9yp3KoHbVz45PmoJi9cz5uLVD9KKzxJUcAN8sqmKUtNS54X8KojvXFJF/nOdT1TRo1NwYQ0BtSPuc7mVQa3RfO1xmHWMyrfDTfckGkDoT47Ejq89ebLuOxxPfP9SEowR0qloV7DKs8aBaGpIymuY+h4NC+UqQwp8vwxyrJTC3Y4Qeg4mDTSBuzmTZts9XfvsD3PPZmb33XzDtrNc1fbm7tfkyJ/pvfH7dXms2xeE5PxoNRXRh9euuH1XOUVQMePoRcCHdnKWObll18eIo+hpJhFKCQo3i2XJ7JlSZ+eZcGlHdLcQun5tF045ZZklklXBE5Gb5KeyVuFJH9a9GkXlVf30iY/H6RBepPeyJJ3Sa+QPiaSJpUSvZu8UFbyCukRQff1xnWpHUBqRD5CcJ37ELQ8DxySHmoD5CXFIj589277wg2RNgUY0Uk1RIRLobn5LnUFESOdDoTESAarV111VfI8bUgZ+E65eRfXSdurpgC5FqNetZGDZIZBWWgfvivim/Svw5MUnsenJ/8zZiEzekYSGdkI6C+nG5IO8Xs9RzqcXJAv6ZT7OmUhl8tDlSns71JfEpFX39Wz6gv0J81XWcFEyBtlhuxJnUX+iJHkkn9IH3VP/fBMqL8sgz5J40NVBfq9VBW4T9vJQ4ffXHAdtSr6vkI4a0Mh4iiVF4WEluFpqH5FOtS395+raHT8T57ko93PfeEmRf6F6QtlSAbP0NfT9OlDoibVIo2tEyFqw5E418ogq5rj+pGSYI6kSkM9h1VuqPPQ1JEU1zEYyHTkvFCmTOIMXoXFTfPHyIBbu3ZtkmaW1I33MAmGA5ZgHHse/K4d2rY5V13ihTN32H9tWWNnd78mRf6D3p+0jc2n2+xGiNjghCbJrNwQZREnyseixyQVSrdVBsrN8TATOnXkn9H/pM8CJd1p765NUhxJdkAR6WNx+e53v3vcMTN6xhyRynI8D5L0UhdpqhHSVZVObbib12aCPNNWWuzSAjNwLAxpYPEXURG5Jx+0OZJmGWbJmDJsD56XGoX6o9cp5S+SONKUtFsLuld74X9J3PNIsfciEobN9m7MvKupkKhxnQWOzQD3pGrj6xoSKMOmImKElJh2Dtue/kfbo7cpEq869ps9b3hFumkhlaUWxV/y6z0mqH+SVyTTkA3KIwmoSAjS3jTfs1kLbZmjVuqRd5Nvb7Sp57nOQicvLzrR8OOMv2xiyR8b2aw+C0mnzrVxpLz+hIx0uU+/Je/yvuLblbxALsh3Xrkgq2yM+O515ckv3/k9kmbGfZHKC3WoeTcrpLKCAOEVRH6ilW/mF+YqxqyMLr23E9Uj9U89SgWpiGSkSYFZL6TKxT3ZZVAXfv2gT1VD1IYrca6lQVY1ZHckJJgjqdLg/TSnqejxt17CKtcbIimuY+g4Oy+UqUifdIXTwidqchFx8AuxFlzus9Aw0WrA7lu/1tbe9U3r7+7KVZfYce4m+38Pb7Lpva+l+z97f8q2Ni+1WY3HumrzJCnPswQLCoNaniCURy8N5jjSq1qkwesJM+FIiqR6lGqJ3HaxWEk65YmRSB9SxwcffHCImPnjWK6DImIsQz9JmUPVCBHwLN1UXy7pFmcFZuDoX9JRr46iY29tpLJc36k9+EBmpMqjTYgnl6iU0Id88IYwgAXgvtotr47IV5a7KJHhrGASIs8iVTrylwRYdc2GgnYXkc0iRiwu3/rWtxIPHArSIOmk2p4NGu3mDXj0jDwyyI+v+hRQftSPJJHPajMZmHq1EMa4JNG0D/OBJHzoFMuTAaRTz9FvuA6h11FrlroGfYi8qd59PcrXLr9DKs37JQnWGFL/lNQ6r89CCulHeepFnhSHx8R857p+U6SGQF1mERXmUtIqUnnh2TIhlalHfp9WftKiDhmzibrali1DXk30jDzNUI95RtFlj+uRuGepckkCz/0yRK0WqgHVSHjLoBqye7IlmCOp0hCGVfYnyuqD9RJWud4QSXEdQzqzeaFMZfiT548RKYAm59CfK5Bunhaygf5+2/DwfbbzqUdz87dm3iE7bdE6+/P9u8wP89/t/Wnb1bzIZjSmk+ks8uWhaHRyNyVpiazNmbhZGFkc0qSbHkzuIigiKSJucu1Enb3+9a9Pns0ytsIw5wtf+MIxerdA6ZJnpIjXXXfdca6vPEIvEGmQNDEPTKCcAFAXXkVG5It8YjjHgkJ/CaV3It4iFnngvo6+dQIhyYOuAx8kJVRb4X/1Xz2XBUnJi/JUBEmmPUkN88P4oAyQ4zxihEcUSa3DPsx1FiDGnAi/f0abA9oFQqS+rDzqf34jAiaLed+uMpiEeMpbRN5RLBJj78Uky5MB84cC6GS57gr9GXvSR76l6iEJrQyDeYZ0+HCNDYbUSWQzoT5BOmxMtSEINyn8jvrVRkcnEF6aqn6HNJS85x3Fh300DSK85JEyhPnRJkh9Tff85kvv4jubFBngaU7nWX2n3TBEpB4lTZeRKu/nOX9SMZzjeupaEvmsuuZ+0VxUS9WAWqsz1Mtx/Uj66vV+miX4UB9mTqBt6yWscr0hkuI6ho7ZNZA8NPkzObLzy/PHyITEYPBhT306ep5JcdfWLbblvu9Y7+5BX5pZ6hKPnL3bfrJjnb1r/2sSv75Ko/123/usu3m2TWvMn0TL6qUxgKVDq4XauzhD31OBBbTo+CNrSamYWKXD6TcGckvnpTRM7PJfyX0mFq5zdMozobspIH0/7iNN5Jg0T7dO703qzS3olFXGgV6SmFY/gHySblZgBupIeq7S5faLqXQlywAVDC2eXo2B7yyaLGJSIZBRlc+7vssVVx7k/qsW8GoLHl6toWijJkPVPOjUQR4UQp1iEVpJmr201Y9LSQTp+1mGoRBYNsPyc5zmmUa6rvSBPE8GpMV9qWD5upB0k/lFm8E0n9iqHzaUnDal6ctSP7yD9ORjN8wPz2pTwHhNazPKSP1o4yqC7dtbOt8KypF1FE/9SS1EGz1vsKdTmzT1HA/NPUUhlTVvSQKaNmalzkE98tvQgFbeI4qkt2WO61kbdMKYVdfS+x5J1YDRYJBVz756R1NY5XpDJMV1DB2zM1jyjtn5nueP0UdaAuFg1HPtPd22/u5vmfVmT4D7O3vtsVU77Pe6N9gFh1577kilxX6974PW2jLRpjTUxqLVl8tLevnujz49/MLvFzGRYy9t03WRUukFUof4+FRdM4Fgqe6l1WmQlwwkZnmR8VgEteD7RVREk7L6coUSIV8u74szLTCDDL4k6QmPvZkUyy401I3S9Ool/pgVkgHBknTWq8vwkYSsiBTLG0itoLYJ9emllqAAHFn65F4NJA/S96QveT0+RVdUJDpvGKb3qW95ApbWrj7/eZ5pRJ7LqBDQhl5dw+tsy/DSE/dwnOm6f5eO4pV/bWaVpqT3fk6TnYEMSDUH+nfptEEbVPV//4zCRkM+FI487Sge/8s8j0cRSfAF1T3klPpRPYU+oSV110ZQXl78OJO+OGkUBVMRCdZpVEiSyh6zlzmu16Yir661hpR9V5YKTrWqAfUi4a0Vaq0akoeyfppHY1CSk41IiusY/pg9SxdYktM8K9Nw8QklHgzBxq3rbf2ja0k4Mz+vzj1k287Ybn+3e4vNdVKwA5UO+3jfL9q0liZra8h211YtRA5DP6QyRqPsCk6g+glJj+oIaAHzk44PEMFxEsQobRfPJOaPztOIsSSh5I3IeEjNvD9XFl3ege4pxBFyKF+tImzy1cr7PGENF2tdK3Mcpzz4hVoSWy3YZSDjwKxjVhFZ6o/3edddWnyZ+JGWaJLOkqbnGeFVC5FxbYwESfy5D5mXW7IwoAR5ZHEON2BZULhxvRuIvGozIWPKUJrsj52LjD4VCCRN75ZyKKqgJz1y9yVpvXSdWSClTiPSKoLNO7mvEwypAggqA+nJoJVFPk0NQ5sqnV5o3Gpcq77liSGNhGrO86HAw/bW86RD4JW8o3hsExSURX1FEmPyyH1UPsgfY9rr07OhRRrt5yadOnmJM+WXnm2ZYCqa04dzzF7muJ7r9BOR/rDv87/Ccpd5l1Rw0owRfZ7zTtHGMqpRDRlOHY2kqsZYQyTFdQ5NkGF8ey0M0oWEbGVZmUK0REqO08Ps67X2zWutcnDwyDIN/Q2oS+yxxTN22hd2brd2R9B2VSbZr/f9Pzavpc+aGwaJXRmd4VCNIwSLB/mGaMlDhJcoUU4kb1dffXWiy8lkHL5bRIh0tNCnQYSRNLP0ShUwQUdPWux8GgpcIL1JGVN5iSCEhvywkOZFmsLYSMY4IVRvMnDjuazADNI/lM54SLBE4uV9whPukHzzCaVy4TGr+qPck4XqAZSzTEAN2hU3YZ40pvWhNNWisB+JQIX9TUfXEJabbrrJ7rvvvsxACBgZevdveZB01kPqBWwoZLzn+7TqVOoXtD+nE1lGnxA16o3+5HWFdaLCuKHt0blHnUeBK0JvIHxoC4icDHRDqE8rxLkMAn3/4Z0c1dKv5enEQypJ5FvklzTC8SoiinQWwk89eRJKXZBPSASEQeplYd+XhFOu9LKO4rUZ5jnVg3d/J//tCjSkjbhvV36PEa7sPMhzGExE7Y6usMJBZ41Z8kp/g4AP55i9zHE9fZv/UWvR+30wFf7SFmXeRbuwuRDZ8ps0+iN9sUwgnbGOMqoh9eTFY7yhnIgo4pRAx+5ayESa5KtYEd9YFOnk8g/L5CR/sFznvoiRR+PhA9a55llryiHEBzv67FtXbLVbJm20z+zcdgwh3lSZYR/v+2U7raXXmhsGr8udVB64Lyv8LEDoFUhDxihSdRC5k84lkeQkxdJmwVueE/mJCYJr0r0UQZMuoSJj5R01Un/nnnvukKRHx6XKE9fRPYbMSPorqZhIs0IFsxjJI4bKCxRpivcUTX7cR3eZ+mTREcngL98VQtqrnYika8HWPS304fG4vnvvBiG5VF16K3t/ZEx/1KmHD6iBpTqTNuWFTPGX71zXIpoHCJaMwLLApK/QvkK4QFB2NlgEQiAgBmXlN/zlO9fpa2WMI70usTZCep9UayC8ksh6lQlJbelDeITAuJN7tCfkmL985zp1Q31Tt/J1rHbgO9fl75fnpbYAJOXVNeoeKEKjSI1OGURK2cD5tpZfar2bBZs8eh/bIqx857o2hVIr8O2hvsVYpE3Un/Wdv+rfLOi0j056/FiUhJMxL1UpHcXTzj5Aj9e9hLAiVWbM8JfvMhLzx8x+jhE056E2I/Ui+rOMpLWhohxFYxZJIi73+L02aXqG72WP2XVcX5SOfD6TV20A+ct3rld7pJ82PwhF45774wFZ/bFWdVS27ceDdL5aRElxHUMSYhGX0F8l17jPZCqfxlo8JdHgOSZ+b0DTyO93bbWWrRuswbKltetnH7bHVu6xT+/dbNd2HWs49+zAYvuTgZ+xhS0cYb62UOgoCOlJmgGXwg9zP0uqrHJJSifXRF7yIgMjyqbQygq9rAVZoZexsOU5FkjeK9JGWsqPCE+Rs/Nrr702IfTyVSvpKgsIRAapCpK5NN1LEWRJ2/KO0SS9JV0dKQuqA+mvIuVUWGgFhhBh5vfk1Rtp+T4kggzhIE2kU97Ij9/wDhlKFR2zythKx61S3fC63NxXQI0sS3WkjbwXibMMr0KyS7spqEia1FFEhTxIChqWjTKLkORJcBRBLM+3tgisvvs2U18nHfLBe3in9z6go2zew/3rr78+qQcklN4HsfTSaT+IqtQ+vISPhVQW59ShVI7C98lmQYRS0k71M+la6/80zxK8j/5DeaQionoPvWbQRzVe5FVD/cNv+DGS8x51pF4kjzqMc/T+JYkMQ5yTJ8htkSSsjO6lDN+oe+rK17XUQuiH0kkO7T8kwJDRqzZ7WWNW92vhgaHscb1/RnMdc2/ZdzFmaIO8OuJaGY8ptQheMVpRz148xgsiKa5z+Eh0fsLWdSYeFhAmnpA8aIFksRoy3Bnot9bNa615/57Mdw40VOyxM/fa7gUH7R+3b7Slvccehd7Zf7HdNf09dn7TIevrG1wAFLqZyR3CKt2/UJ2DRQqyDtH1nha8BbYWbhmcyFDJ6+hJ11ASnLzQy5RdR0lI4UJrbiYMkWx/jJjl7Bw/xLhdg/xStxAZ9ITJzwMPPPDa5iPweOB1FZncIOtZJIzgBiwycp8jss87fLhdFmwWUaSPaRGrWHRl/S5SDrwBkBZwSAREAy8bkjZCurjHezRJy8WPFj0Fu+CdMgD1+qpefYQ6J9+QpzB6mldVkWRN7eIj8VE+CLMCDig6XVhH+k45+S4pptKRDizXKZ9UZ9KMe3iWfqBAESGkW+0juameVUa1PxszqRHQ99T2ipYmq3BBxkv+FEOSXK5BRNKsy/mf9pf3CR/1UW0hbw/a4CgNr+6iZ+nreZ4lZPCnDUha20tPWqoNoVcZqS/QtkWW85wAyXNGuPmkvctIwsroXqocjE2duvn8cI+NC31DwgmvPqF3UDbGKO2VN2Zr7YGhbNS74bxLdcI4zKojCTd4Jmvcj3ePCNGLx6lHJMV1DK/yEB6Ba4HlfyZj+Zr1+odyLC9pZmP3YWtbv9oae7MtTg+19dk9F+60Re1d9u9bNtnkgWMlyf9n4M326rTrbUbfbuvqGcwbC4Dc+jC5Qy4gdTJ0UZ4V2plFzKuApBnSyPhHUkhZRusZFkP+91bRPIfaQdZRkiYc8uRdG5FvpG9y8abJQ/WY5uycPEtCXa3vXP9cFgkLJcPy4eqJtjea5DqLbQgt9vKf6o2QVP/qMzKQEoHmL4u2jGQg8VIBEYGUpbmO4yBOIuyhgaf04kGRRwQZWUk/17c/+YK4ky9PalUX2iySJ22cVH5JR9WPuK6NUx54j6RdoRRYdSrjOEk+veqEbysRXOnhhmVXFD7GkAzmwsAciowmnUG/YfY6g+oDIsRh+bUhZaGUdwTfh7UQk748ImR5llB6eW3P/8wRQKGSNRfoBEIktSjIAel4SdjJ0r2Uv2CNpVCarJMc6lGuzcKNPvflx1nIGrMeWfNDtSiTznDeFW4u0upIfWEkgleMVpyMAB+16kPjBZEU1zG0kEnfNTTe0IIvJ/ber6d3LJ8QkL07rHnTWmvIWfw3z+iyey/YaT/adcA+tm2XeSdUvZUm+5PG99rEJRfarEN7rbv/eP+5WgSY/JkUya8MnHQ0yUdH0ZpEs8ql696QRse40gcuIjNlj5IU7lT51pGydLS5XsbZud8EZOnWiWjkQfqQWkRDtQfAfZ7Lg4yapP8oSbUMrdTPWMyRknPNb6yoJ+n3IkUm3yEJ8cdxSpM6zDqKly5skWW0DJakVy7oO22ExDjLGE9Gln7DmJYfHcvnQV4cVD/eq4j3FOA3qyHB0nvZFBYFlEDymHeEil46bVLk3smTXO95JfT2QJ6kAhEam0EcpY+sjVOadwERQrW9+lvY9qQnA7c0Tw5qo7x3SQKpcM4nKgnzG+aserzggguS+i4izopkJ4LooTGoDYF+O1YkeCeyuQgRPSJErxH1gEiK6xhMzooWJf06r0Igcpnn8aHS32e25gVr2b458z0Vq9gPT99nzy/fb5/ctdP+08FjQ/DurUywP2n/Zbvu8itt88b1trO3Nzn6lHRE0ksmeBY+r5cmIwJJprgO2dECzP2wXFyXhb50qcPjUekAIt0lL4CFPE19osxREu9EKsoGQyobkvRJalkm3CnGORAC6TgeU89Hv3Of5/IgIwzKo5MB8uK9SKDOUSQBELHUkXVY19L7lOQh7ciumuM4bX6kApN2FA/5oc3ygipARiR5DIOlyGiR30m3lfQUbli+a6VLz/tov6z8yBVZHsiTV13xYbjl6o/36Lhf+Qzrkd8hHSZvUpfwnkd4D1J42k36sFmBOVAFKtIZZHzIYC40slQeeS/P8m6F8g69oZCmjESzNk70R2120tzXqe35rTZgaZ4cIPvkkWhrtLs20Oob3pNBLSRhZXQvNb9lEWfsCeSfPMurBOmgIgHGmgeGWm0uxrtHhOg14tQjkuI6hqx/mWylpykdXAUUkCW33JT5Y/+uPbts4Pkf2sChA5nv6Grtt3vP32nd07vt77dutVVHjnXJ9MzAEvvSjF+0S5ctsKWLF9qObVuShU3GfAxakRcFoZBhio78+XjfopQH/d9HH310yPBGoCykwwSLMZb0kn1YU+qAtL3RFEYrMrQT6bvrrrsSQzuv5qCJRXp8kuzKKld60f7oG0gaXxTulPZCVxADKZ8/oKNU7ntvD1kEU+GUQ4m4iJwPQpKVji+PFl8ffUv+cqljiLp8jHqjLUVo04Ygj4TQD3VPXhF8vtkMcZ93oR6QtYAiMST8cB786QL9KDS0k2RYrqWyVAO8JDQL0tGF1Mg4SsZflAlSTr8mLfRhRbhFnIGID2UnYh+kKMwzhBk9btR4pC4RSkqpG21k2DjlbVJIQy7ApNbgyy/dXggt7SH1It7j1YsYj+jQ63ce+k598KykgeG41gmBL7Ok76EPaaUnHXX/rrKBVDyKpLJFm70yxJl55jvf+U7SD6gH9WnNy9ynDeVdgOvyXUzZIUK8n/eMRmJci83FePeIUGZzMd7r6GQjkuI6BhMmi7BIZ5pxE4siixCDxFv9DuzYar0vPT0oKc7Atmndg/rDDd32j5u225zgGPrfKzfa1uXvthuXDU5qsnhn8cD1EJOeP/rEWlmGSCwCOk70ZFZHQ0yUEADIY7hQQxrOP//8hDhIp9STVH5PmZkwyAuE+O677x7yHCH1Cn7LdcCChJRCFt+SKEII5KVB5EOqCt5ISMZ9RbpcPHvNNdckz7LIheWn3Nzne560iGchRFINCF2qcZ378tWalQ6E0Ec0TFNVkTGRCGKakQwbnTJ6bHK3xycrgIMM6CB3WQsoEkOlp/KmESnvWzlcKKT7SxraXKapBug9ReWS5DTLsE2Ga6SJhw3pFyvEM2NVbsy8OoWH9ORJG73pvMAcynPeJkUSbC8t9n2V69wvag8Za+ZtnORyTTqloYoJ72aDx/U8LwUipjLMC/NMXth4lDU2KiuVLZI4FxHnMl4lauldoB5Ri83FeEeso1OLSIrrGH7XKNdTIjTSA/RHUhDondu3264fft96Xn05N+1nlu5LPEzccviQfWrn7mP8Dx+utNn/nf2rdt1b32czJnceo2LAosJfueDyR8gKkSwH9p7E6ehfhnZPPvlkkm9PPPUs11mg5ZZM6gPh8bmOs5EQQ/bkJQKI8LH4cJ8F97vf/e6QXqNUCSBfSHfwCypCDLxBm9fDLUMMmbRuvvnmJHQsuqGy0ocI8B4tfkiLFJxB/kzZbNDe6O/yjAyTQrUHSbZ5fs2aNZnpcKwrSSHtmKZiQDtKb506g6xoQaMddL1MNCp//IfEM83Th47/+E3WAipjE6+j6VV1JLn1Pqu9mgH1oE0MmzXeQX7ki5f8UK+QfX8cmVU2Xy4ItvcaAdn1hm1sVlBt0EZC9ch30lBgE0hS2jOo6UgFIS8whzYY1EuWFwMZB4pk6nnd5zr3eU661WlpQfLlXSBr40Sfkw2AvF2EniU0V7AhJp3Q+wagfiij8pOmNiYfv0XlH2mpLG2qCHppaly19i5Qj7rJw91cRMQ6OpWIpHgUDA6O29MknBBieURAR++Jhx+yysvPWOPh1yycQ/Q0D9j9q3bahjmH7Vf27LMP7NufhHkW1lTm20vX/5W993WvO24Ayt1QuFhrYWKhklN7SbdD6Oieo3F5IAjTYdGivCLYchUlSHLLNY50WQx5bxhcQRIqiBkSY0iSJO4i45LAQcS9R4hQOhVKK8u0G27bsqJosWgi7aK8OvrX8ThkAkKtgCAiF74OFQiD5ySRTEsHvUykVN/+9reTDUC4AYG8ED2Oe7SJfOd66TZtjVePMtGotJGDRGWpB/jjv6wFFOkl6UEw0/RTea9cenFfOsPeGFGSXVzNoYpDG4f54R3KT1HZ+Et9h+msXbs2KdfrXve65DtlD58hTdIgkAYbQtqTkxDfFyFqpCN3dArMEfqEluQX0sW7sk4/ZIin+tOJANBmk+elGkOZw/Izr1DuMt4FdIKl6HVhXdMntXEl3/IZHpZfmyK5WgvHtQxxKU/e6Q9GsbWUypaROKc9Q970TK29C4xW3eTh6oGPB8Q6OjWIpLjOwaRHhDQmcvT+RGiYNLnOAsCku+PlF81eetoac9Qldk0+Yt+7cKf1d/TYn23fZa87fCzZuKvlBlv8s5+1Ny0clHhlGRvlLdayaM/zCCBjttCAyuvdyh2drLi9Rb986/IMi6t/LoSek6swIB1bSa34QFC9y6wsFOmelpnUICCSbKUdj0saRj6kZ+n1KD15F/lQHag+lQ7vQVJKP9GC7HVcuc4H6RwLq4gFH9KUT1jywd+yUrc89YAyIH2O9On/OhkQlD+kiJ5Y+XzLBRjSaen3ZrVRWYkier4Kp+6hEwfuIyHOexf5oq+pXLxHbc+7OVmgz0Aevf6yV1WQVJwNj4LIQCCVZ9Lm9APwG+8OK834j/ucNlDWrPKHLuDSDIC4T3/07v/CepKHDjYSUhVR+ZkXCI+OigbPSR0qfJdIJXnBLiGr/JSlVlLZMv0DFD1TS+8CtZaC15vEOSLiVCCS4jpGlv6Z7jEpPvfss/bcnd+wgZefP0biG+LFhQfsB+fstnkDvfYXm3faCmfUgneJr8z6L/aTv/Cb1tbSVJWxUbhYc90bo4VSYE8sQ0Lsf+P1Rf0HSBWDBUAhrVkovc9iQYRIRFJeK5QOH+na6noacRMZr8UiQf1ISsxirjRlBKdjf5UzrCdd5y+EUN/5nZ6XBJ7NANI06oYAI2nqA0iIIXSqH6/yIu8n3JfRY57UTXqp/B5pVZong2qkc/xGmwblyavUiPz7wCgqB/8jdVNAlrz8yE9uVtmQ6iKR9J5PvCqTTjeUj6x3QXol1ZRhqm972lPH7nn6y6RNnvjfn9rIGJd2Jz+QWW1S/WbQn35wn3xRhuG4gMMO4D/+4z+GjPvSvNN4vXa/idNGV0F7UFGR7nJa9EQIG/02r/ycInm98ROVypbVAy7qQzzDqUwtvAvUWjd5tEqcIyJqjUiK6xhF+medLc321K1fsJ6dg0ZJaehrHLCHVu62VxYcsku7uu1Ptu+0qU5a9P3Kufbg1HfZ3BnzrevQAWubOjVTYuCNjSDGoT6kggTIKl9kTRCx0TUROxC6iRrK/1F3bF5SKt1fFj+OxjHWkxQ7JOEigPLWoXf594mE+6AM4WKl57yf0SJk1aOkmGmuwHT0L0m6JKBeUq5yiERIVcWTeale8Jd24lhaZMIDcoOUUCooWREGuc9zSJ3z3IQhmVaflQeI8H1lpHM+bCz/y72adNlJR+7mpLOqOledqp6R4iqUd1p+0IUF0pEP24NnOJURIfO+jvVXpxs8R56z3sXJhiIJpkF9EZIpl3VpgTm4RvnDaG5Kg99DvrX58qcsgq7LrRq60XkS1SIXcDKklXca7y2H71wX6ZV+e2j4qZDdpKmNcZqBMScAELm88lNP8iGdF7q9SCpbRg+4TB8iDfJUC+8CtdRNHqveMCIiTgSRFNcx8vTPDm7ZaGv+42vWe/hYn8Ie+yb02t0X7bC9k3rtJ/YfsF/ftceUUnelxW5rf6cdnn25tXR1DRmu5EkM+MuHY1tIUqgPCGGU9EtEwUv4gFdhEPkKIfdN8qMbSm49SeV9uF1DZ5hJ3XufUAQudAuRdIZhjkNCLsM+b+CnckgSzWJcBnn1KPKZdzzM8yJGypOkkLrPAka7SaocQtJxSTfToAXZGxKmqSrISJJn09yEIRlUhLhqdCazNg5e1UMSQy81VyhhEXfViTY2+i63XkXlB3nPKL9FZFaGnHnv8puN0G+y/PFCUOk7WcSJvoQLtbx3UWecHqhO/CYU6Lv008u0WZ4LOAg/+cegkY1YeJLExlkGe/xGbhXTIr9pE5MVPZE8sAEpKj/PSuUlL3R7HsroAZfpQ74Oh+tdoFa6yWPdG0ZERLWIpLiOkaZ/xiS2/clHbePD9/Il87dr5x2yB87bZdY0YP995x77iQOvGd89b8vsoVnvtsaJs5OoddK7ZfFQJK08nTjpmHoCygQMIZCldXh0CkSA+Q3SNOkVhsZt/M9iBfkCIXmWVEn5kx9i+SkW8YBMQJjRTeX4V3rKngx7NQHKPBQB0AVF0eLD4iwd3zz9uyLJCx4o5CIs63iYvCtd6c1KnQTpGKSBfEE+lJ80VRVJwPN0GEXMaT+RJNUL75BqiXSURdJF4KgzuQmD/JTVmczbOHAfQied8VD3Fqk1dch16Z9muZvT/bzyg7xndD1NncXXtwKX5KVTlGfKiESeU5As4qSTj6LIeGweuO9PFPw4ktpGNXquWbrymq8oX57bOv+urMhvYfnD6Illyw8BxbBReseq97TQ7Vkoowdcpg/5Ohyud4FqdZOz5quT4Q0jImI0I5LiURTdpr/niK27+9u2d+3qzN/0N1TskbP32AuLD9iUgX77k6077bLuQWlBV6XV7pzwn2z3zKuHFnYvoYLw5EkMUFPAEEbH1VpoRdRYcJk8+V++k/3RrYgvxAlpEs/KAE6EWMfjfOT6TfrDob6s/MICiDEBQdJcIUl/U9b3oVW8yAzkGaKVRUJ5pojMKVx0Xj1i1IYXgjz/04p4J/deaa7NSFMkP5R6i9jKw0Re9LilS5cmZVYevJTcG0hRfvIv3VcvYYdQ066UCx3eIp1JyoLOa9bG4ZJLLhnyc+1d7UkazO94p/TYFTo41GGlPSBo1GNWfiBg/J/3DB5geI/qKM1FIHXEc9RlVjrkhedFRNLyrMhnXMsiTvxGrvbyIqihB/zUU08NGcam+Q6Wn+K8PlJGzzWcr9LUPig//1P+oshvwy0/v6VdmAs0zvjwLL+lH5cJ3V4mypjvQ1mRCsM6zNpc1DryWd585Tf9tfCGEREx2hFJcR2DiU76Z5tWv2h7fnCf9RzYl/n8wfY++95FO2zn1B5b3tNjf7Ftpy08eoT+bPO59sjkt9jBxsnWfJQcihCzOCiscp5OHGQHCQukjMkyPPpk4kSHUdHowghVgMkXV3IQDAgEv2Mh0aLH4kyeKDMkS27UtIAr3yLPfuHlGq7DQlAuRWULQ/0quIKknNxfuXJlpo9Zkbksv8Bnn312KcnLqlWrhoIXUPeqR/IECSQdwDMsav5d0qXUO7yOq68jgXwrnbSjeIgKGx7ypQ2D3zxoYyAfx9Lj1XsgpyLT1HWRziREDcl93saBOpbKSlp4ZvU/fMIiCeR9aVHEON2AAOWVn/z6uk57hj7Bux988MGh8uskQbrObMzof2wIs9KhXZFe4h0hK8+KfJZHnLjvI6iFpw1KR9HqqG+dnnjjQPLP/QsvvDCp8zJ6rllSRz9fZaXDKQljnHwzV/hw0fQjvp9I+dPqkTZjzmJDm6ZTLM8qRVLQMuVSH8pyRehd/9UCZfNU5DGG9qiVN4yIiLGASIrrHEym0/uP2Evf+5ZVcvzkbpzVZfedv9OOtA7Y9Ye77A+277SJlYodbJhovTf9vu3uX2b9q1db34EDQ070fbhg3gOhKdKbk1RBEsLwGR2jsxiH7qskoYEUcE8SHqRCHpKakScZeXnJJPnlw+/KxIDX0SnSHKS0oR9epJvUg4y6snzMiszl+RdG37qMfiYEPMv/NFb80ivUM4pu531Uy2JfeQiNGnUdiRFkDOLLYiiJs67rSJ0yyOdxqLct7xaUi99L11v9QBHPIDaQniKDrKKNA/nknTxDuqGaAXkiP9TFTTfdlBtFDJTR4Sx65sYbb0yekxs0nSTQdwiSovtF6eh9RXkugrxBoDbEX7U3fYv8KB1OUBhDjDnv75t3ki/ul42iVeSloEw6fMrku0z58+qReeiVV14Zmq9Cw8ey+u2gTLmomyzSezL0cYvyRLvcf//9hadWbPrzTknKnBJERIwVRFJcx2BS+o+/+XN75u47s5+xij1xxj57avk+wyfb+/but1/dszfRFd59+o/a9Lf9gdnEWTb7xRftqaPGZkh6vVQJIoM0hb9FenNFUgXSRS+ZtHyUME22XIdYFukDspBBrtAd1RG6JFzcV57LLDbSqYQYyFDO54nrOubMW9RYdLx/4TS/wKTrfaxmWbzzPIZCPIOkTqSWcsn/NMjzUQ2hhUgoeqB3SSfCxn0WR0+G0upbPn15r5eoS+1EEnu/mfLv0qmADI7ydCZlbJW3cfAScH6XdiqhUM1sZtg8ZEU1K8pPNc9AfAnKgrsvJJQQL1zd+Q1imXQgdEV5LgKkRp41GCuh32Tui4Ref/31x22K2CR6wluU77JeCsqkQ/7Ic3i64fNdBnn1SPvUQr+9TP3IYK3I9V+tDdby8kT5qzm1Go43jIiIsYJIiusYiVR00mAo1zR0tfbbvRfstC0zu62lUrFP7dhlbz142A5PP9s63/7/2fRFVyTPabGRbp3C+Cp4g3zjFkkM0HP1+oBhGFdJibkvQhaGaGXhYhFFj1U6n2n6dyyYkD0ZLimYhQgZnzL6gGUj8SFJ3rBhQ+6iJimxjp19nrznA8pE3hSMQ3VEffAXgouEht+HxJ68Sn8b5PmoRmVDLunIa2iMyAKJeoF86Kp9RGZoa+qaNHQtlP5LXYH70oH2/nWBdIyl7+3zkGeQlUdWIOhqG7n6ytI9BbwbcpyHMjqcZZ4hLzLuHA7K5LmM14DQlZr6hydhZQ27ssrv36cxy/8ijyHpK5NOVr+uljxm1WNZvdsi/XbvkiyrXCfLYC1Peu3TT0uzrIcK5qXhesM4GShT9oiIWiOS4jrHNT/5s3bPfY9Y5571x1zfNq3b7rlwpx1u77cZff32Z9t32LkDrWa3/JF1XvIBs6bm4yZsJjp0Y3VELukkiyrXObbPkxhIH/Kb3/xmQshC1QiI5bJlyxJ9YiYxGUp5VQXSRBrKYsXkmxUKGJUGFipJjSFigtytIR0qs8iUicQHIIkizWk+ZqkXFk5JYb2eq8opwx7cZbHR8AaG8iuLriMGi0XqA2X8niLlkZcGH+aYOqMekWIW6e+yGcDAkDaVaohvD9qe+5ICp7kSk+u2ND3yEyUr1FOezqjXPa0njEQghBMhYcMx7NL7aAP6S+jeLHxfPXg7KKN3W0a/vQxJFwEtcllYjcHacPtRNR4qqOvheMOoNWIwkYhThUiK6xy7sHg/83xreexVa+kdJADPLN1vj525xyqNZmcf6bE/37bD5q78CbM3fMps4uzj0pABXFpoWUgi6g5MNBC2IokBunth8Auga0AhW/nuyZOO6b3BDggnXj2bFvUNsLCRvnwrF6FMJD7SKvJnK9IbSktFQhVdTgTVBxzRs1zHFV2t/J6S/7yj8TL6u3Jjp4XcQ1JZThEUfEEW656Ah95AamEkVCvd25HESAVCqJWf2mrex5jjw3vD0OSMQ8Ypz9WTt4Mivdsy46MMSZeKFhv1tNDtOqUpa7BWi35UjYcKlbce3K7FYCIRpxKRFNcxmLiee+5ZWzfhL23rRW32usdnJb6H18/tSu6/8eAh+3jPLJv9vn8xW3BxZjpMKhDMvNCy3Oc5CGKeGyQICmAy1XG6/JwyySIhBYrY5q3I5bqLhY+Jj/JlqSqgM1sU9U3hbovgI/Hl+U9VvrOkKrxXRDdLWkp5UY3gHgtMlvqIIgDWwu9pnpSnjP6uyKl8HXsyLz1iThI4JYBUUB71I20U+F3oDWS4hku10r0dKYxkIIRq/dRWU4a0NqOfUP+8jzYI5xDaj9/yDBu0evJ2MFz99jIkPUtFS2pVbLqpN54bqX5UdvNZTyoJMZhIxKlGJMV1DCaz3bv32Jmd59sP2560f79hk/W2DEpXP7i/166Z+h57cMKF9rqJyy1vf69wuHnRuLjPc5DiLIkBxASJHRIhBW/wUGhVuetKc6UlySKLBEfyWaoKvEf5yor6JnWMIpTxn1rGVy1EVnWZJS1VaGmFOQ7rHDIJcWCSL/ILC4ZrFV6GPCH9E1kQyRVEFvx9SY9Fhlns5UVE+ZFedJq3i7KGXbXQvR1JjKRqQLVSwDLIk/BqrHtXfyG4h7eHevR2MBz99jIkvYyKlgQHRW1fy35UdvNZL4jBRCJONSIprmNIonnl3PfanjUft/9o67X2gYp9uOEcW3jOB2xPQ4v1HfWnmwdIj4hqXmhZnsuDjMaKpCoYSUmdwHs94J3cY4EocltGfniWNMhXmlS2rGSyrMQESKea34ehZZUGhD5LWkqZeKYIkDwITZ7+dpn8cK1MaG7elWXUKLIr11VePUbfdfzLu+kHXsebuoAY+/zcc889x7nkQ9rMNdQ9igyXThWGY9wzkioNtZYCFh1ZY7Apv+FpURj5rlMeSO9o8XbgNxdlg27kqWjlhbhWkJ5a96OiPjvc6HkjiZFWC4qICBFJcR1DUoy+/n5759xftK7tf2eXTflRmzHpPOtvbLWeoxNxkRSDSdm7U8sKLctzRemUCa3Kc0iTWVAUNpnrkFwWQW+wlZWOfCdj1HMgw7dyKJkcrv9U7xeYRTL0HYzHDCRd0qtNk5aiYoAbOdREROQF8s/CSN4xSIQYDyc/kr4V6d+RHpK5rKAC5IUgBwqKES6WOmVAmk4e03wrk0/SooyPPfZYUk/cC4MzcJ37N998c90tyiNp2FQL1EoKWObImnEIYdW4DkmfguNoczVavB1oc5E3PsqQ9DIhrsu2fTX9qGyfrbfNZ72MoYiIEJEU1zEkxcBTwZb+Fjuv9712aPuArd25NiFWcu9VJMWAPCokalFo2TzJg08nL7SqwuoiNUnTFy6jqsACifcEdIvzfCtLMlkr/6lFvoMlnUNiJt1BPQNhIKgG32XRHkrUKB9py6vEcPKD+kkZy3n0crMWda5D9glIoSPgsF1pQ0gP0q5HH300028y9cPv6a/c8waSXFfURO5LfaZecCoMm2qBWkgByxxZi8zSblnjmnxI73i0eTvIGx9lUEZFq2zbl5VeV+NKbrS4QDsVYygiwiOS4joGEwLE76mnnho6MlcwBSR1TFxlAlj4kKhFoWWLJA9FoVV9WF3SYEFVtDe+lw2rK1dJ3AulspAz/nIUj4SyGsOMLImJl5Zl+Q7m/jXXXHOMlEsLvQ+EQIQwSAS/gcCKPErayn3vueJE80N4Y96jBYJ3eemUnpMLvjyjxiuuuMLuvvvu5H06PZCLKRZb7vNckX9Z/EvzPm+M5fuzItFxxFwvpHi0GzYNVwpY9siaza7Gcdq4ZkPImB1N3g5qFXSjlm2vtLJCRjMf1MqVXL25QBuNxoERYwuRFNcxfNANEUMFSfDEUAEseCbLSr8oJCr3JS2DtChABmniv1aSB6XDczr6JB0mUH+f/zG0wt2bJA8sqt7QqoyrJBYArrPQKh2eQSLCfcpbjWFGGf+pWsBlICa1D6VTJJ3j/g033FBoaJYHn588wqvj6izfqAq9rP6SZtTIe173utcl3wm9KxdtPM99CDF9DNKclx/S8So40rX2keh0Pc9gq96Ne2rhVaOW0rvhSvjKHlmz4aEf5Y1rjaVaEZpaSS/LjPu88VGNUdtwxn2YZ5DW3vSrWhmk1ZsLtNFmHBgxthBJcR1DEzbEMC1kMN818SFVSNP19P5c89xbSWIi38Ck4wkWE7wkD6RLdDvpibKQ8F2hiQG/55ic/Ck/ChRSxvuAXCVB+tFD9XrQ5J/gHryXcpQ1zCjjP5W8sBhIP1LSTSZk/peBR5GUa7jH2mWCAShoSJ5vVPJO3ZSpH/oKUmzqCBLDwkrdkKbaoyg4AX1KHjaA99AhDwbcp25Go3HPcMMBV4My76qFhK+aI+uXXnopd1zXktDUSnpZT36TyxB1zcV5LitphyJj5TL5rlcXaKPJODBibCGS4jqGX6xlYJY28TFBohPK8+htaqfPhIaqAxAxznJvxeQjssxiJ5drkkqw+HGfwBOPP/74MZ4FkCw/++yzyfvwLAAh5r1hfpjMw/zkuUpCRQMyxnvkPUOBPViYkVxBwspIucr4T1VkOAU4kQ4vv6HcLPzVGHgM53i4TDAAysG9PN+o/E7ePsoYrvCOc88994TyIz1R6kmLOn1UxEnBTaRTXS8oKykt6kNlwgGXRRnpHaiFhK/skTXzTJlxXQtCUyvpZVE6SHBrZdjl35UWTt3nOY+o+4AiWdJrebgZbr7r2QVaPajX1DvqQQ98rCGS4jpGmcWaSfOZZ55JBoUnRjwvf8BIkJEQ5wU8QOKLlFieIbyklHSYuLlPCOc8zwIYYjHhDzc/kF1JvUOjPrmW4z4SqDJSLvKWJw1RyGjShQToffI1zG9JC1WKkZiwygQDoP5FerN8o/KMDKSGY7hSTXAC3ktepDqhzZMiKXp3bvWAspLSoj5UK4laWemdXOXVIj9FEl7UJu64447S43o4hKZW0ssy6dTKb3I1eaZ+i9zfFUmvSZ+5qMjXeRlXctEF2uhEveiBjzVEUlzHKLNYQzCYtJDchCRTR9uQR0hfXgAEyLD0REEYnhkwcbOI5HkWwBiLNMrmJ4s4YkQif8ShDir/c537SK+KpFyoWmCUlicNQUdSx5FpQUd0nfKXCSQx3AmrTDAAlaXIN6oMpIaj51k2OAH1w33cvEGUdPIgV320Affryfl+GUlpmT5UK4laGekdev5AXkxqkZ88CS/jQ4F7hjPP1Kr8ZcpWJh3GDe4Nh+s3uWyeGRNl3N8Vub5kPkIq/9xzzw0r39EF2uhEvemBjyVEUlzHKLNYs4hBRBkUaYZNPM/AkY5nFvg9v4XAZYVnlveGrIkfYoQ0WemlwecnjzhSPsojiaOPska5+LBw8xzH/XlSrjJ6g3KXxiILaQt9OctrQlE91mrCKhMMgLQkyc/zjSoDqeHoeZYNTiAdb75T9jBPChldb5KnIknpSOqelpHeaaM6UhK+MoF7yswzHlkb4lpJL8umA9Efrh502XeRfjXu7/KkwLhSRFo8nHxHF2ijD/WqBz5WEElxnaNosYaIQBBF5DyZY4LmWUnp8sDvpPuZJimV54AiiDgXBfjgvogjz4kwiThiuCUDQN3zUdYUUIJFpEjKxeJSJA2R5BPwu3BzQZpl6rFWE1aZYAAQdb945vlG5V3D0fMsG5wg1PEO9SGrCWIw0hhuH6pVucpI7xTeXGHQw/Y4kfzkbVKrCdwz3HfVSnpZTTrD9Ztc9l2kV437uyIp8HD1t6MLtNGHetYDHwuIpHgUIG/iU4hdDMRkHOfVHiBnGD4pMEcWmAi9hDiUlHr92rxobQrxTF6zAnxQHkgGUmXIJ5JDEVCIHmVUVDxtArICSsjnMcjSYywjDWERIl0mE20m0vJdVI+1mrDKBgPgCBVdzjIL2nD0PMvmh/ohOt5olTwNpw/Vqlxl3qUAONS1+qf3BsLfMoF9yp5uXHrppYWBe8qMjzLvwgtKLeq62jar5fjIehcnKNW4vysjBR6uQVp0gTa6EPXATy4iKR4lyJr4uM5ihTcIBookm5L46n7RTh+SQzAFyCq/5bvItYJmMPGz4LIQ50Vr41gvL8DHypUrkwmYo9bQkwEqCryLxeXCCy+0hx56KDegRBkJdhlpCFbobB7y8q0AJyMxYZWV4IzUglY2P9TPqZA8nWwr7JGUqJV9Fzq8Tz/99JA3CMoN2WKTWTawT9nTDXT3IcZ33XVXspkNjWz5XmZ8lHkX9gRs9oZb1/XYZpJIlyHqwz3dqQbRBdro8eQQ9cBPLsYkKf6d3/kd+9SnPnXMNSZZJmPAJP7Rj37U/u3f/i0ZADfffLP9r//1vxJSVK/Icz4PIKMQY6lRyBiOhRGUkUwi5YHQ8YGcykCKdKRPSj3K3VtWtDYRMaRBLNAiagrwwbsguxDi0GCP9/FuFl4CSrDY8iwEWjqsSKOvvPLKZBEuizLkUfnOC3AykhNW2WAAI7WglSXgIy15qqUVdpnAHGF7+GiGtUJR21M+ygz5Ypwy7hXYh00w/dYH9skrW9nTjfPOOy8hxgR44TeSTHOqw/Uy46Oad9WiD41kXyz7rmqI+nClwNWgzLtGC3Ecy54coh74ycWYJMUA4yv5zgT+OPzXfu3X7Bvf+IbdeuutScf50Ic+ZO985zvtgQcesHpEGefzEHqO3HwkNibcsoZNknTgmoj3kS6/lX9ZBp8miDLR2hTgg3T1DCoKirIGaZQ0OswH5SO//A6pM8+vWbNmSCKFZwOuV4sy5DEvwEk9T1gjtXiWJeAjRdRraYV9ogvjqYjOV01gH/pFLQJYMDdAtJlntBnWX67zjjIGpGVPUpjTatGHRlIKWuZdo1VdYTQRx7HsySHqgZ9cjFlSLL2sEHSkz33uc/Yv//IvduONNybX/v7v/z4hdEhAOJKvJ1TrfD4kW9UaNmkgSX84vF5m4vd5ZtFWntF7ZgFYvnz50DX53BRk2Md1JMZPPfVUkg7SuDCdE5msypDHrAAnIz1hVRMMYCRRloCfbKJeSyvsaoNlnOz2KGp778s2L7CPovANN4AFkmfcsoV1rXao1oC07ElKrfpQvUlcR5u6wmgjjmPdk8No3ViNBoxZUoybMogUiwVH7Z/+9KcTovPYY48lg/mmm24aepZOxD2O6euJFJ8K5/NIjbJCi/rJIWviL5NnpLD8lsk0TTeZ/8kv/jpH22RVqwnL1yNllG6zpDNhe4xWDOc4tlZGjaciWEZRndTCl63GLsF98vqRn0P0jB/7zCHYG9CPa21AOp6PfkeSqI9H4jjWPTmMto3VaMGYJMUQks9//vPJUTiTL/rF1157bbI4MPEz0YcdnKM67uWBjufVEJjATyZOlfP5rNCitXKYz+LKYJbbs9DnLX+5j/rCaJysajFhqR6RyGhz4L0L1Hv5R+I4tlZGjacqWMZw8lPWly0gLeoiqx9pDkEFQhtjgec47cGeAKO+WhqQ8j55vOGdqEdhYxCPfusLo5k4jnVPDqNlYzWaMCZJ8S233DL0/6pVqxKSjH7rF7/4xWGFmEXaHBrwnUzUo/P5WjnMlx9OVCQUNtYvjNyvxSI8WicsykXd8KE+vTSdzYLqqZ7LX6vj2Cxpcq2MGustWEa1YyhvM6wxVtSPeE660SHx4brcFNbKgBS9fQxaEUTIoBd7gwsuuCAe/dYZRjtxzEP05BAxLkhxCIgJVtgvv/yyveENb0g6OhIWT1jQU03TQfb4xCc+YR/5yEeGvrP4IEE5WTgZzudPNsEom07oh1PXZcnPBIwx33idrFhoIC2UE28boYcOFiHaUj6kRxOqOY6VmkiaNJm/tTiKrzZYRj0E7yjryxYvLvQj0kKynNWPcLPI3yzVqTIqFmXVHmhf1Nsom/wty2CP65DjWhPjseo1YSQwloljVOeJGJekmEXhlVdesZ/5mZ9JXIZBuPC3+a53vSu5j19MjEjQPc6DIq+NFGrtfD7vuLpWBKOaPOf54eTZOFkdL7UbT8exeBxhY5QnTa6FUWM1wTKGq7tfBrUaQ2U9ZCCphTyjN5ylOiUVC4QHaSoW8k5TVNfaEClwD55lwsA9tdZPHateE0YKY5k4Rk8OEeOCFH/sYx+zt771rYnKBLqAn/zkJxOpyLvf/e5k4H7gAx9IJL5IJBgEv/zLv5wQ4noysjuZngxOJsGoNs9ZRH68T1aUE8kex95pxoh817H3WD2OJWBEkTT5mmuuGbbqUNm+BqS7Tz60eaM8pyIQRNEYKtuPJAUsag/SyipfNbrycneYFbiH+7XSTx2rXhNGEmN9Lo6eHCLGPClmUoUAI92gQ7Nw4m5NnftP//RPk8kXSbEP3lGPqLUng1oRjDJBDkaT4/16g0gvhEZeOrwxIguTnhuLx7EQJhbcMoZttbDCLtvXpAsLqfK6sEhRT0UgiLL9iA/1mdaPtLEq45KtrHeaLKDDjJS4KHAPzw0XY9lrwkhjrM/F0ZNDxJgmxUSqywPHg3/1V3+VfEYDauXJoIz1cJl3lTmOrNUkM14nK39kid56GgkZrUeWZY5j5WGhrHFPLaywy/jfRueVdxNB0huHngxd2Fr0/TL9qIxqCHUrbzDD8U7D+8sG7hkuxrLXhFOBsT4XR08OEWOWFI9FlBmwWdJbf1yt414f/aoaguGPI6VjTZppx5HDyXO1ZR+tyCq/P7Jk4WYB55gSssD30XxkWeY4FneKTz75ZGnjnloZUmX1NS91xEWZT5v8h1LHejHsKtuPQJ5bx1p5g+G5MoF7amFAOpa9JpwqjOW5OCICRFI8RpAnvdVxNQujjlBDX6VlrIdDIxnUU4ZjJDPeDWCKyj+WjyyLykY94PGgjHHPSPSjaqSOEL5a5KdW5Srbj/KeqZU3GCTEGPTh/ScrcA+kK5REnwjGsteEiIiIk4NIiscAioxJLrvssoS04mWDRYcFRwsRix8LFJK5oqP4WhrJjHcDmLLlH8tHlkVlK2PcA4EbiX5UVuqICgKuH4ebn1qPjzL9KO+ZWnmD4f6CBQuSuswK3MP9kfbiEREREQEiKR7lqCZELUiTcpVFrYxkxrsBTLXlH8tHlnllKyNNvv/++0ekH5WROsoYbbj5OVnjo0w/OtneYHw6WYF7TpUXj4iIiIhIikcJsnQUyxzrIikBuKjL8mSAv9CRMpIZ7wYw47381SBPeskJx0jVYxmpozdGG05+6rV/nCyvMmHgnnr04hERETE+EEnxKECebiESliIjOpFUFgAW2vAZfod+8EgZyYx3A5jxXv5qkSW9HMl6LCN1rNYYrYxh7MkuV7UYjV5lxrIKUkRERG0RSXGdo0i38Oyzz07ILQuzpMChEV0YojY0YmGhGEkjmfFuADPeyz9a67FI6liNMVoZw9h67R+1UucZSbWgsayCFBERUTtEUlzHKKNbiIU+JJToX1ooRVQ5yoXAnnHGGYmu3nBD1FZrJJMlCRvvBjC+/PwNfceO9fLXCqeiH9XCGI32JgBInmHseB4fpwr14kYvYuy3az3mKWIQkRTXMcrqFkKCgYzpBG9chwQqzw9prY1kitxJjWcDGNUjmxTqiPoTqE/Vz1gtf61wqgyphmOMhpcXvMDkbXS5z3PjdXycCox395BjFfXYrvWYp4jXEElxHaOMbiG6wCyO6DOGYVwnTZo0ZETHsyNlJFPWndR4N4DJIjWR7JRHvfWjMioWZTa65513Xl2VayxjvLuHHKuox3atxzxFHItIiusYZXQLdWzLAEN6lWdEN2fOnJNuJFONO6nxagCjOkJCDMFJC707ll3S1Rpl+9FIHVnm5Wfbtm2ljehqNV4jsjHe3UOOVdRju9ZjniKORyTFo1xnkvvoDpc1ojvZRjLVupOqpQHMaNHT8nWERD9ss+iSrXoU9aORPrLMyk+1RnTRQOzkol7d30WMvXatxzxFHI9IiusYZXQUL7jggkQHsV6Mck6VO6nRpKdVzy63xiLq6chyvBuZ1hviWBybqMd2rcc8RRyPxpRrEXUE6SiyUKIbjCoEf/nO9dmzZyfED4LM4o/Bmwzf+D7SRjleEpaGk+FOSqQHokF5Z8yYkfzlO9e5X084FXU0XhEeWVKnSOd1ZMl1H/FxpDa69TJexzviWBybqMd2rcc8RRyPKCkeAzqT9WRsNNLuxkajnlaUFo4cTsaR5XDVdDRe8WlMH2CMMDZoc/yO19vJxlhGHItjE/XYrvWYp4jjEUnxKEGRbmG9GK1JEoZBUZq7MUm2a5Wv0aindapciY1H1PrIcjSp6UQUI47FsYl6bNd6zFPE8YikeAyhnoxyvI/ktOvjXU+rnqT7Yxm1jA5XK91knw7jVengt5rFMbplGlnEsTg2UY/tWo95ijgWkRRHnBCQAG/cuDHxfEE0OyLZIQmWOgN/CUDAfR0P8xw60bVUZzgZIXHrwXXXWEA9eAOp1ZFlrdR0RqO6z3hAvY7FehhDoxn12K71mKeI1xBJcUTVwNsFYWp3796dhJMmot706dOT8LT4VmUHjPQL0sziD4GGMHM8VGt1hlrradWL667RjnpRM6jVkWWt1HRGo7rPeEG9jcV6GUOjHfXWrvWap4hBRFIcUTUh/s53vpPscAnrrKNfJnCuX3rppUkIaD5M5OyCIc2QZ6TGCgddK3WGWupp1ZPrrtGMeqvHWhxZ1kpNZ7Sq+0SM7zEUETFeEElxRGkg8UVCzILNLhfpL4D4Mmnv3bvXnnnmmeQ+EzhhpkVGISGQYxZ8JLgs/vVEeuKxdm1Qr/U43CPLWqnpnAx1n4ixhXodQxER4wGRFEeUBuoQqEwg6RUhFqQeATFmUR/pyXq4pCcea9cG9VyPwzmyrJWaTnTLFDGax1BExFhHJMURpYH6A2oQSIVZwPlf+sJIgXX0Czlm8kbS4dUnRFYh1UjE6on0xGPt2mC012OWYVOt1HSiW6bxa4Q8XsZQRMRoRiTFYwgn21KZCR6CS0Q9jn/58E7ewQSOhJgPpBfVCfICMSY/LAr8HhIA6u14OB5r1wajuR6LDJtq5U4pumUan0bIeOMZ62MoImK0I5LiMQIW9JMdIQuJB8SWwBxM9hBhCDHEGOILAcb7xKJFi5JnFi5ceFxEO0hAPR4Px2Pt8V2PZQ2bauVOKbplGn9GyKAMMR6tYygiYiwgkuIxACbde+65x7Zv335MBDl8AnPt+uuvrwkxZnJG6kGakoIgAeadfNd9iDjSLwgwkmGOhCHHfK/X4+F4rD1+67Faw6ZauVOKbpnGlxEy908//fRCVYrROIYiIsYKyis6RdQlWNAfe+wx27RpUzIxt7e3J1IK/vKd69yvRSQ5JmmwZMmS5B1IgKX/xneuAyZwJGtIM1C1gJzzl+/17EpIx9qjLd/1htFWj9UYNkVEnKgRMvd5biyOoYiIsYIoKR7lQALx6quvJos3E7J3gcZ3JLbc57lp06YNSzdZBBgVCT6kLVUNdIgBkzfPcX80Hg/HY+3xV4/RsCmilkbIWX2IkwieG4tjKCJirCCS4lEOSRDQ9U2TcnV0dCSBNHiuDCnOMzYKDUBCnTYCc3gDkNF6PDxa811vGC31GA2bImplhJzXh7jPc2NxDEVEjBVE9YlRjjJqETxT5jkZG2HgwXHfjBkzkr985zoTuyQXYXoyAOF+NACJGE2QYVPs1xHDMULGngIBhLfrAHxHSsx9nouIiKhfRFI8ysFijTQYKW3ago4Umfs8V42xEdIOdOFkbMR1rKuxnoYoQ6B5JxM+f/keDUAiRiNk2BT7dcSJgrkSt2vMl6iq+T7Ed65zvxp/xRERESOPqD4xysHRGgZuEFakFIomB8lF/YG/3C86gitrbHTeeeeV9rF6sv0mR0TUqq+NZt/BcZzVB+RuTX6KESSgMkHfqcZPcURExKlDJMWjHCx+F198caIzvHnz5iEizHUW9fnz5yf3ixbJaoyNyhjRFQVCiIioFWrV10ajYVMcZ/UFiC9u14YT0S4iIuLUIZLiMQJIK8e8WECLFCv08skwNsozACkbCCEiYriodV8bTYZNcZzVJyDABDCKiIgYfYjb11EO6QLzl6AZZ5xxhi1fvjz5y3d/fySMjcroJpfJT0REEcZzXxvPZY+IiIg4WYikeJTD6wKzKBK0gyM7/vK9bOCBWhkbxUAIESOF8dzXxnPZI6oHmyMM/rZt25b8jZuliIh0RPWJUY5aBh6ohbFRDIQQMVIYz31tPJc9ojpEvfOIiPKIpHiUo9aBB4ZrbBQDIUSMlOeE8dzXTqTs0UvF+EPUO4+IqA6RFI9ySBeYSY7JzS9y0gVG0ltN4IHhGBudjPxEjE0MV4I1nvtatWWP0sLxh1DvXH1EeufqE/SDuDmKiBhEJMWjHNIFZtfPJIcuIUenSIpYGEc68EC95SdElJaNHQlWvfe1k4lqyl6v0sI4FutH73y0eFyJiDjZiKR4DKDeAg/UW36EKC0bexKseu1rI4EyZa9XaWEciycfUe88IqJ6RFI8RlBvgQfqLT/1Ki0bj6i1BKve+tpIoqjs9SgtjGNxZDCede4jIk4UkRSPIdRb4IF6yU+9SsvGK06GBKte+tqpQF7Z601aGMfiyGE869xHRJwoop/iiDGP8eDTdTT5IfUSrDRECdbYrevxMBbrBbXyPR8RMZ4QJcURYx71Ji0b7/qZUYI1fut6rI/FesN41rmPiDgRRFIcMeYxlnXrRqN+5mj3GjGavCbUW12P5bFYrxjPOvcREdUikuKIMY96k5bVCqNZP3O0SrBGm1S+3up6rI7Fesd41rmPiKgGkRRHjHnUm7SsVqhHzwJjWYI1GqXy9VbXY3UsRkREjA1EUhwxLlBP0rJaYSzoZ44WCdZolsrXW12PxbEYERExNhBJccS4Qb1Iy2qFqJ85chjtUvl6w1gbixEREWMDkRRHjCvUi7SsFoj6mSOHsSCVrzeMpbEYERExNhD9FEdEjFJEP6Tj199vRERERETtEUlxRMQohvQzkQh3dXXZrl27kr98r2fDr9Eqlee4PwyMIqk896NUPiIiImL0IqpPRESMckT9zJOP6DUhIiIiYuwjkuKIiDGAqJ958hG9JkRERESMbURSHBEREVESUSofERERMXYRSXFEREREFYhS+YiIiIixiWhoFxERERERERERMe4RSXFERERERERERMS4RyTFERERERERERER4x6RFEdERERERERERIx7RFIcEREREREREREx7hFJcURERERERERExLhHJMURERERERERERHjHpEUR0REREREREREjHtEUhwRERERERERETHuEUlxRERERERERETEuEckxREREREREREREeMekRRHRERERERERESMe0RSHBERERERERERMe4RSXFERERERERERMS4RyTFERERERERERER4x6RFEdERERERERERIx7RFIcEREREREREREx7hFJcURERERERERExLhHJMURERERERERERHjHpEUR0REREREREREjHs0n+oMjGZUKpXk7/79+091ViIiIiIiIiIiIlIgnibeloVIioeBAwcOJH8XLlx4qrMSERERERERERFRwNumTJmSeb+hUkSbIzIxMDBgmzdvtkmTJllDQ4ONph0TRH7Dhg02efLkU52dMY1Y1yOHWNcjh1jXI4dY1yOHWNdjt56huhDi+fPnW2NjtuZwlBQPA1TsggULbLSCzhgH/sgg1vXIIdb1yCHW9cgh1vXIIdb12KznPAmxEA3tIiIiIiIiIiIixj0iKY6IiIiIiIiIiBj3iKR4HKKtrc0++clPJn8jTi5iXY8cYl2PHGJdjxxiXY8cYl2PDOq5nqOhXURERERERERExLhHlBRHRERERERERESMe0RSHBERERERERERMe4RSXFERERERERERMS4RyTFERERERERERER4x6RFI9RfPrTn7ZLL700ibY3e/Zse/vb324vvvjiMc90d3fbL/3SL9mMGTNs4sSJ9q53vcu2bdt2yvI8WvHXf/3XtmrVqiFH5FdeeaV961vfGrof6/nk4Q/+4A+SaJIf/vCHh67F+q4Nfud3fiepW/8566yzhu7Heq4tNm3aZD/90z+d1GdHR4edd9559uijjw7dxyb+t3/7t23evHnJ/ZtuuslWr159SvM8GrFkyZLj+jUf+jKI/bp26O/vt9/6rd+ypUuXJn12+fLl9ru/+7tJX67Xfh1J8RjFPffckwzshx9+2O68807r7e21N77xjXbo0KGhZ37t137Nvva1r9mtt96aPE/I6ne+852nNN+jEUQ1hJw99thjySJ244032tve9jZ79tlnk/uxnk8OHnnkEfubv/mbZEPiEeu7djj33HNty5YtQ5/7779/6F6s59phz549dvXVV1tLS0uyoX7uuefsj//4j23atGlDz/zRH/2R/fmf/7l99rOfte9///s2YcIEu/nmmxMSF1HdvOH7NOsj+LEf+7Hkb+zXtcMf/uEfJkKjv/zLv7Tnn38++U4//ou/+Iv67de4ZIsY+9i+fTtbs8o999yTfN+7d2+lpaWlcuuttw498/zzzyfPPPTQQ6cwp2MD06ZNq/zd3/1drOeThAMHDlROP/30yp133lm5/vrrK7/6q7+aXI/1XTt88pOfrJx//vmp92I91xa//uu/Xrnmmmsy7w8MDFTmzp1b+cxnPnNMG7S1tVX+9V//dYRyOTbB3LF8+fKkjmO/ri3e/OY3V97//vcfc+2d73xn5T3veU/d9usoKR4n2LdvX/J3+vTpyV+kmkiPOaoQOBpdtGiRPfTQQ6csn2PhuOjf/u3fEok8ahSxnk8OOAV585vffEy9gljftQXHmPPnz7dly5bZe97zHlu/fn1yPdZzbfHVr37VLrnkkkRaibrbhRdeaH/7t387dH/t2rW2devWY+p7ypQpdvnll8f6HgZ6enrsn/7pn+z9739/okIR+3VtcdVVV9ldd91lL730UvL9ySefTE6bbrnllrrt182n5K0RI4qBgYFE55LjuZUrVybX6Iitra02derUY56dM2dOci+iOjz99NMJCebIBz202267zc455xz74Q9/GOu5xmDT8fjjjyfHoCFiv64dWJg+//nP25lnnpkcM3/qU5+ya6+91p555plYzzXGmjVrkmPmj3zkI/bf/tt/S/r2r/zKryR1/HM/93NDdUr9esT6Hh5uv/1227t3r733ve9Nvsd+XVv8xm/8hu3fvz/ZWDQ1NSVCo9///d9PNtigHvt1JMXjRKrGQub1ASNqC4gDBBiJ/L//+78nCxn6aBG1xYYNG+xXf/VXEz3A9vb2U52dMQ1JcwB625DkxYsX2xe/+MXEICaitoILJMX/83/+z+Q7kmLmbPQsmUsiTg4+97nPJf2c05CI2uP/b+8+Q6PoogAMn8TYxRobdo0NG/YuamJBlCCKil0IdlQQRbD9EgVFbCgqGksiIqhYwCixIbHXaJRgYmwYew2JwTIf58Isu3E3lm/X7LjvA5PsZu5MZodh9uzdc8/Ve0ViYqLs2bPHjE/Q90jtoNPzHazXNekT/7iZM2fK0aNH5fTp02ZAmK1GjRrmqyP9lOxOR9nqOvwe7V2IioqS9u3bm8ofbdq0kbVr13Ke/Uy/3nz58qW0a9dOIiIizKIfPnSghj7WHgbOd2Bo71mTJk0kIyOD69rPdOS9frPkrnnz5q50FfucFqyCwPn+c48ePZLk5GSJi4tz/Y3r2r/mzZtneotHjRplqqmMGzfODGTU98hgva4Jiv9RWuZEA2L9Gv/UqVOmJIo7Dd50pLPm+9i0ZJvehDUNAP+/5yc/P5/z7GfR0dEmVUV7HOxFe9j06zj7Mec7MHJyciQzM9MEcFzX/qWpbQVLZmoepvbMK71/a5Dgfr71a2kdrc/5/jPx8fEmf1vHJti4rv0rNzdXwsM9w0xNo9D3x6C9rotkeB8Cbtq0aVaFChWsM2fOWNnZ2a4lNzfX1Wbq1KlW3bp1rVOnTllXr161unbtahb8ngULFpiqHllZWVZqaqp5HhYWZp04ccKs5zwHlnv1CcX59o+5c+ea+4de1ykpKVZMTIwVGRlpKtkozrP/XL582YqIiLCWLVtm3b9/30pMTLTKlCljJSQkuNqsWLHCqlixonXo0CFzn4mNjbUaNGhg5eXlFemxO9G3b9/MtatVPwriuvafCRMmWLVq1bKOHj1q7iMHDhww95D58+cH7XVNUPyP0s873pb4+HhXG73opk+fbsqH6Q146NChJnDG79GSM/Xq1bNKlChhVa1a1YqOjnYFxIrz/HeDYs63f4wcOdKqWbOmua71jU2fZ2RkuNZznv3ryJEjVsuWLU05qmbNmllbtmzxWK/lqxYvXmxVr17dtNH7THp6epEdr5MdP37cvB96O39c1/7z8eNHc2/WDxmlSpWyGjZsaC1cuNDKz88P2us6TH8UTR81AAAAEBzIKQYAAEDIIygGAABAyCMoBgAAQMgjKAYAAEDIIygGAABAyCMoBgAAQMgjKAYAAEDIIygGAABAyCMoBgCHO3v2rISFhbmW8+fPF/UhAYDjEBQDgMPt3LnT4/muXbuK7FgAwKmY5hkAHCwvL0+qV68unz59knLlyklOTo5UqlRJsrOzpWTJkkV9eADgGPQUA4CDHTx40ATEat26deb3u3fv5MiRI0V8ZADgLATFAOBgdqpE69atZdKkSdK0aVOPvwMAfg1BMQA4lKZIJCcnm8djx471+J2UlCSvXr366T7evHkj8+fPN8F06dKlTSpGv379TA+02rFjh2sA38OHD33u5/Pnz7JhwwaJjo6WGjVqSIkSJaRatWoSExMj27Ztk69fv/rpVQNAYJBTDAAOtWrVKpk3b56Eh4fL48ePpVatWpKVlSWNGjUSvbWvXbtWZs2a5XP727dvmwD4xYsXXtdPnjxZunbtanqgle67fv36P7S7deuWxMbGyqNHj3z+r44dO5qUDg26ASAYERQDgEO1adNGUlNTpW/fvnLy5EnX33v06CEpKSnSvn17uXr1qtdt379/Ly1atJBnz56Z5+PGjZPRo0dL1apVJSMjwwTUFy5ckM6dO8ulS5d8BsXatkOHDvLhwwcpX768zJgxQzp16iR16tQxvdCHDx+WzZs3m55i3de5c+ekePHiAT0vAPBHNCgGADjLjRs3tEPDLNu3b/dYt2nTJte6tLQ0r9vPmTPH1WbNmjU/rP/69asVGxvraqNLVlbWD+26detm1rVt29Z69eqV1/917NgxKzw83LTbsmXLH79mAAgkcooBwIHsgXSaBzxs2DCPdSNGjDA5ve7t3OXn55tcYTutYfbs2T+0KVasmOnhLVWqlM9j0F5fe6IQrZUcGRnptd3AgQNl+PDh5rH9fwEg2BAUA4DDaCrCnj17zOMhQ4aYtAV3lStXlkGDBpnHiYmJ8v37d4/1mlKh6RPuA/O80fzfAQMG+FyvqRFKB+m1atWq0GPu1auX+X3lyhUG3QEISgTFAOAwx48fdw2O8xXU2n9/+vSpnD592mPdnTt3XI8177gwmi/si52vnJ6e7jHNtLdl5syZpu2XL1/k7du3v/xaAeBvISgGAIexUyKqVKliUhO8GTx4sFSsWNGjvU0n97DpwLrCFLb+5cuX8idyc3P/aDsACKSIgO4dAOBXWuXBTlvQ6g527nBhDhw4IBs3bpSyZcv69Vi+ffvmqoKRkJDwy9tp6TgACDYExQDgIPv27TMTZfyOnJwcExhr2TVVqVIl1zqd4KNJkyY+ty1sAhDtqbb337Jly986JgAINgTFAOAgdipEzZo1ZfXq1T9tr5N7aF6xbmcHxVqf2Hbt2jXp3r27z+191TlWbdu2NdUnHjx4IM+fPzcz2QGAUzF5BwA4hPtsdTpwbf369T/dZs6cOWYiDvdZ77SnWQNYTcXQkmyXL1/2uq0O5tPJOuye6YKTd5w4ccJVnWLBggWyfPlyv71WAPjbGGgHAA6hvb12P4Zd9/dn7HZals3O+9Xaw+PHj3eVSNOguSBtP2XKlEJTNfr3729mr1MrV640qR2F0WmldapnAAhG9BQDgENERUVJZmamVKtWTbKzs03v789ocFu7dm3TXtMm7HJsWhZNn2vag9LUijFjxnhM86ypERr02j3JDx8+lHr16nnsX49H29hl1rRu8siRI6Vx48ZmAhCtUHHjxg0TDF+8eFHmzp0rq1atCsDZAYD/h5xiAHCAlJQUE4CqoUOH/lJArLSdttfqE2lpaSaHWGsT6wQfSUlJ0q9fPzOYbvfu3WZxN3HiROnZs6crKPY2u52mc1y4cMHMqqcBtwa/hfUGF5xoBACCBekTAOAA7rWGC07r/DPu7d33o6XU7t69a3pvtWe3ZMmSZqrmPn36mBnz4uPj5ePHj672FSpU8Lp/rV5x8+ZNs43+r7p165rpp7VcnA4I7N27tyxatMgE5EuWLPnNVw4AfwfpEwAAn+Li4mTbtm0mBePJkydFfTgAEDD0FAMAvMrLy5NDhw6Zx126dCnqwwGAgCIoBoAQpTnKvr4s1Nnqpk2bJq9fvzbPJ0yY8JePDgD+LtInACBE6UA6HUQ3atQo6dy5s6lqob3DqampsnXrVrl+/bppFxMTY2oSh4WFFfUhA0DAUH0CAELYvXv3ZOnSpT7X62x3e/fuJSAG8M+jpxgAQlR6errs379fkpOTTQ1iLc325csXqVKlinTo0MHUG9Ze5F8t/wYATkZQDAAAgJDHx38AAACEPIJiAAAAhDyCYgAAAIQ8gmIAAACEPIJiAAAAhDyCYgAAAIQ8gmIAAACEPIJiAAAAhDyCYgAAAEio+w+Zrboj8+wnYgAAAABJRU5ErkJggg==", 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" ] @@ -1984,10 +2024,10 @@ "id": "891e3b75", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.198482Z", - "iopub.status.busy": "2024-06-04T23:19:37.198260Z", - "iopub.status.idle": "2024-06-04T23:19:37.214885Z", - "shell.execute_reply": "2024-06-04T23:19:37.214249Z" + "iopub.execute_input": "2025-04-03T19:33:53.212269Z", + "iopub.status.busy": "2025-04-03T19:33:53.211860Z", + "iopub.status.idle": "2025-04-03T19:33:53.259372Z", + "shell.execute_reply": "2025-04-03T19:33:53.257058Z" }, "lines_to_next_cell": 2 }, @@ -2026,16 +2066,16 @@ "id": "54715029", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.218081Z", - "iopub.status.busy": "2024-06-04T23:19:37.217683Z", - "iopub.status.idle": "2024-06-04T23:19:37.501731Z", - "shell.execute_reply": "2024-06-04T23:19:37.500504Z" + "iopub.execute_input": "2025-04-03T19:33:53.263753Z", + "iopub.status.busy": "2025-04-03T19:33:53.263235Z", + "iopub.status.idle": "2025-04-03T19:33:53.665292Z", + "shell.execute_reply": "2025-04-03T19:33:53.663172Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -2088,10 +2128,10 @@ "id": "b3cd8b66", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.505073Z", - "iopub.status.busy": "2024-06-04T23:19:37.504839Z", - "iopub.status.idle": "2024-06-04T23:19:37.519804Z", - "shell.execute_reply": "2024-06-04T23:19:37.519297Z" + "iopub.execute_input": "2025-04-03T19:33:53.669951Z", + "iopub.status.busy": "2025-04-03T19:33:53.669276Z", + "iopub.status.idle": "2025-04-03T19:33:53.713659Z", + "shell.execute_reply": "2025-04-03T19:33:53.710699Z" }, "lines_to_next_cell": 0 }, @@ -2127,17 +2167,17 @@ "id": "ea8d6bc5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.523112Z", - "iopub.status.busy": "2024-06-04T23:19:37.522834Z", - "iopub.status.idle": "2024-06-04T23:19:37.644337Z", - "shell.execute_reply": "2024-06-04T23:19:37.643251Z" + "iopub.execute_input": "2025-04-03T19:33:53.717419Z", + "iopub.status.busy": "2025-04-03T19:33:53.717296Z", + "iopub.status.idle": "2025-04-03T19:33:53.778191Z", + "shell.execute_reply": "2025-04-03T19:33:53.776227Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2190,16 +2230,16 @@ "id": "812a47a1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.648530Z", - "iopub.status.busy": "2024-06-04T23:19:37.647173Z", - "iopub.status.idle": "2024-06-04T23:19:37.756140Z", - "shell.execute_reply": "2024-06-04T23:19:37.755581Z" + "iopub.execute_input": "2025-04-03T19:33:53.781510Z", + "iopub.status.busy": "2025-04-03T19:33:53.780977Z", + "iopub.status.idle": "2025-04-03T19:33:53.897521Z", + "shell.execute_reply": "2025-04-03T19:33:53.894935Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2251,10 +2291,10 @@ "id": "88ff3def", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.759390Z", - "iopub.status.busy": "2024-06-04T23:19:37.759130Z", - "iopub.status.idle": "2024-06-04T23:19:37.788899Z", - "shell.execute_reply": "2024-06-04T23:19:37.788370Z" + "iopub.execute_input": "2025-04-03T19:33:53.903332Z", + "iopub.status.busy": "2025-04-03T19:33:53.902411Z", + "iopub.status.idle": "2025-04-03T19:33:53.948364Z", + "shell.execute_reply": "2025-04-03T19:33:53.947139Z" }, "lines_to_next_cell": 0 }, @@ -2288,16 +2328,16 @@ "id": "29dbc933", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.792129Z", - "iopub.status.busy": "2024-06-04T23:19:37.791875Z", - "iopub.status.idle": "2024-06-04T23:19:37.909465Z", - "shell.execute_reply": "2024-06-04T23:19:37.908764Z" + "iopub.execute_input": "2025-04-03T19:33:53.951460Z", + "iopub.status.busy": "2025-04-03T19:33:53.951209Z", + "iopub.status.idle": "2025-04-03T19:33:54.028760Z", + "shell.execute_reply": "2025-04-03T19:33:54.025536Z" } }, "outputs": [ { "data": { - "image/png": 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+XZkxY0aSz90nn3yievzE70vCOa5NmzbKmjVrlMOHDysLFy5UChcurNtn06ZNyvvvv6+kSpVK6d69u7Jx40bl8OHDyvTp03XnUR8fH6Pfccnl5MmTuhh79+5tct9ly5bp9p00aZLVr5X4733Hjh3KhQsXlKZNmya54A8ODlbatWunXLp0ycafKJkS/piYGKVSpUq6/ebMmaPbduHCBZPH37Rpky4xmzZtmnrQiU7qtWrVUiIjI40ez5IehQTmEv5Lly4p8fHxRp9/4sQJJTAwUAGgfPvtt6r72Jvwr169WhdjgwYNlJiYGIN9hg8fnuQ9Ukv4E+6MpE+fXgkLC1N9rWvXrukS9tatWxtsT9wjljp1atWe89u3bys5c+ZUACgBAQHKgwcPkmyPjo7WfVHXr19fefHihWosf/31l+61Nm7caLA98c/boEED1YQ+8RfBsmXLDLZPnDhRt71Lly6qcXTq1CnJa6md6Fu3bq0A0mt+5coV1eMcOXJEl0gNGjTIYHvi3qo8efIot27dMthn165dup7EhJ6SxO7cuaO7sAgNDTX55XDjxg2DtoTkrUyZMsrDhw9Vn7du3Trd3+tff/1l9PjGPHjwQBdj9uzZVeNI/F7lyJFDNbFNfLfJHg8fPlSePHlidHtUVJRSp04d3Wup9br06dNH97tTu8iNjY1NctFu7NzkLu9/4oQfgPLZZ5+pnic7d+6s2+fIkSNWx3r//n3d8//4448k265fv667KGnUqJHql3RsbKwSFBSkAFC+/vprg+Pbe36PjIzU3dXJlCmTcvHiRYN9zp8/r4SEhJhMJk+fPq37ov/uu+9UY4qLi9N1hAQGBiqPHz82GrcW+vfvr/uZfvjhB4Pt0dHRSt26dTX5zFv6nW8uF5kxY4buOJs3bzbYntwJfwJHndsSmEr44+PjVT+3iQ0dOlT3t6b2HurfJVU7B969e1f3t5g5c2bFy8tL9QL9+PHjut+12necolh2Z87cQ+3vcN26dbrt5u6iHzp0SLevubu/ahLfnfzzzz9N3pEPCgpSNm3aZPVrKIqDE/7nz58r27dvV6pXr54kWTGVkKtp0qSJAkBp1KiRetD/P7a3t7fZJN6RCb8lEr7sS5Qoobrd3oS/QYMGCiA9x7dv31bdJy4uTilRooTRhP/Zs2e6XpTff//d5OtNnjxZl9An7h1RlKR/aD179jR6jMS9Xvp/OAk90P7+/gYXA/oqVKigAOoXHwnH9/f3N9r7FhERoeul69u3r8H2YsWKKYDcZTB24fHs2TMlc+bMRk/0V69e1d0GXLVqlcmf5+uvv9YlWvoSJ/z//vuv0WMkXFiXKVPGYNvAgQN1x1ixYoXJWPQlvsV44sQJk/t+9NFHCiC9u9YaM2aM7nUWLVpkdL/EF2tLliwx2O7oL0VTjh07potF/2I5MjJSd+elfPnyRo9x7949xd/f3+i5yZ3e/8QJf7Zs2Yye7xMP87S1VzphuF3Lli2TtM+ePVsBoBQvXlz336VKlUqyz4EDB3Svr3anwhKmzu+LFi3SHT/xbXl948ePN5loJHQqlCtXzuQFyJMnTxQ/Pz+jia9WoqKilAwZMiiA3I0wFvPNmzeT9Fgm12femu98c0qXLm30+80TE35LxMbG6oaYjh071mC7fg+/MYnv+un/PSeWMNRH7TtOUZIv4U98R1G/g0HfmTNnLMqFjHn//feT5DAJOcrFixeVqKgo5fLly8pXX32l6+BLnz69cv36datfx65Ju8OHD08yeSQwMBDVq1fXTWAJDQ3FihUr4OfnZ/QYDx8+xMWLF3Hq1CndI3PmzACA48ePm3z9ypUrO3UxiidPnuDy5cs4ffq0LvaEpbvPnDmDmJgYh75eXFyc7r2tW7cusmfPrrqft7c32rdvb/Q4O3bs0M08b968ucnXfPfddwHIym+HDx82ul/iiVL6PvzwQ937klAvPcG///4LAKhWrZru924uFlOLp9SpUwehoaGq24KCgnQz369cuZJk2927d3HmzBkAwEcffYS0adOqHiMwMBAfffSR0ddfs2YN4uLikDZtWrz33nvGfxi8/nnu3LmDGzduqO6TPn16k3Wk33rrLQCGPw8ArF69GgCQP39+vP/++yZj0ZfweylcuDBKlixpct+En+PQoUNWTyBN+DykT5/e6KQoAOjcubPBc7QQFRWFGzdu4MyZM7q/cSXR5HH9c1RYWBiePn0KAGjbtq3R42bJkgX16tUzut1d3//mzZsbPd8XLlwYgYGBANQ/r5aoVq0aADmHJZbw/9WrV9dNhD1x4gQeP35ssI+Pjw+qVKli9rWsPb8nvC/e3t5o06aN0eO2bdvW5GTLVatWAQCaNWtmcr/06dPrPhvOXFDq8OHDuuIO7du3Nxpzzpw5UbduXaPH0eozr0ZRFNy7dw8XLlxIkovkyJEDgPlcxFPFx8fjzp07OH/+vO49OXv2LHLmzAnA/PtibII9AJQqVcqq/YydM0aNGoWTJ0/a9Zg5c6bBcRMvpGWuKk7ic56pYhzGvHjxIsnrfv/99/j1119RoEAB+Pr6In/+/Pjpp58watQoAMDTp08xevRoq1/Hrjr8xuTLlw/NmzdH//79VZOvPXv2YMKECdi8eXOSE7I+cxUR3nzzTbtjtdbJkycxbtw4rFu3zuQqwvHx8Xjy5InR5NMWly9fxsuXLwEA5cuXN7lv4ipJ+hJXd8iWLZvFr2/s5/X19U3yx6svderUKFOmDLZt24aTJ0+qxrJhwwaLKw6Yet+LFCli8rkhISEAYDCTPnFclry3kyZNUt2W8PO8fPkSqVJZ/ud179495M6d26C9YMGC8PY2fl1u7OeJiYnBqVOnAABVqlSxuppDws9x/vx5i58bExODx48fW/WZT4ixbNmyJkvoZcmSBXnz5sW1a9d0z0kuL168wIQJE7Bo0SKcPn0acXFxRvfVP0clji3hYsyYcuXKYeXKlarb3PX9N/f3lyFDBjx//tymShaAJPRTpkzBvXv3cO7cOd3rJXSEVK9eHblz50a+fPlw9epV7Ny5E02aNEmyT5kyZYyu1WDP+T3hfcmfP7/uwkBNSEgI8ufPj8uXLxtsu379Oh4+fAgAGDhwYJLy1qZYu6L9gwcP8ODBA9VtAQEByJcvn8XHsvbcuWbNGtVtWn3mE1uzZg3++OMP7Ny50+RnUqvqe65AURTMnz8f06dPx4EDB0wmsObeF1PrICT+G7FkP2O/nxw5cuguzBzJ399f99/m6t4nroyYJk0au14rU6ZMutr/+r766itMmDAB9+7dw5IlSzB58mSrvtsdttKul5cX/P39kSlTJgQHBxt9zrBhw3Srlplj7kopQ4YMlgfrANOnT0e3bt0s7lGw5UrPlMQXR+ZOcFmyZDG6zdiJ3pyEiw19ISEhujq05uLRv8CzJRZT76uxnvkECcmzfhLnqu+tpT+Pfpm0x48f63qirbmoS+Don8OYhPfdki/srFmz4tq1ayY7Cex17do11KxZE1evXrVof/3PYuIytubuWJna7q7vv61/f5ZK6OEHJIEvUqQIbt26hStXrsDLy0u3vXr16rh69Sq2b9+OJk2aIC4uDrt379ZtU2Pv+T3hd2/u956wj1rCr9XvffLkyUa/h6tVq2a2zGRirnruNEVRFHz22WeYPn26Rfs7+rvcVUVGRqJp06YWl3s2976YOh8k7siyZD9rSmI7QlBQkO6/zZWKT9xDn3AX09bXql69utE7CqlSpUKtWrUwf/58PH78GFeuXMEbb7xh8es4fKVdU7Zs2aI7yeTPnx/9+/dHlSpVkDt3bgQEBOh6RIcOHYoRI0aYPZ65JNORzp07p/syCA0NxVdffYWaNWsib968CAoK0vWOzZgxA59++ikAmKwbby976u8m/rI9cuSIxYuTJNzGS45Y3nvvPfz00082H8eRHPHzZMqUCdu2bbP4edb0qGkh4ecoVaoU5s2bZ/HzbO1pSY560rZo164drl69qqvn3apVKxQtWlS3LoSXlxfi4+N1557k+htPqe+/OVmzZkXhwoVx/vx5bN++Hd26ddMN1SlWrJgu2a5WrRpmzpypS1yPHTuGiIgI3TZ9rnJ+T3xuHjp0KFq0aGHR8wICAhweiy0cce7U4jM/Y8YMXbJfunRp9OnTBxUrVkSOHDmQNm1a3d/3J598grlz5ybrd7krGTVqlC7Zr1atGnr06IGyZcsia9asSJMmjS75fvfdd7Fr1y6XeF9u375tcr0gS6jd1Uqc75hbP+TmzZu6/86VK5fVr5/4Oeaen3j7w4cPtUv4rTV16lQA0jO/f/9+oz0hydmDZ6tZs2YhNjYWPj4+2LFjh9Fb18kZe+I7Gvfv3ze5r6ntGTNm1P135syZjSbylnr06BHi4uJMXoAlxJMwBCVxLHfu3EF0dLRVF4+O5uj39tmzZyhatKimF6WJhYSEwNvbG/Hx8bh7967Vz0/4OZ4/f56sv5eQkBDcvXvX7HsOvB62oP8ZcpRz587peoEHDRqEkSNHqu5n6m888efo4cOHJm9VJwzdUJMS339LVatWDefPn9cl+omH8yTQH8efsI+3tzeqVq1qcExHnN8Tfvemfq8JjO2T+NycOnXqZPvdDxs2zKpFoEzRP3ea+sxbcu5M7s888DoXKVCgAPbu3Wt0GIap33fiHmpTvc+Je39dmaIomDZtGgCgatWq2Lp1q9HhpK6Uow0ePFi3OJmt1O5qFSpUCD4+PoiLi8O5c+dMPj/x9qJFi1r9+sWLF9f9t7m7n4m3WzNsGEjGlXbVnD59GgBQo0YNk7c9HbmCoKN6rhJiL1WqlMlxqsm5+uEbb7yhOzEdOnTI5L6mtpcpU0b333v27LE7rujoaJOTd2JjY3Hs2DEAMDiRJ8QSFhZmdpxccko8ScwR721UVJRTV8JMnCzY0hOT8HNcuXLF6vHB1kiI8ciRIyaHUjx48EC3YndyJQMJf+MA0LJlS6P7mfq9Jj5xm5rkbu44KfH9t1RCMp8wjj/xhN0EefLkQd68eaEoCnbu3KnbJ2F1d32OOL8n/O6vXLlisscx4Va8mvz58+vic8S5WQuOPncm92ceeP37fv/9940m+4qi4MiRI0aPkXgYhqnf94ULF2yMUmh19+3x48e6971FixZGk/3nz5/j/PnzmsTkTL6+vrq5kPv27TOZnyScX/z8/FCuXDmrXythEjpgvqBB4qGA1t7d0jThT/hCMXXFe/ToURw4cMBhr5l4MkTiiRXWsiT2u3fv6ioNJIdUqVLpvtQ2btxotOc2Pj7e5BVv7dq1dWPmJkyY4JDbcqZeb/ny5boTYsIS1AkSqseEh4erzpTXSvbs2XVX5kuXLjU6NvHFixcml/pu3Lix7gT922+/OTxOazRu3BgAcPXqVaOTQ41J+L0oioLx48c7PLYECZ+Hp0+fYtmyZUb3mz59uu5zqv8ZcpTECa+pv/OEJdDVlCtXTpewmRqWcP/+fWzYsMHo9pT4/lsq8ZCcBQsW4OLFi0nG7ydIOFdu3boVu3btStKmzxHn91q1agGQ8++CBQuM7jdv3jyj51wfHx80aNAAgJzjz549a/Q4ruKtt97S9fKbGv5y+/ZtbNy40ehxtPrMA5b9vleuXGny7mjiCoGmLgQXLlxofYCJJOQw9uQvlrD0/Ddt2jSHVEZylFmzZkGREvM2P4zNWUmY8B8REWH0/Hjr1i1dha5atWoluRC0VL58+XQXvNu2bdNVUdT37Nkz3Wu98cYbVs/P0zThTyiJuHv3bly6dMlg+8OHD9GuXTuHvmbiN0RtkpSlEmK/ePEi9u7da7D95cuXaN26dbJP7unevTsA+ePv2rWr6u2f0aNHG1TDSSx9+vTo2bMnAGDv3r3o27evyVuS9+/f193qM+aPP/7QDYdI7N69e+jfvz8AmZijXy60ffv2ujFp/fv3x86dO02+zu7duw1K8jlKwnt779499OvXT3Wfvn37mpxcVrhwYd2420WLFuHXX381+ZpXr161+wvBmJ49e+rG9nbt2tVkdRX9MYp169bV9W78/PPPJi9yAKnUkVBO0BodO3bUXXz269cPt2/fNtjn+PHj+OGHHwBIj0bCSdjREv7GAfkSUfPHH3+YvHjy9/fHJ598AkB6M9USl/j4eHTt2jVJ2Td9KfH9t1T27Nl1v6sJEyYASDp+P0HCBcCcOXN0pVLVxu8Djjm/f/jhh7pJq8OGDVP9vrl48aLZohUDBw6Ej48P4uPj0bx5c5Pjh+Pi4jB//nyzY4yTk5+fn64s87Fjx/Dzzz8b7BMbG4vPPvvMZC+pVp954PXve9WqVarDUy5fvowePXqYPEaGDBl0lQJnzpypepzdu3fbffGSkMM8ePDA5upWlsicObOuIs7ChQtVLzAOHTqEIUOGJFsMrqZz5866DpwBAwbg0aNHSbbHxcXh888/1+VhX331lepxhg0bpitfb+y7ZcCAAQDkYqtPnz6q+3z55Ze6uUjdunWz9sexfoUpS1baNWbp0qW652bPnl2ZMGGCsmfPHmXPnj3Kzz//rGTLlk3x8vJKsnyzGmtePyIiQreQQdmyZZWNGzcq58+fVy5evKhcvHhRefnypW5fUwtvHTx4ULctffr0yqhRo5QdO3YoBw4cUCZPnqwULFhQAaBUrlzZ5CIa9i68pSiK0rhx4yQLWyxatEg5fPiwsm7dOqVly5a6RVsS9lFbaTcyMlK37DX+v0jNxIkTld27dytHjx5Vtm7dqvz+++/KBx98oPj6+ipvvfWWwTESFrzInDmzkidPHsXf318ZOHCgsmvXLuXgwYPKxIkTdUtjA1B++eUX1Z9n3759ugVkfHx8lDZt2ihLly5VwsLClIMHDyorV65Uhg4dqpQsWVIB1BcLs/QzYer9j4mJUcqUKaM7Vv369ZUVK1Yohw8fVlasWKFbKTLxe6u24MqjR4+U/Pnz6/Z59913lWnTpin79u1Tjhw5omzatEkZO3asUrt2bcXb21tp1qyZVXEmZm6xuISFzQAoadKkUb744gtl3bp1ytGjR5Vdu3Ypf/zxh/Lee+8p+fPnN3jupUuXkqwK2rhxY2XevHnKgQMHlLCwMGXt2rXKqFGjdIt/9evXz2SsxkyaNEn3GlmyZFHGjRunHDhwQNmzZ48yfPhw3eqmXl5eRhdMcsTiNPHx8UkWrPvoo4+UVatWKWFhYcqKFSuU5s2bG/yNq33eHj16pGTNmlW3T7t27XRL1C9evFi3mmjCQnIAlGvXrhkcx13e/8QLb6mdaxKzd9GfBIlX7QWg9OjRw2RcgCzUaGwVZUed3xcsWKDbnjFjRuWnn35S9u3bp+zbt08ZM2aMEhISoqRPn153vOrVq6vGM27cON1xgoODla+++kpZt26dcuTIEWXv3r3KggULlF69eulWQje1irYWnj59qltRHYDy8ccfK+vWrVMOHz6sLFy4UClfvrzBuVPt/XPEZ96Shbd+/vln3T6FChVSpk+frhw4cEDZsWOH8t133ynBwcGKv7+/UrZsWZPnlT///FN3nGLFiikLFy5Ujhw5omzevFnp27ev4ufnp/t7N3a+MBfvpk2bdNtbt26t7Nu3T5e/mFsVV42pv8EePXroXqtcuXLKggULlEOHDimbN29WvvzyS8Xf31/JlCmTUqhQIaPfT5YuSGbpAmmOWBDVHlOmTNG9/htvvKHMmDFDOXTokLJy5UqlRo0aST7zxiT+GUydIxMWVgWg1KtXT1mxYoVy5MgRZeXKlUm2lSlTRnn16pXVP4umCb+iKErHjh2TnIQTP3x8fJTffvvN7C/Y2tdPWNFU7ZH4A2nudYcPH270OAknH3MfYkck/BEREUm+ePQfZcqUUQ4fPmz2AxYREaE0bdrU5M+U8KhRo4bB8xMnWYcOHdKtvqf2MLYsdoJ9+/YpuXLlsiiW2bNnGzzf0s+Euff/9u3bSuHChY2+dt26dZUNGzaYPaHdvXtXqVq1qkU/T8eOHa2OM4ElJ8NZs2YpadKkMRmDsS+08+fPJ0mCTT2GDx9uMlZTRo0apVtCXe3h5+en+ntP4KjVKI8ePapbNVTtUbJkSeXOnTtmP2/Hjh1LsiKz/qNDhw7K9OnTdf9/79491eO4w/vvjIR/3rx5SeJbunSpydcDoJQuXdrkMR1xflcUWZU4YUVM/UfatGmVNWvW6M4N9evXNxrPX3/9paRNm9bs793X19emxM/RTp06leRCV+0zb8n7Z+9n3pLXiI6O1nXgqD3SpEmjLFmyxOx5JS4uTmnSpInJ88Xdu3dNni/MxRsXF6e7wFF7WMvU3+DTp091qwurPUJCQpQdO3aY/H7ytIRfURRl6NChRv+mASgNGjQwmYBbmvA/e/bM5OcSkBXc7969a9PPoemQHkDKYc2dOxdVq1ZFUFAQ/Pz8kCdPHrRr1w579+5F7969Hf6aP/74I6ZOnYqqVataVDPemKFDh2LNmjWoW7cuMmTIAF9fX+TMmRNNmzbFxo0bMXbsWAdHri4oKAjbt2/H77//jvLlyyMwMBBBQUEoXbo0Ro8ejb1791pUSSMoKAj//PMPdu3ahc6dO6Nw4cIICgpCqlSpEBISgvLly6NHjx5Yu3YtNm3aZPJY5cqVw5EjR/DFF1/gjTfegL+/PzJmzIj69etj7dq1Zm9rVqpUCRcvXsSUKVPQsGFDZM+eHb6+vvD390euXLlQt25djBo1CufOndMNmUgO2bNnx9GjRzFy5EiUKFECadKkQfr06VGpUiVMnjwZ69atM7vqHiDlA3fu3InVq1ejTZs2yJ8/P9KmTYvUqVMjc+bMeOedd9CvXz/s2LEDM2bMSLafB5BhU5cvX8bgwYPx1ltvIX369PDx8UGGDBlQqVIlDBo0COvXr1d9bqFChXDs2DEsWLAAzZo1Q+7cuZEmTRr4+voiW7ZsqF69Or799lscPnwYQ4cOtTnGQYMG4ejRo/jss890k9MDAgJQtGhR9O7dO9l/7wlKly6NY8eOoVu3bsiTJw9Sp06NkJAQVKhQAWPHjsXBgwctGjdZqlQpnDlzBv369UPBggXh5+eHTJkyoUaNGliwYAFmzpypuzULwOjaJSnt/bdU4qE5auP3E6hV7jHGUef3wYMHY8eOHWjSpAlCQ0N133GdOnVCWFgYGjRooPvdm1qz5rPPPsOVK1cwfPhwVK5cGZkyZUKqVKkQEBCAQoUKoVmzZpgyZQpu376NAgUKWBRbcipevDhOnz6Nr7/+2uhn3hJafOZTp06NNWvWYMKECShXrhzSpk2LNGnSoECBAujWrRuOHDliUUlUb29v/P3335g0aRLKly+PgIAABAQE4M0338SoUaNw4MABZM2a1aYYE7/Gxo0b8e2336JUqVIIDAxMtom8wcHB2LNnD0aMGIGSJUvC398fgYGBKFq0KPr374/jx48nmWCaUgwfPhy7d+9G69atkStXLvj6+iI0NBR16tTBggULsGbNmiTzRW0VGBiI9evXY+HChahfvz6yZs2qyxnq1KmDGTNmYN++fTZ/prwUxQUKqZJb6tChA2bPno08efLg2rVrzg6HyK107twZ06dPR86cOZPUcSbPFhMTg+DgYLx69QrffvutRWvOEBHZS/MefiKilO7Vq1e6yb+VKlVycjSkpRUrVugm//J3T0RaYcJPRORgly9fNlqeMC4uDt27d8d///0HAAaVq8i9qVWgS3Dt2jV8+eWXAIAsWbKgXr16WoVFRCmcpivtEhGlBCNGjMDBgwfRqlUrVKxYEaGhoXj16hVOnDiBqVOn6hb0qV27Nho2bOjkaMmRihQpggYNGqBRo0YoXrw4AgIC8ODBA2zbtg1TpkzRlQgdO3as1StlEhHZimcbIqJkcPbsWXz33XdGt1euXBmLFi3SbCVN0kZcXBxWrVpltEa8t7c3Ro4cibZt22ocGRGlZEz4iYgcbODAgShUqBA2b96Ma9eu4eHDh4iJiUHGjBlRrlw5tGzZEq1atTK6fD25r1WrVmHdunXYu3cv7t+/j0ePHsHPzw85cuRA9erV0aNHD5QoUcLZYRJRCsMqPUREREREHow9/CbEx8fjzp07CAoK4m13IiIiIhekKAqePXuG7Nmz886pEUz4Tbhz5w5y5crl7DCIiIiIyIybN28iZ86czg7DJTHhNyEoKAiAfIDSpUvn5GiIiIiISF9ERARy5cqly9vIEBN+ExKG8aRLl44JPxEREZEL4/Br4zjQiYiIiIjIgzHhJyIiIiLyYEz4iYiIiIg8GBN+IiIiIiIPxoSfiIiIiMiDMeEnIiIiIvJgTPiJiIiIiDwYE34iIiIiIg/GhJ+IiIiIyIMx4SciIiIi8mBM+ImIiIiIPBgTfiIiIiIiD8aEn4iIiIjIgzHhJyIiIiLyYEz4iYiIiIg8GBN+IiIiIiIPxoSfiIiIiMiDMeEnIiIiIvJgTPiJiIiIiDwYE34iIiIiIg/GhJ+IiIiIyIMx4SciIiIi8mBM+ImIiIiIPBgTfiIiIiIiD5bK2QEQERERkZtTFKBPHyB3biBfvteP9OmdHRmBCT8RERER2evhQ2DCBMP2YcOA777TPBxKikN6iIiIiMg+V6+qt+fIoW0cpIoJPxERERHZx1jCny+ftnGQKib8RERERGQfJvwujQk/EREREdlHLeH39gZy5dI+FjLAhJ+IiIiI7KOW8OfKBaROrX0sZIAJPxERERHZRy3h53Ael8GEn4iIiIhsFxcH3Lhh2M6E32Uw4SciIiIi292+DcTEGLYz4XcZTPiJiIiIyHas0OPymPATERERke2Y8Ls8JvxEREREZLvoaCA01LCdCb/LSOXsAIiIiIjIjXXpIo8XL4Br1+Rx9SqQNauzI6P/Y8JPRERERPYLCACKF5cHuRQO6SEispSiAFFR8i+RVmJigGXL1KugEBFZgD38RET6nj0DduwALl58fWs64Tb1s2dA+vRA167Ajz86N07ybA8eAFOnAn/8IWUPlywBWrRwdlRE5IaY8BMRAdJzv349sGAB8O+/QGSk8X2fPgW8eYOUksmRI8CECcDChTIZMsHvv1uW8MfH8/NJREkw4SeilCsuDti5U5L8v/+WRN5S5cqZ3yciQsa0+vjYHCKlIFeuAP37A8uXq2/ftQs4dgwoXdr0cb77Drh0CZg0CQgJcXSUROSG2AVARClLbCywdSvw+edAjhxAzZrAtGnWJfsA8NZb5vcZPBjImVNea9MmjsEmdc+fA4MGAUWLGk/2E/z+u+nt+/cDP/wALFoElCwJbNjguDiJyG15KQpnnxkTERGB4OBghIeHI126dM4Oh4hsFR8vidSmTTL58eFD+46XKZOMr/byMr5PbKxcUDx48LotfXqgYUPgww+B+vWl959Srvh4YP584JtvgLt3LXtOqVLA4cPqd41evJDe/0uXkraPGiUXFEQeivmaeRzSQ0Su7+XL1xNmnz9P+nj1SobXVKli/PleXlIj+vFj214/NFQWkMmbFwgKAjJnNp3sA3IXIXGyD8hdhPnz5eHrC7z9NlCrltxlqFABSJ3atvjIvSiK9Lx/9x1w8KBlz2nQAPjiC6BOHePj87/6yjDZB+ROU2wsMHSo7TETGbN1q/ybNy+QKxfPYy6KPfwm8IqRyAliY4HTpyUROngQOHQIOHVKxtsb88035ivmVKkC7NljWQx58gAffww0by7DLNKmtTz+BB06ALNnW75/YCDw7rtyB+Cjj+QuAnkWRQFWrwZGjJDPtTlBQUCnTkCPHkDBgqb3/e8/oHBh0xe1330HDBtmVchEZpUvD4SFyX/7+Mgwxrp1gb/+0iwE5mvmsYefiJwvIkIq4yxZAmzZIj361rBkbHzRoqYT/kyZJNFu3Vp63u2pchIZaX4str7nz4G1a+XRu7ck/u3aAY0aAX5+tsdCzpcwpGzkSJl0a463N9CtG/D990DGjJa9RqZMwMmTwKefSrUpNcOHy0XHsGHm71ARWerq1df/HRcHXL9u/7BJcjgm/ETkHC9eSG/n4sWS5EZF2X4sSxN+fRkzynj6Fi2AGjUcdys6dWq5eFmyRC5k/vvPuufHxgIrV8ojfXq5EPniC65e6Y4OHQJatZIKPJaoUQMYP14m3Fore3b5W/r9d6BPH/UF4r7/XtqHD2fST/Z79gx49MiwPV8+7WMhk5jwE5G2Ll2SSYRLlljfk2+MJQl/kSLyb6ZMr5P86tWTZ7ypjw9Qr548YmPlzsLy5cCKFdL7ZY2nT+XW+PTpUmaxa1fHx0vJJ39+4NYt8/vlzQv88ot8Nu1JxL285OIwXToZDqSW9I8YIe3ff8+kn+yTuHc/sbx5NQ2DzGPCT0TaiowEZs1y7DEtSfjffRc4fx4oUEDbRYlSpQKqVZPHuHEypGPdOpnotmeP6QW+EvPysqwUKLmWjBmBJk3kAldN1qzA11/LEJ40aRz3uh06yGemY0f1pH/kSGkfMYJJP9nOWMLPHn6Xw4SfiLRVogTwzjvA3r2W7Z8/v0wKK19ehiwEBiZ9pEkDBAebP05gIFCokH2x28vLCyhTRh6DBkmyv2+fzFvYtMl0xZYhQyxb7Iu0Zcmqtp06GSb8OXMCAwbImHt//+SJrX17+cx16KCe9I8aJbFzeA/Zigm/22DCT0Ta69rVeMJftCjQtClQubIk+Z5crcbfX8Zs16ghPa6XLwPz5gFz5iQd812hAuuouxJFAVatAsaOlfUUzP1uateWBP/WLRnqMHCgJONaTMb+5BNJ5tu3Nz68x8tLkn4ia3FIj9tgWU4TWOaJyErPn8tk3CxZTO/36pUsSvXkifx/wYJAy5byKF6cvY2KIj3/s2fLiqmHDjn/7gSJS5eA7t2BzZvl/7NlkzUifH1NP2/BArnAa9zYOXXK582TpD8+Xn07S3aSLd5/Xy5+E8uc2XANkmTGfM08Jvwm8ANEZCFFAaZNk2EnVasCS5eaf86wYUB4uPRAli7NJN+Yx4+BkBBnR0HR0dKjP2KE4byLOXOkhKqrM5f0DxsmiT+RpUqWlHVSEqtQAThwQNMwmK+Zx4TfBH6AiCwQGysTA+fNe922fz9QsaLzYkqJHjyQEnlvvOHsSDzPnj0yDO30afXtZcoAhw+7x0XrvHlykW3sq3/4cK7IS5ZRFFkc7sWLpO0tW8qdSQ0xXzNPw1IVRORxoqOlxnjiZB+QqiPsS9DO/fsyD6B6dZkHQI4RESHDd6pUMZ7sA8DRo8COHdrFZY+2bWWomLGLk7lz5ecmMufRI8NkH+CEXRfFhJ+IbBMZKZNr//nHcNvOncCaNdrHlBLduyfJ/pkzMim0Rg3jE+nIcps3y3CFKVNM75c9O/Dbb+5VMrVdOymNq5/0580r5WLZQ0qWYIUet8KEn4is9+IF0KiR6aRefyIXOd7du5Lgnz37uu3mTWm7ds1pYbm1Z89k+E6dOsCNG8b38/ICevaU9753bxna4E4++QSYOfN10p8zpyT7uXI5Ny5yH0z43QoTfiKyTkSErCC7ZYv69oAAWVXWXM8o2e/rr4Fz5wzbr18Hata0bIVXem3zZlkn4q+/TO/35ptSRen33927N7x9e1nBOUcOSfaZqJE1mPC7FSb8RGS558+lpviePerb06UDNm6UlUXdYQKju5s4USpiqLl6VZL+u3e1jckdnT4NtG5tvlc/TRpgzBggLMxzJqV37CgXjQULOjsScjdqCb+XF5A7t/axkFlM+InIcl99JTXh1YSESC/hO+9oG1NKFhwMbNhgfAXeixflAu3hQ23jchdHjwLNmkmv/sKFpvetVk3KD379tXPq6CenwEBnR0DuSC3hz5nT/JoU5BRM+InIMidPGh/qkCULsH27e01c9BTp08tdlbJl1befOSM9148faxqWy1uwQN6zZctM75c2rdxJ2boVyJ9fm9hc1aVLzo6AXIlaws/hPC6LCT8RmacoQN++6gv25MghJQlLltQ+LhIZMkjS/+ab6tuPH5d5F+Hh2sblyho0MD/+vlo1udDt0QPwTuFfl4sWAcWKAePGOTsScgXx8TJXSB8TfpeVws9gRGSR1avVJ+n6+UnPZ+HC2sdESWXMCGzaBBQtqr49LAx47z3PS/pjYoDdu4GRI4FOnYD69YFSpYC6dU0/L316oFcv9W3s1U9q3Djg44/lvf7yS80XVSIXdOeOrMOijwm/y0rl7ACIyMVFRwP9+6tv69cPKFRI23jIuNBQuTB791314Rf79skk35UrgSJFtI/PERRF5iZs3CgXONu2SSlNfdmymT9Wnz6SzL58Kf/v7w906SLj9HPkcGjYbik+Xt6LX35J2v7JJ/JZq1nTOXGR8xkr+8uE32V5KQqXwzSGSzUTARg/XhIjfVmzAhcuuF/98ZTg5k1J+o19KQcFAfPnA40baxqWXRRFxt2PGAGcP29+f29vuVj18TG9X//+wJ9/Ap9/Lr3XWbI4Jl5PsGePrDKsJigI2LVL7qZQyqMowH//yTj+hMe1azL8zQnDO5mvmceE3wR+gCjFe/QIKFAAePrUcNu0acCnn2oeElno6lVJ+k3V4v/+e2DwYNcfn37okCxutW+fdc+7c8d8T//jx5K8ZMxoe3yebNw4uRBSky2b/E7y5NE2JiI9zNfMc/GzPBE51fDh6sl+6dJAhw4aB0NWyZdPxqBnz258n6FDgRYt1IfEuIK7d+VzVqGC9ck+IAm/OSEhTPZN6dtXhu6puXtX1i+Ii9M2JiKyGhN+IlJ39iwwebL6tl9/NT9UgpyvYEHgwAGgfHnj+6xa5XolO6OjgR9/lPkhs2db/jxfX7nQeecdoHlzGZNP9vvpJ0ns1ezdKysOE5FL46RdIlJ39qxUK9Hv/W3SBKhRwykhkQ1y5gR27gS6dVNPnjt3dq0hGQcOSEynTpnf18tLaunXrSuPt9+WylHkWN7ewMyZwIMHwObNhtsHDQIaNZLhf0TkkjiG3wSOCaMU7/59GfYxbZpU7EidWhZy4he7+1EUKTXZt+/rIRh+flLNJ2dO58YGAC9eyHyCCRMkVlNy5waGDZNJx5kyaRIeAYiIAMqUAa5cMdxWtaosvufq80HIIzFfM49/mURkXJYsUsHk2DGgdm2pW85k3z15ecnvb/Pm10ly167mk/2XL4EZM6QiR3L791/TyX7atFKl59w5oGNHJvtaS5dOPgtqdu0CJk3SNh4ishh7+E3gFSNRIooCxMZKLz+5t+vXZdjMnDnmq9isWAF8+KH03FarBjRsKMNnSpSQiwhH2rwZqFNHfVvbtsDo0a5xNyKl69lTPblPm1ZWJuZiZaQx5mvmMeE3gR8gIkrxPvkEmDvXsD1rVrnrU6cOUKuWVANyxAVAx47ArFmv///NN4EpU2R8PrmG58+l1rraOg/Vq8vibxza47nOnJHhgHnzukzxBuZr5jHhN4EfICJK0aKjZViXWmlWfZkySRKY8CheXO4IPXggc0Hu35f/HjfOdPWcR4+AYsWA8HCZP/LVV7yr5Iq2bpULPTWTJslCZuSZ6tWTla79/KSSVpEiQKVKxtdr0ADzNfOY8JvADxARpWgbN8qXuyNdvy6Tbk3ZskWG7hQu7NjXJsfq3l3uvugLCABOn3at6k/kOLlzy2reiVWvDmzb5pRwAOZrluA9NyIiUrd/v+OPef+++X1q1WKy7w5++kn94u3FC+Dbb7WPh5Lf8+eGyT4AFC2qfSxkFSb8RESkbuhQ4PJlYOxYoHJlx4zXtSThJ/cQFCQle9XMn2/ZWgrkXs6fV28vUkTbOMhqbpvwjx49GuXLl0dQUBBCQ0PRpEkTnNf7IEZGRqJHjx7ImDEjAgMD0axZM9znlw2RuqNHZQLm3LnSi0MESMWVfv2A3btlfP2KFUCPHjJ21xYPHjg0PHKyOnWATp0M2xUF+O477eOh5HXunHo7E36X57YJ/44dO9CjRw/s378fmzZtQkxMDOrWrYsXL17o9unbty9WrVqFpUuXYseOHbhz5w6aNm3qxKiJXNicOVIW8ZNPpALLJ58Amza9XqSJKDgY+OADWcDr/Hmp0jJ7tkzWq1NHPjem+PgYrtxM7m/EiKQTsb29gQ4dgF9+cVpIlEyMJfwc0uPyPGbS7sOHDxEaGoodO3bg3XffRXh4ODJnzowFCxagefPmAIBz586haNGi2LdvHypVqmT2mJwEQilGTIxMktTvfQ0IkCEYAQHOiYvcz3//SS32q1flcxMaKpV+QkOBkBCWa/RUX38N/Pwz0KyZXAAwAfRMzZsD//yTtC0gQC7kHb0uhxWYr5mXytkBOEp4eDgAICQkBABw+PBhxMTEoHbt2rp9ihQpgty5cxtN+KOiohAVFaX7/4iIiGSOmshFbNyoPtSiWTMm+2SdTJmAGjXkQSnHN98AH30ElCvn7EgoOan18Bcp4tRknyzjEV0t8fHx6NOnDypXrowSJUoAAO7duwdfX1+kT58+yb5ZsmTBvXv3VI8zevRoBAcH6x65cuVK7tCJXIPawkqADOshIjInY0Ym+54uNha4eNGwneP33YJHJPw9evTAqVOnsGjRIruOM3DgQISHh+seN9VKTxF5mqdPZSKmvpw5pbYyERHR1auyGJ8+Jvxuwe2H9PTs2ROrV6/Gzp07kTNnTl171qxZER0djadPnybp5b9//z6yGplY5ufnBz8/v+QOmci1/P03kGgom07bti6zbDoRETkZJ+y6Nbft4VcUBT179sTy5cuxdetW5MuXL8n2t956C6lTp8aWLVt0befPn8eNGzfw9ttvax0ukeuaM0e9vV07beMgIiLXdfasejt7+N2C2/bw9+jRAwsWLMDKlSsRFBSkG5cfHByMNGnSIDg4GJ9++im+/PJLhISEIF26dOjVqxfefvttiyr0EKUIN28Cu3YZtr/1FlCsmPbxEJHnevZMynemTu3sSMgWaj38Pj5AgQLax0JWc9se/j/++APh4eGoXr06smXLpnssXrxYt8+4cePQqFEjNGvWDO+++y6yZs2KZcuWOTFqIhezapV6OyfrEpGjREYCv/0GvPGG8QIB5PrUEv78+QEOhXYLHlOHPzmwrit5vPfeA9avN2y/fRvInl37eIjIc8TGArNmAcOHA7duSVv+/JI4spffvSiKrKPx9GnS9saNgX//dUpIiTFfM89te/iJyE7PngFbtxq2ly/PZJ+I7Ne8OfDZZ6+TfQC4cgWYN895MZFtHjwwTPYBTth1I0z4iVKqTZvUS6w1bqx9LETkedq0UW8fNUp6/8l9cMKu22PCT5RSGbsN+/772sZBRJ6pWTOgeHHD9suX2cvvboyV5GTC7zaY8BOlRHFxwJo1hu25cwNvvql9PETkeby9gaFD1beNHMlefnfChN/tMeEnSon27wf++8+wvXFjwMtL+3iIyDM1b65e4vfyZWDBAu3jIduoDenJkgXIkEH7WMgmbluHn4jsYKwcJ4fzEJEjJfTyt2pluG3ECKB1ayAVUxGX988/wPnzkvifOyf/Bgc7OyqyAstymsAyT+SxihUz7LEJDJRef9ZUBiCjnsLDXz+ePpV/nz+XtYMCA+URFCT/BgdL1TreICHSExcHlCyp3ks8Zw5X9Sa7MV8zj5fVRCnNpUvqX7z166e4ZF9RgGvXpMPq4kV5axL+vXpV8hRrZMgg11LFi7/+t2RJufNNlGL5+Egv/8cfG24bMULa2ctPlKz4F0aU0hw+LF/A+tmshw/niY+XRP7IkaSPJ08c9xpPngB79sgjsZIlgQYN5PHOO8xtKAVq0UIW4NKf/HnxIrBoEdC2rXPiIkohOKTHBN4iIo/15Amwbp2M5V+3ThbhevAAyJjR2ZE5zKNHwMGDMj95/37gwAEZkuNswcFAvXoyP7pZMyBNGmdHRKSRhQtlzL6+AgWAM2e4+i7ZjPmaeUz4TeAHiFKEmBjg+HGgXDlnR2KzV6+AY8eAQ4fkceCAdBy6upAQ4NNPgc8/B/LmdXY0RMksLg4oUUK9xOMffwDdumkfE3kE5mvmMeE3gR8gIteiKMC9e8Dp068fYWHAyZPuXdLby0t6/Hv1AmrV4sRf8mALFqivwJs1q4y5CwjQPiZye8zXzGPCbwI/QETai4kBbt2SybTXr79+XLoEnDrl2DH3arJlkxEGBQsCuXIB6dPLMJyER1AQEBUl1XqePZN/nz+XmM+ckYuQa9dsf/3ixYEffwQaNmTiTx4oLg4oU0au0vWNHAkMHqx9TOT2mK+Zx4TfBH6AiJJPQm/98eOvH8eOARcuWF8dxxYZMgBvvQWULSv5R5EikugHBtp/7BcvpBDS0aPA+vXApk1ycWCN2rWBX37hwsfkgdaulStafenSyYJcmTJpHxOpe/FCJkTlzClrKrgo5mvmMeE3gR8gIsc7fRqYNg1YsgS4c0eb1wwIAMqXBypWlH/fegvIk0e7HvToaKncs3YtsHq18VXq9Xl7yxj/77+XEQ9EHkFRgOrVgZ07Dbf17Qv8+qvmIZER//4LfPABkDat9IoUKQIULQp06CAXAS6C+Zp5TPhN4AeIyDGeP5cEf+pUqZiTnLy85PuoQgWgUiV5FC/uOqUwFQXYtw/4/Xfg778tm3sQGAgMGQL06ycVVYnc3v79wNtvG7b7+sptvjx5tI+JDA0ZIkOt9J06JSdWF8F8zTwm/CbwA0Rknzt3ZF2defMk6U8O+fJJj32FCq9774OCkue1HO3uXeDPP+Vx7575/atUkfeSuZDnevxYLghv3ZIqlf7+sh6ev788smWTTlYXHl1huaZNgeXLX/9/QADw5ZdA//4yvIecr25dGZOYWGCgLD3uQr0PzNfMY8JvAj9A5DEuXQImTQIaNQKqVpVetGS2Zg3Qvr0M/7RX6tQygbZwYelUSngULeqYMffOFh0NzJolnWkPHpjeN106qWCoVs6c3M+NG8CuXcDu3fLv6dPmn5Mvnwz16tAByJEj2UNMPmfPSplOb2+ga1f5A+Cy1K4jPl5qB+svYFK9OrBtm1NCMob5mnlM+E3gB4g8xi+/SK8ZIN3fdevKpLkWLRyeMcfEAIMGAWPHWve8VKkkgS9VCihWTHqx8+aVf7NmdanOpGQTEQGMGSO/rqgo0/u2aSPXcMHB2sRGjvP0KTBnDvDXX5Yl+MZ4e8vqzZ07y79uuW7VtGmSQBYo4OxISN/583I7Sd8330gpMRfCfM08Jvwm8ANEHqNGDWD79qRtPj7Aw4dSrsZBrl0DWrWSha/MSZMGaN4cqFnzdZLv5+ewUNza9evAwIGyMKkpefJIWfN33tEmLrKdosiaEVOmyO/11SvHHj9bNrlQ/Phjxx6XUrA5c+Q2rb5ly4APP9Q+HhOYr5nnCaMAiciUp09lvIC+ypUdmuyvWCHlLc0l+2XLApMny/j1OXNkWEKZMkz2E0tI5HfvBvLnN77f9evSOTp9umahkZViYuRzXq6czDOZMcPxyT4gf0+tWwN9+rj3InTkQoydzCtW1DYOcggm/ESebuNG9QygUSOHvcSECdLh8/Sp8X0+/BA4fFge3btzKIolKleWtQk6dDC+T0yMDOn48ksmeq4kKkomYxcqJJ2kR45o87rjxwP16ztm7gylcGoJf44cQPbs2sdCdnORQnVElGzWrFFvV1v4xgbr10uvojF+fsBvv8mcPK4ca72gIGDmTOC99+Q9NHZRNW6czIFctIgXU8708qWMzf/5Z+vXmciVS+bUV6kinai+vkBkpDyiomRC94IFsp5DfLzx42zZIhWrVq4ESpa07+ehFOrVK1kNUR97990Wx/CbwDFh5Pbi4mTG63//JW3Pl09WtLQzA790SRILY0looUJSf79UKbtehv7v5k2gXTtgxw7j+xQpImvlFCyoXVwEPHkiQ9XGj5epMZaqWhXo1EnmsuTObdlzbt+Wqk7TpwNXrxrfLyAAmD0baNbM8nhc0unTLlXzPUXYu1duMeobMwb4+mvt4zGD+Zp5HNJD5MkOHTJM9gHp3bcz2X/2TBZgNJbst2kjkxSZ7DtOrlzSeztokPF9zp2TTjgXq5rnsW7dkgXRcucGvv3WsmQ/XTqgVy9Zu2jnThmyZWmyD8ioisGD5YJ79mypz6/mxQuZGD9hguXHdil378qJpEQJ+eCTdjh+3+Mw4SfyZKtXq7fbOX4/Ph745BPgzBn17aNGAXPnus8CWO7Ex0fe3/nzjU90fvIEqFdPFumi5HHmjPTM588P/PqrZQvLFSsmVSjv3JEk3N5Oa29v+TvcvVsuBo3p00fu+riN2Fh5g4oUkTFMANCjh/lateQ4agm/t7esbEhuiQk/kSdTG7+fNi1QrZpdhx05UqryqGndWkpKcrx+8mrdWnqHs2VT3x4TI8N/Ro6UkpBkv/BwYOpUKYNavLjMrYiJMf+8smWBf/4BTp6UBbMCAhwb11tvyd20KlXUtyuKfF7UhmS7HEUB6tQBeveWhSkSnD8vV1akDbWEv0QJz1jpMIViwk/kqW7dkhIv+urUMT4GwAL//gt89536tjJlJCFisq+NChVk1JapTrchQ4DPPrMsMSVDcXEymqRtW7m46tIF2LfPsue+/bZMsA0LA5o2lQ7S5BIaKnF266a+/cULoHFj4N695IvBIby8ZBySmhEjZLEPSl4PHqi/zxzO49aY8BN5qrVr1dvtqM5z4YIkPmoyZQKWL5cbCKSdHDmkp/+jj4zvM326JHuJO0zJuBcv5A5Wp06S5NeuLUOoLK2fX68esHUrsGePVFfS6gLY1xf44w+ZV6nm5k2gSROp+uPSunWT2yL6Xr2Snn9KXhy/75GY8BN5KmPj9xs0sPmQX3whk3X1+fgAf/8tC0aR9tKmldVbBwwwvs+GDcC778qNH0pKUWQC7JQpcj2cMaOsGzFzpuUVd7y9ZZXpI0ekVG2NGs670/XVV3InQs2BAzKsyKWHefn4SMkjtTfw339lfBQln4MH1duZ8Ls1JvxEnujVK/WqFmXKSJewDbZtk6RRzW+/2T0tgOzk7Q2MHi1Jq7GhI8ePSxnVQ4e0jc0V3b4tE8s7dgTy5pUypt27y40xa+aG+vsDn38OXLwoF11lyiRbyBbz8gImTpRVmNUsWCATv11axYoyFk3N559zZbHkpNbDHxgIFC2qfSzkMEz4iTzR9u2yApA+G6vzKIpMxFXToYMU0CDX0LUrsGqV8Ymh9+5JT/+SJdrG5Wx37khC3rWrFH/JmVMq3MyaBdy4Yf3xypUDJk2SC4dJk6RajytJnVo6wgsUUN8+ZIgszOXSRo+WsYL6Hjzg0J7kEh+v3sNfrpzceSG3xYSfyBM5eHXdlSvVO30yZpTefU7SdS0NGsi4/qxZ1bdHRgItW8ocSJce2mGHhw9l1eEuXWQBuBw5pFLNX39JwRdbhIZKzf2TJ+UuyeefAyEhjo3bkUJCZGSfsZWXu3Rx8Y7ykBA5waiZP1+ubMmxLlyQclT6OJzH7THhJ/I0iqI+fj9zZhnPYaW4OOMLPQ0aZDyZIOcqWxbYv19qvxszdKhMwnb5SZwWePUK2LRJFgEtW1aS848/lqpRFy/aftycOWWoz7p1Mv9h7FipTuguChcGli5V75x98EBq9Lu01q1lxrmabt2Mr/xHtrl5E8iQwbCdCb/b81IUT+3fsR+Xaia3dPq0ekbSvr2MX7DSrFkyzllfzpySSNlR4ZM0EB4uk0nXrze+T/ny0hvuasNSzHn8WDp5ly0DNm503IXLW28B778veWbp0p5xB2vSJKBnT/Vtq1bZvRZf8rp9WxY+UOt57tRJylCR4yTMYj9w4PVjxQoge3ZnR2YU8zXzmPCbwA8QuaUff1QfcL90qfH61kZERkoPodoY5+nT5buWXF9srAxFmTDB+D7p0gF//ikXB67szh3JPZYtk6kqcXH2H/ONN4CaNYFataS6Tmio/cd0NYoCfPCB+iiY7NmlnyB9es3DstzMmcZPOOvXSy1USrGYr5nHhN8EfoDILb3zjuHKQKlSAf/9Z/X4m99+A/r2NWwvUkTGMadKZXuYpL0pU6SX11SS3LEj8Pvvjl8N1h4vX8oaD7NnA5s32z/vIFcuqWBTvbok+SmlnKypjvLOnWX4k8tSFFnUQK1UWK5cwKlTctVKKRLzNfOY8JvADxC5pf/+kzH8y5e/HudQu7YMcLZCRIT0fP73n+G2f/6RlUPJ/WzeLDd61JK+BIULyxCf0qU1C8uAogC7dkmSv3Sp+voPlsqdW3ruq1WTJD9vXs8YpmOLGTOkDr+ajRtlIW6XdeOGXLE8f264rVs3WXWMUiTma+Yx4TeBHyByey9eyO3u4GBJ+q0wbBgwfLhhe/nyMqQzpSZMnuDcOVlx1VS1Gl9foH9/mQSr5cTsy5elPv6cOcDVq7YdIzhYhujUqSMf+wIF+HlNoCgy+kXt+j9PHukoDwzUPi6LTZkis6jVbNtmfPEB8mjM18xjwm8CP0CUUj18KBM41TrStmyRZIrc24sXsnLyjBmm98uYEfj2W8mx/PySJ5anT2VdgDlzgD17bDtGyZKyOm6DBjLplsPNjLt+Xeb1q/199+wpQ7pcVny8XMVt22a47Y03gBMnZOlpSlGYr5nHspxEZGDqVPVkoE4dJvueIiBAJl4vWAAEBRnf79EjmcdRtKjsGx/vmNe/fVsmCTdqJOsFdO1qfbJfqRIwZoyUDj9xQu5IVazIZN+cPHnkfVMzcaLhFCCX4u0NTJumntRfvuwGSwgTOQd7+E3gFSOlRPHxQMGCwJUrhtsOHZIFF8mzXL4sNesPHTK/b9680ov+3nsyLt7Syb0RETLRe9MmqRRz5IhtsRYrJhVmW7eW0rBkm/h4+f3t3Gm4rVIlYO9eFx8GpVZRoFkzuWIxtuIceSzma+Yx4TeBHyBKibZtU+/Ff+89YO1a7eMhbURHA0OGAD//bHkVHF9foGpVKQwVECBDfnx95d/4eJkjcPq0PG7etD22kBBJ8Nu3l+E6Lp2IupFLl4A335RFy/QtWQK0aKF9TBaLi5MP3759QJYsstBAs2bOjsp9jRsHHDsmv/Q6dZJv/F4yYb5mHhN+E/gBopSoTRsZuqFv2TIZI02e7dgxYMAA9eqHWkqdGmjYEGjXTob9+Po6Nx5PNXq0+kra+fMDZ864eN539qwsffzzz3JVSLYrUUKuzAGZ9f7BB3Lbr35958ZlIeZr5jHhN4EfIEppnjwBsmUDoqKStoeGArduSRJGKcOWLVKhx9ahN7YqX1568lu2BDJl0va1U6JXr2RdDbXF9X75BfjyS+1jIo2dOSPlTvU1bgz8+6/28diA+Zp5nLRL5AnmzQO2bpUlVe0wf75hsg9IAsZkP2WpVUvG9C9YAOTLl7yvVbYs8N13knccPAj06MFkXytp0gA//KC+bcQI4PFjbeMhJ1i6VL3dpcd0kbXYw28CrxjJLcTGyhjWx4/ltnbjxjL2pm5d+Ta3kKIAZcoAx48bbjt7VnoBKWWKjgZWrpT13NavBx48sO94/v5SWbFxYxm2kyOHY+Ik28THAxUqAIcPG27r2xf49VftYyINJR7Ok8DXV/7QtVyEww7M18xjwm8CP0DkFrZule5Yfb17SyULCx0+rF6Bp0oVWfGUCJDk8PhxYN06Sf737TN9YyljRsknihd//ShfnqXSXc2OHeprVqVOLXdeChTQPCTSwtmzUvpKnxsN5wGYr1mC1YqJ3N3y5ert779v1WGmT1dv//RTK+Mhj+btLXeCypSRyZ4xMcCzZ3IXICrq9b+xsTIfJDSUVXXcQbVqMk9z5cqk7TExwMCBxkd9uLyHD6WCz5AhgI+Ps6NxPRzOk2Kwh98EXjGSy1MUIHdumVGbWEgIcP++xSsQvXwpyVlERNL2oCDg7l3La60Tkfs6f17uwMTFGW7bs0fKr7qVpUtlQsjDhzIuSb9uP3nEcB6A+ZolOGmXyJ2FhRkm+4DcjrViudF//jFM9gGpysZknyhlKFwY6NZNfVu/fpavz+B0Dx5ID/VHH0myD8jtqIsXnRuXqzl71jDZB4B69dwq2SfLMOEncmfGhvNYWTCfw3mICJBqSWodpPv3u8nCe4oiwxn//jtpe2Qk0KmTTEIhweE8KQoTfiJ3ppbwp00rFXosdPGiTNjTV6KETK4kopQjc2b1hbgAYPhwN+jl9/ICxoxR37Z7NzBxorbxuLIlSwzbfH2tnv9F7oEJP5G7OnMGOHfOsL1+favKcc6Yod7euTMnWxKlRL17q5dKPXTI+SswW6RaNaBnT/VtAwcCly9rG48r4nCeFIcJP5G7WrhQvd2K4TyxscCsWYbtvr5A27a2hUVE7s3fHxgwQH2bW/TyA8Do0eorxr18KWMVU/rQHg7nSXGY8BO5I0WRJVD1+foCjRpZfJgdO4B79wzbP/xQ6qcTUcrUuTOQPbth+/79wKZN2sdjtcBAYNo09W07dgAjR2obj6tRS/g5nMejMeEnckf79wNXrhi2N2oEpE9v8WH++Ue9vVMn28IiIs/g7w988436Nrfp5a9Z03jZoWHDZOW4lOjsWeDUKcP2unU5nMeDMeEnckdqvfsA0KaNxYeIiwOWLTNsz5hRvieJKGX77DMga1bD9r17ZYFvt/DTT7JWiT5FAVq3Bq5e1T4mZ5s3T739o4+0jYM0xYSfyN3ExACLFxu2BwcDDRpYfJi9e2VtLn0ffmhVCX8i8lBp0gBff62+zW16+YOCgPnz1VfZffIEaN5cSnamFNevA+PGGbZzOI/HY8JP5G42b369mExizZrJfXgL6ZepTnwYIiIA6NoVyJLFsH3XLmD7ds3DsU2VKsDYserbjhwxXtHHE/XvD7x6Zdj+3nsczuPhmPATuZv589XbrRjOEx+vPpwnfXoO5yGi19KmBb76Sn3b8OHaxmKX3r2Bli3Vt02fbnyCryc5fVq9p8fLCxg8WPt4SFNM+IncyYsXwIoVhu3ZskntaQsdPAjcumXY/v77cmeXiChBt26yIJe+HTvUF+1zSV5ektQXLaq+vWdPWWjAkxUvLouPlS6dtP3TT7nKYgrAhJ/Infz7ryT9+j7+WH2MqhHGqvM0b25jXO4qPh6IiAAePZJFCRzp8mVg7lxg9Wo5PpGbCgiQkSBqRozQNha7BAbKrc2gIMNtUVGyaOH+/drHpaXKlYGwMOCPP4CQELmt+8MPzo6KNOClKG4x7cYpIiIiEBwcjPDwcKRLl87Z4RBJ2c01awzbDx8Gypa16BCKAuTPD1y7lrQ9MFCmBlgxDcA9vHwpg403bgROnAAePwbCw4GnTyXZT7wAT2AgkCGDPNKnl/EMPj6At/frR3y8rEbZvbvp150583V9Uy8v+f3Uri2PypWtWg2ZyNmePwfy5lW/drXi9OMa/vnHeO9G2rTA8uVSotLTPXoEnDwJVK/u7EjsxnzNPNbiIHIXL1/KTDl9hQsDZcpYfJijRw2TfQBo3NgDk31AbtM3bGjZvs+fy+PmTdP7BQWZT/hTp37934oiWdHhw8CYMYCfH1CrFjBwoEwoJHJxgYFAv37AoEGG237+2fjC3y6pWTOZmPDzz4bbYmNTzrjGjBk9Itkny3BID5G7SJtWBt7Pni29T97///Nt00Z6kC2U4qrzvP22ZCuOdPu2+X1M1TaNigLWrgWqVpXfnyXHI3Kyzz9XHw2zdKl6J4JL++EHw158b29g0SImweSRmPATuZOgIOCTT4ANG4A7d4Dx44F27Sx+uqKoJ/xp00pVNrdjyYhEX1/Hlx6yJEFP3MNvyoIFcpfmxx/lQoDIRQUHA126GLbHxamXdndpqVJJAYTEtef//FMWIiHyQEz4idxVlizAF1/IwFoLnToFXLxo2P7ee5L0u4XoaGDbNplFWLAgcP68+efUq+fYGG7fNn+xYc3qZS9eyPCe4sVlki+Ri+rdW/2jPW2aTI9xK2nSyHj+jh2B0aOBzp2dHZHj3LsHrFvn7CjIhXAMP1EK4tLVeRRFerijomRhmMjI1/9GRgJnz8qE5U2bgGfPXj9v6lTji+okSEj4M2WSsnzp00t3ZcK/qVPLRN4nT5I+oqNlkq7+I2NGicnUxNtq1aTix6FDsljatm0ySdiUy5dlMkWnTsDEiZzYSy4nVy4pCjZ3btL2ly+l8IvblXNPlUrq8Fvq8WOpbuPKLl+W4Uo3b0rSX6uWsyMiF8AqPSZw1jd5mhIlZO2VxPz8pDqP2tjcZHH9OjBrFrBqlXwhRUVJ8mzrcJaMGaXH3c/P9H7HjwMlS76e+6C12Fgph7dmDfDbbzI52JRSpWRwdMGCmoRHZKkTJ+TjqS9LFhnL75GT/wE5zxQtKgt49esHFCni7IgMHT0q5UUfPJD/DwyUxRLcqoyS9ZivmcchPUQpxLlzhsk+IJ3fmiX70dGSKQwbJhVrHjyQnnV7xq4/eiRl9MwpVcp5yT4gPYmVKknh8gsXgPbtTe9//DhQrpz6kshETvTmm+qj5O7fN+z59yjffit3FxMW8CpXTiYv3L3r7MjE1q1yZzEh2QekY+G994BLl5wXF7kEJvxEKYSx4TyaVufx9QU++sjxx/3rL8cfMzllyyZ3Ofbtk6TBmIgI+QV9+SUQE6NZeETmfP21evvYsUmXtvAYR49KhbTEDh+Wv82cOWUIzezZ0oGhtYgIYMgQSewTD3dM8OCBzPeiFI0JP5Erc+CIO7WO4tSpZci4phIWo3KE4GC5gOjVy3HH1FKlSsCBAzIPwdRtlj/+YA8duZQaNdRHiVy4IAuCexRFkSE8xs7H8fEyt6hDByA0FPjgA1mYwNywPXvFxACTJgEFCgAjR8odVDUVKgBz5iRvLOTymPATubJff5V758uXyxhwG926BRw5Ytheq5YsKqupihXldritSpYEvvkG2LkT+O8/YPFi9y6l5+0t1UHCwuRnUzN5sn3vGZGDeXnJ2lVq1NazcmsXLgB791q2b3S0XPG0bi3Jf4sWco4yN2HfGooit2yLFwd69pRJWMbUqwds2SIFCyhF46RdEzgJhJwqPl7qsyf07GbPLolh585SKsMKU6aoLww7ZQrQtasDYrXW2LHAgAFA5cpA/vxSjcbPT2b7+fnJ//v7J/03IEASYit/drfy8qV8gc+c+bqtUyfrqogQaSQ2VuaUqy26tWcP8M47moeUfK5fB4YOlYW5jPWkm5I6tdwW+eADqf2fM6d1z4+NlTd15Up5XLli/jlt2gAzZqSIlYOZr5nHhN8EfoDIqTZvBurUMWyvW1cW3rJCw4aysKu+27flOsJhTp4EihUDfHxM7/f0qVTmyZrVgS/uQWbMAHr0AAoVktKeLM9JLmrCBKnNr695cyky5XGePJHe9fnzge3bbT9O167S42JMXJyU1HzwQO5mrl4tBQos4eUld0FHjXJuoQINMV8zjwm/CfwAkVM1b64+0/aff4CmTS0+zIsXUrlSvxDOW2/JKBKHWbNGJph26CBjzr28HHjwFOj4cVkNjWU5yYU9fw7kzi15cGLe3tIJnSePc+LSxM2bMlZ//nypVWqNXr3kasmY+HjpmY+Ls+64tWsDP/0ElClj3fPcHPM181LGpR+Ru7lzR5Z915ctm9WzbDdtUq96mXhFebutWgU0aSIv9OefUsKDfQn2KVXKsmRfUWQwtSUrDhM5WGAg0K2bYXt8vMwn9Wi5csm57vhx4MwZ4LvvLK/NX6iQ6e3e3tYt8PXmm3Lnd9OmFJfsk2WY8BO5ohkz1Ht2Pv1UxoJaYdUq9XaHVee5cwf45JOkk4rHjpXbyZT8ZsyQ97tUKWD0aJbvJM19/rksM6Fv6lS5w5giFC0q64ucOQMcOwYMHCjVc4wpXNj8MTNmNL9PzpxS4vfIERnuSWSE2yb8O3fuROPGjZE9e3Z4eXlhhV5vqKIoGDp0KLJly4Y0adKgdu3auHjxonOCJbJGXJx6XXlvb+Czz6w6VHy8DP3UlzMnULq0beEloSjSvff0qeG2IUNkDColnzt3pFwgIHdXBg2SEnxqJZmIkknOnDICUd/TpymwGqSXl1x8//CDVPc5dUo6PypUSLqfJQm/sco6wcFSBWjJEuDiRVnEz9y8KUrx3Dbhf/HiBUqVKoVJRu4Z/vTTT5gwYQKmTJmCAwcOICAgAPXq1UNkZKTGkRJZad06GRuqr0EDGSxrhYMHky66mKBxYwcNsV+0yPgthK5d1ZfjJMdQFOla1V/o59gxSS6GDbOrlCtpQFGktOzTp24/BK5PH/X2CRM8dCEuS3h5SenMQYNkvY3bt2WibqtWllXpSdzDnyuXjPvfvFnKcM6fLyU//f2TL37yKB4xadfLywvLly9HkyZNAEjvfvbs2dGvXz/0798fABAeHo4sWbJg1qxZaNWqlUXH5SQQcopGjWQCrL7Vq6XcjhUGDZJRHvrWrpVFGe1y/758malVjmjZUiazceJu8lmxwvz6A++8A8ybB+TLp0lIZIGYGGDHDpl8v3y5/B0BUnY2Rw5JBHPkkNmudesCVaq4zd9Rwjpy+tatA+rX1z4et3f6tFQzy5JFPhNu8jlwBuZr5rltD78pV69exb1791C7dm1dW3BwMCpWrIh9+/YZfV5UVBQiIiKSPIg0df26ev3M3Llt+sZU63wPCJBy0Hbr1Us92Q8NBSZO5JdTcqtTR7pVTb3Pe/fK2K3587WKitTExsrfdadOUoq2Th3p6U1I9gEZ7H7hArB1KzB3rqyc+u67ctG2Zo1b3AEw1ss/frymYXiO4sWlnFrOnDyfkt08MuG/d+8eACBLlixJ2rNkyaLbpmb06NEIDg7WPXJ58gI/5JomTVL/Yu/SxeoxmlevyvBRfXXrOuAu8D//GC+yPWkSV3XUQkAAMG6cJPXFihnfLyICaNtWFuHRH/5D2vj9d7k7N3Mm8Pixdc/dv1/u+pUrJ3cEXHh8TLNm0hGtb/164Nw57eMhotc8MuG31cCBAxEeHq573FQbR02UXB49AiZPNmxPlUp6Bq2UbNV5Hj2SseNqmjVTn71HyadSJZmk+9136qVSEixYIL39J09qFhr9X7du9hekP3JE1t8oVQr491/HxOVgqVPLenFqTJWcJ6Lk55EJf9b/r955P/Ht0v//f1YTK3v6+fkhXbp0SR5EmvntN/Uadk2bSv19K6kl/F5eVk8DMNSnj/pM4JCQFFB420X5+ckk3T17TJcCvHZNxoRv3qxVZATISsm//OKYY506BXzwgXQCPH/umGM6UJcu6ncQZ882XJyLiLTjkQl/vnz5kDVrVmzZskXXFhERgQMHDuDtt992YmRERjx9arwLbOBAqw8XHi7zAvVVqiRD7G22dq1MAlUzYYJMLiPnqVABOHrU9B2hiAiZsT17tnZxkVy460+eyZwZ6NwZGDAAaNdOthcsaNmYuzlzgLNnkydWO2TMKD+KvpcvgWnTtI+HiITbJvzPnz/HsWPHcOzYMQAyUffYsWO4ceMGvLy80KdPH4wcORL//vsvTp48iU8++QTZs2fXVfIhcikTJ0oipq9xY5sK5m/YoL7+kl2r68bHA998o76tUSOpC03OFxgITJ8uNbrTp1ffJzYW6NAB+P57t5gM6tKeP5eFj8y9j15eMns1Vy4Z97J1q6yjMHWqlNKaM0faLlyQcf7jx6sPiE8wfDhQvrxDfxRH+eIL9faJE1kplshpFDe1bds2BYDBo3379oqiKEp8fLwyZMgQJUuWLIqfn59Sq1Yt5fz581a9Rnh4uAJACQ8PT4afgOj/IiIUJSREUSRlSPo4eNCmQ7Ztq364U6fsiHPpUvWDBgcryq1bdhyYks2NG4pStar67y3h0aGDokRHOztS9xIfryj37yvKqFGKkjGjvI/r1ln+XEtFRirKlCmKkjdv0t9Z1aqKEhtrW+waqV1b/eP299/Ojow8EfM18zyiDn9yYV1X0sRPP6n3nNerJ+UtrBQbK8N29MfL5ssHXL5sY3W3+HiZLKhW9uePP2RSIrmmqCjg009Nl+asU0fq+qdNq1lYTnfxIjB4MPDqVdIHICuZpk+f9N/Hj2UORMJDf/z8O+8Au3cnT/nEmBip8NO/v6y4feKE1YvwaW31avUCAdWqAdu3ax4OeTjma+aZKOlARMnu5Uvjk/m+/damQ+7dqz457v337chFli1TT/bz5ZNkklyXn5/Udc+bFxg1Sn2fzJllv5QkIsJ4aVlb7N0LbNsG1KzpuGMmSJ1aZsPWqQNcuuTyyT4gC4O/8YZ0MiS2Y4dcr7z5pnPiIkqp3HYMP5FHOHBAJuzqq15dqqnYwOHlOOPjZbywmm+/lWSEXJuXlyzkNHWq4XoOH3wgY9CtXOfB7aVJ4/hjjhzp+GMmli+fJP2WiItL3ljM8PY2XqKTxbyItMeEn8iZatSQFbL69EmagAwZYvMh1Up0p0sHVK1q4wFN9e6rleMg19W5s4y1CAyU/69TB1i82HMu2p49k2pRixeb3zc5Ev64ONcolfnokUzo/ftvp4bRsaP6KLF581iik0hrTPiJnC17dlkx9do1Gctft65h+T4LnT8vRT70vfce4OtrY3ybNqm3Dx7sOYliSlK/PrBrlyyStny5ZwzlOXsW+PJLIGdOoHdvWYTM3Iq0jkz4a9WSgek7dry+mHKWZ89kPM3Ro0DLllKxyUnSpwc++cSw/eVLYMYMzcMhStE4adcETgIhp1AUmwfbjx0LfPWVYfv8+XZWzdy+XYb1JMy2y5tXriyY8JOzPHwILFok5SzDwgy3r10rV7rGREQARYpI4p/wSKh/Hx4uQ+2ePpUEOkHWrHJnK2/e14/KlYHixR32Y9klMlJW1tu6NWn75MlA9+5OCen0aaBECcP2fPlk3nRKG0lGyYP5mnlM+E3gB4jczbvvSudtYj4+khtlyOCAF0hI/Nu25WTdlOLePUl0ne3lS+DcOZnxuWwZsG6d6aLudevKghT2io2VpN/fP3mGATlKbCzQooVUW9KXKhWwbx9QrpzmYQFyA0T/GgSQ4Yc2zy0iSoT5mnms0kPkIR49AvbsMWyvWtVByT4gk4mrV+diTSnFlStA2bIyV2Ps2OQd/vPyJXD3rjzu3ZPHzZsyXOf0aZnrYs3nbuNGeZ69ve+pUjnwDygZeXvLHQc1sbFyi+/IEacMOerVSz3h//13JvxEWmHCT+Qh1q5VH7Zs1+q6xiRHrXFyLZGRQPPmMrxl4kSpKLV0KZAnT/K8Xr9+wJQpjjte5cqv6+qnBN7ewK+/Ahkzqk/6v3hR5jc4YUx/48ZSSfTGjaTtmzbJTZsiRTQPiSjF4aRdIi0lY8+4WnUegD1oZKM+fWTiZ4JDh4AyZaQCTmSk5ce5dk1uP5njiGFDfn5AmzYS6+7dThvC4jReXlIqd/Bg9e0zZgBLlmgbE2RY4eefq2+bOFHbWIhSKib8RFq5cEESkH37HH7oqCj14cpFiwIFCjj85cjTzZsH/PmnYfuTJ0CrVtKL3KSJ1PW/c0cuZB89Ag4fBv75RxaT69BBZmbmywcsXGj+NbNlsz3eKlWAv/6SYUDz5qW8RF/fsGHA22+rb+vSxbCrXQOdO7+eE53Y7Nkyf5qIkheH9BBpIT5evvGOHJGhBl98IaueBgQ45PA7diQtJpKAvftkE29vmaBqbEjMy5fAypXyAORz/OKF8eNt3w707Gn6Na3t4S9QQCaPt20rS7rSa6lSSWmuUqUMTwzh4fKebdumaYmcjBllGoF+Oc7nz2Xdty++0CwUohSJPfxEWvjzz9flcxQFGD8eKFkS2LLFIYc3NpzHpvH77G6j1q2BgweBwoUt299Usg9Iwm+uLr6xhD84GHjnHblgHjdOJuPeuiV3zL77jsm+MfnyGZ8TsWsXMHq0tvFAJu+qmTjR/MeDiOzDspwmsMwTOcSNG1IpRG0Fzt69gd9+s+vwiiLFOfTv0mfKJCMcrOrEe/lSZtdVrCiDbuvXZ6HslOzZM+CzzyxbudacEyfkIteYx4+lqzdbNkn+s2aV/w4O5iRxe7RrJ8Oc9Pn4SFmvihU1DadqVZleoW/dOjndENmC+Zp57OEnSk6PHslYZ7VkP29eYORIu1/ixAn1IbkNG9qQqy9eLDGvXQs0aiS9pz/+CPz3n91xkhsKCpLx93/9Zd8Ye+D1om3GhITIarkffywrTRctKku1Mtm3z6RJ0tuvLy5OxtFo3OdnrJf/9981DYMoxWHCT5RcHj4EatZMWukksalTHVITe9Uq9XabhvP88UfS/79+HRg4ELh0yYaDkUfw8pJe/ps3gb17pQJM6dKWPbdgQXnu/PnARx8la5hkRLp0wIIF6lf/Bw9KbUwNffghkCOHYfu6dTzNECUnDukxgbeIyGb378vykqdPq2/v1Mlh9bArVJAqhIn5+kpHvVXXE4cOycH0lSkj1VfY00qJ3bold4L275cynXnyyF2rhEeePOplWcg5RowAhg41bK9SBdi5U9O/75Ej1ZcK6NNHpmkQWYv5mnlM+E3gB4hscveuJPtnz6pvz58fCAtzyOqdd+6o95bVry89Zlbp1AmYOdOw/a+/pJeWiNzXq1dy7rl3z3Dbtm2ygrZGHjwAcuUCoqOTtqdLB9y+7ZTFgMnNMV8zj0N6iBzp9m354jSW7OfLJ2vMOyDZB4A1a9TbrS7H+fixeq30dOmkYgsRubc0aYCvvlLf9v33moYSGgq0bGnYHhEBzJmjaShEKQYTfiJHUBRZwfLtt6VcoJoCBaRgfp48DntZh62uO2uW+uqpHTo4bK0AInKyrl2lfJe+bdukYo+GTJXo5LgDIsdjwk9krwMHZDGtli1lYqOaQoWkSkmuXA572Zcvgc2bDdtLl7byZeLjDSfrJuje3ZbQiMgVBQQA/fqpbzNXRcnBypcHKlUybD971mHLkxBRIkz4iWx14wbQpo18a+3bZ3y/okWlZ19tsL0d1q9X75S3ujrPli3q5TFq1gSKFLEpNiJyUT16SAlUQCbqfvSR1PYdPFjzUFiik0g7TPiJbLFokaxCumCB6f1KlpSeM2OriNrh77/V261O+CdPVm///HMrD0RELi8oSHr527SRKmKLF5teEC0ZNW+ufmpctQq4elX7eIg8GRN+IluULQtERZnep1o1maAbGurwl4+MVK+/nyePhGaxu3fVJwJky2ZjIX8icnkDB8rqu0WLOjUMX1+ZVqBPUYz3QxCRbZjwE9miUCHpnlKTK5f0/G/dqj5BzgE2blRfvLd5cyvLaS9eLGP49XXpAqRObXN8ROTCXGhNja5dgVSpDNunTQNevNA+HiJPxYSfyFYDByb9/8BAYNQo4Px54OOPAe/k+/MyNpzH2DWIUWpDkry8gE8/tTomIiJrZcsGtGhh2P70qSzQTESOwYSfyFZlysgKV15eQOfOwMWLwKBBUu86GUVFqY/CyZlTfaFcoy5eNFyiF5ChSA6sJkREZIqxybvjx7NEJ5GjMOEn0rdpkxSDtsTYscDJk8DUqckyMVfNli1AeLhhe/PmVt5UMDbhmAttEZGGKlUCypUzbD9zRr30MBFZjwk/UYK4OGDoUKBePaB3b2DnTvPPKV5cHhpyyHAeRVG/X546NdCsmU1xEZEHiYnR7KW8vOSUq+a33zQLg8ijMeEnAmRN9/r1gREjJBmOjwdatQIePHB2ZEnExAArVhi2Z8smi/xa7PBhGdKjr0GD1zW6iShlefVK7vzVrm3Dct32+egj9Zuka9fKtCgisg8TfqLoaOnV1r93fPeu1KqOi3NOXCq2bQOePDFsb9aMw3mIyEbHj8uq2tmyyTlvyxYpBXbjhmYh+PrKmmBqJkzQLAwij8WEn1I2RZEJt8YGim7eDCxdqm1MJjhkOE9cnCwcpi8wUPNePSJyARs3AlOmJJ0cpCjA7NmahtG1K+DnZ9g+a5Z6RwcRWY4JP6Vs334LzJ2rvi1VKmDcOKBlS21jMiI2Fli+3LA9NBSoUsWKAymKDIx9//2ktfabNk32CkNE5ILatQN8fAzbZ81SX6cjmWTODLRta9j+8qXU5Sci2zHhp5RryhTghx/Ut2XNCuzaBfTp4zKL1OzcCfz3n2F706bq39VGpUolA2ZXrgTu3QP++guoXl1u5RNRypM1q8zf0XflimXFCxzI2OTd33+XTg8isg0TfkqZ/v3X+IDRoCBg3TqpFedCHLbYVmIhIcBnn8nkgLp17TgQEbm1jh3V22fO1DSMkiVlzrC+mzfV73ASkWWY8FPKs3+/VOBRu1WdKhXwzz9A6dKah2VKXBywbJlhe6ZMsk4WEZFdGjaUE4q+pUulipmG+vRRbx83TtMwiDwKE35KWW7elImpr16pb58+HahTR9uYLLB7N3D/vmH7hx/KNQoRkV18fWUsv75Xr4AlSzQN5b33gIIFDdv37QMOHNA0FCKPwYSfUo7oaBm7rjYQHgBGjQI++UTbmCyULMN5iIgSc5FhPd7exsfyjx+vaShEHoMJP6UcAwbIcB413boBAwdqG4+FYmLUK4NmyADUqKF9PETkoUqWBMqVM2zfuxc4d07TUNq3B4KDDduXLgVu3dI0FCKPwISfUobly40PAG3cWEpAuEg1Hn2rV6sP52nSJGlVTSIiuxnr5Z81S9MwAgOlnoC+2Fj28hPZggk/eb7Ll41/iRUsCMyb59ID4adOVW+3qopmRARw8KCmNbWJyA19/LH66lezZ2teF7NnT/UVxP/8E3j6VNNQiNweE37ybJGRMm4/8QqSCfz95f5wunTax2WhGzeA9esN2/Pnt3I4z6pVQMWKQK5cQPfuctCoKIfFSUQeIkMGqQag7949YOtWTUPJkwdo0cKw/dkzWUaFiCzHhJ8825dfAkeOqG+bOBEoVUrbeKw0c6YsjKuvc2f1ni+jVqyQf+/ckW/K996TZS3v3XNEmETkSYzdEV28WNs4AHz9tXr7+PHSn0NElmHCT56tTh31mV/t2wOdOmkfjxXi4qRKqD4fH6BDBysOFBmpfpsgRw5ZYZOIKLFatYAsWQzbly3T/M5g2bLqC3HduwfMnatpKERujQk/ebYPPwQOHwbKlHndVrw4MGmSy07STbBxoywboK9RIyBbNisOtHUr8Py5YfsHH9gcGxF5MB8f9bE0T5/KiUlj33yj3j52rHSMEJF5TPjJ873xhpSV69YNCAiQovYBAc6Oyixjk3XVKleYtHKlenuTJlYeiIhSjFat1NudMKynVq2kfTYJLlwwfnojoqS8FEVthDABQEREBIKDgxEeHo50Ljyxk6xw4waQO7ezozDr3j2ZX6tfFCNnTuDaNemAs0h8vAzd0R+rnzUrcPu2lRMBiCjFiI8H8uY1vM0YGCh1gtOm1TScRYukgJC+ChVkeRUXv2FLyYz5mnn8tqeUxQ2SfcB4BbxOnaxI9gEpxak2Mff995nsE5Fx3t5S4Uzf8+fA2rWah9O8OZAvn2H7wYPArl2ah0PkdviNT+RiFAWYNs2w3cvLhnnGxu53c/w+EZmjP6wnbVppy5VL81BSpQL69VPfNmaMtrEQuSMO6TGBt4jIGbZvV6+xX6+eerEdk4oWBc6dS9oWGAg8fCjrEBARGaMoQMmSQKFCkug3bOjU+U8vX0pt/v/+M9x24oSESikT8zXz2MNPnmHJEmDTJmdH4RAOm6x7/rxhsg8A9esz2Sci87y8gOPHpRznRx85vdhB2rRAr17q237+WdtYiNwNE35yf5cuyViXunWB/v3degXZx4+Bf/4xbA8NBRo3tvJgHM5DRPayatJQ8uvRQ32+8IIFwOXL2sdD5C6Y8JN7i4+XZP/FC/n/X34BKlVS79l2AzNmqF+vtG8P+PpaeTC1hN/HR27LExG5oYwZZaVxfXFxwMiR2sdD5C6Y8JN7mzXLsETDsWOyPOONG86IyGbPnwM//aS+Te0LzqT794F9+wzbq1UDMmSwOjYiIlfx5ZcyiVff3Llyw5eIDDHhJ/f14IEM4VHTrp3blOBMMH68zKXVV726zJmzyqpVMuFOHxfbIiI3lyePesWyuDhgxAjt4yFyB0z4yX317w88eWLYnju3DO1xI0+eGJ90NniwDQdcsUK9/f33bTgYEZFrGTwYSJ3asH3ePFmBl4iSYsJP7mnLFrl/q2bSJCk96UbGjgXCww3ba9SQZeWt8vw5sHmzYXuZMtI1RkTkCK9eAVevOuWlc+cGPv3UsD0+nr38RGqY8JP7iYwEunVT39asGdCokbbx2On+feC339S3jRplw5LxGzaoz/xldR4isld0NLB6NdC2rZQP69DBaaEMGqRezGDBAqlKTESvMeEn9/PDD+ozs4KCZCC8mxk9WhaU0dewIfD22zYccPVq9XaO3ycie4wZA2TJIjWC58+Xu4m7dgG3bjklnFy51AsaxMcD33+vfTxErowJP7mXs2eBH39U3/bDD0COHNrGY6ebN4E//lDfZlOJufh4YO1aw/Y8eYA337ThgERE/+fnBzx9mrRNUYDFi50SDgAMHKjey79woXxdEJFgwk/uIz4e6NoViIkx3Fa+PNC9u/Yx2WnECLlDrq9FC6B0aRsOGBUlK9NUrJh0LFDDhjaMDSIiSuSjj9TPIwsXah/L/+XMCXTpYtiuKOzlJ0rMS1HUavcRAERERCA4OBjh4eFIly6ds8OhRYuAjz82bPfxAcLCbMyQnefSJaBIESkll5i3N3D6tGyzy4MHMp5/7VqZ3Va7tp0HJKIUr1YtYOtWw/bz522oH+wYd+4A+fMbTl3y8gJOngSKF3dKWKQh5mvmsYef3EN8vMxgVdOnj9sl+wAwbJhhsg8An3zigGQfkAl17dpJ7xuTfSJyBLVOF8CpvfzZs8vNX32KAgwZon08RK6IPfwm8IrRhaxapV5DPndu6Q53szKc27cDNWsaro2VOrXUkM6b1xlRERGZ8eSJTNzVH1pZuLAMmnfS0MG7d6WXPzLScNuOHcC772ofE2mH+Zp57OEn16coMiFXzbBhbpfsP3kivfhql9qffcZkn4hcWIYMwHvvGbafPw8cO6Z5OAmyZTM+jatfP7lJTJSSMeEn17djB7B/v2F7rlxAmzbax2MHRQE+/1yq8+hLk8bGVXWJiLRkbFjPggXaxqFn0CAgONiwPSzMqSOOiFwCE35yfcZ697/6Sr0emwubP1/mHqsZOVLGohIRubTGjYG0aQ3bFy1yald6pkzAt9+qbxs4UBYGJkqpmPCTawsLAzZtMmzPnFl9XXUXdu2aVMxUU7u2zD0mInJ5AQHqC/ndugXs3q15OIn16gXky2fYfvMmMG6c9vEQuQom/OTazp+XFXT19emj3sPkouLiZNx+RIThtgwZgFmzpBynzZ49s+PJRERWcsFqPYCsDTZmjPq20aOB+/e1jYfIVTDhJ9fWpg1w/bqsUJUxo7SlSycD4d3ImDGyAr2av/5ywALB77wDFCsmw5y2bVNfnIyIyFHq1pXeCn1Llzr9/NO8uZwS9T1/Dnz3nfbxELkCluU0gWWeXMyLF8C0adJd/uWXzo7GYocOyZdPbKzhtg4dgJkz7XyBGzeAPHmStqVLJwWo+/e38+BEREZ07So9FvrWrAEaNNA+nkT27wfeftuw3dsbOHGCi3F5GuZr5rGHn9xHQADQu7dbJfsXLgCNGqkn+/nzAxMmOOBF1q41bIuIAEJCHHBwIiIjXHRYDwBUqgS0amXYHh8vN0KJUhom/ETJ5No1WYX+wQPDbd7ewNy56tMTrKaW8APqtbKJiBylalX18YgrVgAvX2oejr7Ro2VMv7516+QmBFFKwoSfKBncvi0r6d66pb7922/Vx5haLTIS2LLFsL1sWVmJhogoufj4AC1bGrY/fw6sXq19PHry5pWbwmp69nSJaxIizTDhJ3KwBw+kzObVq+rba9c2Xivaatu3q39rOXn8LBGlEMaG9cydq20cRgwaJPX59V27Bnz/vebhEDmNwxL+yMhIRx2KyG09fizFK86dU99eubLc7U6d2kEvaGw4T8OGDnoBIiIT3noLKFjQsH3dOpeogRkcDPz4o/q2X34BTp3SNh4iZ7Er4Y+Pj8eIESOQI0cOBAYG4sqVKwCAIUOGYPr06Q4JkFKY2FjATQtHPXwI1K8PHD+uvr1cORk3GhDgoBdUFPWBqJkyAeXLO+hFiIhM8PIC2rV7/f++vlIXc+XK16WUnaxjR+ls0RcbC3Tr5tTFgYk0Y1fCP3LkSMyaNQs//fQTfH19de0lSpTAtGnT7A6OUqAJE4ASJYCRI4FLl5wdjcWOHpWE/tAh9e0lSgDr10tvk8OcPw/8/yI7ifr1ZWwtEZEWPvlEyuJMngzcvSu1+Bs2BFKlcnZkAKRIwp9/qoezZw8wY4b2MRFpza6Ef86cOfjrr7/Qpk0b+CRKMEqVKoVzxsY0EJmycCFw5ozUkC9YUHqqf/vNpXv9Fy2S3qMbN9S3FyoEbNqUDJ1dq1apt3M4DxFpKU8eYN8+oHt3ly0HXLy48WVJvv5avZoakSexK+G/ffs2ChQoYNAeHx+PGK70Sda6dAkIC0vaFhYmt4a9vJwTkwlxccDAgTJn7dUr9X3y5AE2bwayZk2GAFauNGzz8QHq1UuGFyMicm9DhkjlHn1PnnCNQvJ8diX8xYoVw65duwza//77b5QpU8aeQzvMpEmTkDdvXvj7+6NixYo4ePCgs0MiYxYtUm83VgXCiZ4+BRo3Nj4ZDJAvli1bgFy5kiGAhw+BvXsN2999V325eyKiFC5tWmDSJPVtc+cC27ZpGw+RluwaYDd06FC0b98et2/fRnx8PJYtW4bz589jzpw5WO0CNXgXL16ML7/8ElOmTEHFihXx22+/oV69ejh//jxCQ0OdHR4lpijqqzOmSgU0a6Z9PCbs2QO0bStl3YypXl2GsaqVg3OI1avVhzl98EEyvSARkftr0EDmFP/9t+G2rl2BY8fkwoDI09jVw//BBx9g1apV2Lx5MwICAjB06FCcPXsWq1atQp06dRwVo81+/fVXfPbZZ+jYsSOKFSuGKVOmIG3atJhhZIZOVFQUIiIikjxIIydPyth9fXXrukylh5gYYOhQ6UQ3lez36gVs3JiMyT4A/Puvenvjxsn4okRE7u+339RXOb94ERgwQPNwiDRhdx3+qlWrYtOmTXjw4AFevnyJ3bt3o27duo6IzS7R0dE4fPgwateurWvz9vZG7dq1sW/fPtXnjB49GsHBwbpHrmQZi0Gq1Hr3AZcZznPpkqwiP2KE8RJuvr7A9OlSaMhhdfbVvHolVxT6SpQA8udPxhcmInJ/OXJIITg1v/+uvng5WW7MGOCzzzgR2tV47Eq7//33H+Li4pAlS5Yk7VmyZMG9e/dUnzNw4ECEh4frHjdv3tQiVFIU9fH7/v5OH6KiKMDMmUDp0sCBA8b3y5pVFr3t1EmDoLZsUV9dl8N5iMjVREYCixfLMEQX0qMHULGi+raOHYHwcG3j8RS3bskKxtOmSYW68ePl7jg5n10Jf4YMGRASEmLwyJgxI3LkyIFq1aph5syZjoo12fn5+SFdunRJHqSBAwfUx8g0bqx+31UjERFA69aSxL94YXy/d9+VYkJvv61RYMaG87z/vkYBEBGZoChSprNbN+kNadUKGD7c2VEl4eMDzJ4NpEljuO3mTaB3b+1j8gTffPO6Pyo8HOjTRzrMjAysIA3ZlfAPHToU3t7eaNiwIYYPH47hw4ejYcOG8Pb2Ro8ePVCoUCF0794dU6dOdVS8FsuUKRN8fHxwX29p7/v37yNrstRIJJu54HCeo0dlxXhjhYMAmU88ejSwdavcItZEfLx6/f1s2WTlLyIiZ2vbFnjnHVntKqGrPCwMOH3auXHpKVxYhp+omT0bWLFC03Dc3p49wIIFhu1nzsjiZ+Rcdv0Kdu/ejZEjR2Lu3Lno1asXevXqhblz52LkyJE4fPgwpk6dip9//hkTJkxwVLwW8/X1xVtvvYUtiQbjxcfHY8uWLXhbs65YMisuDliyxLA9XTrgvfc0D0dRpGxbpUqmF/otVEh6LAYM0HhR20OHALUhaY0b84xKRK6henX19tmzNQ3DEj16ADVrqm/r0oXj0C0VFycFK9S0b298+BRpx64MYcOGDUkmxSaoVasWNmzYAABo0KABrly5Ys/L2OzLL7/E1KlTMXv2bJw9exbdu3fHixcv0LFjR6fEQyp27FBPYD/8UMbwa+jpUynX1rMnEB1tfL8uXYAjR5zUof7iBaC2xgWH8xCRq/joI/Xz97x5QGys9vGY4O0t87TURvA+fCijklx4oXeXMX263BnXFxgod8LJ+exK+ENCQrBKZXjBqlWrEPL/5bVfvHiBICeNw27ZsiXGjh2LoUOHonTp0jh27BjWr19vMJGXnMhFhvOcOweULQssW2Z8nwwZgOXL5S51QIB2sSVRs6Zcbdy4AUycKGVLM2QAatVyUkBERHqCg6XTRt/du8CmTdrHY0bu3FJdTc3y5S55Y8KlPHkCDB6svm3oUBlxSs7npSi2X7tOnToV3bt3R4MGDVChQgUAwKFDh7B27VpMmTIFn376KX755RccPHgQixcvdljQWomIiEBwcDDCw8M5gTc5xMUBWbIAjx4lbc+cGbhzRwbJa2DvXhkR8/ix8X3eflvG8+fOrUlI1omKAvz8nB0FEdFrGzcC9eoZtrdsaXpylJMoilyjrFxpuC1tWpmCULSo9nG5g9691S+YChWSJXZ8fZM/BuZr5tmV8APAnj17MHHiRJw/fx4AULhwYfTq1QvvvPOOQwJ0Jn6Aktm+fTKxS1/37sDkyZqE8O+/8v0TGWl8n2++kfr7yVpbn4jIk8TFAXnyALdvJ23385NhnOnTOyUsUx48kOVMHj403Fa8OHDwIFfh1Xf6NFCqlPy69a1ZIysba4H5mnl2d6FWrlwZlStXdkQslNKsXave3qSJJi//119ybWFsIa1MmYC5c4H69TUJh4jIc/j4SLUe/TI4UVFSqKFLF+fEZUJoqAzZbNrUcNvp0zIpdfp07eNyVYoivftqyX7Dhtol+2QZh5X1iIyMRERERJIHkUlr1hi2pU0LVKuWrC+rKMB33wFduxpP9t9+Gzh2jMk+EZHN2rdXb3fhQfEffgh8/rn6thkzgDlztI3HlS1frr4qcerUwLhx2sdDptmV8L98+RI9e/ZEaGgoAgICkCFDhiQPIqPu3FGf0l+7drKOR1cUOZl//73xfZo0kZOYZrX1iYg8UdGiQPnyhu179wIXL2ofj4V++UW9GBogd4XPntU2Hlf06hXQr5/6tr59gYIFtY2HzLMr4f/qq6+wdetW/PHHH/Dz88O0adMwfPhwZM+eHXN4GUymrF+v3p7M9wC/+w6YMsX49u7dgb//Vl99kYiIrGSsl9+FcwR/fxl1pFZg8OVLoEWL16vJplQ//ghcu2bYnjUr8O23modDFrAr4V+1ahUmT56MZs2aIVWqVKhatSq+/fZb/PDDD5g/f76jYiRP9PKlVOPRl4wJ//TpMvnWmJEjZdEtTRfSskRkpHSnEBG5m1at1CsezJljfEylCyhQwPh4/dOnZb2WlOrcOeO19ceMUb9QIuezK+F//Pgx8ufPDwBIly4dHv+/rmGVKlWwc+dO+6Mjz9Wzp1RqOHBAut3LlwfefBPIlStZXm7DBhmzr8bHR8ZmDh4MeHkly8vbZ948KWTcvbuUieAqMETkLjJmVF8Y8MYNYPt2zcOxRosWshKvmpkzZYJvSqMo8l0aE2O4rVIlmadNrsmuhD9//vy4evUqAKBIkSJYsmQJAOn5T++CJbfIxXh7AxUqAMOGSSJ74ECyvMyxY7KCrlolgVSpgBUrAJdefHnmTCA8XMYiVawIlCwpg0zVfiAiIlfjhpN3E4wdK4syqunZE9i1S9t4nG3mTECtP9fbW6ppezusFAw5ml2/mo4dO+L48eMAgAEDBmDSpEnw9/dH37598dVXXzkkQEpB1JZit9PNm1Ie7Plz9e1TpwKNGjn8ZR3n3DmZ4JbY6dOyQrHLjT0iIlJRv776EM5//jF+cnYRCeP51Uq7x8YCzZrJzYqU4MEDoH9/9W19+hif6Eyuwa6Ev2/fvvjiiy8AALVr18a5c+ewYMECHD16FL1793ZIgES2Cg+XKQF37qhv/+47oEMHTUOy3syZ6u2dOmkbBxGRrVKnBtq0MWx/8UKSfhf3xhsy7FPNw4dS2S0lTOLt3x948sSwPXduYPhw7eMh69i10m5kZCT8k6FX1lVw5Tb3pSjSc29sba/27SWXdskx+wliYmROw/37Sdv9/IC7dwGWviUid3HsmHoXcI0awNatmodjiyFDpLiDmpYt5carS3+n2GHLFqmarWbVKuffKWe+Zp5dPfzp06fHu+++iyFDhmDLli14xUoi5CKmTTOe7NeqJavsuvyJef16w2QfkGUgmewTkTspXVoKM+jbtg24fl3zcGwxfLj6/GMAWLxYSlV6olevgG7d1Lc1a+b8ZJ8sY1fCv3nzZtSvXx8HDhzABx98gAwZMqBKlSoYPHgwNm3a5KgYiaxy/brxBUFKlJA7yL6+2sZkEw7nISJPojZ5NyAA+P9cQFfn7Q3MnQsUK6a+ffBg6e32ND/8AFy6ZNgeFASMH699PGQbu4b0JBYbG4tDhw7hzz//xPz58xEfH484N68iwltE7kdRgLp1gc2bDbdlzSrFgJKp8qdjPXggS/3GxiZtz50buHqVpRCIyP3cvy/ntfh4GcrTvr3csQwMdHZkVrl8WSpJq41nDwiQKjbGKvu4myNHpNymWhnOiRONly3VGvM181LZe4ALFy5g+/btukdUVBQaNWqE6tWrOyA88iibN0t3QIMG8siTx+Ev8ddf6sk+IIuouEWyD0jtff1kH5BZxkz2icgdZcki57Z33pHOCzf1xhtSuadePcO1w168kMpw+/cny1ecpl6+BFq3Vk/2K1QwPsyHXJNdPfw5cuTAq1evUL16dVSvXh3VqlXDm2++CS+XHxxtGV4xOlivXtIlkKB4cUn8hw51SA/PtWtSol6tylvHjsarLLgcRZEf5PRpw21XrgD58mkfExERJfHbb0DfvurbihUD9uwB3HlJos8/B/74w7DdxwcIC5NpGa6C+Zp5dnUVZs6cGS9fvsS9e/dw79493L9/nxN3SZ2iGM6iPX0amDULSJvW7sPHxwOffqqe7OfMCfz6q90voZ2wMPVkv2ZNJvtERC6id2/jU6rOnAE+/BCIitI2JkdZvVo92QeAAQNcK9kny9iV8B87dgz37t3DgAEDEBUVhUGDBiFTpkx45513MHjwYEfFSJ7gwgXpndZXv75Dhqj8+afxym7TprlZL4uxWxGcrEtE5DK8vGQB9Dp11Ldv3y4dUY6ZKamd+/eNf92UKydr2JD7cdik3UePHmH79u1YuXIlFi5cyEm7lNSvv6qXzlm0SAoY2+HqVRkB8+KF4bbOnWU1Xbfx8iWQLRsQEZG0PV06qb3vgLshRETkOBERQNWqwIkT6tsHDQJGjdI2JlspCtC4MbBmjeG2tGmBo0eBQoW0j8sc5mvm2dW1umzZMnzxxRd48803kSVLFnTv3h3Pnz/HL7/8giNHjjgqRvIE69YZtnl7S0kdO/XurZ7s58oF/PKL3YfX1t9/Gyb7APDxx0z2iYhcULp0MmI1Z0717T/8kHT6miubMkU92QeAceNcM9kny9jVwx8aGop3331XN2G3ZMmSjozN6XjF6CAvXgAhIUB0dNL2KlWAXbvsOvSBA1IyTM2GDQ65ntBOVBRQtKjcstB34ICURSAiIpd08qR8ran12QBSRe6zz7SNyRrnzkk5UbWpmB98ACxf7roLVjJfM8+uspwPHjxwVBzkybZvN0z2AeC99+w+9JAh6u2ffeZmyT4gXUBqyX7x4lL0mYjIE8XFyffE/PlSgWHWLGdHZJOSJWVhx/feU6+q3LWrLPqotv6Ysz15IpOM1ZL9rFllaKyrJvtkGRb0puSnNpwHkAm7dtixA1Bb0DldOjdc4vzRI2DkSPVtAwfyTEtEnufECeDLL2X8Ze3asrr4woXqK1q5idq1pVCEGkWRybCLFmkbkznR0UDz5tLDr2bmTCBzZm1jIsdjwk/Jb/16w7bQULvqeimK8d79fv1kBJFb+f574OlTw/Zy5WT8PhGRp/nnHxkYfvfu67boaGl3Y+3bA2PHqm+LjwfatnWdH1FRZAEtY1XuevWyu2+OXAQTfkpely7JOuT66tWzqxznpk3qw/9DQoA+fWw+rHMoirwXPj6G2379lSvrEpFnat1avX3+fG3jSAb9+hmvzBMXB7RqBaxapW1Man78UXrw1ZQpA4wZo208lHyYSVDyUuvdB+wav68owLffqm/75hsZ0uNWvLykl+vUKamHluDDD6XWGxGRJypcGHjrLcP2HTuAW7e0j8fBBg2SheTVxMYCzZo5d7rCkiUSo5ocOeSCJE0abWOi5MOEn5KX2vh9Ly/jK5VYYNUq4NAhw/YsWYAePWw+rPMVKQL8+6/cW61YkV0rROT52rQxbFMUYPFi7WNJBsOGSUeUmpgYoGNHYPBgGeqjpX37gE8+Ud8WGCilOXPk0DYmSl52leV88eIFfvzxR2zZsgUPHjxAvN4n9orayqpuhGWe7BQZKWNs9Kf9V6ggZSZtEB8vtxnVFjgZPx744gubDktERM5w544UsNdPRcqUATxkPR9FkbnJv/1mfJ/mzYE5c7TpUT9yRMblP3xouM3bWzrVGjRI/jgcifmaeXaV5ezcuTN27NiBdu3aIVu2bPBiJRFKbNcu9Rpfdgzn+ftv9WQ/Z06gSxebD0tERM6QPTtQsyawZUvS9qNHgbNnZW0SN+flJdOxoqOByZPV9/n7b+DGDWDlSimDmVxWrpSpEy9fqm+fMMH9kn2yjF0J/7p167BmzRpUrlzZUfGQJzE2ft/GKf+xscbHQw4ZAvj723RYIiJyptatDRN+AFiwABgxQvt4koGXlyy1kj69rLyr5uBBGc35zz9SoM2RFEUuOr76yvBmSoLevd18WCyZZNcY/gwZMiDE7eofkmbUxu+HhNi8iNSSJcD584bt+fPLOEgiInJDTZvKilT6Fi0ynp26IS8vqdwzaxaQOrX6PjduSNLfqxcQHu6Y142JkdKb/fsbfzsbNwZ++cUxr0euya6Ef8SIERg6dCheGrs3RCnX9etyO1Zf3brq5SctYOxW6HffGT95uqStW+UMTERE0u3dqJFh+6VLMrTHw7RvL6WlM2RQ3x4fL3cDCheWmxz2XPM8egQ0bAj89ZfxferUkfXObPxqJjdhV8L/yy+/YMOGDciSJQtKliyJsmXLJnlQCrZhg3q7jcN5Tp0C9uwxbC9USL3Ig8vatk3OrjVrymQ1IiKSwvRqXG1ZWgepVg3Yvx8oUMD4Pvfvy/dbnTrAyZPWHf/WLZkonCeP+or0Cbp2lYo8AQHWHZ/cj11j+Js0aeKgMMjjbN6s3l6vnk2H+/NP9fZevdyoV+L2bflSi48Hdu+WKhSLFwPVqzs7MiIi52rYULLOFy+Sti9eLCWKPbAoSKFCkvQ3bQrs3Gl8vy1bgDfflPnLTZoAH3wgI2PV1mQ8exb4+Wdg3jzTN5K9vGQ14L59PfKtJRV2leX0dCzzZIfISElq16+XsfxnzthcZu3lSynkoD+eMU0a6SRPn94xISermBhJ7PfuTdru7S1fZv37OyUsIiKX0bq1jC3Rt3cv8Pbb2sejkehoWfH2hx+AqCjLnpMtG1CpkhTCi4gAnj2Tf2/cMD8EKG1aGSr0wQf2x+4qmK+ZZ1cPf4LDhw/j7P/HaxcvXhxlypRxxGHJnfn7A7Vry2PsWDkL3b9v06EWL1afvNSqlZsk+wDw9deGyT4gvf1xcdrHQ0Tkalq1Uk/4Fy3y6ITf11cq0LVuDfTsaXxEbGJ37wLLl1v/WtmzS519jrpOeezq4X/w4AFatWqF7du3I/3/M6+nT5+iRo0aWLRoETJnzuyoOJ2CV4yuoVIl9XW69u+XagYub9Ei4OOP1be9/z6wYgXvqRIRRUXJkun6PTzZsgE3b7rR+E3bKYqU5ezd2/HTvN5/X4pfeOIKuszXzLNr0m6vXr3w7NkznD59Go8fP8bjx49x6tQpRERE4AsueUoOcPy4erJfqpQs2Ovytm+Xkgxq8ucHZs9msk9EBAB+fsCHHxq2370rCzmmAF5esuruuXMy6dbPz77jeXvLnYNjx2TRLU9M9skydiX869evx+TJk1E00Up4xYoVw6RJk7BOrQY7kZWMTdbt2tUN8uTjx2WQZHS04TZ/f+nGcZsxSUREGkhh1XqMCQqSuvh37gBz5wLNmllXScffH/j8c+DiRWD+fOkko5TNrjH88fHxSK1SAD116tSIj4+359BEeP5cKg3oCwhwg1Kc164B770ns6jU/PEHULq0lhEREbm+mjWBjBmlgHxi//wD/P67my26Yr+QEKBtW3lERkrFnhUrpKpPeDiQLt3rR1CQ/FumjHxHZsni7OjJldiV8NesWRO9e/fGwoULkT17dgDA7du30bdvX9SqVcshAVLKtWiRVB7Q9/HHclJzWf/9J+VH795V396nD9Chg5YRERG5h9SpZUxL4tu73t7SRf3wocw6TaH8/aV6acOGzo6E3JFdQ3omTpyIiIgI5M2bF2+88QbeeOMN5MuXDxEREfj9998dFSOlUMaG83Trpm0cVnnxQlaMvHBBfXurVly/nIjIlIRhPVWqSK/+7duytksKTvaJ7GV3HX5FUbB582acO3cOAFC0aFHUrl3bIcE5G2d9W+nwYeDECaBBA7vvJR45Arz1lmH7W28BYWF2HTr5xMTIhLM1a9S316ol2+ydhUVE5Mni4mTweq5czo6E3ATzNfPsrsPv5eWFOnXqoE6dOo6Ih9zZjBlS88vLS0roNG4svd1vvmn1DFtTk3VdUlycDNMxluyXKQMsW8Zkn4jIHB8fJvtEDsaVdk3gFaMVFAXIk0dqJScWGipj2dXWADfi2TMpu6y/wnpQkHT6BAY6IF5HUhS5Epk6VX17vnyy6FbWrNrGRURElAIwXzPPrjH8RDonThgm+4DMLrIi2QeA1asNk31Aqg64ZLLfv7/xZD9zZlk2kck+EREROQkTfnKM1avV2xs1svpQy5apt3fpYvWhkt9vvwG//qq+LTBQhvgULKhpSERERESJMeEnx1i1yrDN1xeoW9eqw7x6Baxda9hesKCLlq3/8ENZMVefv7+8J+XLax8TERERUSJ2Jfw+Pj548OCBQfujR4/g4+Njz6HJndy/Dxw8aNheo4bVY3A2bgRevjRsb9bMRVfWzZtXlnwvVux1W6pUskhM9erOioqIyHPFxsoqVERkMbsSfmPzfaOiouDr62vPocmdrF0rY9n1NW5s9aH++Ue9vWlTqw+lnezZgR07pGaotzewYIGUJiUiIsdQFODAAeCLL+ScO2uWsyMicis2leWcMGECACnJOW3aNAQm6sWNi4vDzp07UaRIEcdESK5PbTgPYPX4/eho9UPlygWUK2dDXFrKlEnWPN+1y6Z5C0REpCI+Hvj+e2D+fODSpdft8+a5+CqMRK7FpoR/3LhxAKSHf8qUKUmG7/j6+iJv3ryYMmWKYyIk1xYZKeNw9JUsKWU6rbB9O/D0qWF706YuOpxHX3Awk30iIkfy9gbWr0+a7APAnj3AtWsyrJKIzLIp4b969SoAoEaNGli2bBkyZMjg0KDIjezYoV5D05OG88THy79WlhclIiIHaNNGhvPoW7AAGDRI+3iI3JBdGcy2bduY7Kd0DhrOExcHrFhh2B4aClSubH1YDhMbKyvofv65+jwFIiJKXi1byuq7+ubN43mZyEJ2JfzNmjXDmDFjDNp/+ukntGjRwp5DkztQFPWEP3NmoEIFqw61dy+gUvAJTZqon+c1cfOmVBqaOxf480/g66/55UJEpLXQUPUSz2fPAseOaR4OkTuyK+HfuXMnGqhUI3nvvfewc+dOew5N7uDUKeDGDcP2hg2tztJdbjjPihVAqVLA7t2v28aOBUaNclJAREQpWJs26u3z52sbB5Gbsivhf/78uWr5zdSpUyMiIsKeQ5M7cNBwHkVRX103fXrpYNdUZCTQs6csqPXkieH2IUOA/1epIiIijTRpAgQEGLYvXChjQonIJLsS/pIlS2Lx4sUG7YsWLUKxxAsRkWdy0Oq6hw/L6Bl9jRvL4TRz7hxQsSIwaZLxfQICgOLFtYuJiIjk3NukiWH7nTtS4o2ITLKpSk+CIUOGoGnTprh8+TJq1qwJANiyZQsWLlyIpUuXOiRAclF376pXTaheHQgKsupQTh/OExcHjB8PDB5sevXG/PmBJUtkgS0iItJWmzbqQ3jmzwdq1dI+HiI3YlcPf+PGjbFixQpcunQJn3/+Ofr164dbt25h8+bNaKJ2JU6eY/ly9QmsNgznUUv406YF6tWzMTZrnDsHVK0K9OtnOtn/+GPg6FEm+0REzlKnjhSF0PfPP6bP30RkX8IPAA0bNsSePXvw4sUL/Pfff9i6dSuqVavmiNjIlf39t3q7ld3yZ84AFy8atjdoAKRJY0NcloqNBcaMAUqXBvbtM75f2rTAjBnSg5QuXTIGREREJqVKBbRqZdgeEQGsXq19PERuxO6E/+nTp5g2bRoGDRqEx48fAwCOHDmC27dv2x0cuaiHD2XBLX2VKgE5clh1KKcM5zl3DnjnHWDAACAqyvh+b74JhIUBHTu6yVK/REQezli1nnnztI2DyM3YlfCfOHEChQoVwpgxY/Dzzz/j6dOnAIBly5Zh4MCBjoiPXFHGjMDOnUCfPkCuXK/bmze3+lBq1Xl8faWyZ7LYv18uTA4dMr1fr14yR6Fo0WQKhIiIrFahAlCggGH72rXA/fvax0PkJuxK+L/88kt06NABFy9ehL+/v669QYMGrMPvyby9ZfnbceOA69clMf76a6sT/jt3gOPHDdvr1Emm0TO7d8vBw8ON7/PGG3L3YsIEINFnmoiIXICXl3ovf0yMDL8kIlV2JfyHDh1C165dDdpz5MiBe/fu2XNochdeXtLjMmYMkCePVU/dtEm9/YMPHBCXvm3bZBbw8+fq2728gL59gRMngHffTYYAiIjIIT75RH2Y5Z9/siY/kRF2Jfx+fn6qC2xduHABmdVm0hMlsmGDervDq/O8eAF89BHw8qX69sKFpff/119lki4REbmu/PnVvyiuXwfWr9c+HiI3YFfC//777+P7779HTEwMAMDLyws3btzAN998g2bNmjkkQPJM8fHqPfxFigC5czv4xQICpH6+2hCdJk2k3OY77zj4RYmIKNl0767e/scf2sZB5CbsSvh/+eUXPH/+HKGhoXj16hWqVauGAgUKICgoCKNGjXJUjOSBjhwB/vvPsN3KRXotV6MGsHJl0qV7P/pILgSStf4nERE5XMOGSYtGJFi7Frh2TfNwiFydXSvtBgcHY9OmTdizZw+OHz+O58+fo2zZsqhdu7aj4iMPtXGjenuyLrZVt66UBfrwQ6BlS2DmTKnrTERE7sXHB+jSBRgyRP7fywuoX196/tUuBIhSOC9FUVsu1biQkBBcuHABmTJlQqdOnTB+/HgEBQUlV3xOFRERgeDgYISHhyMdF11yqGrVpLJnYr6+wOPHMgInWYWFAWXKyBcGERG5p3v3gLJlZRJvly4ytp9SJOZr5lk9pCc6Olo3UXf27NmI5HLWKcOLF8BffwEPHth9qIgIYO9ew/aqVTVI9gGgXDkm+0RE7i5rVuDmTeDHH5nsE5lh9XiGt99+G02aNMFbb70FRVHwxRdfII2RMdAzWBPXc6xfD3TtKrdL330XaNZMlsPNnt3qQ23bBsTGGrYn2/h9IiLyTOy8IbKI1T388+bNQ4MGDfD8//XMw8PD8eTJE9UHeZC//5Z/4+OB7dtlJdocOYBz56w+lFPG7xMRERGlUFaP4U8sX758CAsLQ8aMGR0Zk8vgmLD/i4wEMmc2XLQqb17gyhX1BVBMKFAAuHw5aVvWrLLyrpWHSiosTG7t/vYbkDOnHQciIiIid8F8zTyre/hDQkLw3//rKdaoUQO+icsckmfauFF9hdrmza3O0C9fNkz2ARnOY1eyHxsLfPYZ8M8/QLFiwMSJXHGRiIiICJy0S5ZIGM6jz4bF1Yytrmv3+P3x44Fjx+S/nz2TIUeVKwMnTth5YCIiIiL3xkm7ZNqrV8Dy5YbtOXMCFSpYfThj4/fr1LH6UK9duwYMHWrYfuAAsGIF8OabdhyciIjczqtXwLRpsuDihg2c3EspntUJ/7x58zBu3DhcvnwZXl5eCA8PZy+/J1u92vhwHm/rbhDFxABbtxq2ly0LhIbaGB8ADBgAvHxp2F6kCPDNN3YcmIiI3MqzZ8CUKcAvvwD370vb33/LYotEKZjVCX+WLFnw448/ApBJu3PnztV80u6oUaOwZs0aHDt2DL6+vnj69KnBPjdu3ED37t2xbds2BAYGon379hg9ejRScWVV6yxYoN7eurXVh9q3T87F+uyqznP9OrB0qfq2v/4C/PzsODgREbmN06elbPTjx0nbR44EWrSwupOKyJPY9em/evWqUyr0REdHo0WLFujevbvq9ri4ODRs2BDR0dHYu3cvZs+ejVmzZmGo2rAPV6IowMWLwOzZQO/eUgLTmZ48AdauNWwvUEAWr7JSsozfnzhR/X367DNZyYuIiFKGwoWBkBDD9lOngFWrtI+HyIXYlPA3aNAA4eHhuv//8ccfk/SyP3r0CMWKFbM7OGOGDx+Ovn37omTJkqrbN27ciDNnzmDevHkoXbo03nvvPYwYMQKTJk1CdHR0ssVls23bgPffl3EthQoBHToAEyYA/2vv3uNsqvc/jr+3GTOuM+7GZUYodMGRpCG5ho7OoVQqitCVk0pOI110SpQ6nS6/qI5KF0JJuVQmtxByOYOUCSnCuIQZ1zGX9ftjZbJnrWH2nj1r773m9Xw89uM0n7Wt+Vhnp7c1n/X9/vRTcPuaOVOyu1633urXkjp28/sVKkht2vjRm2SOGr31lrUeGSk9+aSfJwUAhKXISGnkSPtjzzxj3lQDSii/Av9XX32lzMzMvK+fffZZHTzjR2jZ2dlKTU0tend+WrFihZo2baqaNWvm1bp166aMjAxt2rSpwF+XmZmpjIwMr5cj0tPNuw9/LHeaZ8UKZ75/QaZOta/fcovPpzpwQFq71lrv2FHye2XXd981r11+N91kbgoGAChZ+vWTEhKs9TVrCl41AigB/Ar8+ffqKsLeXcUiLS3NK+xLyvs6LS2twF83duxYxcbG5r3i4+OLtc88iYn29W+/deb729mzx/4J2xYtzIdhfZScbH9zxe/5/dxccylOOw884OdJAQBhLSqq4MUann6au/wosULmCZakpCR5PJ6zvjZv3lysPYwcOVLp6el5r507dxbr98tTs6bUoIG1HszAP22a/R+MfjysK5mB347f8/tz50pbt1rrbdtKrVr5eVIAQNgbONDcvj2/5culb75xvh8gBPi1ZM3pAJ6/VhTDhw/XgAEDzvqeBnah2EZcXJy+++47r9reP5bnirP7Q+AP0dHRig7Wqi5t2kg//+xd++EH88HZypWd78dudR6PR7r5Zp9PZRjSggXWev365vO/fvnPf+zr3N0HgJKtTBlpxAhp+HDrsWeekdq3d74nIMj8CvyGYWjAgAF54fjkyZO65557VL58eUnymu8vrOrVq6t69er+tGORmJioMWPGaN++farxxwLvycnJiomJKdaHiYskMVH64ANrfdUqqXt3Z3vZskVavdpav+oqc8MtH23fLu3YYa136eLXs7/m7rl240b16km9evlxQgCAq9x9tzR2rPXZuK+/llaulK64Ijh9AUHi10hP//79VaNGjbxZ9379+ql27dp5X9eoUUO33357oHvNs2PHDqWkpGjHjh3KyclRSkqKUlJSdPSPDaK6du2qiy66SLfddpvWr1+vr776So899piGDBkSvDv451LQUjXBGOv56CP7up/jPHbZXJI6dfLrdAXf3f/HP8xVGgAAJVv58tJDD9kfGzPG2V6AEOAxQu2J20IYMGCAJk+ebKkvWrRIHTp0kCT9+uuvuvfee7V48WKVL19e/fv317hx43zaeCsjI0OxsbFKT09XTExMoNq3l51tju7k39W2c2fzjoRTDEO68EIp/ypLpUubD/L6se/CLbfY/x0iLc18fMEn+/ZJ8fHW5ULLl5d++02qVMnn/gAALpSRYf7k12ZzTq1bZy5CAVdwNK+FqZB5aNcX7777rgzDsLxOh31JqlevnubNm6fjx49r//79euGFF0J7l93ISOnyy631VauknBzn+khJsYZ9yVxOx4+wbxj2d/gvvtiPsC+ZW6bb7Q1wxx2EfQDAn2JipPvvtz/GXi0oYcIy8LuW3VjP0aPmLoFOKWg3Qj/HeX74wbwpn59f4zyZmdLrr1vrHk/Bf6gDAEqu++83d3jMb/Zs84YaUEIQ+ENJKKzH//jj5vcbOlQ6/RB1uXLmTsB+COj8/vTp0h+rLXm59lrpggv8OCEAwNWqVjWf77Lz+OPO9gIEEYE/lBS0aoCTO+56POZfPF59Vdq9W/ryS+nFF80ZeT/YBX6Px89V0d5/377OUpwAgII8/LA53pNfcjLr8qPEIPCHkipVzAdm8wvWBlyRkebs/j33+PXLc3KkxYut9Usv9XNrgSFDpBtuMNdYPu2SS6SOHf3qDwBQAlSpYr8mvyQ99hi776JEIPCHGruxnm3b7EdZQlxKiv3iCH4vx9mzpzRjhnktJk829ye44w4/F/MHAJQYDzxgBv/8li51diU8IEgI/KGmoPX4nRzrCZCAr79/WkyMdPvt0hdfFLzOMgAAp8XESP/8p7VesaK0a5fz/QAOI/CHGpcH/shI6corne8FAFDCDR0q1ahh/nO5clJSkrkV/IABQW0LcEIIL0xfQjVubK4nn38WJlhz/H46dcr8SWl+rVvbr5AGAECxKl9eGj3a3Gtm5Eg/N4MBwhOBP9SUKmXO8X/xhXd99WozRUdFBacvH61eLR07Zq0XeZwHAAB/3XtvsDsAgoKRnlBkN9aTmWk+BRtoWVlShw7SCy8E9MHgYpvfBwAAgE8I/KHozJV6IiLMdSyHDrVfR7io5s2TliyRRoyQ6taVevWSPv9cys4u0mntAn+ZMgVvNQAAAIDiwUhPKLr8cmnMGPNOf6tWfm96VSiTJv35z9nZ0mefma8FC/y+HX/ihP0jB23bei+hf06GIc2dK3Xp4uMvBAAAwGkE/lBUsaL06KPF/3327DHv8OdXv7455uOnb781HzfIz+e/P6xcKf3tb+ZPNnr3lm65xdxkK5KPLQAAQGEx0lOSvf++uR1ufnfcYT487KeAze9PnWr+b0aG9M47UteuUp060oYNfvcGAECBDhyQxo+3/28jEMa4VVpSGYb3OM9pHo/Uv3+RTm0X+CtWlC67zIeTZGdL06ZZ6ydPSo0a+d0bAAAWBw5IL74ovfqqucRcQoLUp0+wuwIChjv8JdW330o//WStX321+QednzIyzCU582vf3sdJnEWLpH37rPXevZnnBwAExvHj5pr8550njRv353rS//oXd/nhKgT+kurtt+3rgwYV6bRLl9r/GenzOM+UKfb1W27xuScAAGxFRUkff2zdOOaHH8w64BIE/pLo4EH7cZkqVaSePYt06oLm9zt29OEkJ09KM2da6zVr+ngiAADOIjJSeuwx+2Pc5YeLEPjDzdGjBafqwnrlFfttcPv2laKji3Rqu9aqVpWaNfPhJAsWmLNB+d10Eyv0AAACq29fqWFDa527/HARAn84yM01Q3D//lJcnNS9u/mAkT+OHDEDv53Bg/3vUdLvv9tvBtyxo4+L/syebV9nnAcAEGiRkdLjj9sf+9e/zP8GA2GOwB/qFiwwHybq0kV67z3zznxWlvTRR/6db8IE6dAha71HDx9vw1stXmxf92l+3zCkOXOs9bg4qXVrf9oCAODsuMsPlyPwh7rzzpN27rTWJ0/2/VwnTpjLjtkZNcr38+UTkPn9lBRp1y5rvUePIu0NAABAgc42y//009zlR9gjQYW6hg2lK6+01tesMe88+OK//7Vf6rJjRykx0b/+zmAX+GvVkho39uEkBY3z/O1vfvUEAECh9Otnf5f/+++luXOd7wcIIAJ/OLj9dvv6e+8V/hynTknPP29/LAB393fvljZvttY7dTL38io0u8AfHW2ONAEAUFwiI6VHH7U/9uyz5sgpEKYI/OHgppvsV895//3CLxn2/vvSb79Z661b+7FIvtWiRfZ1n069e7f5kwu7k5Qv71dfAAAUWr9+Uny8tb5ypbRkifP9AAFC4A8HsbFSr17W+u7dhVuiMzvb3EHQzqhRPt6Ct1dQGz4F/nnz7OuM8wAAnBAVJY0YYX/s2Wed7QUIIAJ/uOjf375emId3Z8yQtm611ps1k669tmh9/cEu8Nevbz5zXGgFze8HqEcAAM5p0CCpenVrPTnZ/qfQQBgg8IeLq682l6bMb+ZMc239s7HbVVcyZxUDcHd/+3bpl1+sdZ/u7p84Yf5hmt9f/mL/41UAAIpDuXLSAw/YHxs71tFWgEAh8IeLyEhzneD8Tpw49xrBH38sTZkiXXLJn7VGjaQbbghIawEZ51m40Py95MfdfQCA0+67T4qJsdZnzvR9hTwgBBD4w0lBq/W8887ZVw+IjDR3qV2/Xpo1S2rVSkpKkiIiAtJWQNbft9tsS2J+HwDgvEqVpCFD7I8995yjrQCB4DEM1pkqSEZGhmJjY5Wenq4Yu7/pB0OLFubmVPn17m2us1+p0rnPYRjmKwAbWRmGVLu2lJbmXb/wQh9ughiGlJBgXUUoLs7chIsNtwAATtu3T6pXTzp50rseEWE+F+fTQ2ooTiGZ10IMSSrcFHSX/5NPzL8MrFp17nN4PAEL0Zs3W8O+5OM4T1aWOS/Zvr33Tx3YXRcAECw1akiDB1vrOTnSf/7jeDtAUZCmwk3fvlKFCvbHfvnF3JV3/HjHtgEPyPx+VJQ0fLi0eLF5R2XKFHME6cYbA9EiAAD+efhhcyz2tGrVpGeekUaPDlpLgD8I/OGmRg3prbcKPt68ufTtt+YdfwfYBX6Px7xZ75cqVcywP2WK1K1bkXoDAKBI6tUzN+OqU8e8q//LL+b+NYUZnwVCSOS534KQc/PN5mZct98uHTjgfaxiRenDD81lxYpZbq79Drt/+YtUtWqxf3sAAIrfCy+Y/22Nigp2J4DfuMMfrq65xlx1p0MH7/rixebKNsePF3sL69dLhw5Z6z6N8wAAEMqqViXsI+wR+MNZ7drS11+bs4RnPty6cKHUq5d1ZYEAC8j8PgAAAIoVgT/cRURITz5ppu/atf+sJycX+xy/XeCPiJDatSvWbwsAAAAfMMPvFu3bSz/+KL33nrnSTbduUtu2xfbtsrKkb76x1i+/3Bx1BAAAQGgg8LtJTIw0dKgj32rFCunoUWvdp911T51iLhIAEP6ys72X7wRCDCM98MtXX9nXr77ah5N06mQuIzp8uDRvnv3fIAAACFW//CINGWJufJmdHexugAIR+OGX+fOttfLlpTZtCnmCjAxp5Uppwwbp3/82d9WtXFkaMSKgfQIAEHA//ij17y+df770+uvS999LM2YEuyugQAR++OzAAWntWmu9Y0cfJnS++cbcnvxM2dlSzZpF7g8AgGKzfLl08cXmM3Nn/nfs2Wcd2+Ue8BWBHz77+mvJMKx1nzbGXbDAvt6li189AQDgiCuukC64wFr//ntp7lzn+wEKgcAPnxU0v9+1qw8n+fpra61aNalZM796AgDAERERUlKS/bExY+zviAFBRuCHTwzDfn7/vPPsb3jYSksz74Tk16mT9wZiAACEor59pfh4a33VKnMfHCDEkK7gk02bpN27rfVu3SSPp5AnKWiLXsZ5AADhICqq4EUmHnuMu/wIOQR++CQg4zwFze937uxzPwAABMWgQfYLTaxeLc2e7Xw/wFkQ+OETu3GeiAhzGqdQDMN+fr9+falBgyL1BgCAY8qVkx591P7YE0+wYg9CCoEfhXbihLmaZn6tW0uVKhXyJNu2STt2WOvc3QcAhJu77pLq1rXW16+XPvnE+X6AAhD4UWhLl0onT1rrPi3HaXd3X2J+HwAQfsqUMWf27TzxhHW/GSBICPwotGKd3y/0TBAAACHkjjvMsdT8Nm+Wpkxxvh/ABoEfhWYX+CtXllq1KuQJcnPtV+hp3lyqXr1IvQEAEBRRUdKTT9ofe+opKSvL2X4AGwR+FMquXeaSnPl16WI+tFsoKSnSwYP2JwEAIFz17Ss1amStb9smTZ7sfD9APgR+FIrd6jxSAHbXlXhgFwAQ3iIjzbv5dv71Lykz09l+gHwI/CiUYgv8pUtL7dr51RMAACHjppukSy6x1nfulN56y/l+gDMQ+HFOOTn2O4U3aSIlJBTyJCdPSsuWWetXXCFVqFCk/gAACLpSpaSnn7Y/9q9/SenpzvYDnIHAj3Nat076/Xdr3aflOFesMBfyz4/5fQCAW/TsKbVsaa3v3y8984zz/QB/IPDjnAIyzlPQcpzM7wMA3MLjsQ/2rVtLvXs73w/wBwI/zmnOHGstKkpq396Hk/zvf9ZahQrS5Zf73RcAACGne3fzJUl16kjvvy99+605wgoESWSwG0Bo27VLWrnSWm/XTipf3ocTzZ4tpaaa2/UuXWrO8zdpYj60CwCAm7z4orlJzSOP+PgfS6B4EPhxVrNm2dd79fLxRKVKSRdeaL7uusus2c30AwAQ7i66yHxQFwgRjPTgrGbOtK/7HPjtlC0bgJMAAADgbAj8KNCBA9KSJdZ669ZS3brO9wMAAADfEfhRoNmzzTX482OhAQAAgPBB4EeBChrnue46Z/sAAMB1srPtN7kBigGBH7aOHLFff79ZM+n8853vBwAA1/j6a6lFC/NH5rm5we4GJQCBH7bmzZNOnbLWr7/e+V4AAHCFLVvM3Xivvlr6/nvzQbn//jfYXaEEIPDD1qef2td9CvyGYf5BtmEDdzAAACXbnDnSxRdLn3/uXR8xwtz0BihGBH5YnDwpzZ1rrZ9/vnTJJT6c6OefpTvvlJo3l6pUkXr0kMaNM+9wAABQknToINWuba1nZEhDhpg3yYBiQuCHxddfS0ePWuvXXy95PD6caOnSP/85Pd2cExo5Ulq3rsg9AgAQVipUkCZOtD/22WfSxx872w9KFAI/LApancfn+f0zA/+Z2rXz8UQAALhA9+5Sv372x4YOlQ4edLYflBgEfnjJzjZvNORXp47UqpWPJ7ML/A0a2P9IEwCAkuCll6Rq1az1ffuk4cOd7wclAoEfXr75xv4Gw3XXSaV8+bTs3Ws/q8/dfQBASVatmvTKK/bH3n1XSk52tB2UDAR+eAnYOM+yZfb1K6/08UQAALjMzTebC1nYufNO6fBhR9uB+xH4kSc31345zqpV/bgxz/w+AAD2PB5pwgSpYkXrsV9/lQYMYNUeBBSBH3lWrpR277bWe/WSIiN9PJld4K9RQ2rUyJ/WAABwl/h4c6lqO599Jo0f72w/cDUCP/K8+aZ93edxnowMKSXFWr/ySh/X9QQAwMXuuUe66ir7YyNHmjvxAgFA4Ick6cAB6aOPrPXYWKlzZx9PtnKl/c66zO8DAPCnUqWkKVPMn4Dnl5sr9ekj7dnjfF9wnbAM/L/88osGDRqk+vXrq2zZsmrYsKGefPJJnTp1yut9GzZsULt27VSmTBnFx8fr+eefD1LHoe/tt6XMTGt9wAApOtrHkzG/DwBA4dSpI02dar8U3t695gO+2dnO9wVXCcvAv3nzZuXm5uqNN97Qpk2b9NJLL2nixIl69NFH896TkZGhrl27ql69elq7dq3Gjx+v0aNH682C5lZKsJwc89khO/fd58cJ7QJ/hQrSX/7ix8kAAHC5Tp2kp5+2P/bNN9IZ+Qbwh8cw3PEY+Pjx4zVhwgT9/PPPkqQJEyZo1KhRSktLU1RUlCQpKSlJs2bN0ubNmwt1zoyMDMXGxio9PV0xMTHF1nuwzZkj/e1v1vrVV0vz5/t4ssxMqVIl6eTJAJwMAIASIjdX6tnT/I+ynVmzzOOwKCl5rSjC8g6/nfT0dFWpUiXv6xUrVuiqq67KC/uS1K1bN6WmpurQoUO258jMzFRGRobXqyT4v/+zrw8Z4sfJ1q2zhn2JcR4AAM6mVCnpvfek886zHktIkFq2dLwluIcrAv/WrVv16quv6u67786rpaWlqWbNml7vO/11Wlqa7XnGjh2r2NjYvFd8fHzxNR0itm6VvvzSWk9IkK691o8TLl9uX+eBXQAAzq5yZenjj6UzblaqRg1z9926dYPXF8JeSAX+pKQkeTyes77yj+Ps2rVL3bt314033qg777yzSN9/5MiRSk9Pz3vt3LmzSOcLBwXN7t9zjxQR4ccJv/3WWouIkC6/3I+TAQBQwrRsKb36qvnPMTHSV1+xhw2KzNftlIrV8OHDNWDAgLO+p0GDBnn/vHv3bnXs2FFt2rSxPIwbFxenvXv3etVOfx0XF2d77ujoaEX7vCRN+Dp+3FydJ7+oKGnwYD9OaBj2gb9FC6l8eT9OCABACXTnneaOu927s+AFAiKkAn/16tVVvXr1Qr13165d6tixo1q2bKl33nlHpfItZ5WYmKhRo0YpKytLpUuXliQlJyercePGqly5csB7D0dTp0qHD1vrN90kFfL/Bm/bt5tLiOXXpo0fJwMAoITyeKQxYwr33lOnvEeAABshNdJTWLt27VKHDh2UkJCgF154Qfv371daWprXbP6tt96qqKgoDRo0SJs2bdK0adP08ssv66GHHgpi56HDMAL8sK5kf3dfIvADAFBc/vEPqW1bacYM1utHgULqDn9hJScna+vWrdq6davq5nuI5fQqo7GxsZo/f76GDBmili1bqlq1anriiSd01113BaPlkLNypfS//1nrl14qtW7t50nLljV/8bp1UlbWn3UCPwAAgXfggLmyz8mT5k23hARzDCgry6xlZpqvU6fMTXfOfOXmmg8J3367ubmXxxPs3w2KkWvW4S8Obl7XtV8/6cMPrfVJk6SBA4t48pMnpbVrzT98fvzR/kEBAABQNM8+K40aVfTzXHON9NZb5q6/YcjNeS1QCPxn4dYP0MaN5nO0OTne9cqVpd9+k8qVC05fAACgkE6dMtfs37MnMOeLjZX+8x+pf/+wu9vv1rwWSGE5ww//GYY0dKg17EvmnX3CPgAAYWDGjMCFfUlKT5fuuENatChw50TIIPCXMFOmSN98Y62XLl2Eh3UBAICzevaUXntNOv/8wJ3zuuukjh0Ddz6EDEZ6zsJtPyJKT5eaNJHsNhpOSpLGjnW+JwAAUAS5udLcudLrr5vPzmVmStHRf77KlDGX7YyI8H79/LP50O9pVatKmzZJNWsG7/fiJ7flteIQlqv0wD+jR9uH/fh46bHHHG8HAAAUValS0t/+Zr58cfCgNGyY9MEH5tevvRaWYR+FQ+AvITZu/HOn7vxeeomNcAEAKFGqVJHef1+68Ubpyy+lPn2C3RGKEYG/BDAMcz7f7kHdrl2l6693vicAABAC/v538wVXI/CXAB9+KC1daq2XLm3e9S/y6ltjxkj795sbbLVpI+XbDA0AAADBwyo9LpeeLo0YYX/s4YelRo0C8E3ee096+WXzx4Hx8eZOfw8/HIATAwAAoKgI/C6WmSndckvBD+oGYnM+HTgg/fSTd23nTu8n/wEAQPhKTZU+/TTYXaAICPwulZUl3Xyz9MUX9sf/858APai7cqV9vU2bAJwcAAAETVqadO+90sUXSwMGcDMvjBH4XSgnR7r9dmnWLPvjXbuae2sExLff2tcJ/AAAhKejR6WnnjI39Zo40QwWGRnSM88EuzP4icDvMrm50qBB0kcf2R+vWtX8d7fID+qeZhf4Y2Kkiy4K0DcAAACO2rjR3Lzn2DHv+uuvmxt2IewQ+F3k9PKbkyfbH4+NlebPl+rXD9A3zMqSvvvOWk9MNDcCAQAA4Scx0X4UICtLevRR5/tBkZHKQkR2dtF+/S+/SH37mnfv7VSoYO6rcemlRfs+Xtavl06csNYZ5wEAILyNHStFRFjr06dLO3Y43w+KhMAfAnJypHr1pM6dpXHjpHXrzNGcwvj1V+muu6QLLpCmTrV/T9my0ty50hVXBK5nSczvAwDgVo0bmwEjP8Mo+CFBhCyPYRhGsJsIVRkZGYqNjVV6erpiYmKK7fusWSO1auVdq1ZN6tLFfMD26qvNvawyM6VDh8zX779LH3wgvf22+RO2gkRHS3PmmOcKuJtvlqZN866VKmU2WIzXCwAAOGD3bjOA5I+K7dtLixcHpSU7TuW1cMZOuyEgOdlaO3DAfPD29MO3ZcvaT8+cTenS0iefFFPYl+zv8DdtStgHAMANatc2f2q/fLl3felSaf9+qXr14PQFnzHSEwLmzz/3e3wN++XKmWN2PXr419M57dhhbrCVH+M8AAC4h93Du7m50uzZzvcCvxH4g+zoUetfnIsiMlIaPFjatEnq1Stw57VYutS+TuAHAMA9Ctq4Z+ZMZ/tAkRD4gyw729zbokMHcwTHXxER0sCB5u7Xb70lnXdeoDosQEGBv127Yv7GAADAMQ0aSM2bW+vJydKRI873A78Q+IOsUiVp5Ehp0SLp4EFzNZ1hw6QLLyzcr4+IMHe7Tk2VJk0y/710hF3gT0gwlxsCAADuYXeX/9Qp6YsvnO8FfuGh3RBSoYL017+aL0n67TfzL9DffWf+e1W5slSlivm/lSubu+a2bGnWHHXggPTDD9Y6d/cBAHCf664zd97Nb+ZM6aabHG8HviPwh7C6daU77jBfIWXZMvs6gR8AAPdp2lRq2FDats27Pm+euWZ4dHRw+kKhMdID3zG/DwBAyeHx2I/1HDkiLVjgfD/wGYEfvrML/FWrFv7BAwAAEF5YrSesEfjhm6NHpXXrrPV27cw7AAAAwH2uuEKKi7PWP/9cyslxvh/4hMAP35QqJU2eLN11l/cdfcZ5AABwr1Kl7Df42b8/sBsKoVgQ+OGbcuWkvn2lN94wV+rZt0/69FPp+uuD3RkAAChOBY31fPqps33AZ6zSg6KpXr2Yt/QFAAAhoUMHcwOhw4fNr1u3Nv8S0Lt3EJtCYRD4AQAAcG5RUdKjj0rly0s9e0p16gS7IxQSgR8AAACFM2JEsDuAH5jhBwAAAFyMwA8AAAC4GIEfAAAAcDECPwrn+HHp1KlgdwEAAAAfEfhROO+8Yy7F1amT9OST0tdfm7vuAgAAIKSxSg8KZ+lS6cQJadEi8yWZy3P9/rtUoUJwewMAAMF38qRUurQUERHsTpAPd/hxboYhffONtd6kCWEfAICSav9+6bPPpH/+U2rbVoqNldatC3ZXsMEdfpzbzz9Le/ZY6+3aOd8LAAAIvqVLpauustaXL5datXK+H5wVd/hxbkuX2tcJ/AAAlEx/+YtUyiZGLl/ueCs4NwI/zs1unEci8AMAUFJVrCg1a2atL19ujgIjpBD4cXaGIX31lbXesKFUu7bz/QAAgNDQtq21tmeP9MsvjreCsyPw4+xSUqTdu631Tp0cbwUAAIQQu8AvSd9+62wfOCcCP85uzhz7+rXXOtsHAAAILQUFfub4Qw6BH2c3d661Fh0tde7sfC8AACB0JCRIdeta6wT+kEPgR8H27ZO++85a79hRKl/e+X4AAEBosbvLv3GjlJ7ufC8oEIEfBfviC/sn7Xv0cL4XAAAQeuwCv2FIK1c63wsKROBHwQqa3yfwAwAAiTn+MEHgh71Tp6T58631iy6S6td3vh8AABB6mjWzH/Ml8IcUAj/sLVsmZWRY66zOAwAATouMlK64wlpftUrKzna+H9gi8MOe3eo8EuM8AADAm91Yz7Fj0vr1zvcCWwR+2LOb369USWrTxvFWAABACCsoGzDWEzII/LDaskX66SdrvXt380d3AAAAp11xheTxWOsE/pBB4IdVQeM8zO8DAID8YmOlpk2t9eXL7Zf3huMI/LAqW1Zq2NC7VqqUeYcfAAAgP7s5/l27pB07nO8FFgR+WN19tznWs3mz9OKL5s667dpJVasGuzMAABCKWI8/pDGQDXsej9S4sfl66CEpJyfYHQEAgFB1ZuCPiZESE83apZcGryfkIfCjcCIigt0BAAAIVfXqSZMmSZddJl18MbkhxBD4AQAAUDQejzRwYLC7QAGY4QcAAABcjMAPAAAAuBiBHwAAAHAxAj9M8+dL+/cHuwsAAAAEGIEf0uHDUu/e5hP2Q4ZI27YFuyMAAAAECIEf0sSJ0tGj0okT0uuvS40aSTfdJKWmBrszAAAAFBGBv6TLzJReftm7lpsrzZghHTsWnJ4AAAAQMAT+ku6DD6S0NGu9Sxd2xwMAAHABAn9JlpsrvfCC/bERI5ztBQAAAMWCwF+SzZkjbd5srTdvLl19tfP9AAAAIOAI/CXZ+PH29X/+09wiGwAAAGGPwF9SrVghLVtmrSckSDfe6Hw/AAAAKBYE/pLIMKQxY+yPPfSQVLq0s/0AAACg2BD4S6KXX5bmzrXWK1eWBg1yvh8AAAAUGwJ/SbNokfTww/bH7rtPqlDB2X4AAABQrAj8Jcmvv5o76ObkWI9VriwNG+Z8TwAAAChWBP6S4sQJ6frrpQMHrMc8HnMDrurVne8LAAAAxYrAXxIYhnTXXdK6dfbHn35a+utfne0JAAAAjiDwlwSvvGLewbdz3XXSyJHO9gMAAADHhG3g//vf/66EhASVKVNGtWrV0m233abdu3d7vWfDhg1q166dypQpo/j4eD3//PNB6jaIkpOl4cPtj114oTR5slQqbD8GAAAAOIewTXodO3bU9OnTlZqaqk8++UTbtm3TDTfckHc8IyNDXbt2Vb169bR27VqNHz9eo0eP1ptvvhnEroNgzx5zpCe/2Fhp1iypYkXHWwIAAIBzPIZhlwbDz+eff65evXopMzNTpUuX1oQJEzRq1CilpaUpKipKkpSUlKRZs2Zp8+bNhTpnRkaGYmNjlZ6erpiYmOJsv3jNmiXdfLOUmWl+7fFIs2dLPXoEtS0AAICick1eK0Zhe4f/TAcPHtSHH36oNm3aqPQfu8SuWLFCV111VV7Yl6Ru3bopNTVVhw4dsj1PZmamMjIyvF6u0KuX9OWXf97Nf+opwj4AAEAJEdaB/5FHHlH58uVVtWpV7dixQ5999lnesbS0NNWsWdPr/ae/TktLsz3f2LFjFRsbm/eKj48vvuad1qGDtGSJ9OCD0qhRwe4GAAAADgmpwJ+UlCSPx3PW15njOCNGjND//vc/zZ8/XxEREbr99ttVlAmlkSNHKj09Pe+1c+fOQPy2QkeLFtK//81DugAAACVIZLAbONPw4cM1YMCAs76nQYMGef9crVo1VatWTY0aNdKFF16o+Ph4rVy5UomJiYqLi9PevXu9fu3pr+Pi4mzPHR0drejo6KL9JgAAAIAQElKBv3r16qru526vubm5ksw5fElKTEzUqFGjlJWVlTfXn5ycrMaNG6ty5cqBaRgAAAAIcWE527Fq1Sq99tprSklJ0a+//qqFCxfqlltuUcOGDZWYmChJuvXWWxUVFaVBgwZp06ZNmjZtml5++WU99NBDQe6+GBw6JL3+upSVFexOAAAAEGLCMvCXK1dOM2fOVOfOndW4cWMNGjRIzZo105IlS/JGcmJjYzV//nxt375dLVu21PDhw/XEE0/orrvuCnL3xeCpp6QhQ6Tmzc3VeAAAAIA/uGYd/uIQFuu6bt4sNW0qZWf/WfvrX6UXX5SaNAleXwAAAA4Ii7wWZCE1ww8/DB/uHfYlad48KSpK+vTT4PQEAACAkBGWIz34w5dfmuE+v9Klpeeec74fAAAAhBwCf7jKyjI30bLzj39IjRo52w8AAABCEoE/XE2YYM7v51etmvT44873AwAAgJBE4A9Hv/8ujR5tf+zpp6VKlZzsBgAAACGMwB+ORo82197Pr2lTafBgx9sBAABA6CLwh5u9e6WJE+2PvfSSFMnCSwAAAPgTgT/cTJ9uXYZTknr2lDp3dr4fAAAAhDQCf7iZMsW+/vzzzvYBAACAsEDgDyfbtkkrV1rr7dqxDCcAAABsEfjDydSp9vVbb3W2DwAAAIQNAn+4MAzpww+t9chI6YYbnO8HAAAAYYHAHy7Wr7ffaKtbN3OzLQAAAMAGgT9cFPSwLuM8AAAAOAsCfzjIzbWf3y9XTvr7353vBwAAAGGDwB8Oli2TfvvNWu/ZU6pQwfl+AAAAEDYI/OHg+HHpkkusdcZ5AAAAcA4E/nDQvbu0caO0YYOUlCTVqydVqSJ17RrszgAAABDiPIZhGMFuIlRlZGQoNjZW6enpiomJCXY7f8rNlX75RWrQINidAAAABFXI5rUQwh3+cFSqFGEfAAAAhULgBwAAAFyMwA8AAAC4GIEfAAAAcDECPwAAAOBiBH4AAADAxSKD3QAK8Mor0sqVUseOUqdO5qo8Hk+wuwIAAECYIfCHqk8+kb75Rpo61fw6IUG69lrp//4vuH0BAAAgrDDSE4qOH5dWrPCu7dghbdkSnH4AAAAQtgj8oWj5cikry1rv1Mn5XgAAABDWCPyhaNEi+3rHjs72AQAAgLBH4A9FCxdaaxUrSi1bOt8LAAAAwhqBP9RkZEhr1ljr7dtLkTxjDQAAAN8Q+EPN0qVSTo61zvw+AAAA/EDgDzV24zwS8/sAAADwC4E/1NgF/ipVpGbNnO8FAAAAYY/AH0p+/11av95a79hRKsX/VQAAAPAdKTKULFkiGYa1zvw+AAAA/ETgDyXM7wMAACDACPyhxC7wx8VJTZo43wsAAABcgcAfKtLSpB9/tNY7dZI8Huf7AQAAgCsQ+EPFokX2deb3AQAAUAQE/lDB/D4AAACKAYE/VNjd4a9XT6pf3/leAAAA4BoE/lDw66/Stm3WOvP7AAAAKCICfyhgfh8AAADFJDLYDUBS795SjRrmHP/ChVJKirkBF/P7AAAAKCICfyioWFH661/NlyQdPCitWiXVqRPcvgAAABD2GOkJRVWqSNdcE+wuAAAA4AIEfgAAAMDFCPwAAACAixH4AQAAABcj8AMAAAAuRuAHAAAAXIzADwAAALgYgR8AAABwMQI/AAAA4GIEfgAAAMDFCPwAAACAixH4AQAAABcj8AMAAAAuRuAHAAAAXIzADwAAALgYgR8AAABwMQI/AAAA4GIEfgAAAMDFCPwAAACAixH4AQAAABcj8AMAAAAuRuAHAAAAXIzADwAAALgYgR8AAABwMQI/AAAA4GIEfgAAAMDFIoPdQCgzDEOSlJGREeROAAAAYOd0Tjud22BF4D+LI0eOSJLi4+OD3AkAAADO5siRI4qNjQ12GyHJY/DXoQLl5uZq9+7dqlixojweT7DbKbSMjAzFx8dr586diomJCXY7rsa1dg7X2jlca+dwrZ3DtXaO09faMAwdOXJEtWvXVqlSTKvb4Q7/WZQqVUp169YNdht+i4mJ4Q81h3CtncO1dg7X2jlca+dwrZ3j5LXmzv7Z8dcgAAAAwMUI/AAAAICLEfhdKDo6Wk8++aSio6OD3Yrrca2dw7V2DtfaOVxr53CtncO1Dj08tAsAAAC4GHf4AQAAABcj8AMAAAAuRuAHAAAAXIzADwAAALgYgT+MjR07Vq1atVLFihVVo0YN9erVS6mpqV7vOXnypIYMGaKqVauqQoUK6t27t/bu3RukjsPXhAkT1KxZs7xNRBITE/XFF1/kHec6F49x48bJ4/HogQceyKtxrQNj9OjR8ng8Xq8mTZrkHec6B9auXbvUr18/Va1aVWXLllXTpk21Zs2avOOGYeiJJ55QrVq1VLZsWXXp0kVbtmwJYsfh6bzzzrN8rj0ej4YMGSKJz3Ug5eTk6PHHH1f9+vVVtmxZNWzYUE8//bTOXAuGz3XoIPCHsSVLlmjIkCFauXKlkpOTlZWVpa5du+rYsWN573nwwQc1e/ZszZgxQ0uWLNHu3bt1/fXXB7Hr8FS3bl2NGzdOa9eu1Zo1a9SpUyf17NlTmzZtksR1Lg6rV6/WG2+8oWbNmnnVudaBc/HFF2vPnj15r2XLluUd4zoHzqFDh9S2bVuVLl1aX3zxhX744Qe9+OKLqly5ct57nn/+eb3yyiuaOHGiVq1apfLly6tbt246efJkEDsPP6tXr/b6TCcnJ0uSbrzxRkl8rgPpueee04QJE/Taa6/pxx9/1HPPPafnn39er776at57+FyHEAOusW/fPkOSsWTJEsMwDOPw4cNG6dKljRkzZuS958cffzQkGStWrAhWm65RuXJl47///S/XuRgcOXLEuOCCC4zk5GSjffv2xrBhwwzD4DMdSE8++aTRvHlz22Nc58B65JFHjCuvvLLA47m5uUZcXJwxfvz4vNrhw4eN6OhoY+rUqU606FrDhg0zGjZsaOTm5vK5DrAePXoYAwcO9Kpdf/31Rt++fQ3D4HMdarjD7yLp6emSpCpVqkiS1q5dq6ysLHXp0iXvPU2aNFFCQoJWrFgRlB7dICcnRx999JGOHTumxMRErnMxGDJkiHr06OF1TSU+04G2ZcsW1a5dWw0aNFDfvn21Y8cOSVznQPv888912WWX6cYbb1SNGjXUokULvfXWW3nHt2/frrS0NK/rHRsbq9atW3O9i+DUqVP64IMPNHDgQHk8Hj7XAdamTRstWLBAP/30kyRp/fr1WrZsma655hpJfK5DTWSwG0Bg5Obm6oEHHlDbtm11ySWXSJLS0tIUFRWlSpUqeb23Zs2aSktLC0KX4W3jxo1KTEzUyZMnVaFCBX366ae66KKLlJKSwnUOoI8++kjr1q3T6tWrLcf4TAdO69at9e6776px48bas2ePnnrqKbVr107ff/891znAfv75Z02YMEEPPfSQHn30Ua1evVr333+/oqKi1L9//7xrWrNmTa9fx/UumlmzZunw4cMaMGCAJP78CLSkpCRlZGSoSZMmioiIUE5OjsaMGaO+fftKEp/rEEPgd4khQ4bo+++/95rBRWA1btxYKSkpSk9P18cff6z+/ftryZIlwW7LVXbu3Klhw4YpOTlZZcqUCXY7rnb6LpwkNWvWTK1bt1a9evU0ffp0lS1bNoiduU9ubq4uu+wyPfvss5KkFi1a6Pvvv9fEiRPVv3//IHfnXpMmTdI111yj2rVrB7sVV5o+fbo+/PBDTZkyRRdffLFSUlL0wAMPqHbt2nyuQxAjPS4wdOhQzZkzR4sWLVLdunXz6nFxcTp16pQOHz7s9f69e/cqLi7O4S7DX1RUlM4//3y1bNlSY8eOVfPmzfXyyy9znQNo7dq12rdvny699FJFRkYqMjJSS5Ys0SuvvKLIyEjVrFmTa11MKlWqpEaNGmnr1q18pgOsVq1auuiii7xqF154Yd4I1elrmn+1GK63/3799Vd9/fXXGjx4cF6Nz3VgjRgxQklJSbr55pvVtGlT3XbbbXrwwQc1duxYSXyuQw2BP4wZhqGhQ4fq008/1cKFC1W/fn2v4y1btlTp0qW1YMGCvFpqaqp27NihxMREp9t1ndzcXGVmZnKdA6hz587auHGjUlJS8l6XXXaZ+vbtm/fPXOvicfToUW3btk21atXiMx1gbdu2tSyZ/NNPP6levXqSpPr16ysuLs7remdkZGjVqlVcbz+98847qlGjhnr06JFX43MdWMePH1epUt4xMiIiQrm5uZL4XIecYD81DP/de++9RmxsrLF48WJjz549ea/jx4/nveeee+4xEhISjIULFxpr1qwxEhMTjcTExCB2HZ6SkpKMJUuWGNu3bzc2bNhgJCUlGR6Px5g/f75hGFzn4nTmKj2GwbUOlOHDhxuLFy82tm/fbixfvtzo0qWLUa1aNWPfvn2GYXCdA+m7774zIiMjjTFjxhhbtmwxPvzwQ6NcuXLGBx98kPeecePGGZUqVTI+++wzY8OGDUbPnj2N+vXrGydOnAhi5+EpJyfHSEhIMB555BHLMT7XgdO/f3+jTp06xpw5c4zt27cbM2fONKpVq2b885//zHsPn+vQQeAPY5JsX++8807ee06cOGHcd999RuXKlY1y5coZ1113nbFnz57gNR2mBg4caNSrV8+IiooyqlevbnTu3Dkv7BsG17k45Q/8XOvA6NOnj1GrVi0jKirKqFOnjtGnTx9j69atece5zoE1e/Zs45JLLjGio6ONJk2aGG+++abX8dzcXOPxxx83atasaURHRxudO3c2UlNTg9RtePvqq68MSbbXj8914GRkZBjDhg0zEhISjDJlyhgNGjQwRo0aZWRmZua9h8916PAYxhlbogEAAABwFWb4AQAAABcj8AMAAAAuRuAHAAAAXIzADwAAALgYgR8AAABwMQI/AAAA4GIEfgAAAMDFCPwAAACAixH4AQAAABcj8AOAS6xYsUIRERHq0aNHsFsBAIQQj2EYRrCbAAAU3eDBg1WhQgVNmjRJqampql27drBbAgCEAO7wA4ALHD16VNOmTdO9996rHj166N133/U6/vnnn+uCCy5QmTJl1LFjR02ePFkej0eHDx/Oe8+yZcvUrl07lS1bVvHx8br//vt17NgxZ38jAICAI/ADgAtMnz5dTZo0UePGjdWvXz+9/fbbOv0D3O3bt+uGG25Qr169tH79et19990aNWqU16/ftm2bunfvrt69e2vDhg2aNm2ali1bpqFDhwbjtwMACCBGegDABdq2baubbrpJw4YNU3Z2tmrVqqUZM2aoQ4cOSkpK0ty5c7Vx48a89z/22GMaM2aMDh06pEqVKmnw4MGKiIjQG2+8kfeeZcuWqX379jp27JjKlCkTjN8WACAAuMMPAGEuNTVV3333nW655RZJUmRkpPr06aNJkyblHW/VqpXXr7n88su9vl6/fr3effddVahQIe/VrVs35ebmavv27c78RgAAxSIy2A0AAIpm0qRJys7O9npI1zAMRUdH67XXXivUOY4ePaq7775b999/v+VYQkJCwHoFADiPwA8AYSw7O1vvvfeeXnzxRXXt2tXrWK9evTR16lQ1btxY8+bN8zq2evVqr68vvfRS/fDDDzr//POLvWcAgLOY4QeAMDZr1iz16dNH+/btU2xsrNexRx55RAsXLtT06dPVuHFjPfjggxo0aJBSUlI0fPhw/fbbbzp8+LBiY2O1YcMGXXHFFRo4cKAGDx6s8uXL64cfflBycnKhf0oAAAhNzPADQBibNGmSunTpYgn7ktS7d2+tWbNGR44c0ccff6yZM2eqWbNmmjBhQt4qPdHR0ZKkZs2aacmSJfrpp5/Url07tWjRQk888QRr+QOAC3CHHwBKoDFjxmjixInauXNnsFsBABQzZvgBoAR4/fXX1apVK1WtWlXLly/X+PHjWWMfAEoIAj8AlABbtmzRM888o4MHDyohIUHDhw/XyJEjg90WAMABjPQAAAAALsZDuwAAAICLEfgBAAAAFyPwAwAAAC5G4AcAAABcjMAPAAAAuBiBHwAAAHAxAj8AAADgYgR+AAAAwMX+H8yDWKVnhkgGAAAAAElFTkSuQmCC", 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", 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" ] @@ -2331,10 +2371,10 @@ "id": "cdb535b5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.912957Z", - "iopub.status.busy": "2024-06-04T23:19:37.912683Z", - "iopub.status.idle": "2024-06-04T23:19:37.946458Z", - "shell.execute_reply": "2024-06-04T23:19:37.945953Z" + "iopub.execute_input": "2025-04-03T19:33:54.037314Z", + "iopub.status.busy": "2025-04-03T19:33:54.036928Z", + "iopub.status.idle": "2025-04-03T19:33:54.091522Z", + "shell.execute_reply": "2025-04-03T19:33:54.087570Z" }, "lines_to_next_cell": 0 }, @@ -2364,10 +2404,10 @@ "id": "2acadc27", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:37.950913Z", - "iopub.status.busy": "2024-06-04T23:19:37.950529Z", - "iopub.status.idle": "2024-06-04T23:19:38.082439Z", - "shell.execute_reply": "2024-06-04T23:19:38.081725Z" + "iopub.execute_input": "2025-04-03T19:33:54.097247Z", + "iopub.status.busy": "2025-04-03T19:33:54.096681Z", + "iopub.status.idle": "2025-04-03T19:33:54.164314Z", + "shell.execute_reply": "2025-04-03T19:33:54.163688Z" }, "lines_to_next_cell": 0 }, @@ -2384,7 +2424,7 @@ }, { "data": { - "image/png": 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4+GvVqlWnHCe5CDGU4yQX7fC1fzoCn/P7778/6DG5UqVKab4nNb6mqlataruNnyVcDOb0ug4FjyfE5+2PP/445dhHXFjF9w4XSvH5nTdvXso+S5YsSenQFsqiNi5C5iKwwM+BwKoXqf8+XIzFY4T/NeSEC0Z5cxLpY6eozmvS4wGBQdGTTz5pG0gsWLDA6nrDg5vdSk+/9FbuOx38ookrol977TXrAyxY9zCuSmdw6xRIZQQD5QMHDlj/ZrWDUKs9pMZGEn5OK3LtOP2+XNka7CDLAzk/jHjQ5vNnN5avvvoq5JXhwZ73888/P+j3ctU3pW4fGjiuUJ7bN99803ab//fh3yl1YJPe78RqD6kx2ObJSLi/D082+CFKrG4QbpMQ/+/B1eDhrNjn+zmc17x/jKyUEazMFVeh80SVQYL/e6KBlRzuuusuDB06FJ9++ql1nAo8kRk5cmRKgN6mTZtTvpcBydKlS61/v/POO9btdF7PPBHiOBhYBWt368Zx0v+c873OKjHBsIIFq3pwUoIVEfg9kXqfhhu8Ek/4/T/Pf/LvD0r5df369db9XOUfuA8bXvC94/Scs2LIZ599Zv2ewVbzp/77BL5+/SefTlipwunkJNLHTlGprKTBEh/80OeNb0g2Jdi1a5d1tszyMXYfYiz9wQMCPxiCBa4UrJQQsdSPm1gWiB+ynJEJ5SCQ3vjDFfh8pRcg8MPeybZt2zL08/2Bs90HDQ/0oYwn9d88I2MJ9rw6zZj6+QNB/8xHvD+3of4+qUt48ffxf6CGc4ISrd/Dif95DyXgZUmiwO+JFv8sLwMnlhkK5J+NZYDG2btAHFdGyoalfs74d+MYOHvLADa9AM6N46T/Oed7Pb2TMv/fib+HU6m9jL5PQ8Ux8KoBBZZD9M+8BgavTvvwhNuu4QWvQDEYZmkqzoanV4Yq9d8n8DlJb8Y02PZIHztFM69J3WErmFmzZlmpBv5LmZyZZSDLGSfW0/QfFPv06YN+/fql+3jpBUyRxEtP7dq1sz6c+Ht36dIF1157rTUbxEty/lmjDz74IOWyWjRr651Ou93ADwReIgu1sDtrpkZrLLzE7lQv022R+H3OPPPMlFqNoV6tiCf+34Mz6h9//HHI3+e/lB2ueGofzRNUBi4///yzFazee++91v0//vhjSmpJ6lnX1O8rBp8dO3YM6eelnpnkMYQnysRZzk6dOlnBMp9bBn3+4x7HxZng9I4zkTxOxtPfKT0MTHnlwB+MslYyr17xd/DPzPq/ss4xA3Smr/hr1QbO3vpxJplpQzt27LCOm4899pjVAp21rXmS4E8V4QTOOeecE9XPgXg8dnqdglexxSLvxDc5izw7nVVGe2YlI1jom4ErPwh4MHS67BXNsQfOoDD3Lphg21ng3o9/A6egNFQ8kPNAGuxD0j8e/+XAwLEw55MfCuGcCMX7c8vZMub+uXmCFYjPM2evOCPLfN5w+X8PNgyJ5t+F4+T40nvOyX+1I/VrKBoYfLZv3956r/OyMk8u/LOuDCDvvPPONN8TOC4GLBl93vzHSTak+O6776xUhlgfJ/2/G9/rPA4Gm331/50YJLp9dSwQg0+mbvjzXv05+5wx93/2lC1bNiUdhekZbCLhXw9gl+/Khjj+PFw2iQnMxQ71bxP4nHDNR7ATPm53Ei/HzkSitAGxxYR2qlu3btDLIYE5mfEyU+AfO2eiguVrRXLsqfFM3v9BxsUxwQTb7l8M4c8/Pl08eAZbNMIPO38uYOqDrH8sfN6CdQyKtsBFSZF4btnlLJqvhfRwVsj/XM+fPz/s2Z/ABTPRzJPzj5FXAIJdcuclUn+nPjc+qFu2bGm91/i88cSVl1vHjBljbbvttttsLydzBpUdwU73feU/1nCRnVPgynHxOXOL/znne9T/Xnby008/peRr2+W7uiUw+GRaQOqUgdT7Be7DE79rrrnG8W9DzZs3d/zZwd77/tcIBVsEm97jxMuxM5EoeBVb/g8nrs50wkt1vDwXKWy55xdu29Rwx84ZJP8K0GgIbOn49ddfO86ocbbN3y7TDtt6+nPOuCAlEpe1gv28iRMnpuR58WcH8q+C52yH3eput7Byg3+V9Lhx4xxzw/j3Z752sJXd/hOmQYMGIZY4FuLM4aRJk8L6Xv/fha+NwYMHI1r8rwfmyrN9qRNeRve/TlO/hqKBFQf8lTj42mbuq39Gzi5lIPXzxpk+LqSJ1rGGf8+MzKhnVOBzzrQGJ1zN7k+tcOPvFAxzvf3VRRiYpl6sZRe8+vfxty5PLfAEy+nvw+Ovf/bcDn+eP6eXaR9OOCEQbFIgXo6dCSWgbJYkaYetYPURWXCc3XtSY626Cy+8MGhtOwrn57Nwvn//1N15wqnzyg4o/tqy7HSTGuuCBta3daore7p1Xlnz0//4fD5ZkzBYTUy7Oq+BNUb9HWac2jkSW3+yq0x6TQpYND81tq9kRyvuw2L0qWuwshamvxsVGzwEa91K/BmsJZzR10Sw558dwfyPwzqRdtq2bZtukwJ/3VPeXnnllaDjYRMDdj0KZ5yhvmb53PuL+6fXpGDjxo2OtSFZk5K1eoNhLdhg9WhDaVLAZhZ2zRhY79Lf/CPaTQqcjh2sF8qv55xzTtBi/3yv+MfKBh9Onc8Cm46krnfq7xzIWqeB9UcDm6X466A6/c6BdV6d2kOH69JLL03pCGXXGIHF+f1j53HS7vUW2B42mEj9Pf3vV39LW9Z0TV0T1f9ccZu/vqtT5zN2SPM/r/3797fdJ/DY6vT8+z8LnZoUHDhwIN0mBZE6dspJCl4T2OkEr4EtY3nwZbDAQJA3Fq/nwZ4HkKuuuipiwSuLc/vbDbIlITuMsA0kg2feeJAIJRBgC8TAot9sn8qDBYt3s5D/eeedZ21j+9toBq+pD3xsH8ji7mwHyG5H7CjD+/0fNE4HTx74AlsPsssMO0yxyDjbQM6ePdtqYMC2myzsz244wdrD8kOGzzML4/MgyeeLjxf4IesUyLEtJJsu+AMltuHla4VFxPk4kyZN8vXp0yflg9GusUIkglcWjmexf/9jsWsNi43zueVXtlFN/dzaBa8MONgW1L9PrVq1rG5x/D3ZLYpd5NgCle1m+SF/2223hTXOcBprBLaHzZUrl9WJi68T/o35d2L7THYT4njtgqRChQqdcrLE7kd8zfNvwzazfB/4GyWwbfLptodlg4jXXnvN+hk8LrBbnD8YtGsPG83gldhpKzCAYLvW9DDA8Xc+43uCRf35GubriG1Q2U6aAY7/NZK6mQWPhf6fx5///vvvW88Hjzf8e7PBCB/X397ZreA1sD0sv/LvzWCILU3ZxSnwNZ9ee1i3gtfAbl28cXIk2Lj8N/697LBRC08E/ceqhx56yGr9y/cDj8Ps+Jj6c8Du+ednj/+kzd8elsdcPs7w4cNTGqYEtqKO1rFTTlLwmsBOJ3il1q1bn3KQCLzxzTdo0KB0P5DD/fmpz4QDb4HBR3o/N3Xb1dQ3HszT6+gVieCVAXngwTH1jQEYPyjT+/Di47DfdbDfyX+rW7du0A8YfoD5e3rb3ezal6Y+CPtnEdK7sQtSNIJXf/ebihUrOv5sBrBsjRksePXPevpbjaZ343si3HGG0xWOH4YMXIONwSlI4IleYLe2YDe+PzKKQbC/zazdjR/Qdn/3aAevgR3AOD67GWo7nIUODPydbnxMBi2BOLPsP1Gyu/FvyS5jwX7naASvxNe+fxbT6da+fXvHKzluB6+cyU89tmA/z/83YatbJwxW/RMidjdegeOMe3rPPydS/FdG7G58b/fu3dv6N39etI6dcpJyXsUR86WY58NkeJaYYmkRrvhkpxGurA21vEw4BgwYYOUg8WeGUpPUCUt4se4iu1txxSgXI3ClPjsDMQf15Zdfhhv4vDE36/XXX7cK6rPrE+9jnhZrD/J5DGVFNr+HRba5oIerZlkXkfcxt5bfz8fmiuupU6dixowZ6RbT5gKSDh06WAvLmGvM1bA33HCD9f3p5U1eeeWVVrHvt99+2+ooxRxUPr98HK4A5nP+/PPPW7mE/tJF0cCfy7zr5557zlqkwgUzLJ/D8XF1MZtThLIIhXUmuXqZXa64+Iel4ZhnzIVUXKxYo0YNPPHEE9YCkWA5hJHAbmssEdSzZ0+rKDp/H74H+Brm7/XUU09h+vTptt/LEkBcoDNq1ChroRLL2vE54XPAnELm7/Xq1ctaeML3R0ZxDHze27Ztm7IwkeXzmIfMY0K0/+5OAjsgsXtbqJU5mG/MXGMeE1hSj7WB+bfn78XKBazhyiL3XOXOBayBuB+PM8xH5/uKrxt+H6sPsFwf32fsBBYLfB+ynjf/XjzecOEaj+F8XfB1zmPJG2+8EbSxhpu4kt9fsipYx6zA+9nUIViVhAYNGliLpO6++27reOF/T7O6wbBhw6ySkHztpoevJ9ZHf+ihh6zPQL6n+Drh8Y/vR9ZE37Nnj7WvXf5tvB07E0EmRrCxHoSIRNd9991nLWbhgZcfwiKJhidt/PCnsWPHBm0NLBJpXPTGYJj10HliINEVH6dcIiIip8E/K86rCCxGL+IW1nDl1Rv/7KpEn4JXERHxNKZa+NvDtm7dOqV7kkgkMP3CCUv18crW0aNHrf/rcr871GFLREQ8hy1EDxw4YDVn6Natm1XXk7mDnTt3jvXQJMFwnQFrxTIVhbnoXGfAznzMp2V+vT+4ZbvxwCYqEj0KXkVExHO46MjfZcmvX79+1iIYkUhjoBqsi1bTpk2thbniDgWvIiLiWVzhz0oLnTp1sqo1iEQaq02w++Ds2bOxadMmbN++3eoiV7RoUSvHla+7Ro0axXqYSUXVBkRERETEMxJ+5pW9i7kSkDUx/X3MRURERCR+cC6VucRM/Umv/nDCB68MXFn8V0RERETi28aNG9NtMpLwwStnXP1PBjuMiIiIiEh8YZcyTjb647akDl79qQIMXBW8ioiIiMSvUFI81aRARERERDxDwauIiIiIeIaCVxERERHxDAWvIiIiIuIZCl5FRERExDMUvIqIiIiIZyh4FRERERHPUPAqIiIiIp6h4FVEREREPEPBq4iIiIh4hoJXEREREfEMBa8iIiIi4hkKXkVERETEMxS8ioiIiIhnKHgVEREREc9Q8CoiIiIinqHgVUREREQ8Q8GriIiIiHiGglcRERER8QwFryIiIiLiGQpeRURERMQzFLyKiIiIiGcoeBURERER48QJxDsFryIiIiICHD0K3HADMHQo4pmCVxEREZFk5/MB7dsDM2YAjzwCdOgAHDuGeKTgVURERCTZDRoEvPvuyf+//jpw883A7t2INwpeRURERJLZl18CTzyR9v7p04HmzRFvFLyKiIiIJKtffgFatDBpA6nlyQMMGIB4o+BVREREJBlt3WpSA/btS7stUybgk0+Aiy5CvFHwKiIiIpJs9u0DmjQB/vrLfvvAgUDjxohHCl5FREREksl//wHXXQf88IP99jZtgCefRLzKGusBiIiIiIhLtm8Hrr8eWLrUfnvt2qbOK9MG4pSCVxEREZFksHmzmXH9/Xf77eecA3z2GZA9O+KZglcRERGRRLdhA1CvHrBunf324sWBKVOAwoUR7xS8ioiIiCT64qxatYCNG+23lykDzJoFnHsuvEALtkREREQSWd68QLdu9tsYsM6f75nAlRS8ioiIiCS69u1N+atAF14IzJtnZl49RMGriIiISDLo2hXo3dv8+5JLgG++AUqUgNco51VEREQkWfTtCxQrBtx9N5A/P7xIwauIiIiI161dC2TJApQrF3w/1m9lCoGHKW1ARERExKv27wd69TL5qx4PSkOl4FVERETEa3w+YOxY4PzzgeefBw4fBqZOBb78EolOwauIiIiIVxw/brpgXXklcOedwKZNp27v1MkEsglMwauIiIhIvDt0CBg2DKhUCWjWDPjpJ+fc11dfRSLTgi0RERGReMWZ1eHDgddfB7ZtS3//zJmBXbuQyBS8ioiIiMSTPXuACROAkSOBOXNMfmsoatUChgwBqlVDIlPwKiIiIhJre/cCM2cCn34KTJoEHDwY+veWKgW8/DLQvLkphZXgFLyKiIiIxApnVRs3BqZPB44eDe97zz4beOIJ4L77gNy5kSwUvIqIiIjEin+mNJzA9ZJLTKvXW28FsiZfKKdqAyIiIiKRtn078PXXwLRp6e/bqFFoC7EaNgRmzQIWLgTuuCMpA1dKzt9aREREJFJ1V9etA379FfjlF2DJEnPbuNFsv/hiE3QGE2w7F1/dcw/QogVQsmRkx+5RCl5FRERE0sPC/2vWAKtWAStXmmB1xQrgjz9MDVYny5cDR44A2bM771O2LHDBBcBvv5n/n3UWcNddwN13A1WqRP538TgFryIiIiJ+W7cCP/9sAtXAYHXDBuDEifAfj7msDHSrVw++X4cOwH//mRQCBqxJUDUgoxS8ioiIiPiNGWNarEYSg+H0gteHHorsz0xgCl5FREQksXJQN282eaipb8wZZfH/YM49N/JjYg5smzaRf9wkpeBVREREvNeByh+Qrl17aoD655/OZadKl07/sc877/TGljOnWWTFclZcrMXbhRee3mPKKRS8ioiISHziYqfFi02A6r8xQP3334w93qZNZuFVjhzO+5QrZ8pSpZffyjJVFSuawDTwxpnbJC1h5RY9uyIiIhKf3ngDGDYsst2sODNboYLzPqwKwNX/69eb/xctavZnoBp4Y3erbNkiNzYJmYJXERERiQ4Gi1y9zxX7/tvq1eZ2yy3ACy8E//5zzon8mDhzGyx4pQ8/BM44w/x8fpW4ouBVRERETs++fSdLSvEWGKzu3Wv/PeXLp/+4kVo8lTu3mSnlLW/e9PevXTsyP1eiQsGriIiIhDaLypxRFuUPvDFY5er+cDGwTU+oM6+sicrC/v4AlTcGx7zxMXjpX3VTE4aCVxEREUlfzZrA999H9vL9sWPBFzcxCPXLlcsEov5bYKDKRVbBFmFJQlHwKiIikqzlpn7/3cyeXnSRKe8UDBcxRTJ4ZeDKxVPBZlfz5QMWLDAzqMWLa/ZULApeRUREEvlS/99/mwCVgao/WOXXLVtO7tenT/rB6/nnR2ZMBQqYWqqh1lOtUSMyP1cShoJXERERr2Pt0jVrTP5p6pxUpwVTgRjMpiec4JWF+rmiP/WNAWvhwppBldOi4FVERE7O0vFS7qFDJ2/MR2TdS+YT8ivrWirwiL333wd++eXkCn9efk+vqH40glcukuL9qWugspMVC/2LRIGCVxGRZLBjB7BsmelQxMvFvHGFuP/fnJ1jsBpKAMTLuMxDDOa//8yCHOYpcqW3irmHhm1N+TdIb/HR4MGm+1SkMAg+fhzIksV5H86c9u5tglXe+P9Qyk6JRJiCVxGRRPbTT0CzZsDGjZF7zDx50t9n7lzg1lvNvzlTywC2VKlTb5y1C/yaDMXgObPNkwXOlG7YYL76/80Ti7/+At59F2jdOvjjMHCMZPB65IjpKBWsripX+z/7bOR+pkgGKXgVEUlkJUpENnD1L7hJD7sqpe6yxNuSJcFXljOI5Zj9t5IlzVfO4F5xhSk2H68zptu3m99x2zbz9Z9/TF1UznD7v/I+znAGwxnr9PDSfEbxOa5Uycye8qv/VqxYxh9TxEUKXkVEvIiXlhmUsnxRMJzVLFQI2Lkzcj+7YMH092GQFi6mLvgXGdnhzCDreQYLILlqPn9+M0vIQDfwK3N2mYfJS+O8+f/N2dDAGx+HM5H79wP16pngOb1ZULY7jRTOwJ5u8Mo0Dc6i+vNR/cEqb8kwwy0JTcGriIiXcEaPfdeHDTP/5wrzYAtjeMm+enVg1qz4D17TU6RI8O27dwMDBkT2Z06dmn7wypnlSApl5pUBM3G2lMEp/+//ykCVdVGDFf8X8TC9skVE4h2DI+aQvv02MHGimRn0mzEDaNAg+PfbBa8MbPyX5XkZmbOznJ1kiSP/jYuGeImbZZg4E+n/WqeO+8ErZ0/Ty7XdtQsRx9nXSATzkZ55veQS8/tyllkkySh4FRGJ56CVwWmvXsDChfb7vPNO+sHr9debIIxBbNWq5tI7ZzGjWcqoWzegcWNTIN9f2cB/Y2Abblmn9GZdvRy88kSCpaX4d/G3PeXzE+zvw7QABa6SpBS8iojEo2+/BXr2BObNC77f5MkmOOQMqpPrrjM3N111lbnZYV4p0x8CFzL5/83fxR/wsn1pOMEr0wZiEbymt4AtsNICc5B5Y64ybwxY+bcLVqJKROIneJ03bx5eeuklLF68GH///TcmTpyIJk2anLLP77//jm7duuGbb77BsWPHcMEFF+Czzz5DmTJlYjZuEZGoWbzYzLROnx7a/rys//HHQNeu8AzONPqDucsvd97vwAETyPIWSmOEWM28cuab1RCYf8obg1X/V6ZmpFezVUS8E7zu378f1apVQ5s2bXCrvx5ggLVr1+Lqq6/G/fffj759++KMM87Ar7/+ipzMxRIRSbQmAgxAP/ggtP0ZzDEdoF074KabkJCY58rL6LyFgp8j+/aZqgUHD5rgl1/9//Y3AGDAH/iVs54MqHkpnl/9/+bPT6+aA7Vpc9q/qoiELpPPF+llkhmTKVOmNDOvd955J7Jly4aRI0dm+HH37NmD/PnzY/fu3VbwKyISV3gI5jHuiSeAf/9Nf39ePr//fqBtW5MbKSKSAMKJ1+K28fCJEycwZcoUVKhQAQ0aNEDRokVxxRVX4PPPPw/6fYcPH7aegMCbiEhcYkvO+vWBVq3SD1x5CXrQINOBqX9/Ba4ikrTiNnjdtm0b9u3bhwEDBuCGG27A119/jaZNm1rpBcx/ddK/f38rcvffSnMFp4hIPOHla7bZrFIFmD07/ZXsDFZZ+7NjR1PCSkQkicVt2sCWLVtQqlQptGjRAqNGjUrZ75ZbbkGePHkwevRox5lX3vw488oAVmkDIhIXuNK+Vi3g+++D78eapo8/bm6htGMVEUmStIG4LZV15plnImvWrFZ1gUCVKlXCtywh4yBHjhzWTUQkLnExEFMFggWvrI86ZAigqioiIt5JG8iePTsuu+wyrFy58pT7V61ahbKhrP4UEYlXffoAV16Z9n7W/2QHLeb2K3AVEYm/mVfmtK5hX+7/W79+PZYuXYpChQpZdVy7dOmC5s2bo1atWqhbty6mT5+OL774AnPZJlFExMuzr598AlSrZko7sZNShw4mDzZfvliPTkQkrsU055VBKIPS1Fq1aoXhw4db//7ggw+sRVibNm1CxYoVrXqvjXlJLUQqlSUiceujj4AXXjBNBi69NNajERGJmXDitbhZsBUtCl5FJG7x8HvkiDowiUjS25MIdV5FRDyJqVBckLVpU2hdshS4ioiERcGriEikTJliLv/PmgU0a8bafbEekYhIwlHwKiISicv/zz8P3HQTsHu3ue/HH80iLBERiSgFryIip4Ozq2zv2qtX2m3DhgHvvReLUYmIJCwFryIiGbVjB3DddcDIkc77MHg9ftzNUYmIJDQFryIiGcEGKmw0MH++8z533w3Mng1kyeLmyEREEpqCVxGRcLFRylVXmcoCdhisDh5s6rjmzu326EREElpMO2yJiHgOGwq0aQMcPWq/nfUJP/0UaNDA7ZGJiCQFzbyKiIRaUeDll4F77nEOXMuWBRYsUOAqIhJFCl5FRNJz4gTwxBNAly7O+1xxhSmPVbmymyMTEUk6ShsQEUmvFFbr1sDo0c773H47MGIEkCuXmyMTEUlKmnkVEXGyZw9w443BA9du3YAxYxS4ioi4RDOvIiJ2tm0DbrgB+Pln++2ZMpmKAo895vbIRESSmoJXERG7HNeGDZ0D1+zZTWOCO+5we2QiIklPaQMiIqllzgwMGABky2ZfCmv6dAWuIiIxouBVRMQO276OGmUCWb/ixYF584C6dWM5MhGRpKbgVUTESbNmwLBh5t/nnQd8/z1QrVqsRyUiktSU8yoiEsz995v0ATYeKFYs1qMREUl6Cl5FRNJz772xHoGIiPyf0gZEJLnLYYmIiKcoeBWR5PTmm8A555g8VhER8QwFryKSfAYOBB59FNi3L3g9VxERiTsKXkUkefh8QO/eQPfuJ+/bvRu4/nrgt99iOTIREQmRglcRSZ7A9fHHgeeeS7vt339NXdf//ovFyEREJAyqNiAiydHu9eGHT9ZstcPAtmBBN0clIiIZoOBVRBLbsWNAmzbAyJH22zNlAt56C2jXzu2RiYhIBih4FZHEdeQI0LIlMH68/Xa2fh0+HLjnHrdHJiIiGaTgVUQS06FDpr3rlCn229k1a/Ro4Lbb3B6ZiIicBgWvIpJ49u8HmjQBZs60354jBzBhAtCokdsjExGR06TgVUQSy549wI03At9+a789d25g8mSgXj23RyYiIhGg4FVEEsfOnUCDBsCiRfbbzzgDmDoVqFnT7ZGJiEiEKHgVkcSwdaup1bp8uf32QoWAr74CLr3U7ZGJiEgEKXgVEe/btAmoXx9YudJ+e9GiwIwZQNWqbo9MREQiTMGriHjbhg3AtdcC69fbby9VCpg1C6hY0e2RiYhIFCh4FRFvYx5r3rz228qVA2bPBsqXd3tUIiISJZmj9cAiIq5gLitTAlLPrFaoAMyfr8BVRCTBKHgVEe8rVszUdPUHqlWqAPPmAWedFeuRiYhIhCl4FZHEwECVua2NGwNz5piAVkREEo5yXkUkcXDm9fPPYz0KERGJIs28ioiIiIhnKHgVkfg3dCjQvj3g88V6JCIiEmNKGxCR+MVgtX9/oGdP8/8CBYDnn4/1qEREJIY08yoi8Ru4du16MnClF14AXn45lqMSEZEYU/AqIvHn+HGgbVv7QLVLF+C992IxKhERiQNKGxCR+HL4MNCyJfDZZ877/PefmyMSEZE4ouBVROLHnj1A06ampaudTJmAd94xs7IiIpKUFLyKSHzYuhVo1AhYssR+e7ZswMcfA3fc4fbIREQkjih4FZHYW7cOaNAAWLPGfnuuXMCECcANN7g9MhERiTMKXkUktpYtM0HpP//Yb8+fH5gyBahZ0+2RiYhIHFK1ARGJnW++AWrVcg5cS5QA5s9X4CoiIikUvIpIbIwfD1x/vVmkZee884DvvgOqVHF7ZCIiEscUvIqI+4YMMQuvjhyx337JJcC33wLlyrk9MhERiXMKXkXEPSdOmK5ZHTuaDlp26tcH5swBihZ1e3QiIuIBWrAlIu5gsHrffcDIkc77NG8OjBgB5Mjh5shERMRDNPMqIu5gg4FgC686dAA++USBq4hIHPj1V2DnTsQlBa8i4p6HHgJ69Up7/0svAYMGAVmyxGJUIiISgL1iWAiG5bd370bcUfAqIu569lmTPuDvmjVqFPDkk2ZmVkREYuqnn4B69cys66JFpgz33r2IKwpeRcRdDFKHDTP5rdOnAy1axHpEIiICU52Qa2Z37Tp53w8/mM7d+/cjbih4FRH3ccZ1zBjg2mtjPRIREQEwb55JE7CbZWXlwuefR9xQ8CoikS2FtXZtrEchIiJhmD0baNgQ2LfPfjtnXvv0QdxQ8CoikcFrSrffDlx+ObBqVaxHIyIiIfj6a+DGG4EDB+y333ILMGECkDMn4oaCVxE5fVu2ALVrmyMcs/x5mr59e6xHJSIiQUyZAtx8M3DokP32224Dxo2LvwqGCl5F5PT8/LOZbV28+OR9TB1o3Bg4eDCWIxMREQeTJgFNmzp36b7zTrM0IXt2xB0FryJyeke/q68GNm9Ou+3774G2bWMxKhERCeKzz4BmzYCjR+2333OPaYaYNU77sCp4FZGMtXp9+WVz2u6UKFW4sGlKICIicWPsWFOp8Ngx++1t2gAffhi/gSvF8dBEJC4dPmyC0hEjnPc5/3zgyy+Bc85xc2QiIhIEO3Dfe68pDGOHh/a33gIyx/nUpoJXEQnd1q3ArbeaStZO2JqFGf4FC7o5MhERCYLzDa1bmwtndh57DBg82BvNDuM8thaRuLFsmVmYFSxwffBBYNo0Ba4iInHk/feDB66PP+6dwJUUvIpI+iZOBGrUAP76y347j3ivvAK8/bbpniUiInHh7beBBx5wDly7dTNLGLwSuJKCVxFxxqNdv34mVcBpYVa+fMAXX5hTdy8d/UREEtzrrwMPP+y8vVcvoH9/7x26lfMqIvbYJ/C++0xNFSdnnw1MngxceKGbIxMRkXS8+irwxBPO2/v2ja+Wr+FQ8Coiaa1fb5oMLF/uvE+dOsD48aYkloiIxI2BA4Hu3Z23P/cc0LMnPEtpAyJyql9/BS67LHjg2q6daYitwFVEJK4891zwwPXFF70duJKCVxE51XnnmTqtdrJkAd54Axg6VAuzRETibInCM88AvXsHTyXo0gWep+BVRE7FRtbMcz3rrFPv5yzrjBlA+/axGpmIiDgErpxNZR5rsMVbnTsjIcQ0eJ03bx5uvvlmlCxZEpkyZcLnn3/uuG+7du2sfQYNGuTqGEWSUrFiAN+POXOa/1etCixcCNStG+uRiYhIqsCVs6msGhCsXNajjyJhxDR43b9/P6pVq4Y333wz6H4TJ07EDz/8YAW5IuKSSy4xla2bNQMWLADKl4/1iEREJFXg2rGjKbNthyWw3nvPtH1NJDGtNtCwYUPrFszmzZvx2GOP4auvvsKNN97o2thEBMBddwEtWnivCKCISII7ccJkcXFW1U7mzMAHHwCtWiHhxHXO64kTJ3DPPfegS5cuuDDEOpKHDx/Gnj17TrmJSIBDh0xi1P79oe2vwFVEJK4cP266cTsFrlxbO3JkYgaucR+8Dhw4EFmzZkWHDh1C/p7+/fsjf/78KbfSpUtHdYwinrJxI1CrllmSGqxfoIiIxKVjx0z/GGZ12cmaFRg92lw4S1RxG7wuXrwYgwcPxvDhw62FWqHq0aMHdu/enXLbyA9rEQHmzjV5rFx4RWPGmLopIiLiCUePmqD044/tt7OCIXvH3H47ElrcBq/z58/Htm3bUKZMGWv2lbc///wTTzzxBMqVK+f4fTly5MAZZ5xxyk0kqXF29bXXgPr1ge3bT93WtSswc2asRiYiIiE6fNgEpePG2W/PkcMUiWFzxEQXt+1hmetanx+2ARo0aGDd37p165iNS8RTmNfatq25huSU8c/tq1ap6YCISJw6eBC47TZg2jT77axqOGkScP31SAoxDV737duHNWvWpPx//fr1WLp0KQoVKmTNuBZO1XoyW7ZsKF68OCpWrBiD0Yp4zNq1wK23Ar/84rwP30s8VVfgKiISt3MQnE2dNct+e548wJdfAnXqIGnENHhdtGgR6gYUPX/88cetr61atbJyXUUkg6ZOBVq2BHbtct6nSRNgxAhAqTUiInGJBZNuuomplPbb8+Uzs7E1ayKpxDR4rVOnDnxhrHbesGFDVMcj4nlMA3j+eeDpp50rCXABZL9+XN1oCgGKiEjc2bkTuOGGk2tsUytQAPjqK+Dyy5F04jbnVUTCxFnWe+8FvvjCeR8e7UaNYocQN0cmIiJh2LYNuO4656yvwoWBGTOA6tWRlBS8iiSCFStMfuvq1c77VKnCXsvAOee4OTIREQnD5s1AvXrAypX224sWNfmvlSsjaemaoYjXjR0LXHFF8MCVhQG//16Bq4hIHFu/HrjmGufAtVQp4JtvkjtwJQWvIl5us/Lkk8CddwIHDjj3CBw0yFS05pJUERGJSwxYGbgygLXDEvdcuHX++W6PLP4obUDEqwlRzZubrllOeG3p00+B2rXdHJmIiIRp6VJTozV1Hxm/ChVMqsBZZ7k9svikmVcRL9q3D1i2zHn7lVcCS5YocBURiXPffWdqtDoFrlyuMG+eAtdACl5FvOjss03VAJa9Sq1dOzMjy+QoERGJW+zOzaoCu3fbb7/0UmDOHKBYMbdHFt8UvIp4FQsAsl5rYGPrDz4Ahg41/xYRkbjF5oY33ui8ZIGNBxjcpmo2Ksp5FfE4NhpgBeuffwYmTAAuuSTWIxIRkXSMHAm0bg0cP26/nfmvPKRrna09Ba8iXsYOWWzxevQocOaZsR6NiIikY8gQoGNH5+0s2c2sMF1Ac6a0AZF4xNauLOYXivz5FbiKiHjgsM7O3cEC11atTOluBa7BKXgViTdMgGKbVy4/5em3iIh42okTwKOPAs8+67zPY4+ZZQtZdU08XQpeReLJhg3A1VebpgL0wAOmAKCIiHjSkSNAy5bAW28579OrFzB4sMkEk/TpaRKJF1xWyrooXHzld/CgSYDauTOWIxMRkQzYvx9o3BgYM8Z5n5dfNoVj7Cofij0FryLxkAjFo1eDBsCOHWm3s1fg3Xeb/URExBN4OK9fH5g+3bl79/DhwBNPuD0y71NmhUis81vvvz/4aTkXZDFZSqflIiKe8OefZj5i5Ur77VyQxe7dt9zi9sgSg4JXkVjmtzZtGjyntXJlYOJE4Nxz3RyZiIhk0C+/mB4yf/9tvz1fPuCLL9S9+3QobUAkFmbPNvmtwQLX228Hvv9egauIiEewM/c11zgHrkWLmiqIClxPj4JXETcxb5VLStk+xS6/lbjcdOBAU+wvb163RygiIhkwbpxJFdizx357uXLAt98C1au7PbLEo7QBEbccOgS0a2c6YjkpUMDkv/IIKCIinuma1amT87raiy4Cpk0Dihd3e2SJScGriBu2bDH5rT/9FDy/9fPPgXPOcXNkIiKSQcePA08+CQwa5LxPvXrAhAnAGWe4ObLEprQBkWj78UeT3xoscGUtV+a3KnAVEfFMsRguTQgWuLZoAUydqsA10jTzKhJNTBF48EHTYsUOy1+xOvVTT6kUlrjWppK9L3jjhy9v/DdTrfkBy5XQ/Jo9e6xHKhK/tm0zZa44N+Gkc2dTwltdsyJPwatItCxYANx3n/N2RgijRgE33ujmqCRJuvosXw6sWQOsXXvyK2/80A0F61DyJVqypLkgkPpWtqw+lCU5rVoFNGwIrFvnvM9LL5l0AokOBa8i0VKjBtC2LfDuu2m3VagATJ4MVKwYi5FJguHqZp4rsQQPS/UsXgwcO3Z6j3n4MLB9u7ktW2bfO+Oyy4DLLz95K1Hi9H6mSLzjeyxYx26e9H30EXDHHW6PLLlk8vkSu+fknj17kD9/fuzevRtnKOlE3MZ0AWbrsz6KH6tXjx5tKguInEaPi08+MWv8liwx6QCxVro0cO21pljGddcBZ54Z6xGJRM4HH5iCMUeP2m8vXBiYNAmoWdPtkSVfvKbgVSTaeJ2WC7Y2bjTXkQYMME2tRcK0axcwfjwwciQwbx7iGlO4L7nEBLK8xHrVVUozEO9WFOje3eSvOmEqDUthnXeemyNLLApeAyh4lbjw88/AihXAPffEeiTiQUwJYB1Jzurwcr4XMXf2ttvM5VRm1CiQFS/Yuxdo2dK0c3Vy5ZUmC6xIETdHlngUvAZQ8CoiXsQj88yZwPPPmzy708E8vLPPNp2GudCKh8Lcuc0tVy5zY9oBc2f9N35oswnc+vVmoRdnfSMZyDZrBtx5p/ngV6ENiUd//mkqCvzyi/M+PCHjlRC+h+T0KHgNoOBVooaf8EwDYHShU26JEAaRnOXhy2rhwvC/nwupatUyvdN52Z4BK4PF053p5AIVf+UCfpizbDHHxyD3dJx/PtCmDXDvvUCxYqf3WCKRwrQc1nANVp2jZ0/g2Wd1FSFSFLwGUPAqUfHXX8BNN5l6RLwGOmsWkDNnrEclHjdjBvDEE+ZlFSqmT3NxFHNLGbBWrepeSjUD7ZUrTSD73XfA11+bhWQZwTHzLcVAtlEjIKtq4UgMMCIaOhTo2NG5YgdrIL//PnD33W6PLrHtUfB6koJXibhFi4Cbbwb++efUNipc+q3rn5IBDPgefxyYODH07+EaQH548tJ7vMxY8tNk9Wrgq6/Mbc4c0wQhXKVKAQ8/bCrNFS0ajZGKpMV88vbtTWDqhK9HVvjgAkSJLAWvARS8SkQxumD2PlsSpdanD9C3byxGJR7Fl9HAgeZ26FD6+xcqZBq28RJ7pUrwRDDAixLjxpm3zu7d4X0/Z7i4wOuxx0wdWZFo2bLF1G8N1jGrcmWT0lOunJsjSx57FLyepOBVIoJvk1deAbp2Nf+2U7w48NtvQMGCbo9OPIizN506mUUh6eFLi+kErDGZNy88W/KYaREMZPm7hxvIsiEC220yD1EpBRLpah5cQBh4MS01LtziwiyFEfERrynNWCQ9THzitaQuXZwD1ypVzCm7AldJx3//AXfdBTRtmn7gWqYM8OabZsU/1wZ6NXD1z6KyE/Lw4SZIYJ+O+vVDz7Th4jA+b1yANngwsG9ftEcsiY6H81dfNbniwQLXp582Vw4UuMYPzbyKpNcknvmswYr8sWPW2LE6skm6OPPYujWweXP66QH9+pmcz2zZkPD5viNGAB9+GNostB/PE5kXy5QCzkyLhIMz/3wvBssz58kiZ1ubNHFzZMlrj2ZeRSKANVLq1g0euPLTk9sVuEoQXLTUoQNw/fXBA1fOQjI1YNUq4JFHEj9wJeYPcmZr3TrToYiVBkKZjeUM9gsvmLq1fBtydlokFEuXmjJywQJXdsrixTQFrvFJwauIHUYPXE7qVGiTn66vvWau6SoBT9IpTnHxxcDrrwffj/3QFy82ZXrYIz3ZsFYmL2JMmWLefsxvZc3aUHJp337bBButWgF//OHGaMWLeJ2ZlQR4aGfNYidsZ8zybxdc4OboJBwKXkVSY8FK1m7lVJAdtlKZMMGstlFpLAnyQcmgii8l1kINdvmbl83nzweqV3dzhPGLea3MReQsNZ/DChVC6z//0Ucm4ODiG3ZkFglME2DO9AMPOFf24OGcBWN4Ma1AAbdHKOFQ8CoSiA2q69UzfTHtnHmmKV6pa0mSTgms++83l7OPHnXej2kEbEjA0lc6D0orTx7goYeA3383FQquvjq0k4bPPjOz3VwhzplvSW4//ABcdBEwZozzPjy0szYxKx661eRDMk7Bq4jfBx+YQn9Op+WcDvr+e+CKK9wemXgIFx0xyOICJCecvH/jDWD6dFOQX9JPKWjc2MxO8y3It2kowT5n0Fhii1UOeBlYkgtn49lmme/HYJ3fmEbAmXp2qhNvUPAqwqmaAQPMVBmPdnYYsDKdgAGsSJBqAlwIsmSJ8z4MpvhByeprmm0N35VXmplV5rZytXgoKedTp5q3MHNqGfxK4tu0yVxE69XL+bBOzK3+5hvgrLPcHJ2cLgWvIrxO1KOH83ZO+cyeDRQp4uaoxIM9LBgcOWWcUPfupiB6xYpuji4xMQ+WF0u48ObRR4GcOdP/Hl4WZg4y/068lCyJ+V5keSuW3mZQGizXnCdBzK1OhqoeiUbBqwgTD50++Vi3iEe43LndHpV4BGd1WGuUTQROnLDfJ18+s8avf399UEYaGzmwkgMvC7MBXiiNHBjE8lIxV5UriE0cbDTA5h/MId+1y3m/WrWAZctM+ol4k4JXkWuuMZn8TKwL9MwzwFtvKXtfgvaw4IclK6Y5Of98k2/J/SR6ihUDBg40QWzPnuaEIT3MOVYQmxjYJ6ZyZWDSJOd9eCh/9llzIa10aTdHJ5Gm4FXEnxrwzjvm30xEZLFNVk5XUqI42Lo1/R4Wt91mAlcGsOIO1sh97jmzcI4ZQaHUilUQ613btwN33AHceWfwlB02s5g3D+jdW/MRiUDtYUUCvfgicM45JuoQccC6rQx0nLo68ZyHKQK8jK3zn9ji5ePBg01PEdb6DAVzYnnuysVhEp+YosOKHnyP7dwZfN+WLU11D9VuTZx4TcGriEgYWHTipptMe1I7TJ8eNUppAvEYxA4ZYhbohBrENmhgZurY/Uzix2+/meUILJ0WDNfY8oKa3ouJF68pbUCSQ7BK8SIhmjXL1IJ0Clz9PSz0YRl/OOvGNALmxDKdPZR0Ai7sYo3Qa681f9fEnurxRvMPlr5iw4H0Ald2Wfv1V70XE5WCV0l8K1aYpMP0jnYiQXz5pSl2f+CA/fbzzjM1RHWpOf6DWKYEMIjl11CCWAauDGC5tpMBrYJYd/H5Zoc1lr9i04FgcxGFCgGjRwOffqrqholMwasktqVLgTp1gHXrzLXeYNXjRRyMG2dmcA4ftt/OxT7qYeG9IJYzsOHMxLJGL/NhL7/cVNALVvxeIoMlrdhsgO8/1vQNhksVONvKxVvKNU9sCl4lcbGpOadL/EtQ9+wxSWxslC4SouHDzYfhsWP22/mhynQCpgyIt2diQw1ieWjhZekLLgDef9/5pEZOr5pH27ZA9epm5jsYVhLglZHx44Hixd0aocSSgldJTLx+y9P11MmJ//4L1K9v6uiIpINlftmC1Kn5wD33mMuTuXK5PTKJZhDLWqDswJSeVauABx4Azj7bdFjj+bGcnr17TakzpuG8917wFA2WvGK1Ac62MqVHkoeCV0k8335rumY5fZKwNycTo0SCYGmd9u2dt3O1M2dls2Z1c1TiRhDLCgMMYlnuLJQZ9S1bTIe1s84COnd2LqEmwRt+sFJh+fLm+WcQG8wVV5gsMDamyJPHrVFKvFDwKomF15eYGrBvn/12bpsyJbT2O5K0WF6HLV+dPP64mZVN3ZRNEgcr9XTvboJYzqqGcjmaAdegQSb3mfmXPI/W4q70KwiwBi9nr7t1C95ogEqUMCeNzDGvWtWtUUq80aFXEgcTDxs1cl4OzgVbXLKqa7wSBAufc1bVCS8tv/yyFoQkC87q8WSFaz7ZeI9BVnqYZjJhgqlOcNllJi+WM4tyEjO6/D1h+Pxu2xZ8f9ZPZpkspmq0aqUTx2SnJgWSGObONYErT+OdVtWMGQNkz+72yMRDPv4YuPde59kyfth26eL2qCSecOEeFwYNGGBWwoeKHz98bT30EFC5MpIWTwI4O/3BB6EH9C1amOe7TJloj05iSU0KJLmwYTWz9Z0C1+bNgbFjFbhKUFx4xRkdp8CVl44VuApznFl94uefgalTgVq1Qvs+puAzj5q1Stn4gDP8ybLAi+8pltlmKgUXYr3+emiBK9fccu0tO9YpcJVAmnkVb2NSGQsvOh0J777bfEpoVY0EwWwSlj5yqtv5wgtAjx5uj0q8YvFiM5vIiztOJdWcLoU3bmwOU0zHz5YNCWXzZuCjj8whePXq0L+P6Rb9+gG1a0dzdOLleE3Bq3gXT8lZVcBpcRav0fHaFOupiDiYOdNknDh17WHtT+a5ioRSdeDNN4G33wZ27gzve1nVgBeJmOHE2VyvBrK8AMY1sTz0shuZU5k5O+xOx6CVM67KKU8+exS8nqTgNUH9+KNpMu9UT6VlS2DECAWukm6x+bp1nc9/nnrK1JzUB6mEg2tGP/nEBLEZaerHcl08oeKsLC8sxftHFysEsEnApEkmYHVaM+uEpbdZaoxzEXqvJa89Cl5PUvCagJhsxohj92777UxIGzlSqQIS1B9/mMuT7Fth54kngJde0oepnP4JEoNY5m06peUHwxlYvk55CZ031jdlukEs8SoFO29zucEXX5h81nBmWP2/1113mUoDKnklpOA1gILXBMPWrrym5hRx3H67+ZRQ4CpBbNoE1KgBbNxov/3RR4EhQxS4SuTs2mWqWbBsFgO/jMqRwwSwDGTZOpULwFi+K5qlo5gCwTkDBqlcZvDDDxkv/cX+MCxFx/cYa7aK+Cl4DaDgNYHwpcqkqJ9+st9+661mxYRXk8XEtUucnMnieZAdLp5hxonqSEq0rFhhAlmmFvBE6nTlzm3KbzGQZQNBBoWBN6YhOJ2IccaUgTXfF7xt3w6sWWPeH7w6wRvvOx18L3FBGlst33KLCcBFUlPwGkDBawIWCWQ2P9veBGJyGGsdqRyWBMHZIubXcebIDiuuTZyo8x9xBwPHb74xWU6seMHC/dHAwyIDRi4BCLzx8j9nVcO95B8qdhpr08asnS1VKjo/QxKHgtcACl4TEKcquFiLUwLEU3quFNDpvATBEkY8x2FtTjtMI5gxw8xiibiNgSQvyU+ebA5n69fDky680LzPeGN3MaXeSKgUvAZQ8Jqg2EuQQSv/ptOmKeKQoHiUY57dsGH223nJlYtPChZ0e2Qi9q9XphYwkGXXa1YFPHQIcYkpATzxa9LEBKycbRXJCAWvARS8JjAmavHIqb+rpIOtJZ2aDJQrByxYAJQs6faoREJz+DCwcKFJMeDtu+8yvmDqdHGegEsP2CWMueNcPJYvX2zGIolFwWsABa8iyW30aFOSx06RIiZwZctKEa9gJzguqlq+/NTb2rXO7Y3Dxcv9PLE7//yTt2rVgIsvVk64RIeC1wAKXkWSF1MBmB595Ij9DNLcuSYvTyQRMLXgn3+Av/8+9cZqAcz5ZtDr/8obF22xdBW7exUufPLGqxA8ocuVK9a/kSSTPWHEayqGKfGnVy9T7+Wee2I9EvEwrudjHp5d4MpsE87IKnCVRMLmBZwt5U0kkSl4lfjy2mvA88+fXJTFNkciYdq6FWjY0Ln00ODBpt6kiIh4j8pwS/xg1W72CvRjs+uuXSOXxCVJc+m0adO0pYD9eD7E7j4iIuJNCl4lPnz9tWm/khqbyz/4YCxGJB7E85y2bU1pITvNmgEvvuj2qEREJJIUvErsLVsG3HabWUlgp25dt0ckHi6JxQl8O1ddBXz0kdq+ioh4XUwP4/PmzcPNN9+MkiVLIlOmTPic/fH+7+jRo+jWrRuqVKmCPHnyWPvce++92LJlSyyHLNHolsWenPv22W8fNMi5zpFIgAkTgKeest929tmm4LtWT4uIeF9Mg9f9+/ejWrVqePPNN9NsO3DgAJYsWYLevXtbXydMmICVK1fiFq2ySBy7dwONGgGbN9tvZ1X5jh3dHpV40M8/OxenYMWVL7805YBERMT74qbOK2deJ06ciCasbeNg4cKFuPzyy/Hnn3+iTJkyIT2u6rzGKdYv4ozrzJn221u1Aj78UI2xJV2sY3n55WYSPzWmCEydajoJi4hI/ErYOq/8hRjkFihQwHGfw4cPW7fAJ0PiDM+XuAjLKXCtV880oVfgKuk4eNDUcrULXP1ZJwpcRUQSi2eWLhw6dMjKgW3RokXQiLx///5W5O6/lS5d2tVxSgiefRYYMcJ+W+XKwGefAdmzuz0q8eA50EMPAT/9ZL+9XTuVxBIRSUSeCF65eOuOO+4AMxyGDh0adN8ePXpYM7T+28aNG10bp4Tgk0+AZ56x38aehLzGmz+/26MSD+Ks6siRzpP3Q4Zo8l5EJBFl9UrgyjzX2bNnp5sHkSNHDusmcYjFN++/335b3rzAlCmAZsolBMw4YQ8LOxUqAOPGAdmyuT0qERFBsgev/sB19erVmDNnDgoXLhzrIUlG/fmnSU4MyEdOkSULMH48cNFFsRiZeMy6dUDz5sCJE2m38dyWJbEKFozFyEREJO6DV+ah5syZM8Pfv2/fPqxZsybl/+vXr8fSpUtRqFAhlChRAs2aNbPKZH355Zc4fvw4/vnnH2s/bs+unEjv2LsXuPlmYNs2++1vv61VNRISlgNu3BjYuTPtNqYIjB4NVKwYi5GJiEjc5ryeOHEC/fr1Q6lSpZA3b16s4zQIYNVjff/998N6rEWLFqF69erWjR5//HHr33369MHmzZsxefJkbNq0CRdddJEVzPpv3333XbjDllg5ftw0GVi+3LnR/AMPuD0q8egCrfvuA1assN/+/POmbLCIiCS2sIPX5557DsOHD8eLL754yuxn5cqV8d5774X1WHXq1LEWYaW+8fHLlStnu403fp94xBtvmArxdm66CRg40O0RiUe98IIpRGHn9tuB7t3dHpGIiHgieP3oo48wbNgwtGzZElmYq/h/7JT1xx9/RHp84nWs53rHHWnvr1IFGDXK5LuKpGPaNF7dsd9Wtar6WYiIJJOwg1dezj/33HNt0wm4wErkFGwmP2YM8PTTJ+8rWhT44gsgX75Yjkw8gplJLVuatIHUuIbz88+BPHliMTIREfFE8HrBBRdg/vz5ae4fP358Su6qyCk4JcbarlxNwxqujDbKlo31qMQDDhwAbrsN+O+/tNs4af/pp0D58rEYmYhIAvPZzBZ4udoAF1O1atXKmoHlbOuECROwcuVKK52AVQFEHN15J9CwoZoQSMjHTnbJWrrUfvtLLwHXXuv2qEREEtjRo0DPnqas5eDBSJiZ18aNG+OLL77AzJkzkSdPHiuY/f333637rrvuuuiMUhKHAlcJ0VtvOXfQ4nlQp05uj0hEJIFt2WJmBDgzwBaFY8ciXmXycfl+AtuzZw/y589vtYpNrzuXiMQHVsOrXRs4dizttsqVgR9+UJ6riEjEzJplyloG1mNn58tFi1wrnh1OvBb2zKuILTabYE1XkdPEXiTNmtkHrjyeTZigwFVEJCLYqvC554Drr0/bSIhdYXgw5uKDOBN28FqwYEGrw1XqG1u3snFB7dq18SHr1kjy2LgRqFEDuPFG+5U1IiFiwMrWr3//bb/944+B885ze1QiIgno4EHTtp11CO36bdPq1cCPP8LzwStzXDNnzowbb7wRffv2tW78N+9r3749KlSogIcffhjvvvtudEYs8YVJ3VwOvn078NVXwGWXAb/+GutRiUc99RQwb579Nh5f2WVYRERO06FDJnBl2UonLOXCHK66deH5agPffvut1WWrHZcBB3jnnXfw9ddf47PPPkPVqlUxZMgQtG3bNpJjlXjUuTOwcOHJ/69dC1xxBbtZALfeGsuRicdMnGjWCdi54YZTSwWLiMhpBK5NmwJff+28zy23AMOH83I74lHYM69fffUV6tevn+b+evXqWduoUaNGWMfK4pLYuBR86NC09+/fb7Yl9lpAiSBembrvPvtt5coBn3yiZmwiIhG7Wjp9uv12HmhffNHUY4/TwDVDwSvzW1kWKzXex220f/9+5FP3pMS2fDnw0EP227gyccQI9euUsBoR7NmTdluOHGyAwuNOLEYmIpJggWuzZsDUqfbbeaCdPRvo0iXuP7/DThvo3bu3ldM6Z84cXH755dZ9CxcuxNSpU/H2229b/58xY4a1cEsS1O7dJtpgsndqXAbO5eAqSyYh4OT8ww+bcyE7r78OXHKJ26MSEUkwR44Ad9wBODWTYuDKclkXXQQvyFCd1wULFuCNN96wOmtRxYoV8dhjj6EGV5zHGdV5jTC+XBi4MkHRzqhRQIsWbo9KPGrYMOcJ/FatABYuifMJABGR+HbihCnjwstYdpgeMHMmcPHF8Eq8FvbMK9WsWdO6SRJ6+WXnwPWxxxS4SsgWLzYvGTtVq5oOWwpcRURO09NPOweuBQrwcnnMA9dwZSh49Tt06BCOcCo6gGY3E9j8+UCPHvbbrrzSBLYiIdi1C7j9dnMlKzUeQj77DMidOxYjExFJIKNHmyYETu3aGbh6MDcr7AVbBw4cwKOPPoqiRYsiT548VtOCwJskKNZxZUN5uy5aZ54JjBsHZM8ei5GJBzNPWrcG1q+3387qLOee6/aoREQSzMKFQJs29ts4S8DA9dJL4UVhB69dunTB7NmzMXToUOTIkQPvvfee1aigZMmS+Ii1PSUx82WYgLhlS9ptmTMDY8YAZ50Vi5GJB732mqnCYufJJ035QRERiUApl1y50t6fLZtpTsCmQh4VdvDKklhvvfUWbrvtNmTNmhXXXHMNevXqhRdeeAGfsBijJB6mA0ybZr+tXz8W+XV7ROJRbNbSrZv9tquvBl54we0RiYgkqNq1gZ9+AipVOvV+1mevVQteFnbwunPnTpx99tkp+a38P1199dWY59TXUbwdbbBnp50GDYDu3d0ekXjUv/+aBa/HjqXdVqSImcDnhICIiETIuecC338PNGxo/t+pE3D//fC6sINXBq7r/5+sdv755+PTTz9NmZEtwFVrkjh27HDOcy1RwrSAZdqASAiZJ3ffDWzalHYbKwqwwlqpUrEYmYhIgsuf36QJsDahUw9ujwk78mjdujWWLVtm/bt79+548803kTNnTnTu3NnKh5UEW1WzcWPabQxYuYKxaNFYjEw8iOkA/+8ebVvFxabjtIiIREqWLEDbtkDW0yoy5e0mBYH+/PNPLF68GOeeey6qsjhjnFGTggx69VXgiSfst/XtC/Tp4/aIxKPmzDHBKWdfU+P9bLHN46qIiCSvPdFsUsDarpxp9Stbtqx1kwTDa7h88aRuOH/ttUDPnrEalXjM1q3AXXfZB64lSwJc46nAVUREopo2wLzWWrVqoXfv3pg1axYO2vW3F+/jyhqmh1x11cn7ihVTtCEhY6o0A9d//km7jS+hsWOVeSIicto+/hj45hskk7CD15kzZ+KGG27Ajz/+iMaNG1uNCVhpoGfPnpjBgreSOMqVA1hBondvkyfDN0jx4rEelXgEq6jNnu2cA8vSWCIichqWLwceeMBcFX3mGftyLgnotHJejx07hoULF+Kdd96xaryeOHECx+1WpseQcl4j5K+/gDJlYj0K8Qiex7KSmt3R5aabgEmTVKhCROS07N9vGg38/vvJ+1i/lVdIPdg4KKo5r7Rq1SrMnTs35Xb48GHcdNNNqFOnTkbHLPFOgauEiI3YWra0D1z5MhoxQoGriMhp69Tp1MCVeLX0oouAlSuBwoWRqMIOXkuVKmXluTJQ5a1bt25WlYFMLNYoIkmNV6xatAC2b0+7jZknzHMtVCgWIxMRSSDjxwPvvWe/je3cEzhwpbDnP4oUKYIDBw7gn3/+sW5bt27Voi0RsTDlyqnR3osvAlde6faIREQSsIxLu3b22y65BOjfH4ku7OB16dKlVtDKBgVMF3jqqadw5plnokaNGtaiLfFg+1cmIIqcJjYh4EIsO02amCtcIiJyGpiP1a6d6YCZWt68ps929uxIdKe1YGvHjh1WzuukSZMwevRoLdjyGtZwZWOJP/8EHn4YeOUVIFeuWI9KPGjzZpNm9e+/9kUrliwBChaMxchERBIIq/7cc4/9tpEjTR9ujwonXgt75nXChAno0KGDledarFgxPPzww9i3bx9eeeUVLOEnlHhHhw4mcKWhQ82qRZbdEMlAnqtd4JotG/DppwpcRUQiMkvw6KP222691ayUTRJhz7wWLVrUalLAxVq1a9dGlSpVEM808xok2fv229PenyMHsGCByZsRCcFTTzmnWA0ZAjz2mNsjEhFJMAzVGjUy/bRTK1IEWLHC811foloqa9u2baczNomXWkYPPWS/jStqeP1XJATTpjkHrrfd5jxJICIiYXj/ffvAld5+2/OBa7hUbTHZsMl869bAzp1pt/FMh0U41f5VQrBpk3Pq1dlnm2OtKuiJiJymDRuAzp3tt911l0kZSDIKXpPNm28CX39tv+2tt4CyZd0ekXg0z/XOO+0XvHKhK/Nc8+ePxchERBJswun++4F9+9JuK1ECeP11JCMFr8nkt9+Arl3ttzVvbs7gRELAqnhMjbbDohVKmRYRiQA2Ipg9237bu+8mbdcXBa/J4sgRsxLx0KG020qVMtUGdI1XQvDll6bhgJ1mzYD27d0ekYhIglYX6NLFflubNsCNNyJZKXhNFn37ssOE/TbmuaqWkYTgr79M50GnPFdOEugcSEQkAtUFOBPAeuyplS4NvPoqklnY1Qb279+PAQMGYNasWVblATYmCLRu3bpIjk8i4YcfgAED7Lex7VG9em6PSDw6ec/sEru1fspzFRGJcDlLp+6Xw4Yl/cE27OD1gQcewDfffIN77rkHJUqUQCZNs8S3/fuBe+81Sd+pXXhhUvRAlsjo0cOcB9l57TXluYqIRMzixfb3s4PWDTcg2YXdpKBAgQKYMmUKatasCS9I+iYFrBD/xhv2rY9++kk1XSUknABo0sR+G2djR49WuoCISETNmgU8+CAvaZv/n3km8Pvv5msCimp72IIFC6JQkq5u8+QL3y5wpaefVuAqIVm/HrjvPvtt551nrmApcBURiTCm9LFl+5NPApkzm5aFCRq4Rn3m9eOPP8akSZMwYsQI5M6dG/EuaWded+8G2Lp348a02664Avj2WyBr2FkjkmQOHwauvhpYtCjttpw5TRpBtWqxGJmISBJZuRKoUCGhZwr2RLM97CuvvIK1a9eiWLFiKFeuHLLx8nOAJUuWhD9iibyOHe0D11y5gI8+UuAqIXniCfvAlTgJoMBVRMQFFSvGegRxJewIpolT4pvEj88/N+Wv7AwcaM7eRNIxdqxpyGaHJYMfeMDtEYmIiGQgeH2auZIS37ZuNbWLWNso0LXXqoK8hHyFyik45QTA228n9NUrERGJYxm+drx48WL8zlVvVsWlC1G9evVIjktOx0MPAawGwRJZP/9s7mP+yIcfmqRvkSAOHDCdsuxaaTPrhOUH8+aNxchERBLQsWNK5QtT2JEMGxNce+21uOyyy9ChQwfrdskll6BevXrYvn17uA8n0VK5sllN07s3kCULMHgwUKZMrEclHsDJ+RUr7LdxxpUvLRERiQCuTWHZluHDTVctiU7w+thjj2Hv3r349ddfsXPnTuu2YsUKa5UYA1mJI0wdePZZE4k49fQUCfDBB+YYaodpBJzMFxGRCNZi37ABaN3apPYxZ0siXyqLZQxmzpxpzbwG+umnn3D99ddj165diCdJWypLJEy//GKqqB06lHYbSwJ/951JGxARkQgtrm7aNO2kU69e5pZkCwv2RLNJwYkTJ9KUxyLex20i4s2ywLfdZh+48hgybpwCVxGRiNmzB3j00bT3c6E1O8MkWeAarrCDV+a7duzYEVu2bEm5b/PmzejcubOV9yoi3sJrL+ygtWaN/Xau8zv3XLdHJSKSwLgeZfPmtPezg9ZLL8ViRIkdvL7xxhvW1C4bFJxzzjnWrXz58tZ9r7/+enRGKc5Rxx9/xHoU4nGvvGKuXtnp1Am49Va3RyQiksAWLgSc4qVXXwUKF3Z7RImf80r8Fua9/vH/wKlSpUqoX78+4lFC57y++y7w8MNAz57mxlwZkTB8841pn338eNptV15ptutlJSISwbJYXDO0dGnabTwYz5iRtCkDe8KI1zIUvHpJwgavf/5pahb5i3FyRQ27alWtGuuRiUf8/TfA8szsaWF35Yolgs86KxYjExFJUK+9Bjz+eNr7c+QwlYGSOEdrTzQXbEkc4MK4Nm1OrSLPs7hLL3Xu5ykS4OhRoHlz+8CVfSzGjFHgKiIS8ZquzHW1w/uTOHANl4JXL2Kl+Nmz7SOScuViMSLxmKeeAubPt9/Wr5+5eiUiIhHEWvj796e9v1IloEuXWIzIsxS8es3atc4vchY5vvFGt0ckHvPZZ8DLL9tvu+kmoHt3t0ckIpLgJk92XhnLCSktLgiLglevpQswQGXz+dR4jZe5NCJB/P67KYtlh5P2H31k0gZERCRCmOJnV9OV+Jleq5bbI/K8sD+msmTJgm3btqW5f8eOHdY2iaIhQ5yv9b7/PtufuT0i8VhNbDZzCUyVDlwrwBnZggVjMTIRkQT2zDMm3zU1lsR68cVYjCj5glen4gSHDx9Gdk17R8+qVUCPHvbbHnoIuP56t0ckHpu0v/de57bZb7wBXHyx26MSEUlwy5YBgwbZb2P+Fku7SNiyhrrjEM76sbZWpkx47733kDdv3pRtx48fx7x583D++eeHPwJJH4tw8lqvXe9OXutVNw5Jx4ABwKRJ9tvatgUeeMDtEYmIJMGsASeX7App164NtGoVi1ElV/D62v/zKTnz+vbbb5+SIsAZV3bc4v0SBey48f339ts++ADIl8/tEYmHfPUV0KuX/bbLL3du9CIJ8MH5ww8A07wCb3v3msUhzBXx3/j/QoWAs8825Xp4UqwraSKn5513gB9/THt/tmxmkVaSNiNwNXhdv3699bVu3bqYMGECCio5zh0sWuwUeTABvG5dt0ckHsK3bYsWppNwakWKAOPHm9hFEhRnd9jRJ1xctVe6tAlkq1Qxj8NFJQxwRSS0LjBOpVu6dQN0pfq0qMNWPDtyxPToZKuj1M45x+TS5MkTi5GJB7AoRc2a9l0IeeGEXQh17uNRrOnM2Zv0lCoFbNkSuZ/LDn5Tp5rHFRFnc+cCzZpxNXvaz+7ly4FcuWI1suTssHXbbbdh4MCBae5/8cUXcfvtt4f7cBLMc8/ZB6681DB8uAJXccRTUjZhswtciQtcFbh6DNuhDRsG3HCDmbUJZd6haNHIt6UuXjyyjymSiOrUAf74I21twqFDFbhGQNjBKxdmNWrUKM39DRs2tLZJhCxcCLzwgv22J54Arr7a7RGJh/D8cuxY+21sC9u5s9sjkgxhgLpggZnBKVnSLP5gEvO6dcCiRe4Hr9dcY6btRSR9rCTw4YfAnDlAhQpAy5bAddfFelTJGbzu27fPtiRWtmzZrClfiYCDB01dI7sVihdeaPp3ijjgVV22f7XDl89772mdgCfSAkaPBq64wpyosggvF2AF4n3hBq/8w3OBZ86cGXsRcDYpPYcPmytD/Coi5n3zyy+mJqHEJnitUqUKxtpM6YwZMwYXXHBBZEaV7Hr2NJcbUsua1bRA4gePiA3WcXVaoMU1liyXFVDlTuIN+56z9B1X/d91l7kC44TBa3qpA48/DsycaT44//nH5NFzkoEnyDw55v9ZfWD7dpNjwhV8nLZ/8EHg2mvT1qDkwq30jBplugaxYgFrtO3aFeIvL5LAuDK2QIFYjyJ5F2x98cUXuPXWW3HXXXfhWh7cAMyaNQujR4/GuHHj0KRJE8QTzy3YYhcOJnRz5iW1vn2BPn1iMSrxgN27zUSdXSMCLh6fPl1XrOIWA8mRI82JazgLrLjwo3Ll6I2LHw/sKczFJyzXx0ugPIl2wtlhjoff48eZXqY7dOkS+TQGEUkY4cRrGao2MGXKFLzwwgtYunQpcuXKhapVq+Lpp59G7VDOyl3mueCVONvCtIHA2ddLLwW++y60FcaSlLFP48Z8b9pvf+UVMwkncWjWLJPHzuohobrkEq6eNTOc8bSAavJk80K0wyCWwXnHjrp6JCLuB69e4snglXhZj/Vd2RyCOcasOlCpUqxHJXGKnYN5hdbO3XebbBPlucaZ334DunZ1PuNIjTVWOYPJS/q8JB+PmJ/LBWbBcOxMTWB1Gr0oJREw/UZNPeK7VBbt2rXLahH71FNPYefOndZ9S5YswebNm8N6HFYnuPnmm1GyZEmr7eznn39+ynbG1X369EGJEiWsGd769etj9erVSAospcHpMq5SZCcOBa7igIGpU+DKCXtWV1KMEEeYEvTss0C1aqEFriyLxWMAU4pYgSReA1fmtrK4cHo2bDAlL1iE2K77kIjXMD/9jjtMXrm4Iuzg9ZdffkGFChWsWq8vvfSSFcgSu2714PRPGPbv349q1arhzTfftN3O2rFDhgyx2s7++OOPyJMnDxo0aIBDhw4haTAVI3WdOJH/+/ZboG1b+23FigETJ6qkYFxhjioTk59+Ov3OV5zFZOmIX381M665cyOucTHK4sXA118D9eunvz9zaNmE5ZFHzKIxES/iKlgunhw3zkwyvftu2sogEnm+MNWrV8/XpUsX69958+b1rV271vr3ggULfGXLlvVlFIcyceLElP+fOHHCV7x4cd9LL72Uct+uXbt8OXLk8I0ePTrkx929e7f12PwqkkjWrfP5zjyT7520t2zZ+J6M9QjlFJs3+3w5ctj/wQJv553n8/FYeOKEz9MWL/b5brst/d+XN352fP11rEcsEh7GFaVKpX0916rl823YEOvReU448VrYM68LFy7EQ5wFSKVUqVL4J4JT5uvXr7cej6kCfsyFuOKKK/A9z9gdHD582MqbCLyJJBq+rG++Gfj3X/vt77wD1Kjh9qgkKH+TgWA5rYMHAytWAKza4vVcj4svNqW3mAN7+eXpd+66/npzGYFlM0S8gAW17dIl+R7WJa+oCjt4zZEjh21AuGrVKhQpUiRS40oJhIvx2mcA/j9YkNy/f38ryPXfSpcujbhO8hYJE68233mnuZpsh2uAuAhd4hBzVs8999T72LGKpSDWrAE6dEi8hR88i+KEwyefAOkdj9lBg6W2pk1za3QiGcPX9Ftv2W979VWVhYu34PWWW27Bs88+i6P/r0PKhVZ//fUXunXrhttYuiXGmHfLlWr+20YucohHXNjAcjfPPJN+7ptIgCefdP5sZ5Wi/v3dHpGELE8eUyvVP6vKxi4//GAWZ7KLRKJioWEuamER4ueeCz4rtWkTwBbkLB+mE3yJR3xd8iqBXbEmXi1mqUuJr+D1lVdesVrEFi1aFAcPHrRqu5577rnIly8fnn/++YgNrPj/axdu3br1lPv5f/82p5lhllgIvMUlFuzmpQU2HmC/8LVrYz0i8QCe6PPKsh0uXv/4YxMnSBzjQiwGZt27mwVOLAmRLBi0stYrF66lVxecL+b/V7MRiSss9WZ36Yv1i1kZxOspPx4QpFWKPV6KnzFjBhYsWIBly5ZZgezFF198Sm5qJJQvX94KUtm966KLLrLuY7oCqw48/PDD8DSuSgy83MCZF/6OQ4aYygJ64YtD/ffHHrPfxvO5L75Q69eYO3zYtIFMz4svJvf7nF0EZ882H/TMc2Fb3EB8bphmEE8NGETIf/XADq+k8rUtURfSHE2hQoXw7/9XhrRp0wZ79+5FzZo18cgjj6Br164ZDlwZ+LJLF2/+RVr8N9MQmI7QqVMnPPfcc5g8eTKWL1+Oe++916oJG28taMPCvLb77097/759JqBlqyQRm6ZrzHO1q8DCk31Wa4nn9O6kwPdvlSqhzRYmc+Dqx0sELJPFWdh69U7d1rt3aOW2RNzEA/ADD9ins/DSl9oYuieU8gV58uRJKYmVOXNm37Zt206/JoLP55szZ45VFiH1rVWrVinlsnr37u0rVqyYVSKLZbpWrlwZ1s+Iq1JZBw/6fNWr25eKyZ3b5wvzd5PkwLde0aLOVYbGjIn1CJMcS1p1737yD9Kwoc93/HisR+W95/Ctt0wpsTp1fL5jx2I9IpG0Xn/d/iCcObPPt3BhrEfneeHEayG1h73uuuusXNNLLrkEI0aMQPPmza2OV3Y++OADxJO4ag/LWYahQ+23jRihJG9JY8cOs1h71Sr77VycxdRJiREuXOVMDNucBWITAl5ClPAsW2ZWaZcoEeuRiKTtDMdKGKlTXKhTJ9PKXeKrPezHH3+MRo0aWZf5iQ/833//2d7EwdixzoEr6xopcJVUDh401QOcAleWDO3Wze1RSQoeD1lsN3XgSlyIGUrrV0l76TXUwJUpWCJu4BwfqwvYBa7lyzvnwErUhDTzmnoh1aJFi1C4cGF4QVzMvDL6YFms/wf/p+CZHPt7x3vrR3EVU5+Z48oa73ZuvBH4/HMga9hLLiUitm0zf4RFi5z3eeklU9dMIo+rE7n2gTPczI9VDrFE0/vvmyssdmbOTJuzLfEx8xq4YKtu3brInmhFtKM9fXbHHfaBK2s+svKAAlcJwNPJRx91Dlx5HjRmjALXmFm3zuRyOAWu/MOMHKnANVpYYpA1Y7l4hsErz/JYN1skGthBi6Xt7HA2VoFrTIQUvB45ciSlqxZzXg8dOhTtcSVOFMJru8zjssMyMeef7/aoJM716WNeGnbKlQO+/FIlsWIauNap41yXmX8YpgvcfbfbI0sO27ebVI3AyYBPPwWuvda5V7LI6XyGszSnXcviUqXM1RWJiZDmbq666iqrPBUXbDHLoEOHDp5ZsBVTfGFzBsYOL0HoA05SGTTIOX2KDZimTlXpy5gu2KhbF3Dq2seFRmx9dvHFbo8sObATYbNm5u+QGlOvatYEvvrKnOGJRAIvcTFFxQ5nGPLnd3tEktEFW6y/qgVbIVaUd1oGXrWqaUggEoDrfjp3tt/mr+VaqZLboxLLn3+awPWvv+y3n3uu6XWuwDV6mI7RvDmQJYvz2oKrrnK+0iUSbl67U1eYli2Bm25ye0QSQAu2ooFFt5kTZ5fnyjH89BNQsaI7YxFP4Ml906b2PSr4WT1xorlaKjHAgJWpAuvXOychc0qcM68SfezMxRlYp8kSHmO5mpEnGyIZtWsX0KFD2qunfJ//9hvgkRgoqRdsBWIXLK8ErjHLybrlFvvAlR1leBlCgasEmDfPrOlzaq7GTBwFrjHCFAEGQU6B66WXmtXGClzdw/xWpgmwRJEdrs+44QaTCyuSUQUKmMthn30GFCly8v433lDgGgdCDl6ZNsBo2G/AgAHYxTOT/9uxYwcuuOACINn7mt96q31OFr38MtCwodujkjj23Xem4pLTGkjWvVYJ4Bj5+28TuHKRltOM69dfmw85cdd555k3T/Xq9tvZvpNVCJxqa4uEip/prHDB0mz8N2f9xTtpA1myZMHff/+Nov+fYeCU7tKlS3H22Wdb/2cHrpIlS+K40/RRMqQNPPgg8O679tvuv99sUz1C+T9mj7B9+9699tt79QL69XN7VJIye1erlnP+JHNbOePKVXQSO3zzMKDg38LJiy8CXbq4OSpJRAyVOMvgsFhd4jRtIHWMG2aqbHJo0cL+w+yaa4C33lLgKikWLwauv945cG3XDnj2WbdHJSmzdgyInALXiy4CZsxQ4BoP8uUzpclY99VJ166mHqw+s+R08PNbgWvcCDvnVYLgJUZOpwXWbmXZFubMqLGD/N/SpcB119mXDiRe7WRalc51YmT4cGDWLOf2pZzlK1TI7VGJEx5b02sKwTNBFppXACuSXMErS2Txlvo+sSmZ88MPZsEAC5ZzGXlgsrckNRaiYKqA00Lp224zawScqgGJC9g1p1u3tPezThkDVy3WiD9cDMu62kwRcMIEcl7SiLPUNhEJX8gNJpkmcN999yFHjhzW/9llq127dsjDFqfWWqXDGfjxCYqFixm0/v47ULlyrEcjcYI5/+wkuGOH/fbGjYFRo4Bs2dwemZyCJ+UDBpgOOh07mtm6kiWB6dOBM8+M9egkGOa2ctLgkUfstw8bBuzfb2bX1V9Z/FhE+/LLgRIlYj0SifSCrdatW4f0gB9++CGQ7HVeRRxyXHfutN/OigPMLvn/uaHEi/HjgfbtTY4rm4uIN/DyBT+zTpxwDnKDzdJK8mBuOwNXnvSwOgXrFkrcx2thNynwGgWvEmsLFrDUnFnAbqdBA1NTnV20JA5xpu7/V5jEQ3g2yEW0R4+mLbM1d66ZTZfkdvCgqdXMpgN+7OL25ptKD0q0JgUiEjqu++GMq1PgyvxXds9S4BrHFLh6ExPIU58Vck3CnDkKXMXgIr/AwJXGjjVXWZwO2hIXFLyKRMmXX5p0gAMH7Lcz/5WpVqq+IhIlvOTB1r08ATnnHBO4MpdZZPJkU8LSTsuWps2wxC0FryJRMG4c0LSpabpmh0Etg9vcud0emViYLcWmIbxsKIlfwpCd0JgqcNZZsR6NxIMtW4A2bZwbkDz3nNsjkjApeBWJsLffNrVajx2z387ughMmKFUgpgYNMh3x2EVr06ZYj0airUYNBa5icBFfq1b2ZV84m8CSL6rLHvcUvIpEcDKPjXwefth5kfO99wKjR+vYGFOchfMXtF+0yCzY+O67WI9K4oXTm1cSA+v9OrUTHjIEqFjR7RFJBih4FYkAzrI+9FDwlq6sj85KciovGUNr1pjVxIEBytat5tIyuzRJcmOjg2CXTcTbliwBevRwviTmlEogcUcfoyKniQuyWJGH+f9O2JmSn4tqShfjPxQTkXftSruN5ZRUGie5sTGFP7Bhx66PP9aZZiJhP27WcE1dOo2YUsIGFjpAe4bemSKngU0Hbr45+FXnF14AunfXcTHmOnQwbc7s9O9vVqZLcuICnd69Ty2XxB7N6tWcODldnFVduzbtNh6YeaJSsGAsRiYZpOBV5DSuQN90E7Bypf12fua99x5w331uj0zS4IfT++/bb+O0edeubo9I4mnGNTBw9ePCHb6JmeujANb7CzS5StYOZ9tr13Z7RHKalPMqkgHz5wNXXOEcuHLRKtMIFLjGgT/+MAnHdi66yJxhaFo8eV1zjXMjCuZBt22rRVxexstiTienrELxzDNuj0giQMGrSJj4ecYGA0wZsMPUydmzdRU6bvJcb7/dtHhNLV8+4NNPVWw32dWsaRoZOL0OOPMarISIxK9//zULNO0W4J15pkkPyZYtFiOT06TgVSRE/Ozq1cuUu7LL+aeyZYEFC8ysrMSBjh2d81zZpIB97kVY73fKFOd2d1zM89hjJndSvHPAvvtu+zrOvNLCtBDV/vUsBa8iIWAjprvuAp5/3nkflgv9/nuVCYyrPFemBNjhTBpnZET86tQBvvjCuXsIW4l26qQA1it4sP7qK/ttffoA113n9ogkgjL5fIn9TtyzZw/y58+P3bt34wz1KpYM+OsvU2GJJQKd3HqrSSfQFeg4wWTkSy6xTxdgnivPMtTiTOzMmGFKiDj1dlbdu/jHgzVnE+zCm/r1genTtQjP4/GaZl5FgvjmG3MMDBa4dusGjBunwDVuHDlipsmD5bkqcBUnnJGbONG5Dd4rrwBPPaUZ2HhWvTrQt2/a+0uWBD75RIFrAlDwKmKDn0tvvGFO0rdvt9+H9ctZfYmVdljTXOIE25w5nW0oz1VC0bAhMH6882Ief3ktBbDxibPi/Pt89tnJWQUGrDxxLVo01qOTCNBHrkgqhw4B999v1mc4dYlkPeuvv1Y3wbgsi8OGA3aU5yrhYOqAv1mBU04lcycVwMYv5nPxmMCVtAMHmsoSkhCU8yqSKr+VlZV++sl5n0qVgEmTNIEXd/buNfms69al3XbBBcCiRc6ryUWcMCeIjSyOH7ffzgDW7hK1xFdrWH7+K085rinnVSQDmMPPVKlggWuTJsCPPypwjUuPP24fuPLSLysPKHCVjODZLFdjOuUGMU1FwWt8y59fgWuCUfAqSY8TKpw8YVMBp8YDxM8nplBxzY/E6SXC4sXtgwuelYhkFGdeP/rIOYBll6Yvv3R7VCJJS8GrJDUuxrrhBqBfP+fUNQarTBNggKuFWXG+yGb5chPE+l19NdClSyxHJYmiZUtgxAj7GbwHH1RLvVhYswZYvTrWo5AY0EexJC12wuKE3MyZzvtUqGDSBG65xc2RSYax5SNXiQ8fDpQqZWbLVBZHIoUdm1IHsA89BAwdqjNbt23dCjRoANSoASxcGOvRiMv0bpOkTBPgQuHatYHNm4OnuvGYyAVa4iEMLFq1AtauBcqXj/VoJNHccw/w4YfmdcYKFuy8pcDV/cWZN95octz//ReoW9csWpCkkTXWAxBx099/m8mT2bOd92H9VtYhZ6ks5fh7WI4csR6BJCqeHHHV5pVXKnCNRROSZs2AxYtP3seGJCxt9sEH5uRCEp7edZI0pk0DqlULHriedRYwbx7QoYMCVxEJgperFbi668QJU1ybRbZTY1HuN990Ls4tCUXvPEmKE3Wu2eF6CqduWf6ukGzMdNVVbo5ORBLarl3ONWIlPN27m/audjgT/sUX5tKZJDwFr5LQ/vjDBKMvv+y8D9fzPPecmZktUsTN0UmG8AxkypRYj0Ikff/9Z/Ix77rLnEVLxr34IvDSS/bbWCLvq690AE8iCl4lIbHs1dtvAxdf7NzmnkqXBr75BujZU4vSPYM5HTfdZJKXd+yI9WhEnLs6cTX80qXAp5+anMx9+2I9Km9i0Nqtm3Mtw6lTtTgzySh4lYSzbRvQuLFZCHzwoPN+TZuazxW1u/YQFtwdM8b8m5cP2faVpbFE4m01PPOUAks4MU+zfn2dcGUkcO3a1X4bu+dNnKgmJElIwaskFF76r1rVpD4FW4TOvH52yypUyM3RyWlfguUZSeozFdY0Y3kIkXjRvDnw3Xdp72fR6GuuATZtisWoEitwJdZxrlfPzRFJnFDwKgmBV+MY13Cyg7WrnXCijp8fjzyiagKe88QTptZZagUKmJxCkXhaWHTGGfbbfv/dXO5ZudLtUSVW4MrGEHfe6eaIJI4oeBXP4wTHRReZHNdgWLd10SJTLks8Zu5cUxjezqBBQIkSbo9IxFmtWiaZvlgx++1//WVaF7PNn6TFFbbBAlc2hmjXzs0RSZxR8CqexcW7PXqYq3BspuSEnx/M5x8yBMiVy80RSkQcPuz8QXXDDcC997o9IpH08YyawanTQiJ2hrr2WufST8lax5Wz1qxtGCxwTZ0+JElHwat40i+/AJdfDgwYYI53TrjAl/s2bOjm6CTiJXLsLrHmzQu8847yPyR+nXOOCWCrVHE+A2fVjN69gx/IkuUklc/FwIHO+yhwlf9T8CqewuYpzz8PXHopsGyZ8365c5uUKC5OL1rUzRFKRK1ebf7gdlict0wZt0ckEh6mtDCFIFhZE76WW7QIXh4lkbG9K6+ijB7tvA9X2Spwlf9T8Cqe8dtvpuFAr17A0aPO+3EfBra80qxJOY8X623f3szIpMYCvo8+GotRiYSvYEFTKos1/JywFmydOvaLEhMd87nYaCBY4MpVtiL/p+BV4h47K3LhKeMVLrhywpJ//fsD8+cD557r5gglKljPdcaMtPeznzzTBdRVQryEl4NYny9YPudPPwGXXGJmapMJ39PDhwO1a6c9qI8cqcBV0lDwKnGNqY5ckMWFp3YTcH5MKWM9cOb6K6ZJkH7wnTvbb+NsLPNGRLyGByfmcL/3HpA1q/0+nHnlQq4XXkiuPFgW4GbDAdYzJJYamz7d5MGKpKLgVeJ2tpV157lg9/vvg5+ws2sgA1eVwEogLCNhV7CX+YPMDxTxsvvvN2kETCeww6CVPatZuJrdupIFnw+WhuFqXC50YxAvYsPh1E8ktrOtbdrYN6gJVLGiudJ05ZVujUxc8cMPJi3AzuDBzsXfRbykbl3zWr/pJrMw0SmIZbpBMilb1jwvWrAgQWjmVeJytjVY4Mpj2uOPAz//rMA1IV8EXFHMxVqpsd5Zs2axGJVIdFSoYAI1rrS3u8rw8ceJkQfF9/O775pLZKFQ4CrpUPAqcZXb+uSTwKFDwcsmci0Dg1w1HEhAw4YBS5emvZ9/bK441oeaJJpChYApU8xqU3+gynyoUaMSo87fqlXA9dcDDz4I3HGHyWcXOU0KXiXmE23sBMh81WC5rdShgymBxSBXEhA7DjHPz06fPs6dikS8jsEqV5vOmQOUKgX07WvKZnkZa9byfcvVtDNnmvs2bDA5YXZXVkTCoJxXiZk//gBatzZXzYI5+2zT1p7twiWBPfUU8N9/9snNzBMRSXQ8M2dLwPz5Q9v/11+BSpVM8BtPWCWAdZjt+nazogB7dXfsGIuRSYKIs1e8JMtsK6vFMLc1vcCVs608litwTQIPPQRccUXa+/lBlz17LEYkEps0glDyXBkYXnaZmdlkg4N4KKvFy2fsyc38dLvA1Y+1DzdudHNkkmAUvIqrfv/ddElkeatgdVv9ua1cXJ4nj5sjlJhhcXau1OM0e5Ei5r5bbzX5ciJyEi+782SPl+bZerB5c5N7NX68+0EsxzJtmmkwUKMG8OWXwffnQrRx44DSpd0aoSQgBa/i6mxr9erAjz8678f1OJ06abY1afHy5333mUUebFLw6quxHpFI/PnoI2DWrFPvW7ECuP12c0lr6FCTQx5NBw4An3xifh7r0c6bl/57m6kCzBe75Zbojk0SXiafL7Ezp/fs2YP8+fNj9+7dOEP1IWOCxyrGI8GCVjrvPOCDD4Crr3ZrZCIiHrNtm8lz3bkz+H7s4MWrFi1aAI0bA/nynf7P3rHDzKwyb5VNFjjzGwqmAzGg5uyFSATiNS3YkqjOtnLirHfv4CkCnG3lJFu/fslXj1tEJCzr1plOVOkFr8eOmW5VvLHUHJsiMLWAt6pVzWyBU4va1DZvBu65x8yu8sAeqgIFgAEDgLZt429RmXiaZl4lKnjVl7Ot6ZW/Yo1upjgyVUpERELAwJQNDHjGz2A2I3LmNAFsjhym0kfTps77sgoIF5KFisE1qw1wxe2ZZ2ZsfJJ09oQRr+lUSCKKawW4OJxpUMECV862PvGEqUevwFVEJAycMeXsAHOy3n8fKFcu/MdgN5jly4FFi9LPj2UwWqxY+o/JGrXsIPPnn8CzzypwlahR8CoRw/rT9eqZnPxgqVA82Z8/3zQnUJesJH6xhHP5UUTSypbNFP3npS4GsRntlx1Kaa7zzw++jQsWOAvMmsyRyK8VCULBq5w2Jp68954pNzh3bvq5rZxtZbksSVJMgK5fH7j0UlMPTUQiE8Tychfrqz7/PHDBBdENXitXNgsaFi825brYcUb1mMUlWrAlp+Wff0wufnql/Vi3dfhwVRIQmOK9/gLmbIHJWq4vvWRaqYnI6eH7iDmsPXqYtIAxY8xCK/57zx777wllMRUXenHWoUkTczv33IgPXSRUWrAlGcZqKQ8+mH66FPP2ueBUzQYEW7eavJG9e0+9nzM2nDW6+OJYjUwksfGjnrmoLKK9bJn5yrJbTN/p00fNQCTmVCpLooon78xr5UxqMGXKmDQo5sGKWHr2TBu4+md1uMpPRKKDeVtc2MWbmgSIxyl4lbBwoRXL/fEEPpj77zc1XjXZLSl+/tmczdgZNEh1IEVEJCT6tJCQHD0K9Opl2lcHC1yLFzf5r1zApcBVTrlkyel6uywldgBSvTQREQmRZl4lXVxb07Jl+u1due7mnXdU2k9sjB9vpu1TY620gQNjMSIREfGouJ55PX78OHr37o3y5csjV65cOOecc9CvXz8k+BqzuMGneeRIk4oYLHBlSb8RI0x8osBVbIuhd+liv433ly7t9ohERMTD4nrmdeDAgRg6dChGjBiBCy+8EIsWLULr1q2t1Wgd2HZOomb3buDhh4HRo4PvV6sW8NFHQNmybo1MPFkayy7XhN14unaNxYhERMTD4jp4/e6779C4cWPceOON1v/LlSuH0aNH46effor10BIauwU2bx68ZTZrYj/3nGnxGkp9a0lSLMXzwgv225guoPppIiKSSGkDNWrUwKxZs7CKre/A0nTL8O2336Jhw4aO33P48GGrVljgTUJPE+AkGdfOBAtcK1QwJTk5aabAVYJ65hn7wuhXXAHcdVcsRiQiIh4X1zOv3bt3t4LP888/H1myZLFyYJ9//nm05OohB/3790ffvn1dHWci+O8/013w88+D7/fAA6aqkSbMJF2//w4MG2a/jXXUWHdSREQkkWZeP/30U3zyyScYNWoUlixZYuW+vvzyy9ZXJz169LC6M/hvGzdudHXMXvTDD0D16sED14IFzYKsd99V4Coh4mIsdu9J7fbbVRpLREQSsz1s6dKlrdnX9u3bp9z33HPP4eOPP8Yff/wR0mOoPawz/uWHDAGefBI4dsx5v6uvBkaN0qJwCcPMmcB116W9n21gOSPL/usiIiIZiNfieub1wIEDyJyq6w7TB06cOBGzMSWKfftMymGnTs6BK6/qPvUUMGeOAlcJA2dbuZLPDquEKHAVEZFEzXm9+eabrRzXMmXKWKWyfv75Z7z66qtow+RMybCVK01Dgd9+c96nSBHg44+B6693c2SSEJjW88svae8vXBjo2TMWIxIRkQQS12kDe/futZoUTJw4Edu2bUPJkiXRokUL9OnTB9l5+TEEShs41YQJwH338bl13qduXeCTT4ASJdwcmSTMlD7LUfz9d9ptzFF57LFYjEpEROJcOPFaXAevkaDg9eSVXKYAvPii8z5ME+jdG+jTRyWwJIMmTwaaNgVSp/YwoF2xwhQIFhERSdScV4mMXbuAm24KHriymsCUKQCrjClwlQy75RZg6dK0+SYvvaTAVUREIkLBa4Jjf4crrwSmT3fe5+KLgcWLgSC9H0RCV6UK8NVXwLRpwIUXAnXqMIE91qMSEZEEEdcLtuT0MH5gm9fdu5334dq3N98EcuZ0c2SSFG64AahfH9i5Uw0JREQkYjTzmoCYxcwGRo0aOQeuXO/GhgPvv6/AVaIoa1agaNFYj0JERBKIZl4TzJEjQLt2wIcfOu/DKgITJ5r28iIiIiJeouA1wRZm3XYbMHu28z6XXmrawJYq5ebIRERERCJDaQMJ4s8/gZo1gweuLVsC8+YpcBURERHvUvCaABYtMikATh2zuFZm4EBg5EggVy63RycJ6+BBYOhQk6siIiLiEgWvCVATvnZtYOtW++1585p9unbVgm+JsMGDgUceASpVAsaNMysFRUREokzBq4dx0qtJE+DAAfvtTA/49lvToEAkorZvB/r3N/9etw644w6gRg1gwYJYj0xERBKcglcP4gQXO2Fx0stpsqtqVeCHH4Bq1dwenSSFfv3Yy+/U+/iC69JFM7AiIhJVqjbgMWwZ37Ej8MYbwWvDjx0LpNMaWCRj1qwx0/52Xn5Z+SkiIhJVmnn1EK6Lufvu4IHrQw8BX3yhwFWiqEcP4NixtPffeqtJHRAREYkizbx6xP79poYrW746YQpit26a+JIoYmrA+PH2nbQGDIjFiEREJMkoePWA//4zrV4ZN9jJnNm0em3Txu2RSVJhLitzWu2wrdt557k9IhERSUIKXuPcjh3AddcBP/9svz1HDmDMGFN1QCSqJk0y5StSy5cP6NMnFiMSEZEkpOA1jm3bBtSvDyxfbr+dMQNruNap4/bIJOkcPWpyUux07w4UKeL2iEREJEkpeI1T//wD1Kvn3DWLscL06cDFF7s9MklK778PrFqV9v6SJYFOnWIxIhERSVIKXuPQli3AtdcCK1faby9TBpg5UymG4pK9e4Gnn3au95o7t9sjEhGRJKbgNc5s3GgCV5bStFO+PDBnDlC2rNsjk6TF2q3MYUmtcmWgVatYjEhERJKYgtc4snmzyV9lt007nGmdPRs46yy3RyZJ6++/TfBq58UXgSxZ3B6RiIgkOTUpiBNbt5ocV6fA9fzzgblzFbiKy1hF4MCBtPfzxcpWbiIiIi5T8BpH5bCcclx5dZaBK9fGiLjm11+BDz5wnnVVNwwREYkBBa8xtns30KCBczmsiy4yOa7Firk9Mkl6XbsCJ06kvb9lS5W5EBGRmFHOawzt22c6Zy1ebL+9ShVTVaBwYbdHJkmPydVTp9p3xXj++ViMSERExKKZ1xg5eBC45Rbgu+/st1esCMyYocBVYmT1aiBnzrT3d+igUhciIhJTCl5j4Ngx4I47TDqAnbPPBmbNUqqAxNBDD5mmBPfeezK3tVAh4KmnYj0yERFJcgpeXebzmbjgyy/tt7OaAAPXUqXcHplIKqVLAyNGmLwWVhfo3RsoUCDWoxIRkSSnnNcYVB5yWsDNmVYGruXKuT0qkSCqVzc5LDzzEhERiTEFry566y3guefstzG3lYuzKlRwe1QiIWDqgEpjiYhIHFDagEsmTAAefdR+G1vDT5tm6rmKiIiIiDMFry6YNw+46y77q65ZswKffQZcdlksRiYiIiLiLQpeXWhSxJJYhw/bb3//fXXZFBEREQmVgtco2rYNuOkm00XLzoABphKRSEzNnw88+STw33+xHomIiEi6FLxGyaFDQNOmwIYN9ttZ653dN0Viiu1fH38ceOUV4NxzgddfB44ejfWoREREHCl4jQLmtrZt69w9iw0KXntNi7clDoweDSxaZP69c6c5q+LKQbvWsCIiInFAwWsU9O8PfPyx/bZrrgE++gjIrGde4qFHcY8eae9nZ63ffovFiERERNKlECrCxo8HevZ0bvvKklk5crg9KhEbnP7fuDHt/eXLA489FosRiYiIpEvBawTx6qvTAqwzzgC++AI480y3RyViY+tWc4nAaSWhzrBERCROKXiNkM2bgcaNzZXY1LJkAT79FLjggliMTMTG008D+/alvf+qq4Dbb4/FiEREREKi4DUC9u8Hbr4Z2LLFfvvgwUCDBm6PSiRI8eF337XfxqoDWkkoIiJxTMFrBLz1FvDzz/bb2BK2fXu3RyQSpBQGS2OxRJZdGQzOvIqIiMQxBa8RwFigS5e093O2lWtiROIGS2B9/XXa+7NnN7muIiIicU7BawQwp/XFF02r16xZzX2VKgFjx578v0jMHTlizrTssL4rqwyIiIjEOQWvEdSmDTBzJlCxIvDll0D+/LEekUiAN980NVxTK1IE6NUrFiMSEREJm+YFI6x2bbMehrOxInFj+3agb1/7bc8/rzMtERHxDM28RoECV4k7ffoAu3envb9aNXPJQERExCMUvIokul9+AYYNc67jprMtERHxEAWvIoleGqtzZ/vSWLfdZvJcREREPETBq0gimzwZmD077f1s//rSS7EYkYiIyGlR8CqSyKWxnnjCfhtLZqk0loiIeJCCV5FElS0b0K8fUKLEqfcXLw706BGrUYmIiJwWBa8iiSpTJqBFC2DlSuDJJ092zOjfH8iXL9ajExERyRAFryKJjoEq81uXLQMeewy4995Yj0hERCTD1KRAJFlccAEwZEisRyEiInJaNPMqIiIiIp6h4FVEREREPEPBq4iIiIh4hoJXkUSxdGmsRyAiIhJ1Cl5FEsE33wDVqwNNmgDr18d6NCIiIlGj4FXE644eBdq3N/+eNMlUFejbFzh4MNYjExERiTgFryJe9/LLwK+/nvz/oUPAM88ANWoAJ07EcmQiIiIRp+BVxMvWrgWefdZ+W6tWQGa9xUVEJLHok03Eq3w+oF07M9OaWtWqJ1MJREREEoiCVxGv+uQTYObMtPdnygS8+y6QLVssRiUiIhJVCl5FvGjHDqBzZ/ttnHG9/HK3RyQiIuIKBa8iXtSlC/Dvv2nvL1kSeP75WIxIRETEFQpeRbxm7lzgww/tt73+OnDGGW6PSERExDUKXkW8hIuzHnrIftsttwBNm7o9IhEREVcpeBXxEqYErFqV9v68eYE33jCLtURERBKYglcRr1i8GOjf337bc88BpUu7PSIRERHXKXgV8YLDh4H77gOOH0+77ZJLgEcfjcWoREREXKfgVcQL2EVrxYq092fNamq6ZskSi1GJiIi4TsGrSLxbuBAYMMB+W8+eQPXqbo9IREQkZuI+eN28eTPuvvtuFC5cGLly5UKVKlWwaNGiWA9LxL3qAq1aASdOpN1WrRrw1FOxGJWIiEjMZEUc+++//1CzZk3UrVsX06ZNQ5EiRbB69WoULFgw1kMTcccrrwC//26fLjBiBJA9eyxGJSIiEjNxHbwOHDgQpUuXxocBBdnLly8f0zGJuKpDB2DTJuDtt0+9v08fM/MqIiKSZOI6bWDy5Mm49NJLcfvtt6No0aKoXr063uXilCAOHz6MPXv2nHIT8ax8+YChQ4Gvvz5ZCuvii4Hu3WM9MhERkZiI6+B13bp1GDp0KM477zx89dVXePjhh9GhQweM4OVSB/3790f+/PlTbpy5FfG8664z1QbYXWv4cCBbtliPSEREJCYy+Xw+H+JU9uzZrZnX7777LuU+Bq8LFy7E999/7zjzypsfZ14ZwO7evRtnqOe7iIiISNxhvMZJx1DitbieeS1RogQuuOCCU+6rVKkS/vrrL8fvyZEjh/VLB95EREREJDHEdfDKSgMrV6485b5Vq1ahbNmyMRuTiIiIiMROXAevnTt3xg8//IAXXngBa9aswahRozBs2DC0b98+1kMTiTy71q8iIiLineD1sssuw8SJEzF69GhUrlwZ/fr1w6BBg9CyZctYD00ksmbPNlUEfv011iMRERGJa3G9YMvtBGCRmNi+3dRs/ftvIFcuYNAgoG1bIFOmWI9MRETEFQmzYEskKVIF2P6VgSsdPGjKYTVvDuzaFevRiYiIxB0FryKx1Ls3MG1a2vvHjQNGj47FiEREROKagleRWBk7ll017Lc1bGhmYEVEROQUCl5FYmHpUqB1a/ttxYubLlqZ9fYUERFJTZ+OIrFYoNW4sclvTY1tXz/7DChaNBYjExERiXsKXkXcdPQocPvtgFOXuKFDgRo13B6ViIiIZyh4FXFT587AN9/Yb3v0UeD++90ekYiIiKcoeBVxy1tvAW++ab+tTh3g1VfdHpGIiIjnKHgVcQNLX3Fm1U7ZssCnn5p8VxEREQlKwatItM2aBdx9N2DXzC53buDzz4EiRWIxMhEREc9R8CoSTYsXA02aAEeO2G//8EPgoovcHpWIiIhnKXgViZbVq02zgX377LcPGADccYfboxIREfE0Ba8i0bBlC3D99aamq53HHwe6dnV7VCIiIp6n4FUkGl55BdiwwX7bPfcAL70EZMrk9qhEREQ8T8GrSDQwJaBVq7T3N2oEvP++Wr+KiIhkkD5BRaKBZa+4GKtLl5P3XXWVSmKJiIicpqyn+wAi4oBpAS++aMpgjRgBfPklkCdPrEclIiLiaZp5FYk2zr4uWgQUKhTrkYiIiHieglcRN+TMGesRiIiIJAQFryIZNXMm8NNPsR6FiIhIUlHwKpIRb78N3HADcMstwF9/xXo0IiIiSUPBq0g4jh0DOnYEHn4YOH4c2LoVuPlmYO/eWI9MREQkKSh4FQnVnj1mpnXIkFPv/+UXoGVLE8yKiIhIVCl4FQnFqlVAzZrAtGn227/4Ahgzxu1RiYiIJB0FryLp+egj4OKLgRUrnPd59lngrrvcHJWIiEhSUpMCESfMY33kEeDjj4OXwBo+HGje3M2RiYiIJC0FryJ2Fi8G7rwTWLPGeZ9ixYBJk4ArrnBzZCIiIklNaQMiqasJvPQScNVVwQPXqlVNjVcFriIiIq5S8Crit3AhcNllQNeuwNGjzvs9+CDw/fdAmTJujk5EREQUvIr8vwRWhw5mFnXpUuf98ucHPv0UeOcdIHduN0coIiIi/6ecV0lePh8wcSLw2GPAli3B961RAxg1Cihb1q3RiYiIiA3NvEryOnEC6NkzeOCaKZPZ55tvFLiKiIjEAQWvkryyZAGef955e6VKJmh97jkgqy5SiIiIxAMFr5LcmjY1i7QC5cgB9Otn8l+vuSZWIxMREREbCl4l8VMDgmFawAsvnPx/vXrA8uVAr15A9uxRH56IiIiER8GrJGatVlYFYK3W999Pf//69YEWLYCRI4EZM4DzznNjlCIiIpIBSuSTxLF+vWnl+t57wF9/nSyD9cADZoY1GFYSEBERkbin4FW87b//zCwrZ00XLEi7/bffgK+/Bho0iMXoREREJMIUvIr3bN0KTJ0KfPEFMGUKcORI8P1fe03Bq4iISIJQ8CreWHS1ZIkJVHljG9dwfPWVmYG94IJojVBERERcouBV4s/hw8CiRcC335ob0wGYHpARXHzVqZMaDIiIiCQIBa8Sf1iuyi5/NVRsKNCwIfDgg0CjRkBmFdUQERFJFApeJfp8PpOnunq16WpVo0bw/S+/PGPBK7/vnnuA5s2BIkUyPFwRERGJXwpe5fQD0127gL//NreNG9Pe/vwT2LfP7H/ttcCsWcEf8+qrzSKrUFx4oemSdffdQMWKp//7iIiISFxT8CrB/f478McfZub0n39OfuWNwSq/Mkc1VGvWpL9PzZrO23LmBOrWBW66yaQElCsX+s8WERERz1Pwmqyr9xl05sgBFC4cfN8XXwSGD4/cz+ZM7KFDJgh1UqwYcO65JtDNlg249FIzG1urlpm5zZ07cuMRERERT1HwmuiX9HnJfvly4JdfzNcVK4C1a00A+cILQI8ewR+DgWSkx8Sfz8v9wbz6KpA/P3DZZUCuXJEdg4iIiHiWgtdEwoD0xx+BuXPNjbVR2R41WDvV9BQvjojjwq30gtebb478zxURERHPU/DqZZzFZD1UdptisPr99+Hln4YSvGZ05pVVBUqVAkqXNrezzzapAKy7WrVqxh5TREREkp6CVy9atQoYNcrcOIuZURmZeWVQWrSoCWpLlDh54378WrKkCVb5b+4rIiIiEkEKXr1i505gxAjgk0+AxYsj85jMhz1+PHiQWb06MGOGCU4ZsHKBl4r+i4iISIwoePWK3buBLl1MsJlR2bMDlSoBlSsD55wDlC+ffvBaoABQv37Gf6aIiIhIBCl49QoGmuweFWrZKq7QZycrdp1ijmmVKkCFCqb0lIiIiIhHKXj1kqeeAj76yNRptQtWWdy/Th1zY4kpzrSKiIiIJBAFr17ClfotWpi8V2Jwyi5Td91lOk6pHqqIiIgkOAWv8YAtVydNAh58MP19e/Y0bVkZsN56K1CwoBsjFBEREYkLCl5jXad1zBjg0UdNNQHmtV53XfDv4YKrWbPcGqGIiIhIXFHNo1hhsNqsmZlB5b/p/vuDd8QSERERSXIKXmNhwwZTCWDChFPv37gReOKJWI1KREREJO4peHXb0qXAVVcBK1fab3/vPeCrr9welYiIiIgnKHh10+zZQK1awD//OO9z++3AxRe7OSoRERERz1Dw6hYuzLrhBmDvXvvtbLs6dizw6adAkSJuj05ERETEE1RtwA2DBgGdOztvr14dmDIFKFHCzVGJiIiIeI5mXqNt8ODggWv9+sDcuQpcRUREREKg4DWavvgieODKMlmccT3jDDdHJSIiIuJZCl6j5eefTStXNiKw8+STwMiRpsWriIiIiIREOa/RsHkzcPPNwP799ttfew3o1MntUYmIiIh4nmZeI23fPhO4MoC18/LLClxFREREMkjBayQdPw60bGlSBuw8+CDw+ONuj0pEREQkYSh4jaTu3YHJk52rCrzxBpApk9ujEhEREUkYCl4j6dprgXz50t5fqRIwbhyQLVssRiUiIiKSMBS8RlLDhsCCBUCZMifvO/NM4MsvgQIFYjkyERERkYSg4DXSqlQBfvwRuOwyIEcOYNIk4OyzYz0qERERkYSgUlnRULy46Zq1aBFQo0asRyMiIiKSMDTzGi25cwO1asV6FCIiIiIJxVPB64ABA5ApUyZ0Up1UERERkaTkmeB14cKFeOedd1C1atVYD0VEREREYsQTweu+ffvQsmVLvPvuuyhYsGCshyMiIiIiMeKJ4LV9+/a48cYbUZ+F/tNx+PBh7Nmz55SbiIiIiCSGuK82MGbMGCxZssRKGwhF//790bdv36iPS0RERETcF9czrxs3bkTHjh3xySefIGfOnCF9T48ePbB79+6UGx9DRERERBJDJp/P50Oc+vzzz9G0aVNkyZIl5b7jx49bFQcyZ85spQgEbrPDtIH8+fNbgewZZ5zhwqhFREREJBzhxGtxnTZQr149LF++/JT7WrdujfPPPx/dunVLN3AVERERkcQS18Frvnz5ULly5VPuy5MnDwoXLpzmfhERERFJfHGd8yoiIiIi4pmZVztz586N9RBEREREJEY08yoiIiIinqHgVUREREQ8Q8GriIiIiHiGglcRERER8QwFryIiIiLiGQpeRURERMQzFLyKiIiIiGcoeBURERERz1DwKiIiIiKeoeBVRERERDxDwauIiIiIeIaCVxERERHxDAWvIiIiIuIZWZHgfD6f9XXPnj2xHoqIiIiI2PDHaf64LamD171791pfS5cuHeuhiIiIiEg6cVv+/PmD7YJMvlBCXA87ceIEtmzZgnz58iFTpkyunDkwUN64cSPOOOOMqP88MfS8x4ae99jQ8x4bet5jQ897cjzvPp/PClxLliyJzJkzJ/fMK5+As846y/Wfyz+03mTu0/MeG3reY0PPe2zoeY8NPe+J/7znT2fG1U8LtkRERETEMxS8ioiIiIhnKHiNsBw5cuDpp5+2vop79LzHhp732NDzHht63mNDz3ts5Ijj5z3hF2yJiIiISOLQzKuIiIiIeIaCVxERERHxDAWvIiIiIuIZCl5FRERExDMUvNro378/LrvsMqsrV9GiRdGkSROsXLnylH0OHTqE9u3bo3DhwsibNy9uu+02bN269ZR9/vrrL9x4443InTu39ThdunTBsWPHUrZ/++23qFmzpvUYuXLlwvnnn4/XXnsNycqt5z3QggULkDVrVlx00UVIVm4973PnzrW63KW+/fPPP679rsn4Wj98+DB69uyJsmXLWquGy5Urhw8++ADJyK3n/b777rN9rV944YVIRm6+3j/55BNUq1bN2qdEiRJo06YNduzYgWTU38Xn/c0330SlSpWsWKZixYr46KOPovvLsdqAnKpBgwa+Dz/80LdixQrf0qVLfY0aNfKVKVPGt2/fvpR92rVr5ytdurRv1qxZvkWLFvmuvPJKX40aNVK2Hzt2zFe5cmVf/fr1fT///LNv6tSpvjPPPNPXo0ePlH2WLFniGzVqlPVz1q9f7xs5cqQvd+7cvnfeeceXjNx63v3+++8/39lnn+27/vrrfdWqVfMlK7ee9zlz5rCyiW/lypW+v//+O+V2/PhxX7Jx87V+yy23+K644grfjBkzrOPMd9995/v22299ycit533Xrl2nvMY3btzoK1SokO/pp5/2JSO3nne+rjNnzuwbPHiwb926db758+f7LrzwQl/Tpk19yaiBS8/7W2+95cuXL59vzJgxvrVr1/pGjx7ty5s3r2/y5MlR+90UvIZg27Zt1ofuN998k3JgypYtm2/cuHEp+/z+++/WPt9//731f/6B+Sb6559/UvYZOnSo74wzzvAdPnzY8WfxTXb33XdH9ffximg/782bN/f16tXL+kBJ5uDVrefdH7zypEHcec6nTZvmy58/v2/Hjh2u/05e4NaxfeLEib5MmTL5NmzYEPXfKZmf95deesmakAg0ZMgQX6lSpVz6zZLzeb/qqqt8Tz755Ck/6/HHH/fVrFkzar+L0gZCsHv3butroUKFrK+LFy/G0aNHUb9+/ZR9eMm/TJky+P77763/82uVKlVQrFixlH0aNGiAPXv24Ndff7X9OT///DO+++471K5dO8q/kTdE83n/8MMPsW7dOqsAs7j7emeKBi/nXXfddVbahkTvOZ88eTIuvfRSvPjiiyhVqhQqVKiAJ598EgcPHnT5N0zuY/v7779vPSZTNyR6z/tVV12FjRs3YurUqZyYsy5/jx8/Ho0aNXL5N0yu5/3w4cPImTPnKT+L6QM//fST9fjRoOA1HSdOnECnTp2s3NTKlStb9zFHL3v27ChQoMAp+/KP68/f49fAP7Z/u39boLPOOsvKReOHDHNPHnjgASS7aD7vq1evRvfu3fHxxx9b+a7izvPOgPXtt9/GZ599Zt1Kly6NOnXqYMmSJUhm0XzOeYLG3PoVK1Zg4sSJGDRokPVh/sgjjyDZuXFspy1btmDatGk6rrvwvPMxmfPavHlz6/GKFy+O/PnzW/mYye5EFJ93BrPvvfeeFQzzpGHRokXW/xm4/vvvv1H5ffTJnQ4Gkzzw8wMgWubPn499+/bhhx9+sIKqc889Fy1atEAyi9bzfvz4cdx1113o27evNQsl7r3emcTPm1+NGjWwdu1aa5HiyJEjkayi+ZzzA4sLhfiBzg9xevXVV9GsWTO89dZb1uxIsnLj2E4jRoywggMulpHoPu+//fYbOnbsiD59+lgB1d9//20tLmrXrp01+53M2kfxee/du7cVyF555ZVW8MrgtlWrVtYVn8yZozNHqpnXIB599FF8+eWXmDNnjjU76sezuSNHjmDXrl2n7M9LFNzm3yf1ij3///37+JUvX96alm/bti06d+6MZ555Bsksms/73r17rbNC/gzOuvL27LPPYtmyZda/Z8+ejWTl1us90OWXX441a9YgWUX7OedsN9MF/IErcUUwP2A2bdqEZOXWa53PMys73HPPPdYMV7KL9vPO1fWcWWTAWrVqVSuA5Uka/wYMZJPVo1F+3nkSzOf4wIED2LBhg1WdgFVNWOWgSJEi0fmlopZN62EnTpzwtW/f3leyZEnfqlWr0mz3JzmPHz8+5b4//vjDNsl569atKfuwigCTnA8dOuT4s/v27esrW7asLxm58bxzZfvy5ctPuT388MO+ihUrWv8OXIWZLGL5eucK1mRcCezWc87/58qVy7d3796UfT7//HPr+w4cOOBLNm6/1v2LFHlsSWZuPe+33nqr74477jjlsVldg4+zefNmX7I5EcNje61atXwtWrTwRYuCVxsMZrhCd+7cuaeUOwk82LO8BEtOzJ492yovwdV2vKUuL8EyTCxRMX36dF+RIkVOKS/xxhtvWKUk+KLi7b333rPKTfTs2dOXjNx63lNL9moDbj3vr732mhU4rV692vow79ixo3VQnDlzpi/ZuPWcM2g966yzfM2aNfP9+uuv1irj8847z/fAAw/4kpHbxxhWjmGZsmTn1vPOslBZs2a1SjexZBNLZ1166aW+yy+/3JeMHnbpeWf5Q5b6ZBzz448/WpV8WBqOpfmiRcGrDZ512N34xvA7ePCg75FHHvEVLFjQqs3K2SO+KAKxLErDhg2tmQ/WRXviiSd8R48ePaWEB2vQ8ft5FlO9enXrTZeMdS/dfN5TS/bg1a3nfeDAgb5zzjnHlzNnTuvAVqdOHeuAmYzcfK2z9A1nuLkPA1mWsEnGWVe3n3fOanH7sGHDfMnOzeedn6sXXHCBtU+JEiV8LVu29G3atMmXjODS8/7bb7/5LrroIms7Y5nGjRtbM7jRlOn/v6CIiIiISNzTgi0RERER8QwFryIiIiLiGQpeRURERMQzFLyKiIiIiGcoeBURERERz1DwKiIiIiKeoeBVRERERDxDwauIiIiIeIaCVxERERHxDAWvIiIxwgaH9evXR4MGDdJse+utt1CgQAFs2rQpJmMTEYlXCl5FRGIkU6ZM+PDDD/Hjjz/inXfeSbl//fr16Nq1K15//XWcddZZEf2ZR48ejejjiYi4TcGriEgMlS5dGoMHD8aTTz5pBa2cjb3//vtx/fXXo3r16mjYsCHy5s2LYsWK4Z577sG///6b8r3Tp0/H1Vdfbc3QFi5cGDfddBPWrl2bsn3Dhg1WgDx27FjUrl0bOXPmxCeffBKj31REJDIy+XikFBGRmGrSpAl2796NW2+9Ff369cOvv/6KCy+8EA888ADuvfdeHDx4EN26dcOxY8cwe/Zs63s+++wzKzitWrUq9u3bhz59+lgB69KlS5E5c2br3+XLl0e5cuXwyiuvWMEwA9gSJUrE+tcVEckwBa8iInFg27ZtVrC6c+dOKyhdsWIF5s+fj6+++iplH+a/cqZ25cqVqFChQprH4KxskSJFsHz5clSuXDkleB00aBA6duzo8m8kIhIdShsQEYkDRYsWxUMPPYRKlSpZs7DLli3DnDlzrJQB/+3888+39vWnBqxevRotWrTA2WefjTPOOMOaYaW//vrrlMe+9NJLY/AbiYhER9YoPa6IiIQpa9as1o2YBnDzzTdj4MCBafbzX/bn9rJly+Ldd99FyZIlceLECWvG9ciRI6fsnydPHpd+AxGR6FPwKiIShy6++GIrfYCzqf6ANtCOHTus9AEGrtdcc41137fffhuDkYqIuEtpAyIicah9+/ZW/ivTAhYuXGilCjD/tXXr1jh+/DgKFixoVRgYNmwY1qxZYy3ievzxx2M9bBGRqFPwKiISh5gGsGDBAitQZdmsKlWqoFOnTlZZLFYS4G3MmDFYvHixlSrQuXNnvPTSS7EetohI1KnagIiIiIh4hmZeRURERMQzFLyKiIiIiGcoeBURERERz1DwKiIiIiKeoeBVRERERDxDwauIiIiIeIaCVxERERHxDAWvIiIiIuIZCl5FRERExDMUvIqIiIiIZyh4FRERERF4xf8A4WuGJgNDEsoAAAAASUVORK5CYII=", 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" ] @@ -2417,16 +2457,16 @@ "id": "a2b8e481", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.085348Z", - "iopub.status.busy": "2024-06-04T23:19:38.085138Z", - "iopub.status.idle": "2024-06-04T23:19:38.186025Z", - "shell.execute_reply": "2024-06-04T23:19:38.185091Z" + "iopub.execute_input": "2025-04-03T19:33:54.167414Z", + "iopub.status.busy": "2025-04-03T19:33:54.167155Z", + "iopub.status.idle": "2025-04-03T19:33:54.249548Z", + "shell.execute_reply": "2025-04-03T19:33:54.246730Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2464,10 +2504,10 @@ "id": "1a76b516", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.189290Z", - "iopub.status.busy": "2024-06-04T23:19:38.188978Z", - "iopub.status.idle": "2024-06-04T23:19:38.226707Z", - "shell.execute_reply": "2024-06-04T23:19:38.226124Z" + "iopub.execute_input": "2025-04-03T19:33:54.253007Z", + "iopub.status.busy": "2025-04-03T19:33:54.252338Z", + "iopub.status.idle": "2025-04-03T19:33:54.343941Z", + "shell.execute_reply": "2025-04-03T19:33:54.340978Z" } }, "outputs": [ @@ -2511,10 +2551,10 @@ "id": "7a68bf04", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.229152Z", - "iopub.status.busy": "2024-06-04T23:19:38.228941Z", - "iopub.status.idle": "2024-06-04T23:19:38.236919Z", - "shell.execute_reply": "2024-06-04T23:19:38.236171Z" + "iopub.execute_input": "2025-04-03T19:33:54.348524Z", + "iopub.status.busy": "2025-04-03T19:33:54.348254Z", + "iopub.status.idle": "2025-04-03T19:33:54.360318Z", + "shell.execute_reply": "2025-04-03T19:33:54.359165Z" }, "lines_to_next_cell": 0 }, @@ -2618,10 +2658,10 @@ "id": "a05df53b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.239488Z", - "iopub.status.busy": "2024-06-04T23:19:38.239277Z", - "iopub.status.idle": "2024-06-04T23:19:38.264822Z", - "shell.execute_reply": "2024-06-04T23:19:38.264344Z" + "iopub.execute_input": "2025-04-03T19:33:54.364260Z", + "iopub.status.busy": "2025-04-03T19:33:54.363536Z", + "iopub.status.idle": "2025-04-03T19:33:54.409320Z", + "shell.execute_reply": "2025-04-03T19:33:54.408390Z" } }, "outputs": [ @@ -2728,10 +2768,10 @@ "id": "ee9db82c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.267342Z", - "iopub.status.busy": "2024-06-04T23:19:38.267144Z", - "iopub.status.idle": "2024-06-04T23:19:38.271357Z", - "shell.execute_reply": "2024-06-04T23:19:38.270406Z" + "iopub.execute_input": "2025-04-03T19:33:54.413076Z", + "iopub.status.busy": "2025-04-03T19:33:54.412575Z", + "iopub.status.idle": "2025-04-03T19:33:54.417902Z", + "shell.execute_reply": "2025-04-03T19:33:54.416893Z" } }, "outputs": [ @@ -2765,6 +2805,20 @@ " known smoothing parameters, but when smoothing parameters have been estimated, the p-values\n", " are typically lower than they should be, meaning that the tests reject the null too readily.\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70598/2135516388.py:1: UserWarning: KNOWN BUG: p-values computed in this summary are likely much smaller than they should be. \n", + " \n", + "Please do not make inferences based on these values! \n", + "\n", + "Collaborate on a solution, and stay up to date at: \n", + "github.com/dswah/pyGAM/issues/163 \n", + "\n", + " gam_full.summary()\n" + ] } ], "source": [ @@ -2787,10 +2841,10 @@ "id": "cf0a024d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.274135Z", - "iopub.status.busy": "2024-06-04T23:19:38.273728Z", - "iopub.status.idle": "2024-06-04T23:19:38.283548Z", - "shell.execute_reply": "2024-06-04T23:19:38.283095Z" + "iopub.execute_input": "2025-04-03T19:33:54.424651Z", + "iopub.status.busy": "2025-04-03T19:33:54.423895Z", + "iopub.status.idle": "2025-04-03T19:33:54.439571Z", + "shell.execute_reply": "2025-04-03T19:33:54.438937Z" } }, "outputs": [], @@ -2813,10 +2867,10 @@ "id": "0fe9f031", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.286193Z", - "iopub.status.busy": "2024-06-04T23:19:38.285994Z", - "iopub.status.idle": "2024-06-04T23:19:38.372533Z", - "shell.execute_reply": "2024-06-04T23:19:38.372014Z" + "iopub.execute_input": "2025-04-03T19:33:54.451992Z", + "iopub.status.busy": "2025-04-03T19:33:54.451137Z", + "iopub.status.idle": "2025-04-03T19:33:54.849000Z", + "shell.execute_reply": "2025-04-03T19:33:54.847284Z" }, "lines_to_next_cell": 2 }, @@ -2847,17 +2901,17 @@ "id": "bdc73505", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.375490Z", - "iopub.status.busy": "2024-06-04T23:19:38.375048Z", - "iopub.status.idle": "2024-06-04T23:19:38.476110Z", - "shell.execute_reply": "2024-06-04T23:19:38.475521Z" + "iopub.execute_input": "2025-04-03T19:33:54.855349Z", + "iopub.status.busy": "2025-04-03T19:33:54.853647Z", + "iopub.status.idle": "2025-04-03T19:33:54.928284Z", + "shell.execute_reply": "2025-04-03T19:33:54.926859Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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tNuqGuJ9//tmWL18ennbdiCArVqywAwcOpOn36SayYNk2bdrU3UgWTc6cOa1169Yxx/Ppp5+6G+xEN+L4NGrUyP2veVm8eHHM4SJvrkxON1IEyyX5jUTvv/+++183fAbrPaVpUXvfsVx66aVuu4tGN5HphrLghpZIGzZscOtMdMOTbjqLRtu43o/lww8/dOtJn7/88svjmp/169fHvNlLy003TsZSt27dqPMjas5RdKPPNddcY6kRrJdq1aq59ozjmQ/daJnaGwCD7UHzef3118ccTjcJJ/9MetB2KNrWI29G0z4jumFTN5zp5ift/5E3SwbDSDxdHu/YscPWrFmT5PgRbHfBe5Ei51s3OcWi7UU31aW0bnWTo+/YnCtXLndjXkr7XDTaDoLloWOVbiSMRTdeBZ/xdXCh5kpT2g9i7QvHYnsP1o/Ohb7jr46VqbkZ9Ujo5tLgRr57773XtU9+NHT8//33391N08G2quNWsJ2ldK4+GrMitvu2bdt680CNGjUO+0xyWr+6UTQarRfdUHik21F6oR3mLEx33yqI6a7vaOHliy++cL1vaaPeunVrzPGk1OJErI0+PelO/qefftq1TuDrxVB3c+uEGiu8HQmF8+AgqFY64m2lJLmvv/46/LdaoYhXrPnVXeRqUSIWtQSgg9CcOXPc8os2LdOnT4/7JOJb7tWrV/d+Vi0BSPKukyOnK55l++yzz0Z9L5gfrScFjnhpnqJ1sKGArwug1M6PTnA6qQUhL7Un6GA+dCd5vJ/Vd2p/Ts02H0yjWnjxNQmnO9h1caw76IPPpIcg6CoI6a77yy67zD0PgpzeV8hUSxranvW6WriIHEYXfjVr1ow6frXioZZs1GpEZOtBsY5/utgJBPOt71frIbEoHGmaZs+efdh7uphbsmSJ+3vs2LHuEY/U9tiqsBEcq9QSi0/k+75169u3g/1AjqRb9LTY3oNjiM5/agElFm0f2paTXxClpW+//Tb8d8OGDY9oHJo/tUjyyiuvuPGplahY0rN1qGX/t03oPBPsa75tSaFehXCaXn0mrc4RGYnAnEV6+rv77rvd3zrIqIkXHSjUxFksavZLJdPx8DW7JWpW6FhSk0IdO3aMuxQtpelPrciLi5RCiQJGLJs3bz6i74/VbJMOMCmVYATTk/wC6UimxbdcY5UMB4LwqeCQCMs23vlJ3sSZ5idohi81F0XpNR+xBMs9npCtJroUmH0X2UdLJZX6FUGlywrACsw68QYlrEGg1v9BYO7atat7LShRDUqpk9NFtgoS4l1GybfzoInHePa3WL/WaNkdSTOAR7pe41m3Wq/RPpeafSHyojL5vn2stvfUbMs6hqRnYI4MsEey/2te9MuA71fF9DzXJZ+WYLtPqRAi2JZ07NP+Eu1YfaTniIxEYM6iPf35qMQjCMsqOVEJtEq/VLKmtiGDnaF///72yCOPpDi+o/2ZKTVWrVoVDsua7549e9rFF1/sSgr0U39QOvbiiy+GfzZKzy62j+YnvcgDgdptjbezB7Whml7TouoLQ4cOtcwgLeZHF44KVPFSqVRmEsyHfjl49dVX4/6c2rs+Eun9E3W8dAzST7v6xSMoMdZP7woEKggIfq4NQvFnn33mLlZ0Ug+q9EQLzAowt9xyiwtYCuQ69qkd7ZNPPtmNNygJ00/pav83vY4fkfu+qrncd999cX0uWkldoq3bY7W9J8L8pkTbRRCWmzdvbnfeeaf7RVfnPhWMBfOoc/e6devS9VyXlZbrkSIwZ0Pq+CEoGVbj4L5SkMxGDasrLCukqyQp1s866TntkSXqmzZt8g7rez+yfqPWQawgHK+//vrLnXB8FzDB9ET+dBpMi+rCqRQvNRdfmX3Z6uc81ac7lhd1kbScVVKiMKf62akVzIdKWtNzvWg6NX0pLfPIagHJt6G0psCrwBzUYw6Csy7ug/WpKhkKDkE9ZlWXCkJDtPrL6nhn27Zt7u933nnHdYKR2uNHsI1qmJT2N90bEk3kstP0pte6jfyelNZtZHWP9F636bm9B+snnm3ZN0xkabmvoyR1AhZLZJUQ7V+puSBX/Xl1LhbUG/ddQPg6tkorJf5vm9B5RudgXylzsC0pXB/rX6DTEzf9ZUO6uSXo9cd3g1dkHdvMclUaTLtKIHx1oNJy2pNTaZRuNgpKvXx87welZEF98qOlsOu76UMHuaDeZPKTUTAtWm6+OnLpLfJGn7RYtuptMT23hZToV4NgWX/++eepLgGKvPEltfVXUyOYRv3S4asqoJ/Mgzq/6X1hlbwec1DVIjIIB/WYRYE6GEZBJVr94uD4oZN/rLAsvm0mGK+2rWB80UTWU45WUhyMJy32/Vj0C2Lw0/fChQu9wy5atCj8d0ZdNKfF9h4cQ1TVQuHOdzHj681Ov1jGE0h/+OGHmO/pnoCAfgVJDdX/DW5YV49+vl9dfb00ptW5t9b/bRM6P8TarpNvS7r342h+FclsCMzZUHBC9F0Z6+aClA6wqaFSoEBqu4xO7bTrSj642zo96Mo6OGnPmDEjZsmhSiWCroKj0Qk7OJnp5su0+DnN930qUQsO/MnDQtB6g1rteOmllyyjqMWR4A7ryZMnx6yTp/U/adKkmONRN7LBieK///2vZSRNS3ACf++991L12WC9aNsYMWKEpZdge1Dp65QpU7z3DwTbqS9wpgV1E60qYjJz5kybP39+1JLj4LkCc1AKrRYUogWF4PihbpdjlRqquoZusIolqKohvuHUUosvsAXrVoFHJenpdawKqqZoGaqFhVjGjRsX/kw8rYukh7TY3oPtUuN4+eWXvb9W+o65kaXBvguoN954I+Z7KpQKtuFnnnkmVfVxIy9cfee75F2/p9e5t0nE/q4qj7HoPoOgWlR6HyOONQJzNhQ06aVSm59++inqlbevuaQjEXnDg342Pdpp19V3cAJNfrJTHcX0vPkhuNEyOADdddddUQ+EQ4YMOaw1ikhqwqtLly7ub81Lt27dvD/96efD4KQWy+jRo916TU6lNaqvKQrpyZtb0vOgySkNl1JpSGSJX3otW01zjx49og6jZeW7QUjNUrVs2dL9/eabb9pTTz3l/U6FWd+J72hoHQcnTW0rvhYIkgca3fATtLQybNgw70WCaHtTyw+ppSa2gos3LfM//vjjsGH068Vjjz0WrjOqOpXpXTqvpuOCoK7QEFl/ORAEQtU7DpZtrBv+guOHjhPRlqX2Y9UpVvWkWNTEW9AykEJQtIIFHUO1jaZUP1X1qIPl7yutDgJ4ZPN58ercuXO4ZFD3dURrZlMBSBf/omYFj+QGtbSQFtu7tstg+nUPjlrcSE6BbvDgwSmWqAbVEEaNGhU1bGr6dGHvO8ZrnxdVLdKNqbFCutZL5DGtatWq4Ys+FYRE+5zmXdN2LM699erVcxexQbXOaK2/qMAlmF9VaQmO5VlGRjcEjfTtGjul7rLVOLi63FQ3nXqoQwv1KqeeyyK7Oo0mNd+vziqCHrrUJaZ6iVu9erXr+lOP3bt3x9VxSWS3ueodT11HqzMDNaquzj2Crk7V9bev0fm06Bo76Gks6GBBHT6om1j1QnXjjTe6188+++yYjdqLOizQZyO7rVVPd2r8Xd2QqnMGdWqi3szU2Ycav/d1ja1G4bWc1VmGeqDT8tL4gkbg9Rg+fHjU+VG3pOqIJejJSV2Qa1tRD4QajzpiUPerQTev0TpbiXeb8C1/ddQQdCkc9DyoDj+C7ovVhXTyZRutQX416B/Z21qjRo1cr5WaT/Vap94s1bGBOmxQ5zstWrRI1XSmprOdyK6x8+fP73oE1Haidaz1NHr0aNfBhKY3Ws+W6rI4+Ly2O/WEpW1e60ZdbGs/CDpPUZfxR9s1tjqNUZfT+g4dF9RrZdB7ZrSusdO645JoHaXoceWVVx42zJ49e8LbbUodIqgXv2BY7SfqSVDdDKub6PHjx4e7ZI88fkTbtrTOgp7mgq6xg/1NxyF1ya6OQoIeAdWLZjTq/jroJVLTow4htJ9pW1cHKeqIQh3rBNtxSp3wxNKyZcvw/Oj4q+1H2472gbZt26aqa+yj6fUuHmmxvWu5RZ4nhgwZ4vZ7dY2tbrLV8Y0eVatW9e7fkZ0OqYMUHX907NC+q94ZtQ0EPROmpmts9U6o6dF61vpWBzjqnCb557W9B5/TcUrbS7AMtN50nNY5L+jESftfNEEvr+qGWt+nHiODc6/OzUfSNXaePHncslcnadp/NE+Rx9uUusaONa3pdSxJCwTmbBiY5Y477khygol8aCdUT1cphYDUfn/Qo1q0R+RJKaXvTd7ldPKHduKUdvy0CMw60ESeWJM/FPp0QEzpBKLxqPcs3zwFj8aNG3sPLDpwlSxZMubno3XdHEkHcZ3s45mWCRMmHPE2kdLyV++J1apVi/ndCs3qmtUXamTDhg2hhg0bxjU/2idSO52p6Z1SgUxh2TcNsU4OuriM7DXS99D+caQURIIgGO2hsBltvafXSS6y17BoPVRG65FRYStW98Py4osveudRF7sK0SltW1qfCsXRxqHe8l544YVQq1at3PPq1avHnB71IBkZEGM9NM26gD4SuqhQj2m+8euiWoEommMZmNNqe9e2ElwIJH/oIkddhqe0fyvsBsE82kO96ar755TmWV1864I9pXlJ/nn1zFipUqWYw+u95cuXpxhCg55iU/rOeNbz9OnTk3QrHu2hLsAje9+MRGBGwgVmeeWVV1yYKFy4sDsRasPUAV5X8pLWgVknMZ1E9J06QQTdeSY/KcUTPlTCpdCkbnF1pasraIVOlVzLsQjMQWmoSlrPOeccVwKnZalSJZVo6CSVmhOISqjatWvngqLGo5OulpPGrQOQShWida2a/MCig6yC8cknn+xKrY4//nhXSqvPx0Ol3uriVqUbOolq+Wo8CtJa5gpVKqE4mm0inuWvXx1UwqgTp4KmSop08tLBXwfj1HT5qpOjSsxVAqKTpYKOSmVUOqQLLP1KcaTTmZru3NXl8IMPPuhKmTQ/2ge0DWu+1O3xypUrY35W6/711193JeE6UWqZaN3oFyGduFXKqQu0o6XS2fbt27vtR99RsGDBUI0aNUL33Xdfit3ap/VJbt++feGuhfXQBWFKy7958+Ypjlel5hpO24C2BS1D7SPqPl7i3ba0rLRdBfuJSgnVZbNKh0W/DEXr4ju57du3u187Lr74Yle6r2nSsleJ4FVXXRV66qmnvN1ax0vhXMfJYHq17WnadLxSd/OxHOvAnFbbu9az5rd06dLhc5xKhlesWJGq45COeSol1jQoLOqYrF/uNI2pmecpU6aEbrjhBne+0vTouKpjkn4BeO2116J2kf7nn3+GevbsGTr11FPdZ1Qyrl8itc1v3bo17hCq0mkdv7UsdG450sAsmzdvdscrneu0PDRdWkfaF3Qe80nkwJxD/2R0tRAAR65NmzaujlvlypW9d30DOLZUD1X1Rm+77TbvDYIAMj9u+gMAII2p2cPgJqug6TsAiYvADABAKkVrYSig5uTat28fbiva144ugMRAT38AAKTSpZde6trqve6661wzc2ryTu2cqyOS5557Ltw+e79+/ZL0+AYgMRGYAQBIJd3+M2fOHPeI5e6777a+ffse0+kCkD4IzAAApJJutFXHEerkR6XJ6qxEveSVLVvWGjRoYB06dAh3vAIg8dFKBgAAAOBBCXM6UPfG6lq1cOHC4a4tAQAAkHmozPiff/6x8uXLu+68fQjM6UBhuWLFihk9GQAAAEjBunXrrEKFCt5hCMzpQCXLwQooUqRIRk8OAAAAktmxY4cr4Axymw+BOR0E1TAUlgnMAAAAmVc81WfpuAQAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAABAVgjMo0ePttNPP92KFCniHvXr17ePP/44/P7evXutc+fOdvzxx1uhQoWsRYsWtmnTpiTjWLt2rV155ZVWoEABK126tPXs2dMOHjyYZJi5c+faWWedZXnz5rWqVava+PHjj9k8AgAAIPNJmMBcoUIFe/zxx23x4sX29ddf28UXX2zXXnutLV++3L3frVs3++CDD2zy5Mn26aef2vr16+36668Pf/7ff/91YXn//v02f/58mzBhggvD/fv3Dw+zZs0aN0zjxo1tyZIl1rVrV2vXrp1Nnz49Q+YZAAAAGS9HKBQKWYIqUaKEDRs2zG644QYrVaqUvf766+5vWbVqldWoUcMWLFhg5513niuNvuqqq1yQLlOmjBtmzJgx1rt3b9uyZYvlyZPH/f3hhx/asmXLwt9x00032bZt22zatGlxT9eOHTusaNGitn37dlcaDgAAgMwlNXktYUqYI6m0+M0337Rdu3a5qhkqdT5w4IA1adIkPEz16tWtUqVKLjCL/q9du3Y4LEuzZs3cwgpKqTVM5DiCYYJxxLJv3z43nsgHAAAAsoaECsxLly519ZNVv7hjx472zjvvWM2aNW3jxo2uhLhYsWJJhlc41nui/yPDcvB+8J5vGAXgPXv2xJyuIUOGuCuU4FGxYsU0m2cAAABkrIQKzNWqVXN1ixcuXGidOnWy1q1b24oVKzJ6sqxPnz6uOD94rFu3LqMnCQAAAGkklyUQlSKr5QqpW7euffXVVzZixAi78cYb3c18qmscWcqsVjLKli3r/tb/ixYtSjK+oBWNyGGSt6yh56rXkj9//pjTpRJvPQAAAJD1JFQJc3KHDh1y9YcVnnPnzm2zZ88Ov7d69WrXjJzqOIv+V5WOzZs3h4eZOXOmC8Oq1hEMEzmOYJhgHAAAAMh+EqaEWdUeLr/8cncj3z///ONaxFCbyWryTfWG27Zta927d3ctZygE33PPPS7oqoUMadq0qQvGrVq1sqFDh7r6yv369XNtNwelw6oXPWrUKOvVq5fdeeed9sknn9ikSZNcyxlI2YYNG9zjWClXrpx7AAAApKeECcwqGb799ttdIFNAVicmCsuXXnqpe//pp5+2nDlzug5LVOqs1i2ee+658OePO+44mzp1qqv7rCBdsGBBVwf64YcfDg9TpUoVF47VprOqeqjt53HjxrlxIWVjx461QYMGHbPvGzBggA0cOPCYfR8AAMieErod5swqu7bDnNoSZrU80qBBA/f3vHnzvPXEo6GEGQAAHIu8ljAlzMj8Uhtg1Y52oE6dOq7UHwAAILNJ6Jv+AAAAgPRGYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAALJCYB4yZIidc845VrhwYStdurQ1b97cVq9enWSYvXv3WufOne3444+3QoUKWYsWLWzTpk1Jhlm7dq1deeWVVqBAATeenj172sGDB5MMM3fuXDvrrLMsb968VrVqVRs/fvwxmUcAAABkPgkTmD/99FMXhr/88kubOXOmHThwwJo2bWq7du0KD9OtWzf74IMPbPLkyW749evX2/XXXx9+/99//3Vhef/+/TZ//nybMGGCC8P9+/cPD7NmzRo3TOPGjW3JkiXWtWtXa9eunU2fPv2YzzMAAAAyXo5QKBSyBLRlyxZXQqxg3KhRI9u+fbuVKlXKXn/9dbvhhhvcMKtWrbIaNWrYggUL7LzzzrOPP/7YrrrqKheky5Qp44YZM2aM9e7d240vT5487u8PP/zQli1bFv6um266ybZt22bTpk2La9p27NhhRYsWddNUpEiRdFoCiU8XO/olQHbu3GkFCxbM6EkCAADZxI5U5LWEKWFOTjMnJUqUcP8vXrzYlTo3adIkPEz16tWtUqVKLjCL/q9du3Y4LEuzZs3cAlu+fHl4mMhxBMME44hm3759bhyRDwAAAGQNCRmYDx065KpKXHDBBVarVi332saNG10JcbFixZIMq3Cs94JhIsNy8H7wnm8YheA9e/bErF+tK5TgUbFixTScWwAAAGSkhAzMqsusKhNvvvmmZQZ9+vRxJd7BY926dRk9SQAAAEgjuSzBdOnSxaZOnWqfffaZVahQIfx62bJl3c18qmscWcqsVjL0XjDMokWLkowvaEUjcpjkLWvoueq25M+fP+o0qTUNPQAAAJD1JEwJs+5NVFh+55137JNPPrEqVaokeb9u3bqWO3dumz17dvg1NTunZuTq16/vnuv/pUuX2ubNm8PDqMUNheGaNWuGh4kcRzBMMA4AAABkL7kSqRqGWsB47733XFvMQZ1j1RlWya/+b9u2rXXv3t3dCKgQfM8997igqxYyRM3QKRi3atXKhg4d6sbRr18/N+6ghLhjx442atQo69Wrl915550unE+aNMm1nAEAAIDsJ2GalcuRI0fU11966SVr06ZNuOOSHj162BtvvOFarlDrFs8991y4uoX89ttv1qlTJ9c5iZoxa926tT3++OOWK9f/f+2g99Sm84oVK1y1j4ceeij8HfGgWbn40KwcAADIKKnJawkTmBMJgTk+BGYAAJBRskU7zAAAAMCxQGAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAAyCqB+bPPPrOrr77aypcvbzly5LB33303yfuhUMj69+9v5cqVs/z581uTJk3sxx9/TDLM1q1b7dZbb7UiRYpYsWLFrG3btrZz584kw3z//ffWsGFDy5cvn1WsWNGGDh16TOYPAAAAmU9CBeZdu3bZGWecYc8++2zU9xVsR44caWPGjLGFCxdawYIFrVmzZrZ3797wMArLy5cvt5kzZ9rUqVNdCO/QoUP4/R07dljTpk2tcuXKtnjxYhs2bJgNHDjQnn/++WMyjwAAAMhccoRULJuAVML8zjvvWPPmzd1zzYZKnnv06GH333+/e2379u1WpkwZGz9+vN100022cuVKq1mzpn311Vd29tlnu2GmTZtmV1xxhf3+++/u86NHj7YHH3zQNm7caHny5HHDPPDAA640e9WqVVGnZd++fe4RGbpVMq3vV0k2Yl8AFSpUyP2tUn5d4AAAABwLymtFixaNK68lVAmzz5o1a1zIVTWMgBbCueeeawsWLHDP9b+qYQRhWTR8zpw5XYl0MEyjRo3CYVlUSr169Wr7+++/o373kCFD3HcFD4VlAAAAZA1ZJjArLItKlCPpefCe/i9dunSS93PlymUlSpRIMky0cUR+R3J9+vRxVyfBY926dWk4ZwAAAMhIuTL027OIvHnzugcAAACynixTwly2bFn3/6ZNm5K8rufBe/p/8+bNSd4/ePCgazkjcpho44j8DgAAAGQfWSYwV6lSxQXa2bNnJ6nMrbrJ9evXd8/1/7Zt21zrF4FPPvnEDh065Oo6B8Oo5YwDBw6Eh1GLGtWqVbPixYsf03kCAABAxkuowKyWFJYsWeIewY1++nvt2rWu1YyuXbvao48+au+//74tXbrUbr/9dtfyRdCSRo0aNeyyyy6z9u3b26JFi+yLL76wLl26uBY0NJzccsst7oY/tc+s5ucmTpxoI0aMsO7du2fovAMAACBjJFQd5q+//toaN24cfh6E2NatW7um43r16uWaKlO7yipJbtCggWs2Th2QBF577TUXki+55BLXOkaLFi1c280BtXIxY8YM69y5s9WtW9dKlizpOkOJbKsZAAAA2UfCtsOcVdr1y85ohxkAAGSUbNkOMwAAAJAeCMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMAjl+9NAACQ+W3YsME9jpVy5cq5B9IX6zXzIDAD8OKAnfWwTrOesWPH2qBBg47Z9w0YMMAGDhx4zL4vu2K9Zh4EZgBeHLCzHtZp1nPXXXfZNddcE/fwe/bssQYNGri/582bZ/nz50/V93EBdGywXjOPHKFQKJTRE5HV7Nixw4oWLWrbt2+3IkWKZPTkZFq7du2yQoUKub937txpBQsWzOhJQhqURqbFAZuDdvpinYLjb9bEek2/vEYJM4A0DTs6YAfq1KnDATsTYp0CQOrQSgYAAADgQWAGAAAAPAjMAAAAgAeBGQAAAEivwLx3796j+TgAAACQ9QLzoUOH7JFHHrETTjjBNV3yyy+/uNcfeugh+9///pce0wgAAAAkTmB+9NFHbfz48TZ06FDLkydP+PVatWrZuHHj0nr6AAAAgMQKzC+//LI9//zzduutt9pxxx0Xfv2MM86wVatWpfX0AQAAAIkVmP/44w+rWrVq1KoaBw4cSKvpAgAAABIzMNesWdM+//zzw15/66237Mwzz0yr6QIAAAAyhVR3jd2/f39r3bq1K2lWqfKUKVNs9erVrqrG1KlT02cqAQAAgEQpYb722mvtgw8+sFmzZlnBggVdgF65cqV77dJLL02fqQQAAAASpYRZGjZsaDNnzkz7qQEAAAAyGXr6AwAAANKyhLl48eKWI0eOw17Xa/ny5XMtaLRp08buuOOO1I4aAAAAyBo3/Q0ePNguv/xyq1evnntt0aJFNm3aNOvcubOtWbPGOnXqZAcPHrT27dunxzQDAAAAmTcwz5s3z/X217FjxySvjx071mbMmGFvv/22nX766TZy5EgCMwAAALJfHebp06dbkyZNDnv9kksuce/JFVdcYb/88kvaTCEAAACQSIG5RIkSrgm55PSa3pNdu3ZZ4cKF02YKAQAAgESqkvHQQw+5Ospz5swJ12H+6quv7KOPPrIxY8a452py7sILL0z7qQUAAAAye2BWvWR1jz1q1CjXy59Uq1bNPv30Uzv//PPd8x49eqT9lAIAAACJ0nHJBRdc4B4AAABAVndEgTmwd+9e279/f5LXihQpcrTTBAAAACTuTX+7d++2Ll26WOnSpa1gwYKuI5PIBwAAAJCtA3PPnj3tk08+sdGjR1vevHlt3LhxNmjQICtfvry9/PLL6TOVAAAAQKJUyVDzcQrGF110kev+umHDhq477MqVK9trr71mt956a/pMKQAAAJAIJcxbt261k046KVxfWc+lQYMG9tlnn6X9FAIAAACJFJgVltesWeP+rl69uk2aNClc8lysWLG0n0IAAAAgkQKzqmF899137u8HHnjAnn32WcuXL59169bN1W8GAAAAsnUdZgXjQJMmTWzVqlW2ePFiV4/59NNPT+vpAwAAABIrMKvtZZUoB3Sznx4AAABAVpTqwKx6yvXq1bMLL7zQtZSh7rDz58+fPlMHAAAAJFod5lmzZtlll11mCxcutGuvvdZ1VqIWMh588EGbOXNm+kwlAAAAkCiBWeG4b9++NmPGDNu2bZvNmTPH1V8eOnSoC9IAAABAtq6SIT/88IPNnTs3/Ni3b59dddVVrooGAAAAkK0D8wknnGB79uxx4ViP3r17u9YxcuTIkT5TCAAAACRSlYxSpUrZ7t27bePGje6xadMmF6ABAACArCjVgXnJkiUuKKvTElXFUH3mkiVLutYydOMfAAAAYNm9DrOalrvmmmvsggsucEH5vffeszfeeMO1nDF48OC0n0oAAAAgUQLzlClTwjf7rVixwkqUKOFazhg+fLhrmxkAAADI1oG5Y8eO1qhRI+vQoYMLyLVr106fKQMAAAASMTBv3rw5faYEAAAAyAo3/QEAAADZCYEZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAACkZSsZu3btsscff9xmz57tWsw4dOhQkvd/+eWX1I4SAAAAyDqBuV27dvbpp59aq1atrFy5cpYjR470mTIAAAAgEQPzxx9/bB9++KHrFjsre/bZZ23YsGG2ceNGO+OMM+yZZ56xevXqZfRkAQAAILPXYS5evLjrDjsrmzhxonXv3t0GDBhg33zzjQvMzZo1o9MWAACAbCjVgfmRRx6x/v372+7duy2reuqpp6x9+/Z2xx13WM2aNW3MmDFWoEABe/HFFzN60gAAAJDZq2QMHz7cfv75ZytTpoydeOKJljt37iTvq0Q2ke3fv98WL15sffr0Cb+WM2dOa9KkiS1YsCDqZ/bt2+cegR07dhyTaQUAAEAmDMzNmze3rOzPP/+0f//9110QRNLzVatWRf3MkCFDbNCgQZaRTnzgQ0s0h/bvDf9d46FpljNPPks0vz5+ZUZPAgAAyGyBWfV6kZRKo1XnObKEuWLFihk6TQAAAMigwBxQtYWVK1e6v0877TQ788wzLSsoWbKkHXfccbZp06Ykr+t52bJlo34mb9687pGRErGkU216F3r6//298pHLrGDBghk9SQAAAEd/059airj44ovtnHPOsXvvvdc96tata5dccolt2bLFEl2ePHnc/KhjloA6Z9Hz+vXrZ+i0AQAAIAEC8z333GP//POPLV++3LZu3eoey5Ytc9UQFJ6zAlWveOGFF2zChAmuFL1Tp06uNFStZgAAACB7SXWVjGnTptmsWbOsRo0a4dfU9Jo6+mjatKllBTfeeKMrLVfzeeq4pE6dOm6+k98ICAAAgKwv1YFZ1ROSNyUnek3vZRVdunRxDwAAAGRvqa6SofrL9913n61fvz782h9//GHdunVz9ZgBAACAbB2YR40a5eorq9OSk08+2T2qVKniXnvmmWfSZyoBAACARKmSofaF1Zuf6jEHHXmoPrN6wgMAAACymiNqhzlHjhx26aWXugcAAACQlaW6SgYAAACQnRCYAQAAAA8CMwAAAOBBYAYAAADSMjAfd9xxtnnz5sNe/+uvv9x7AAAAQLYOzKFQKOrr+/btszx58qTFNAEAAACJ16zcyJEjw03KjRs3zgoVKhR+799//7XPPvvMqlevnj5TCQAAAGT2wPz000+HS5jHjBmTpPqFSpbV859eBwAAALJlYF6zZo37v3HjxjZlyhQrXrx4ek4XAAAAkJg9/c2ZMyd9pgQAAADICjf9tWjRwp544onDXh86dKi1bNkyraYLAAAASMzArJv7rrjiisNev/zyy917AAAAQLYOzDt37ozafFzu3Lltx44daTVdAAAAQGIG5tq1a9vEiRMPe/3NN9+0mjVrptV0AQAAAIl5099DDz1k119/vf3888928cUXu9dmz55tb7zxhk2ePDk9phEAAABInMB89dVX27vvvmuPPfaYvfXWW5Y/f347/fTTbdasWXbhhRemz1QCAAAAiRKY5corr3QPAAAAIKtLdR1m2bZtm+seu2/fvrZ161b32jfffGN//PFHWk8fAAAAkFglzN9//701adLEihYtar/++qu1a9fOSpQo4Xr/W7t2rb388svpM6UAAABAIpQwd+/e3dq0aWM//vij5cuXL/y62mamHWYAAABYdg/MX331ld11112HvX7CCSfYxo0b02q6AAAAgMQMzHnz5o3aQckPP/xgpUqVSqvpAgAAABIzMF9zzTX28MMP24EDB9zzHDlyuLrLvXv3thYtWqTHNAIAAACJE5iHDx/uuscuXbq07dmzx7W9XLVqVStcuLANHjw4faYSAAAASJRWMtQ6xsyZM+2LL76w7777zoXns846y7WcAQAAAGTLwKxm41RHuWTJknbnnXfaiBEj7IILLnAPAAAAwLJ7lYz9+/eHb/SbMGGC7d27N72nCwAAAEicEub69etb8+bNrW7duhYKhezee++1/PnzRx32xRdfTOtpBAAAADJ3YH711Vft6aeftp9//tk93759O6XMAAAAyBbiCsxlypSxxx9/3P1dpUoVe+WVV+z4449P72kDAAAAEqMOs276+/PPP93fjRs3tjx58qT3dAEAAACZAjf9AQAAAB7c9AcAAACk5U1/6gqbm/4AAACQXXDTHwAAAJCWXWOvWbMmtR8BAAAAsvZNf3LFFVe4qhgBlThv27Yt/Pyvv/6ymjVrpv0UAgAAAIkQmKdPn2779u0LP3/sscds69at4ecHDx601atXp/0UAgAAAIkQmNU6hu85AAAAkK0DMwAAAJAdxR2Y1ZycHslfAwAAALKyuFvJUBWMNm3aWN68ed1ztcPcsWNHK1iwoHseWb8ZAAAAyHaBuXXr1kme33bbbYcNc/vtt6fNVAEAAACJFphfeuml9J0SAAAAIBPipj8AAAAgLXv6A5C9bNiwwT3itWfPnvDfS5Yssfz586fq+8qVK+ceSD+s06yHdZo1sV4zkRDS3Pbt29VItfsfse3cudMtJz30NzKnAQMGhNfTsXjo+5C+WKdZD+s0a2K9Zp68lkP/ZHRoz2p27NhhRYsWdV2JFylSJKMnJ9PatWuXFSpUyP29c+fOcIsrSOwSjqNFCUf6Y51mPazTrIn1mnnyGoE5HRCY40NgBgAAiZDXuOkPAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAALJCYB48eLCdf/75VqBAAStWrFjUYdauXWtXXnmlG6Z06dLWs2dPO3jwYJJh5s6da2eddZblzZvXqlatauPHjz9sPM8++6ydeOKJli9fPjv33HNt0aJF6TZfAAAAyNwSJjDv37/fWrZsaZ06dYr6/r///uvCsoabP3++TZgwwYXh/v37h4dZs2aNG6Zx48a2ZMkS69q1q7Vr186mT58eHmbixInWvXt3GzBggH3zzTd2xhlnWLNmzWzz5s3HZD4BAACQueQIhUIhSyAKwQq627ZtS/L6xx9/bFdddZWtX7/eypQp414bM2aM9e7d27Zs2WJ58uRxf3/44Ye2bNmy8OduuukmN65p06a55ypRPuecc2zUqFHu+aFDh6xixYp2zz332AMPPBB1mvbt2+cegR07drjPbN++3YoUKZIuyyEr2LVrlxUqVMj9vXPnTitYsGBGTxIAAMgmduzYYUWLFo0rryVMCXNKFixYYLVr1w6HZVHJsBbG8uXLw8M0adIkyec0jF4XlU4vXrw4yTA5c+Z0z4NhohkyZIhb4MFDYRkAAABZQ5YJzBs3bkwSliV4rvd8wyhU79mzx/78809XtSPaMME4ounTp4+7Ogke69atS8M5AwAAQLYNzKrikCNHDu9j1apVltnpBkIV5Uc+AAAAkDXkysgv79Gjh7Vp08Y7zEknnRTXuMqWLXtYaxabNm0Kvxf8H7wWOYwCbv78+e24445zj2jDBOMAAABA9pKhgblUqVLukRbq16/vmp5TaxZqUk5mzpzpwnDNmjXDw3z00UdJPqdh9LroxsC6deva7NmzrXnz5uGb/vS8S5cuaTKdAAAASCwJU4dZbSyrKTj9r3rG+lsPta4gTZs2dcG4VatW9t1337mm4vr162edO3d2VSakY8eO9ssvv1ivXr1cVY/nnnvOJk2aZN26dQt/j5qUe+GFF1yzdCtXrnTN2Kk1hzvuuCPD5h0AAAAZJ0NLmFND7SkrxAbOPPNM9/+cOXPsoosuclUppk6d6gKuSozVRFnr1q3t4YcfDn+mSpUqrlk5BeQRI0ZYhQoVbNy4ca6ljMCNN97omqHT9+lGvzp16rgm55LfCAgAAIDsIeHaYc5q7fplZ7TDDAAAMkq2bIcZAAAASA8EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAACR6YP7111+tbdu2VqVKFcufP7+dfPLJNmDAANu/f3+S4b7//ntr2LCh5cuXzypWrGhDhw49bFyTJ0+26tWru2Fq165tH330UZL3Q6GQ9e/f38qVK+e+q0mTJvbjjz+m+zwCAAAgc0qIwLxq1So7dOiQjR071pYvX25PP/20jRkzxvr27RseZseOHda0aVOrXLmyLV682IYNG2YDBw60559/PjzM/Pnz7eabb3bh+9tvv7XmzZu7x7Jly8LDKGSPHDnSjX/hwoVWsGBBa9asme3du/eYzzcAAAAyXo6QilQTkALx6NGj7ZdffnHP9feDDz5oGzdutDx58rjXHnjgAXv33Xdd4JYbb7zRdu3aZVOnTg2P57zzzrM6deq4gKxFUb58eevRo4fdf//97v3t27dbmTJlbPz48XbTTTfFNW0K70WLFnWfLVKkSDrMfdagdVGoUCH3986dO93FCQAAwLGQmryWECXM0WjmSpQoEX6+YMECa9SoUTgsi0qGV69ebX///Xd4GFWxiKRh9LqsWbPGBe7IYbQgzz333PAw0ezbt88t9MgHAAAAsoaEDMw//fSTPfPMM3bXXXeFX1PQVUlwpOC53vMNE/l+5OeiDRPNkCFDXLAOHqo/DQAAgKwhQwOzqkzkyJHD+wiqUwT++OMPu+yyy6xly5bWvn17ywz69OnjSryDx7p16zJ6kgAAAJBGclkGUl3hNm3aeIc56aSTwn+vX7/eGjdubOeff36Sm/mkbNmytmnTpiSvBc/1nm+YyPeD19RKRuQwquccS968ed0DAAAAWU+GBuZSpUq5RzxUsqywXLduXXvppZcsZ86kheP169d3N/0dOHDAcufO7V6bOXOmVatWzYoXLx4eZvbs2da1a9fw5zSMXhc1W6fQrGGCgKz6yGoto1OnTmk23wAAAEgcCVGHWWH5oosuskqVKtmTTz5pW7ZscXWKI+sV33LLLe6GPzUZp6bnJk6caCNGjLDu3buHh7nvvvts2rRpNnz4cFfVQ83Off3119alSxf3vqqAKEw/+uij9v7779vSpUvt9ttvdy1nqPk5AAAAZD8ZWsIcL5UC60Y/PSpUqJDkvaBVPN1sN2PGDOvcubMrhS5ZsqTrgKRDhw7hYVWV4/XXX7d+/fq5NpxPOeUU1+xcrVq1wsP06tXLNXemz23bts0aNGjgQrY6OgEAAED2k7DtMGdmtMMcH9phBgAAGSVbtMMMAAAAHAsEZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPAgMAMAAAAeBGYAAADAg8AMAAAAeBCYAQAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAGSFwHzNNddYpUqVLF++fFauXDlr1aqVrV+/Pskw33//vTVs2NANU7FiRRs6dOhh45k8ebJVr17dDVO7dm376KOPkrwfCoWsf//+7jvy589vTZo0sR9//DHd5w8AAACZU8IE5saNG9ukSZNs9erV9vbbb9vPP/9sN9xwQ/j9HTt2WNOmTa1y5cq2ePFiGzZsmA0cONCef/758DDz58+3m2++2dq2bWvffvutNW/e3D2WLVsWHkYhe+TIkTZmzBhbuHChFSxY0Jo1a2Z79+495vMMAACAjJcjpCLVBPT++++7sLtv3z7LnTu3jR492h588EHbuHGj5cmTxw3zwAMP2LvvvmurVq1yz2+88UbbtWuXTZ06NTye8847z+rUqeMCshZF+fLlrUePHnb//fe797dv325lypSx8ePH20033RTXtCm8Fy1a1H22SJEill1s2LDBPeK1Z88ea9Cggft73rx5rkQ/NfQrgB4AAACplZq8lssS0NatW+21116z888/34VlWbBggTVq1CgclkUlw0888YT9/fffVrx4cTdM9+7dk4xLwyhUy5o1a1zgVjWMgBbkueee6z4bKzArtOsRuQKyo7Fjx9qgQYOO6LNBcE6NAQMGuF8RAAAA0lNCBebevXvbqFGjbPfu3a5kOLKkWEG3SpUqSYZXyXDwngKz/g9eixxGrwfDRX4u2jDRDBky5IiDYlZy1113ubrmxwqlywAAIMsHZlWZUAmwz8qVK91NetKzZ09X//i3335zAfX22293oTlHjhyWkfr06ZOk5FolzLrpMLuhigQAAMiKMjQwq65wmzZtvMOcdNJJ4b9LlizpHqeeeqrVqFHDhdIvv/zS6tevb2XLlrVNmzYl+WzwXO8F/0cbJvL94LXI4KfnquccS968ed0DAAAAWU+GBuZSpUq5x5E4dOiQ+z+oO6zQrJv+Dhw4EK7XPHPmTKtWrZqrjhEMM3v2bOvatWt4PBpGr4uqdCg0a5ggIKu0WK1ldOrU6SjnFgAAAIkoIZqVU2BV3eUlS5a46hiffPKJax7u5JNPDofdW265xd3wpyoby5cvt4kTJ9qIESOSVJW47777bNq0aTZ8+HDXcoZuGPv666+tS5cu7n1V7VCYfvTRR10rHEuXLnXVPtRyhlrkAAAAQPaTEDf9FShQwKZMmeJaRVCzcKoucdlll1m/fv3CVSHUmsWMGTOsc+fOVrduXVd1Qx2QdOjQITwetarx+uuvu8/17dvXTjnlFNdCRq1atcLD9OrVy32HPrdt2zbXeoNCtjo6AQAAQPaTsO0wZ2bZtR1mAACArJjXEqJKBgAAAJBRCMwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAHgRmAAAAwIPADAAAAHgQmAEAAAAPAjMAAADgQWAGAAAAPAjMAAAAgAeBGQAAAPDI5XsTRyYUCrn/d+zYkdGTAgAAgCiCnBbkNh8Cczr4559/3P8VK1bM6EkBAABACrmtaNGivkEsRyieWI1UOXTokK1fv94KFy5sOXLkyOjJyfRXd7qwWLdunRUpUiSjJwdphPWa9bBOsx7WadbEeo2fIrDCcvny5S1nTn8tZUqY04EWeoUKFTJ6MhKKdmp27KyH9Zr1sE6zHtZp1sR6jU9KJcsBbvoDAAAAPAjMAAAAgAeBGRkqb968NmDAAPc/sg7Wa9bDOs16WKdZE+s1fXDTHwAAAOBBCTMAAADgQWAGAAAAPAjMAAAAgAeBOZu699577cQTT3QdqyxZsiRdv0vfsW3btiSv6buD7126dKldfPHFdsYZZ1itWrXsnHPOsWXLlkUd186dO61r165WtWpVq127tvvMbbfdZmvWrEmTaS1ZsqT9+uuvllXs3bvXmjdvbqeeeqpbVpdeeqn99NNPcX22TZs29t///jfJawMHDnTLXw4cOOC2o9NOO82Nu2bNmvbUU0/FHN+bb77p1u0pp5xiZ599tjVs2NDefvttSwv333+/m7ZE07RpUzv99NOtTp06bnl8++23cX3u008/tfr167vPablfcMEFtmnTJssoEydOdOu0WrVqVrduXbv66qvdfu0zd+5cN/2ifa5YsWKWFb300kvuGPjuu+9m6eU5c+ZMa9SokZ100klu2uvVq2fPP/98mox71KhR7niUlnQO0vLVMtNDyzxeuvWrSpUqdskllxzz6U5rN9xwg40fPz7qe9pudZ7VMerUU0+1m2++2VasWGHZFR2XZFPaSXr16mUNGjRIk/Ht3r3b7Vz58+dP9We1Ez7yyCN23XXXuefqnSja3b06SF1xxRVWo0YNd/LQd6lXxbfeest+/vlndwCLpPckpd57sroOHTrY5Zdf7taPDuDt2rVzJ9ejNWLECNej5XfffWe5cuVy4VzrIZpx48bZk08+aVOmTHEBT1avXm3vv/9+1OEPHjzoxpnVTZo0KRxs3nnnHXdy1fL00bLRvjJr1iw766yzwsuyYMGCllGBcMiQIS4QBut28eLFbtvQyTY7U3B94YUX7LzzzsvSy3PGjBlu29Wx+Pzzz3ev/f77727eM/P+rZAcXGSkxuzZs91++/3337vCmuTnnqzk888/d/N66NAhdwGki/NvvvkmzeY5s2wL8cjeSSIbU0nA0fZGqA39o48+ciW81atXtz/++OOIxqMD6wknnBB+ri49S5cuHfUgpROQQl8QzBWG//Of/1iTJk3cc5UytmjRwpo1a+ZKqzds2OBKH1WyqQOj5lvhIqDApgCuK2hdQGQ1+fLlcxcZQRftOnGnVQm61pvWU3Cw03eptDkarReVVgcBQFS607NnzyQlYr1793YhUOtY61ulqGeeeaYb7//+97/wZ7VetY41Pq17TUsiiiwF3L59e3g9+agbV3V9W7Zs2STLslChQu5v/YKgZRKUXEeWbGr8gwcPtnPPPdeVsOk9hTOVCKrkP/JCavr06e6CWiWcKi2cM2dO1OlR81XJ160+o/UTjEfrVNNz4YUXxlVC9dVXX7lfnTRdWv+TJ08Ovzd27FhX2qVx6kI7cpn5PnesKWDo4vSZZ55JVfNeibg8H374Yevfv384LIvOL4MGDQo/13g1bzoW9+nTxxV6aPvS92peH3300STb+I033ui2aw2TUun6saZjUfv27e2WW26xF198Ma7p1jL++uuvw89VqhsUEumXueAcpf8XLFgQHk77qZatjoUKqZHLSedcFX4FpcAPPfRQeDo0fdpv9boKTfbv3+/eW7VqlVtPOqbq10cdS+KRM2dO69ixo9sOn3vuubi+R9Os77n++uvdr2lBSbYuru688053PtZ5Wl555RV3XNL2oNcjCw5U2KLv0HuXXXaZ/fbbb5Yh1Kwcsq/KlSuHvv3227iHP3ToUOjzzz8PderUKXTiiSeGbrnlltD7778f2rdvX8zPaDOrVatW6Iwzzgg/cufOHf7eJ598MlSgQIHQxRdfHOrbt2/om2++iTqeJ554InTNNdd4p2/AgAGhcuXKhTZu3Bh+bfPmzeG/33jjjVCzZs3c35s2bQqVKFEitHz5cvd87NixblrXrFkTyqpuu+220L333hvXsK1btw6VL18+yXorU6ZM6L777nPvL1u2LFShQoVQ9erVQ+3atXPL9uDBg4eNR8tZy3Xr1q0xv0vLXMNMmDAh/JqGD8b3119/hSpVqhRat26de37DDTeE+vXr5/7+/fffQyVLlnTrPhG1atXKLUc9vv/++7g+o3VQqFCh0OWXXx56+OGHQ6tXrw6/V69evdCYMWPc3z/88IPbxn/99Vf3XMv4v//9r/t71qxZoYIFC4Zeeukl93zSpEmhs88+2/39888/h84777zQ9u3b3fMff/wxVLZs2dDevXtTtW6DfSyYr1dffTVUo0YNdxyZM2eO26aC9V+0aFH3999//x2qU6dOaP369e75li1bQhUrVnTreenSpW46NmzY4N7r37+/+/6UPpcRhg0b5qZPLrzwwtA777yT4mcSdXnmz58/5nE7oPEOGjQo/HzHjh3h7Wn37t3uuxYsWOCe33///W6/0Hxt27bNHWN0PErrc1/t2rXduenOO+9Mcp7w0bGoWLFibvl89913br/9999/U5zuwYMHhzp37hweT6NGjdy5UyK/W8ugWrVqSabznnvuCa+DIkWKhNfBRRddFHrsscfCwwbjad++ffhYqmlp27ZtaOjQoe659vFx48a5v7Ud5cmTJ3wMiLbONJ+RnnrqKXfcied7XnzxRff3ihUrQnnz5g1/j5bJ6aef7rYBmTdvnhtnsD189tlnoZo1a7q/X3vtNXd+Cc4FL7/8cuiKK64IZQQCczaX2sB87bXXhgoXLhwaPXq0O8jFI9pOl/x7FXBff/31UMeOHd1J/M0330wxMGun0gni5JNPDj300EPuNYUm7bSRtMPp5H/aaae5k4tCn7z33nvugBPQDqmDR1YNzDpgazns2rUrruF1UHv66aeTvKblGwRm0YXS7NmzQ4888kjo1FNPjXogixYCtNx1otJnRMtcF1HBiScIadddd51bb1rP2i4+/vhj917x4sXDIVB0wkvUwBwYP358+EQUD82/TkC6CNIFpy5kdQLKlStX6MCBA+HhtM+88sor7m+thyAc6YSu53v27AmPLwhZzz77rLsIibxY0sWTAnhqAp4CgcJiJH2HLnxiBbwPP/zQhYLI71ZQ03Y2YsSIUJs2bcLj0niCgOf73LGmIKp9bf/+/WkamDPr8kwemFWQElxgBxddGm9wwRvMq7ZdHQcUnrRP67wiZ555Zmju3LnhYXVRmNaB+bfffnP/ax316tUr7n1v5MiRoZtvvjn8vG7duqGPPvooxenWvGufUijUBakuVIL9dPr06S5AB8c6Lavg/KpzZXAhIbqw0L7+zz//uH09WmFVqVKlkhRS6TjboUMHty70mciCDRVUpSYwDx8+PLysUvM9l1xySZLArHNGoGfPnocVzmj5aBm0bNnSFc4Fr+v79MgIiVFxBJnG448/7n460U+GH3zwgat/fO2111rhwoWParxlypRx49KjcuXK9tprr7mftiLpJ0H9VK+bzXLnzu1uktKNg/q5P/KmwuCnaVm7dq116dLF/bR48sknuzpn+rknmnh+Dk9UQf1h1XstUKBAmo03T5487idbPfTzc7ly5Wzr1q1WokSJ8DCqtqEqN4sWLQr/rKyf91UNI7L+oKYrsr65fv5TdRLdGKh1o5/jVE86q6671q1bu3n+66+/7Pjjj09xeO0n+mlTD9VfVn1o3XyZ0rJR1Rk57rjjDnuualaic6VuEH399de906B1q5/e9ROy1lVa0HfrZ9z58+cf9l7ym4Ej5833uYyo96ntW9VcZOPGje7nalUl6tSpU5Zbnjo2a//W/6LjdzC+4F6S5Mfmvn37upusdaOrqnXpZ/tjuX9XqlTJ/a9ziW5kVpWJeKtjaH2qqkRQLUGv6T4R33Rrvapqy3vvvWfLly93VRk136rCoHnXMVHVMVRFomjRorZv375w1cNgH02+n8aidafjZvJ5ilb9IrXL9quvvgpXozia74ncFjQeHf8ee+yxqPOiKjzafzIadZiRKqqrrDqQqp/Ur18/W7hwoas/pXpUmzdvPqJx6mYnhWDRgUChVuE2OdXLVP3m++67z/bs2RN+fdeuXTHHrXqhOiAqyGnHU+AOqH6VvkvzIqqLFtS/ykpUP+6NN95wd7Gn5Z3zn332mQsAAd2YpKAc7TtUB69bt27hZZ3SepO///7bhUIdaPVdkXXatC0EdQc1DbFuHszMdJGnG7kCqk+soBx5sRGrpZiPP/7Ybc+ifWHlypVun9GFqy4sdONYUJ953rx5MS8SY9GFjS6utH8EFIii0QVr9+7dk6xbhSDdCKY686rHGQQztZSii6fIexaSU/1K3Uil7w/owlj7ZuPGjV0d3uBYE1mv3fe5Y02hWNulQrMeWg66YcoXlhN5earurOoxf/nll6navxUiFRp1X4mOT5H7t7ZhbeMKXzp+pSVNW2Qhi8YfhH0fHeO2bNni9ttg3epGZy1DvZ7SdN9xxx3uuPXyyy+7OryiiwQt0yDAq857PBQ4tV8PHz48/JqmQVQ3+YknnggHay1rHQuKFCni5lPfLwruOj7E49ChQ+4mzmnTpoW3Y9/36OL91Vdfda9r/fq+55prrnHDqoAr+K6gvre+Y8yYMa4gRpQV4m1NKK1RwpxN3XXXXfbhhx+6K2WdHHWiDZobU0mhNmA9fBQ49VBpsw6qR9rLuko+H3jgAXdjzL///usq90feLBJQcFJQUFDXFa5K1TTdasZIV6DRKMzfdNNNrqREYUQ7X6BUqVLu4KUbL1RSqpsJ4inZSyS6Ga5Hjx5uGenkKFrOutAJgmz58uVdyWZq6eCmkhkd8LX8dABX6Um0VklUOqD1pVIVXcRo2avU5Nlnn/X+mnH33Xe7G5FUEq0bQiJb6FDJqm4WUlhQCXei0XJo2bKlC7xaZlomU6dODZfExNoPtZ/pBKILR5VA6QSibbdz587h0j2tT10calxqoSQ4GcdLzTaqdFnHCbWAoxO6TrTRSpzbtm3rpuPWW291YV4BSOFdNxNqnjQ9t99+uzupFi9e3N045ivV0jA6NulmXW27mj9Nvy4otD9r/9ed+tr3Nd8qjUvpc5mNb79LxOWpzyls6yZehUlNp44JCn+xfn3U97Zq1comTJjg5i9yH1YA1/avAhqNSzfQqcQ1ragJRt0crvON9icdH4MQ6dv3NI86n0Qe41RAoF9j9MtrStOtX2MVNvXLg242F4VL3cin855K3DX+eOk777nnHnd+U8GQxq9z59NPP+3OqTpualq1DQ0dOtTt15pPBXcFbU1HShfT+iVX29fevXvdxfgXX3wRbiEjpe/RRcGwYcPcc5Wexyqw0XfoczoXa7vW8ebKK690JfLaD/SrW3D+0vsabzwXOGkth+plHPNvBQDgCOgn8CCE6cJJJV66kMaRYXkiPezcudMVkihs65cKFa6pOod+JU5UlDADABKGSrRUyqUST5XSqlk0HDmWJ9LD/Pnzw82GqiRfpdGJHJaFEmYAAADAg5v+AAAAAA8CMwAAAOBBYAYAAAA8CMwAAACAB4EZAAAA8CAwA0AWpTZQM0PHHepkJrLTIABINARmAEgACp0KwMkf6mUts1BXwZomdaEcSR1ijB8/PsOmCwCOFh2XAECCUDh+6aWXkrymrs4zu6C7ZQBIVJQwA0CCUDguW7Zskkfx4sXdez/++KM1atTI8uXLZzVr1rSZM2cm+ezcuXNd6e+2bdvCr6kkWK+pZDigXt8uuugiK1CggBt3s2bN7O+//3bvqdvkBg0aWLFixez444+3q666yn7++efwZ6tUqeL+P/PMM914NZ5oVTL27dtn9957r5UuXdpNr8apbnOTT+vs2bPt7LPPdtNy/vnn2+rVq9NhqQJAygjMAJDgDh06ZNdff73lyZPHFi5caGPGjLHevXunejwK0JdccokL3AsWLLB58+bZ1Vdf7bq2lV27dln37t3t66+/dmE2Z86cdt1117nvl0WLFrn/Z82aZRs2bLApU6ZE/Z5evXrZ22+/bRMmTLBvvvnGqlat6oL51q1bkwz34IMP2vDhw9335cqVy+68884jWDoAcPSokgEACWLq1KlWqFChJK/17dvXlcKuWrXKpk+fbuXLl3evP/bYY3b55ZenavxDhw5143ruuefCr5122mnhv1u0aJFk+BdffNFKlSplK1assFq1arm/RaXPKv2ORqF79OjRrk5zMH0vvPCCKxH/3//+Zz179gwPO3jwYLvwwgvd3w888IBdeeWVtnfvXlcqDQDHEiXMAJAgGjdu7EqBIx8dO3a0lStXWsWKFcNhWerXr3/EJcyxqNrHzTffbCeddJIVKVLETjzxRPf62rVr4/4OVeE4cOCAXXDBBeHXcufObfXq1XPzEen0008P/12uXDn3/+bNm1M1TwCQFihhBoAEUbBgQVd94Uio+oSEQqHwawqukfLnz+8dh6pnVK5c2ZUIK5yrKoZKlvfv32/pQUE6oDrNElT/AIBjiRJmAEhwNWrUsHXr1rl6w4Evv/wyyTBBdYnIYZI3/6YSXdVNjuavv/5yN93169fPlULrO4ObAQOqQy1BnedoTj75ZDecbi6MDO666U91pwEgM6KEGQAShFqX2LhxY5LXdDNckyZN7NRTT7XWrVvbsGHDbMeOHe6GuUgqmVa1jYEDB7q6wT/88IO7oS5Snz59rHbt2nb33Xe7qh4KtnPmzLGWLVtaiRIlXN3k559/3lWPUDUM1SuOpFYvVEqt1jQqVKjg6honb1JOpeSdOnVydZU1zkqVKrm607t377a2bdum+TIDgLRACTMAJAgFUYXVyIeaZFN1i3feecf27Nnj6gK3a9fOheLk1RveeOMNd3OgSpKfeOIJe/TRR5MMo9A9Y8YM++6779x4VA/6vffec6Fc3/Hmm2/a4sWLXTWMbt26uXAeScONHDnSxo4d66psXHvttVHn4/HHH3c3ELZq1crOOuss++mnn9wNi0ETeQCQ2eQIRVZoAwAAAJAEJcwAAACAB4EZAAAA8CAwAwAAAB4EZgAAAMCDwAwAAAB4EJgBAAAADwIzAAAA4EFgBgAAADwIzAAAAIAHgRkAAADwIDADAAAAFtv/B6S5NUrA6S55AAAAAElFTkSuQmCC", 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" ] @@ -2891,10 +2945,10 @@ "id": "44f57a30", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.478694Z", - "iopub.status.busy": "2024-06-04T23:19:38.478494Z", - "iopub.status.idle": "2024-06-04T23:19:38.491764Z", - "shell.execute_reply": "2024-06-04T23:19:38.491357Z" + "iopub.execute_input": "2025-04-03T19:33:54.932765Z", + "iopub.status.busy": "2025-04-03T19:33:54.931894Z", + "iopub.status.idle": "2025-04-03T19:33:54.954335Z", + "shell.execute_reply": "2025-04-03T19:33:54.949457Z" }, "lines_to_next_cell": 0 }, @@ -3000,10 +3054,10 @@ "id": "3b061ffd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.494311Z", - "iopub.status.busy": "2024-06-04T23:19:38.494116Z", - "iopub.status.idle": "2024-06-04T23:19:38.498822Z", - "shell.execute_reply": "2024-06-04T23:19:38.498423Z" + "iopub.execute_input": "2025-04-03T19:33:54.958044Z", + "iopub.status.busy": "2025-04-03T19:33:54.957514Z", + "iopub.status.idle": "2025-04-03T19:33:54.965133Z", + "shell.execute_reply": "2025-04-03T19:33:54.964174Z" }, "lines_to_next_cell": 0 }, @@ -3034,10 +3088,10 @@ "id": "220e26ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.501236Z", - "iopub.status.busy": "2024-06-04T23:19:38.501049Z", - "iopub.status.idle": "2024-06-04T23:19:38.540965Z", - "shell.execute_reply": "2024-06-04T23:19:38.540424Z" + "iopub.execute_input": "2025-04-03T19:33:54.969033Z", + "iopub.status.busy": "2025-04-03T19:33:54.967633Z", + "iopub.status.idle": "2025-04-03T19:33:55.092228Z", + "shell.execute_reply": "2025-04-03T19:33:55.089007Z" }, "lines_to_next_cell": 2 }, @@ -3077,16 +3131,16 @@ "id": "94209d95", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.543510Z", - "iopub.status.busy": "2024-06-04T23:19:38.543313Z", - "iopub.status.idle": "2024-06-04T23:19:38.635421Z", - "shell.execute_reply": "2024-06-04T23:19:38.634852Z" + "iopub.execute_input": "2025-04-03T19:33:55.095854Z", + "iopub.status.busy": "2025-04-03T19:33:55.095223Z", + "iopub.status.idle": "2025-04-03T19:33:55.148813Z", + "shell.execute_reply": "2025-04-03T19:33:55.147907Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3111,16 +3165,16 @@ "id": "b86a257f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.637945Z", - "iopub.status.busy": "2024-06-04T23:19:38.637732Z", - "iopub.status.idle": "2024-06-04T23:19:38.738536Z", - "shell.execute_reply": "2024-06-04T23:19:38.737576Z" + "iopub.execute_input": "2025-04-03T19:33:55.152362Z", + "iopub.status.busy": "2025-04-03T19:33:55.151749Z", + "iopub.status.idle": "2025-04-03T19:33:55.224762Z", + "shell.execute_reply": "2025-04-03T19:33:55.224034Z" } }, "outputs": [ { "data": { - "image/png": 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o0a5t3nrrrTOqtX///pYkKzEx0e0xxrKsHN+Iv/322znWRerr3pd8FbqPHDliTZs2zercuXOOwJDXJ6hPnz6uT6ruOG87KirK54EjkKHbH//73/8sSVbDhg3drj/b0N2zZ09LMi2ou3btcrtNRkaG1bBhQ4+h+/Dhw64WzPfff9/r/X3wwQeuN/XsrSCWlTN0Dxw40ONtfP/9967tTu8K4WyJLVy4sM9P9K1atfL4AcB5+4ULF/b4LUNqaqqrleLBBx/Mtd55sCxXrpzHA8zhw4etMmXKeAzdW7dutQoVKmRJssaOHev18Tz66KOusHm67KH7999/93gbzg+3TZs2zbVuyJAhrtv49ddfvdZyuhkzZrj2XbFihddtr776aksyra159eqrr7rux9tXqdnD0Q8//JBrvR2h21uXqMGDB1uSaeXz9k2Fu9f5XXfd5boPT99QWJZlXX755X6H7rOp1ZdAvUb9EchjfyCOCc4w7as7wpo1azwGj+zvQRdeeKHX2/HHOeec47FeX/LyfuiPX3/91XV7//33X671wQ7dzlbZvLR8+mv+/Pmux3LXXXd53G7WrFmu7e65555c67OH7uXLl7u9jczMTKtixYqWJOvyyy8/o3qnT5/uup/Ro0e73ea+++6zJFmxsbG5GlYi9XXvS0SfSPnss8/mOCmiePHi6ty5s+tkjLJly+rXX39VXFycx9v4999/tXHjRq1atcq1lClTRpK0fPlyr/ffvn37vHWgD7CDBw9q8+bNWr16tav2pKQkSdKaNWuUnp4e0PvLyMhwPbfdunVTxYoV3W4XFRWlAQMGeLyd6dOnKyUlRZJ05ZVXer3P888/X5KUnp6uxYsXe9wu+wk6p7v88stdz4tzPGWn33//XZLUqVMn19/dVy1z5871uM1FF12ksmXLul0XHx+vc845R5K0ZcuWHOv27NnjOrnm6quvVtGiRd3eRvHixXX11Vd7vP8///xTGRkZKlq0qC6++GLPD0ZZj2f37t3asWOH222SkpJ0ySWXeLyN5s2bS8r9eCTpjz/+kCTVrFlTl112mddaTuf8u9SpU0eNGjXyuq3zcSxcuDDPJ+w5/x+SkpJ0xRVXeNzutttuy7WP3fr16+dxnfN5tyxLW7duzdPtOutv2rSpGjRo4HG7G2+80e/btKtWKfCvUadgHfvP9JiQmprqOt76Ok7Wq1dPpUuXluT9sXv7O/mrQoUKkqSxY8cG7cR9yZwQt23bthzvd9lHC/L1NwsG53Pz/fff69ixYwG97ezHnVtvvdXjdu3bt3edwOjtWNWoUSOdd955btc5HA41bdpUkvtjuz/OP/98nXvuuZKkL7/8Mtf6tLQ0ffPNN5KkXr16uf5/nSL9de9JRI/T7UmNGjV05ZVX6uGHH3Z7sJs9e7bee+89TZo0SQcOHPB4O74OKJ7+Ye20cuVKvf322/rrr7+8zraZmZmpgwcPejzYn4nNmze7DiQtW7b0um320WNOt2jRItfPzoOUPzw93tjYWDVu3NjjfjExMWratKmmTp2qlStXuq1lwoQJfo804e15r1u3rtd9S5YsKUm5pgnPXpc/z+3w4cPdrnM+nmPHjik62v+Xd3JysqpWrZrr+nPOOUdRUZ4/m3t6POnp6Vq1apUkqUOHDnkexcP5ONavX+/3vunp6Tpw4ECe/uedNTZr1szrcH/lypVT9erVtW3bNtc+dvP2v+R83qXcz703J06c0KZNmyRlhWFPWrRo4fft2lGrUyBfo6E49p/pMWHp0qWu0Yquu+46XXfddX7dn7fjUyDeswYMGKAZM2Zo06ZNql27tq644gpddNFF6tixoypXrnzWt5/df//9p7feeks///yzNm7c6HWUimB+APBkwIABev755zVnzhzVqFFDV111lbp06aIOHTr4DI6+OI87sbGxatKkiddtW7durbVr12rjxo1KS0tTbGxsrm3O9P8yL2699VY99thjmjJlirZv365q1aq51v3+++/av3+/JOmWW27JtW+kv+49iejQnX1GSofDocKFC6t06dJKTEz0uM/QoUNzDCPkjbch1SQzHngwff7557rrrrv8bs3zVX9eZf9n9RVsypUr53Hdvn37zuj+PbUclCxZUoUKFfKrntNfcGdSi7fn1VMLtZMzwGZkZOS4PlyfW38fz+lDGR44cMD1BpmXD1ZOgX4cnjifd3+Cevny5bVt2zavB+1A8vbcZ/8gdPr/kjeHDh1y/ewrBOQlJNhRq1OgXqOhOvaf6THBjtdAIN6zbrnlFm3evFmvvfaaUlJS9OWXX7paMmvVqqXevXvr3nvvVc2aNc/qfhYvXqzu3bu7gpkvgX6/OxNPPfWUdu3apS+//FL79u3T8OHDXQ0kDRo0UN++fXXPPfd4PYZ74jzulCxZ0meDSvny5SWZb5cOHjzo9v7O9P8yLwYMGKAnn3xS6enpGjlypJ5++mnXui+++EKSVKlSJXXv3j3XvpH+uvckokO3uxkpvZk8ebLrya9Zs6YefvhhdejQQVWrVlWxYsVc/8hPP/20nn/+eZ+35yvoBdK6detcgbts2bJ65JFHdOGFF6p69eqKj493tdJ98cUXrq+evLUKnK2zGX84+4t4yZIlfk8o4qkVJRC1XHzxxXrttdfO+HYCKRCPp3Tp0po6darf+9WoUeOM79MOzsfRuHFjjR492u/9KlWqdEb3d7bjacM+gXiNRuKxP/tx8uOPP1a7du382s9bMAhU3S+++KLuuOMOff3115o8ebLmzZunY8eOafPmzXrrrbf0/vvv67333tNdd911Rreflpamq6++Wvv371dMTIzuu+8+9e7dW+eee65KlCjh6jK6ZcsW1apVS5K973f+iomJ0eeff65Bgwbp22+/1ZQpU7Ro0SKlpaVp9erVWr16td566y2NHj1avXv3PqP7iKRjVbly5XTppZdqzJgxGjFihJ566ik5HA7t3r1bf//9tyTTjc3d/2V+fd1HdOjOq08//VSSOSjNmzfPY0tOsFqy8mLEiBE6deqUChUqpOnTp3v8asjO2rMfzPfu3et1W2/rS5Uq5fq5TJkyZ/2V5P79+5WRkeH1BeGsJ/tX3c5adu/erbS0tDx9gAu0QD+3hw8fVr169YL6wTC7kiVLKioqSpmZmdqzZ0+e93c+jiNHjtj6dylZsqT27Nnj8zmXsr66PP1/KJI4z22QTN9Gb3ytD5ZAvEYj8dif/ThZtGjRkB6f3KlWrZoef/xxPf7440pPT9fChQv1ww8/6OOPP9aJEyd0zz33qHXr1q6+wXkxZcoUV1/iDz74IMc5FdkF8u/l6Vu70x09etTnbdWvX1/PP/+8nn/+eZ04cUKzZs3SN998o1GjRunIkSO67rrrtHnz5jx9C+g87uzfv1+nTp3y2trtPFY5HI6gfyN/uttuu01jxozR1q1bNX36dHXu3FmjRo1yhWpP52Pl19d9RJ9ImVerV6+WJF1wwQVevzrN3uf4bAXqU6mz9saNG3vtixXI2k9Xq1YtFSlSRJI5ac0bb+uzH4Rnz5591nWlpaV5PQHi1KlTWrZsmSTlevE6a3G2RoRK9pMFA/Hcnjx50tb/BV9iYmJcz/XMmTPz3AqV/SQeb31Uz5azxiVLlnjttrVv3z7XzLbhFn7yonDhwq6WQW8nJkv2HkvyIhCv0VAc+89WkyZNXO8fgThO2ikmJkbt2rXTO++84zo5zrIs/fTTTzm28/f90Pn3kqRrrrnG43a+/l55ef+Nj4+XJK+zIEvyOvunO4ULF1bXrl31xRdf6PXXX5dkujE4TzT3l/O4k5aW5no/88Q5G+g555zjtj93MPXo0cPVsObshuS87Nixo+tE4tPl19d9gQrdzjdVb59Uly5dqvnz5wfsPgsXLuz6Oa/To2fnT+179uxxnfFrh+joaNe00n///bfHFszMzEzXtNjudO3a1dWf7L333gvI14Le7m/MmDGuA2nXrl1zrHOOquHsmxgqFStWdJ1x/uOPP3rsW3b06FH98MMPHm+nV69erjead955J+B15kWvXr0kSVu3btVvv/2Wp32dfxfLsvTuu+8GvDYn5//DoUOH9Msvv3jc7vPPP3f9n57+PxRpunTpIskc67KHm9ONGjUqWCV5FYjXaCiO/WerTJkyatOmjSTpm2++CZtvHnxx/n9JuU9M8/f9MPsHYE9/s8zMTFdLpid5ef91drFbsmSJx/ek1atXa8WKFV5vxxtvz40v2Y87zv7Q7sydO9c1ElY4HKuioqJcrdk//fSTxo8f7/rg4u4ESqf8+rovUKHb+Ylq1qxZrjP4s/v333/Vv3//gN5n9q+PNm/efMa346x948aNmjNnTq71x44d0/XXX2/7ySR33323JHMAu/POO92eZPHyyy/nGiUku6SkJA0cOFCSNGfOHD344INev9Lbu3evPvvsM691ffjhh5o1a1au65OTk/Xwww9LMl/Rnj6U4YABA1SlShVJ0sMPP6wZM2Z4vZ9Zs2Zp+vTpXrc5U87nNjk5WYMGDXK7zYMPPuj1BJM6deroqquukiR99913euutt7ze59atW/Xtt9+eYcXeDRw4UMWKFZMk3XnnnV5H/fjnn39y/N6tWzfXCDivv/661w8akhn9ZezYsXmu8eabb3Z9ABw0aJB27dqVa5vly5frpZdekmT6jPfp0yfP9xNO7rjjDtcHs9tvv93tMePnn3/WmDFjgl2aW4F4jYbi2B8ITz75pCQzfOCVV16Z40TY0508eVLDhw/XiRMnbK1p9OjRXr8VcvbVlXKfK+Lv+2H21s8RI0a43WbIkCFasmSJ11rz8v7bqVMnSWYIVXfHxMOHD3sdqu/AgQMaO3as10Ykb8+NL61atXKNKPTpp59q8uTJubZJSUnRnXfeKcmEXed7SqjdcsstcjgcOnbsmCuAx8fHu96r3Mm3r/uzGuU7BPydBt6d7LOsVaxY0Xrvvfes2bNnW7Nnz7Zef/11q0KFCpbD4bDatm3rdUD9vNx/amqqa0rVZs2aWX///be1fv16a+PGjdbGjRtzzNTkbSD/BQsWuNYlJSVZL774ojV9+nRr/vz51gcffOCasKB9+/ZeJx8IxDTwvXr1ct1H69atre+++841ze0111xjSbJatGjh2sbTNPDZp5lu3LixNWzYMGvWrFnW0qVLrSlTpljvv/++1bt3bys2NtZq3rx5rtvIPg18tWrVrMKFC1tDhgyxZs6caS1YsMAaNmyYa4B/Sdabb77p9vHMnTvXiouLsyQz01e/fv2sH3/80Vq0aJG1YMEC67fffrOefvpp1xSw7ib08fd/wtvzn56ebjVt2tR1Wz169LB+/fVXa/Hixdavv/5qdevWLddz62kWwJo1a7q2Of/8863PPvvMmjt3rmuq5jfeeMPq2rWrFRUV5XbiDX//T3xNPpF9GvgiRYpY999/v/XXX39ZS5cutWbOnGl9+OGH1sUXX2zVrFkz176bNm2ySpYs6dq/V69e1ujRo6358+dbixYtssaNG2e9+OKLrgl6Bg0a5LVWT7JPA1+uXDnr7bfftubPn2/Nnj3bevbZZ12zvLqbBt7JjslxvMl+HHT3P+Dr73fHHXe49m/QoIE1YsQIa9GiRdaUKVNc08A7J5yQZA0dOtS2Wv1xtq/RUBz7A3FMsCzLeuCBB1y3Vb58eWvo0KHWpEmTrKVLl1qzZs2yRowYYd16661WiRIlLEnW4cOHc+wf6AlpnK+Tu+++2/rqq6+sOXPmWEuWLLH++usv66GHHnJN/128ePFcM7z6+3545MgRq2zZsq6/95133mmNHz/eWrRokfXdd99ZXbp0yfV+5+59ZuPGja713bp1s6ZPn25t2LDBdX/ZZ9Tct2+flZCQ4JqQ5dlnn7XmzZvneo+tXbu2VbhwYdcx+vTX+tatWy1JVvXq1a2HHnrI+v7776158+ZZixYtssaOHWvdcccdrpkPK1WqlOvv5I/s08DHxsZagwYNsqZNm2YtXLjQ+uSTT3Ic931NA382kwCdiYsuushVmyTr1ltv9blPJL7ufSlQoduyLOvmm2/O8YfPvhQqVMh65513fL6Z5PX+nTP/uVuyvwn5ut/Tp1c/fRk0aJDPA2wgQndqamqOg93pS9OmTa3Fixd7PRg6b+eKK67w+picywUXXJBr/+wHhYULF1qlS5f2uL+7acqzmzt3rmsKX1/LyJEjc+0fqDfYXbt2WXXq1PF43926dbMmTJjgM8Ts2bPHNV2zr+Xmm2/Oc51O/gSvESNGuN6IPS2eDuzr16/PMbupt+XZZ5/1Wqs3L774ousN0d0SFxfn9u/uFGmh++TJk9all17q8fHWqFHD2rRpk+v3V155xbZa/XW2r9FgH/sDdUzIzMy0nn32WSs6Otrn4y5WrFiuKbftCN2+lsTEROuvv/5yu7+/74fjx493BXR3S+fOna1Vq1a5fvf0PuOcsdbdcvrz8cMPP7hm9D19KVKkiPXjjz96fK07Q7evpUKFCtaiRYvO+PmfMGGC68OBp+Xee++1MjIy3O4fqtCdfWZoSdbs2bP92i/SXve+FKjuJZLpC/XVV1+pY8eOio+PV1xcnKpVq6b+/ftrzpw5euCBBwJ+n6+88oo+/fRTdezY0a8xpT15+umn9eeff6pbt24qUaKEYmNjVblyZV1xxRX6+++/9cYbbwS4cvfi4+M1bdo0vf/++2rZsqWKFy+u+Ph4NWnSRC+//LLmzJnj1wgP8fHx+vnnnzVz5kzddtttqlOnjuLj4xUdHa2SJUuqZcuWuvfeezVu3DhNnDjR6221aNFCS5Ys0f33369atWqpcOHCKlWqlHr06KFx48b57Bfcpk0bbdy4UR999JEuueQSVaxYUbGxsSpcuLCqVKmibt266cUXX9S6devyNFNfXlWsWFFLly7VCy+8oIYNG6pIkSJKSkpSmzZt9MEHH+ivv/7y68SY8uXLa8aMGfrjjz/Ur18/1axZU0WLFlVMTIzKlCmjdu3aadCgQZo+fbrX/oGBMGDAAG3evFlPPPGEmjdvrqSkJBUqVEglSpRQmzZt9Pjjj2v8+PFu9z333HO1bNkyffPNN+rbt6+qVq2qIkWKKDY2VhUqVFDnzp315JNPavHixTnGgM2rxx9/XEuXLtXtt9/uOmG4WLFiqlevnh544AHb/+7BFhsbq99//11ffvmlOnTooMTERBUtWlT16tXT448/rsWLF+cYPcPb3AfBcrav0VAc+wPB4XDo6aef1oYNG/Too4+qRYsWrveR+Ph41a9fX/369dPIkSO1Z88e18nudlm1apVeffVV9erVS/Xr11epUqVUqFAh13HqmWee0fr169WjRw+3+/v7fti9e3ctWrRIN9xwgypWrOg6dnXq1EmffPKJJk+e7Oq+5s3o0aP12muvqVWrVkpMTPQ62ddVV12lOXPm6PLLL1eZMmUUGxurKlWqaMCAAVq4cKHXmUGrVaumBQsWaOjQoerWrZvq1KmjpKQkRUdHq3Tp0jr//PP1+uuva926dT4npvKmW7du2rRpkx5//HE1adJECQkJiouLU9WqVdWvXz/NnDlTw4YN8/o4Q6FPnz4qXry4JDMxj79DYOa3173j/1M8EHFuuukmjRw5UtWqVdO2bdtCXQ6Qr8yaNUsdO3aUZKaTzn4SGADkxcaNG13Twr/66qt69NFHQ1xRaITXRyEAQFhwnkwWExNzVi1zAOD8RjU6OjpffWuYV4RuAChg/vvvP68jYUyYMEEff/yxJDN0V/ZJdQAgLw4dOqRPPvlEkulm4pymviAqUDNSAgBMv9zevXvrqquuUteuXVWrVi1FRUVp+/bt+v333zV69GhlZGSoSJEiruESAcBf+/btU2pqqnbv3q2hQ4fqwIEDcjgcGjJkSKhLCylCNwAUQKmpqfr888/1+eefu12fkJCgH3/80dUPEwD89eijj+aatO6ee+5Rs2bNQlRReCB0A0AB06JFC40YMULjx4/X8uXL9e+//+rQoUNKSEhQ7dq11aNHDw0cONDr1MkA4EtsbKxq1aql22+/Xffdd1+oywk5Ri8BAAAAbEZLtw+ZmZnavXu34uPjXVMnAwAAIHxYlqXDhw+rYsWKYTdOuROh24fdu3erSpUqoS4DAAAAPuzcuVOVK1cOdRluEbp9iI+Pl2T+iAkJCSGuBgAAAKdLTU1VlSpVXLktHBG6fXB2KUlISCB0AwAAhLFw7gocnp1eAAAAgHyE0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2Cw61AUAACLUqVNSerq5LFRIio2VonlbAQB3ODoCQEGWlibt2SPt2pVz6ddPatLE+759+kh//pnzuqgoKS7OLIULS4mJUsmSUokSWZelSkmVK+dcEhPteoQAEBYI3QCQ31mWtHu3tHattGGDtH69WTZskLZtM+tP16SJ79BdqFDu6zIzpePHzSJJycn+1RgfL1WtKv32m1Srln/7AEAEIXQDQH5iWdL27dKSJdLixeZyyRJp37683U5Kiu9tAtmV5PBhafVqqXRp39tu2SJVq+Y+9ANAmCJ0A0Ck27lTGjNGmjFDmjkz7wHbnUOHfG8T6P7b5cr57mZy9KhUu7ZUpIjUooXUvr3Urp3Utq3ptgIAYYrQDQCRbsUK6YEHAnubwW7plqQ6dXxvs2KFac0/dsx8yJgxI2td3bomhF94odSliwnxABAmCN0AEOnat5ccDvd9s8/UsWO+t7nsMql6ddPNIyNDOnky53L8uGkxP3BAOnjQXHprQT/3XN/3uWyZ53Xr1pnl88/N7w0bSl27muX8802/cQAIEUI3AISjf/+Vfv1Vmj1b+vJLE6o9SUqSGjf2HkhPV66caVmuU0eqWVOqVClrqVjRv4B6zTVmyYuMDPPY/vnHLDt3mssdO6TOnX3vv3Sp//e1apVZ3nlHiokxt9+rl1mqV89b3QBwlhyWFcimkfwnNTVViYmJSklJUUJCQqjLAZCf7d4t/fKL9PPPpttEZqa5fulS3yOJPPCA9N57ua+Pj5eaNpWaNTOX9eqZFuVIHaKvVStp4cKzv51GjUz4vvxyqXlz7x9qAIS9SMhrtHQDQCilppqg/dVX0tSp7ruI/Pyz79B9/vnSsGGmxbtjR3NiYbNm5qTDqHw0+fAzz0gLFkjz5pklNfXMbmflSrO89JK0aJEJ3gBgI1q6fYiET04AIkx6ujRxognav/2WNaa1J/Xrm+H0vDl+3Ex0E6kt2GciI0Nas0aaM8d0w5k504w7nhfVq5shCGnpBiJaJOQ1WroBIFi2bJE++0z64gtp717/91uzxpwgWLeu522KFDFLQVKokOkm0qiRdOed5rotW6TJk6VJk8zl/v3eb+PqqwncAIKC0A0AdkpPl37/XfrkE+nvv8/sNpo18x0eYdSsaZbbbzd94pctk8aPN3+DBQtyd9/x50TQn3+Wvv9eGjBA6tGDSXkAnBG6l/gQCV9XAAhDu3dLH34offpp3lq1ndq2lfr2la64QqpRI/D1FUR790p//imNHWs+AFWsKG3Y4Lulu0cPacIE83OVKtIdd0i33SaVL29/zQD8Egl5jdDtQyT8EQGEmTfekIYMkU6dytt+TZpI/fub1tdKlWwpDf/v+HFp61bTX96bXbukqlWzRpJxio6W+vSR7r5buuACuqgAIRYJeY3uJQAQaPXq+R+4K1eW+vWTbrjBTOaC4ChSxHfglszJrqcHbsn8fX/6ySx165ohG2+8USpaNPC1AsgXaOn2IRI+OQEIM5mZJoht3Oh+fUyMGR/69ttNKyl9hMOTZZlgvm6df9uXLCnddZd0772m6wqAoImEvJaPBm8FgDARFSXdf3/u62vVkl591czA+P33ZnpyAnf4SkszfeorV/Zv+wMHzLjf1aubVu/ly20tD0BkIXQDgL/27ZMee8yMB+3LgAFSQoIJ4Jdfboaw27BBevRRqWxZ+2vF2YuLk1580Yz9PXasdMkl/vXdTk833VKaNJEuvdSMIw6gwKN7iQ+R8HUFAJvt2ye9/ro0fLg5Aa97dzMMnS+//y6dd55p+UT+sG2bGf7x88/N/4W/OnWSnnjCfLvBSZdAwEVCXiN0+xAJf0QANtm3z4xEMny4dOxYznVLlkhNm4amLoReWpoZv/vtt6WFC/3fb8AAacQI28oCCqpIyGt0LwGA0x04IA0ebMbHfv313IFbMn2zUXDFxkrXXSfNny/NmmX6fkf58Zbas6f9tQEIS4RuAHA6dkx65ZWsEx7dhW2nH3+UNm0KXm0ITw6H1L69afXetMkMHVismPttzznHTHgEoEAidANAerr00UdS7dpmUptDh3zvc/nl9M1FTjVqSO+8I23fLj3zjFSiRM71gwczWg1QgBG6ARRclmVarOvXNzML7tnje5++faUVK8ykKLVq2V8jIk+pUtLQoSZ8v/66mS6+ShUzAZIvu3Z5Ht8dQEQjdAMomObPlzp0kK6+2r9uIn37mnGXf/pJatTI/voQ+eLjpYcfNtPN//mn6Qfuy5Ah5kPgwIF5Gx0FQNgjdAMoWHbsMNOut2nj3/jJvXpJy5aZsH3eebaXh3yocGH/PqgtXy6NHm2mmB8+3HyT8vzz3s8tABAxCN0ACoajR804yXXqSN9843v7Dh3MqBS//y41bmx/fcBjj5kuT05HjkhPPy3VrWtmMGWEXyCiEboBFAwZGdIXX0gnTnjfrlEj6Y8/pBkzzKgUQDBMnixNmOB+3c6d0rXXSuefLy1eHNy6AAQMoRtAwZCQ4H1s7cqVpVGjpKVL/Z/uGwiUVaukmBjv28yaJbVsKd16q7R3b3DqAhAwhG4ABccNN0ht2+a8rnhx6YUXpPXrpf79GdINofHAA9LatdI113jfzrLMNzbnnisNG2a+wQEQEQjdAAqOqCgTVBwO8/Ptt5vh2Z54QipaNNTVoaCrVUv67jtpwQLfXZtSU6X77jMt3/PnB6c+AGeF0A0gf1i92r+v3Js1k957z3Qj+eQTM4YyEE5atpRmzjQBvEoV79suXWq+vbnzTmn//uDUB+CMELoBRLZjx8zYxk2aSIMG+bfPwIEM/4fw5nCYribr1pmJdooU8bytZZkPkHXqSCNHMsoJEKYI3QAi17hxUoMG0iuvmLGNv/7ajAIB5BdFi5op5devNyOYeLN/v3TTTVK3btKWLUEpD4D/CN0AIs+//0rXX29GGdm2Lee6u+/2PSwgEGmqVJG+/dZ8qKxTx/u2kyaZSXYAhBVCN4DIYVnSV19J9eqZAOLOxo3Sa68Fty4gWC68UFqxQnrpJc9dTurXNxPtAAgrhG4AkWH7dunii6Ubb/R+wlh0NMOoIX+LjTXnMaxZI/XunXOdwyF9+qkUFxea2gB4ROgGEN4yM80wfw0aeJ6xz6lDBzOaw7PPBqc2IJSqV5d+/dUslSqZ6+65R2rXLoRFAfCE0A0gfG3ZYr5Ov+8+6ehRz9slJZnWvenTpYYNg1YeEBZ69zat3oMHm24n/mCEEyDoCN0Awk9mpjR8uBnWb/p079v27WsCx223mQlvgIIoIUF6+WVz6cvGjVLr1tKiRfbXBcCFdygA4WXbNqlrVzOWtrfW7QoVpF9+kX76yfwMwLeMDOnmm6WFC6U2baTnnjPDbQKwHaEbQHiwLNNFpFEjaepU79vedptp3b788uDUBuQX778vzZ5tfs7IMGOAt28vbdgQ2rqAAoDQDSD0Dh6ULrtMuuMO6cgRz9tVrWrGIP70U9OPG4D/Nm6UHn889/ULFkhNm0offkhfb8BGhG4AoVe0qLRjh/dt7rxTWrVK6tIlODUB+c3rr0vHj7tfd+yYGfnkkkukPXuCWxdQQBC6AYReXJyZQS82Nve6KlWkv/+WPvpIio8Pfm1AfjF8uOlOUqiQ523++sucwDx2bPDqAgoIQjeA8NCokfTiizmvu/1207p90UWhqQnIT2JipKFDpTlzpHPP9bzdf/+Z7l733ee5ZRxAnhG6AYSPBx+Uzj9fKl9e+vNP6ZNP/BsCDYD/WrUyk0jde6/37YYNM9uuWhWcuoB8jtANIHwUKiR9/bW0cqXUs2eoqwHyr6JFTageP977kJurVkktWphtOckSOCuEbgD2++Yb6dJL/RsPuHJlqXRp+2sCIHXvbj7k9u3reZuTJ01Xk8svlw4cCF5tQD5D6AZgnyNHpAEDpH79THeRl18OdUUATleqlPTjj2YozqJFPW/3229maME5c4JXG5CPELoB2GPZMql5c2nUqKzrnn1Wmjs3ZCUB8MDhMJNOLV4sNWniebsdO8x5F6+8ImVmBq08ID8gdAMILMsyQ5O1aZN7lruMDNPqnZoamtoAeFe3rjRvnjRokOdtMjKkGTOCVxOQTxC6AQTOwYOmb+jAgaYfqDtbt0o//BDcugD4Ly5OeuMNc5Jl2bK511esKI0cKUURIeAG34B4xCsGQGDMn2/6e44Z43mbxETpu+/M19gAwlv37qab2IUXZl0XFWVOjC5TJmRlIYwdPCg1a2bOEUAuhG4AZ8eypHfflTp2lLZv97xd69ZmbOBrrglebQDOToUKZkbY554zgfuZZ6ROnUJdFcKRZUk33SQtXy5dfbV0//1SWlqoqworERe6hw8frurVq6tw4cJq3bq1FixY4HX7H3/8UXXr1lXhwoXVqFEjjRs3LkiVAgVASop05ZXS//4npad73u6RR6SZM6UaNYJWGoAAKVRIeuopacEC6YknQl0NwtVbb0m//571+/vvm8aYbdtCVlK4iajQ/f333+uhhx7SM888oyVLlqhx48bq3r279u3b53b7OXPm6LrrrtOtt96qpUuXqk+fPurTp49WMbsWcPaWLjWjk/zyi+dtSpeWxo2TXnvNTEENIHI1b24CuC+HD5vhB5lMp+CYPVt67LHc1y9YYBplIElyWFbkvCpat26tli1batiwYZKkzMxMValSRffdd58GDx6ca/trrrlGR48e1R9//OG6rk2bNmrSpIk++ugjv+4zNTVViYmJSklJUQLTUQPmjfTTT81Xh55OlpSkzp3N7JIVKwatNAAhZlnS9debczcuv1z68ktzLgfyr3//Nefz7NqVe13Zsua8AG+zngZIJOS1iGnpTktL0+LFi9W1a1fXdVFRUeratavmehj3d+7cuTm2l6Tu3bt73F6STp48qdTU1BwLgP937Jh0883SnXd6DtwOh/kqetIkAjdQ0Hz0kQnckjmpukUL08cX+VNmptS/v/vA7XBI334blMAdKSImdP/333/KyMhQuXLlclxfrlw5JScnu90nOTk5T9tL0ssvv6zExETXUqVKlbMvHsgPNm+W2rUzQ4V5Urq09Ndf5qQrf76GBpB/LF6cuyvBpk1mzP4vvwxJSbDZyy9LEya4X/fsszlHvkHkhO5gGTJkiFJSUlzLzp07Q10SEHqnTkk9enhvsWrf3vTz7t49eHUBCA/p6dK117ofreLECemWW6Rbb5WOHw9+bbDHpEnS00+7X3fRRdLjjwe3nggQMaG7dOnSKlSokPbu3Zvj+r1796p8+fJu9ylfvnyetpekuLg4JSQk5FiAAi862swy6XC4X//II9LUqVLlysGtC0B4iImRPvjAfNvlyRdfSG3bmtZvRLaNG6WrrnI/EU7FitLo0Xzb6UbEhO7Y2Fg1b95ckydPdl2XmZmpyZMnq23btm73adu2bY7tJWnixIketwfgRbdu5uvC7BISpF9/ZXQSAKZ1c+lSE6w9Wb7c96hHCH8JCVK9ermvL1TI9Ol3N5MpIid0S9JDDz2kTz/9VCNHjtTatWt199136+jRo7r55pslSTfeeKOGDBni2v6BBx7Q+PHj9eabb2rdunUaOnSoFi1apIEDB4bqIQCR7YknpEsuMT83amT6cPbuHdqaAISPypWl6dOlBx/0vE1qqtS3rzRokPfx/RG+ypUz327edFPO6196yYzNDbciKnRfc801euONN/T000+rSZMmWrZsmcaPH+86WXLHjh3as2ePa/t27drpm2++0SeffKLGjRvrp59+0q+//qqGDRuG6iEAkS0qSvrqK/NmOW+eVLt2qCsCEG5iYsxEKT/9JMXHe97urbekCy5wP/IFwl9cnOky9MYb5r3h2mtNV0N4FFHjdIdCJIz7CASEZXnusw0AZ2LjRjNr7YoVnrcpU8aM6X/RRcGrC4E1bZrUqpVUtGjISoiEvBZRLd0AbPLff+YN7/vvQ10JgPzknHOkuXPN+P6e/PuvGfXomWekjIzg1YbA6dw5pIE7UhC6gYJu+XKpZUtp8mQzrJe3FikAyKuiRU03hM8/lwoXdr+NZZnx/bt3l04bdQzILwjdQEH2009mwptt28zvx46ZqZsPHAhpWQDyoVtuMa3etWp53mbyZKlJE2nBgqCVBS/27vU8+zDyjNANFESZmWaq9quuMkE7uy1bpOuv52teAIHXpIkZ9ejyyz1vk5YmeZlPA0Fy4IDUpYvUs6d0+HCoq8kXCN1AQZOaat7wXnjB8zZr1kj//BO8mgAUHImJ0s8/S2+/bSbeOt2oUVLVqsGvC1mOHZMuvVRavVqaMsWMMrNvX6iriniEbqAg2bzZTFzx+++et+nYUVq0SKpWLXh1AShYHA7pf/+TZs6UqlTJuv6xx7LmAkBopKebb0Hnzs26bvFiqUOHrK6IOCOEbqCgmDrVDOm0Zo3nbe6+W5o0idnEAARHmzZmFsuePc0Hfm/fwMF+mZmm7/24cbnXbdwo3Xln8GvKR9x8rwMg3/nwQ+n++6VTp9yvj46Whg3jgAog+EqVksaONf2G3XU3QXBkZkr33SeNHu1+fbVqZhQanDFauoH8LD1duvde6Z57PAfuMmVMnz0CN4BQiYoyfb19sSwz9fjo0eZnBEZGhnkP+OAD9+tLl5b+/luqVCm4deUzhG4gvzpwQOrRw/NBVJKaNjX9tzt2DF5dAHCmPv9cGjlS6t/fjLJ08GCoK4p8p06ZDzKffeZ+ffHi0vjx0rnnBrWs/IjQDeRH69ZJrVubFmxPrrzSnMTEKAEAIsGGDdIDD2T9/t13UuPGZgpynJn0dPPhxVOXkthY6bffpObNg1tXPkXoBvKbiRPNyUmbNnneZuhQM+V7sWJBKwsAzlh6unTDDbnnFdi5U7rwQjPqCZO45M3Jk2aUkh9/dL8+Ntasu/DC4NaVjxG6gfzkww+liy+WUlLcry9SxBxEn3nG9KEEgEgwdqy0cKH7dZYlvfaa1KyZ522Q09GjUp8+phXbncKFzdCyl10W1LLyO951gfzCsqQZMzzPJFmlijR7tulWAgCR5IorTINBiRKet1mzxnzLN3iwdOJE8GqLNDt3mjG3x493v75oUTNkYPfuwa2rACB0A/mFw2GGc2rdOve6Nm2kBQvMiZMAEImuvFJascJMTe5JZqb06qvmWDdvXvBqixTz50stW0rLlrlfHx9vRim54IKgllVQELqB/KRIEenXX3PO8HbddWZinPLlQ1YWAARE5comFL7xhulz7Mm6dVL79tKgQWb8b0jffit16iTt3et+fVKSmRytffugllWQELqB/KZ8edMXr3hx6bnnpK+/Nv3zACA/iIoyYXrhQu/f3mVmSm+9JdWrZ7qmFORxvUeONKOUeDrZtHTprFmLYRtCN5AfNWlipux96inT7QQA8pvzzjPdJZ5/XoqJ8bzdrl3S1VebeQs2bgxefeHk0kulWrXcr2vQwHQ/bNIkqCUVRIRuIJJYljnr3B90JwGQ38XESE8+KS1ebEYv8ebvv6WGDaWnn8499GB+V6qUGQEmISHn9T17SnPmSDVqhKauAobQDUSKEyfM14O9eklpaaGuBgDCR6NG5sTJF1/03tc7Lc20jP/5Z/BqCxf16kk//JA1XOyDD5quiKcHcdjGYVkFuZOTb6mpqUpMTFRKSooS+MdEqOzbZ8ZUnTvX/H7rrdKnn9J1BABOt3atdO+9po+yO82bm+4UBXWuguHDpbg46bbbQl1JQEVCXiug/3FABFmzxgwD6AzckvT55+bsfQBATvXqSZMnm5PI3XWze/XV/Be49+0zI1f54957813gjhT57L8OyGcmTpTatpW2bcu97rHHpDFjgl4SAIQ9h8N0x1u3Trr//qyQ3a2b93G+I83x49LLL0u1a0vXXOP+vQJhg9ANhKuPPzZTuqemul+fkEBfPADwJjFRevddadEiqWNH6ZVXfO+Tni517WqOwceP21/jmcjMlEaPlurUkR5/3IxFnpZmfkbYok+3D5HQRwj5TEaG9OijZnxZT2rWNCcC1a0bvLoAoCD4+mvphhvMz2XKSLfcIvXvb4bWC7XUVBO2hw83XQ/dmT+/QI63HQl5jdDtQyT8EZGPHDki9etnzij3pH1703evdOmglQUABYJlmQl3li/Pva5JExO+r7tOqlAhuHWtWCF9+KEJ3EeOeN+2QwdpxowCd6J9JOS16FAXAOD//fOPGQ5w2TLP2/TrZ06ijIsLWlkAUGBMmuQ+cEvm2LxsmfTII9IFF0gXXWQumzWTogMcpzIzzX1NnGgaYebM8X/fChXMfA7Fiwe2Jpw1QjcQDhYvli67TNq92/M2zz7LDJMAYKfXX/e9TWamGR1l8mTze0KCdP75JoA3aWJmfqxcWSpUyP/7TUmRtm41fc8nTjS3vX9/3mpv00Z6802pXbu87YegIXSHs1OnAv/pGeHn119NC7anGdJiY6UvvzRn4gMA7HP77dLBgyb8+is1VfrjD7M4xcSYWR5r1pQ++0yqVMnz/vfdJw0bduY116hhhkG88koaZcIco5eEo4wM06LZs6cJ3sifLMu0qlxxhefAXaaMmeCBwA0A9rvqKjNxztSpZgi+woXP7HbS06UNG6Tx46X4eO/buhtL3B9t20qjRplhEa+6isAdAWhGDTcHD5pWz7/+Mr8/8YT5BIv8JS1Nuusu04LtSb16ZoSSGjWCVxcAFHQOh9S5s1lSUqRffpG++kqaNs00luRF6dK+h3bNyzG+WDGTEe6+23RlQUShpTucrFghtWiRFbgl6bXXpB9/DF1NCLz//jMn4HgL3BddZE6cIXADQOgkJko33yxNmSJt324mounQwXQf8UetWr63qV7d+/qYGKlTJ+n996Vdu8z44QTuiMSQgT4EbQiajAwzBuj69bnXFStmxt0MhzFCcfaWLTPD/nnqUnLnnebg6u9BHQAQXEePmoaRKVNMV5RFi8z7+Omuv96M++3Nnj1SxYo5r2vQwDS+XHSROUmTkUh8ioQhAwndPgT1j7hggZkxKy0t97pzzpEWLjSfuhH5fvlF6ts353UOhznz/H//o28eAESSw4el1aulLVukzZuzlj59pEGDvO9rWdLgwVK1aqbVu2nT4I8Dng8QuvOBoP8RP/9cuu029+t69TIjXUTRKyhfePnlrCl7ixeXvv1WuvTS0NYEAEAEioTQTXoLN7feKt1xh/t1Y8dKL7wQ3Hpgn8GDpRtvlKpWlWbPJnADAJCP0dLtQ0g+OZ08aU6amD/f/foffzTjcSLynTxpzo4vWzbUlQAAELFo6caZiYuTfvrJcxDr31+aOze4NSFv/J1JLC6OwA0AQAFA6A5XlStLP/zgfhrZEyfMlOGbNwe/Lvj2669mqL8xY0JdCQAACBOE7nDWqZP01lvu1/33n5mx0t8WVdgvM1N65hnp8svNmez9+5ux1wEAQIFH6A53990nDRzoft2GDWY4ohMngloS3Dh0SOrdW3ruuazrjh4130j8+2/IygIAAOGB0B3uHA7pnXfMcIHuzJplZsvKzAxqWchmzRqpVSvpjz9yr9u+3Zz06m7sdQAAUGAQuiNBoUJmDOfmzd2v/+67rPGeEVy//CK1bi1t3Oh5mzp1glcPAAAIS4TuSFGsmGlJrVbN/fpXX2UM72A6dcp80OnbVzpyxP020dFmOvePP5ZiY4NbHwAACCuE7khSvrz055+ep4J/5RVp587g1lQQJSdLF11kZpT0pGxZacoU0x+fKd0BACjwCN2RpkED06UhOjrn9fHx0vjxUpUqoamroJg2TWra1Fx60rq1tGSJ1LFjsKoCAABhjtAdiS68UBo5MqsFNSlJmjRJ6tAhpGXla5mZpmW7SxfT0u3JbbdJ06dLlSoFrzYAABD2on1vgrB0/fVmqMDHHpMmTJCaNQt1RfnX/v3SjTdK48Z53iYmRho2TLrjjuDVBQAAIgahO5Ldcot0xRWmpRv2mDzZBO7duz1vU62amT20Vavg1QUAACIK3UsiHYHbHmlp0qOPmhMmvQXuSy81/bcJ3AAAwAtCd0ExcSIT6OTFihXSm29KluV+faFCZrSY336TSpYMbm0AACDiELoLgs8+k7p1k264gZkR/dWihfTUU+7XVahghgN87DEpipcQAADwjcSQ3/3+u3Tnnebnb7+VLrlEOnw4tDVFiieflNq1y3ldjx7S0qXS+eeHpiYAABCRCN352axZ0jXX5OxWMmmSCYxbt4aurkgRHS2NHi0lJJgZJd95x0xOVK5cqCsDAAARhtCdXx0/Ll11lRlW8HTLlknNm5sACe9q1DDBe+FC6YEH6E4CAADOCAkivypSRPruO9NK687Bg2bkjSeflDIygltbqKWlSW+84bnP9ul69ZLOO8/emgAAQL5G6M7POnWSZsyQypf3vM2LL0rdu0v79gWvrlD6+28ToB95RHrpJWnevFBXBAAACgBCd37XuLE0Z45Uv77nbSZPNjNaTpkSvLqCbetW6fLLzQeM9evNdZmZZoIhd11wAAAAAojQXRDUqCHNny9dd53nbXbtkrp0kW6+2Ux7nl8cPiw984xUr57066+5169dKz3/fNDLAgAABQuhu6AoXlz6+mtp2DApJsbzdiNGSHXrmpMHPU0MEwmOHpVef12qWVN67jnp5EnP2772mvTPP8GrDQAAFDiE7oLE4ZDuvVeaOVOqUsXzdv/9J/Xvb7pibN4cvPoC4fhx6e23Tdh+9FHzWLxp3Nh0q6lcOTj1AQCAAonQXRC1bi0tWWJmqfRm4kTTF/yuu6QdO4JT25k6eFB66y0Tth96yPeJoSVKSMOHS4sXSx07BqdGAABQYBG6C6rSpaVx46R33zVdTzxJS5M+/lhauTJ4teXF0qXSbbdJlSpJgwZJycnet3c4zIeIjRule+6RChUKTp0AAKBAI3QXZIUKSfffL61ZI112meftmjWTevYMXl2+HD0qffWV1Latqe3zz023El8uuMC0bH/4oVSqlP11AgAA/D9CN0z/7l9/lX7+WapYMff6J580LcThYsMG6cYb/R9ju0MH0297yhSpaVN7awMAAHCD0A3D4ZCuuMK0et9zT9Z0540aSb17+97/8celJ56QfvvNDD9o58gnTZpIVav63q5NG9MvfcYM08oNAAAQItGhLgBhJjHRnGD4v/9JL78sXXJJVgD35OhR0zf82LGs68qXl1q0MN0/KlSQkpLMUqKEuZSkAwdyLsnJ0rJlZjzxG2/0fH8Oh/kg8P777td36iQNHmxGXwmnFnoAAFBgOSwrkgdjtl9qaqoSExOVkpKihISEUJcTnr7/Xrr22sDd3i23mH7a3kyeLHXtmvV7fLwJ6nffLTVoELhaAABA2IuEvEZLN87ed98F9vaWLPG9zfnnmxbzypVNd5gbbjDBGwAAIAwRunF2Dh0yQw8G0urVZgbJuDjP28TEmGEMK1WiCwkAAAh7nEiJs3PggJlkx9tY33mVnm6Cty+VKxO4AQBARKClG2enZk1p7FgpI8MM5bd4sbRokVmWLPFv/Ozs4uPNsH5pafbUCwAAEAKcSOlDJHTMD1sZGdLevaYLinM5eNAsDodUsmTWUqKEWUqWpPUaAADkSSTkNVq6YZ9ChcxkO+4m3AEAAChA6NMNAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANgsYkL3gQMH1K9fPyUkJCgpKUm33nqrjhw54nX7++67T3Xq1FGRIkVUtWpV3X///UpJSQli1QAAAEAEhe5+/fpp9erVmjhxov744w/NmDFDd9xxh8ftd+/erd27d+uNN97QqlWrNGLECI0fP1633nprEKsGAAAAJIdlWVaoi/Bl7dq1ql+/vhYuXKgWLVpIksaPH6+ePXvqn3/+UcWKFf26nR9//FE33HCDjh49qujoaL/2SU1NVWJiolJSUpSQkHDGjwEAAAD2iIS8FhEt3XPnzlVSUpIrcEtS165dFRUVpfnz5/t9O84/hLfAffLkSaWmpuZYAAAAgLMREaE7OTlZZcuWzXFddHS0SpYsqeTkZL9u47///tPzzz/vtUuKJL388stKTEx0LVWqVDnjugEAAAApxKF78ODBcjgcXpd169ad9f2kpqbqkksuUf369TV06FCv2w4ZMkQpKSmuZefOnWd9/wAAACjY/OvYbJNBgwbppptu8rpNzZo1Vb58ee3bty/H9adOndKBAwdUvnx5r/sfPnxYPXr0UHx8vMaMGaOYmBiv28fFxSkuLs6v+gEAAAB/hDR0lylTRmXKlPG5Xdu2bXXo0CEtXrxYzZs3lyRNmTJFmZmZat26tcf9UlNT1b17d8XFxen3339X4cKFA1Y7AAAA4K+I6NNdr1499ejRQ7fffrsWLFig2bNna+DAgbr22mtdI5fs2rVLdevW1YIFCySZwN2tWzcdPXpUn3/+uVJTU5WcnKzk5GRlZGSE8uEAAACggAlpS3defP311xo4cKC6dOmiqKgo9e3bV++9955rfXp6utavX69jx45JkpYsWeIa2aR27do5bmvr1q2qXr160GoHAABAwRYR43SHUiSM+wgAAFCQRUJei4juJQAAAEAkI3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2I3QDAAAANiN0AwAAADYjdAMAAAA2O6vQfeLEiUDVAQAAAORbeQ7dmZmZev7551WpUiUVL15cW7ZskSQ99dRT+vzzzwNeIAAAABDp8hy6X3jhBY0YMUKvvfaaYmNjXdc3bNhQn332WUCLAwAAAPKDPIfuUaNG6ZNPPlG/fv1UqFAh1/WNGzfWunXrAlocAAAAkB/kOXTv2rVLtWvXznV9Zmam0tPTA1IUAAAAkJ/kOXTXr19fM2fOzHX9Tz/9pKZNmwakKAAAACA/ic7rDk8//bQGDBigXbt2KTMzU7/88ovWr1+vUaNG6Y8//rCjRgAAACCi5bmlu3fv3ho7dqwmTZqkYsWK6emnn9batWs1duxYXXTRRXbUCAAAAEQ0h2VZVqiLCGepqalKTExUSkqKEhISQl0OAAAAThMJeY0ZKQEAAACb5blPd4kSJeRwOHJd73A4VLhwYdWuXVs33XSTbr755oAUCAAAAES6MzqR8sUXX9TFF1+sVq1aSZIWLFig8ePH695779XWrVt1991369SpU7r99tsDXjAAAAAQafIcumfNmqUXXnhBd911V47rP/74Y/3999/6+eefdd555+m9994jdAMAAAA6gz7dEyZMUNeuXXNd36VLF02YMEGS1LNnT23ZsuXsqwMAAADygTyH7pIlS2rs2LG5rh87dqxKliwpSTp69Kji4+PPvjoAAAAgH8hz95KnnnpKd999t6ZOnerq071w4UKNGzdOH330kSRp4sSJ6tSpU2ArBQAAACLUGY3TPXv2bA0bNkzr16+XJNWpU0f33Xef2rVrF/ACQy0Sxn0EAAAoyCIhrzE5jg+R8EcEAAAoyCIhr+W5e0l2J06cUFpaWo7rwvWBAgAAAKGS5xMpjx07poEDB6ps2bIqVqyYSpQokWMBAAAAkFOeQ/cjjzyiKVOm6MMPP1RcXJw+++wzPfvss6pYsaJGjRplR40AAABARMtz95KxY8dq1KhR6ty5s26++WZ17NhRtWvXVrVq1fT111+rX79+dtQJAAAARKw8t3QfOHBANWvWlGT6bx84cECS1KFDB82YMSOw1QEAAAD5QJ5Dd82aNbV161ZJUt26dfXDDz9IMi3gSUlJAS0OAAAAyA/yHLpvvvlmLV++XJI0ePBgDR8+XIULF9aDDz6oRx55JOAFAgAAAJHurMfp3r59uxYvXqzatWvrvPPOC1RdYSMSxn0EAAAoyCIhr+X5RMoTJ06ocOHCrt+rVaumatWqBbQoAAAAID/Jc+hOSkpSq1at1KlTJ3Xu3Fnt2rVTkSJF7KgNAAAAyBfy3Kd70qRJ6tGjh+bPn6/evXurRIkS6tChg5544glNnDjRjhoBAACAiHZWfbpPnTqlhQsX6uOPP9bXX3+tzMxMZWRkBLK+kIuEPkIAAAAFWSTktTx3L5GkDRs2aNq0aa7l5MmTuvTSS9W5c+cAlwcAAABEvjyH7kqVKun48ePq3LmzOnfurMcee0znnXeeHA6HHfUBAAAAES/PfbrLlCmjY8eOKTk5WcnJydq7d6+OHz9uR20AAABAvpDn0L1s2TIlJydr8ODBOnnypB5//HGVLl1a7dq10xNPPGFHjQAAAEBEO6sTKffv369p06bpt99+07fffsuJlACAiGJZUlqadPy4dOyYWY4fl06ezL2kpUmnTknp6VnLqVNSZqaUkZHz0tM7a1SUWQoVynkZEyNFR5tL58+xsVJcXO6lSJGcS1ycRA9PFHSRkNfy3Kf7l19+cZ1AuWbNGpUsWVIdOnTQm2++qU6dOtlRIwAAuZw4IR08KB04YJaDB6VDh6SUlNzL4cPSkSNZl87l2DETkiOZw2HCd7FiUvHiuS8TErKW+HhzmZgoJSXlXBITzfYEeMAeeW7pLlu2rM4//3x17txZnTp1UqNGjeyqLSxEwicnAMgPjh6V9u7NuezbJ/37r/Tff1nLv/9K+/ebFmkEVnS0VKKEVLJkzqV06dxLmTJS2bJm+6g8d1YFAisS8lqeW7r37dtnRx0AgHwqPV3as0faudMsu3aZ33fvNovz5yNHQl0pTp0yH2r+/df/fQoVygrgZctK5cpJ5cvnXipUMAGelnQUVGc0TjcAAE5Hjkjbtknbt+e83LHDhOzk5MjvwgHPMjLM3zg52fe2sbFSxYo5l0qVpMqVzVKpklkKF7a/biDYCN0AAK8sywSqjRulzZvNsmVL1pKXVlEUbGlp5gPZtm3etytdWqpaVapSxVw6f65WzSzlytGlBZGH0A0AkGROMly/Xlq3zlxu2GCC9saNkdv1o3Bhc4JgQoI5SbB4cXMyofNEw2LFpKJFzVKkiLksXNgsztFCnD87RxbJPsJITEzukUgKFXLfhcKyzHL6SCenTmUt2UdGSUszy8mT5qRR5ygqJ05kjbZy/HjWz0ePZp0g6vz58GGzpKZG1rcNzv77S5a4Xx8ba4J4tWpS9epmqVEjaylfnm4sCD+EbgAoYPbvl1avNsuaNdLatSZo79oV6so8c57MV6aMuSxVyiwlS+Y88c85CodziY0NdeXhwbJMOE9NNaO5pKaakV5OX7KPBuNc/vvPBPdwkpYmbdpkFncKFzZBvFYtqWZNs2T/uUiRoJYLSCJ0A0C+deyYCdYrVphl1Srz+969oa7MSEw0fXorVDCXzhPuypXLuZQqZVqVceYcjqwW/fLl875/Wpr5sHb6KDL79uVcnKPOpKQE/jHkxYkT5oPkunXu11eqJNWuLZ1zjrnM/nPRosGtFQVHng9jR48e1SuvvKLJkydr3759yjzt+6otW7YErDgAgG+WZUYAWbpUWrZMWr7cLBs3ep6kxW6Jiear/6pVzQlyVaqYxXnCXMWKpmsHIkNsrPlwVKGCf9sfP27Ct/MEyz17co5Ys3u3+Wblv//srduTXbvMMn167nWVK5sAfu655rJOHbPUqMGHP5ydPP/73HbbbZo+fbr69++vChUqyEGnKQAIGsuStm6VFi82y7JlJmwHezTXokWzvqqvWdMEkurVs050S0oKbj0IL0WKZPW19ubkSRPA//nHLLt2ZQ0tuXOnGQEn2P/bzlqmTs15fUyM6aJy7rlS3bpmqVfPXPL/Dn/keXKcpKQk/fnnn2rfvr1dNYWVSBhsHUD+ZFlm+L2FC03AXrTInFh28GBw7r9o0Zwtfs6lVi3T7YM2FwTDiRMmBO/YYV4P2Zdt20w4P3UqtDWWK2cCeL16Uv36WZec0Bk8kZDX8tzSXaJECZUsWdKOWgCgQPvvPxOwFywwy8KFwRmOr1KlrBY751KnjrmewIBQK1w4q9+1O6dOmRbyrVtzL5s3+zd++Nly9mWfNi3n9UlJJnzXry81aJB1WbEir62CKM8t3aNHj9Zvv/2mkSNHqmgBONsgEj45AYg8aWmma8j8+dK8eeZy82Z777N8ealhQ/Om77ysX9/0vwbyq6NHTQDfssW8xpyjnmzaZFrKQzGUYmJi1uuwUSNz2bChGZkHZyYS8lqeQ3fTpk21efNmWZal6tWrKyYmJsf6JZ4G1YxQkfBHBBD+9uyR5syR5s41l0uWmP6sdoiLM2/g552XtTRqZIbbA5DFOVnPpk1Z49I7L3fsCP6JyOXKmddq9qV+fUZU8Uck5LU8dy/p06ePDWUAQP5x6pS0cqU0e7YJ2HPmmP6ndkhMlJo0kZo2zVrq1jUnfQHwLjbWnLNw7rlSz5451x0/bsL4+vVZk0U5J4+ya0hEZzeVSZOyrouKMl1rnB+gGzc2l9Wq0UUl0uS5pbugiYRPTgBC6/Bh00Vk1iwTsOfNs2cGx6QkqXnznEvNmrzxAsFkWSYYO8cBX7s2a4KpnTuDV0dCQlYIb9zYfPhu2LDgTvwTCXntjEP34sWLtXbtWklSgwYN1LRp04AWFi4i4Y8IILiSk03AnjVLmjnT9M0OdL/QIkVMqG7ZUmrVylwSsIHwdviwCd/OmV6ds75u3RqcripRUabVvkmTrG/AmjSRypa1/75DLRLyWp5D9759+3Tttddq2rRpSvr/gSkPHTqkCy64QN99953K5LNOg5HwRwRgH+e42DNnSjNmmMuNGwN7Hw6H6bfZpo3UurVZ6tdnIg4gvzh2LCuEZ1+2bQvO/VeokLsbWn77EB8JeS3Pofuaa67Rli1bNGrUKNWrV0+StGbNGg0YMEC1a9fWt99+a0uhoRIJf0QAgWNZ5s1xxoysZdeuwN5HqVJS27YmZLdpY1qxObwABc/hw6YlfNUqs6xYYc4HCcZQoQkJJog3a5a11KkTuR/2IyGv5Tl0JyYmatKkSWrZsmWO6xcsWKBu3brp0KFDgawv5CLhjwjgzGVmmje56dOzQnYg3/AcDjM0WLt2Zmnb1kwwk59amAAE1t695ri0cmVWEF+1yr4Rj5yKFDH9w50hvHlzc/yKhBOzIyGv5fnzTGZmZq5hAiUpJiZGmaEY7BIA8iAjw/TBnj7dLDNnBnaGxyJFTPeQ9u3N0rYtU0QDyJty5czStWvWdadOma5tK1aYZflys/zzT+Du9/hxcyL4vHlZ18XFmRM2nSG8eXNzwmZsbODut6DIc0t37969dejQIX377beqWLGiJGnXrl3q16+fSpQooTFjxthSaKhEwicnAJ6dOiUtXWoC9rRpJmSnpgbu9suUkTp2lDp0MEuTJpHRKgQgf9i/PyuAL1tmljVrzLHPLrGxpkW8eXOpRYvwaBGPhLyW59C9c+dOXXbZZVq9erWqVKniuq5hw4b6/fffVblyZVsKDZVI+CMCyOIM2dOmZYXsw4cDd/s1a5qQ7VzoKgIg3Jw8aYK3M4QvXWouA3ksPF3hwqbRoUULs7RsafqIFypk331mFwl57YyGDLQsS5MmTdK6deskSfXq1VPX7N+B5COR8EcECjK7Q3aDBlKnTlkhu1KlwN02AARLZqa0ZYs5Xi5dambFXbLE3pM2ixUz3VLatZNeecW++5EiI68xOY4PkfBHBAqSU6dMi83UqYEP2VFRpqXm/POzgnapUoG5bQAIN5Yl7d6dFcCXLDGBPNCT/DRrJi1eHNjbPF0k5LUIHRgGQEHhPPFx2jQTtAPZJ7tQIdMXsVMns3ToYKZVB4CCwOEw395VqiT16pV1/b59JoAvXpy17Nhx5vfTosXZ15ofELoBhJXsIXvaNDOEX6BCdnS06WfYqZPUubP5yjM+PjC3DQD5RdmyUo8eZnH67z8TxBctMiF80SL/gzih2yB0Awip7H2ynUP4BTJkt2plArYzZBcrFpjbBoCCpHRpqVs3szj9+2/OEL5okfvJxE6b2qXAInQDCKr0dHOAdo6TPWtW4PpkE7IBIHjKlJEuvtgsTrt35wzhq1aZE9JxBqG7UKFC2rNnj8qWLZvj+v3796ts2bLKyMgIWHEAIt/Jk+bA6wzZs2dLR48G5rad3UU6d5YuuICQDQChVrGiWbL3EYeR59DtabCTkydPKpbpiYAC79gxM5uZc0r1uXOlEycCc9vR0aZv4AUXZLVkFy8emNsGAMBOfofu9957T5LkcDj02WefqXi2d7qMjAzNmDFDdevWDXyFAMLaoUOm9XrmTBOyFy0yXUgCIXtLNiEbABDJ/A7db7/9tiTT0v3RRx+pULYphmJjY1W9enV99NFHga8QQFjZvdv0w5450ywrVpixXgMhe8ju1Elq356QDQDIH/wO3Vu3bpUkXXDBBfrll19UokQJ24oCEB4yM6X1603Idgbt/z8UBERMTM6QTUs2ACC/ynOf7qlTp9pRB4AwcOyYtHChNGeO6TIyd6504EDgbj8uTmrdOmsymrZtpaJFA3f7AACEqzyH7r59+6pVq1Z67LHHclz/2muvaeHChfrxxx8DVhwA+1iWmdhg7tysZelSM252oBQtalqvzz/fLK1bS4ULB+72AQCIFHkO3TNmzNDQoUNzXX/xxRfrzTffDERNAGxw9KgZO3X+fDO6yNy50p49gb2PxETTD/v8801LdrNmEoMaAQBwBqH7yJEjbocGjImJUWqgppEDcFYyMqS1a01XkfnzzbJypbk+kMqXlzp2NMv550sNG0rZzrEGAAD/L8+hu1GjRvr+++/19NNP57j+u+++U/369QNWGAD/WJa0ZYsJ2M5lyZLATUCTXd26UocOWUvNmpLDEfj7AQAgv8lz6H7qqad0xRVXaPPmzbrwwgslSZMnT9a3335Lf27AZpmZ0ubNppuIc1myREpJCfx9xcaaiWjatzf9stu3N1P+AgCAvMtz6O7Vq5d+/fVXvfTSS/rpp59UpEgRnXfeeZo0aZI6depkR41AgXTypLR6tTm5cdkyc7l8uXTkiD33V66cGU3EGbCbNzejjQAAgLPnsDzN6w5JUmpqqhITE5WSkqKEhIRQl4N8yLKk5GQzyczy5WZZsUJaty6wI4lkFx0tNW5sQrZzqV6driIAgMgUCXktzy3dknTo0CH99NNP2rJlix5++GGVLFlSS5YsUbly5VSpUqVA1wjkG4cPS6tWmWXlSrOsWiX995+991utmhmuz7k0ayYVKWLvfQIAgCx5Dt0rVqxQ165dlZiYqG3btum2225TyZIl9csvv2jHjh0aNWqUHXUCEeXIEWnNGtM9xHm5erUZF9tupUubWR6zL+XK2X+/AADAszyH7oceekg33XSTXnvtNcXHx7uu79mzp66//vqAFgeEM8syLdRr1+Zedu4MTg0lS5q+182bm9brFi3oJgIAQDjKc+heuHChPv7441zXV6pUScnJyQEpCggn6elmSL71600/a+flunWBnSLdl4oVpaZNzdKkiQna1aoRsAEAiAR5Dt1xcXFuJ8HZsGGDytg4ntiBAwd03333aezYsYqKilLfvn317rvvqnjx4j73tSxLPXv21Pjx4zVmzBj16dPHtjoRmTIzpV27pI0bzbJhgwnXGzaYwB3oSWW8iY4242Gfd55ZnCG7bNng1QAAAAIrz6H7sssu03PPPacffvhBkuRwOLRjxw499thj6tu3b8ALdOrXr5/27NmjiRMnKj09XTfffLPuuOMOffPNNz73feedd+SgObDAy8yUdu+WNm3KWpwBe/Nm6fjx4NdUsaKZxbFhQzOayHnnSfXqMVQfAAD5TZ6HDExJSdGVV16pRYsW6fDhw6pYsaKSk5PVtm1bjRs3TsWKFQt4kWvXrlX9+vW1cOFCtWjRQpI0fvx49ezZU//8848qVqzocd9ly5bp0ksv1aJFi1ShQoU8t3RHwhA0yJKebk5W3LzZLJs2Zf0cqmAtmb7XDRpkLY0amaBdqlRo6gEAID+JhLyW55buxMRETZw4UbNnz9by5ct15MgRNWvWTF27drWjPknS3LlzlZSU5ArcktS1a1dFRUVp/vz5uvzyy93ud+zYMV1//fUaPny4ypcv79d9nTx5UidPnnT97q4rDUIrNdV0+di8OevS+fP27cHtCnK6cuVMS7VzcYbssmXpew0AQEHmV+guWbKkNmzYoNKlS+uWW27Ru+++q/bt26t9+/Z21ydJSk5OVtnTOrRGR0erZMmSXk/efPDBB9WuXTv17t3b7/t6+eWX9eyzz55xrTh7p05J//xjQvTWreYye8jevz+09UVFSTVrmn7XdetKdepkheySJUNbGwAACE9+he60tDSlpqaqdOnSGjlypF599dUcwwWeqcGDB+vVV1/1us3atWvP6LZ///13TZkyRUuXLs3TfkOGDNFDDz3k+j01NVVVqlQ5oxrgnmWZ4OwM1Fu35vx5+3b7ZmLMi9KlTaA+99yspW5dqVYt+lwDAIC88St0t23bVn369FHz5s1lWZbuv/9+FfEwnd0XX3zh950PGjRIN910k9dtatasqfLly2vfvn05rj916pQOHDjgsdvIlClTtHnzZiUlJeW4vm/fvurYsaOmTZvmdr+4uDjFkajO2rFj0rZt7kP11q1mZsZwkJBgwvQ55+ReaLUGAACB4lfoHj16tN5++21t3rxZkjmZ8sSJE2d952XKlPFrmMG2bdvq0KFDWrx4sZo3by7JhOrMzEy1bt3a7T6DBw/WbbfdluO6Ro0a6e2331avXr3OuvaCLiPDdAHJHqazX+7dG+oKs5QqZVqna9c2yznnZP1cqhR9rQEAgP3yPHpJjRo1tGjRIpUK8rALF198sfbu3auPPvrINWRgixYtXEMG7tq1S126dNGoUaPUqlUrt7fhcDgYvSQPDh3K6k99+rJjhxkpJBw4HFLlyiZY16xpLp0hu1Yt6bQvOwAAQD4TCXktzydSXnDBBYqNjbW7rly+/vprDRw4UF26dHFNjvPee++51qenp2v9+vU6duxY0GuLVM7W6uyjgGQ/afHgwVBXmKVYsaxAXbOmWWrUML9Xr04fawAAEN78aukuXry4VqxYoZo1a6pQoUJKTk62dfbJcBIJn5y8SUszXT6yTwjjHGJv2zazPhxERUlVqmQF6Ro1cobrMmXoBgIAANyLhLwW0hMpERhpaaZ12jmF+caNWQF7xw4zE2M4KF06K0Q7Q7XzskoVKSYm1BUCAADYI88nUjocjoCdSAn/ZWRIO3eaKctPX7ZvD49gXaSI51BdvboUgFEmAQAAIlLEnEgZKsH+uiIlRVq/PmtZt85cbtwoZZsoMyQcDvddQJwhu1w5uoAAAIDgyzfdS7LbunWrHXUUaGlpUrduJlx7mWAzKIoXzxr9I/uJizVqSNWqSSE4hxYAACDi+R26e/bsqW+//VaJiYmSpFdeeUV33XWXa/KZ/fv3q2PHjlqzZo0theZnsbGmRTtYY1uXL581nN7pC+NWAwAABJ7foXvChAk6ma1/w0svvaSrr77aFbpPnTql9evXB7zAgqJOncCG7ipVck4C41xq1jTD7wEAACB4/A7dp3f9zmNXcPhQp440Y0be9qlQwQTr06cxr1XLnNQIAACA8JDnPt2wR9267q8vVswE8rp1TbiuUycrZDMaCAAAQGTwO3Q7HA45Tuvse/rvOHPNmpmTKevWNcHaGbQrVqSPNQAAQKTLU/eSm266SXH/P9/2iRMndNddd6nY/3cQPhnq8ewiXOfOZgEAAED+43foHjBgQI7fb7jhhlzb3HjjjWdfEQAAAJDP+B26v/zySzvrAAAAAPKtqFAXAAAAAOR3hG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmhG4AAADAZoRuAAAAwGaEbgAAAMBmERO6Dxw4oH79+ikhIUFJSUm69dZbdeTIEZ/7zZ07VxdeeKGKFSumhIQEnX/++Tp+/HgQKgYAAACMiAnd/fr10+rVqzVx4kT98ccfmjFjhu644w6v+8ydO1c9evRQt27dtGDBAi1cuFADBw5UVFTEPGwAAADkAw7LsqxQF+HL2rVrVb9+fS1cuFAtWrSQJI0fP149e/bUP//8o4oVK7rdr02bNrrooov0/PPPn/F9p6amKjExUSkpKUpISDjj2wEAAIA9IiGvRUST79y5c5WUlOQK3JLUtWtXRUVFaf78+W732bdvn+bPn6+yZcuqXbt2KleunDp16qRZs2Z5va+TJ08qNTU1xwIAAACcjYgI3cnJySpbtmyO66Kjo1WyZEklJye73WfLli2SpKFDh+r222/X+PHj1axZM3Xp0kUbN270eF8vv/yyEhMTXUuVKlUC90AAAABQIIU0dA8ePFgOh8Prsm7dujO67czMTEnSnXfeqZtvvllNmzbV22+/rTp16uiLL77wuN+QIUOUkpLiWnbu3HlG9w8AAAA4RYfyzgcNGqSbbrrJ6zY1a9ZU+fLltW/fvhzXnzp1SgcOHFD58uXd7lehQgVJUv369XNcX69ePe3YscPj/cXFxSkuLs6P6gEAAAD/hDR0lylTRmXKlPG5Xdu2bXXo0CEtXrxYzZs3lyRNmTJFmZmZat26tdt9qlevrooVK2r9+vU5rt+wYYMuvvjisy8eAAAA8FNE9OmuV6+eevToodtvv10LFizQ7NmzNXDgQF177bWukUt27dqlunXrasGCBZIkh8OhRx55RO+9955++uknbdq0SU899ZTWrVunW2+9NZQPBwAAAAVMSFu68+Lrr7/WwIED1aVLF0VFRalv37567733XOvT09O1fv16HTt2zHXd//73P504cUIPPvigDhw4oMaNG2vixImqVatWKB4CAAAACqiIGKc7lCJh3EcAAICCLBLyWkR0LwEAAAAiGaEbAAAAsBmhGwAAALAZoRsAAACwGaEbAAAAsBmhGwAAALAZoRsAAACwWcRMjgMAABA0x45J//4rpaRIqanmMiVFOnxYiouTEhOlpKSsyxIlzKXDEeLCEa4I3QAAoGDbulWaOlVasyZr2b4977dTpozUpIlZOnSQLrss0JUighG6AQBAwTZ1qnTrrWd/O//+K02caJYVKwjdyIE+3QAAoGCrXz/wt9mkSeBvExGN0A0AAPKfnTult94yiy/16gX+/ps29b3N0aPSunWBv2+EJUI3AADIH44ckT79VGrfXqpaVRo0SHrlFenUKe/7JSZKlSoFthZ/Qvdnn5lW9r59pUWLAnv/CDsOy7KsUBcRzlJTU5WYmKiUlBQlJCSEuhwAAHC6FSukjz+WvvrKjC5yukmTpC5dvN9Gt26mL3apUlKDBiYM16qVNUKJcyleXDp50oxkcuhQ1qgm27dLy5aZJS3NjHgS5aVtMy3N3P4//2Rd16WL9Pjj0oUX5v05KOAiIa9xIiUAAIg8J09KP/wgffihNHeu922//9536P7wQykhwYxAcjYsS9q3z3vglqSvv84ZuCVp8mSz9O4tvf++VKXK2dWCsEJLtw+R8MkJAIACIyXFtGq/8460Z49/+5QsKSUnSzExtpbmt4wM05q+fr3nbYoVk55/XrrvPimaNlJfIiGv0acbAACEv927pcceM321H3vM/8DtcEjnnWdan8PFypW+xwE/elR66CGpZUtpwYLg1AVbEboBAED42rJFuu02qUYN6bXXTF9pf7Rsabpo7N5txuEO9ImSZ6NJE2nbNmnwYNOlxZtly6Q2bUyL9/HjQSgOdqF7iQ+R8HUFAAD5zvbt0gsvSCNG+B59xKlYMalfP+nOO6VmzWwtL2BSUkx/8jfekPbv975t48bSzz+bEzCRQyTkNVq6AQBAeBk+XDrnHDOknj+Bu2FDs8+uXaa/d6QEbsmMiDJ4sBmv++abvW+7fLnUvLn0++/BqQ0BRegGAADhpUkTKT3d93ZdukgTJpghA++5xwTYSFW6tPTFF9L06d4n60lJMaObPP64/98AICwQugEAQHhp317q3t39uqgo6eqrpYULzfjb3bqZkyXzi/PPN/24X3hBiovzvN3LL5vnKJxOEIVXhG4AABB+nn025+/R0dLtt5th9r7/XmrRIjR1BUNsrPTEE6Y7ScOGnrebMsU8D0wlHxEI3QAAIHgsy8zk6Evr1lLPnlKhQtKtt0obNkiffCLVrm17iWGjTh1p3jzphhs8b7Nzp+n7jrDHaOsAACA4Fi40Y0/HxJiZF311C3n7bdOdpCAF7dMVKyaNGiW1ayc98EDuvu5XXim9+mpoakOe0NINAADstXOn1L+/1KqVNGuWGTfbnxE4zj23YAduJ4dDuvtu89xlnxr+oouk0aPNtwEIe4RuAABgjyNHpKefNt0kRo/Oue6RR6S0tNDUFalatZKWLJE6dDDdb375xfvJlggrhG4AABBYmZkmZNepIz3/vPuZFDduNJPCIG9KlzbDJI4bJxUvHupqkAeEbgAAEDgLFpgh//r3N1Owe/PFFyagI2+KFpVKlgx1FcgjQjcAADh7e/aYGRVbtzYjbnhTtKj03HPS3LnmREnYIzNTWro01FXg//GfDgAAztzJk9Jrr5mTHkeM8L6tw2GC+caN0lNPmfANe5w6ZYZabN1a+vvvUFcDEboBAMCZGjdOatRIeuwxc9KkN+efLy1ebLqUVKwYnPoKqhMnpKuuMh+C0tOlyy833yogpAjdAAAgbzZskC65xCwbN3rftlo16ccfpWnTpKZNg1JegXb4sPm7/Ppr1nXHjpmJhlasCFlZIHQDAAB/HT5sWrUbNjSt3N4ULWpGLlm71kzg4msiHATGl1+a6eFPd+iQ1L27tHlz0EuCQegGAAD+WbTI9N8+fVbE011/vbR+vfTkk1KRIsGpDcZ995l+8+4kJ0uXXiqlpAS3JkgidAMAAH9dcIHUt6/n9c2amVkTv/5aqlw5eHUhi8MhffKJ6cftzrp15kNRRkZw6wKhGwAA5MEbb0iFC+e8rnRpE/ScY3QjtKKjpW++kS680P36ceOkJ54Ibk0gdAMAgDyoXl169FHzc6FC0gMPmBMrb7/d/I7wULiwOZmyWTP361991XwjgaAhdAMAAMmypKlT/Zsh8rHHpBtukJYvl955RypRwvbycAbi403wLlfO/fpbb5UWLgxqSQUZoRsAgIJu3TozpNyFF0ojR/revmhR6auvpAYN7K8NZ6dKFWnMGCk2Nve6kyelPn2k3buDXlZBROgGAKCgOnBA+t//zAQ348eb64YMkVJTQ1oWAqxtW+mjj9yv273bnHR54kRwayqACN0AABQ06enS++9L55wjvfuumTLcae9e6cUXQ1cb7HHzzdKDD7pft2CBNGhQcOspgAjdAAAUJH/9JZ13nnT//aal25233/Y90yQiz2uvmQly3PngA+nnn4NbTwFD6AYAoCBYtUrq0cP03V63zvu2JUpI27YFpSwEUXS09N130rnnul9/66383W1E6AYAID/bu1e6806pcWNpwgTv20ZHSw89ZIYAvOii4NSH4EpKkn77TSpWLPe6lBTpuut8zziKM0LoBgAgPzp+3PTNrl3bTFzjayjAyy6TVq+W3nxTSkwMTo0Ijbp1peHD3a+bN0/6/PPg1lNAELoBAMhPMjPNcH516khPPikdOeJ9+4YNpYkTTeunp24HyH9uvNGMtZ5dVJQ0dKiZ6AgBR+gGACC/mDhRat7cBKqdO71vW7q0GUZu6VKpa9fg1Ifw4XCYkyfPOcf8XrGiNGWK9MwzzCxqE0I3AACRzrLMWMvduknLlnnfNjbWTOO+aZPp6x0dHZQSEYbi46Xvv5euuMLMLtqpU6grytd4pQEAEOkcDtOdxJdrrpFeflmqUcP+mhAZmjZlqMAgIXQj/Pz3n/Tvv9LBg2YM2SNHpCJFpOLFzdnWzssSJcxZ2AAAafBg6dNP3Y+93aaN9NZbZmZCACFB6Eb4uewyae5c/7YtUcL0R8u+NGhgTgyiTxqAgiQpSXrqqZyzDtaqJb30knTVVaY1HEDIELoRHJZlZjfz58z4kiX9v92DB830tQsW5Lw+Pt606HToILVvb34uUiRvNQNAuEhLk06ckBISvG93993Se+9JqanmhLg77zR9uAGEHCdSwj6ZmdKcOdLDD0s1a0r16pluI76UKHH29334sPT339LTT0tdukhLlpz9bQJAsGVkSKNGmXGVn3zS9/ZxcdIvv0ibN0v33UfgRmBs3mxmM92wIdSVRDRCNwIvJUV69VWpWjXTyvzmm2Za2cxM6ddffe+fl5Zuf8TFSS1aBPY2AcBOmZnSTz9JjRpJAwZIW7dKH38sbd/ue98mTZjcBoGRmWkm0TnvPDOb6U03mQ+COCOEbgTO7t1mGKoqVcwJPf/8k3sbf86QDkRLd3YtWpjg7cuWLYG9XwDIK8uSxo2TWrY0/bDXrs1al5YmPfdc6GpDwbJtmxm/feBA6dgxc93cudI774SyqohGn26cvfXrpddfNzOgpaV533byZNMP21uwvuQSqVw5s03Jkma0khMnpKNHzXLkiFn++ceMM7txo/nq6+RJ97fXoYPvx7B5s5kquWlTM6nE9ddLZcv63g8AAsGyTJe4Z56R5s/3vN3IkdJjjzFzJOz3/vvS1Km5r3/iCfM+Xbdu8GuKcA7LsqxQFxHOUlNTlZiYqJSUFCX4OoGloElJMX2mhw0zX0H5a+RIE2wDKSPDhPBVq0w/8tmzzRvXiRPS779LvXp53//ZZ83Ut06FCkl9+kj33CNdcAFn/QOwh2VJkyaZsO3vqE2PPSa98oq9dQFHj0qNG5tGqdO1aWPeZ6PCp8NEJOQ1QrcPkfBHDDrLkr77TnroISk52b994uNN8O3b15yMUbSovTVKptV96VKpfn1z/55Ylmnl9tS9pG5dE75vvJF+kgACw7LMN3/PPivNmuXfPnXqSM8/b46jYRR2kI/NnGlmqXQXFT/6yIyOEyYiIa8Run2IhD9iUK1dK917r/uvnNzp0kW6/36pe3f/+lWHwqxZUseOvrcrVky64Qbpf//jazUAZ8bZjeS558y3cv6oVs18E3fDDUzZjuB76CHp7bdzX5+UJK1bZ7qDhoFIyGt8VIZ/0tPNpAuNG/sO3FFR0tVXSwsXmq9NL7ssfAO3JI0e7d92R4+a0QPq1TOt9tOnu//0DwCnsyzpzz/N1/I9evgXuCtWNP1q1683o0YQuBEKL7xgJlk63aFDJpDDb4Ru+LZzp/l66YUXTPj2JDZWuusu8wbx/feRM0zfa69Jn39uHqO//vhD6tzZjDDw3XfSqVO2lQcggmVmmlGbWrSQLr0090Re7pQrZ0aI2LTJjBwRzo0WyP+KFpU++MD9um++kSZODG49EYzuJT5EwtcVtjpxwvR33rXL+3bdupkTKs85Jzh12WXbNumzz6RPP5X27fN/v2rVpEcekW65hZkvAWTJzDRjHK9e7XvbMmXMcKt33RWc816AvLjuOtPIdLrataWVK6XChYNfUzaRkNdo6YZ3hQubbiWeVKok/fijNH585AduSape3bTo79wpffutf8MNSmbCioEDzf6vvmq6ogBAVJQ0ZIj3bcqWNd+4bd1qvq4ncCMcvfWW5C7Mbtokvfxy8OuJQLR0+xAJn5xsZ1lSv34mhDoVKmROKHzmGe8jg+QHK1aYGblGjTIt/74kJpoQzkgnACTT/ezcc02ozq58eTP83x13ELQRGT74wAymcLrYWPNeWadO8Gv6f5GQ12jphm8OhzmB0Pliql3b9Et84438H7gl89Xwxx9LO3aYEQTKlPG+/b33EriBguKff3yfUB0dbcK1U6VK5gTJLVtM4wWBG5HizjulVq1yX5+WZrpF0Y7rFaEb/omPN91I+veXFi+WmjULdUXBV6aMadnfvt2EcHczwhUpIj3wQPBrAxBcS5aYbwCrV5emTfO9/YABprvaxx+byUYGDuT8D0SeQoXM/7C7ceKnTfN/NLACiu4lPkTC1xUIkcxMM9vlyy9njUhw333Se+/53vfw4YLxLQGQn2RmmmH/3norZ9Du0UP666+QlQUEnaexu8uXlzZsCMn7WyTkNVq6IX3xhfmKFHkTFWWmip83z4xH3q2b9PDDvvf791+pShXpttvcT68LILwcOyZ9+KGZFOuyy3K3bI8fb/qzAgXFc89JlSvnvj45WXrppeDXEyEI3QWZZUlPPindeqt08cXSwYOhrigyORxm5s0JE6SqVX1v/9ZbUkqKGRu8Th3ztfOGDfbXCSBvtm83fbErV5buuUfauNHztq+9Fry6gFArXlx691336956iwYlDwjdBZVlmRP+XnzR/L5qldS7t3T8eGjryu/27zfjmTtlZJhRUerVM/1D16wJXW0AzLFx+nSpb1+pZk0Tpv1pkPj5ZxouULBcfrlpcDpdWpr0ySfBrycCELoLIssy3SA+/DDn9TNnmuCXkRGaugqCd96RjhzJfX1mppnZq2FD6corpWXLgl0ZULAdPWomxmra1Mw2+8sv5nXpS3y89OCDZibeEiVsLxMIGw6HeU/LflJl+fLSyJGM2+0BobsgeuYZ8/WPO2PGSJMnB7eeguLkydwfdE5nWabFrGlTqVcv/6aMBnDm1q83w/ZVqiTdfru0fLl/+1WtKr35pplI6623/OtaBuQ3DRtKd99txukePNh0lbzxRvejm4DRS3yJhLNh8+TVV80Lw53oaHNSZf/+wa2pINm6VXrlFenLL6X0dP/26drV/M0uvNC0LAA4e3/9ZcLypEl5269VK2nQIOmKK8wxEyjoDhwwXatq1QppGZGQ1/goUpAMG+Y9cP/yC4HbbjVqmDFON20yJ2bFxvreZ9IkE7zbtDHfRPjzlTcA7yZM8D9wR0dL114rzZ5tRiu6+moCN+BUsmTIA3ekIHQXFF98YcaQdicqyvQn7tUruDUVZFWrmqnlt2yR7r9fKlzY9z4LFpjWtQYNpBEjzMkqAM7M7bf73qZMGTPC07Zt0rffSu3a8W0TgDNG6C4IfvnFjAntyZdfSlddFbx6kKVSJTPs0rZt0qOPmmGYfFm3Trr5ZtNq/tVXtpcI5EsNGkjt27tf17y5OS7u2CE9/7x5nQLAWSJ053eLFkk33GBO0HPnww/NSQ8IrXLlTH/7bdukp5+WEhN977N7N11NgOxSUsxQZe3amRMcfbnjjqyf4+Kkm24y3ygtWmR+9ucbKADwEydS+hAJHfM92rXLnPSze7f79W++aaZyRfhJSTEfiN5+W9q3z/02FSqYkO5Pv3Agvzp1yoy4NGqUOefBOdfA88+briHeHD8udexo+mvffLNUqpT99QKwRSTkNUK3D5HwR3Tr6FHzZrJ0qfv1zz0nPfVUcGtC3h0/bvpvv/aaCdjZvfSSNGRIKKoCQm/5ctO96uuvzdTTp6tZ08wgydBlQIEQCXmNo1F+lJlpupR4Ctz33OO7BQjhoUgRMwbqxo3S6NFmTFRJKlZMuvNO3/tPmmT6im/dam+dQDBs22aG3DzvPKlJE/NtnbvALZmTlGfODGZ1AOAVLd0+RMInp1wGDzb9g93p1k3680+Gu4pUliX9/bcJ0Xfd5Xv7iy+Wxo83P190kRmxoXdvuqQgcuzdK/3wgxk9ZO7cvO17441mdjwA+V4k5DWSV37z5ZeeA3e9eubNi8AduRwOqXt3/7bdsCErcEvSxIlmKVPGnCR2223SuefaUiZwVvbtM/2zf/xRmjr1zE4YbtVKuuCCwNcGAGeIlm4fIuGTk8vy5VLLlu5nOixVypyVX7Nm8OtCaPzvf2Y4Qm/atzetgVdfLSUlBaMqwL1Dh8x8AT/9JE2ffmZBu0IFqV8/acCArK5YAAqESMhrhG4fIuGP6HLqlPTMM+YEu+xiY83Z/R06hKYuBN+RI2Zs4dRU/7aPizPdTm680XRBiomxtz7gdNu3S9Wr532/okXNpFH9+0tdukiFCgW8NADhLxLyGidS5ifR0dKLL0rjxplpWZ0+/ZTAXdCkpko9e/rflejkSdP16NJLTVi/+27ztX5Ghr11Ak7Vqplv6vxRqJD5cDhihOnz/dVX5ncCN4AwRku3D5HwycmtnTula66ROnWSXn451NUgVPbtMyeSffKJtGlT3vcvV0668krT/aRDB4ZfQ96lpppzCWrXlho39r7t66+b0XY8ad9euu46M4Nu2bKBrRNARIuEvEbo9iES/ogepaebkETrDyzL9JP95BPp55+ltLS838all0pjxwa+NuQvliWtXGlG2Rk3zgzbd+qUdO+90rBh3vfdujX3eSctW5oPftdcY1rDAcCNSMhrDGORn9EvF04Oh9S5s1n275e++860gC9c6P9tnH++XdUh0iUnm9bsv/82l3v35t7mzz+l9983/4ue1KghtWhhukVdeaXUt++Z9fMGgDBES7cPkfDJCThja9ea/rBffSX984/3bdev9z3E4Pr1pqWSD3z523//mW9Opk6Vpk2TVq/2b7/Vq6X69b1vc/y4mRQKAPIgEvIaoduHSPgjAmctM9OEp++/N91P9u/Pub5uXRPQfd1G+fLSiROmRf2CC8xl48b0BY90//wjzZ5tlmnTTPeRM/Haa9IjjwS0NACQIiOv0b0kEs2da4YG/OwzqWrVUFeD/CAqSrrwQrMMG2ZaML//XvrlFzN+cu/evm9j2TLp33/Nz2PHZvX/LlHCdE3p3NmcjNm4MS3hkWDRIumtt0zQ3rEjMLeZl+5MAJDP0NLtQ9h9cjp6VGrSxIxEkZBg+kj27++9nyRwptLSzBjvtWtL55zjfduXXpKeeML3bRYpYvrttm2btZQrF5h6EThTp5oPYWejUCGpXTvpkkvM0qABxyoAtgi7vOYGoduHsPsj3nOP9OGHOa+74grpo4/M9N5AqHTqJM2YcWb7VqokNWuWc6lUiYAWSMeOmW4hy5dLK1ZI77zjfRz3o0elxMS8j9V+zjlmzOxu3cy3G+Fw3ASQ74VdXnOD0O1DWP0Rx4+XLr7Y/bo33pAGDQpuPYBTSopUurQZGi5QypWTdu1iyMu8OnTInNC6Zo3ph79mjVm2bTPD+TmtWSPVq+f9tlq2NN1MvKlQIasP/0UXMdoIgJAIq7zmAX26I8WBA9Itt7hf17at9MADwa0HyK5YMWnSJGnCBNMdZfHis5/NskQJ34F7xw7Tul6rljm/oUKF/H/SZnq6tHu3eezbtkmbN5vuZs7l9JNgPVm2zHfobt8+d+guX958q+E8Ufbcc/lGAgD8QOiOFPfcI+3Zk/v6okWlUaP8n+4bsEN0tAlinTqZ31NTpVmzzEgX06aZEJ6ZmbfbbNDA9zbTpkkDBmT9HhMjVa5sAnjVqlLFiiYkli9vWs6dl0lJkRXOd+6Urr3WBO3du/P+XLqzfLmZ3dGbjh3Nh6kOHUwAb9/ejKVNyAaAPCOpRYLvvjMjSbjz5pvmJDcgnCQkSD17mkUyIXz+fDPyzty50rx5phuEN/6E7tOntk9PN7Mabt3qfT+HwwTvEiXMUrKk+b1UqdznTJxu//6cfdctK6vbRkaGdPKkOQE1++XRo9KRI2Y5fNhcpqZKBw+ab7EWLjSt9J7Ex0tz5nivK6+WLfO9Td++ZgEAnDVCd7jbtcu0crvTo4d0553BrQc4EwkJpr/vRReZ3zMzTb/jefOkJUvMsmyZOdnP6UxCt78sywTegwdzXl+6tO/QvXatOXk5kPbv9x66ExNN8D58OLD3CQAIGkJ3OLMs6Y47cgcDybTOff45X/MiMkVFmf7E9epJN99srsvIkDZsyArgLVv6vp2NGwNbV/HivrdJTw/sfUqmtdsbh8N0l/F35sfskpLMB5h69cxskE2amLHSS5Y8k0oBAGeI0B3Ovv9eGjfO/boPPzT9VYH8olChrCDer5/v7S0r8KG7WDHf26SlBfY+Jd+hW/IeuuPizMmktWubpVatrOeyXDk+nANAGCB0h6sDBzyPSHLdddI11wS3HiAcrVplgvf27eYkw9OX48fzdnv+hO5QtHRLZpIZZ4t39qV6dTOmeSSdGAoABRChO1w9/LC0b1/u68uVM9N0AwWdw2FGKqlc2f16yzJ9oPfulZKTsy737cs6gdHZr/vAAXNiY9myvu/3bEJ3kSKmC0t8vLl0nsRZqZLvfZ988szvFwAQcoTucDRlivTll+7XvfcefTEBfzgc5gTOhATfU9jnRceOZqhChyOr24bzZ4fDdPWIi5NiY7MunWGboT0BoMDiHSDcHD9uTp5059JLpauuCm49AHIqXTprPHIAAPxEJ8Bw8/zzZoa50xUvLn3wASdEAQAARCBCdzhZsUJ6/XX36158UapSJbj1AAAAICAI3eEiI0O67Tbp1Knc61q3lu69N/g1AQAAICAI3eEiNdVMQX266Gjp00/NGMYAAACISITucFGihJkI5+uvpTJlsq5/9FGpUaPQ1QUAAICzRugOJw6HdP310tq10oAB0rnnSk89FeqqAAAAcJYYMjAclSoljRhhJvYoXDjU1QAAAOAs0dIdzuLjQ10BAAAAAoDQDQAAANiM0A0AAADYjNANAAAA2IzQDQAAANiM0A0AAADYLGJC94EDB9SvXz8lJCQoKSlJt956q44cOeJ1n+TkZPXv31/ly5dXsWLF1KxZM/38889BqhgAAAAwIiZ09+vXT6tXr9bEiRP1xx9/aMaMGbrjjju87nPjjTdq/fr1+v3337Vy5UpdccUVuvrqq7V06dIgVQ0AAABIDsuyrFAX4cvatWtVv359LVy4UC1atJAkjR8/Xj179tQ///yjihUrut2vePHi+vDDD9W/f3/XdaVKldKrr76q2267za/7Tk1NVWJiolJSUpSQkHD2DwYAAAABFQl5LSJauufOnaukpCRX4Jakrl27KioqSvPnz/e4X7t27fT999/rwIEDyszM1HfffacTJ06oc+fOHvc5efKkUlNTcywAAADA2YiI0J2cnKyyZcvmuC46OlolS5ZUcnKyx/1++OEHpaenq1SpUoqLi9Odd96pMWPGqHbt2h73efnll5WYmOhaqlSpErDHAQAAgIIppKF78ODBcjgcXpd169ad8e0/9dRTOnTokCZNmqRFixbpoYce0tVXX62VK1d63GfIkCFKSUlxLTt37jzj+wcAAAAkKTqUdz5o0CDddNNNXrepWbOmypcvr3379uW4/tSpUzpw4IDKly/vdr/Nmzdr2LBhWrVqlRo0aCBJaty4sWbOnKnhw4fro48+crtfXFyc4uLi8v5gAAAAAA9CGrrLlCmjMmXK+Nyubdu2OnTokBYvXqzmzZtLkqZMmaLMzEy1bt3a7T7Hjh2TJEVF5WzML1SokDIzM8+ycgAAAMB/EdGnu169eurRo4duv/12LViwQLNnz9bAgQN17bXXukYu2bVrl+rWrasFCxZIkurWravatWvrzjvv1IIFC7R582a9+eabmjhxovr06RPCRwMAAICCJiJCtyR9/fXXqlu3rrp06aKePXuqQ4cO+uSTT1zr09PTtX79elcLd0xMjMaNG6cyZcqoV69eOu+88zRq1CiNHDlSPXv2DNXDAAAAQAEUEeN0h1IkjPsIAABQkEVCXouYlm4AAAAgUhG6AQAAAJsRugEAAACbEboBAAAAmxG6AQAAAJsRugEAAACbEboBAAAAmxG6AQAAAJtFh7qAcOecOyg1NTXElQAAAMAdZ04L5zkfCd0+HD58WJJUpUqVEFcCAAAAbw4fPqzExMRQl+EW08D7kJmZqd27dys+Pl4Oh8P2+0tNTVWVKlW0c+fOsJ3GND/ieQ8NnvfQ4HkPDZ730OB5D41gP++WZenw4cOqWLGioqLCs/c0Ld0+REVFqXLlykG/34SEBA4OIcDzHho876HB8x4aPO+hwfMeGsF83sO1hdspPD8KAAAAAPkIoRsAAACwGaE7zMTFxemZZ55RXFxcqEspUHjeQ4PnPTR43kOD5z00eN5Dg+c9N06kBAAAAGxGSzcAAABgM0I3AAAAYDNCNwAAAGAzQjcAAABgM0K3DV5++WW1bNlS8fHxKlu2rPr06aP169fn2ObEiRO69957VapUKRUvXlx9+/bV3r17c2yzY8cOXXLJJSpatKjKli2rRx55RKdOnXKtnzVrltq3b69SpUqpSJEiqlu3rt5+++2gPMZwFKznPbvZs2crOjpaTZo0sethhbVgPefTpk2Tw+HItSQnJwflcYabYP6vnzx5Uk888YSqVaumuLg4Va9eXV988YXtjzEcBet5v+mmm9z+vzdo0CAojzPcBPP//euvv1bjxo1VtGhRVahQQbfccov2799v+2MMR8F83ocPH6569eqpSJEiqlOnjkaNGmX74wsJCwHXvXt368svv7RWrVplLVu2zOrZs6dVtWpV68iRI65t7rrrLqtKlSrW5MmTrUWLFllt2rSx2rVr51p/6tQpq2HDhlbXrl2tpUuXWuPGjbNKly5tDRkyxLXNkiVLrG+++cZatWqVtXXrVuurr76yihYtan388cdBfbzhIljPu9PBgwetmjVrWt26dbMaN24cjIcYdoL1nE+dOtWSZK1fv97as2ePa8nIyAjq4w0Xwfxfv+yyy6zWrVtbEydOtLZu3WrNmTPHmjVrVtAeazgJ1vN+6NChHP/nO3futEqWLGk988wzwXy4YSNYz/usWbOsqKgo691337W2bNlizZw502rQoIF1+eWXB/XxhotgPe8ffPCBFR8fb3333XfW5s2brW+//dYqXry49fvvvwf18QYDoTsI9u3bZ0mypk+fblmWOaDGxMRYP/74o2ubtWvXWpKsuXPnWpZlWePGjbOioqKs5ORk1zYffvihlZCQYJ08edLjfV1++eXWDTfcYNMjiSx2P+/XXHON9eSTT1rPPPNMgQ3dp7PrOXeG7oMHDwbvwUQQu573v/76y0pMTLT2798fxEcTOYJ1bB8zZozlcDisbdu22fhoIoddz/vrr79u1axZM8d9vffee1alSpXsfkgRwa7nvW3bttbDDz+c474eeughq3379nY/pKCje0kQpKSkSJJKliwpSVq8ePH/tXOvIVHlfRzAvzMM5mlLp1pXx8a0y2qlRobUmt1ezDoURRaxYVciYysDuxdEbZcXUct2b8gyJUoocChi2S1YpiK7bXZjtFIzh9ZSFzfaVbKa9Lcvnschq+fV4/84M34/MC9mzt/zP98ff44/DucceL1e2Gw235ihQ4diwIABuHHjBgDgxo0bSE5ORmRkpG+M3W7HP//8g/Ly8s/Oc+/ePVy/fh0TJ05UFSWgqKx7YWEhnj59ih9++EGPKAFD9VofOXIkLBYLvv32W1y7dk11nIChqu7nz59Hamoqdu/ejf79+yM+Ph5r165FS0uLXtH8ml7n9uPHj8NmsyE2NlZVlICiqu5paWn4448/8Msvv0BE0NDQgOLiYkyZMkWvaH5NVd3fvn2L0NDQDnNpmobff/8dXq9XaSa9selWrK2tDStXrkR6ejqSkpIAAPX19QgJCYHZbO4wNjIy0nePan19fYdF2r69fduHrFYrevTogdTUVOTk5CA7O1tRmsChsu5VVVXYuHEjTp06BZPJpDhJ4FBZc4vFgiNHjsDpdMLpdCImJgaTJk3C3bt3Fafyfyrr/vTpU5SUlKCsrAxnz57Fvn37UFxcjOXLlytO5f/0OLcDwIsXL/Drr7/yvP5fKuuenp6OoqIizJ49GyEhIYiKikJ4eDgOHz6sOJX/U1l3u92O/Px83LlzByKC0tJS5Ofnw+v1orGxUXEyfbFjUCwnJwdlZWUoKSlRNsfVq1fR3NyMmzdvYuPGjRgyZAiysrKUzRcIVNW9tbUVc+bMwbZt2xAfH9+p+w50Ktd6QkICEhISfN/Hjh2L6upq7N27FydPnuz0+QKJyrq3tbXBYDCgqKgI4eHhAIA9e/Zg1qxZcDgc0DSt0+cMFHqc2wHgxIkTMJvNyMzMVDpPoFBZ94cPHyI3NxdbtmyB3W5HXV0d1q1bh6VLl+L48eOdPl8gUVn3zZs3o76+Ht988w1EBJGRkVi4cCF2794NozG4rg0HVxo/s2LFCvz888+4dOkSrFar7/eoqCi8e/cOr1696jC+oaEBUVFRvjEfPwHc/r19TLuBAwciOTkZS5YswapVq7B169bODxNAVNa9qakJpaWlWLFiBUwmE0wmE7Zv344HDx7AZDLB5XKpDeen9FrrHxo9ejSePHnSSQkCk+q6WywW9O/f39dwA8CwYcMgIqitrVURKSDotd5FBAUFBZg/fz5CQkIUJAksquu+c+dOpKenY926dRgxYgTsdjscDgcKCgpQV1enMJl/U113TdNQUFCA169fw+Px4NmzZ4iLi0Pv3r0RERGhMFkX6NI7yoNUW1ub5OTkSHR0tFRWVn6yvf3hg+LiYt9vjx8//uzDBw0NDb4xeXl5EhYWJm/evPmfc2/btk1iY2M7L0wA0aPura2t4na7O3yWLVsmCQkJ4na7OzzV3R105Vq32Wzd9q0CetU9Ly9PNE2TpqYm35hz586J0WiU169fq4rnt/Re7+0PELvdbkWJAoNedZ85c6Z89913HfZ9/fp1ASDPnz9XEc2vdeX5fcKECZKVldWJafwDm24Fli1bJuHh4XL58uUOr3368J/U0qVLZcCAAeJyuaS0tFTS0tIkLS3Nt739NTsZGRly//59uXDhgkRERHR4zc6hQ4fk/PnzUllZKZWVlZKfny+9e/eWTZs26ZrXX+hV949157eX6FXzvXv3yrlz56Sqqkrcbrfk5uaK0WiU3377Tde8/kKvujc1NYnVapVZs2ZJeXm5XLlyRb7++mvJzs7WNa+/0PscM2/ePBkzZowu2fyZXnUvLCwUk8kkDodDqqurpaSkRFJTU2X06NG65vUXetW9oqJCTp48KZWVlXLr1i2ZPXu29O3bV2pqavSMqws23QoA+OynsLDQN6alpUWWL18uffr0kZ49e8qMGTOkrq6uw348Ho9MnjxZNE2TL7/8UtasWSNer9e3/cCBA5KYmCg9e/aUsLAwSUlJEYfD0W3fXaxX3T/WnZtuvWq+a9cuGTx4sISGhkrfvn1l0qRJ4nK59Irpd/Rc648ePRKbzSaaponVapXVq1d3y6vcIvrW/dWrV6Jpmhw9elSPaH5Nz7ofOHBAhg8fLpqmicVikblz50ptba0eMf2OXnV/+PChjBw5UjRNk7CwMJk+fbo8fvxYr5i6MoiIdPIdK0RERERE9AE+SElEREREpBibbiIiIiIixdh0ExEREREpxqabiIiIiEgxNt1ERERERIqx6SYiIiIiUoxNNxERERGRYmy6iYiIiIgUY9NNRERERKQYm24iogAlIrDZbLDb7Z9sczgcMJvNqK2t7YIjIyKij7HpJiIKUAaDAYWFhbh16xby8vJ8v9fU1GD9+vU4ePAgrFZrp87p9Xo7dX9ERN0Fm24iogAWExOD/fv3Y+3ataipqYGIYPHixcjIyEBKSgomT56MXr16ITIyEvPnz0djY6Pvby9cuIBx48bBbDajX79+mDp1Kqqrq33bPR4PDAYDzpw5g4kTJyI0NBRFRUVdEZOIKOAZRES6+iCIiOj/k5mZib///hszZ87Ejh07UF5ejsTERGRnZ2PBggVoaWnBhg0b8P79e7hcLgCA0+mEwWDAiBEj0NzcjC1btsDj8eD+/fswGo3weDwYOHAg4uLi8NNPPyElJQWhoaGwWCxdnJaIKPCw6SYiCgJ//vknEhMT8fLlSzidTpSVleHq1au4ePGib0xtbS1iYmJQUVGB+Pj4T/bR2NiIiIgIuN1uJCUl+Zruffv2ITc3V884RERBh7eXEBEFga+++grff/89hg0bhszMTDx48ACXLl1Cr169fJ+hQ4cCgO8WkqqqKmRlZWHQoEEICwtDXFwcAODZs2cd9p2amqprFiKiYGTq6gMgIqLOYTKZYDL957Te3NyMadOmYdeuXZ+Ma789ZNq0aYiNjcWxY8cQHR2NtrY2JCUl4d27dx3Gf/HFF+oPnogoyLHpJiIKQqNGjYLT6URcXJyvEf/QX3/9hYqKChw7dgzjx48HAJSUlOh9mERE3QZvLyEiCkI5OTl4+fIlsrKycPv2bVRXV+PixYtYtGgRWltb0adPH/Tr1w9Hjx7FkydP4HK5sHr16q4+bCKioMWmm4goCEVHR+PatWtobW1FRkYGkpOTsXLlSpjNZhiNRhiNRpw+fRp37txBUlISVq1ahR9//LGrD5uIKGjx7SVERERERIrxSjcRERERkWJsuomIiIiIFGPTTURERESkGJtuIiIiIiLF2HQTERERESnGppuIiIiISDE23UREREREirHpJiIiIiJSjE03EREREZFibLqJiIiIiBRj001EREREpNi/P6ijONEY3cEAAAAASUVORK5CYII=", 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", 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" ] @@ -3144,17 +3198,17 @@ "id": "075082df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.741522Z", - "iopub.status.busy": "2024-06-04T23:19:38.741306Z", - "iopub.status.idle": "2024-06-04T23:19:38.853209Z", - "shell.execute_reply": "2024-06-04T23:19:38.852962Z" + "iopub.execute_input": "2025-04-03T19:33:55.228948Z", + "iopub.status.busy": "2025-04-03T19:33:55.228423Z", + "iopub.status.idle": "2025-04-03T19:33:55.297977Z", + "shell.execute_reply": "2025-04-03T19:33:55.296769Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3192,17 +3246,17 @@ "id": "ecef97c8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:38.854567Z", - "iopub.status.busy": "2024-06-04T23:19:38.854494Z", - "iopub.status.idle": "2024-06-04T23:19:38.953710Z", - "shell.execute_reply": "2024-06-04T23:19:38.953441Z" + "iopub.execute_input": "2025-04-03T19:33:55.301548Z", + "iopub.status.busy": "2025-04-03T19:33:55.300921Z", + "iopub.status.idle": "2025-04-03T19:33:55.392273Z", + "shell.execute_reply": "2025-04-03T19:33:55.391971Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -3242,8 +3296,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -3255,7 +3309,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/Ch08-baggboost-lab.Rmd b/Ch08-baggboost-lab.Rmd index f515e7a..6671a7e 100644 --- a/Ch08-baggboost-lab.Rmd +++ b/Ch08-baggboost-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Tree-Based Methods diff --git a/Ch08-baggboost-lab.ipynb b/Ch08-baggboost-lab.ipynb index 65136ce..3661e94 100644 --- a/Ch08-baggboost-lab.ipynb +++ b/Ch08-baggboost-lab.ipynb @@ -1,11 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "004a2f9a", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "id": "934eb5b1", @@ -35,10 +29,10 @@ "id": "0f4c7654", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:40.079955Z", - "iopub.status.busy": "2024-06-04T23:19:40.079505Z", - "iopub.status.idle": "2024-06-04T23:19:40.907476Z", - "shell.execute_reply": "2024-06-04T23:19:40.907166Z" + "iopub.execute_input": "2025-04-03T19:33:56.538518Z", + "iopub.status.busy": "2025-04-03T19:33:56.538444Z", + "iopub.status.idle": "2025-04-03T19:33:57.461439Z", + "shell.execute_reply": "2025-04-03T19:33:57.461138Z" }, "lines_to_next_cell": 0 }, @@ -67,10 +61,10 @@ "id": "8d6cbed4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:40.909130Z", - "iopub.status.busy": "2024-06-04T23:19:40.909012Z", - "iopub.status.idle": "2024-06-04T23:19:40.959537Z", - "shell.execute_reply": "2024-06-04T23:19:40.959331Z" + "iopub.execute_input": "2025-04-03T19:33:57.463014Z", + "iopub.status.busy": "2025-04-03T19:33:57.462900Z", + "iopub.status.idle": "2025-04-03T19:33:57.545573Z", + "shell.execute_reply": "2025-04-03T19:33:57.545313Z" }, "lines_to_next_cell": 2 }, @@ -115,10 +109,10 @@ "id": "d85f1550", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:40.960966Z", - "iopub.status.busy": "2024-06-04T23:19:40.960851Z", - "iopub.status.idle": "2024-06-04T23:19:40.965270Z", - "shell.execute_reply": "2024-06-04T23:19:40.965080Z" + "iopub.execute_input": "2025-04-03T19:33:57.547131Z", + "iopub.status.busy": "2025-04-03T19:33:57.546998Z", + "iopub.status.idle": "2025-04-03T19:33:57.551787Z", + "shell.execute_reply": "2025-04-03T19:33:57.551553Z" } }, "outputs": [], @@ -146,10 +140,10 @@ "id": "36229722", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:40.966447Z", - "iopub.status.busy": "2024-06-04T23:19:40.966356Z", - "iopub.status.idle": "2024-06-04T23:19:40.975698Z", - "shell.execute_reply": "2024-06-04T23:19:40.975481Z" + "iopub.execute_input": "2025-04-03T19:33:57.552963Z", + "iopub.status.busy": "2025-04-03T19:33:57.552868Z", + "iopub.status.idle": "2025-04-03T19:33:57.563391Z", + "shell.execute_reply": "2025-04-03T19:33:57.563145Z" }, "lines_to_next_cell": 0 }, @@ -181,10 +175,10 @@ "id": "587701c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:40.976920Z", - "iopub.status.busy": "2024-06-04T23:19:40.976850Z", - "iopub.status.idle": "2024-06-04T23:19:40.981259Z", - "shell.execute_reply": "2024-06-04T23:19:40.981057Z" + "iopub.execute_input": "2025-04-03T19:33:57.564667Z", + "iopub.status.busy": "2025-04-03T19:33:57.564592Z", + "iopub.status.idle": "2025-04-03T19:33:57.570215Z", + "shell.execute_reply": "2025-04-03T19:33:57.569974Z" }, "lines_to_next_cell": 2 }, @@ -637,10 +631,10 @@ "id": "a0194963", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:40.982399Z", - "iopub.status.busy": "2024-06-04T23:19:40.982321Z", - "iopub.status.idle": "2024-06-04T23:19:40.984527Z", - "shell.execute_reply": "2024-06-04T23:19:40.984345Z" + "iopub.execute_input": "2025-04-03T19:33:57.571414Z", + "iopub.status.busy": "2025-04-03T19:33:57.571323Z", + "iopub.status.idle": "2025-04-03T19:33:57.573854Z", + "shell.execute_reply": "2025-04-03T19:33:57.573655Z" }, "lines_to_next_cell": 2 }, @@ -684,10 +678,10 @@ "id": "ef173e93", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:40.985725Z", - "iopub.status.busy": "2024-06-04T23:19:40.985647Z", - "iopub.status.idle": "2024-06-04T23:19:40.988104Z", - "shell.execute_reply": "2024-06-04T23:19:40.987929Z" + "iopub.execute_input": "2025-04-03T19:33:57.574968Z", + "iopub.status.busy": "2025-04-03T19:33:57.574883Z", + "iopub.status.idle": "2025-04-03T19:33:57.577601Z", + "shell.execute_reply": "2025-04-03T19:33:57.577406Z" } }, "outputs": [ @@ -727,17 +721,17 @@ "id": "06cd42f6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:40.989180Z", - "iopub.status.busy": "2024-06-04T23:19:40.989116Z", - "iopub.status.idle": "2024-06-04T23:19:41.199225Z", - "shell.execute_reply": "2024-06-04T23:19:41.198700Z" + "iopub.execute_input": "2025-04-03T19:33:57.578703Z", + "iopub.status.busy": "2025-04-03T19:33:57.578619Z", + "iopub.status.idle": "2025-04-03T19:33:57.790070Z", + "shell.execute_reply": "2025-04-03T19:33:57.787052Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -775,10 +769,10 @@ "id": "b6812ee3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.202511Z", - "iopub.status.busy": "2024-06-04T23:19:41.202113Z", - "iopub.status.idle": "2024-06-04T23:19:41.207460Z", - "shell.execute_reply": "2024-06-04T23:19:41.206250Z" + "iopub.execute_input": "2025-04-03T19:33:57.795177Z", + "iopub.status.busy": "2025-04-03T19:33:57.794516Z", + "iopub.status.idle": "2025-04-03T19:33:57.803228Z", + "shell.execute_reply": "2025-04-03T19:33:57.801841Z" } }, "outputs": [ @@ -840,10 +834,10 @@ "id": "7c731ea6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.210453Z", - "iopub.status.busy": "2024-06-04T23:19:41.210211Z", - "iopub.status.idle": "2024-06-04T23:19:41.223833Z", - "shell.execute_reply": "2024-06-04T23:19:41.223373Z" + "iopub.execute_input": "2025-04-03T19:33:57.806328Z", + "iopub.status.busy": "2025-04-03T19:33:57.806104Z", + "iopub.status.idle": "2025-04-03T19:33:57.821842Z", + "shell.execute_reply": "2025-04-03T19:33:57.820604Z" }, "lines_to_next_cell": 0 }, @@ -896,10 +890,10 @@ "id": "84680f6a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.227054Z", - "iopub.status.busy": "2024-06-04T23:19:41.226781Z", - "iopub.status.idle": "2024-06-04T23:19:41.230834Z", - "shell.execute_reply": "2024-06-04T23:19:41.230042Z" + "iopub.execute_input": "2025-04-03T19:33:57.825954Z", + "iopub.status.busy": "2025-04-03T19:33:57.825446Z", + "iopub.status.idle": "2025-04-03T19:33:57.830795Z", + "shell.execute_reply": "2025-04-03T19:33:57.829694Z" }, "lines_to_next_cell": 0 }, @@ -929,10 +923,10 @@ "id": "1c5dbf1d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.233920Z", - "iopub.status.busy": "2024-06-04T23:19:41.233718Z", - "iopub.status.idle": "2024-06-04T23:19:41.243506Z", - "shell.execute_reply": "2024-06-04T23:19:41.242998Z" + "iopub.execute_input": "2025-04-03T19:33:57.837384Z", + "iopub.status.busy": "2025-04-03T19:33:57.835775Z", + "iopub.status.idle": "2025-04-03T19:33:57.846267Z", + "shell.execute_reply": "2025-04-03T19:33:57.844749Z" }, "lines_to_next_cell": 0 }, @@ -969,10 +963,10 @@ "id": "8cf23460", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.246374Z", - "iopub.status.busy": "2024-06-04T23:19:41.246047Z", - "iopub.status.idle": "2024-06-04T23:19:41.251681Z", - "shell.execute_reply": "2024-06-04T23:19:41.251155Z" + "iopub.execute_input": "2025-04-03T19:33:57.849358Z", + "iopub.status.busy": "2025-04-03T19:33:57.848842Z", + "iopub.status.idle": "2025-04-03T19:33:57.862585Z", + "shell.execute_reply": "2025-04-03T19:33:57.860405Z" }, "lines_to_next_cell": 0 }, @@ -999,10 +993,10 @@ "id": "62abb2be", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.255029Z", - "iopub.status.busy": "2024-06-04T23:19:41.254557Z", - "iopub.status.idle": "2024-06-04T23:19:41.484614Z", - "shell.execute_reply": "2024-06-04T23:19:41.484364Z" + "iopub.execute_input": "2025-04-03T19:33:57.872936Z", + "iopub.status.busy": "2025-04-03T19:33:57.872306Z", + "iopub.status.idle": "2025-04-03T19:33:58.107084Z", + "shell.execute_reply": "2025-04-03T19:33:58.106846Z" }, "lines_to_next_cell": 0 }, @@ -1033,7 +1027,7 @@ "id": "0787b680", "metadata": {}, "source": [ - "Let’s take a look at the pruned true." + "Let’s take a look at the pruned tree." ] }, { @@ -1042,17 +1036,17 @@ "id": "8311d318", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.485939Z", - "iopub.status.busy": "2024-06-04T23:19:41.485856Z", - "iopub.status.idle": "2024-06-04T23:19:41.957648Z", - "shell.execute_reply": "2024-06-04T23:19:41.957387Z" + "iopub.execute_input": "2025-04-03T19:33:58.108440Z", + "iopub.status.busy": "2025-04-03T19:33:58.108366Z", + "iopub.status.idle": "2025-04-03T19:33:58.530145Z", + "shell.execute_reply": "2025-04-03T19:33:58.529868Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1084,10 +1078,10 @@ "id": "36d1cd8f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.959153Z", - "iopub.status.busy": "2024-06-04T23:19:41.959051Z", - "iopub.status.idle": "2024-06-04T23:19:41.961196Z", - "shell.execute_reply": "2024-06-04T23:19:41.960938Z" + "iopub.execute_input": "2025-04-03T19:33:58.531485Z", + "iopub.status.busy": "2025-04-03T19:33:58.531382Z", + "iopub.status.idle": "2025-04-03T19:33:58.533531Z", + "shell.execute_reply": "2025-04-03T19:33:58.533312Z" }, "lines_to_next_cell": 0 }, @@ -1124,10 +1118,10 @@ "id": "f0f7bd8c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.962629Z", - "iopub.status.busy": "2024-06-04T23:19:41.962538Z", - "iopub.status.idle": "2024-06-04T23:19:41.967670Z", - "shell.execute_reply": "2024-06-04T23:19:41.967362Z" + "iopub.execute_input": "2025-04-03T19:33:58.534778Z", + "iopub.status.busy": "2025-04-03T19:33:58.534693Z", + "iopub.status.idle": "2025-04-03T19:33:58.539435Z", + "shell.execute_reply": "2025-04-03T19:33:58.539165Z" }, "lines_to_next_cell": 2 }, @@ -1230,10 +1224,10 @@ "id": "663d8cba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.969083Z", - "iopub.status.busy": "2024-06-04T23:19:41.969008Z", - "iopub.status.idle": "2024-06-04T23:19:41.977175Z", - "shell.execute_reply": "2024-06-04T23:19:41.976947Z" + "iopub.execute_input": "2025-04-03T19:33:58.540825Z", + "iopub.status.busy": "2025-04-03T19:33:58.540752Z", + "iopub.status.idle": "2025-04-03T19:33:58.550403Z", + "shell.execute_reply": "2025-04-03T19:33:58.550172Z" } }, "outputs": [], @@ -1260,10 +1254,10 @@ "id": "e2f6482c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.978554Z", - "iopub.status.busy": "2024-06-04T23:19:41.978483Z", - "iopub.status.idle": "2024-06-04T23:19:41.980648Z", - "shell.execute_reply": "2024-06-04T23:19:41.980414Z" + "iopub.execute_input": "2025-04-03T19:33:58.551716Z", + "iopub.status.busy": "2025-04-03T19:33:58.551637Z", + "iopub.status.idle": "2025-04-03T19:33:58.554034Z", + "shell.execute_reply": "2025-04-03T19:33:58.553810Z" } }, "outputs": [], @@ -1291,16 +1285,16 @@ "id": "55ff65dd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:41.981863Z", - "iopub.status.busy": "2024-06-04T23:19:41.981787Z", - "iopub.status.idle": "2024-06-04T23:19:42.157435Z", - "shell.execute_reply": "2024-06-04T23:19:42.157194Z" + "iopub.execute_input": "2025-04-03T19:33:58.555212Z", + "iopub.status.busy": "2025-04-03T19:33:58.555138Z", + "iopub.status.idle": "2025-04-03T19:33:58.697536Z", + "shell.execute_reply": "2025-04-03T19:33:58.697210Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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"text/plain": [ "
" ] @@ -1345,10 +1339,10 @@ "id": "681b183e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:42.158836Z", - "iopub.status.busy": "2024-06-04T23:19:42.158742Z", - "iopub.status.idle": "2024-06-04T23:19:42.194904Z", - "shell.execute_reply": "2024-06-04T23:19:42.194697Z" + "iopub.execute_input": "2025-04-03T19:33:58.699190Z", + "iopub.status.busy": "2025-04-03T19:33:58.699058Z", + "iopub.status.idle": "2025-04-03T19:33:58.739446Z", + "shell.execute_reply": "2025-04-03T19:33:58.739180Z" } }, "outputs": [], @@ -1380,10 +1374,10 @@ "id": "78a255e3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:42.196310Z", - "iopub.status.busy": "2024-06-04T23:19:42.196237Z", - "iopub.status.idle": "2024-06-04T23:19:42.198627Z", - "shell.execute_reply": "2024-06-04T23:19:42.198405Z" + "iopub.execute_input": "2025-04-03T19:33:58.740764Z", + "iopub.status.busy": "2025-04-03T19:33:58.740684Z", + "iopub.status.idle": "2025-04-03T19:33:58.743246Z", + "shell.execute_reply": "2025-04-03T19:33:58.743035Z" }, "lines_to_next_cell": 2 }, @@ -1391,7 +1385,7 @@ { "data": { "text/plain": [ - "28.06985754975404" + "28.069857549754044" ] }, "execution_count": 22, @@ -1426,17 +1420,17 @@ "id": "d6f01ff8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:42.199859Z", - "iopub.status.busy": "2024-06-04T23:19:42.199787Z", - "iopub.status.idle": "2024-06-04T23:19:42.377590Z", - "shell.execute_reply": "2024-06-04T23:19:42.377308Z" + "iopub.execute_input": "2025-04-03T19:33:58.744386Z", + "iopub.status.busy": "2025-04-03T19:33:58.744313Z", + "iopub.status.idle": "2025-04-03T19:33:58.885793Z", + "shell.execute_reply": "2025-04-03T19:33:58.885495Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1487,10 +1481,10 @@ "id": "6f7ba658", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:42.379199Z", - "iopub.status.busy": "2024-06-04T23:19:42.379090Z", - "iopub.status.idle": "2024-06-04T23:19:42.503843Z", - "shell.execute_reply": "2024-06-04T23:19:42.503577Z" + "iopub.execute_input": "2025-04-03T19:33:58.887312Z", + "iopub.status.busy": "2025-04-03T19:33:58.887194Z", + "iopub.status.idle": "2025-04-03T19:33:59.020842Z", + "shell.execute_reply": "2025-04-03T19:33:59.020537Z" }, "lines_to_next_cell": 2 }, @@ -1935,10 +1929,10 @@ "id": "82e484c3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:42.505229Z", - "iopub.status.busy": "2024-06-04T23:19:42.505151Z", - "iopub.status.idle": "2024-06-04T23:19:42.567276Z", - "shell.execute_reply": "2024-06-04T23:19:42.567039Z" + "iopub.execute_input": "2025-04-03T19:33:59.022227Z", + "iopub.status.busy": "2025-04-03T19:33:59.022133Z", + "iopub.status.idle": "2025-04-03T19:33:59.069912Z", + "shell.execute_reply": "2025-04-03T19:33:59.069626Z" } }, "outputs": [ @@ -1954,7 +1948,7 @@ }, { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -1988,10 +1982,10 @@ "id": "c8de558e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:42.568858Z", - "iopub.status.busy": "2024-06-04T23:19:42.568766Z", - "iopub.status.idle": "2024-06-04T23:19:43.181163Z", - "shell.execute_reply": "2024-06-04T23:19:43.180926Z" + "iopub.execute_input": "2025-04-03T19:33:59.071344Z", + "iopub.status.busy": "2025-04-03T19:33:59.071248Z", + "iopub.status.idle": "2025-04-03T19:33:59.727901Z", + "shell.execute_reply": "2025-04-03T19:33:59.727665Z" }, "lines_to_next_cell": 0 }, @@ -2037,10 +2031,10 @@ "id": "bbebeb55", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:43.182422Z", - "iopub.status.busy": "2024-06-04T23:19:43.182350Z", - "iopub.status.idle": "2024-06-04T23:19:43.262543Z", - "shell.execute_reply": "2024-06-04T23:19:43.262340Z" + "iopub.execute_input": "2025-04-03T19:33:59.729294Z", + "iopub.status.busy": "2025-04-03T19:33:59.729211Z", + "iopub.status.idle": "2025-04-03T19:33:59.818356Z", + "shell.execute_reply": "2025-04-03T19:33:59.818091Z" }, "lines_to_next_cell": 2 }, @@ -2080,10 +2074,10 @@ "id": "36dcca85", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:43.263779Z", - "iopub.status.busy": "2024-06-04T23:19:43.263707Z", - "iopub.status.idle": "2024-06-04T23:19:43.268724Z", - "shell.execute_reply": "2024-06-04T23:19:43.268512Z" + "iopub.execute_input": "2025-04-03T19:33:59.819714Z", + "iopub.status.busy": "2025-04-03T19:33:59.819634Z", + "iopub.status.idle": "2025-04-03T19:33:59.824993Z", + "shell.execute_reply": "2025-04-03T19:33:59.824736Z" }, "lines_to_next_cell": 0 }, @@ -2238,10 +2232,10 @@ "id": "4478fc0c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:43.270020Z", - "iopub.status.busy": "2024-06-04T23:19:43.269951Z", - "iopub.status.idle": "2024-06-04T23:19:45.951552Z", - "shell.execute_reply": "2024-06-04T23:19:45.951319Z" + "iopub.execute_input": "2025-04-03T19:33:59.826285Z", + "iopub.status.busy": "2025-04-03T19:33:59.826212Z", + "iopub.status.idle": "2025-04-03T19:34:02.831808Z", + "shell.execute_reply": "2025-04-03T19:34:02.831548Z" } }, "outputs": [ @@ -2690,16 +2684,16 @@ "id": "57ac9a1e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:45.952863Z", - "iopub.status.busy": "2024-06-04T23:19:45.952788Z", - "iopub.status.idle": "2024-06-04T23:19:46.246579Z", - "shell.execute_reply": "2024-06-04T23:19:46.246323Z" + "iopub.execute_input": "2025-04-03T19:34:02.833132Z", + "iopub.status.busy": "2025-04-03T19:34:02.833055Z", + "iopub.status.idle": "2025-04-03T19:34:03.101998Z", + "shell.execute_reply": "2025-04-03T19:34:03.101670Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2740,10 +2734,10 @@ "id": "5e9127e2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:46.248030Z", - "iopub.status.busy": "2024-06-04T23:19:46.247950Z", - "iopub.status.idle": "2024-06-04T23:19:46.256507Z", - "shell.execute_reply": "2024-06-04T23:19:46.256269Z" + "iopub.execute_input": "2025-04-03T19:34:03.103498Z", + "iopub.status.busy": "2025-04-03T19:34:03.103396Z", + "iopub.status.idle": "2025-04-03T19:34:03.112948Z", + "shell.execute_reply": "2025-04-03T19:34:03.112658Z" } }, "outputs": [ @@ -2781,10 +2775,10 @@ "id": "f76d94d9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:46.257975Z", - "iopub.status.busy": "2024-06-04T23:19:46.257861Z", - "iopub.status.idle": "2024-06-04T23:19:48.006426Z", - "shell.execute_reply": "2024-06-04T23:19:48.006184Z" + "iopub.execute_input": "2025-04-03T19:34:03.114370Z", + "iopub.status.busy": "2025-04-03T19:34:03.114270Z", + "iopub.status.idle": "2025-04-03T19:34:05.116839Z", + "shell.execute_reply": "2025-04-03T19:34:05.116587Z" }, "lines_to_next_cell": 2 }, @@ -2816,7 +2810,7 @@ "id": "5f4ff1aa", "metadata": {}, "source": [ - "In this case, using $\\lambda=0.2$ leads to a almost the same test MSE\n", + "In this case, using $\\lambda=0.2$ leads to almost the same test MSE\n", "as when using $\\lambda=0.001$.\n", "\n", " " @@ -2848,10 +2842,10 @@ "id": "a310d4bd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:48.007901Z", - "iopub.status.busy": "2024-06-04T23:19:48.007823Z", - "iopub.status.idle": "2024-06-04T23:19:49.070813Z", - "shell.execute_reply": "2024-06-04T23:19:49.070586Z" + "iopub.execute_input": "2025-04-03T19:34:05.118221Z", + "iopub.status.busy": "2025-04-03T19:34:05.118141Z", + "iopub.status.idle": "2025-04-03T19:34:06.308660Z", + "shell.execute_reply": "2025-04-03T19:34:06.308427Z" }, "lines_to_next_cell": 2 }, @@ -3293,10 +3287,10 @@ "id": "4e45c7e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:49.072153Z", - "iopub.status.busy": "2024-06-04T23:19:49.072078Z", - "iopub.status.idle": "2024-06-04T23:19:49.296517Z", - "shell.execute_reply": "2024-06-04T23:19:49.296307Z" + "iopub.execute_input": "2025-04-03T19:34:06.310069Z", + "iopub.status.busy": "2025-04-03T19:34:06.309992Z", + "iopub.status.idle": "2025-04-03T19:34:06.543437Z", + "shell.execute_reply": "2025-04-03T19:34:06.543204Z" }, "lines_to_next_cell": 2 }, @@ -3332,10 +3326,10 @@ "id": "77037b29", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:49.297765Z", - "iopub.status.busy": "2024-06-04T23:19:49.297699Z", - "iopub.status.idle": "2024-06-04T23:19:49.299962Z", - "shell.execute_reply": "2024-06-04T23:19:49.299756Z" + "iopub.execute_input": "2025-04-03T19:34:06.544739Z", + "iopub.status.busy": "2025-04-03T19:34:06.544664Z", + "iopub.status.idle": "2025-04-03T19:34:06.547184Z", + "shell.execute_reply": "2025-04-03T19:34:06.546986Z" }, "lines_to_next_cell": 0 }, @@ -3384,8 +3378,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -3397,7 +3391,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/Ch09-svm-lab.Rmd b/Ch09-svm-lab.Rmd index 4e4a7ec..998e71b 100644 --- a/Ch09-svm-lab.Rmd +++ b/Ch09-svm-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Support Vector Machines diff --git a/Ch09-svm-lab.ipynb b/Ch09-svm-lab.ipynb index ff1be02..eac6a74 100644 --- a/Ch09-svm-lab.ipynb +++ b/Ch09-svm-lab.ipynb @@ -1,11 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "024023fd", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "id": "15f8edb2", @@ -37,10 +31,10 @@ "id": "0f8bbe77", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:50.217026Z", - "iopub.status.busy": "2024-06-04T23:19:50.216649Z", - "iopub.status.idle": "2024-06-04T23:19:50.906470Z", - "shell.execute_reply": "2024-06-04T23:19:50.906177Z" + "iopub.execute_input": "2025-04-03T19:34:07.672406Z", + "iopub.status.busy": "2025-04-03T19:34:07.672262Z", + "iopub.status.idle": "2025-04-03T19:34:08.366716Z", + "shell.execute_reply": "2025-04-03T19:34:08.366424Z" }, "lines_to_next_cell": 0 }, @@ -67,10 +61,10 @@ "id": "adea8726", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:50.908150Z", - "iopub.status.busy": "2024-06-04T23:19:50.908035Z", - "iopub.status.idle": "2024-06-04T23:19:50.930344Z", - "shell.execute_reply": "2024-06-04T23:19:50.930125Z" + "iopub.execute_input": "2025-04-03T19:34:08.368334Z", + "iopub.status.busy": "2025-04-03T19:34:08.368206Z", + "iopub.status.idle": "2025-04-03T19:34:08.392522Z", + "shell.execute_reply": "2025-04-03T19:34:08.392254Z" } }, "outputs": [], @@ -95,10 +89,10 @@ "id": "2eecca71", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:50.931785Z", - "iopub.status.busy": "2024-06-04T23:19:50.931697Z", - "iopub.status.idle": "2024-06-04T23:19:50.933292Z", - "shell.execute_reply": "2024-06-04T23:19:50.933058Z" + "iopub.execute_input": "2025-04-03T19:34:08.393984Z", + "iopub.status.busy": "2025-04-03T19:34:08.393870Z", + "iopub.status.idle": "2025-04-03T19:34:08.395470Z", + "shell.execute_reply": "2025-04-03T19:34:08.395268Z" } }, "outputs": [], @@ -116,7 +110,7 @@ "We now use the `SupportVectorClassifier()` function (abbreviated `SVC()`) from `sklearn` to fit the support vector\n", "classifier for a given value of the parameter `C`. The\n", "`C` argument allows us to specify the cost of a violation to\n", - "the margin. When the `cost` argument is small, then the margins\n", + "the margin. When the `C` argument is small, then the margins\n", "will be wide and many support vectors will be on the margin or will\n", "violate the margin. When the `C` argument is large, then the\n", "margins will be narrow and there will be few support vectors on the\n", @@ -135,17 +129,17 @@ "id": "6af148c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:50.934425Z", - "iopub.status.busy": "2024-06-04T23:19:50.934363Z", - "iopub.status.idle": "2024-06-04T23:19:51.021100Z", - "shell.execute_reply": "2024-06-04T23:19:51.020567Z" + "iopub.execute_input": "2025-04-03T19:34:08.396571Z", + "iopub.status.busy": "2025-04-03T19:34:08.396491Z", + "iopub.status.idle": "2025-04-03T19:34:08.501305Z", + "shell.execute_reply": "2025-04-03T19:34:08.497275Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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AAACORVkFAACAY3nsDgAAsSJYVq6iNz9Q2Y+b5U3roIyTxyguua3dsQAgqlFWAaAF5M16Rd9d8zdVB4pr7l8eNOVKTFCfm69Qj6mTZRiG3REBICpRVgFgPxW88Ja+ufiGXw4ETUmSWVau1dPukuFyq8fVF9iUDgCiG2tWAWA/WKapVdPvCjnm+9seULC0LEKJACC2UFYBYD/sWvqNyvM2hxwTLC7RlncWRSgRAMQWyioA7IfKHbuaNm5708YBAOqirALAfkjq2qlp47o1bRwAoC7KKgDsh3YH9ZZvaH/J1cCPU8OQNztdHY8dGdlgABAjKKsAsJ8O+tdNcsV5aras2pPLkAxDAx6+TYbbbU84AIhylFUA2E/thw/UiA+eUfvDBtc5nnxwXx361uPKOGG0TcmAX1QXl2jD/bO1ePCJej9zuBYPPlEb7p+t6uISu6MBIRmWZVl2h2hJgUBAPp9Pfr9fycnJdscB0MqUrs9TWX6hvGmpatu3p91xAElSxdYdWnLMOSpZs6HmgGVJP92oom3fHhrxwdOK75hqY0K0Ns3pa9wUAECrUBUolv+LbyXLUvLgAxWfmhKWr5PUI0dJPXLC8rmBffXtJTeqdO2mmpL6s5/+XPL9Rn1z6U0a9tKDNqWLfbu/W6eCF99S1fZdSuqRo07nnCxvege7Y0UNyiqAmBYsr9DqG+5W7mMvyCyvkCQZ8XHq/H8TdOBd0+Rp19bmhEB4lW7KV9HcD+oW1T1YwaCKXp+vstwCJXbJjnC62BasqNQ3F9+ggufelOFxS4YhK2hq9Q13q+/fruPOdk3EmlUAMcsKBrXs1Cna+NDTtUVVkqzKKuXNekWfjbtQwYpKGxMC4bfrs68bLKq1LEu7ln4dmUCtyMorblXBC29JkqzqoKyqask0ZVUHter6v+vHp1+zN2CUoKwCiFlFcz/U1vc/kkxz75NBU7uWfq38Z16PfDAggoxf71LREHasaFFlPxYqb9Yr9f/8+cn3tz4gK8R51KCsAohZuf99KfQLsMtQ3uMvRi4QYIPUI4bVvAUdguHxKPXwoRFK1DoUvbGg0TFlG3/U7pVrI5AmulFWAcSsso0/SsFgwwNMS6Wb8iMXCLCBN6Ojss86ce99gH/mdqnTuSdxwU8LC5aUyGjoZiF7jisujUCa6EZZBRCzvBkdG76zlFRzdyleoNEK9L//JrUfMaTmg59L60//TB05VAfd9xebksWutn17ygr1y7IkuV1K6tU1MoGiGLsBAIhZnf/vd9r+4aehx5x/aoTSAPbxtG2jw+bNVtGbH+jHWa+o7MdCJXbOVOdJpyrjxGPk8lAHWlra+KPlzeyoii3bJXPvC9wMj1sZJx8nbxr72zaGmwIAiFnBikr97/CJKl6xdq8ZDsPjVmKXbB3x+WuKS2b7KgAtb9sHS7T0txdJplXnZ5DhcSs+vYMO/99LSuycaWNC+zSnr7EMAEDMcnvjddh7s5Q29si9zrU/fKhGfPgMRRVAs1X5d2vjQ0/r68nT9e2lN6nwjfn1vuXf8ZgRGrnoOaWNO7L2jmGuBK9yLjhNRyx5udUW1eZiZhVAq1CydqO2L1oqWZbajxyidgf1tjsSgChUNPcDfXXOVAXLyn/aFsyQVV2tNn266dC3/qOkbp3rfV5VoFjVuwKKT+8gd4I3sqEdqDl9jbIKAADQBP7lq/S/EafVzKL+qj79vLToqG/eltsbb1PC6MEyAAAAgBa2/p//kWTVe0cwqzqo0vV5KpzzfuSDxTjKKgAAQBMUvjZPVnWI7ahcLhW9MT9ygVoJyioAAEAjLMuSWVERepBpKlhaHplArQhlFQAAoBGGYaht3561V/XXy+1Su/59IheqlaCsAgAANEG3Kf8nKcR16aalLpMnRixPa0FZBQAAaIKcC09T+m9G7T27+tOtaw+690Yldc+JfLAYR1kFAABoApfHo6EvPah+d05TYtdOtcdTRw7RIW8+qm5TzrUxXexin1UAAIBmsixL1YFiueI8cicl2h0n6jSnr3kilAkAACBmGIahOF87u2O0CiwDAAAAgGNRVgEAAOBYlFUAAAA4FmUVAAAAjkVZBQAAgGNRVgEAAOBYlFUAAAA4FmUVAAAAjkVZBQAAgGNRVgEAAOBYlFUAAAA4lsfuAAAA4BeWaWrr+x/pxyfnqLxgixI6Zyrn/FPU8diRMlzMMaH1oawCAOAQwdIyfXHKZdq24BMZbresYFCGx63NL7yltPFHa+iLD8id4LU7JhBR/IoGAIBDrLzqr9r24aeSJCsYrPlndc0/t773kVb96R+2ZQPsQlkFAMABKoq26cen5kimWf8A01Tu4y+qaqc/ssH2k2lasizL7hiIYiwDAADAAbYvXlo7i9oQq7JKOz7+QhknHhuhVPvGsiy9v3CLXnj9R639oVgut6FDBrXXWb/rrKED29sdD1GGsgoAiBqlG/K08eFntPmVd2WWVyh5YD91u+wcpf/2GBmGYXe8/dJYUf2Z2cRxdrEsS/944HvNnVcow5AsScGgpc+/2qFPl+3QtZf11oTx2XbHRBQJ6zKAxYsX68QTT1R2drYMw9Brr73W6HMWLlyoIUOGyOv1qlevXpo1a1Y4IwIAosT2xUu1aOBvtfGBJ1Wet1mVW3do+4ef6otTLtO3l94U9W81pwwb0Pggw1DK0P7hD7MfFn6yTXPnFUqS9vyWBH9a3XDPI2v1Y0GZDckQrcJaVktKSjRw4EA99NBDTRq/YcMGnXDCCRo9erSWL1+uq666ShdddJHee++9cMYEADhcdXGJvjjlMpkVlbUXHkm/XISU958X9eOsV+yK1yLa9O6mjmMOl+Fx13vecLuVfsIoJXZx9qzky2/mK9QOW4Yhvf5uQeQCOVSVf7f8y1epZO3GqP9FK9zCugxg/PjxGj9+fJPHz5w5U927d9c999wjSerXr58+/vhj3XvvvRo7dmy4YgIAHK7gubmqDuyueU+5Poah9ffNUs4Fp0U0V0sb+PgMfXL0WSrL21z3QiuXocTunXXwzL/aF66J1qzb3eA1YlLNv9bqtbsjF8hhKoq2adUNd6vg+bmyKqskSW0O6KE+N1+h7Im/sTmdMzlqN4AlS5ZozJgxdY6NHTtWS5YsafA5FRUVCgQCdR4AgNiyc8lXMlz1zzhKkixLxd+tVbA0ut9eTuiUoSM/n6M+t/xRid07y52UoKQeOTrg9qk64tNX5M3oaHfERnk8oauFYUjx8Y6qHxFTsXWH/nf46Sp49o3aoipJJd9v0FdnX60NDz5lYzrnctT/LYWFhcrIyKhzLCMjQ4FAQGVl9f8AmjFjhnw+X+0jJycnElEBAJHkMqSmXD8VA3d4imvvU+/pl+qY7xdonP9rjV4zX72u/73ifO3sjtYkRx7WQe4Qv1fIkg4/tEPE8jjJur89rPIfC/e+mO6nZQCrrvu7KrZstyGZs0X93+rp06fL7/fXPvLy8uyOBABoYR2PGRH6anmXSymHHszdnRzg9JM7S5ah+jZncLkkny9OY0dn7H0yxpmVlcp74uU6a65/zTJN/fj0a5ELFSUcVVYzMzNVVFRU51hRUZGSk5OVmJhY73O8Xq+Sk5PrPAAAsSXz1HHyZqWpwSk701SPay6KbCjUq3f3trp92oHyeGoKq2HUTIxLki85TvfdfrDaJLW+nTMrt+1UsCT0MhXD7VLpD7kRShQ9HPV/y4gRI/T222/XOTZv3jyNGDHCpkQAACdwe+N16NzH9dnYSarcvqv2bVPD45ZVHVTvm65Q1ilciOsUR43oqFefOExvzSvUqu93y+02dOiQ9hpzZLoSEkKtEYhdnnZtapp7qCv/LUtxKUy6/VpYy2pxcbHWrVtX+/GGDRu0fPlypaamqkuXLpo+fbry8/P15JNPSpL+8Ic/6MEHH9T111+vCy+8UB988IFefPFFvfXWW+GMCQCIAskH99Wo795T3qxXVfja+wqWlCl58IHqeslZTdujFBHV3hevc0/rYncMx/C0a6u0cUdp2/sfN7gUwKoOKosdAfZiWGHc3GvhwoUaPXr0XsfPP/98zZo1S5MmTdLGjRu1cOHCOs+5+uqr9d1336lz5876y1/+okmTJjX5awYCAfl8Pvn9fpYEAAAAx9j52ddaMupsWWZQMn9Vv1wuZZx4jIa93LS96aNdc/paWMuqHSirAADAqba8t1jLz7tOVTt2yfB4ZJmmZJrKOv03GvjY3+ROqv8anVjTnL7mqDWrAAAAsSx97FEak/eRCl+fr+JVP8jdNkmZJ41Rm15d7Y7mWJRVAACACHLFx3O3qmZw1NZVAAAAwJ6YWQUAoB7VQUufLN2uFWsCcrukQwa11+ABKTLq2+0eQNhQVgEA+JU163Zr2l9XaOv2SnnchixJT72Upx5d2+jOm/orMz3B7ohAq8EyAAAA9rBlW4X++OevtX1npaSaGdZgsGbjnE15Jbrihq9VXh7i1q8AWhRlFQCAPbwyN19l5UGZ5t7ngqa0uahc8z/aEvlgQCtFWQUAYA/zFm2pt6j+zDCkBYu3Ri4Q0MpRVgEA2ENpWei3+C1LKi6pjlAaAJRVAAD20KVzokJd8O92GereJSlygYBWjrIKAMAefvebbIW6EXnQtHTSuOzIBQJaOcoqAAB7OO7oDB02NHWv2dWfPzztxGz17xv6XuYAWg5lFQCAPXjchmb8+SBNPqebUnxxtcczMxJ07WW9deXFvWxMB7Q+hmWFerMj+gQCAfl8Pvn9fiUn85svAMSKvIJSvT2/SEVby+VLjtNxR6frwD7h/TlfHbRUtKVcbreh9I5euVzcvQpoCc3pa9zBCgDgaJZl6ZHZG/TsK3lyuyRLkmEYeumNfB15WAfdct2B8saH541Cj9tQp6zEsHxuAE3DMgAAgKO99Ea+nn0lT1LNpvymqdo7Sn382Xbd8/D3dsYDEGaUVQCAY1VXm3rqpdwGz1uW9O4HRdq6vSKCqQBEEmUVAOBYq9cVa6e/KuQY05KWfLEjQokARBplFQDgWJWVoe8mJdXc/rSiovFxAKITZRUA4Fhdc9qosQvwLUvq2b1tZAIBiDjKKgDAsTq0j9dRIzrK3cCrlcsldc5O1OD+vsgGAxAxlFUAgKNddUkvdezgletXr1hulyFvvFu3XNtPxq9vNwUgZlBWAQCO1jHVq8f/OURnnNxZbZLckqQ4j6Gxo9P1n/uGqG/vdjYnBBBO3MEKABA1TNNSWXlQXq9bHjezqUC04g5WAICY5HIZapPUel+6ygu3qrJou7yZHeXN6Gh3HCAiWu/feAAAooR/2QqtvvGf2jb/fzUHDENpxx2hA+64Rr5B/ewNB4QZa1YBAHCwHZ98qU+OPkvbPlzyy0HL0rYFn+iTI8/QrqXf2BcOiADKKgAADmVZlr79w19kVlVLQbPuuWBQZmWVvrn0LzalAyKDsgoAgEPtWvqNiletk0yz/gGmqd3frJb/q+8iGwyIIMoqAAAOVbJuY4uOA6IRF1gBAOBQcSlN24KxqeOAXzMrK1U4Z562vL1QZmWlkgcdqJxJpzpqtwn2WQUAwKGC5RWa32mkqgPFDY6JS/VpTN7HcsXHRzAZYkHJD7n6bNwklW3Ml+F2y7JMSYYMt0sD//N3dTrrxLB97eb0NZYBAICDBb5ZrYKX3taWdxYpWFZudxxEmDvBq943Tgk5pvdfrqCootmCFZX6bNwklecVSqq5YE+mJZmmrKpqLZ90vXZ88qXNKWuwDAAAHCjwzWp98/s/y79sRe0xT7s26jntD+p53cUyDO7e1Fp0v+oCBUvLtPaOh2UFgzUzYNVBGXEe9bnpCnWbcq7dERGFCue8r7KN+Q2eN1yG1v/zP0odOSSCqepHWQUAhyle/YM+GXW2gqVldY5X7y7Rmj/fo2r/bvW94xqb0iHSDMNQ7z9PUddLzlLBi++oonCLvFnpyj79N4rv0N7ueIhSW976UHK79toS7WdWdVBb3l4oy7Js/+WYsgoADvP9bQ/ILC1v8EXkh7seU9dLz1Fi58wIJ8OeysqDMiQlJLgj8vXiO6aq22XnRORrIfaZFZU1b/uHYFVV12yb5o7M/+MNYc0qADhI9e5iFc55v2b9WENchgqeeyNyoVDLsiy9Pb9Q513+uY6b+LHGTPxYF161TPMXb1GMXa+MGJc8sK8UasbUMNS2Xy8ZNhdVibIKAI5SuW2nrOoQRVWS4XKpvGBLhBLhZ5Zl6Z8z1+pv/1qjDbmltcfXri/WLXet0szZG2xMBzRPzoUTZbhCv73f7fL/i1Ca0CirAOAgcR3ay3CH/tFsmaa8WekRSoSfffblTs15e7Mkac9J1J///MwrefrmO78NyYDmS8hK18GP/k0yjLqzp4YhGYYyTh6jLpMn2hdwD5RVAHCQuOS2yphwfOi33kwrrPsfon6vvpWvUL9HuN2G5rxdELlAwH7q/H8TNOKDp5U2/qjanzlt+nRX/wdu1tDn/+WIJQASF1gBgOP0ufmP2vre4pp9Veu5yKrHNZOVmJNlQ7LWbe364oaueZMkBYOWvv+h4c37ASdKPWKYUo8YJss0ZQWDcsXF2R1pL8ysAoDDtOvXUyM+eEbJAw6oc9zdNkl9bruKbats4vU2PsuUkMDLKqKT4XI5sqhKzKwCgCP5Bh+oIz9/Tf4vV6p4zXp52iapwzEj5GmTZHe0Vmv04R31zCt5MhuYXTUMadTItMiGAloByioAOJhvyEHyDTnI7hiQ9LvfdNLLbxaoojK4V2F1uaQ2SR799nj2vgVaGu9XAADQBOkdvbr39gFqk1Qzz+N2G7V7pfuS4/Svvx6s9r54GxMCsYmZVQBohu9/2K0X38jXZ8t2yDQtHXygTxNP6qQhB3Pby9agf1+f5jxxmOZ/tEXfrPRLhqEhA1I0+og0eeOZ/wHCwbBi7JYbgUBAPp9Pfr9fycnJdscBEEPe+7BId9y7WoZL+vkGU26XoaBp6eJzu+n8M7raGxAAokRz+hozqwDQgDXrdmvdxhLFx7mUk52oO+5bXXMr7T1uMBX86d7ajz29UQP6JTPDCgAtjLIKAL+yMa9Et92zus6emaFuoS3VzLC+9EY+ZRUAWhhlFQD2ULilXJdet1wlZdV1jje2YCpoWvqaW20CQItjNTgA7OHpl/NUWrb31kRN4XI1Mv0KAGg2ZlYB4CemaendDwpr16E2h9ttaPgQlgCg5VWXlGrzS+9o93fr5GmTqIyTj5NvUD+7YwERQ1kFgJ9UVpoqr9iHKVXVFN3TT+rcwonstW1HhRZ9sk27i6uVnZmgo0d0bNItR9FyCl+fr+UXXK/g7hIZcR7JsrT2rw8pbdxRGvzMvYpLbmt3RCDsKKsA8BOv16WkJLdKS4OND/6J66fFVDdc2VcH9GoXpmSRFQxaeviJH/TSm/myrJrlDcGgpaQkt669tLeOH5Vhd8RWYccnX2rZGVdIP830W1W/rKPeOu9/+vL0K3ToO/+V0djVf0CUo6wCwE8Mw9CJx2fppTd+DLlm9ZQTsrV2fbGqg5YGHeTThPHZ6pSVGLmgYfbIrPV64fX82o+DwZqyVFoa1G33rFabJI8OP7SDXfFajXV3PFzzh/qu7gsGtW3BJ9q19Bu1Hz4wYpksy9L2D5Zo6/sfyaoOyjdsgDJPGSu3lzt3IXwoqwCwh7NPydGCxVu0c1elgvUU1gnjszT1D70jHyxCtu+s1Etv5Dd43jCkfz+5QSMPSWVGL4yqS0q1dd7HIbehMDwebX75nYiV1bK8zfr8pN9r94rvZXg8klEz2xt39R0a9vKDSj1iWERyoPVhNwAA2EOH9vGaeddgDRucWud4UqJbk8/uGtNFVZIWL9kmM0RBsixp/aYS5eWXRTBV6xMsKWt8vzRDqt5dEpk8FZX69PjzVbz6B0mSVV1duyyhaqdfS0+4SCXrNkUkC1ofZlYB4Fcy0xN0zy0DtLmoXD9sKpY3zqUB/XxKSIj9i4t2F1fVrlENOa6kOuR57J+4VJ88vnaq9u9ucIwVDKrtAd0jkmfzy++otKEyapoyKyq14f7Z6n//TRHJg9aFmVUAaEBWRoKOOLSjDhmc2iqKqiR1ykpstKgaRk2hR/i4PB51ueh0yd3wy7Thdqvz/02ISJ7CV9775WrCeljBoApeeCsiWdD6MLMKAKh1xPCOatvGrZKSoOqrrG6XNHxoqjq054KacOs17Q/a8vZClXy/UVZwjx0qXC7JNNX/XzcpvmNqw5+gBVXvLlZjd8oIlsbm0pAq/27lP/OG/MtWyBUfp7RxRyn9hFFyeahQkRKRmdWHHnpI3bp1U0JCgoYPH66lS5c2OHbWrFkyDKPOIyGB3+ABIBK88S5df/kBklEzg7onl0tKSvToiot62hOulYlLSdbIRc+p66Vny93ml90mfEMO0rA5j6jLxWdELEvbfr1keEK8u+Ay1PaAHhHLEylb3lmkBV2O1Mqrblf+s68rb9bLWnbaFC3qP16l6/PsjtdqhP3XghdeeEFTp07VzJkzNXz4cN13330aO3as1qxZo/T09Hqfk5ycrDVr1tR+zBWnABA5xxyRpqTEAXr0yQ36fn2xpJriOvKQDppyYQ/lZCfZnLD1iGvv00H33qi+f7tW5QVb5E5KUEJW/a+d4dTl4jO06ZFnGh5gWup66TmRCxQBgW9W64tTLquZ1bYsWdW/zG6XbcrXp8efr6NXvCN3gtfGlK1D2MvqP//5T1188cW64IILJEkzZ87UW2+9pf/+97+aNm1avc8xDEOZmZnhjgYAaMBhQ1N12NBU5W8uU2B3lTLSEpTKW/+2cScmqE3PLrZ9/eQBB6jXny+r2fvVMOruVGAY6jjmcHU+b4Jt+cJh/X1PSLLq3ZXBqg6qbFO+Nr/yrjqfc3Lkw7UyYV0GUFlZqWXLlmnMmDG/fEGXS2PGjNGSJUsafF5xcbG6du2qnJwcnXzyyVq5cmWDYysqKhQIBOo8AAAto1NWovr1SaaoQgfccqUGzrpTbfv+sgwkPr2D+tzyRx3y2iNyxcXZmK7lFc55v85s6l5cLhW9sSBygVqxsM6sbtu2TcFgUBkZdW/Nl5GRodWrV9f7nAMOOED//e9/dfDBB8vv9+vuu+/WyJEjtXLlSnXuvPd9t2fMmKFbb701LPkBAMAvOp9zsjqdfZIqirbJqqpWQna6DHds7pRhllc0MsCM2YvKnMZxW1eNGDFC5513ngYNGqSjjz5ar776qtLS0vTvf/+73vHTp0+X3++vfeTlseAZAIBwMQxDCZlpSszJitmiKkltD+wtuRq+ZsZwu9Wuf58IJmq9wlpWO3bsKLfbraKiojrHi4qKmrwmNS4uToMHD9a6devqPe/1epWcnFznAQBALNq2vUJr1u3W1u2NzPphv3Wbcq5khribm2mqy0WR25GhNQtrWY2Pj9fQoUO1YMEvazpM09SCBQs0YsSIJn2OYDCob7/9VllZWeGKCQCAo61Zt1tX3fi1Jkz6VJOv/lK/m/Sp/njD11r1PddphEvn836njJOOrbmgbM9diX66UcOB99xg60VvrUnYlwFMnTpVjz32mGbPnq1Vq1bp0ksvVUlJSe3uAOedd56mT59eO/62227T+++/r/Xr1+vLL7/Uueeeq02bNumiiy4Kd1QAABxnxeqALr1+ub78dled48tX7tJlf1qur1f67QkW41wej4a8cL8OvHu6Ert2qj3efsQQDXv93+p+xXk2pmtdwr511RlnnKGtW7fqpptuUmFhoQYNGqR333239qKr3Nxcufa4hdvOnTt18cUXq7CwUO3bt9fQoUP1ySef6MADDwx3VAAAHMWyLN314Peqrjb3ekfaNCVZlu58cI2efvgQ9iQPA5fHo+5/PF/drjhP1YFiueI8ciclNv5EtCjDsurZQCyKBQIB+Xw++f1+1q8CAKLa6nW7ddHVXzY6buZdg9W/L695iB7N6WuO2w0AAADUyN/ctK2RCgrZQgmxi7IKAIBDJbdt2mq9tm3CvqoPsA1lFQAAhxrUP0XJ7UIX0bZtPBo6sH2EEgGRR1kFAMCh4uJcuuicbiHHXHhWV3njeTlH7OJ9AwAAHOx3v8lWeYWpx57eoOpqSy6XIdO05HEbuvDsbpp4UqfGPwkQxSirAAA4mGEYOvuUHP32+Ex9+PFWbdtRqQ7t43XMEWlKbhdndzwg7CirAABEgeS2cTp5XLbdMYCIY5ELAAAAHIuyCgAAAMeirAIAAMCxKKsAAABwLMoqAAAAHIuyCgAAAMeirAIAAMCxKKsAAABwLMoqAAAAHIuyCgAAAMfidqsAgKhSWWXqo0+3acOmEiUkuHXUYR3VpXOS3bEAhAllFQAQNT77coduvXuVArur5XEbMi1LM2dv0NEjO+rGq/sqMcFtd0QALYxlAACAqPDd9wFdf9sK7S6uliRVBy2ZZs25jz7dppvv/M7GdADChbIKAIgKs57fJMuyZFl7nzNN6ZPPd2j12t2RDwYgrCirAADHKysPaskXO2pnUuvjdhta8NGWyIUCEBGUVQCA45WVBeudUf214tJg+MMAiCjKKgDA8ZLbeZSUGPriKdO01DkrIUKJAEQKZRUA4Hgej0snjs2SK8SrlsswNP6YzMiFAhARlFUAQFQ4b2IXZWck7FVYDaPmn5dP7qHU9vGRDwYgrCirAICo4EuO08y7huiEMZmKjzNqj3ftnKRbr++niSd1tjEdgHAxLKspS9ajRyAQkM/nk9/vV3Jyst1xAABhUFJarcIt5UrwupWdmSDDMBp/EgDHaE5f4w5WAICI2bKtQu99WKRtOyqUmhKv40dlKCuj+RdFtUnyqGe3tmFICMBpKKsAgLCzLEuPPrVRz7ycKxk1F0OZlqXHnt6oM07upCkX9pTLxewogL2xZhUAEHbPzflRT72UK9OqudvUnrdKfeH1fD3x/CZ7AwJwLMoqACCsKipNPfli6DL63Kt5Ki2tjlAiANGEsgoACKuvV+xScUnoO0uVV5j6fPnOCCUCEE1YswoACKuy8qbdArW0ieMQfSzL0vYPlmjnki8ll0sdRh2m9iMGs4sDmoSyCgAIqy6dk5o0rlsTxyG6FK/+QV+cOkUl32+Q4am5Ze73N/9LyYMP0rBXHlJiTpbNCeF0LAMAAIRV9y5tdFDf5AZvlepyST27tlHf3u0iGwxhV7F1h5Ycc65Kf8iVJFnVQVnVNTPou79drU/H/J+qS0rtjIgoQFkFAITdny7vo8QEt9y/etVxu6T4eJemX3UAbwnHoNxHn1Pl9l2ygnsv8bCqgyrdkKeCZ9+0IRmiCWUVABB2Pbq20eP3DtExR6bL7a4ppS6XdNTIND12zxD17cWsaizKf/ZN1e5RVi9D+c+9EbE8iE6sWQUAREROdpJuvrafrpvSR4HdVUpu61FSEi9DsaxqVyD0AMtS1c5GxqDV46cEACCikhLdSkp02x0DEdCmdzdVbtvZ4Oyq4XGrzQE9IpwK0YZlAAAAICy6XnJWyGUAVnVQXS46PYKJEI0oqwAAICyyJo5X2tijJFc9F88ZUqdzTlbHY0dGPhiiCmUVAACEhcvj0bBXH1Kv6Zcqrr2v9nh8Rgf1/du1GvifGewCgUYZlmVZdodoSYFAQD6fT36/X8nJyXbHAYBWbdv2CpVVBJXewSuvl3WqrVmwolKlazdKLpfa9Okml4fLZlqz5vQ1/k8BALS4j5du0xPPbdKadcWSpASvS789LksXntNVyW3jbE4HO7i98WrXv4/dMRCFKKsAEONKy4LalFcit9tQj65t5PGEdwXYG+9t1p0Pfq89390trzD16tv5+nz5Dj1y12AKK4Amo6wCQIwqLQvq0Sc36M33N6uisuaK7JTkOJ35u846+5Qcueq76GU/7fJX6Z8z10qSfr3IzDSlvIIyPf1Sri67oGeLf20AsYkLrAAgBlVUBHXln7/Wq2/n1xZVSdoVqNLM2Rt054PfKxyXLLz7YaGCwYY/r2lKr7+7WdXVoe5qBAC/oKwCQAyaO69Qq9fubnCLy7nzCvXtqpa/c1BeflmjM7YlpUH5d1e3+NcGEJsoqwAQg157p0Ch5k3dbmnu+5tb/Os25c5UhqRELy8/AJqGnxYAEIM2F5WHPB8MSj9uLmvxrzv6iLSQywBcLunQIe2VlMQlEwCahrIKADGobZvQZdDlknzJLX9Ffr/e7XTo4PZyhXh1Of+Mri3+dQHELsoqAMSgccdkhCyMpikdd3RGi39dwzD012kHaviQVEmS223I465Zw5qU6NZfpx2kgw/0hfoUAFAH78MAQAw67bed9OZ7m1VcUq3gry6ycrmknt3a6KjDOoTlaycleXTXzQO0dkOxFi/ZpvLyoLp3aaNjjkhTQgJ3sQLQPNxuFQBi1IbcEt04Y6U2/VhWM8tqSaYlDRuYoluuO1ApPjbmB2APbrcKAFD3Lm309MOH6KsVfn23JiC329Chg9urZ7e2dkcDgCajrAJADDMMQ0MGpGjIgBS7owDAPuECKwAAADgWZRUAAACORVkFAACAY0WkrD700EPq1q2bEhISNHz4cC1dujTk+Jdeekl9+/ZVQkKCBgwYoLfffjsSMQEAAOAwYS+rL7zwgqZOnaqbb75ZX375pQYOHKixY8dqy5Yt9Y7/5JNPdNZZZ2ny5Mn66quvNGHCBE2YMEErVqwId1QAAAA4TNj3WR0+fLgOOeQQPfjgg5Ik0zSVk5OjK664QtOmTdtr/BlnnKGSkhLNnTu39thhhx2mQYMGaebMmY1+PfZZBQAAcLbm9LWwzqxWVlZq2bJlGjNmzC9f0OXSmDFjtGTJknqfs2TJkjrjJWns2LENjq+oqFAgEKjzAAAAQGwIa1ndtm2bgsGgMjLq3n86IyNDhYWF9T6nsLCwWeNnzJghn89X+8jJyWmZ8AAAALBd1O8GMH36dPn9/tpHXl6e3ZEAAADQQsJ6B6uOHTvK7XarqKiozvGioiJlZmbW+5zMzMxmjfd6vfJ6vS0TGAAAAI4S1pnV+Ph4DR06VAsWLKg9ZpqmFixYoBEjRtT7nBEjRtQZL0nz5s1rcDwAAABiV1hnViVp6tSpOv/88zVs2DAdeuihuu+++1RSUqILLrhAknTeeeepU6dOmjFjhiTpyiuv1NFHH6177rlHJ5xwgp5//nl98cUXevTRR8MdFQAAAA4T9rJ6xhlnaOvWrbrppptUWFioQYMG6d133629iCo3N1cu1y8TvCNHjtSzzz6rG2+8UTfccIN69+6t1157Tf379w93VAAAgBZnWZZ2/m+ZitdskKddG6WNPVJxvnZ2x4oaYd9nNdLYZxUAADjFziVfafmFf1Lpuk21x1zeeHW/+kIdcMsfZbjdNqazT3P6WthnVgEAAFqjwNer9enx58msrK5z3Kyo1A//mKlgcYkOuvdGm9JFj6jfugoAAMCJvr/1X7KqgpJp7n3SkjY+9LRKN+VHPliUoawCAAC0sKpdARXN/VBWMNjwIJehgufnNnweklgGAACoR1WgWDs++lxmZZV8A/spqQd3BwSao2qHX2rksiDD5VJF0bYIJYpelFUAQC2zulrf33SfNjz4lMyy8trjaccfqQEzb1diTpaN6YDoEZ/WXobHLau64ZlVK2jyd6oJWAYAAJBUs73O1xdO0w93P16nqErStg8+0SdHnqGKLdttSgdEF0+7tso6dZwMT8NX+xsuQ9lnnRjBVNGJsgoAkCT5P/9WBc+9We9bl1Z1UOWFW7Xh/tk2JAOiU59brpS7TVKD21P1vnGKEjLTIpwq+lBWAQCSpLwnXw05C6Sgqbz/vBi5QECUa9Orq0Z+9ILaHz60zvH4tFQddN9f1OuGy2xKFl1YswoAkCRVbN4Scn2dJFVu2ynLNGW4mOsAmqJdv54aseAplazdqOLva+5g1X7EYLni4uyOFjUoqwAASZI3o2OjF4TEtfdRVIF90KZ3N7Xp3c3uGFGJnzgAAElSp3MnhCyqhtutnAtOjWAiAKCsAohyufmlev3dAr32ToHWbyqxO05Uaz9isDJ/d7zkMvY6Z3jciuvYXt2vusCGZABaM5YBAIhKu/xVuv2fq/TZlzvrHB88IEU3X9NXHTt4bUoWvQzD0OCn79F31/1duY+9IKvql/uZpwwfpEH//YcSstJtTAigNTIsq5HbK0SZQCAgn88nv9+v5ORku+MACIOKSlMXT/1Sm/JKFPzVLbfdLikzPUFP/GuokpL4fXxfVW7fqe0ffqpgRaV8gw5Uu4N62x0JQAxpTl/jJzmAqDN/8ZYG3/IPmlJBUbneml+oiSd1jnCy2BHfob2yThtvdwwAYM0qgOjzzoJCGXsvq6xlWdJb8wsjFwgAEDbMrAKIOjt2VdZ3k6U6dvmr9jpWllugTf9+TlveWSSrOqjUw4eq66VnK/ngvmFKCgDYX5RVAFEnKz1BPxaUyTTrP28YNetW97T1/Y/0xamXyayq1s8LXUvWblDuf17UQf/6i7pdek64YwMA9gHLAABEnROPz2qwqEo1ywBOGptV+3F54VZ9ceoUmRVV2vOKLKs6KFmWVv7xNu345MtwRgYA7CPKKoCoc+RhHXXIoJR61626XFL/vskac/QvWyzl/edFmZVVamjtgOFxa8P9s8MVFwCwHyirAKKO223o738ZoNNO7CRv/C8/xuLiDJ14fJbuvf1gxcf9cnzbgiUKNRVrVQe1fcEnYc0MANg3rFkFEJW88S5deXEvXXRON61eu1uWpD492yq5bdxeY5uynbSlmNpyGgBiBmUVQFRrk+TR0IHtQ47pcPSh2rnkS+11B4GfGB63Ohx1aDjiAQD2E8sAAMS8LhedIcPlUkObs1rVQXX/4/kRTgUAaArKKoCYl9g5U0Oe+5cMt1uGx117/Oc/9/37depw9HC74gEAQmAZAADHM01L735QpJffzNe6jcXyeFw6/JAOOuuUzjqwT+h7Sv8s8+QxOmr5m9r48DM/3RSgWqlHDFO3S89R+xGDw/xvAADYV4bVlCsPokggEJDP55Pf71dyctNexAA4l2lauu2fqzV/0RYZxi+7T7ndhizT0s3X9dOxR6aH/iQAAEdpTl9jGQAAR3t7QaHmL9oiqe42qcGgJdOSbv/nau3YWWlTuti0bXuF5i/eovcXFim/sMzuOABaOZYBAHC0F1/PrzOj+mtm0NLceZt13uldIxssBpWWVuuuh9dqweItMvf47z1iWKqm//EApbaPty8cgFaLmVUAjhUMWlq/qaTBoipJlqQ164ojlilWVQctTb35Wy34qG5RlaSlX+7QlOnLVVpabU84AK0aZRWAY7lcNWtTQzEMyRMXegwat3jJNq1YHaj3Rl9BU8rLL9NJ5y3Rvx5bp81F5ZEPCKDVoqwCcCzDMDR8SHu5Q/ykMk1p5LAOkQsVo95ZUChXI68I5RWmXp2br/Ov+EJr1u2OTDAArR5lFYCjnXNqTkM3npLbJaV19GrU4WmRDRWDtm6vqHdW9deCplReEdQNf1upYDCmNpMB4FCUVQCONvCgFN1w5QFyuVQ78/fzjajap8TrvtsOljeeH2X7K72jt9GZ1Z+ZplS0tUJLv9oR3lAAIHYDABAFfjMmU0MHpmju+4Va88Nuxce5NPKQDjr2yDR5ve7GPwEadcJxWfrk86aXT7fb0Kq1uzWCJRgAwoyyCiAqZKQlaPI53eyOEbOOOLSDhgxI0fIVu/baDaA+lmXJ08jFbwDQEnjvDAAgt9vQnTf11wnHZzaphJqmNHxIagSSAWjtKKsAAElSQoJbf7r8AL02e4ROGpfV4Di3y9Cg/j4d0KtdBNMBaK0oqwCAOlJ8cbrust464+ROkn7Z6/bnC7C6d03S7dMOtCsegFaGNasAgL0YhqErLuqlsaMz9MZ7hcorKFW7Nh6NOSpdRwzvII+HuQ4AkUFZBQA0qE/Pdrr2Mt7uB2AffjUGAACAY1FWAQAA4FiUVQAAADgWZRUAAACORVkFAACAY1FWAQAA4FiUVQAAADgW+6wCQBTbmFeiTz7foaoqU316ttWhg1Nr7zgFALGAsgoAUai4pFq33bNan3y+XS5XzR2ngkFLGWle3T7tQB3YJ9nuiADQIlgGAABRxjQt/en2Ffp02fafPpaCQUuStHV7hf7456+Vm19qZ0QAaDGUVQCIMsu+3qmvV/plmnufM02pqtLUc3N+jHwwAAgDyioARJn5H22V293w+aApvf9hkSzLilwoAAgTyioARJndxdUKBkOPqag0Faxn5hUAog1lFQCiTKfMBLldoa/475gaLw+7AgCIAZRVAIgyvz0uS0Gz4bf4XS5pwvjsCCYCgPChrAJAlOmak6T/m5hT7zmXS+qW00ann9QpwqlaJ8uyWBsMhBllFQCi0O//r7uuvay30jt6a4/Fxxk68fgsPfT3QUpKYhvtcLEsS/nPvamPR56mtxMO1Dtt+uvzCZdo++KldkcDYpJhxdivhIFAQD6fT36/X8nJbIoNILaZpqWNeaWqrDKVk52oNpTUsLIsS99edrPyHn+hZhr7p/3DDI9bVtDUgIduVZeLz7A5JeB8zelrzKwCQBRzuQz16NpGfXu1o6hGQOGr79UUVUl7bnRrVQcly9K3l9+skh9ybUoHxCbKKgCgQes3leipl3L1n2c3atGSbaqubt37YW148CnJ3fBLp2G4lPvY8xFMBMS+sJbVHTt26JxzzlFycrJSUlI0efJkFRcXh3zOqFGjZBhGnccf/vCHcMYEAPxKcUm1rr3lG513+Rd67OkNevLFXP35byt1ygWfavmKXXbHs03gq5UKtYGtFQxq1xffRjAREPvCWlbPOeccrVy5UvPmzdPcuXO1ePFi/f73v2/0eRdffLE2b95c+7jzzjvDGRMAsAfLsvSn21do6Vc7JdW82x0M1lzesMtfpak3fav1m0rsjGgbI66RpRaGIbfXG3oMgGYJW1ldtWqV3n33XT3++OMaPny4jjjiCD3wwAN6/vnnVVBQEPK5SUlJyszMrH2EWnhbUVGhQCBQ5wEA2HdffbtLX6/077kks5Zp1RTXZ15pnesyM357jAxPiHvdSko/YXSE0gCtQ9jK6pIlS5SSkqJhw4bVHhszZoxcLpc+++yzkM995pln1LFjR/Xv31/Tp09XaWlpg2NnzJghn89X+8jJqX/vQQBA08xfvFXuEHe/CpqWPvhoq8wQNyaIVd2vvKDmD0Y9/33cLsWlpqjTuSdHNhQQ48JWVgsLC5Wenl7nmMfjUWpqqgoLCxt83tlnn62nn35aH374oaZPn66nnnpK5557boPjp0+fLr/fX/vIy8trsX8HAGiNikuqZTVSRKuqLVVVtb6LrXyD+mnwM/fWLAdw/fQS+tOtb+NTUzT83ScUl9zWxoRA7Gn2PifTpk3TP/7xj5BjVq1atc+B9lzTOmDAAGVlZenYY4/VDz/8oJ49e+413uv1ysv6IABoMZ2zEyVDUoi+2j4lTvHxrXNDmaxTxir18KHKe+Jl7Vr6jYw4j9KOP1LZZ54gT5sku+MBMafZZfWaa67RpEmTQo7p0aOHMjMztWXLljrHq6urtWPHDmVmZjb56w0fPlyStG7dunrLKgCgZf32uEw99WLDa1JdLmnC+GwZ9b0V3kp4Mzqq1zR2qgEiodllNS0tTWlpaY2OGzFihHbt2qVly5Zp6NChkqQPPvhApmnWFtCmWL58uSQpKyuruVEBAPsgOzNRk8/tpsef3rjXOZdL6to5SWdO6Bz5YABapbC9h9OvXz+NGzdOF198sZYuXar//e9/uvzyy3XmmWcqOztbkpSfn6++fftq6dKa+yn/8MMPuv3227Vs2TJt3LhRb7zxhs477zwdddRROvjgg8MVFQDwK5PO6Ko/X3WAOmUl1B7zxrt08rhsPfyPwdwtC0DEhPWnzTPPPKPLL79cxx57rFwul0499VTdf//9teerqqq0Zs2a2qv94+PjNX/+fN13330qKSlRTk6OTj31VN14443hjAkAqMf4YzM17pgM5RWUqbLSVHZmopISQ2/bBAAtzbAsK6b2HgkEAvL5fPL7/SH3ZwUAAIA9mtPXWuelnAAAAIgKlFUAAAA4FmUVAAAAjkVZBQAAgGNRVgEAAOBYlFUAAAA4FmUVAAAAjkVZBQAAgGNRVgEAAOBYlFUAAAA4FmUVAAAAjkVZBQAAgGNRVgEAAOBYHrsDAAivnbsqtWrtbhmGdNAByUpuF2d3JAAAmoyyCsSo4pJq3ffoOs1buEVB05IkeTyGThiTqSsm91RCgtvmhAAANI6yCsSgikpTV974tdauL5Zp/nK8utrSm+9tVm5+qe69faA8bsO+kAAANAFrVoEY9P6HRVqzrm5R/ZlpSV9969dHn26LfDAAAJqJsgrEoDfe3ywjxKSpyyXNfX9z5AIBALCPKKtADNqytUKW1fB505QKt1RELhAAAPuIsgrEoNT28SHPuwypY2roMQAAOAFlFYhBvz0uM+QyANOSfjMmM3KBAADYR5RVIAaNPzZTOdmJctfzN9zlkg7o2Vajj0iLfDAAAJqJsgrEoKREtx76+yANG9R+r3NHHNpB9/71YMXH8dcfAOB87LMKxKj2KfG659aD9WNBmb5d5ZcMaXD/FGWmJ9gdDQCAJqOsAjGuc3aiOmcn2h0DAIB9wvuAAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgC0IpZlqbQsqIqKoN1RAKBJPHYHAACEXzBo6fV3C/TiG/n6saBMkjSgX7LOOS1HRxza0eZ0ANAwZlYBIMaZpqVb716lf85cp/zNZbXHV64JaNrtK/Xsq3k2pgOA0CirABDj5i/eog8+3ipJsqxfjptmzT8ffmK9NuaV2JAMABpHWQWAGPfy3Hy5jIbPu1zS6+9ujlwgAGgGyioAxLj1G0tkWg2fN01p7friyAUCgGagrAJAjIuPC/2j3jCkhAR3hNIAQPNQVgEgxo06PE3uEOsALEs66jB2BADgTJRVAIhxp5/cSS53zQzqr7ldUlqHeB13dHrkgwFAE1BWASDGdctpo3/c2F8JXrcM1RRUt7umuaZ19OpfdwxUIssAADhU2MrqHXfcoZEjRyopKUkpKSlNeo5lWbrpppuUlZWlxMREjRkzRmvXrg1XRABoNQ4dkqrXZx+mqZf21vGjMjT+mAz9ddqBev7fh6pLpyS74wFAg8JWVisrKzVx4kRdeumlTX7OnXfeqfvvv18zZ87UZ599pjZt2mjs2LEqLy8PV0wAaDWSkjz63W+y9eer+2raHw/QqMPT5PHwBhsAZzMsywqxocn+mzVrlq666irt2rUr5DjLspSdna1rrrlG1157rSTJ7/crIyNDs2bN0plnntmkrxcIBOTz+eT3+5WcnLy/8QEAANDCmtPXHPMr9YYNG1RYWKgxY8bUHvP5fBo+fLiWLFnS4PMqKioUCATqPAAAABAbHFNWCwsLJUkZGRl1jmdkZNSeq8+MGTPk8/lqHzk5OWHNCQAAgMhpVlmdNm2aDMMI+Vi9enW4stZr+vTp8vv9tY+8vLyIfn0AAACEj6c5g6+55hpNmjQp5JgePXrsU5DMzExJUlFRkbKysmqPFxUVadCgQQ0+z+v1yuv17tPXBAAAgLM1q6ympaUpLS0tLEG6d++uzMxMLViwoLacBgIBffbZZ83aUQAAAACxI2xrVnNzc7V8+XLl5uYqGAxq+fLlWr58uYqLi2vH9O3bV3PmzJEkGYahq666Sn/961/1xhtv6Ntvv9V5552n7OxsTZgwIVwxAQAA4GDNmlltjptuukmzZ8+u/Xjw4MGSpA8//FCjRo2SJK1Zs0Z+v792zPXXX6+SkhL9/ve/165du3TEEUfo3XffVUJCQrhiAgAAwMHCvs9qpLHPKgAAgLNF5T6rAAAAwK9RVgEAAOBYlFUAAAA4VtgusAIAAEDklZUH9cPGEhmG1KtbG3m9brsj7RfKKgAAQAyoqAjq0ac26PV3N6u8wpQktUly65QTOmny2V3l8UTnG+qUVQAAgChXXW3qultXaPmKXTL32OeppDSop1/O1cbcEt1xw0FyuQz7Qu6j6KzYAAAAqDV/8VZ9+W3dovozy5I++my7Pvlie+SDtQDKKgAAQJR7/d0CGSEmTV0u6c33CiMXqAVRVgEAAKJcQWG5Qt3myTSlHwvKIheoBVFWAQAAolxyu9CXIRmGlOKLi1CalkVZBQAAiHLjj80MuQzAsqRxozMiF6gFUVYBAACi3G+Pz1RaB6/c9TQ7t0vq0ilRxx2dHvlgLYCyCgAAEOWS28bp4X8MUu+ebSVJLkO1M60DDvTp/r8NVEJCdN4cgH1WAQAAYkBmeoIe/+dQrfo+oK+/88uQoSEDU9S7e1u7o+0XyioAAEAM6dcnWf36JNsdo8WwDAAAAACORVkFAACAY1FWAQAA4FiUVQAAADgWZRUAAACORVkFAACAY1FWAQAA4FiUVQAAADgWNwUAAACIIpZlacdHn2vXF9/KFRentOOPUNsDetgdK2woqwAAAFFi94rvtezMK1WyZr3kdkmWJZmW0n8zSoNm36W4lNi5c9XPWAYAAAAQBcp+LNSSY85V6bpNNQeCpmRakqSt732kpSdcJCsYtDFheFBWAQAAosCG+2erOlBcbyG1gkHtWvq1tryzyIZk4UVZBQAAiAL5T70WeubU7VbB83MjFyhCKKsAAABRoMofCD0gGFTF1h2RCRNBlFUAAIAokNglO+R5w+NWmx5dIpQmciirAICoEAxasizL7hiAbbr8/kzJZTR43qoOKufC0yKYKDLYugoA4Fimaent+YV68Y18rd9UIrfb0PAh7XXOqTkaeFCK3fGAiOp6yVnKf/YNFa9YW+/a1ZyLz1DKIQfbkCy8mFkFADiSaVq67Z5V+vsD32tDbomkmtnVz5bt0JRpX+ut+YU2JwQiy9MmSSMWPK3OF5wqlze+9nh8Wqr6zrhWAx68xb5wYWRYMfaeSiAQkM/nk9/vV3Jy7G2MCwCtxdvzC/W3f61p8LzLJb30+HBlpCVEMBXgDFX+3dq9cq1c8XFKHthXrrg4uyM1S3P6GjOrAABHevnNfBkNL8+TJL353ubIhAEcJs7XTqkjhyhl2ICoK6rNRVkFADjSuo3FCvXen2lK368vjlwgALagrAIAHMnjCf0SZRhSfBwvY0Cs4285AMCRDj+kg9whXqUsSxp5SIfIBQJgC8oqAMCRzjqlsyxLqm/ZqtslpXWI17FHpkU8F4DIoqwCABzpwD7Juvm6fnJ7DLmMmrf9XT+9aqWmevWvvw6U1+u2NySAsOOmAAAAxzr2yHQN7p+iufM2a826YnniDI0c1kGjj0hjvSrQSlBWAQCOlto+Xued3tXuGABswq+lAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAAByLsgoAAADHoqwCAADAsSirAAAAcCzKKgAAABwrbGX1jjvu0MiRI5WUlKSUlJQmPWfSpEkyDKPOY9y4ceGKCAAAAIfzhOsTV1ZWauLEiRoxYoT+85//NPl548aN0xNPPFH7sdfrDUc8AAAARIGwldVbb71VkjRr1qxmPc/r9SozMzMMiQAAABBtHLdmdeHChUpPT9cBBxygSy+9VNu3bw85vqKiQoFAoM4DAAAAscFRZXXcuHF68skntWDBAv3jH//QokWLNH78eAWDwQafM2PGDPl8vtpHTk5OBBMDAAAgnJpVVqdNm7bXBVC/fqxevXqfw5x55pk66aSTNGDAAE2YMEFz587V559/roULFzb4nOnTp8vv99c+8vLy9vnrAwAAwFmatWb1mmuu0aRJk0KO6dGjx/7k2etzdezYUevWrdOxxx5b7xiv18tFWAAAADGqWWU1LS1NaWlp4cqylx9//FHbt29XVlZWxL4mAAAAnCNsa1Zzc3O1fPly5ebmKhgMavny5Vq+fLmKi4trx/Tt21dz5syRJBUXF+u6667Tp59+qo0bN2rBggU6+eST1atXL40dOzZcMQEAAOBgYdu66qabbtLs2bNrPx48eLAk6cMPP9SoUaMkSWvWrJHf75ckud1uffPNN5o9e7Z27dql7OxsHX/88br99tt5mx8AAKCVMizLsuwO0ZICgYB8Pp/8fr+Sk5PtjgMAAIBfaU5fc9TWVQAAAMCeKKsAAABwLMoqAAAAHIuyCgAAAMeirAIAAMCxKKsAAABwLMoqAAAAHIuyCgAAAMeirAIAAMCxwna7VQCIBcUl1Zo7b7Penl+knf5KZaYn6OSxWTp+dIbi4/h9HwDCjbIKAA3Yur1Cl/1puQq3lOvnG1Pv8ldp1fe79eb7m3Xv7QOVlOi2NyQAxDimBQCgAbfevUpbtlbUFlVJtX9etXa3HvzPOnuCAUArQlkFgHqs31Si5Sv8CppWvedNU3p7QZECxVURTgYArQtlFQDq8c13/kbHVFdb+v6H4gikAYDWi7IKAPUwjCaOC28MAGj1KKsAUI8hB6c0OsYb71K/3u3CHwYAWjHKKgDUIyc7SSOGpcrVwE9Jw5AmjM9SUhKbqgBAOFFWAaABN17dVz26tJH0y7KAn8vr8CGpuuT8HjYlA4DWgykBAGiALzlOj/5ziD74aKveWVCoHbsqlZWRoBOPz9LIQzrI7WbFKgCEG2UVAEKIj3Np3DEZGndMht1RAKBVYhkAAAAAHIuyCgAAAMeirAIAAMCxKKsAAABwLMoqAAAAHIuyCgAAAMeirAIAAMCxKKsAAABwLMoqAAAAHIuyCgAAAMeirAIAAMCxKKsAAABwLMoqAAAAHIuyCgAAAMeirAIAAMCxKKsAAABwLMoqAAAAHIuyCgAAAMeirAIAAMCxKKsAAABwLMoqAAAAHIuyCgAAAMeirAIAAMCxPHYHiGZbt1fovQ+LtG17pdqnxOn4URnKykiwOxYAAEDMoKzuA8uy9PgzG/XUi7mSIbkMQ6Zl6bGnN2riSZ10xeSecrkMu2MCAABEPZYB7IPnX/tRs1/IlWlJpilVBy2ZZs25l97I1xPPbbI3IAAAQIygrDZTZZWp2S/khhzz7Kt5Ki2tjlAiAACA2EVZbaavV+xScUnoIlpRaWrpVzsjlAgAACB2UVabqbTcbNK4svJgmJMAAADEPspqM3XtnNi0cTlJYU4CAAAQ+yirzdQtp40G9EuWq4H/ci6X1KNrkvr1bhfZYAAAADGIsroPrr+8jxIT3HL/6r+e22UoPs6lG67sK8Ng6yoAAID9RVndB927tNHj9w7RMUemy+2uKaUul3TkYR306D1D1JdZVQAAgBZhWJZl2R2iJQUCAfl8Pvn9fiUnJ4f965WWBeUPVCm5nUdtkrjHAgAAQGOa09doV/spKdGtpES33TEAAABiEssAAAAA4FiUVQAAADgWZRUAAACORVkFAACAY1FWAQAA4FhhK6sbN27U5MmT1b17dyUmJqpnz566+eabVVlZGfJ55eXlmjJlijp06KC2bdvq1FNPVVFRUbhiAgAAwMHCVlZXr14t0zT173//WytXrtS9996rmTNn6oYbbgj5vKuvvlpvvvmmXnrpJS1atEgFBQU65ZRTwhUTAAAADhbRmwLcddddeuSRR7R+/fp6z/v9fqWlpenZZ5/VaaedJqmm9Pbr109LlizRYYcd1ujXiPRNAQAAANA8zelrEV2z6vf7lZqa2uD5ZcuWqaqqSmPGjKk91rdvX3Xp0kVLliyp9zkVFRUKBAJ1HgAAAIgNESur69at0wMPPKBLLrmkwTGFhYWKj49XSkpKneMZGRkqLCys9zkzZsyQz+erfeTk5LRkbAAAANio2WV12rRpMgwj5GP16tV1npOfn69x48Zp4sSJuvjii1ssvCRNnz5dfr+/9pGXl9einx8AAAD28TT3Cddcc40mTZoUckyPHj1q/1xQUKDRo0dr5MiRevTRR0M+LzMzU5WVldq1a1ed2dWioiJlZmbW+xyv1yuv19vk/AAAAIgezS6raWlpSktLa9LY/Px8jR49WkOHDtUTTzwhlyv0RO7QoUMVFxenBQsW6NRTT5UkrVmzRrm5uRoxYkRzowIAACDKhW3Nan5+vkaNGqUuXbro7rvv1tatW1VYWFhn7Wl+fr769u2rpUuXSpJ8Pp8mT56sqVOn6sMPP9SyZct0wQUXaMSIEU3aCQAAAACxpdkzq001b948rVu3TuvWrVPnzp3rnPt5t6yqqiqtWbNGpaWltefuvfdeuVwunXrqqaqoqNDYsWP18MMPhysmAAAAHCyi+6xGAvusAgAAOJtj91kFAAAAmoOyCgAAAMeirAIAAMCxKKsAAABwrLDtBmCXn68XCwQCNicBAABAfX7uaU25zj/myuru3bslSTk5OTYnAQAAQCi7d++Wz+cLOSbmtq4yTVMFBQVq166dDMOwOw6aKBAIKCcnR3l5eWw5FsX4PsYGvo+xge9j7IjF76VlWdq9e7eys7MbvcNpzM2sulyuvW5CgOiRnJwcM38RWzO+j7GB72Ns4PsYO2Lte9nYjOrPuMAKAAAAjkVZBQAAgGNRVuEIXq9XN998s7xer91RsB/4PsYGvo+xge9j7Gjt38uYu8AKAAAAsYOZVQAAADgWZRUAAACORVkFAACAY1FWAQAA4FiUVQAAADgWZRWOsnHjRk2ePFndu3dXYmKievbsqZtvvlmVlZV2R0Mz3XHHHRo5cqSSkpKUkpJidxw0w0MPPaRu3bopISFBw4cP19KlS+2OhGZYvHixTjzxRGVnZ8swDL322mt2R8I+mDFjhg455BC1a9dO6enpmjBhgtasWWN3LFtQVuEoq1evlmma+ve//62VK1fq3nvv1cyZM3XDDTfYHQ3NVFlZqYkTJ+rSSy+1Owqa4YUXXtDUqVN1880368svv9TAgQM1duxYbdmyxe5oaKKSkhINHDhQDz30kN1RsB8WLVqkKVOm6NNPP9W8efNUVVWl448/XiUlJXZHizj2WYXj3XXXXXrkkUe0fv16u6NgH8yaNUtXXXWVdu3aZXcUNMHw4cN1yCGH6MEHH5QkmaapnJwcXXHFFZo2bZrN6dBchmFozpw5mjBhgt1RsJ+2bt2q9PR0LVq0SEcddZTdcSKKmVU4nt/vV2pqqt0xgJhXWVmpZcuWacyYMbXHXC6XxowZoyVLltiYDIDf75ekVvl6SFmFo61bt04PPPCALrnkErujADFv27ZtCgaDysjIqHM8IyNDhYWFNqUCYJqmrrrqKh1++OHq37+/3XEijrKKiJg2bZoMwwj5WL16dZ3n5Ofna9y4cZo4caIuvvhim5JjT/vyfQQA7J8pU6ZoxYoVev755+2OYguP3QHQOlxzzTWaNGlSyDE9evSo/XNBQYFGjx6tkSNH6tFHHw1zOjRVc7+PiC4dO3aU2+1WUVFRneNFRUXKzMy0KRXQul1++eWaO3euFi9erM6dO9sdxxaUVUREWlqa0tLSmjQ2Pz9fo0eP1tChQ/XEE0/I5eINAKdozvcR0Sc+Pl5Dhw7VggULai/IMU1TCxYs0OWXX25vOKCVsSxLV1xxhebMmaOFCxeqe/fudkeyDWUVjpKfn69Ro0apa9euuvvuu7V169bac8zsRJfc3Fzt2LFDubm5CgaDWr58uSSpV69eatu2rb3h0KCpU6fq/PPP17Bhw3TooYfqvvvuU0lJiS644AK7o6GJiouLtW7dutqPN2zYoOXLlys1NVVdunSxMRmaY8qUKXr22Wf1+uuvq127drXrxn0+nxITE21OF1lsXQVHmTVrVoMvivyvGl0mTZqk2bNn73X8ww8/1KhRoyIfCE324IMP6q677lJhYaEGDRqk+++/X8OHD7c7Fppo4cKFGj169F7Hzz//fM2aNSvygbBPDMOo9/gTTzzR6HKsWENZBQAAgGOxGBAAAACORVkFAACAY1FWAQAA4FiUVQAAADgWZRUAAACORVkFAACAY1FWAQAA4FiUVQAAADgWZRUAAACORVkFAACAY1FWAQAA4Fj/D4kuVVWBFtIzAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -180,10 +174,10 @@ "id": "01cf2e1a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.025822Z", - "iopub.status.busy": "2024-06-04T23:19:51.025389Z", - "iopub.status.idle": "2024-06-04T23:19:51.034733Z", - "shell.execute_reply": "2024-06-04T23:19:51.033849Z" + "iopub.execute_input": "2025-04-03T19:34:08.520528Z", + "iopub.status.busy": "2025-04-03T19:34:08.514664Z", + "iopub.status.idle": "2025-04-03T19:34:08.537554Z", + "shell.execute_reply": "2025-04-03T19:34:08.536661Z" }, "lines_to_next_cell": 2 }, @@ -628,16 +622,16 @@ "id": "39826ae8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.037704Z", - "iopub.status.busy": "2024-06-04T23:19:51.037494Z", - "iopub.status.idle": "2024-06-04T23:19:51.214727Z", - "shell.execute_reply": "2024-06-04T23:19:51.214362Z" + "iopub.execute_input": "2025-04-03T19:34:08.544733Z", + "iopub.status.busy": "2025-04-03T19:34:08.544393Z", + "iopub.status.idle": "2025-04-03T19:34:08.679072Z", + "shell.execute_reply": "2025-04-03T19:34:08.678826Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -673,17 +667,17 @@ "id": "7f7abd49", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.216395Z", - "iopub.status.busy": "2024-06-04T23:19:51.216304Z", - "iopub.status.idle": "2024-06-04T23:19:51.349071Z", - "shell.execute_reply": "2024-06-04T23:19:51.348816Z" + "iopub.execute_input": "2025-04-03T19:34:08.680528Z", + "iopub.status.busy": "2025-04-03T19:34:08.680425Z", + "iopub.status.idle": "2025-04-03T19:34:08.833774Z", + "shell.execute_reply": "2025-04-03T19:34:08.833542Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -719,10 +713,10 @@ "id": "ddce63fa", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.350548Z", - "iopub.status.busy": "2024-06-04T23:19:51.350429Z", - "iopub.status.idle": "2024-06-04T23:19:51.352612Z", - "shell.execute_reply": "2024-06-04T23:19:51.352356Z" + "iopub.execute_input": "2025-04-03T19:34:08.835166Z", + "iopub.status.busy": "2025-04-03T19:34:08.835063Z", + "iopub.status.idle": "2025-04-03T19:34:08.837321Z", + "shell.execute_reply": "2025-04-03T19:34:08.837086Z" }, "lines_to_next_cell": 2 }, @@ -757,10 +751,10 @@ "id": "28860549", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.353955Z", - "iopub.status.busy": "2024-06-04T23:19:51.353852Z", - "iopub.status.idle": "2024-06-04T23:19:51.375266Z", - "shell.execute_reply": "2024-06-04T23:19:51.375045Z" + "iopub.execute_input": "2025-04-03T19:34:08.838534Z", + "iopub.status.busy": "2025-04-03T19:34:08.838436Z", + "iopub.status.idle": "2025-04-03T19:34:08.867170Z", + "shell.execute_reply": "2025-04-03T19:34:08.866914Z" }, "lines_to_next_cell": 2 }, @@ -805,10 +799,10 @@ "id": "ebfcf7a0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.376507Z", - "iopub.status.busy": "2024-06-04T23:19:51.376434Z", - "iopub.status.idle": "2024-06-04T23:19:51.378557Z", - "shell.execute_reply": "2024-06-04T23:19:51.378347Z" + "iopub.execute_input": "2025-04-03T19:34:08.868589Z", + "iopub.status.busy": "2025-04-03T19:34:08.868487Z", + "iopub.status.idle": "2025-04-03T19:34:08.870887Z", + "shell.execute_reply": "2025-04-03T19:34:08.870644Z" }, "lines_to_next_cell": 0 }, @@ -846,10 +840,10 @@ "id": "18762933", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.379730Z", - "iopub.status.busy": "2024-06-04T23:19:51.379662Z", - "iopub.status.idle": "2024-06-04T23:19:51.381453Z", - "shell.execute_reply": "2024-06-04T23:19:51.381251Z" + "iopub.execute_input": "2025-04-03T19:34:08.872090Z", + "iopub.status.busy": "2025-04-03T19:34:08.872005Z", + "iopub.status.idle": "2025-04-03T19:34:08.873843Z", + "shell.execute_reply": "2025-04-03T19:34:08.873556Z" } }, "outputs": [], @@ -875,10 +869,10 @@ "id": "19e59dd8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.382589Z", - "iopub.status.busy": "2024-06-04T23:19:51.382517Z", - "iopub.status.idle": "2024-06-04T23:19:51.386928Z", - "shell.execute_reply": "2024-06-04T23:19:51.386708Z" + "iopub.execute_input": "2025-04-03T19:34:08.875248Z", + "iopub.status.busy": "2025-04-03T19:34:08.875145Z", + "iopub.status.idle": "2025-04-03T19:34:08.879738Z", + "shell.execute_reply": "2025-04-03T19:34:08.879511Z" } }, "outputs": [ @@ -962,10 +956,10 @@ "id": "cf34cb53", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.388280Z", - "iopub.status.busy": "2024-06-04T23:19:51.388213Z", - "iopub.status.idle": "2024-06-04T23:19:51.391898Z", - "shell.execute_reply": "2024-06-04T23:19:51.391668Z" + "iopub.execute_input": "2025-04-03T19:34:08.881038Z", + "iopub.status.busy": "2025-04-03T19:34:08.880915Z", + "iopub.status.idle": "2025-04-03T19:34:08.885391Z", + "shell.execute_reply": "2025-04-03T19:34:08.885132Z" } }, "outputs": [ @@ -1053,16 +1047,16 @@ "id": "10f65bff", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.393232Z", - "iopub.status.busy": "2024-06-04T23:19:51.393148Z", - "iopub.status.idle": "2024-06-04T23:19:51.453307Z", - "shell.execute_reply": "2024-06-04T23:19:51.453073Z" + "iopub.execute_input": "2025-04-03T19:34:08.886615Z", + "iopub.status.busy": "2025-04-03T19:34:08.886536Z", + "iopub.status.idle": "2025-04-03T19:34:08.930189Z", + "shell.execute_reply": "2025-04-03T19:34:08.929963Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1091,10 +1085,10 @@ "id": "f5cbec92", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.454747Z", - "iopub.status.busy": "2024-06-04T23:19:51.454671Z", - "iopub.status.idle": "2024-06-04T23:19:51.458209Z", - "shell.execute_reply": "2024-06-04T23:19:51.457954Z" + "iopub.execute_input": "2025-04-03T19:34:08.931460Z", + "iopub.status.busy": "2025-04-03T19:34:08.931384Z", + "iopub.status.idle": "2025-04-03T19:34:08.935705Z", + "shell.execute_reply": "2025-04-03T19:34:08.935451Z" } }, "outputs": [ @@ -1178,17 +1172,17 @@ "id": "eeecb206", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.459452Z", - "iopub.status.busy": "2024-06-04T23:19:51.459383Z", - "iopub.status.idle": "2024-06-04T23:19:51.550420Z", - "shell.execute_reply": "2024-06-04T23:19:51.550152Z" + "iopub.execute_input": "2025-04-03T19:34:08.936866Z", + "iopub.status.busy": "2025-04-03T19:34:08.936789Z", + "iopub.status.idle": "2025-04-03T19:34:09.014377Z", + "shell.execute_reply": "2025-04-03T19:34:09.014032Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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gV8vnpSV0CvW5dk13qRJ/tvkOm3YnpHGM82kMn6pVP08In8crn87yXnlbTnWJkhVQVj+JaCZEW58PVBbJlel/7uzHc/1uuYBEhEyRq8Soamy/mmVOO64vL8QFBU45TzRTiTmaH6kqwQ3lRdjcP4zdHi8OjPjgCUfkkPxcezYWO+24uDD981/X5Dk026Uq1grKbmZtLhlM0ai6kxjcbjdcLhe2bm6E05kHI1I1fAodG5rk/60hN2oXiOH3OUqGT4Hhk4hmwxuO4LDXJw8RpLJNZtTarbKJell26pqek3a+fqAFu4dHx99AJOL2+RW4uChfgzNlBrfbg8KydRgaGkJ+/vSPGyueaaZy9bPyCv3M/YwNuzOAEtFMiDmbK/Mc8iBjEFMpvrS/JaFziOkEc+zZuKDQmMUuFbCOrAjO/dQG530SEWWmZc4cvK+8MO4OBqbjxzfmV8qpBJQcrHgqRMVV76ea+6lq5VNg9ZOISB1iB6DGUT8Oj/jQGwzJ28qsVixy2jHPnq3ptpofrymRPT1fmmUHA1GFE5fxrflVmJ/DRUXJxOCpGJWH3vUUPgUuPCIiSh9PKIy/dA/gye4hDIbCY6vgj2fMWNujEmsWri0rwLvLCpCjQcsq0cHgm/Mr8bv2PjzU0S9/5unmfIqfWmS14Nb5lZx6kQJcXKQwVcOn0PvUTrngqHBh0fht2QvPg4oYPomIUmvb0Ah+3NQJdyh82p2jRPArzs6Sux1pGfwOen14sK0PW4ZGZPVT9PkUicc0oYtBnsWMq8sK8P6Koozq1ZrOxUUMnjqgagAV4TOmrOpNVKwsVTZ8CgygRETJ97eeQfzsWLcMlDPdrjQW+W5NwmrynkAQuz2jcqh/KBSWVVHRp3Rhrg0r83JSurWnUXFVu8GoOvdTDLvHdMvh9zdRgS3Khk8OvRMRJdeWQY8MnZhF6Jx43/840oliaxbO0LDyWZptxduLxMH2SCpgzNcJPax6724/C527ehA4tAWq4o5HRETJIYbVf3K0M6FgIYZgf9jUidHIbGIr6QmDp87Cp8oBVITPtu6LxsNn7FARwycRkbYe7RqAJxSZVaVzMvG93YEQnuoe1PDKSCUMnjqkcvgUTedF+Nz1TI08VK6AMnwSEWkjGI3gye5BTXYNElXPJ7oHofASFEoA53jqlKrzPifveNS2IR/YtVnZuZ/s+UlElLiDHh+Gw9oNj3cFQmjxBdO+BzxpjxVPHVN96H1iBVTlyqfA6icRUfwOjfo1DxSHvT6Nz0gqYPA0AIZPbTB8EhHFpzcQglnDXSZFo/mewNguR2QsDJ4GoXr1U0/hkwGUKD5iTl4gEoE/EuH8PCKaEud4Gozqcz/bNmB8zmdWnlPeruK2m+z5STQzI+EI/t43JPs3HvD44ImMBc5cswkLc+04ryAX7yh2IS/Lku5LpSQqyc7C8b96TYSjov8mI4oR8W/VgPQQPt3PNiM33wpHcSOc5b3IXXMxVMOFR0SnFolGZfuc37X3InB8TcnE3DESiWLn8Kg8ftPaiw9VleCfKgrlrjFkPAtzbAm1UZpKvcOu8RlJBRxqNyiVh95F+PQvWYv+qlU4cuwC9B0dxsj2TVAVh96JTm4U/uX9Lfjv1l74I2OBc6piV+z2YBS4v60XX9jXjIEg5+0Z0SKnXe57rpXy7CzU2q2anY/UweBpcKqGz+K6XHnE5n4yfBLpgzccwTcOtuDAyOxXHDd6/fjqgRYMh7To9kgqEfudX11WAItGweTdZQUwsTpuSAyeGUDl6qegp/DJAEqZ7r6WbjR5A3E1Chff0+4L4p5mNacCUWLeW14IZ5Y5oWAhgquY23lVWYGGV0Yq4RzPDKKHuZ/BoZ2owCYl53zGcOERZapdw1483etO6BwifP6j34PLikew1pWr2bXRzLT6Ani2dwh7PKM4OhqALxKBzWRCXY4Ny/JycHmxK+6m7flZFnxlXgW+c7g9oWv8el0FcsysixkVg2eGUT18dohV7zvHwqdj6SJ5u8lRBdVw4REZiWh91BEI4rDXL+dvWmBCld2KhQ47HBPm7T3aOSD7K4oVx0iwqvVIVz+DZwr1BoO4+2g3Xh0akY//xIp1KBrFnhEf9nl9+FPnANbmO/CFeeUoz579HMvzCpz4wtwy/Nexbln5nOmCo9iz7BvzK3BGngNaPrcPeH04OOJDmy8IMcM432LGAocNy505KLQyBqUaH/EMFBt2VzGATgyfBV1j75rt5QeVrYCy+kl6JuZaPtUzhP/rHkTvFIt+xAy7NfkOXFdeiMUOG7YMjUy5iGi2ROh50z0qw1CJlQtIkk20urrrSAcCx3urnmqaROwNxZvDXnxyTxO+XleJtxXmzfrnXVVagNJsK37c1CnfyERmEDqLs7PwjboKrNQodIq94//aPYjHugbl9pviuSzeNMWEomNvgNYVOPHBqiKuoE8hU1ThLr9utxsulwtbNzfC6Zz9k59OT8XwKbS/2jP+cbn/ZVSsCisbPgWGT9KbVwaG8Z/HuuAJRaYNBrHq2BKHHfs13sLwjgWVWB9HsKHZhc7vHm4/ZeeBU4lltG8uqMSFcf4diTc2j3UP4MnuIQyGwieEPxH8hBJrllxIdG1ZAXI0WhV/ZNSPuxo70OwLnPbPLK5HpKB/rizCh6uK2e4rTm63B4Vl6zA0NIT8/Pxp78vgScqGz4khVA/hU2AAJT14uKMfv2nrndVQqHg51vLFIssE3FhVjBsr+W8mWboDQXxyz1H4I9G4/u7E37nVDPxq+TxU2eKb9xkbyhdh8PCIb7yyXpYtpnLY5NxSs4Zhb69nFLcebEEwcurK7lTEFZxf4MS3FlQyfCY5eHKonZQeeheq1pWi/dX1wM6XT5j7qeL8Tw69k+qe7hmSoVOYTcPvZFQo/GGtW47TRHcf7UIwztApRI8Pv//0aCd+tHhO3NeRZTJhkcMuj2QH7dsOtsoNDSJx/FlfGfTgvpYefHZOWZKukAQuG6NxKrdcEuGzy7YenTst6N/ULg/vvoOIehNbPZkMbLlEqurwB3BPixpvMMULvV3DhuN0oubRALa6vXG1vZpIBM+dwz4c0niahdbC0Qi+19ghq7uRBJ6Tj3cPYuewV+Oro4lY8STdVD9jlc+ugbHPyztFBfQgHEvVrHwKrH6SSv6ntTfhFelaEdchhlkpOTb0DmnSgSA2LWJDrxsL56i1AEfMFNzu9srFca+7R8bnjSZCvBX6dUsP7l42V4tLpCkweJKu2i6J8Bnz1vC7muFT4NA7qaI/GMJLA56EK2BaEbPoFueqFWSMZLdnVLM3GSLQ7VasCtjpD+DHRzuxa9h3UnuoRIhq6QGvH41eHxZwpXtScJyDdDn0Pnn4XdVhd4E7HpEKtg6OKBM6RVA4O9+BIvZQTJqjo35NzydXiCuyFlmE4E83HEPD8NjwfzgJwej1IbWCtpEweJKut9ucGD6HtjUoGz4Fhk9KJzFHTwyZqkAEhRsqi9J9GYYm5jpqSVRPQ0lZYjY7ohJ5+yGxgCia1DdSqs9p1TMGT5oRPYTPviN2hk+iU+j0BzWZA6fFi86VJflYpeHuNHSybI1f3cXpssa7e6aHaAr//SMdCMWxan02xLl7AidvqEDa4DgHGWrhEY68DKAB1ryD8nbRekm1uZ9ceETpEFVkiL3OYcNnatmuJtlq7Ta5BapWqu1WmNLc3/LxrkG0+oIpeS4rMjhgSAyeZKiFRyJ8du/zyM/zIs2oGW6A6xwuPCIqtFo0W+UcrzPycnBHfbVmO9TQqa1w5sh5nlpUucXzRpwvnSLRqGx1lIqnr/jzVtm4lWuy8F8/GWroXYRPsd+7OPxL1qK1oVLp4XcuPKJUWeiwy60Btaxe/uvcUjiPh8ipXkxitznMJnxubhn+Y1ENchk6U+IdJfmaTa0Qb1YuL55+N5pkOzDiS9nwt/h3spAdF5KGFU8y5NC7UFyXiz6I8Pm6HH5XtfIpsPpJybYmPxcR9GgWOlfm5+Dq0kJcXuzCiwMebB30YN+ID33Ht0QstGZhaa4dZ7tycXFRHmxmBs5Uv9FY4bTLv5NEqtyi+jc/x4Zlaa54HvT6NN+29VTEzzi3IDcFPykzMXiSYYfeBYZPojFzcrLHgojHl/huNgDeXVogP842m3Fpcb48SC1fnleBTzccTfg8X62rSPv8zjZfUIbgZC+QE2+qzsp3JLQ3PU2Pb0HJ8G2XRPjUw7C7wKF3SqZPVJckvBpYvPgvdthwXoFTo6uiZKmxZ+NL8yoSOsctc8qU2GEqVT1oRb7+dO1bG5WQ9hg8SVN6CZ+BQ1vkoWoIZfikZFie58B7ywviXrFrPn58ra4S5jRXwGhm3lGcj6/OK5c9XEU1byYs43N4y3D18cp2uuWnaG7wx6pLME+BoG1kHGqnjBl6jw27H959GNgN2M2DqFmu7vA7h94pGT5RU4quwNj2mdHZhk4TcGd9tRy2J/24vMSFxbk5+FFTBw56/afsbhC7fZ4jW765EHM7VbHAYUvaMHts7ugN5YV4X3lhcn4IjWPwpIxaeCTCJ+pWyY/7mkaUn/vJnp+kNYvJhG/Or8Tv2vvwUEe/fNENzyB0lmVn4db5lWlfZEJjxPaVezyjcs6uaJvki0RhM5tktW6p044znDknzMucm5ONu5fOwV7PKDb0ueW2k+3+sb2IxL0qsrOwIi8HV5S4TvpeFSx35mi6J/vE57Zddl0ox6VFecr9uY0oqcHzl7/8pTyOHh2b3Lx8+XJ85zvfwZVXXpnMH0sKUbX6OdXCo/wl/TBXrICKWP0kLYlh8puqS7C+0Ik/tPfj1UGPnPs5cUtNseOiuK0gy4JrywrwvopC2LkyXYnA+VTvEP7U0Y/OQEgGp9ibh7Gh9GH5sXij8E8VhXKoPDYtQoQqMd1CHLGdgIIRwGoGrCa1/25Fl4TzC514ZdCjWS9aMXx/dVmBfH4XWVmHSxVTVDyLk+Svf/0rLBYLFi5cKP+x/Pa3v8WPfvQjvPnmmzKEno7b7YbL5cLWzY1wOvOSdZmUAqqGz1jl07b/ddQs74Cjeux5lpXnVDaEMoCS1kQLpD0eLw6N+OEOhZFlMqHSZsWiXLusNInPKf26A0Hc1diBhpGZtxZakmvHbfMrUGmAVdpin/Zb9jYnvEDOZgK+t6hGNsXnXGVtuN0eFJatw9DQEPLz89MXPKdSVFQkw+cnPvGJ096XwdN4VA2gInxG9x4e/7x2wS6Urp7D8ElE0rHRgKwMHxzxoTMQlMWU0mwr6nNtODs/Vwa8ZA7TdvgD+NL+FgwFw7MabhbzNvMsFvx0SS2q7foPn79r78WD7f0J9fO8dX4F3l7E9l/pCp4pqy2Hw2E88sgjGBkZwbp166a8j9/vl8fE4EnGourQ++S5ny17xUcifELJ8Mmhd6LUNS6/t7kbezw+OZQtAk+s4tY0GsBrQyP43/Z+1OVkyzY8olG/1gKRCL55sA1DodmFTkEMSw+HwrjtYCt+tWKe7qdLfKiyWL4JmO3iuJgPVBZhbX4ujoyOZY2irCwUWGe63p+0kPSK5+7du2XQ9Pl8cDqd+MMf/oB3vetdU973u9/9Lu68886TbmfF05hUDKCTK6CqVz4FBlAi7YmXxgc7+vG/7X0zXoAlAulVJS58dm6ppnMmf93ag0c6BxKq8omrua6sAJ+ZUwa9C0ej8jH5S9eg/LuJzKDqK+4ndtLq8AfRGzzxb7PIasG6AieuKStQaiW/nig11B4IBNDc3Cwv5s9//jN+/etfY/PmzVi2bNmMKp61tbUMngbG8KkNhk8i7YiXxXuau/F/PUOz/l4RcM5xOXBHfbUmc2PF0PoHdjVq0kpIBLAHV843zEIasTL/N6292DviO2GRFY4vlIstQirNzkJ3IHTKNlJC7GtvL3Lis3PKkZ/FKqhug+dkl112GRYsWID77rvvtPflHM/MwPCpHQZQosT9rWcIPzsW/+8lEYDeX1GIj9ckvgPOI539MlwluqBGEOHso9UlcrjZSJpG/dg+NIJDXh96jq/0F4upymxZeLrHjf5gaMaPn4ibInT+x+IaNpLX+xzPmEgkckJVk0jVnp9vtVyqP2HOZ4yKIZRzP4kS0xMI4t6WxH4XiWrOw50DuKAwT3YGSMQbbm9CQ+wTifC13T1iuOAptvScvK2nCJuf29uMgdDMQyeOV0xFZ4ev7m/B3cvmGKIbgGqSOsv4tttuwwsvvCD7eIq5nuLzTZs24cYbb0zmjyWdUnm7zZKrVqGlcSWObbTKo+eNZkQ690BF3G6TKH6Pdw3K3pZavLiKBv2JEqvotRyWPDTig9GJgdyfHe3CQDAUV89PET5HIhH86EinPBdpK6kVz+7ubnzkIx9BR0eHHDJfuXIlNmzYgHe84x3J/LGkYypXP0X4FEPvQt/eXOVXvQusfhLNXCgaxdO9g5rsjiPOIZqdi8pbvHMqRejxhLUYZH/LaCQq/5xG7s26ZWhEHokQgXXPiA8b+9xyy1HSSfD8zW9+k8zTk4Ep3XZJqFuFlqegdPgUOPRONHPNvgA8Wm2Lc3xoe59nFOsL41ujIPqCmpO0TaSR/aVrQJPtNcXj9GjXAN5RnM+tNDVk9Ocf6ZiqQ+8xseF3lYfdY+GTw+9Ep3dY42FosbL6sDexNQ1iRbaWSqxZht6tRwyv7xwe1SSsR473am3xBTU4G8UweJLy4VPlAKqX8CkwfBJNzxMOa/6imOhQ+TJnzvE92BMn/mxihyUjE3NiNT+n1/jzYlOJwZN0QU/hM+ptl4eKGD6JTk3Me9R6KYmoeibibYVOzYbaRQS+sMgJI2vxBTQNNuLvT5yTtGOMLrKUEVSd9zkePp8CQsMtsOf5YHEMwFHdjOyF50E1XHhENLUau03T4Cmmi9YmuD/6eQVOFFstGAiGE+rlKfJvfpYZ6wuNHTxFo32zCYho+BcZjGi7wCvTseJJuqLy0LsIn8dC5+LAwGoc3r0Unbt6EDi0Bapi9ZPoRPUOmwxoWhHZZ2GCQ9sWkwmfn1OWcAN5cS2fm1um6VaeKsrNMsfVQulURDclJ3cx0pSxn4FkWKqGz6p1pfIQIbS7/SyGTyIdETvWrM53aDKnUgTYKlsW6jXY/eb8wjy8U6ysTuBaLi3Kw0WF0+8oYwQLcjSuWss3JMaeF5tqDJ6kWypXP4XJ4VPVuZ9c9U70luvLCzWbU/me8kLN2vB8YV45LoxzmPz8Aie+XKfu70otLXDYYNcw2Yg93BczeGqKwZN0Tw/hs+XFIPo3tWNoW4OyFVCGTyLg7HwH1hc4E6p6Wo4HoKtKCzS7LjHkfvv8StwypxTZ5tO/eFuOL4z5TG0Jvr2g0vBD7DE2sxnvKCmQgTFR4hxicZfLyqF2LWXGM5EMT/Xw2WVbL+d+7tt3rtLD76x+UqYTFcp/nVuGwuysuMKLiCg2swm31lXKsKj1tb27rBD3r6iT+60XnGLuYb7FjBsqCvHAGXV4T3mRoft2TuW9ZQWazNUV8zv/qcJY+9qrwBRVeCNSt9stt9rcurkRTmd8Oz9Q5lF15XtMx4YmVJdtRsXKUiVXvcdw1Ttlsg5/AF8/0IqeQGjGC3tEDMyxmPGDRTVYlIJ+meLluzsYQpPXD18kArvZjHk52SjPtmb8TjuPdvbjvtbeuL9fPHoi3N9UXaLpdRmV2+1BYdk6DA0NIT9/+rnErHiS4ahc/RQqr6hDW/dFSlc+BVY+KZNV2rJx3/J5eGfJ2IvodNXP2NfOduXi1yvmpSR0CiJcipApWi5dXJQv/19hy8740BmbX3tZUZxblQI415WLD1XxzXcysI8nGTp8qlr9lOFzg9jqfTMqsEXZyid7flImc1jM+OK8ClxXXoi/9gzhpf5hDIROXHrktJhxnisXV5cVYGmunaFPEeLv4at1FSiwWvBo16AMk6erXJuP3+fKknzcMrdMbihA2uNQOxmequFTT8PuAsMn0dhe4N2BEKKIosiahVJrFsOm4nYPe3FvSw8Oef2yOi2ay8eCj/ibE7eJxvNz7VZ8urZMVq4peUPtDJ6UEfQQPovnvfUcz11zMVTFAEqklp5AEBt63dgx7EXjiA/eSFSGqSqbVe71/vbifKxy5mR8QBb7uG8Z8uCgx4fOQFCGz7LsLCzOzZHdDMRjlemPUbwYPIl0FkBF+IyJhVCGTyKajicUxn0tPdjY55afTzWUnHW8mldrt+Ir8ypkuCJKZ/DkHE/KKKru9y7mfMbIuZ/YDGCTsuFTzP1k+CRKn4NeH751sA3Doen3cBehU2j3BfGl/S1ywcyHKotY2aO0YfCkjKNq+Dxp4ZEOwqfAAEqUWoe9Pnx1fwuCkeiMd1mK3e/37X0IRiL4eE1pEq8ws0SiUewYHsVezyiOjPrhDYdhM5kxz2GTux6dXeDImAb+M8HgSRlJN6veFQ+fAqufFK+RcASNXh8GQ2G5yKPSZsXcnGy+SE9D9Ov8/w63zyp0TvZQ5wBW5DlwDhfRJETMVHyydwgPdfTLfq9iXq2YvCgq0OL5/Jp7RFac8yxmuRXr+ysL+dxm8KRMp3L1k+GTjCgYjeD5Pg+e6B6Qq4ynmpO4rsCJd5cVYGWeIy3XqLL/be+Tq+pn2tR+KiL6/LipE79bWSebzlN8C7q+39iBhhHf+G3hCStmohOmOQyHI7LS/I8+N75VX4X5OTZkMgZPyngqVz8nh09r/lhjahVbL3HonU5n/8gofnikE63+4Cl3LxEv1q8MePDigEfuk/35OeWyFyMBo+EI/q97IKHQKYjvF1XmTf3DeGeJS6OryxxdgSC+tK/5pJ6u0xEZtMMfxBf3NePHi2tTtsmAivhWh0jxHY9iOx3t2rYau56pQcuLQYxs3wRVcccjmsrz/W58aV+LfPEVpgtPsZdzEUBv3nsUrb5ASq5RdSKM+xJNnRNe/P/WM6TNyTKsYv/tQ20ydE6scM6EeF4HI1F881Ar3LMIrUbD4Emkk/ApjpKrVqHLth6dOy3Kh08GUIp5bWgEPzjSKV94Z/NyK+47GAzjqwdaZOP2TCcqxmIqghYixxcphdTtqKikhzoGcGw0MOvQOfE57QlF8PNm9UbYUoXBk2iK8KlqABWq1pXqInwKDJ8kKjs/bOqI+/vFC/VQMIy7j3XLxRyZ7PCIf3zeoBbEudp8YxVoOj1vOII/dfaN73qUyHN6U78HLRlayWfwJDoFPYXPwKEt8lARw2dm+0N7n6zwRBN8oX5p0IOdnlFkstFIOCmr5Glm/tHvRkCjh8sipzoMIhNxcRGRThceifDZ/up6DLzSLj8vzG1ChXsTHEsXweSogkq48CgzjUYieLp3KO62PxOJVjWPdw3izAxe6Z5jFnFF2wolV7XP3A63V7ZJ0qLoHAaw3e1FJuIzjkjH1U8RPsW8z4lzP737DiLqHQujqmH1M/NeqEcj2owNizl1Wwc9CGRwhW5Brk2zOZ6COFeVnfWnmTow4ku4o8BELaMBuVgp0zB4Euk8fE41/K56+GQAzQwHR3yaBiVRJWoazcx5ccLiXLtmczzFX8uCHBsbms+C1ivRw2ITBS0n7eoEn3FEBl14pHL4FBg+ja/TH5Q7uWipw5+5wfPCwjzYNHrVFn8tV5YWaHOyDGFJwv72Fu1PqTwGT6I4MHxqg+HT2MKIajIfbiKNRu51yWEx4+qSgoRfuMX3u7LMuKQoT6MrywxVNqum53OaTXBaMi+GZd6fmCgDw2ekc488VAyhHHo3roKsLJg1rui4MnwXo49Ul6AoOyuhF28xq/BLcyuQk4GhJxFLnTmaTR0xA1jotMOUhCqq6visIzLo0HssfDZtKcGxjVZ5DG1rUDJ8CgyfxlPvsGnad1KeMydztxoURFi8Y0FVQkO07y0rwPmFzhnPa9zuHpG7HD3VMyh3kxL7lGdiT9ULC52aPZ8jAC4pykcm4nI2Ig2I8Klqy6W+prXwA/B3ejHSIFqxNMB1DpRruRQLn2y5ZByr8h2atZ8R55ljt2Z8xTO2g1Ewzgf1/RWF+Hh1ybT3iUSjeGFgWLav2jvim/I+NTYrrisvwOUlroxpybTCmYO5ditafMGEVrebxLQJswkXZ+hUh8x4thBlcPWzuC5XHiKE+pesRWtDpfKVT1Y/jaE824o1+Q7ZLDtRIme9u7wQmU5UHu9p7on7+0fDkWmHd9t8AXxpfwu+f6QT+08ROuX9/EF5HZ/ecxR7hjOjH6V43L44tzzhN1JRALfMLc+YwD5ZZv6piZJIxfAZIwKoHsKnwPBpDB+pLk6496EIrmXZWbisODOHJmOaRwMJ7/H9fz1DeHXQM+XX9npGcfPeY7INlhA5TXgSR3cghK8eaMVzfW6kkuh/Kfpqit1/Hu7oxyOd/XIaQG8wuVuALs9z4AOVRQmFrrcVOnFphlY7BQ61E2XQ0HssfPZBhM/XlR52F7jjkf4tyc3BP1UU4k+dA3FXikQA+npdRcZWiGLubu5MuNomHsGfHu3C2lWOE3p4tvoCuPVgKwKR6KzeKMTu+8OmTriyLFjrykUydQeCeKxrAE/3DMEbicphazHfVXQ7iF3Lslw7rq8owIUFeUlZvPPRqmJZOX6se3DWU0nOceXi1vkVGbmoKCaz/xUTZeDQ+1SVT5VXvQusfurbTdUluKDQKV+kZ8N0/BDDmyszeKtMoWnUj53DPrmDUyJEOBsMhfFiv+eEOZ0iOAajswudE4m/J3GOYY2brMeIxUximsEn9zTJuacidMrbAbngZ+J1iykC32vsxDcOtsqgqjURGj9TW4rb51cg12I67fNaBC2rCbi5thR31ldlfNP+zP7TE6WAHsJnz2tOeag8/M7wqe/G27fPr8QNFYVjFaqZfA8gX9S/vaASV5a6kOme73Nr1mxcvPBvnDA0vnlgWIa1REKtCH4idP6psx/JCJ2/bOnBz451wRcZ2/HndNci7PaM4pa9x3B0VCyv1D58XlyUjwfOmI9P1ZSgyjb1AHKR1SKH5h84ow7XlxdmdKUzhkPtRCkMn6oNv48Pux//3NbgUXr4nUPv+g6fn6wpxfoCJ37b3os33KPydtEXMRZ4RLAS1SuxO887il34cFUxCq18mRL2JRgMJwezA55RGehEEHqsa1CG0UTn4opA+FTPkPx7y9ZwWsRDnQN4vHtw9tcTBTyhCL5+oBW/Wj4PBUnoiJCfZcH7KorkIVpPiZDrDUdgM5sxNycbRXz+noSPCFGGz/0U4TNGT3M/GT7124T7B4tq0e4PYId7FIe9PgwGwxCFoEqbFQtzbVib70Qum5uftLBIS55IFO5QRA5VT7d6fdbnDUfQ4PHhrHxtpkYcGfXjd229CYVhUYkVi7K+tSC5v89ECM30KSEzweBJlGIqhs/pFh7FqBZCWf3UtypbNqpKs8VeROm+FF0Q8y+1FkIUR7zaDkOLtwuHRrQLnr9q6R6bQJrAH1+EzxcGPNjnGZVvfCi9GDyJ0kDVoffJ4TN8fL6nxTGA/CX9MFesgGpY/aRMIPb0FtVELTnMZnRpvPhGbJHaqdE5232B8SkZiRLTOP6ve5DBUwEcyyBKI9UXHh0YWC0PuQDpjWa58l1FXHhERrcw167pC3Z5dpbcflNEWVWXu2wdGtHszyzme74y6MnIrT5Vw4onUZqpOvQu534en/8ptt3s2S8+akbpaihb+RRY/SQjOiMvBy8NTN34PZ7q35l5Y5W/oiyLJluaxohzabWIRwzZy0XgGl3gaCSKjkBQTvOg9GHFk0gBKvf8nFgBbWlcqXTlU2D1k4zo0qJ82QFAq+rfVaUF8uN6h02bk0449yKHXZNziSF7rVbyx/QEQtqekGaNwZNIIaqHT9OyeoZPojTIy7Lg2rKChF+0RS3yDKcdi3PHwmF5thWV2VmaDbeLcLxco3mUyRgV50B7+jF4EilG5eqn3sInAygZyUeqS1CanTWjBvxTEeEyywx8te6tLRvF/99dPlb9TJS4LrEHuQjJWiizxf9nPZVi9tVMOwZPIkXpJXyObN8kD1VDKMMnGUWO2Yz/b2E1ciymWQcy0/EX/NvnV6Fy0hzHK0sKUJydlXg11Qx8oEq7OdYLHXZNK5R2M1Bjs2p4RooHgyeRwlQPn0eOXYBd21Zj3yv1SldAWf0ko6jLseGnS+aiwjbz4XHL8XZM/7awGusKnCd9Xaxu/3pdRcIh71M1pZou3Dnb5Ux4N6WJC6rE+bhlZfoxeBIpTtWhdxE+K6+ok0fJVat0M/xOpHdiK8b7ls/DP1cWwS4aZx6fWzk5bJqPB65LivLwmxV1WOt6a5eyyc7Mc+DL88plmI0nmr23vADXHl+wpOWfc4XTrslwu1ikJObIUvpxsgORTqjadilGhs+nxEe7lG25JLDhPBmB2Av9Y9Ul+OeKIrw4MIwGzygOef0YCYVhs5gxP8cmFxBdXJQ34/3uryhxwZVlwY+PdmIkFJE7/kxHhFoRbj9dWypDZzKqiaKK+sX9LQmdQ1znWXkOrGTzeCWYogp3U3W73XC5XNi6uRFOZ166L4dIGSoH0N6ndqJ2gQifc5QNnzEMoEQnc4fCeKijD3/rGYI3EpUVx4mZMhQdq7BeUpiHG6uKUWVPbl/M37T24OHOgbi+VwTjHLMJv1oxD6XZnN+ZLG63B4Vl6zA0NIT8/Pxp78uKJ5EOqVz91EvlU2D1k+hk+VkWfLq2TK6i3zM8Vknt8gcRRRRF1iwsdNiwIs8h75cKorLbGwjhuf7hWX2fuLpsswl3La5h6FQIgyeRTuklfDrLe+Vt1nw7sheeB9VwxyOiqdnNZjkvdLq5oalgNpnk4qe5OTb8rr1XLoKarrF8rDhb57Dh1rpKzMnhTkUqYfAk0rHYoiMVA6gIn0c25APHAGvIjbKqN1GBLUqGT4HVTyJ1ifmjYjHV+QVO/KmzD//oH5ZD/hOnAYjPhWqbFe+pKMSVJS5YuIpdOQyeRAagavVTrHiP6ZYVUIZPIoqfqF5+ta4Sn6ktw/4RHw57/XCHQsgymVBtz8aiXDvm2bPZNklhDJ5EBqFy9TNWAdVL+BQYQInU5cyyKDENgGaPwZPIYFStfk4VPmNUDKGsfhIRaY/Bk8iA9BA+u9vHPld57iern0RE2mLwJDIolYfeRfiMaRMLkHZtVjZ8Cqx+EhFpg1tmEhmcitttTl6A1NZ9ETp39SBw6K3hd9Vwu00iosSx4kmUAVQeeh8Pnxugi8qnwOon0ZjuQBBvur2yyfxAMCR7aJZlZ8nV5Wvyc5GXoibzpB8MnkQZQuWhdz2FT4FD75TpGr0+/LatD1uHRmRDd7GFpmjqLoKn2TTWU9NqAi4tysdHa0pQPMP94sn4ONROlGFUHnqfPOwe6dwjDxVx6J0yUTQaxf+29+GWvc147XjoFETQFB9HJjRyD0aBjf1ufGJ3Ezb1u9N52aQQvgUhykDxVj/D4Qi6hv3oHPIhGI7AZrWgymVHqdMGkyhzaFj5dD/bjNx8KxzFjShc6FGyAsqhd8q00PmTo514tm/me6aLKuhoNIrvH+nEYCiM68oKk3qNpD4GT6IMNtO5n51Do3jxUC9eO9qPwBSbJDusFqxbUIwL6ktQmJutSfjsayqDX/zsg5WoHlV7+J1D75QJHu4cmFXojIn9xvhFcw+qbdk4m03fM5opKt7CKMrtdsPlcmHr5kY4nXnpvhwiwzpV+AyFw3i2oRt/3ye+bkJk/CVk6nk7FrMJ166qkgFUqwqo0LGhCdVlm1E8Lw+5ay6GyhhAyYiOjvpx895jsoIZL/E7osBqwW9W1CHXwpl+RuJ2e1BYtg5DQ0PIz8+f9r78myciWfmcPPfTHwzjl5sasXFfl5y3NV3oFMR9gpEoHn2zDf+7tVkOy2s997Pv6DBGtm+Cyjj3k4zoDx19b5Uu4yR+IwwEw/hbz6BWl0U6xOBJRONi4TMaieL+l4+iqdcb12vNG80DeGzH8a2JNMLwSZQeok3SiwMehDU4l/h98kT3oJwvSpmJwZOITgqfzwxEsL9rWFYo4iFeUl463Iv9He6khk/VV70zgJIR7PaMJjTEPll3IITOQFC7E5KuMHgS0Qm8/tBYtdM69QKAcMiH5x78sDzEx9P9cnnk9RZZPU1G+GzaUoJjG63o2Kx2BZThk/Su0euXfTq1dNgrlg5SJmLwJKITbDrQjUBorNZ5qvA5E+IMfd4gDnTNfhXsTMKnf8la9FetwrHQuejcaVE+fDKAkl4NBkOan3MopMXAPekRgycRneClSQFJhM94A6gFJuxsHUIyFNflyqNqXSm6bOuVD58CwyfpkcVkSkL40P6cpA8MnkT0lmgUh7uHT1pQFAn60AiLHFoPh94aIhMfj902dkwWRhTN/SNJv+zJ4TPqbZeHihg+SW8qsq3QeMYMKm1WbU9IusEG8kQ0zuMPYzR48pKiow9+cOz/k25/8dHPnfD5pTf+/qTv7fMEkAoifLa/uh7Y+TIKusZCp738oJJ9P7njEenJIqc97oWGp1LvsGl8RtKLpFY877rrLpx99tnIy8tDWVkZrrvuOhw4cCCZP5KIEpCcFiepa5sSq3weGFgtD9WH31n9JD1Y7rQjX6OG7xYAZ+blIC9LfESZKKkVz82bN+OWW26R4TMUCuH222/H5Zdfjr179yI3l1tmEakmJ9si53OFJwXQeTf+YfzjaMiHYw9/XH78tvf+HJas6SsXudmpHVgR4TMmVgGtwCYlK58Ct9sk1VlNZlxTVoA/dvQnXPkUS4quKyvQ6MpIj5L6ivDMM8+c8PkDDzwgK5/bt2/HhRdemMwfTURxyLKYMa/EgcaeE+dlmq328Y8nvvC02Ivk1+qCU8/jFDWSecVOpMvE4XcRPh1LF8HkqIJqOPROqruhoggbet3oD4biDp+ixrkyPwfrCtL3O4EybHGR2MNTKCoqmvLrfr9f7s8+8SCi1FoztxCz3Wb9VKvexQvUovL0vshMXHjk3XdQ2UVHAofeSVUOixm3zq+AWOBuijN0inN8ZZ44B1e0Z7KUBc9IJIIvfvGLWL9+PVasWHHKOaEul2v8qK2tTdXlEdFxVyyvEIvbZ22q8GnPMuGsuekfVpsqfKq68p09P0lVK/Mc+Pb8SlhMY0FypsR9cy1m/GhJLcqyuZo905miKdow9eabb8bTTz+Nl156CTU1NaeseIojRlQ8RfjcurkRTmdeKi6TiAD84vnD2NDQGXcLldjQ+7tXVeKSJWP7v6ug/dUelPtfRsWS42HY1qfs8LvAoXdSUaPXh/9o6sTR0YCsXkWmCZxiTufZ+Q58qa4cJVaGTqNyuz0oLFsnR7bz8/OnvW9KZv1/7nOfw5NPPokXXnjhlKFTsNls8iCi9PrY+jq8drQf/SNBROJ4b3os24mLnBFctKgMKonN+eze6ZGf50VMqBlugOscKBk+ufCIVLTAYcc9y+bghX4PnugaxAGv76TeFSJ0nu3KxbvLC7A6z8HhdUpN8BTF1M9//vN47LHHsGnTJtTV1SXzxxGRhqvb/+3dK/CNR3fJ3p6zCZ9mkwmVLhs+/t6VyHJkI9LWBZWMrXofW/ne/qoTrQ3iI7XDp8AASqqtdL+0OF8e3nAEh70+DIRCMMGE8mwr6nKykW1WZ48afySCVl8A/kgUdosZtXar/DOQwYKnaKX0hz/8AU888YTs5dnZ2SlvF/M3c3JykvmjiShBNUUO/OSGM/HjZw/I/dZFvWK6+CkKGiKfrp1XgC9cugj5OWPDaubqcuXC54kV0GXKh0+B1U9SlVg0JOZ/qmYkHMHf+4bwt54hHBsNnDAlQMxTnZ9jw1WlLlxSlI8cjfqUUprneJ6qtH7//ffjpptuOu33izmeIqRyjidR+kQiUTzT0Im/bG9F17BfrngXVY0oovL/ohoqfonUlTjwT2trcUF9yVgKnXweRcNnbO5n/uBe1CzvgOuc5cqGzxgGUKJTE7Fm88Aw/t+xbhk+5W1T3C/2Zrogy4IvzyvHeWzzlJI5nilbXBQPBk8idUQjURzocuNAlwctfV74QxHYsy2oK8nF0op81JXObFMIVQNoX9MIbPtfZ/jUMU8ojPZAUL5ZyrdaUJlt5dzCDCMizd3N3XiyZ+i0ozQxsQVS768oxMerS/icMcLiIiLSP5PZhCWVLnkkQtWh9+K6XPRhLVobXpfD7ta8g/J2VVe9c+h9THcgiKd6hvCPPje6AqETvmY3m3BmngNXlxXIldUMFMZ3b0uPDJ3CTKtqsSH4hzsH5LzUD1fx31UyMXgSUcqpHj737euWn+dFmpVf9S5kYgANRiNyC8c/tvfLz0Xbnsl8kSheGxrBlqERLHLY8PW6SszJyU75tVJqiL/rx7oHEzrH/7b3YU2+A8ucXIeSLJxNS0RpC5/iUDF8Vl5RJw//ElEBrcTQtgYlm83HZFrD+eFQGF/Z34oH2/tl4JwqdMbEvtY46sdn9h7FKwNjrbTIWMLRKH56rCvhUCO+/ydHO+WQPSUHgycRpZWK4XNiCGX4VK/S+c2DbTg0cnLvyOmEo2PHvx1pxxtubxKvkNJh66AHvYH495Gf+EalxRfELs+oRldGkzF4ElHaqVr9nCp8Bg5tkUekcw9Ukwnbbf6+rU82LJ+uynkqIqiKQtYPjnTIqikZxz/6h2e1jed0skzA833DGp2NJmPwJCJlqB4+D+9eil3P1ODgs070vNGsZPgUjBo+RQPwP3UOzKrSOZmoiInQ+ft2Yz5GmWrvSHxvRqYSiorzseKZLFxcRERKUXnhEepWjbde6tkvPmpG6WrAXLECqjHiwqMnuwdli5xEiYDydO8gbqoukc3PSd9C0agcZtdSuz+g6fnoLQyeRKRs5VPFADpx9bvq4XM2bZdafAG8OuDBIa8P7f6grCqWWLOwMNeOtfkOLMm1p7UdkVjssbHPrVlVyx8Btg555K41pP/gqTUxH5iSg8GTiJSlavXTSOHzyKgf9zZ3Y8fwqJx7JaJlLNwdgR+vuUfksLTYe/tTNaVY65rZRgFa6wmGMHx8Fxqt5vEd9PgYPA3AZjLJv08xRK4Vh0L7zBsNgycRKU3l6qfewqcQC6CigijmSz7Q1jt+n8mxTn5+/MVc7HV9+6E2XFmSj8/NLYPVlNoX5lZfUNPziZDS7ONwqhGISnxdjg2HvH5tzgdgUa5Nk3PRyRg8iUgXVK1+xsJny94CALtQuHCsT2RWnlPJECoCaM6CIvyqtQePds282XYslD7T65bz6b67sCql4TMZw6lB9mo0jLPyHTji9WsyFUMEz1V56ansZwLWkolIN1Re9W5aVo+WxpU4umW+PFRe9f789hZs2dEW1/eKqPa624v7W9+qlKaCU+NFQOJs+VlaNeChdHtXiUuz+b8ieF5ewikYycLgSUS6omrPTxE+S65aheGqOfIQIVTF8DkUCuNPnf3yxbW6YyTu8CmqpftT2GRbDKVqubRJnGuhw67hGSmdquzZuLjImXAvTxGKrix1ocjKAeFkYfAkIl1SMXzGAmgshKoYPl8YGEYgGh3vhTk5fIZHfXjugvfKQ3w83YvHHzrG9klPhRyLGQscNs1etER17Iw87sdtJLfUliPXYo77OSJCqwicn6wp1fjKaCIGTyLSLVWrnzGTw2fsSJdIFHix33NSA3YRPmdb/RTBbevQCHqD2i76mc61pQUJb4kYq3bW2q1YmsuKp5G4rBb828JqWM2zDzcidNrNJvz7omr2dk0yPrpEpHt6CJ/HNlrlIUKo2HIzHboCIYxETh3dZhs+RYBtSOFw+8XFeSjNzkr4hUtc94eritPal5SSY6kzBz9ZPAcls3ieiGdBpc2K/1w6B/NzuJo92Rg8icgQVA+f/VVjhwihA4f60xI+W/1Tt5sJ+vzjR+mxgfHbwz6/HG6PHZOJ3omNI9q0sJkJu9mMb9RVJFT1FJWtda5cXFSYp+GVkUoW5drx3yvm4b3lhbKKGXuuThT7PNdswgcri3Dv8rlyHjElH2fPEpFhqN7zU6pbhZanxAe7UIgtyF54XsquYSQckdWdyUPtf/zwp6a8/4vXfPyEzy996dGT7qNlU/eZWJnnwGfnlOIXzT1xhc45Odn4Wl0Fq50Gl2M241O1pfhQVTFeHvRg/4gPR7w++CJRGUbrHXYsddpxfoETNjaLTykGTyIyHFV7fp4w/J6G8JkF00mhM+FzpiHAXVdWKHeWubu5C6HIWzstzaTX4+3zK+FkG6WMIRalXVacLw9SA4MnERkSw+fJymxT/8r/wO//e/zjkN+PRz75OfnxDb/+ObJsY8OPHRWOKfezrrFbkQ6Xl7jkqvRftfTilUGPrORO3O5TfGw5vo1iWXYWPlJVgncU57HSSZRmDJ5EZFgqD71PDJ/+0TdRgS2wVs+Rt5scVUn5eXPsY70wJ1c9rfap57aJ0Bn72pzBMNomdR8S51mUxl6YlbZs3FFfhe5AEC8NDOPwiB/HfAGEo1G5wnmxw45V+Q6sznMwcBIpgsGTiAxP5eqnCJ/dTwHBoSYUVLTD4hiAo7o5KRVQMbdtudOOvR5fXAt0xKr3tsq3thIUlcQlCrQkKsu24j3lRem+DCKaAc6oJaKMoHLPTxE+u2zrcWBgNfbtOxedu3qStur9kqL8hFaFx3p+ivrhe8oLWEkkollhxZOIMoqq1c+qdbHdUkrRtkFM/dwsh9+1rnyKCuVZeQ7sHPZOGUDF0PpHHvndaSsWa/oDuGZNgabXRkTGx4onEWUcVSufMZVX1KGt+6KkVT7fX1EEV5YlrhcA0/GV7B+rLkGwcQDew32aXx8RGReDJxFlJJWH3pMdPvOyzPjSvHIUZllkkJzNC4bdZMLn55ah2vbWanaGTyKaKQZPIspoegqfUW+7PLRQbM3C7QuqsL7AedoXg1jXy8W5dnyrvnLKbQUZPoloJjjHk4gynsptl2T43AAEX9yp+ap3scr9A5VFuKgoDy8MeLDDPXLSTkQ5JhOW5eXgbYVO1OfYMd1aolj4dNQXJ3xtRGRMpmg0qvVGFppxu91wuVzYurkRTif31SWi5FMxfArtr45tEWlye1BdthkVK0uT0nJpKBTGQDAke3TmZ1lQlJU1bdg8FYZPoszhdntQWLYOQ0NDyM+ffpcoVjyJiCbI9FXvYtGROBLF6icRTYVzPImIJsnkhUda49xPIpqIFU8iIp1VPyfO/RSVz2L3pvHbc9dcDBXDJyufU+sJBLFvxIejo374whHYLWbMy7HJfqtiRyYio2HwJCLSefhs6x77vMy2ExXYpGz4FBhAx7w+NII/dfZjx/Co/Dxrwjza0PGVF2fm5eCGiiKc7Xprm1IivWPwJCLS+ar3mPZXncDOl5UNn0KmVz89oTB+3tyNf/QPj7epmhg2J9o9PIodw214e5ETn5tTDqcGc2+J0o1zPImIZkjleZ+xBUhiz/fOnRaMbH9r+F01mTrv0x0K48sHWrC5f1h+Hj7N/WNf39zvwZf2t2AoeLrvIFIfgycRkYEWHukpfGZSAI1Eo/j2oTa0jAZOGzgnE/dv9QXw7cNtCKvbAZFoRhg8iYjiwPCpjUwJn492DchFRPHWLMX3HRjx4dHOAY2vjCi1GDyJiDIgfIq2S6q2XjJ69dMbjuD37b0Jn0fUOn/f0SvPR6RXDJ5ERAYdeo+Fz32v1GPXMzVoeTGodAXUqOHzuX43/BplxUAE+HvfkDYnI0oDBk8iIg2oHD5LrlolDz0MvxsxfL42OII4dh09pW2DIxqejSi1GDyJiDKg+qmnuZ9GG3rfP+KDVoPjYrj9gNen0dmIUo/Bk4hIYwyf2jBC+IxGoxgMadsGaSgUkecl0iMGTyKiJGD41IYRqp9aDrMT6R2DJxFRBg69n2rVe9TbDhXpNXyaTCYUWrXdcagwyyLPS6RHDJ5EREmmp1XvQ9saGD41tiTXrtmLrTjP4ly7RmcjSj3u1U5ElKLwqeJe7yJ8AqXH93qvwmjDXgANcJ0DmBxVUDV86mm/9/MKnHhFo5XoYpHSugKnJuciSgdWPImIUkTlofdYCHUXLENrQ6XSlU+9VT8vLsqDw6zN0Lg4zyXFeZqciygdGDyJiFJMT+Ez0rlHHiqGUL2ET7vZjE/UjlWVE/XxmhJ5PiK94lA7EVEaw6eqw+/tr4rwCeS2WOVthbVqDr/rZej96hIXXh4Yxg73aFw9PcXypDPycnBNaUESro4odfi2iYgojVStforw6V+yFv1Vq+Sh+vC76tVPsQr9Owuq5cKg2b7wivsvdNjx3fpqrmYn3WPwJCJKM1XDZ3Fd7vghQqgewqfKAdRhMeM/Ftfg3WUFsren5TQZMvb1a8sK8MMlNfL7ifSOQ+1ERApQeehdEOGzDyJ8vq70qndBhE9Vh97F/Myb55ThkqI8PNo1gJcGPBD7GolIKdYfRaJjK9fF0PoFBU68p7wQS5056b5sIs0weBIRKUTVtksCw6d2ljhz8E1nDkbCERzy+nB01A9fOAqb2YQ6hw31OTY4s7RtPE+kAgZPIiLFqFz9nBw+85f0y9tN+UXKhVA9LDzKtZhxZp5DHkSZgBNGiIgUpfLcz9icz57XnPKItV5SkcrzPokyDSueREQKU3XoPVb5bOz0ys/zmz0o9TajdDVgrlgB1eih+kmUCRg8iYgUp+rQuwifEAeAvqa16NkvPlI3fOph7ieR0XGonYhIJ1Qdep84/N7TNAc9bzQrO+wucOidKH0YPImIdIThMzN6fhIZFYfaiYh0RtWh94lzPycOu4sV74Jqq94FDr0TpRaDJxGRTqm+8EiEz9DwMOx5PlgcA7L1kopzPxk+iVKHwZOISMdUrX7Gwucxsep9AMhubEetd5eyC4+46p0oNRg8iYgMQMXq58RV70ApWp4S/1c3fAqsfhIlFxcXEREZhMoLj4SSq1ahpXElFx4RZTBWPImIDETVofcTwufxyqezvFfellNdomQFlNVPIu0xeBIRGZCKQ+8Tw+eRDfnAMcAacqN2gbrD7wyfRNpi8CQiMqh4qp8dg6M40DWM1v5RDI0GYTIBxc5s1BY5sKwyHwWObE2urfKKuvGPYxXQwoUeZC88D6rhwiMi7TB4EhEZ3Eyqn43dHvx1VzuO9nmPT/43IYKo/MjSbcKWI/0wAVhRnY9rV1WjJM+WlOH3QmxRMnwKrH4SJY7Bk4gog8NnOBzBX3d2YNOhnvHVphH53+hb9zn+sfjvnrZh7G3fj+tXV2N9fUlGhk+BAZQoPlzVTkSUQeFz4sp3ETofeOUoNh/qmRA4pyeqoKFoFI9sb8XGhs6krHofONSPwKEtUBlXvRPFhxVPIqIMrX4+vacTe9rdE2qbs/PUnk6U5+dgZa0rOave3ZvkbdZ8u5IVUA69E80egycRUQY6kuXAc/u74w6dgpjz+fDrzVhQthS5tqykrHoXqss2o0LR4XcOvRPNDofaiYgy0EOvteBothNN1tjOQicKh3x47sEPy0N8PBURWkcDYbzaqP2ws1j1Hjvaui9C564epYffOfRONDMMnkREGabP48e2o/2IRMfqnacKnzMh5oW+JOaIHj9XMjB8EhlHUoPnCy+8gGuuuQZVVVUwmUx4/PHHk/njiIhoBhrEvM5JOVGEz3gD6KAvhF5PAMmkp/DJAEqUpuA5MjKCVatW4Z577knmjyEiolk40uOBRXSGnyQS9KERFjm0Hg75x28XH4/dNnZMpXVwFMmml/ApMHwSpWFx0ZVXXimPmfL7/fKIcbvdSboyIiJtBENhvNLYh9eaBrC/040Bb0BWE4tys7G4Ig9nzyvE+fUlyM6yQBXDo6Epbz/64AfH/j/p9hcf/dwJn1964+9P+l5vYOpzJiV8bhCL3scWHMVw4RGRPii1qv2uu+7CnXfeme7LICI6vWgUf9/bjf95pQnDvhDMJtP4nEmha9iPHo8fLxzqxb2bj+Cm9XV45/JyyD0o08xs0f4azHKNO1IaPrufGStOlFW9qeyqd4Ftl4gUDZ633XYbvvzlL59Q8aytrU3rNRERTeYLhPGDZ/Zj+7GB8dsmhs63bhv7/0ggjHueP4xXDvfitnctRU52equf1a4cRKdopDTvxj+MfxwN+XDs4Y/Lj9/23p/DkjX9FpklTu220JztXu9tovXS8QoowyeR2pRa1W6z2ZCfn3/CQUSkkkAojO/83x682fxW6Jypna2D+NZju+ELhpFO9WXO8VA8kdlqHz9MWfbx21vsRWjOKYYlyy6PyUSts6YwB+mil7mfXHhEpFjwJCJS3W9fOYoDncNTBrfTEd9zuMeD+19uQjotqcyDK8c66++batW7GGJfWpEHe5qruJPDZ6RzjzxUxPBJmYzBk4gyXiQSxfBoEB5fUH58Kgc63fjrzo64Quf4z4oCf9vdib3tQ0iXLIsZ16yshDmOaZmTw6fYu/3CRaVQQSx8HnzWiWMbrejYPIyR7WPbbqqG1U/KVEmd4+nxeHD48OHxz5uamrBjxw4UFRVhzpw5yfzRRETTGhwJ4Nm9ndjWNCDbCwWPp8lsiwnzS504t64Y71hWBpcje/x7HnujTfYkjibYLF0sRPrLG21YVqXdHuezdd1Z1di4t0sugDrVsPv8m/4ybfhcEPRieXUellTkQRUifPY1lUH0Rwkc7IZv505UYBNy11wMFXHuJ2UaUzTR36DT2LRpEy655JKTbv/oRz+KBx544LTfLxYXuVwubN3cCKdTnV9sRKTv9kd/3NaCR99ok5s+nqp6KRafi16X/7S2BjesrZXzMm/89daEqp2Tz/+7j5+DggnBNtVEBffWR3cjHI3OeuMhEZ4LcrLw/y6qRl4cw/ap0rGhCWW2nahYFVY2fAoMn6RnbrcHhWXrMDQ0dNr1OUmteF588cUJVwaIiLTS7/HjO0/sQXP/6BRruk8kfnWFolEZUrc29eN9q2s0C52x8x/uHsbaeekLHIsr8nHHNcvwb0/uRSgy9cr8qYgh+kKHFXe95wy4CsYWFUXauqAiUQHtEH0/dVD5FBhAyeg4x5OIMoJ7NIhvPLobLQO+04bOicR9m3q9uPeFI3HNiZyuYijOm25nzinE3R9cjYVlzvHrOpXY18Sczp9/8CxUHg+d8mvV5VCVCJ/d/lXo3GlRds5nDOd9ktEp1ceTiCgpolH8/B+H0D0s5jPOvmwpvkcEVy2JCJfutkoxVQU5+NH7VuL1Y/14cmcHdrUNITSpvGu3mnH+/GJctbIKi04xp1OET5Urn+2vOoGdL8vKZ051ibzdXLECqmH1k4yMwZOIDG9bUz9ePdKf0Dm0njQkzpdtUWfQyWQ24ey6YnmEwhG0DoxiYCQgF1OV59tQkW+X9zmdWOVTxQBata4U7a+ux+iWvcjNtyI7ux8F9WoPvzN8ktEweBKR4f3ljVY5TK7lHM1EiSrq3OKT+2KqQLRbmleSK494qVr9FOGzr2mtXPXu7/TCd7wCyvBJlBrqvN0mIkqCnmE/Gjria/iebAvLx+ZVGpWq8z6L63LlIUJol2298nM/2fOTjITBk4gM7WDXMFQjFumsqnWhOMX7m6crfKoaQAW9hE+B4ZOMgMGTiAytuW9E9uNUiRhmf89ZNcgkDJ/aYPgkvWPwJCJD84eicgW5VsS5TAlWOy9aVIrVcwuRaVSufk4On1Fv+/ihGg69k54xeBKRoTmyzYhofM4Ch3XafpenIr6nssCOz1y0AJlMD+Gzf1O7PIa2NSgZPgWGT9IjBk8iMrR5xblx9e48FXGmmy9egPrS2a34FjG1rsSBH7znDDjtbCiievg8MLBaHn1H7MqHTwZQ0hP+9iMiQxPNzkVxUqvsKeaLrqwpwDnzivCXN9vwx63N483WT/UjxM9fVpGPq1ZWKtW7M91U7fkpwmeM6PuJIy8DaIDrHMDkqIKK2HaJ9ILBk4gMrcCRLUPia0cHEq58iqHyCxaWINc29qvzhrW1uHJFJZ7b34WtR/pxsMst55SeJArs7xxGQ4cb2RYTLltWjg+cM0deG6nb83Ni0/mJ4VNQMYAyfGqnJxDEwREfWn1BBKNROLPMWJBjQ32uHTlmvnlMBIMnERnee1dXY2tTYjsXCVFEcf1Z1SfcJobNr1lZheHREPa0Dcn5S5PnlIooGj4eegPhKJ7Z04XNB3rwxcsW4bwFDAoqVz9P2PGoYS/C3nbYC2yA7SAcSxcpF0C53Wb8otEoXhz04C+dA9g74pO3WY6PWIhBDfHv2mYG3lHswnvLC1Ft5xvHeDC2E5HhLa1y4aozKuXuRfESLz6iBdKCshObvkciUfzk2QP40+stMmDOZCGTqLx6A2F8/2/7sLGhM/6LMiCV5366C5bh4LFF2LVzLpq2lCg/95NmV+G8/VAb/r2xA/uPh04hDEAMYsT+XfsjwNO9Q/hUw1E80tkvwyrNDiueRJQRPrZ+Hhp7PLKh/Gx3MRKB9YxqF248t/akr/1hWzNeONQ76+uJXcLdzx9GpSsHK2pcsz6HUak69D4293Ns/mdfUxlaG15Xeu4nq58z0+oL4Cv7W+AOi5h5+jeP4eP/eP+7tRdHvH58ra4iri4XmYoVTyI6QTQSReuAF3vbh7C/w43BkQCMwGa14N/evQKr58y8f2bspWTd/GJ855plsGaJgbe3HO7y4JHXWxK6LvEzfvr3g/AHx170SP2en4LYctO/ZC1aGyqVrnwKrH6e2nAojK8faIE7FB4PlLPxXP8wftM6+zeemYwVTyKSYfON5gH8bXcHdrQOIjBpgUxBjhUXLirBlWdUoqbQAb2yZ1twxzXL8Pd93Xjg5aMY8gVlpWLyoqPYba4cKz75tjrZ8F2OtU/yh23HjkfH+IfbRPVV7Cf/3P5uvOuMyrjPY1SqVj9j4bMPa5WvfApceDS1e1u6MRAMyyH1eP25awDrCnKxIk+/vxtTyRRVeIKC2+2Gy+XC1s2NcDrz0n05RIbT1DOCzQe7sXFvF9y+0LT3FWFM/Lq4dlUVPrxurqwg6lkoHMGWI314/diAXHE+4PHLEFnszMbiijycM68Y59QVwnKK9ke9w358/LevadKmSUTXmsIc/OJDaxI/mUGpGj6FvqYR2Pa/jprlHXCds1zZ8BnDADrmyKgfn2kQbx4TI34T1jtsuHvZXGQqt9uDwrJ1GBoaQn5+/rT3ZcWTKAPtaB7E7149ikPdnhl/T6wq+Ndd7XizeQD/fv0KFObaoFdZFjMuWFgqj3jsah3UrDeoOE3LwCiGvAG42GJJd6veJ1c+rXkH5e051SUwV6yAalj9HPNk9yAsprfmbMZLVEsPeP045PVhocOu1eUZFud4EmWQYCiMe/5xCN9+Yg8OzyJ0Th4abh304fbH9mA0kLnzEsVCJdFMXkvx/p1kElXnfcbmfO7bdy52bVstV72793cg0rkHKuK8T+ClAU/CoXNi1XPr4Ig2JzM4VjyJMkQoFMG/P7lPzuEUEvl9K6qf7YOjuP/lJnz2knokTTSKPW1ubG8ewKGuYfR6AnKqZUW+HQvLnDh3fvFJ7Y1SZWg0JPt6asntC2p6PqNStfopwifq6k5Y9V6DDoiBR1Urn0ImVj8HgiEMhrR74yx+Exya0IaJTo3BkyhD/M/LTXhTw+FhUfl8ek8nLl9Wjvpy7edgbzvSh/95+SjaBkdlZVGE3dili9veaB7EH19rkQH00xfOx5LK6ecVaU0M0WnNNL6Onoy08Ejl8JmpQ+89wenntM+WaMHUGeAbx5ngUDtRBmhoG8Jfd3VoFjonLjh6cne75tMBfrbxAP7tqX1oHxqVt4ldfyZeuvhzxOaciiHvr/95F37/6lG5Oj9VyvLFXC5tg2JFAeeHGWXofXLLpZ43mpUddo+Fz4wafk/CrwplV2orhsGTKAP8cVtzUhoci/D34sFehMMz2a9nZtMB/u3JfXj+QI/8fCZBWWRNcbc/vd6Ke54/PLNv0oCotCa69/vkJvXzS3I1O18mUbnnZyx89jTNUT58CpkSPkuyszQPUxXZVk3PaVQMnkQG1zk4ip2tQ5qGpInE3uNiRbYWfrflqJyDGm/hcsPeLtmLNBVW1RbAbtXmV6h4U7B2biGyJzWop9nRS/gMHNoiD1VDaCaEzyJrFgqytItA4n39wlyOWMwEgyeRwe1uG0r6zxALjRJ1oNONx99sT7hg+ZuXmtDtTv4kf9HH9Irl2myVJ94UXL1S7d6PeqFq9TMWPlsaV2LXMzXyULkCmglD7+cVODWbqy1Wx5/j4ojFTDB4EhlcY8+I5m1/JgtpMLfykddbYdLgOkMR4K87U7N94T+fXYs8e9ZUmxrNmAiuolH9WXMKtLy0jKdq+Cy5atX4IUKoyuFTMHL4vLasQJN2SubjDeQXs+I5IwyeRAYnWvRo3fZnMqctsflSYtegbUf7NZkOIM7xbEOXnC+abE67FV+6bGFCoVME18+JllRJfnOQiVQMnxPpKXwaMYDWO+y4pMgpe3AmQvym+XRtfBtRZCIGTyKDyxKrVpJsfmliQ0z7Ooc1XRPkDYbRPOBFKqyZV4SvvGORXBw0m2H3WOj8wXtOvwPUwIgf24/248WDPdjS2IfWfm9KV/DrmapD71OFT879TL1b5pQjL8sSd/gU/+KvKyvAmdynfcbYx5PI4CpdsbY/2gcV0/HzFyS4zeOR3rHpAKJtklaO9IxgfmlqmstftLgMFS47/vPZg2gf8skQeqpcKAKnqMqePa9QVjoLcqd+7Dy+IDY0dOHp3R3oGhb7yJ9ILGy6ZHEZrl5ZiTnFnFum556fMnw+BQz1jT0XXMWvo3S1mn0/jdbzMz/Lgh8ursFXD7RgJBSR21/O5vff2wqd+BdWO2eFwZPI4BaWa9v2Z7KrVlYmfI4Rn7bNnEXw8/i1PefpLK7Ix90fPAsvHOiV+9mLMH3ydZlk4LxmVRVW1bhOObz+0qFe/PwfhzAaDJ8ywPqCERlMn2noxHvOrMYHz5vDVfE6D599TWPPGffeAIBdSodPwSgBdF6ODT9fOgc/bOrEHo9PDgVPN1FH/iszAR+uKsY/VxQlpVWdkTF4EhncypoC5GZbMKLxvuoi3OXbrbhsaeLDmFkabwMkcrY1GVsLnYYIfpctL5eHxxfCkR4PBkeD8rES23zOLXbAOl04jEbxwCvH8OgbrTOqUcfeUPxlRxt2tQ3h/3v3Cjjt/LWux+02x7fcFOrGKqAqh0+jVT8rbNn4yeJaPNc/jL90DeCwd2yUIev4rxHxBjBy/PNLCvNwQ2WRDKw0e/wNRWRwIgxdeUYl/vJGa9z9MacizvWFyxbCkeDCIqGm0KFpVTZ6/JzpJALgytrZrVT/8/ZWGTqF2Twa4qET3Qvu/GsDfvCeM2CxcPq+nqufE4ffGT5TR3TVuKw4Xx5tvgAOen1o8QVk1w6nxYL6XDsWOWxwcmQhIQyeRBnghjU1eH5/Fwa8Qc3C503nz8PaeUWaTQfQcjKAKFIsSNH8Tq00dnvw+y3H4v5+Edz3dw7jL2+24Ya1tZpem1GpXP2cHD4LF3qQvfA8qMhoQ+9CtT1bHqQ9vi0mygCiKvmVy5fISJbIALSYy2Q1m3DLJfV475oaza5PbBVZXZCjyc7n4hrXzC3U3ZDzvZsbNdn7/cEtzXIVPM2cHla9DxzqH1/xzlXvpGcMnkQZ4owaF775riWwmE1xTYYX3yEWxtxz42q8c0WFthdnMuHdZ1ZpUvUUlb9rV+lrF6CjvSOyWqnFdAPRs3XjXjUreCrTQ/hs21KIYxut462XVGTUnp+kHQZPogxyzvxi/Nc/n4m6krH5jzNp8VnlsuOj6+biNzedjW9dvQyVBTlJuTax/eTCMmdCK0TF915QX4Kz5hZCT14+3KvZylgxlWLzgR5NzpVpVO75KcJnf9XYceTYBejc1aNs+BQYPulU9DUWRUQJEz0ff3LDKrx2dABP7mrHnjb3Sf0zHVYLLlhYgnedUYkFZamZK2k2m/DVKxbjyw/vwGgwMuvqnwhupc5s3HzxAujNoS4Pohourmod9CEQCrO9ksEWHr216r0ObRvE1M/NqMAWped+GmneJ2mDwZMoA4lVz+ctKJaH2FryWL8X7tGgXNUp2v5UuGxp2cKxqiAHd71nJb71+G54/KKH5czC2Fi7Ihv+/fozkJ9jhd60DY5qurhKPG49w35Up3llv56pvvCo8gr9hE+BAZRiONROlOGyssyyqimGp8+cU4CKAnta9w2vK83FL25cjXULxlbMTzcEHfvaVWdU4r8+cBZK8/TZVy8ZDf6TuGdARlF16H08fHZfpPywu8Chd4phxZOIlONyZOPWK5fKFkN/292BbU39shH7RKV52Th/wdh0AFEp1bNCRza6p9gWMxF6rPyqStWh96kqn9bqOfJ2k0O9BXasfpLA4ElEyhKV2M9fulB+PDgSkH1IRZGzxGnTtF2SmGZwuNuDlgEvgqEocm0WWXkVbZ5SMU9SLKoSP1+rveqLHFYGzwwaeo+Fz+CLO1FQ0Q6LYwCO6malh98ZPjMXgycR6UJBbrY8tLSjeQCPv9mON5oH5BzLsVX+JrnQR3yebTHh0qXlstVTMudLrqotwJO7OzQ5l5h+sFpnq/r1RNXqpwif7a860TUAmI55UN2n/txPhs/MxOBJRBlneDQoG7a/cGisjVGszji2q9NbVcdAOIoNDV14tqELH143F9efVS1X32tN9EctyLGeNJ0g3vmi71pRqcl1kb7CZ9W60uMflXLhESmLi4uIKKP0efz48p924qXjL3qnW9gjvi6GwB945Sh+tOEAwuFIUroMfOi8uQmfR4TotXMLsbAiT5PrIn32/Jxq4VHU2y4PFXHhUWZh8CSijCF6W37r8T1yIU88K8lFo/f/frEpKdd2xfJyrKp1xd1IXhRi7VlmfP7t9ZpfG52aHsJny4tB9G9qx9C2Boxs3wQVMXxmDgZPIsoYYh9z0TMz3vZF4rue2t2BHc2Dml+bWDV16zuXorYwZ9bhU9w/y2LCd69dhiKnPltK6ZnK1U8RPrts63FgYDX27TsXfUeHlQ6fDKDGx+BJRBmh2+3DYzvaEu5vKSqL973QmJRGmWKl/g/euxLnzR/rYTqT/Gk63jz/h+9dhaVVLs2viWZO1fAp5n6KY3z4fadF2fApMHwaGxcXEZHhiP6fz+/vxr4O91iLpHBUBkYtsqJYgNQ6MCq3Gl1R40pK+LztXUvxyuFePPRaM5p6vTJcmmGSldqxMDr2cb49C9esrMJ71lRze0xFqLrwKEaEzw6x8GjnTlRgE3LXXAwVceGRcTF4EpFhtPSN4O7nD2Nfx7Acfk7GjkCCxWTCa0f7kxI8Y86vL8H5C4rR2DOCve1DONI7Aq8/DKvFjJqiHNSXOXFmTYHceYrUonLPTz2FT4Ftl4yHwZOIDGHDnk78clPjhNZIydszUqxyP9g1jKQzmWQTfXGQ/qhc/WT4pHRh8CQi3RPbaorQmUpdbl9Kfx7pk8rVz8nhM0bFEMqhd+PgGA0R6drhrmHctzm1oVNIXj2VjEjVhUcifHb7V2HXttXy4MIjSjZWPIlIt6KRKP5z48G0/GyxXzyREYbeRfiMEdtuYufLSg+/s/qpb6x4EpFuiT3WWwZEX87U/lyxuGhROeddkrF6fgqi7ZLo+6l65VNg9VOfGDyJSLee3dsV904/iS4uWjN3rNcmUTz0FD7FlpuxbTdVw/CpPxxqJyLdamh3J3X1+lREzC3Ns+Gs2oKU/lwyHpUXHonw2f7qegy8MhY2C3ObULHqIBxLAZOjCirh0Lu+MHgSkS55fEEMjQZT/nNFzP3EBXUwiY70RAae+ynCJ1B6fO5nlZz7WTzcANc56oVPgW2X9IHBk4jSpnXAK3cAOtLjgccfQpbZhCpXDurLnVhV44J1mt14RoMRpJrImheIxu71JSn/2WRsqobPyRVQHHlZjDUoHT4FBlB1MXgSUcrtbBnEH7ceQ0PHsBy6FvM0o+MNisZ2HHLasnDVykq8b00N7NaTA6jVnPrQuaLKhS9ctjC1P5gyhspD73oKnwKrn+ri4iIiSplAKIyf/+MQvvX4HuzrHNv5J3p8sY5YmT52jAVQUQF95PUWfPbB7djf4T7xRNEofvfqsZRcswjFIhxfu6oK3712GfdEp6TTw8KjviN2DG1rUHLBUQwXHqmJFU8iSgl/MIzv/l8D9h4PkTNpgSTu0+cJ4La/7MYd1yzDmXMK5e1/3dmOjfu6k1LVlNXX6FgYFgvmz55XiPetrcGSinzNfx6RHofeJ1c+HdXN8nZr9RzlKqAcelcPgycRpcTP/3FYhs7Z9twU9xfD8P/25F78/IOrZTC8/+Wjml+fqGq+c0UFrBYzcrMtqCtxYnGFE4W5bBRP6aGLVe+724HdgN08iJrl6g6/c+hdHQyeRJR0Wxr7sOlgT9zfLyqQoQjw078fxIISJ8Iad1ASYfb8BcW4+eJ6bU9MlCGr3vuaRtDa8LrScz8ZPtXA4ElEySXnYx6Vw9aJtNwUcz/3dQyjsdujee9Oe5YZn37bW9sGEqlG5eqnUFyXiz6s1UX4FBhA04eLi4goqfZ1uOW2llpkRTEHM6BxuVMMsX/tnYtRyL3XSQdUXngkwqd/iQiflVx4RKfE4ElESbWjZVCzbS2TsSf7RYtKsXYet78k/WD41C58MoCmHofaiSipDnd7JvToVIvFZEJhrjXdl0FkqKH3ycPu+Uv65e2m/CJlh9859J46rHgSUVL1jwQ1GWZPliwzfw2Sfqla/ZxY+Ty20SoPUQGNdO6Bilj5TB1WPIko6VVFVYlendWFOem+DCJDVj9jlU//8c9HGoKoQQdER1xzxQqohguPUoNv9YkoqaoK7ZrN8UyGRWXOdF8CkWGrnyJ8xo5YBdS9v0PZyqfA6mdyMXgSUVLVlzk1neMpKqhidXuixCnmFuWgtsihxWURKUHF8BkzVfgUh4oLkLjwKHkYPIkoqc6bXzy2IbsGROV09dwCTVa3i1Ncd2YNZINRIoOFT1UD6MTw2fOaUx6c+5lZUhI877nnHsybNw92ux3nnnsutm3bloofS0QKKMu3Y+28Qk2G26PRKD6+fh7WzE3sfOJ7F5fn4e1LyxK+JiJVqR4+G91z5HF491L0vNHM8Jkhkh48H374YXz5y1/GHXfcgTfeeAOrVq3CFVdcge7u7mT/aCJSxCcuqEt4eFx8/zWrKlFTlIt/fXs9CnKy4gqf4ntyss34yuWLYNZizJ5IYSqHT7HlpjhMy+rR0rhS+fDJAKqT4Pmf//mf+NSnPoWPfexjWLZsGe699144HA78z//8T7J/NBEporrQgU9dOD/u7xdhsaogBx9ZN09+XuS04QfvXYmiXOusAq04j9NmwV3Xr0RlAVezU2ZQeeg9FkL1ED4Fhk/Fg2cgEMD27dtx2WWXvfUDzWb5+auvvnrS/f1+P9xu9wkHERnDu86oxEfWzZUfz6ZQKYJlpcuG71+3AjarZfx2ERzv/sBqvH3J2HD5dNXP2NfWzS/CL25cjbrS3Pj/IEQ6pXr4LLlqFcNnBkhqH8/e3l6Ew2GUl5/4ZBef79+//6T733XXXbjzzjuTeUlElEY3rK3F3CIH/t8/DsM9Gpx2zZEIi5FoFO9YWo6PX1AHh+3kX1dOexa+cNkivPvMavxtdwdebezD4GjwhPvk2bNwbl0RrjqjEvXleUn4UxHph6o9P2Nk+HxKfLQLpavHdjsSVNvxiD0/DdJA/rbbbpPzQWNExbO2tjat10RE2jpnfjHurXLh7/u68OSuDnS5fSfdJ8tswtsWluDqlVVYVHH6sDivJBefvaReHgMjfvR5AnK3pCJnNopzs7lynWiKAKp6+AwNt8Ce54PFMSC33VS16TzDp0LBs6SkBBaLBV1dJz65xecVFRUn3d9ms8mDiIxNVCqvO6taHgMePxp7R+DxBZFlMcu5nHOKHPLjeBTm2uRBRPoOn8derQIGgOzGdtR6xyqgqoZPgQFUgeCZnZ2NNWvW4LnnnsN1110nb4tEIvLzz33uc8n80UQZQwxZv3y4Fwe7hnG014tgOCKHlxeUOrGsKh/nzCtCVpa6LXsLnTasdTIoEqWDykPvYsX7mNITht9VDJ8Cq5+KDLWLofOPfvSjWLt2Lc455xz87Gc/w8jIiFzlTkTxG/IG8MArR/H8/h45F1LMiRR7j8fs6xjGEzvbkW/PwvvW1ODaVVWwxFlFJCJjU7n6OdXcT4ZP/Up68Hz/+9+Pnp4efOc730FnZyfOPPNMPPPMMyctOCKimdt+tB8/fvYgvIGwDJ3CxNA58XO3L4T/efkoNh/swTevWobSPFYXiUhf1U+9hU+BAXRqpqjYCkRRYnGRy+XC1s2NcDq5GpVI2NLYh7ue3icXz8zmH6+oiIqm6z+6YZXcTYiI6FRUDZ9C71M7UbtAhM85yobPmEwJn263B4Vl6zA0NIT8/Pxp78txNyIdaRvw4ofP7J916BREZXRwNIR/f3IvQuFIkq6QiIxA5Z6fE/t9jmzfJI/AoS1QEXc8OhmDJ5FeRKP46cZDCMcROieGz6N9Xjz2ZpvGF0dERqPyjkcifB45dgF2bVuNfa/Uo3NXj7LhU2D4fAuDJ5FO7G5z40DX8PiczniJ7/7z660IhMKaXRsRGZeq4bPyijp5iBDa3X4Ww6dOMHgS6cQzezqm3RZyNrzBsGzBRESk5/AZI8JnW/dFugif3gwPoAyeRDqxs3Uo4WpnjMVkQkObW5NzEVFmUHnoXRDVz1j41MPcz0yl1JaZRDS1QW8AQ5P2IE+EaLUkhu31wOMLoaV/BP5QBHarRe5qNNW+7USUGir3/JThcwPQ1j32eXXZZlRgC7IXngfVeDO05yd/exPpwLBPu9AZ4/GHoHJz/I17u7ChoQudU+zlXluYgytWVODSJeVy+00iSi2Ve36K8BkjQih2qR0+hUwKoBxqJ1JcNBLFg1uaNT9vllmb+aKaikbx7J5OfOK3r+P3W45NGTqFloFR/PrFJnz0f7birzu4Qp8oXVQeep88/K7qsHumDb0zeBIp7sld7Xi5UdtfSiJzzi12QCXhcAQ/efYg7n7+sBxWj8xgOmsgHMWvXmzCJx54Da39I6m4TCKahOFTG94MWXjE4EmksK4hH+5/+WgSzmzCwjK1dgP7xfONclvPeHQP+3HLH97EM3s6Nb8uIjLWwiOVw6dg9PDJ4EmksCd2tMmG8VoTq+MvXFQKVbx6uA/P7uuKuzG+ICqk9zx/GI+zOT5R2ugpfMYOFXkNHD45K59IUcFQGBv3dWnWQilG9AI9q7YAlQU5UEEoFME9mw5DzDjV4k/6m5eaML80FytrCjQ4GxEZbeGRWHDU/cxYO7myqjdR7N6E3DUXQzVegy48YsWTSFHH+rzwBbXfUz3LDHzmovlQxatH+mSrKK3itQiwP914kDszEaWZqtXP2G5HsabzfUeHZc9PVXkNVv1k8CRS1JGe5CyWufnielQoUu0UxLxOLRfYiwDb6wnghYPcmYko3VQNn5OH3/UQPr0GCaAMnkSKGgmENNsiM+aM6nxctkytF4IDnWL/eW3PKR62v+3q0PakRGTohUeqh0/BCOGTwZNIUaLPZlSzAeixuZ2LFFvJLobDBzXckSlGTItt7PVwuJ1IIXoIn507LQyfScbgSaSo6kKHDFBaEYuUaorU6t0ZiSTx3FGguc+bvB9ARIYLn93+VePhM9K5Rx4q8up46J3Bk0hR9WVOzc+5MAnnTER2ljmpOyiNBFjxJFKNykPvsfDZtKUExzZa0bFZ7eF3rw7DJ9spESkqP8cq2x7tbB1KuKWSiHY1hTlp262osduDVxp7caBrGB2DPoQjURQ4rFhUnocSp+2UW2MacltQIpJE+FS15VJfUxn8APydXvh2vowKqNlyKRY+9dRyicGTSGHXnlmFN1sGEz6PiK3XnVk9tuomhQ53DePezUdk4BRzTKPRt2at9o0EcLTXi7DGfUonEmGbiNSlas/P4rrcsQ/qctH+6npAB+FT0EMA5VA7kcLWzi3EuvlFCa1uF9+7uDwvtavZo1E8vK0ZX3lkJw51D8ubRNV2csRMZugszrXC5chO2vmJSDuqDr0LVetK0WVbf9Lcz6i3Harx6mDoncGTSGUmE265pB7FTmtc4VOMNDuyLfjK5YtgTtWwczQq95f/363NcoGP1q2SZkL8US9bWpH6H0xEhg6fE+d+evcdVDZ8ehUOoAyeRIoTVbsfvGclyvJss2q0LoJqnt2Ku95zRkq3x3zhUC/+kub90k0w4Z0rGDyJ9EblhUcifPqXrEV/1SocC50rK6Cqhk9B1fDJ4EmkA2X5dtz9gbPGw9R01c/Y19bXF+MXN67GvJLjc5VSYHg0iF88P7bvejp96Ly5KMmzpfkqiCheqoZPMfdTHBOH3xk+Z4eLi4h0wp5tkdtdXruqCk/v7sTLjb1ya8iJCh1WnFNXhCtXVGJBGlonbWjowmgwrGHb+9kRFeEzql24/qyqNF0BERl94VGMCJ9vLTw6CMdSwORQ73ePV7GFR6aoWGaqKLfbDZfLha2bG+F0qrXjCpEKPL6gDJ/iX3FhrhUFaV5M84kHXkP3sGhCkh5n1hbgm1cthd1qSds1EJH2VA2fQvurPSj3v4zi+T64zlmuZPiMSVb4dLs9KCxbh6GhIeTn5097X1Y8iXTMabfKQwUDHr/modOWZYI/dPr3xmJ6wU3nz8N1Z1bBxN6dRIajas/PEyqfR14G0ADXOW99TbUQ6lWg+sk5nkSkiSO9I5qfcyahUxADN88f6EpaI3oiSj/VFx6JOZ99R+zo39Quj6FtDZz7OQUGTyLShDeN21OKeHqsbxRffWQn2ga4PzuRkakePg8MrJZHa0Mlw+cUGDyJSBNWS3p/nYgG9R5/GHc80QB/kHu0ExmZqtVPET5jh2i9pIfw6U1xAGXwJCJN1BSlf3tKET67PX7875Zj6b4UIkoBFcNnjGi7pIfwKaQyfDJ4EpEmql05sFvT/ytFrPD/664O2VOUiIyP4VNf1c/0v0oQkSGI1eSXLilPaF95LSufz+3vTvdlEFGGD73rLXwKyQ6fDJ5EpJmrV1Ygmrb28Sfa1TqY7ksgohTTS/gc2b5JHqqG0GSGT/bxJCLN1BTl4r2ra/Do9ta0xk8x3H6wy5PGKyCidFG156cIn31Yi337xkZj8iLNqBke6/upWr/PZPb8ZPAkIk194Jxa7G4bwqEujxzyThevP5S2n01E6aXqdpvFdblAXZ38uK+pDK0Nr483nVcxfMYCqJbhk0PtRKSp7CwL7rx2OZZWpH6v+InM3MGIKOOpOvQ+1fB7pHMPVKXl0DsrnkSkuVxbFr53/Rl4fEe7bG0kKp+RUxQ/xWIk8fUsswmhU90pDlUFds3ORUT6pWr1c+Lwu6h81qAD1rZeeXtOdQnMFStgxKF3Bk8iSgqLxYz3rqnBZUvLsHFvN1441C13F5o4/F7izMaaOYW48oxK/HHbMbx2dOCUAXVWP9tkwuKK/MRPRESGoZe5n9aQG7ULdqF0NZQLn1oMvTN4ElFSuRzZeN/aGnkEQ2H0eYKIIIp8uxVO+1u/gi5cVIatTQOa/MxwNIoLF5Zqci4iMg6VwyfG536OoGWv+Ejt8CnEE0AZPIlmKRyO4M2WAexpc+NwtwfDvpDcLnJOUQ4Wlufh/AXFMmzRyaxZFlQUWKb82vnzi+HKscI9GkxoRbyY2llVkIMzqlnxJCJ9Db2/VQGtVz58xlv9ZPAkmqFoJIqn93TgoddaMOANyuFcUVmLESH07/u6cd/mI7h4cQluWl+HAgbQGcvKMuOWi+vx/af3JXQe8Vfyr2+vBxRoZE9E6lK1+qnH8Iky24zvz+BJNAMDI378x9P70dAxPH7bxNA58XPx/+cP9GLLkX585fJFOLtO2x5oRrauvhiXLy3Hxn1dcVc9/+nsWiytcml8ZURkRCpXP4v1FD6P9M/4vmynRHQaAx4/vvbILuzrnHlDcrGAxhsI49+e2oeXDvUk9fqM5rOXLMBFi2c3PzNW23z3mVX40LlzknJdRGRcqrZdKq7LhWlZPVoaV6LnjWYEDm2Rh8qtl06HFU+i0wyvf//p/ejxBGbdDF3c2xQFfrzhIGoLHZhbkpu06zTaavivvGMRVla78KsXjyAQipxypbtpQvumz7+9HufXl6TyUonIQNReeLQKLU8BLY1jt6m86v10GDyJpvHkrnbs73xreH22osePn/79IH76T2fCxKbmM2My4R3LK3Du/GJs3NuFZxs60T7kO/EugFzQ9c7lFbhkabkMn0RERh16L7lq1fjHIoSqPvx+KvxNTXQKoXAED7/emvB5RKW0sWcE25sHsHZekSbXlinyc6yyF6g4RvwhtPR74Q9FYLeaMbcoF/bsqVfIExEZsfo5MYTqNXwyeBKdwmtNAxgaDWpyLrE7j1gRz+AZP1HRXFLJFklElBoMn8nBxUVEp7CnfVC2TNKCqHruaXWP9fohIiLdhE9VFx6Nh8/jC4/0suCIFU+iUxB9OSe3TEqENxhGl9uPchf3ECci0hOVq58lkyqfMapWQBk8iU7B7Qtpfk6PPwR13zsTEZFeFx61PAV4+0bk547iRhQu9CB74XlQDYMn0SlkJWEFejLOSUREqaNq9bPkqlVyn3ehb28u/KNvogJblAufnONJdApzihxy32+tiHNVcJidiEj3VJ33WVyXKw8RQrvbz0Lnrh7ZcF4lDJ5Ep7CwPE/T81UX5MBmZfsfIiIj0MPCo+4J4VMsPlJhARKDJ9EpXFBfEvd+4VNVO9++pEyjsxERkSr0ED4PPuvEsY3W8W0304nBk+gUSvJsOK+uSPbgTJRoy3TZMnV/ORERkTGrnyVXrYJ/yVr0V63CkWMXpH34ncGTaBqffNt8WC2JB8+b1s9DgSNbk2siIiI1qT73s/KKOrR1X5TW8MngSTSNsnw7Pv/2+oSG2FfXFuCalVWaXhcREalJ1fAZk+7wyeBJdBoXLS7D5y+phxhxn+2w+6qaAtx+1VKY2EaJiChjqDz0nu7wyT6eRDNw+YoKzCvJxX9uPIC2QZ+sZEZOsfLIfDygfvT8ebh2VRXMDJ1ERBlJ1Z6f4+Fzg9jwaLPs92mtniNvNzmSO0LH4Ek0Q4sq8vDzD67GliP9+NvuduzvGEZwUvqsyLfj0qVluGJZOQqdtrRdKxERqUEP4TP44k4UVLTD4hiANe8gctdcnLSfyeBJNAtZFjMuWFgij3A4gvbBUYwEQsgym1FVkAOHjf+kiIhIP9ttVl5Rh/ZXnegaAEzHPKgu2wxgU9LCJ18lieJksZhRW5yb7svQv2gUjT0j2Nc+hCO9I/AGw7CazagtysGi8jycUe2SjzURkd6ZFa1+Vq0rPf5R6djwO5IXPhk8iSg9olG80tiHh15rRlOvd2zxFkyIIIqxWbEmRKJRFORYce2ZVbjuzCpYs7jzExHpm1nh6ucJcz+TFD5ZRiCilPP4Qvj+3/bhrqf342ifV94WjQLhaFT+X0ydFaFTGBwN4vdbjuHzD+3A0d6RNF85EZE2zDpY9d53dBgj2zdpem4GTyJKKY8viFsf3YmtTQPy8+P5clriPh2DPnztzztxsHM4+RdJRJQC5gwMnwyeRJQy0UgU3//bfrQM+MYrmjMl7h8IRXDH/+3BoDeQtGskIkols8I9P8f7fe60yPAZOxLB4ElEKfNMQyd2tw3NOnTGiCF4byCCXzx/WPNrIyJKJ7PC4bPbvwq7tq2WRyyExouLi4goJULhCB7c2pzweURoffVIP5p6RlBXyq4CRGQcZkUXHonwGSNaL2Hny6iIc+ERK55ElBJbm/oxNBrU5FxiZ6i/7enQ5FxERKoxK1r9jLVe6rKtj7vyyeBJRCmxo2UQllnudT9d1XP7sX5NzkVEpCKzjsKnd8eLM/5eDrUTUUoc6hqW7ZK00jMckG2ZnHb+GiMiYzIrOvQeC5/tr65H9zYPRgJjbfFmgr+xiSglkrESXbRmYvAkIqMzK73jUSk8Xg/w3zP7Hg61E1FKiHmZWjMl4ZxERCoyKzz0PhtJC57f+973cP7558PhcKCgoCBZP4aIdKKqIEfT84n5osW52Zqek4hIZWaFe36mPXgGAgHccMMNuPnmm5P1I4hIRxaW52la9ZxTnIOsLA7aEFHmMes4fCZtctSdd94p///AAw8k60cQkY6sX1CCP29v1eRcIr9euLBMk3NRZvMFwjjYPYwjPSNysZrZNFadry93okZU6TmdgxRlVnjh0XSUmpXv9/vlEeN2u9N6PUSkHfFCXl/qxJHekbh3Lpo4zP6OZQyeFL+OwVE8+kYbnt/fhUA4KgOnCWMhM9Z9YW5RDq47swZvX1oGs7gDkYLMii48OhWlxqnuuusuuFyu8aO2tjbdl0REGrr54gWIIvGWSh86by5cDs7vpDhEo3hiRxs+++Ab2Lh3LHTGtmMVgXNiy6/m/lH81z8O4et/3oX2wdE0XjSRcYbeZxU8b731VrmKdLpj//79cV/MbbfdhqGhofGjpaUl7nMRkXoWVeThn9fG/4ZSzBFdVpmH68+q1vS6KDNEI1Hc/dxh/PrFJoQi0dNW3mNfPdTtwZce3oHGbk9KrpPIyAuPZjXU/pWvfAU33XTTtPeZP39+3Bdjs9nkQUTG9cFz58AbCOOJne1yYHOm9U8x1W5hmRPfuWY5hz0pLn/Y1oxn981+SFIEVF8wjG89vhu/+OBqFDr5OkXqMis+9D6r4FlaWioPIqK4mUz45NvqsLDciV883whfKCyHOaercorh+fecVYMPnluL7CxLKq+WDOJQ5zAefj3+UTTxHPUGIvh/zx/GHVcv46IjUppZ4YVHSVtc1NzcjP7+fvn/cDiMHTt2yNvr6+vhdDqT9WOJSA9MJly0uAxn1hbg2b1deGp3B/o8J+9sZLea8fYlZbjqjErMKc5Ny6WSMdz/StOsKuynqny+fnQAe9rcWFHj0vDqiDKn+pm04Pmd73wHv/3tb8c/P+uss+T/n3/+eVx88cXJ+rFEpCNigdANa2txw5oa9I0E0NTrwYg/jGyLGbVFOagqcHBYnRLWOuDF7jZtuqSICvyTu9sZPEk3zIqFz6QFT9G/kz08iWhGxC5ETps8iLQmqpTi/ct0UzpmW/UUC5VMfFNEOmFWaOhdqXZKREREWhtbja5dSPSHImgfYnsl0h+zAqveGTyJiMjQeoZ9CW9aMNlUc5KJ9MCc5rZLDJ5ERESzxVF20jlzmsIngycRERlaWb5dLgrSUgnnI5MBmNMQPhk8iYjI0BaUiRZ+2g2126xmVObbNTsfUSYNvTN4EhGRoZ09r0iTFe2CqJyeM6+IK9rJcMwpCp8MnkREZGhVBTlYVePSZLhdLFK6emWlJtdFlInhk8GTiDKWxxfCrpZBvHCwBy8e7MHBzmEEQuF0XxYlwcfW1yU83C6C67l1RVhWma/ZdRFl2tB70hrIExGpKBgKY/PBXjy5qx2NPSNThou1cwtw9coqnDWngHtyG2ie5wfOnoMHtzXH9f3ieZFrs+CWSxbwOUEZwZykHY8YPIkoYxzqHMZPNh5E2+Co3MnmlDvTHBvEtqMDWDuvEP96ST0KuYLZEP75nFr0ewN4ek/nrENnjtWM7113Bgpz+VygzGFOwo5HHGonoozw0qEefPXPO9Ex5JOfT7fYJNZs/I1jg/j8H3fgWO/JlVHSIZMJn714Af7lwvnItphOO+cz9uZkSYUTP3v/WagrzU3NdRIpRsuhdwZPIjK8Hc0D+NGGAxB5cjY72Ij7DvtDuP2x3ejz+JN6jZQiJhOuXlWFX9y4Bu9aUQFb1tjLoMigFtOJYbSuJBdffsdC/OA9K1FRwPZJlNnMGoVPDrUTkeEXEP342YPy43iWlojw6fGHcfdzh/Hda5dxfp9BlLvs+JeLF+Cm9fNwpMcj93MXbzIsZhOqChxYWOaU9yEibYfeGTyJyND+/HoLhn3BhPo4ivC5vXkArx/rx9p5xVpeHqWZzWrB0iqXPIgo+QuPONRORIYlWiOJhSRaNA8XQ7B/3dGhxWUREWVs2yUGTyIyrN2tQ/AGtenLKaqeb7YMYjTAPp9ERDGzDZ8MnkRkWIe7PZrsVhMjCqdiPiAREb3FXFmGmWLwJCLD6nSPtU7SUqwdExERzR6DJxEZ1mxaJ81YEk5JRJQpGDyJyLAKcrKhdfOj/Bw2AyEiiheDJxEZ1vzSXIQ1rnrWlzk1PR8RUSZh8CQiw1pZ7TrlnuyzJU5T6bKjiPu2ExHFjcGTiAyr0GnDuXVFmq1sv2ZlpSbnISLKVAyeRGRoHzx3bsIrgkTVtNiZjXcsq9DsuoiIMhGDJxEZ2rySXHzg7DkJnUNME/3SZYtgz7Zodl1ERJmIwZOIDO/9Z9fiksWls17hHrv/Zy9ZgJW1BUm4MiKizMK+IERkeCazSVYsK/LtePj1FhkpT9fjU8wLzbGa8a+XLsT59SUpu1YiIiNj8CSijAmfHzxvLs6dX4w/bD2G144NyCF0i8mE6PE5oCaYZPulbIsJly4txwfPmYOC3Ox0XzoRkWEweBJRRllQ5sS3r1mOnmE/drYMorHbg8HRoKxwlufbZJ/OVbUFyLXx1yMRkdb4m5WIMlJpng2XLSuXBxERpQYXFxERERFRSjB4EhEREVFKMHgSERERUUoweBIRERFRSjB4EhEREVFKMHgSERERUUqwnRIRSdFIFF1uP3yhMOxZZpTl22E2z3aTSSIiolNj8CTKYMFQGK809mFDQycOdA0jEHprG0mxe8/CMicuX16BCxaWIDvLktZrJSIi/WPwJMpQu1oG8dO/H0SvJwBR2IxM2ro8EI5iX+cwGjqG8cArR+We5WvnFaXrcomIyAA4x5Mo00SjeGhbM775+B70jwTkTZNDZ0zs9qHRIO7861787pWj8vuJiIjiweBJlGH+vL0VD25tnjZwTha73yMTvpeIiGi2GDyJMsjBzmH8bsuxhM7x8GstaGgb0uyaiIgoczB4EmWKaBT/77mDMCGxleomE/Bffz8kV8ETERHNBoMnUYbY0+bGsf5RRBKcoynyZofbhzeaBzS7NiIiygwMnkQZYtPBHphFuVID4jybD/Zoci4iIsocDJ5EGWJ/x1DC1c4YcZ59HcOanIuIiDIHgydRhmgf9Gl6vi63DxHO8yQiollg8CTKECGN+2+Ks4UjEU3PSURExsbgSZQhcjTe8jLLbILVwl8hREQ0c3zVIMoQ80tzNT1fXXHuWG8lIiKiGWLwJMoQZ1S7NF3VvrLGpcm5iIgoczB4EmWIy5aVIypnZmqzqv0dy8s1ORcREWUOBk+iDFGWb8fFi0oTrnqK7z9/QTGqCx2aXRsREWUGBk+iDPLpC+cjz54Fc5zZU3xfjtWMmy9aoPWlERFRBmDwJMogTrsV37p6KbIsplmHT3F/i8mEb121DAW52cm6RCIiMjAGT6IMs6QiH3ddvxKFjuwZh09xv3y7Ff9+/Qqs4KIiIiKKE4MnUQZaVJGHX35oDa4+oxLZFhNE/hTVzMlzOcVNol/nO5dX4N4Pr8GyKoZOIiKKX1YC30taiUbR0O7GjpZBHOr2oH8kICtMVQU5WFjmxHnzi1FZkJPuqySDycm24FMXLcAHzp2Llw73Yn+nG43dHowGw7BnWbCgLBeLK/Jx4cISOURPRESUKFM0qvE+ehpyu91wuVzYurkRTmcejGhLYx9+++pRtA6MygqTaHcT+xuJDYOK7bDPqi3Ap95Wh1rRtJuIiIhIER7PMM69aAGGhoaQn58/7X051J4mgVAYP9lwAN/72z60DY6O90ac+DZABE5xCDtbh/D5P+7AEzvaZIWUiIiISG841J6m0HnHEw3Y2+GWn88kR4pQKvz6xSZ4fCHceN7cZF8mERERkaZY8UyD37zYJENnrJo5Ww+91oKXD/dqfVlEREREScXgmWK7Wgfxtz2dcYdOQUz9/Pk/DmN4NKjlpRERERElFYNnij20rSXuXWNiRGb1BkJ4pqFTq8siIiIiSjoGzxTqGBzF7rahhKqdMeIcT+1q50IjIiIi0g0GzxRqaBtbTKSVvpEgejwBTc9JRERElCwMnil0pNdz0u4wiRINv4mIiIj0gMEzhTz+kGwQr/U5iYiIiPSAwTOFLGaxJ7a2FU+xjzYRERGRHjB4plB1QY7mFc+aQu7hTkRERPrA4JlCC8vzNFnRHiPmi84tdmh3QiIiIqIkYvBMoeVV+XDlWDU5l9lkwvr6YlizLJqcj4iIiCjZGDxTKMtixjUrK6HFwnaxd/s1qyq1uCwiIiIifQfPo0eP4hOf+ATq6uqQk5ODBQsW4I477kAgkNl9J69fXY2KPHtCuxeJ771saRmWVLq0vDQiIiIifQbP/fv3IxKJ4L777kNDQwN++tOf4t5778Xtt9+OTJadZcHX37lYrkaPJ3yKIfaKfDs++bb5ybg8IiIioqQxRaOp23PxRz/6EX75y1/iyJEjM7q/2+2Gy+XC1s2NcDrzYCR7Wodw55MNCISicth8JkRQrSzIwfevW4Eipy3p10hERER0Oh7PMM69aAGGhoaQn5+vzhxPcUFFRUWn/Lrf75dhc+JhVCtqXPj5B1bLBUexSuapiK+Jr16zsgr/9f4zGTqJiIhIl7JS9YMOHz6Mu+++Gz/+8Y9PeZ+77roLd955JzJFucuO71+/Arvb3HhqVzveaB7AaDBywn0KHVZcuLAU7zyjAjWFbJ1EREREGTTUfuutt+I//uM/pr3Pvn37sGTJkvHP29racNFFF+Hiiy/Gr3/962krnuKIERXP2tpaQw61TykaRZfbj4HRAMwwoTzfBpcjO91XRURERKTJUPusg2dPTw/6+vqmvc/8+fORnT0WmNrb22XgPO+88/DAAw/AbJ756L6R53gSERERZVrwnPVQe2lpqTxmQlQ6L7nkEqxZswb333//rEInERERERlL0uZ4itApKp1z586V8zpFpTSmoqIiWT+WiIiIiDIteG7cuFEuKBJHTU3NCV9LYQcnIiIiIlJE0sa+b7rpJhkwpzqIiIiIKPNw0iURERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlBIMnEREREaUEgycRERERpQSDJxERERGlRBYUFo1G5f89I8PpvhQiIiIimkIsp8Vym26D5/Dw2B/k0nedme5LISIiIqLT5DaXyzXdXWCKziSepkkkEkF7ezvy8vJgMpmgIrfbjdraWrS0tCA/Pz/dl6MkPkbT4+NzenyMTo+P0fT4+JweH6PT42M0NRElReisqqqC2WzWb8VTXHxNTQ30QDwB+SScHh+j6fHxOT0+RqfHx2h6fHxOj4/R6fExOtnpKp0xXFxERERERCnB4ElEREREKcHgmSCbzYY77rhD/p+mxsdoenx8To+P0enxMZoeH5/T42N0enyMEqf04iIiIiIiMg5WPImIiIgoJRg8iYiIiCglGDyJiIiIKCUYPImIiIgoJRg8iYiIiCglGDw1dO2112LOnDmw2+2orKzEhz/8YbnlJ405evQoPvGJT6Curg45OTlYsGCBbEsRCATSfWnK+N73vofzzz8fDocDBQUF6b4cJdxzzz2YN2+e/Hd17rnnYtu2bem+JKW88MILuOaaa+RWdWJr4ccffzzdl6SUu+66C2effbbcermsrAzXXXcdDhw4kO7LUsovf/lLrFy5cnw3nnXr1uHpp59O92Up6wc/+IH8t/bFL34x3ZeiSwyeGrrkkkvwpz/9Sf5Se/TRR9HY2Ij3ve996b4sZezfvx+RSAT33XcfGhoa8NOf/hT33nsvbr/99nRfmjJECL/hhhtw8803p/tSlPDwww/jy1/+snyD8sYbb2DVqlW44oor0N3dne5LU8bIyIh8XERAp5Nt3rwZt9xyC7Zs2YKNGzciGAzi8ssvl48bjRFbU4swtX37drz++ut4+9vfjne/+93y9zSd6LXXXpOvYSKoU5xEH09KjieeeCJqMpmigUAg3ZeirB/+8IfRurq6dF+Gcu6///6oy+WKZrpzzjknesstt4x/Hg6Ho1VVVdG77rorrdelKvEr/bHHHkv3ZSitu7tbPk6bN29O96UorbCwMPrrX/863ZehlOHh4ejChQujGzdujF500UXRL3zhC+m+JF1ixTNJ+vv78eCDD8phU6vVmu7LUdbQ0BCKiorSfRmkaPVXVGAuu+yy8dvMZrP8/NVXX03rtZG+f+cI/L0ztXA4jIceekhWhMWQO71FVM6vuuqqE34n0ewxeGrsG9/4BnJzc1FcXIzm5mY88cQT6b4kZR0+fBh33303/uVf/iXdl0IK6u3tlS+C5eXlJ9wuPu/s7EzbdZF+iak+Yl7e+vXrsWLFinRfjlJ2794Np9Mpt4L8zGc+g8ceewzLli1L92UpQ4RxMd1HzBmmxDB4nsatt94qJxFPd4i5izFf+9rX8Oabb+LZZ5+FxWLBRz7yETGdAUY228dIaGtrwzvf+U45n/FTn/oUjCyex4eIklOx2rNnjwwRdKLFixdjx44d2Lp1q5xj/tGPfhR79+5N92UpoaWlBV/4whfkKKZY5EiJ4V7tp9HT04O+vr5p7zN//nxkZ2efdHtraytqa2vxyiuvGHrIYraPkVjpf/HFF+O8887DAw88IIdPjSye55B4XERlZnBwEJk81C5W9//5z3+WK5FjxAuieFw4mnAy8SZGVKomPl405nOf+5x8zoguAKKzBk1PDCeLziNiIU2mE50irr/+ellMihGjMeLfm3j98vv9J3yNppd1mq9nvNLSUnnEO6wjiCelkc3mMRKVTrH6f82aNbj//vsNHzoTfQ5lMhHExfPkueeeGw9S4t+U+FyECKKZELWVz3/+8zKQb9q0iaFzhsS/NaO/ds3UpZdeKqciTPSxj30MS5YskdPrGDpnh8FTI2J4QrRZuOCCC1BYWChbKX3729+W7xiNXO2cDRE6RaVz7ty5+PGPfywrgTEVFRVpvTZViHnBYmGa+L94Ry2GvoT6+no5/yrTiFZKosK5du1anHPOOfjZz34mFz2IX/o0xuPxyPnSMU1NTfJ5IxbPiL7CmU4Mr//hD3+Q1U7RyzM2P9jlcsl+wgTcdtttuPLKK+XzZXh4WD5eIqRv2LAh3ZemBPG8mTwnOLaWg3OF45DuZfVGsWvXrugll1wSLSoqitpstui8efOin/nMZ6Ktra3pvjSlWgSJp9xUB4356Ec/OuXj8/zzz0cz1d133x2dM2dONDs7W7ZX2rJlS7ovSSniuTHVc0Y8l2isxdRUh/h9RGM+/vGPR+fOnSv/jZWWlkYvvfTS6LPPPpvuy1Ia2ynFj3M8iYiIiCgljD/BjoiIiIiUwOBJRERERCnB4ElEREREKcHgSUREREQpweBJRERERCnB4ElEREREKcHgSUREREQpweBJRERERCnB4ElEREREKcHgSUREREQpweBJREREREiF/x+M0yJLJ+7O/AAAAABJRU5ErkJggg==", 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" ] @@ -1223,10 +1217,10 @@ "id": "6208e1b2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.551786Z", - "iopub.status.busy": "2024-06-04T23:19:51.551712Z", - "iopub.status.idle": "2024-06-04T23:19:51.555477Z", - "shell.execute_reply": "2024-06-04T23:19:51.555225Z" + "iopub.execute_input": "2025-04-03T19:34:09.015768Z", + "iopub.status.busy": "2025-04-03T19:34:09.015657Z", + "iopub.status.idle": "2025-04-03T19:34:09.019689Z", + "shell.execute_reply": "2025-04-03T19:34:09.019408Z" } }, "outputs": [ @@ -1310,17 +1304,17 @@ "id": "a87a5d6e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.556768Z", - "iopub.status.busy": "2024-06-04T23:19:51.556697Z", - "iopub.status.idle": "2024-06-04T23:19:51.659397Z", - "shell.execute_reply": "2024-06-04T23:19:51.659155Z" + "iopub.execute_input": "2025-04-03T19:34:09.020858Z", + "iopub.status.busy": "2025-04-03T19:34:09.020787Z", + "iopub.status.idle": "2025-04-03T19:34:09.108406Z", + "shell.execute_reply": "2025-04-03T19:34:09.108082Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1363,10 +1357,10 @@ "id": "ac2545b2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.660768Z", - "iopub.status.busy": "2024-06-04T23:19:51.660690Z", - "iopub.status.idle": "2024-06-04T23:19:51.662380Z", - "shell.execute_reply": "2024-06-04T23:19:51.662172Z" + "iopub.execute_input": "2025-04-03T19:34:09.109990Z", + "iopub.status.busy": "2025-04-03T19:34:09.109884Z", + "iopub.status.idle": "2025-04-03T19:34:09.111917Z", + "shell.execute_reply": "2025-04-03T19:34:09.111647Z" } }, "outputs": [], @@ -1391,17 +1385,17 @@ "id": "a8f4a9d5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.663566Z", - "iopub.status.busy": "2024-06-04T23:19:51.663493Z", - "iopub.status.idle": "2024-06-04T23:19:51.724988Z", - "shell.execute_reply": "2024-06-04T23:19:51.724724Z" + "iopub.execute_input": "2025-04-03T19:34:09.113194Z", + "iopub.status.busy": "2025-04-03T19:34:09.113106Z", + "iopub.status.idle": "2025-04-03T19:34:09.156099Z", + "shell.execute_reply": "2025-04-03T19:34:09.155768Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1434,10 +1428,10 @@ "id": "2374664b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.726454Z", - "iopub.status.busy": "2024-06-04T23:19:51.726333Z", - "iopub.status.idle": "2024-06-04T23:19:51.729864Z", - "shell.execute_reply": "2024-06-04T23:19:51.729604Z" + "iopub.execute_input": "2025-04-03T19:34:09.157655Z", + "iopub.status.busy": "2025-04-03T19:34:09.157536Z", + "iopub.status.idle": "2025-04-03T19:34:09.161398Z", + "shell.execute_reply": "2025-04-03T19:34:09.161108Z" } }, "outputs": [ @@ -1886,16 +1880,16 @@ "id": "eeb838df", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:51.731202Z", - "iopub.status.busy": "2024-06-04T23:19:51.731137Z", - "iopub.status.idle": "2024-06-04T23:19:52.039122Z", - "shell.execute_reply": "2024-06-04T23:19:52.038848Z" + "iopub.execute_input": "2025-04-03T19:34:09.162695Z", + "iopub.status.busy": "2025-04-03T19:34:09.162616Z", + "iopub.status.idle": "2025-04-03T19:34:09.457591Z", + "shell.execute_reply": "2025-04-03T19:34:09.457276Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1930,16 +1924,16 @@ "id": "3c41c695", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.040527Z", - "iopub.status.busy": "2024-06-04T23:19:52.040435Z", - "iopub.status.idle": "2024-06-04T23:19:52.194270Z", - "shell.execute_reply": "2024-06-04T23:19:52.194007Z" + "iopub.execute_input": "2025-04-03T19:34:09.458927Z", + "iopub.status.busy": "2025-04-03T19:34:09.458830Z", + "iopub.status.idle": "2025-04-03T19:34:09.595713Z", + "shell.execute_reply": "2025-04-03T19:34:09.595401Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1974,10 +1968,10 @@ "id": "2c435bdf", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.195541Z", - "iopub.status.busy": "2024-06-04T23:19:52.195464Z", - "iopub.status.idle": "2024-06-04T23:19:52.269515Z", - "shell.execute_reply": "2024-06-04T23:19:52.269287Z" + "iopub.execute_input": "2025-04-03T19:34:09.597249Z", + "iopub.status.busy": "2025-04-03T19:34:09.597139Z", + "iopub.status.idle": "2025-04-03T19:34:09.677077Z", + "shell.execute_reply": "2025-04-03T19:34:09.676772Z" } }, "outputs": [ @@ -2022,10 +2016,10 @@ "id": "a66d5368", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.270984Z", - "iopub.status.busy": "2024-06-04T23:19:52.270901Z", - "iopub.status.idle": "2024-06-04T23:19:52.543736Z", - "shell.execute_reply": "2024-06-04T23:19:52.543467Z" + "iopub.execute_input": "2025-04-03T19:34:09.678435Z", + "iopub.status.busy": "2025-04-03T19:34:09.678365Z", + "iopub.status.idle": "2025-04-03T19:34:09.935535Z", + "shell.execute_reply": "2025-04-03T19:34:09.935277Z" } }, "outputs": [ @@ -2087,7 +2081,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2156,17 +2150,17 @@ "id": "f920d30b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.545283Z", - "iopub.status.busy": "2024-06-04T23:19:52.545186Z", - "iopub.status.idle": "2024-06-04T23:19:52.607319Z", - "shell.execute_reply": "2024-06-04T23:19:52.607077Z" + "iopub.execute_input": "2025-04-03T19:34:09.936889Z", + "iopub.status.busy": "2025-04-03T19:34:09.936791Z", + "iopub.status.idle": "2025-04-03T19:34:09.981558Z", + "shell.execute_reply": "2025-04-03T19:34:09.981319Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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++6wSEhJks9lkt9u1ceNGjRgxQv369SuOGgEAAIACuRxoJ06cqAYNGigiIkJpaWlq1KiRbr/9dkVFRWnMmDHFUSMAAABQIJsxxhRlx/j4eO3atUtpaWlq3ry56tat6+7aikVqaqqCg4OVkpKioKCgC43p6VJg4IWv09KkgIDSKxAAAOAak28+c4FTD1bIz/XXX6+IiAhJcvqxuAAAAIC7FenBCrNnz1bjxo3l6+srX19fNW7cWO+//767awMAAAAuy+UR2nHjxmnKlCkaOnSo2rZtK0natGmThg8frvj4eE2YMMHtRQIAAAAFcXkObaVKlfTWW2+pV69eudo//vhjDR06tMyvRcscWgAAgLLlSufQujzlIDs7W61atcrT3rJlS50/f97lAgAAAIAr4XKg7du3r95999087TNnzlSfPn3cUhQAAADgLKfm0MbGxjq+ttlsev/997Vy5UrdfPPNkqTNmzcrPj6eBysAAACgxDkVaHfs2JHrfcuWLSVJhw4dkiSFhoYqNDRUu3fvdnN5AAAAQOGcCrRr164t7joAAACAIinSOrQAAABAWVGkJ4Vt3bpVn3zyieLj45WVlZVr2+LFi91SGAAAAOAMl0doFy5cqKioKO3du1eff/65srOztXv3bn3zzTcKDg4ujhoBAACAArkcaCdOnKg33nhD//nPf+Tt7a0333xT+/btU/fu3XX99dcXR40AAABAgVwOtIcOHdK9994rSfL29lZ6erpsNpuGDx+umTNnur1AAAAAoDAuB9oKFSrozJkzkqRq1app165dkqTTp08rIyPDvdUBAAAAl+HyTWG33367Vq1apSZNmuihhx7SsGHD9M0332jVqlW6++67i6NGAAAAoEAuB9pp06bp3LlzkqTRo0fLy8tL33//vR544AGNGTPG7QUCAAAAhXE50FasWNHxtYeHh0aOHOnWggAAAABXOBVoU1NTnT5gUFBQkYsBAAAAXOVUoA0JCZHNZiu0jzFGNptNOTk5bikMAAAAcIZTgXbt2rXFXQcAAABQJE4F2nbt2hV3HQAAAECRuLwOLQAAAFCWlHqgnT59uiIjI+Xr66s2bdroxx9/LLT/6dOnNXjwYFWpUkU+Pj6qV6+eli1bVkLVAgAAoKxxedkud1q0aJFiY2M1Y8YMtWnTRlOnTlVMTIz279+vsLCwPP2zsrLUvn17hYWF6bPPPlO1atV07NgxhYSElHzxAAAAKBNsxhhTWidv06aNbrrpJk2bNk2SZLfbFRERoaFDh+a7vu2MGTP02muvad++ffLy8irSOVNTUxUcHKyUlJT/LTGWni4FBl74Oi1NCggo0rEBAADgunzzmQuKNOXg/PnzWr16td577z2dOXNGknTixAmlpaU5fYysrCxt27ZN0dHR/yvGw0PR0dHatGlTvvssWbJEbdu21eDBg1W5cmU1btxYEydOLHSpsMzMTKWmpuZ6AQAA4Orh8pSDY8eOqWPHjoqPj1dmZqbat2+v8uXL65VXXlFmZqZmzJjh1HGSkpKUk5OjypUr52qvXLmy9u3bl+8+hw8f1jfffKM+ffpo2bJlOnjwoJ566illZ2crLi4u330mTZqk559/3rUPCQAAAMtweYR22LBhatWqlf7880/5+fk52rt166Y1a9a4tbhL2e12hYWFaebMmWrZsqV69Oih0aNHFxqiR40apZSUFMfrt99+K9YaAQAAULJcHqFdv369vv/+e3l7e+dqj4yM1PHjx50+TmhoqDw9PZWYmJirPTExUeHh4fnuU6VKFXl5ecnT09PR1rBhQyUkJCgrKytPTZLk4+MjHx8fp+sCAACAtbg8Qmu32/Ods/r777+rfPnyTh/H29tbLVu2zDWqa7fbtWbNGrVt2zbffW655RYdPHhQdrvd0XbgwAFVqVIl3zALAACAq5/LgbZDhw6aOnWq473NZlNaWpri4uLUqVMnl44VGxurWbNmad68edq7d6+efPJJpaena+DAgZKkfv36adSoUY7+Tz75pJKTkzVs2DAdOHBAS5cu1cSJEzV48GBXPwYAAACuEi5POZg8ebJiYmLUqFEjnTt3Tr1799avv/6q0NBQffzxxy4dq0ePHvrjjz80btw4JSQkqFmzZlq+fLnjRrH4+Hh5ePwvc0dERGjFihUaPny4brzxRlWrVk3Dhg3Ts88+6+rHAAAAwFWiSOvQnj9/XgsXLtTPP/+stLQ0tWjRQn369Ml1k1hZxTq0AAAAZcuVrkPr8gjtuXPn5Ovrq4cfftjlkwEAAADu5vIc2rCwMPXv31+rVq3KdXMWAAAAUBpcDrTz5s1TRkaGunTpomrVqukf//iHtm7dWhy1AQAAAJflcqDt1q2bPv30UyUmJmrixInas2ePbr75ZtWrV08TJkwojhoBAACAAhXpprBL7dmzR3369NHPP/+c7xq1ZQk3hQEAAJQtV3pTmMsjtBedO3dOn3zyibp27aoWLVooOTlZ//rXv4p6OAAAAKBIXF7lYMWKFfroo4/0xRdfqFy5cnrwwQe1cuVK3X777cVRHwAAAFAolwNtt27d9Le//U3z589Xp06d5OXlVRx1AQAAAE5xOdAmJiaqfPnyxVELAAAA4DKnAm1qaqpjgq4xRqmpqQX2LcpEXgAAAKConAq0FSpU0MmTJxUWFqaQkBDZbLY8fYwxstlsZX6VAwAAAFxdnAq033zzjSpWrChJWrt2bbEWBAAAALjCqUDbrl07x9c1a9ZUREREnlFaY4x+++0391YHAAAAXIbL69DWrFlTf/zxR5725ORk1axZ0y1FAQAAAM5yOdBenCt7qbS0NPn6+rqlKAAAAMBZTi/bFRsbK0my2WwaO3as/P39HdtycnK0efNmNWvWzO0FAgAAAIVxOtDu2LFD0oUR2l9++UXe3t6Obd7e3mratKlGjBjh/goBAACAQjgdaC+ubjBw4EC9+eabrDcLAACAMsHlJ4XNmTOnOOoAAAAAisSpQHv//fdr7ty5CgoK0v33319o38WLF7ulMAAAAMAZTgXa4OBgx8oGwcHBxVoQAAAA4AqbMcaUdhElKTU1VcHBwUpJSfnfPOD0dCkw8MLXaWlSQEDpFQgAAHCNyTefucDldWjPnj2rjIwMx/tjx45p6tSpWrlypcsnBwAAAK6Uy4G2S5cumj9/viTp9OnTat26tSZPnqwuXbro3XffdXuBAAAAQGFcDrTbt2/XbbfdJkn67LPPFB4ermPHjmn+/Pl666233F4gAAAAUBiXA21GRobKly8vSVq5cqXuv/9+eXh46Oabb9axY8fcXiAAAABQGJcDbZ06dfTFF1/ot99+04oVK9ShQwdJ0qlTp3jYAgAAAEqcy4F23LhxGjFihCIjI9W6dWu1bdtW0oXR2ubNm7u9QAAAAKAwRVq2KyEhQSdPnlTTpk3l4XEhE//4448KCgpSgwYN3F6kO7FsFwAAQNlypct2ufzoW0kKDw9XeHi4fv/9d0lS9erV1bp166IcCgAAALgiLk85sNvtmjBhgoKDg1WjRg3VqFFDISEheuGFF2S324ujRgAAAKBALo/Qjh49WrNnz9bLL7+sW265RZK0YcMGjR8/XufOndNLL73k9iIBAACAgrg8h7Zq1aqaMWOG7rvvvlztX375pZ566ikdP37crQW6G3NoAQAAypYSf/RtcnJyvjd+NWjQQMnJyS4XAAAAAFwJlwNt06ZNNW3atDzt06ZNU9OmTd1SFAAAAOAsl+fQvvrqq7r33nu1evVqxxq0mzZt0m+//aZly5a5vUAAAACgMC6P0LZr104HDhzQ/fffr9OnT+v06dO6//77tX//ft12223FUSMAAABQIJdGaI8ePapVq1YpKytLPXv2VOPGjYurLgAAAMApTgfatWvX6m9/+5vOnj17Ycdy5fTBBx/o4YcfLrbiAAAAgMtxesrB2LFj1b59ex0/flz//e9/NWjQID3zzDPFWRsAAABwWU6vQxsSEqLvv/9ejRo1kiRlZGQoKChIiYmJuu6664q1SHdiHVoAAICypcTWoU1NTVVoaKjjvb+/v/z8/JSSkuLySQEAAAB3cemmsBUrVig4ONjx3m63a82aNdq1a5ej7dIniAEAAADFyekpBx4elx/MtdlsysnJueKiihNTDgAAAMqWK51y4PQIrd1ud/ngAAAAQHFz+cEKAAAAQFniVKD94YcfnD5gRkaGdu/eXeSCAAAAAFc4FWj79u2rmJgYffrpp0pPT8+3z549e/Tcc8+pdu3a2rZtm1uLBAAAAAri1BzaPXv26N1339WYMWPUu3dv1atXT1WrVpWvr6/+/PNP7du3T2lpaerWrZtWrlypJk2aFHfdAAAAgCQXVjm4aOvWrdqwYYOOHTums2fPKjQ0VM2bN9edd96pihUrFledbsMqBwAAAGVLia1ycFGrVq3UqlUrl08EAAAAFAdWOQAAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZ2RYH23Llz7qoDAAAAKBKXA63dbtcLL7ygatWqKTAwUIcPH5YkjR07VrNnz3Z7gQAAAEBhXA60L774oubOnatXX31V3t7ejvbGjRvr/fffd2txAAAAwOW4HGjnz5+vmTNnqk+fPvL09HS0N23aVPv27XNrcQAAAMDluBxojx8/rjp16uRpt9vtys7OdktRAAAAgLNcDrSNGjXS+vXr87R/9tlnat68uVuKAgAAAJzl8qNvx40bp/79++v48eOy2+1avHix9u/fr/nz5+urr74qjhoBAACAArk8QtulSxf95z//0erVqxUQEKBx48Zp7969+s9//qP27dsXR40AAABAgVweoZWk2267TatWrXJ3LQAAAIDLXB6hrVWrlv773//maT99+rRq1arllqIAAAAAZ7kcaI8ePaqcnJw87ZmZmTp+/LhbigIAAACc5fSUgyVLlji+XrFihYKDgx3vc3JytGbNGkVGRrq1OAAAAOBynA60Xbt2lSTZbDb1798/1zYvLy9FRkZq8uTJbi0OAAAAuBynA63dbpck1axZU1u2bFFoaGixFQUAAAA4y+VVDo4cOVIcdQAAAABFUqRlu9LT0/Xtt98qPj5eWVlZubY9/fTTbikMAAAAcIbLgXbHjh3q1KmTMjIylJ6erooVKyopKUn+/v4KCwsj0AIAAKBEubxs1/Dhw9W5c2f9+eef8vPz0w8//KBjx46pZcuWev3114ujRgAAAKBALgfanTt36p///Kc8PDzk6empzMxMRURE6NVXX9Vzzz1XHDUCAAAABXI50Hp5ecnD48JuYWFhio+PlyQFBwfrt99+c291AAAAwGW4PIe2efPm2rJli+rWrat27dpp3LhxSkpK0ocffqjGjRsXR40AAABAgVweoZ04caKqVKkiSXrppZdUoUIFPfnkk/rjjz/03nvvub1AAAAAoDAuj9C2atXK8XVYWJiWL1/u1oIAAAAAV7g8QluQ7du3629/+5u7DgcAAAA4xaVAu2LFCo0YMULPPfecDh8+LEnat2+funbtqptuusnxeFwAAACgpDg95WD27NkaNGiQKlasqD///FPvv/++pkyZoqFDh6pHjx7atWuXGjZsWJy1AgAAAHk4PUL75ptv6pVXXlFSUpI++eQTJSUl6Z133tEvv/yiGTNmEGYBAABQKpwOtIcOHdJDDz0kSbr//vtVrlw5vfbaa6pevXqxFQcAAABcjtOB9uzZs/L395ck2Ww2+fj4OJbvAgAAAEqLS8t2vf/++woMDJQknT9/XnPnzlVoaGiuPk8//bT7qgMAAAAuw2aMMc50jIyMlM1mK/xgNptj9YOyKjU1VcHBwUpJSVFQUNCFxvR06f8HdaWlSQEBpVcgAADANSbffOYCp0dojx496vLBAQAAgOLmtgcrAAAAAKWBQAsAAABLI9ACAADA0gi0AAAAsDQCLQAAACytSIH20KFDGjNmjHr16qVTp05Jkr7++mvt3r3brcUBAAAAl+NyoP3222/VpEkTbd68WYsXL1ZaWpok6aefflJcXJzbCwQAAAAK43KgHTlypF588UWtWrVK3t7ejva77rpLP/zwg1uLAwAAAC7H5UD7yy+/qFu3bnnaw8LClJSU5JaiAAAAAGe5HGhDQkJ08uTJPO07duxQtWrV3FIUAAAA4CyXA23Pnj317LPPKiEhQTabTXa7XRs3btSIESPUr1+/4qgRAAAAKJDLgXbixIlq0KCBIiIilJaWpkaNGun2229XVFSUxowZUxw1AgAAAAWyGWNMUXaMj4/Xrl27lJaWpubNm6tu3brurq1YpKamKjg4WCkpKQoKCrrQmJ4uBQZe+DotTQoIKL0CAQAArjH55jMXlHN1hw0bNujWW2/V9ddfr+uvv97lEwIAAADu5PKUg7vuuks1a9bUc889pz179hRHTQAAAIDTXA60J06c0D//+U99++23aty4sZo1a6bXXntNv//+e3HUBwAAABTK5UAbGhqqIUOGaOPGjTp06JAeeughzZs3T5GRkbrrrruKo0YAAACgQC4H2r+qWbOmRo4cqZdffllNmjTRt99+6666AAAAAKcUOdBu3LhRTz31lKpUqaLevXurcePGWrp0qTtrAwAAAC7L5VUORo0apYULF+rEiRNq37693nzzTXXp0kX+/v7FUR8AAABQKJcD7Xfffad//etf6t69u0JDQ4ujJgAAAMBpLgfajRs3FkcdAAAAQJE4FWiXLFmie+65R15eXlqyZEmhfe+77z63FAYAAAA4w6lH33p4eCghIUFhYWHy8Cj4PjKbzaacnByXi5g+fbpee+01JSQkqGnTpnr77bfVunXry+63cOFC9erVS126dNEXX3zh1Ll49C0AAEDZcqWPvnVqlQO73a6wsDDH1wW9ihJmFy1apNjYWMXFxWn79u1q2rSpYmJidOrUqUL3O3r0qEaMGKHbbrvN5XMCAADg6uHysl3z589XZmZmnvasrCzNnz/f5QKmTJmiQYMGaeDAgWrUqJFmzJghf39/ffDBBwXuk5OToz59+uj5559XrVq1XD4nAAAArh4uB9qBAwcqJSUlT/uZM2c0cOBAl46VlZWlbdu2KTo6+n8FeXgoOjpamzZtKnC/CRMmKCwsTI8++uhlz5GZmanU1NRcLwAAAFw9XA60xhjZbLY87b///ruCg4NdOlZSUpJycnJUuXLlXO2VK1dWQkJCvvts2LBBs2fP1qxZs5w6x6RJkxQcHOx4RUREuFQjAAAAyjanl+1q3ry5bDabbDab7r77bpUr979dc3JydOTIEXXs2LFYirzozJkz6tu3r2bNmuX0GrijRo1SbGys431qaiqhFgAA4CridKDt2rWrJGnnzp2KiYlR4MVVASR5e3srMjJSDzzwgEsnDw0NlaenpxITE3O1JyYmKjw8PE//Q4cO6ejRo+rcubOjzW63X/gg5cpp//79ql27dq59fHx85OPj41JdAAAAsA6nA21cXJwkKTIyUj169JCvr+8Vn9zb21stW7bUmjVrHIHZbrdrzZo1GjJkSJ7+DRo00C+//JKrbcyYMTpz5ozefPNNRl4BAACuQS4/Kax///5uLSA2Nlb9+/dXq1at1Lp1a02dOlXp6emOG8z69eunatWqadKkSfL19VXjxo1z7R8SEiJJedoBAABwbXAq0FasWFEHDhxQaGioKlSokO9NYRclJye7VECPHj30xx9/aNy4cUpISFCzZs20fPlyx41i8fHxhT7MAQAAANc2p54UNm/ePPXs2VM+Pj6aO3duoYHW3SO47saTwgAAAMqWK31SmFOB9mpCoAUAAChbSuTRt3+1ffv2XDdmffnll+ratauee+45ZWVluVwAAAAAcCVcDrR///vfdeDAAUnS4cOH1aNHD/n7++vTTz/VM8884/YCAQAAgMK4HGgPHDigZs2aSZI+/fRTtWvXTh999JHmzp2rf//73+6uDwAAAChUkR59e/FhBqtXr1anTp0kSREREUpKSnJvdQAAAMBluBxoW7VqpRdffFEffvihvv32W917772SpCNHjjiW2gIAAABKisuBdurUqdq+fbuGDBmi0aNHq06dOpKkzz77TFFRUW4vEAAAACiM25btOnfunDw9PeXl5eWOwxUblu0CAAAoW6502S6XH3170bZt27R3715JUqNGjdSiRYuiHgoAAAAoMpcD7alTp9SjRw99++23CgkJkSSdPn1ad955pxYuXKhKlSq5u0YAAACgQC7PoR06dKjS0tK0e/duJScnKzk5Wbt27VJqaqqefvrp4qgRAAAAKJDLI7TLly/X6tWr1bBhQ0dbo0aNNH36dHXo0MGtxQEAAACX4/IIrd1uz/fGLy8vL8f6tAAAAEBJcTnQ3nXXXRo2bJhOnDjhaDt+/LiGDx+uu+++263FAQAAAJfjcqCdNm2aUlNTFRkZqdq1a6t27dqqWbOmUlNT9fbbbxdHjQAAAECBXJ5DGxERoe3bt2vNmjWOZbsaNmyo6OhotxcHAAAAXI5LgXbRokVasmSJsrKydPfdd2vo0KHFVRcAAADgFKcD7bvvvqvBgwerbt268vPz0+LFi3Xo0CG99tprxVkfAAAAUCin59BOmzZNcXFx2r9/v3bu3Kl58+bpnXfeKc7aAAAAgMtyOtAePnxY/fv3d7zv3bu3zp8/r5MnTxZLYQAAAIAznA60mZmZCggI+N+OHh7y9vbW2bNni6UwAAAAwBku3RQ2duxY+fv7O95nZWXppZdeUnBwsKNtypQp7qsOAAAAuAynA+3tt9+u/fv352qLiorS4cOHHe9tNpv7KgMAAACc4HSgXbduXTGWAQAAABSNy08KAwAAAMoSAi0AAAAsjUALAAAASyPQAgAAwNIItAAAALC0IgXa9evX6+GHH1bbtm11/PhxSdKHH36oDRs2uLU4AAAA4HJcDrT//ve/FRMTIz8/P+3YsUOZmZmSpJSUFE2cONHtBQIAAACFcTnQvvjii5oxY4ZmzZolLy8vR/stt9yi7du3u7U4AAAA4HJcDrT79+/X7bffnqc9ODhYp0+fdkdNAAAAgNNcDrTh4eE6ePBgnvYNGzaoVq1abikKAAAAcJbLgXbQoEEaNmyYNm/eLJvNphMnTmjBggUaMWKEnnzyyeKoEQAAAChQOVd3GDlypOx2u+6++25lZGTo9ttvl4+Pj0aMGKGhQ4cWR40AAABAgWzGGFOUHbOysnTw4EGlpaWpUaNGCgwMdHdtxSI1NVXBwcFKSUlRUFDQhcb0dOli/WlpUkBA6RUIAABwjck3n7nA5RHai7y9vdWoUaOi7g4AAAC4hcuB9s4775TNZitw+zfffHNFBQEAAACucDnQNmvWLNf77Oxs7dy5U7t27VL//v3dVRcAAADgFJcD7RtvvJFv+/jx45WWlnbFBQEAAACucHnZroI8/PDD+uCDD9x1OAAAAMApbgu0mzZtkq+vr7sOBwAAADjF5SkH999/f673xhidPHlSW7du1dixY91WGAAAAOAMlwNtcHBwrvceHh6qX7++JkyYoA4dOritMAAAAMAZLgXanJwcDRw4UE2aNFGFChWKqyYAAADAaS7NofX09FSHDh10+vTpYioHAAAAcI3LN4U1btxYhw8fLo5aAAAAAJe5HGhffPFFjRgxQl999ZVOnjyp1NTUXC8AAACgJDk9h3bChAn65z//qU6dOkmS7rvvvlyPwDXGyGazKScnx/1VAgAAAAWwGWOMMx09PT118uRJ7d27t9B+7dq1c0thxSU1NVXBwcFKSUlRUFDQhcb0dCkw8MLXaWlSQEDpFQgAAHCNyTefucDpEdqLubesB1YAAABcW1yaQ/vXKQYAAABAWeDSOrT16tW7bKhNTk6+ooIAAAAAV7gUaJ9//vk8TwoDAAAASpNLgbZnz54KCwsrrloAAAAAlzk9h5b5swAAACiLnA60Tq7uBQAAAJQop6cc2O324qwDAAAAKBKXH30LAAAAlCUEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWRqAFAACApRFoAQAAYGkEWgAAAFgagRYAAACWViYC7fTp0xUZGSlfX1+1adNGP/74Y4F9Z82apdtuu00VKlRQhQoVFB0dXWh/AAAAXN1KPdAuWrRIsbGxiouL0/bt29W0aVPFxMTo1KlT+fZft26devXqpbVr12rTpk2KiIhQhw4ddPz48RKuHAAAAGWBzRhjSrOANm3a6KabbtK0adMkSXa7XRERERo6dKhGjhx52f1zcnJUoUIFTZs2Tf369bts/9TUVAUHByslJUVBQUEXGtPTpcDAC1+npUkBAUX+PAAAAHBNvvnMBaU6QpuVlaVt27YpOjra0ebh4aHo6Ght2rTJqWNkZGQoOztbFStWzHd7ZmamUlNTc70AAABw9SjVQJuUlKScnBxVrlw5V3vlypWVkJDg1DGeffZZVa1aNVco/qtJkyYpODjY8YqIiLjiugEAAFB2lPoc2ivx8ssva+HChfr888/l6+ubb59Ro0YpJSXF8frtt99KuEoAAAAUp3KlefLQ0FB5enoqMTExV3tiYqLCw8ML3ff111/Xyy+/rNWrV+vGG28ssJ+Pj498fHzcUi8AAADKnlIdofX29lbLli21Zs0aR5vdbteaNWvUtm3bAvd79dVX9cILL2j58uVq1apVSZQKAACAMqpUR2glKTY2Vv3791erVq3UunVrTZ06Venp6Ro4cKAkqV+/fqpWrZomTZokSXrllVc0btw4ffTRR4qMjHTMtQ0MDFTgxZUKAAAAcM0o9UDbo0cP/fHHHxo3bpwSEhLUrFkzLV++3HGjWHx8vDw8/jeQ/O677yorK0sPPvhgruPExcVp/PjxJVk6AAAAyoBSX4e2pLEOLQAAQNli6XVoAQAAgCtFoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaeVKuwAAAK4mxhidP39eOTk5pV0KUKZ4eXnJ09OzWI5NoAUAwE2ysrJ08uRJZWRklHYpQJljs9lUvXp1BQYGuv3YBFoAANzAbrfryJEj8vT0VNWqVeXt7S2bzVbaZQFlgjFGf/zxh37//XfVrVvX7SO1BFoAANwgKytLdrtdERER8vf3L+1ygDKnUqVKOnr0qLKzs90eaLkpDAAAN/Lw4H+tQH6K8y8W/NQBAADA0gi0AADA7SIjIzV16lSn+69bt042m02nT58utpr+qm/fvpo4cWKJnOtasGfPHlWvXl3p6emlcn4CLQAA1zCbzVboa/z48UU67pYtW/T444873T8qKkonT55UcHBwkc7nip9++knLli3T008/nWfbxx9/LE9PTw0ePDjPtrlz5yokJCTfY9psNn3xxRe52v7973/rjjvuUHBwsAIDA3XjjTdqwoQJSk5OdsfHyFdycrL69OmjoKAghYSE6NFHH1VaWlqh+xw6dEjdunVTpUqVFBQUpO7duysxMTFXn+3bt6t9+/YKCQnRddddp8cffzzXcRs1aqSbb75ZU6ZMKZbPdTkEWgAArmEnT550vKZOnaqgoKBcbSNGjHD0vbjGrjMqVark0s1x3t7eCg8PL5GVId5++2099NBD+S4fNXv2bD3zzDP6+OOPde7cuSKfY/To0erRo4duuukmff3119q1a5cmT56sn376SR9++OGVlF+oPn36aPfu3Vq1apW++uorfffdd4X+YpGenq4OHTrIZrPpm2++0caNG5WVlaXOnTvLbrdLkk6cOKHo6GjVqVNHmzdv1vLly7V7924NGDAg17EGDhyod9991+nvEbcy15iUlBQjyaSkpPyvMS3NGOnCKy2t9IoDAFjW2bNnzZ49e8zZs2dLu5QimzNnjgkODna8X7t2rZFkli1bZlq0aGG8vLzM2rVrzcGDB819991nwsLCTEBAgGnVqpVZtWpVrmPVqFHDvPHGG473ksysWbNM165djZ+fn6lTp4758ssv85zrzz//zFXL8uXLTYMGDUxAQICJiYkxJ06ccOyTnZ1thg4daoKDg03FihXNM888Y/r162e6dOlS4Gc8f/68CQ4ONl999VWebYcPHzZ+fn7m9OnTpk2bNmbBggWF/vv8lSTz+eefG2OM2bx5s5Fkpk6dmm/fi5/R3fbs2WMkmS1btjjavv76a2Oz2czx48fz3WfFihXGw8MjVy46ffq0sdlsjmv63nvvmbCwMJOTk+Po8/PPPxtJ5tdff3W0ZWZmGh8fH7N69ep8z1XYz0i++cwFjNACAFBcjJHS00vnZYzbPsbIkSP18ssva+/evbrxxhuVlpamTp06ac2aNdqxY4c6duyozp07Kz4+vtDjPP/88+revbt+/vlnderUSX369Cn0z+8ZGRl6/fXX9eGHH+q7775TfHx8rhHjV155RQsWLNCcOXO0ceNGpaam5vmz/6V+/vlnpaSkqFWrVnm2zZkzR/fee6+Cg4P18MMPa/bs2YX/wxRgwYIFCgwM1FNPPZXv9oKmLUjSDTfcoMDAwAJf99xzT4H7btq0SSEhIbk+W3R0tDw8PLR58+Z898nMzJTNZpOPj4+jzdfXVx4eHtqwYYOjj7e3d64VPPz8/CTJ0Ue6MMrerFkzrV+/vsAaiwvr0AIAUFwyMqRieCqSU9LSpIAAtxxqwoQJat++veN9xYoV1bRpU8f7F154QZ9//rmWLFmiIUOGFHicAQMGqFevXpKkiRMn6q233tKPP/6ojh075ts/OztbM2bMUO3atSVJQ4YM0YQJExzb3377bY0aNUrdunWTJE2bNk3Lli0r9LMcO3ZMnp6eCgsLy9Vut9s1d+5cvf3225Kknj176p///KeOHDmimjVrFnrMS/3666+qVauWvLy8XNpPkpYtW6bs7OwCt18MkvlJSEjI87nKlSunihUrKiEhId99br75ZgUEBOjZZ5/VxIkTZYzRyJEjlZOTo5MnT0qS7rrrLsXGxuq1117TsGHDlJ6erpEjR0qSo89FVatW1bFjx5z6rO7ECC0AACjUpaOZaWlpGjFihBo2bKiQkBAFBgZq7969lx2hvfHGGx1fBwQEKCgoSKdOnSqwv7+/vyPMSlKVKlUc/VNSUpSYmKjWrVs7tnt6eqply5aF1nD27Fn5+Pjkmau7atUqpaenq1OnTpKk0NBQtW/fXh988EGhx8uPuYLR8Ro1aqhOnToFvqpVq1bkY+enUqVK+vTTT/Wf//xHgYGBCg4O1unTp9WiRQvHiOwNN9ygefPmafLkyfL391d4eLhq1qypypUr51l32c/Pr1Qe/cwILQAAxcXf/8JIaWmd200CLhnpHTFihFatWqXXX39dderUkZ+fnx588EFlZWUVepxLRyxtNpvjxiNn+19JWJQuBNWMjAxlZWXJ29vb0T579mwlJyfnGgG12+36+eef9fzzz8vDw0NBQUFKT0+X3W7PFeQuLjV2cYWGevXqacOGDcrOznZ5lPaGG24odITztttu09dff53vtvDw8Dy/IJw/f17JyckKDw8v8JgdOnTQoUOHlJSUpHLlyikkJETh4eGqVauWo0/v3r3Vu3dvJSYmKiAgQDabTVOmTMnVR7qwysJffwkpKQRaAACKi83mtj/7lyUbN27UgAEDHH/qT0tL09GjR0u0huDgYFWuXFlbtmzR7bffLknKycnR9u3b1axZswL3u7htz549jq//+9//6ssvv9TChQt1ww03OPrm5OTo1ltv1cqVK9WxY0fVr19f58+f186dO9WiRQtHv+3bt0u6EGSlC+Hvrbfe0jvvvKNhw4blqeH06dMFzqO9kikHbdu21enTp7Vt2zbHSPU333wju92uNm3aFLjfRaGhoY59Tp06pfvuuy9Pn8qVK0uSPvjgA/n6+uaaiiJJu3bt0oMPPnjZc7kbgRYAALikbt26Wrx4sTp37iybzaaxY8cWOtJaXIYOHapJkyapTp06atCggd5++239+eefhS79ValSJbVo0UIbNmxwBNoPP/xQ1113nbp3755n306dOmn27Nnq2LGjbrjhBnXo0EGPPPKIJk+erFq1amn//v36xz/+oR49ejimA7Rp00bPPPOM/vnPf+r48ePq1q2bqlatqoMHD2rGjBm69dZb8w260oUpB0XVsGFDdezYUYMGDdKMGTOUnZ2tIUOGqGfPnqpataok6fjx47r77rs1f/58x3SNOXPmqGHDhqpUqZI2bdqkYcOGafjw4apfv77j2NOmTVNUVJQCAwO1atUq/etf/9LLL7+cK5gfPXpUx48fV3R0dJE/Q1ERaAEAgEumTJmiRx55RFFRUQoNDdWzzz6r1NTUEq/j2WefVUJCgvr16ydPT089/vjjiomJkaenZ6H7PfbYY5o/f77jBrYPPvhA3bp1yzcIP/DAA+rbt6+SkpIUGhqqRYsWKS4uTn//+9914sQJVa9eXd26ddPYsWNz7ffKK6+oZcuWmj59umbMmCG73a7atWvrwQcfVP/+/d33j3CJBQsWaMiQIbr77rvl4eGhBx54QG+99ZZje3Z2tvbv359rnuv+/fs1atQoJScnKzIyUqNHj9bw4cNzHffHH39UXFyc0tLS1KBBA7333nvq27dvrj4ff/yxOnTocEWhvKhs5kono1hMamqqgoODlZKSoqCgoAuN6en/uwvVjXeFAgCuHefOnXPcEe/r61va5VyT7Ha7GjZsqO7du+uFF14osN/Zs2dVv359LVq0SG3bti3BCq9eWVlZqlu3rj766CPdcsst+fYp7Gck33zmAkZoAQCAJR07dkwrV65Uu3btlJmZqWnTpunIkSPq3bt3ofv5+flp/vz5SkpKKqFKr37x8fF67rnnCgyzxY1ACwAALMnDw0Nz587ViBEjZIxR48aNtXr1ajVs2PCy+95xxx3FX+A15OKyYqWFQAsAACwpIiJCGzduLO0yUAbwYAUAAABYWpkItNOnT1dkZKR8fX3Vpk0b/fjjj4X2//TTT9WgQQP5+vqqSZMml33MHQAAAK5epR5oFy1apNjYWMXFxWn79u1q2rSpYmJiCnwU3vfff69evXrp0Ucf1Y4dO9S1a1d17dpVu3btKuHKAQDI6xpbPAhwWnH+bJT6sl1t2rTRTTfdpGnTpkm6sORGRESEhg4dqpEjR+bp36NHD6Wnp+urr75ytN18881q1qyZZsyYcdnzsWwXAKA45OTk6MCBAwoLC9N1111X2uUAZU5KSopOnDihOnXq5HkksKWX7crKytK2bds0atQoR5uHh4eio6O1adOmfPfZtGmTYmNjc7XFxMToiy++KM5SAQAolKenp0JCQhx/YfT39y/0iVXAtcRut+uPP/6Qv7+/ypVzf/ws1UCblJSknJwcx3OBL6pcubL27duX7z4JCQn59k9ISMi3f2ZmpjIzMx3vS+NJJgCAa0N4eLgkFThtDriWeXh46Prrry+WX/Su+mW7Jk2apOeff760ywAAXANsNpuqVKmisLAwZWdnl3Y5QJni7e0tD4/iuX2rVANtaGioPD09lZiYmKs9MTHR8VvupcLDw13qP2rUqFxTFFJTUxUREZG7k7//hbmzF78GAOAKeHp6ytPTs7TLAK4ZpbrKgbe3t1q2bKk1a9Y42ux2u9asWVPgs5Xbtm2bq78krVq1qsD+Pj4+CgoKyvXKw2a7cCNYQMCFrwEAAGAZpT7lIDY2Vv3791erVq3UunVrTZ06Venp6Ro4cKAkqV+/fqpWrZomTZokSRo2bJjatWunyZMn695779XChQu1detWzZw5szQ/BgAAAEpJqQfaHj166I8//tC4ceOUkJCgZs2aafny5Y4bv+Lj43PNt4iKitJHH32kMWPG6LnnnlPdunX1xRdfqHHjxqX1EQAAAFCKSn0d2pKWkpKikJAQ/fbbb0Va5wwAAADudfEep9OnTys4ONjl/Ut9hLaknTlzRpLy3hgGAACAUnXmzJkiBdprboTWbrfrxIkTKl++fK510C7+ZsDI7dWLa3z14xpf/bjGVzeu79WvoGtsjNGZM2dUtWrVIi3tdc2N0Hp4eKh69eoFbi9wJQRcNbjGVz+u8dWPa3x14/pe/fK7xkUZmb2oVJftAgAAAK4UgRYAAACWRqD9/3x8fBQXFycfH5/SLgXFhGt89eMaX/24xlc3ru/Vr7iu8TV3UxgAAACuLozQAgAAwNIItAAAALA0Ai0AAAAsjUALAAAAS7umAu306dMVGRkpX19ftWnTRj/++GOh/T/99FM1aNBAvr6+atKkiZYtW1ZClaKoXLnGs2bN0m233aYKFSqoQoUKio6Ovuz3BEqfqz/HFy1cuFA2m01du3Yt3gJxxVy9xqdPn9bgwYNVpUoV+fj4qF69evz3ugxz9fpOnTpV9evXl5+fnyIiIjR8+HCdO3euhKqFq7777jt17txZVatWlc1m0xdffHHZfdatW6cWLVrIx8dHderU0dy5c10/sblGLFy40Hh7e5sPPvjA7N692wwaNMiEhISYxMTEfPtv3LjReHp6mldffdXs2bPHjBkzxnh5eZlffvmlhCuHs1y9xr179zbTp083O3bsMHv37jUDBgwwwcHB5vfffy/hyuEsV6/xRUeOHDHVqlUzt912m+nSpUvJFIsicfUaZ2ZmmlatWplOnTqZDRs2mCNHjph169aZnTt3lnDlcIar13fBggXGx8fHLFiwwBw5csSsWLHCVKlSxQwfPryEK4ezli1bZkaPHm0WL15sJJnPP/+80P6HDx82/v7+JjY21uzZs8e8/fbbxtPT0yxfvtyl814zgbZ169Zm8ODBjvc5OTmmatWqZtKkSfn27969u7n33ntztbVp08b8/e9/L9Y6UXSuXuNLnT9/3pQvX97MmzevuErEFSrKNT5//ryJiooy77//vunfvz+Btoxz9Rq/++67platWiYrK6ukSsQVcPX6Dh482Nx111252mJjY80tt9xSrHXCPZwJtM8884y54YYbcrX16NHDxMTEuHSua2LKQVZWlrZt26bo6GhHm4eHh6Kjo7Vp06Z899m0aVOu/pIUExNTYH+UrqJc40tlZGQoOztbFStWLK4ycQWKeo0nTJigsLAwPfrooyVRJq5AUa7xkiVL1LZtWw0ePFiVK1dW48aNNXHiROXk5JRU2XBSUa5vVFSUtm3b5piWcPjwYS1btkydOnUqkZpR/NyVt8q5s6iyKikpSTk5OapcuXKu9sqVK2vfvn357pOQkJBv/4SEhGKrE0VXlGt8qWeffVZVq1bN84OFsqEo13jDhg2aPXu2du7cWQIV4koV5RofPnxY33zzjfr06aNly5bp4MGDeuqpp5Sdna24uLiSKBtOKsr17d27t5KSknTrrbfKGKPz58/riSee0HPPPVcSJaMEFJS3UlNTdfbsWfn5+Tl1nGtihBa4nJdfflkLFy7U559/Ll9f39IuB25w5swZ9e3bV7NmzVJoaGhpl4NiYrfbFRYWppkzZ6ply5bq0aOHRo8erRkzZpR2aXCDdevWaeLEiXrnnXe0fft2LV68WEuXLtULL7xQ2qWhjLkmRmhDQ0Pl6empxMTEXO2JiYkKDw/Pd5/w8HCX+qN0FeUaX/T666/r5Zdf1urVq3XjjTcWZ5m4Aq5e40OHDuno0aPq3Lmzo81ut0uSypUrp/3796t27drFWzRcUpSf4ypVqsjLy0uenp6OtoYNGyohIUFZWVny9vYu1prhvKJc37Fjx6pv37567LHHJElNmjRRenq6Hn/8cY0ePVoeHozLWV1BeSsoKMjp0VnpGhmh9fb2VsuWLbVmzRpHm91u15o1a9S2bdt892nbtm2u/pK0atWqAvujdBXlGkvSq6++qhdeeEHLly9Xq1atSqJUFJGr17hBgwb65ZdftHPnTsfrvvvu05133qmdO3cqIiKiJMuHE4ryc3zLLbfo4MGDjl9WJOnAgQOqUqUKYbaMKcr1zcjIyBNaL/7ycuGeI1id2/KWa/erWdfChQuNj4+PmTt3rtmzZ495/PHHTUhIiElISDDGGNO3b18zcuRIR/+NGzeacuXKmddff93s3bvXxMXFsWxXGefqNX755ZeNt7e3+eyzz8zJkycdrzNnzpTWR8BluHqNL8UqB2Wfq9c4Pj7elC9f3gwZMsTs37/ffPXVVyYsLMy8+OKLpfURUAhXr29cXJwpX768+fjjj83hw4fNypUrTe3atU337t1L6yPgMs6cOWN27NhhduzYYSSZKVOmmB07dphjx44ZY4wZOXKk6du3r6P/xWW7/vWvf5m9e/ea6dOns2zX5bz99tvm+uuvN97e3qZ169bmhx9+cGxr166d6d+/f67+n3zyialXr57x9vY2N9xwg1m6dGkJVwxXuXKNa9SoYSTlecXFxZV84XCaqz/Hf0WgtQZXr/H3339v2rRpY3x8fEytWrXMSy+9ZM6fP1/CVcNZrlzf7OxsM378eFO7dm3j6+trIiIizFNPPWX+/PPPki8cTlm7dm2+/2+9eF379+9v2rVrl2efZs2aGW9vb1OrVi0zZ84cl89rM4YxewAAAFjXNTGHFgAAAFcvAi0AAAAsjUALAAAASyPQAgAAwNIItAAAALA0Ai0AAAAsjUALAAAASyPQAgAAwNIItAA0d+5chYSElHYZRWaz2fTFF18U2mfAgAHq2rVridRT1owdO1aPP/54iZxr3bp1stlsOn36dKH9IiMjNXXq1GKtxdVzuOvnwJnvR1ctX75czZo1k91ud+txgasFgRa4SgwYMEA2my3P6+DBg6VdmubOneuox8PDQ9WrV9fAgQN16tQptxz/5MmTuueeeyRJR48elc1m086dO3P1efPNNzV37ly3nK8g48ePd3xOT09PRURE6PHHH1dycrJLx3Fn+E5ISNCbb76p0aNH5zr+xTq9vb1Vp04dTZgwQefPn7/i80VFRenkyZMKDg6WVHBI3LJlS4mF7LLu6NGjevTRR1WzZk35+fmpdu3aiouLU1ZWlqNPx44d5eXlpQULFpRipUDZVa60CwDgPh07dtScOXNytVWqVKmUqsktKChI+/fvl91u108//aSBAwfqxIkTWrFixRUfOzw8/LJ9Lgas4nbDDTdo9erVysnJ0d69e/XII48oJSVFixYtKpHzX+r9999XVFSUatSokav94vdKZmamli1bpsGDB8vLy0ujRo26ovN5e3s7dT3KyvdlWbBv3z7Z7Xa99957qlOnjnbt2qVBgwYpPT1dr7/+uqPfgAED9NZbb6lv376lWC1QNjFCC1xFfHx8FB4enuvl6empKVOmqEmTJgoICFBERISeeuoppaWlFXicn376SXfeeafKly+voKAgtWzZUlu3bnVs37Bhg2677Tb5+fkpIiJCTz/9tNLT0wutzWazKTw8XFWrVtU999yjp59+WqtXr9bZs2dlt9s1YcIEVa9eXT4+PmrWrJmWL1/u2DcrK0tDhgxRlSpV5Ovrqxo1amjSpEm5jn3xT7w1a9aUJDVv3lw2m0133HGHpNyjnjNnzlTVqlXz/Pm2S5cueuSRRxzvv/zyS7Vo0UK+vr6qVauWnn/++cuOYpYrV07h4eGqVq2aoqOj9dBDD2nVqlWO7Tk5OblG4+rXr68333zTsX38+PGaN2+evvzyS8co6rp16yRJv/32m7p3766QkBBVrFhRXbp00dGjRwutZ+HChercuXOe9ovfKzVq1NCTTz6p6OhoLVmyRJL0559/ql+/fqpQoYL8/f11zz336Ndff3Xse+zYMXXu3FkVKlRQQECAbrjhBi1btkxS7ikH69at08CBA5WSkuL4LOPHj5eUezpA79691aNHj1z1ZWdnKzQ0VPPnz5ck2e12TZo0yfHv1rRpU3322WeFfvZLOftz8MUXX6hu3bry9fVVTEyMfvvtt1zbi/J9UZiLv1x06NBBtWrV0n333acRI0Zo8eLFufp17txZW7du1aFDh4p8LuBqRaAFrgEeHh566623tHv3bs2bN0/ffPONnnnmmQL79+nTR9WrV9eWLVu0bds2jRw5Ul5eXpKkQ4cOqWPHjnrggQf0888/a9GiRdqwYYOGDBniUk1+fn6y2+06f/683nzzTU2ePFmvv/66fv75Z8XExOi+++5zhKi33npLS5Ys0SeffKL9+/drwYIFioyMzPe4P/74oyRp9erVOnnyZJ5QIEkPPfSQ/vvf/2rt2rWOtuTkZC1fvlx9+vSRJK1fv179+vXTsGHDtGfPHr333nuaO3euXnrpJac/49GjR7VixQp5e3s72ux2u6pXr65PP/1Ue/bs0bhx4/Tcc8/pk08+kSSNGDFC3bt3V8eOHXXy5EmdPHlSUVFRys7OVkxMjMqXL6/169dr48aNCgwMVMeOHXP9afqvkpOTtWfPHrVq1eqytfr5+TmOM2DAAG3dulVLlizRpk2bZIxRp06dlJ2dLUkaPHiwMjMz9d133+mXX37RK6+8osDAwDzHjIqK0tSpUxUUFOT4LCNGjMjTr0+fPvrPf/6TK1yuWLFCGRkZ6tatmyRp0qRJmj9/vmbMmKHdu3dr+PDhevjhh/Xtt99e9rNd5MzPQUZGhl566SXNnz9fGzdu1OnTp9WzZ0/H9qJ8X9xxxx0aMGCA03VKUkpKiipWrJir7frrr1flypW1fv16l44FXBMMgKtC//79jaenpwkICHC8HnzwwXz7fvrpp+a6665zvJ8zZ44JDg52vC9fvryZO3duvvs++uij5vHHH8/Vtn79euPh4WHOnj2b7z6XHv/AgQOmXr16plWrVsYYY6pWrWpeeumlXPvcdNNN5qmnnjLGGDN06FBz1113Gbvdnu/xJZnPP//cGGPMkSNHjCSzY8eOXH369+9vunTp4njfpUsX88gjjzjev/fee6Zq1aomJyfHGGPM3XffbSZOnJjrGB9++KGpUqVKvjUYY0xcXJzx8PAwAQEBxtfX10gyksyUKVMK3McYYwYPHmweeOCBAmu9eO769evn+jfIzMw0fn5+ZsWKFfked8eOHUaSiY+Pz9X+1+Pb7XazatUq4+PjY0aMGGEOHDhgJJmNGzc6+iclJRk/Pz/zySefGGOMadKkiRk/fny+51y7dq2RZP78809jTN5rf1GNGjXMG2+8YYwxJjs724SGhpr58+c7tvfq1cv06NHDGGPMuXPnjL+/v/n+++9zHePRRx81vXr1yreOS8+Rn/x+DiSZH374wdG2d+9eI8ls3rzZGOPc98Vfvx+NMaZv375m5MiRBdZxqV9//dUEBQWZmTNn5tnWvHnzAv/tgWsZc2iBq8idd96pd9991/E+ICBA0oXRykmTJmnfvn1KTU3V+fPnde7cOWVkZMjf3z/PcWJjY/XYY4/pww8/dPzZvHbt2pIuTEf4+eefc92cYoyR3W7XkSNH1LBhw3xrS0lJUWBgoOx2u86dO6dbb71V77//vlJTU3XixAndcsstufrfcsst+umnnyRdGDFs37696tevr44dO+pvf/ubOnTocEX/Vn369NGgQYP0zjvvyMfHRwsWLFDPnj3l4eHh+JwbN27MNfKWk5NT6L+bJNWvX19LlizRuXPn9H//93/auXOnhg4dmqvP9OnT9cEHHyg+Pl5nz55VVlaWmjVrVmi9P/30kw4ePKjy5cvnaj937lyBf4I+e/asJMnX1zfPtq+++kqBgYHKzs6W3W5X7969NX78eK1Zs0blypVTmzZtHH2vu+461a9fX3v37pUkPf3003ryySe1cuVKRUdH64EHHtCNN95YaP2FKVeunLp3764FCxaob9++Sk9P15dffqmFCxdKkg4ePKiMjAy1b98+135ZWVlq3ry50+dx5uegXLlyuummmxz7NGjQQCEhIdq7d69at25dpO+Li9MmnHH8+HF17NhRDz30kAYNGpRnu5+fnzIyMpw+HnCtINACV5GAgADVqVMnV9vRo0f1t7/9TU8++aReeuklVaxYURs2bNCjjz6qrKysfP8HPH78ePXu3VtLly7V119/rbi4OC1cuFDdunVTWlqa/v73v+vpp5/Os9/1119fYG3ly5fX9u3b5eHhoSpVqsjPz0+SlJqaetnP1aJFCx05ckRff/21Vq9ere7duys6OtrlOZR/1blzZxljtHTpUt10001av3693njjDcf2tLQ0Pf/887r//vvz7JtfQLzo4qoBkvTyyy/r3nvv1fPPP68XXnhB0oU5rSNGjNDkyZPVtm1blS9fXq+99po2b95caL1paWlq2bJlvne5F3SDVWhoqKQLc2Iv7XPxlx9vb29VrVpV5co5/7+Dxx57TDExMVq6dKlWrlypSZMmafLkyXmCuyv69Omjdu3a6dSpU1q1apX8/PzUsWNHSXJMRVi6dKmqVauWaz8fHx+njl+Un4P8FPX7whknTpzQnXfeqaioKM2cOTPfPsnJydxQB+SDQAtc5bZt2ya73a7Jkyc7Rh8vztcsTL169VSvXj0NHz5cvXr10pw5c9StWze1aNFCe/bsyROcL8fDwyPffYKCglS1alVt3LhR7dq1c7Rv3LhRrVu3ztWvR48e6tGjhx588EF17NhRycnJeeYZXpyvmpOTU2g9vr6+uv/++7VgwQIdPHhQ9evXV4sWLRzbW7Roof3797v8OS81ZswY3XXXXXryyScdnzMqKkpPPfWUo8+lI6ze3t556m/RooUWLVqksLAwBQUFOXXu2rVrKygoSHv27FG9evVybcvvlx9Jatiwoc6fP6/NmzcrKipKkvTf//5X+/fvV6NGjRz9IiIi9MQTT+iJJ57QqFGjNGvWrHwDbX6fJT9RUVGKiIjQokWL9PXXX+uhhx5yzNtu1KiRfHx8FB8fn+t7xBXO/hycP39eW7dudXzv7d+/X6dPn3b85cFd3xeXOn78uO688061bNlSc+bMcdT4VxdH410ZlQauFdwUBlzl6tSpo+zsbL399ts6fPiwPvzwQ82YMaPA/mfPntWQIUO0bt06HTt2TBs3btSWLVsc/0N/9tln9f3332vIkCHauXOnfv31V3355Zcu3xT2V//617/0yiuvaNGiRdq/f79GjhypnTt3atiwYZIu3J3+8ccfa9++fTpw4IA+/fRThYeH57u+aVhYmPz8/LR8+XIlJiYqJSWlwPP26dNHS5cu1QcffOC4GeyicePGaf78+Xr++ee1e/du7d27VwsXLtSYMWNc+mxt27bVjTfeqIkTJ0qS6tatq61bt2rFihU6cOCAxo4dqy1btuTaJzIyUj///LP279+vpKQkZWdnq0+fPgoNDVWXLl20fv16HTlyROvWrdPTTz+t33//Pd9ze3h4KDo6Whs2bHC63rp166pLly4aNGiQNmzYoJ9++kkPP/ywqlWrpi5dukiS/vGPf2jFihU6cuSItm/frrVr1xY41SQyMlJpaWlas2aNkpKSCv1zee/evTVjxgytWrUq1/UoX768RowYoeHDh2vevHk6dOiQtm/frrffflvz5s1z6nM5+3Pg5eWloUOHavPmzdq2bZsGDBigm2++2RFwi/J90a9fv0KXQzt+/LjuuOMOXX/99Xr99df1xx9/KCEhQQkJCbn6/fDDD/Lx8VHbtm2d+szANaW0J/ECcI/8biS6aMqUKaZKlSrGz8/PxMTEmPnz5xd4405mZqbp2bOniYiIMN7e3qZq1apmyJAhuW74+vHHH0379u1NYGCgCQgIMDfeeGOem7r+qqAbgy7Kyckx48ePN9WqVTNeXl6madOm5uuvv3ZsnzlzpmnWrJkJCAgwQUFB5u677zbbt293bNclN+HMmjXLREREGA8PD9OuXbsC/31ycnJMlSpVjCRz6NChPHUtX77cREVFGT8/PxMUFGRat26d7406F8XFxZmmTZvmaf/444+Nj4+PiY+PN+fOnTMDBgwwwcHBJiQkxDz55JNm5MiRufY7deqU499Xklm7dq0xxpiTJ0+afv36mdDQUOPj42Nq1aplBg0aZFJSUgqsadmyZaZatWqOm90K+rf4q+TkZNO3b18THBzs+J45cOCAY/uQIUNM7dq1jY+Pj6lUqZLp27evSUpKMsbkvSnMGGOeeOIJc9111xlJJi4uzhiT/w1be/bsMZJMjRo18twAaLfbzdSpU039+vWNl5eXqVSpkomJiTHffvttgZ/j0nM4+3Pw73//29SqVcv4+PiY6Ohoc+zYsVzHvdz3xaXfj+3atTP9+/cvsM6LN6Pl9/qrxx9/3Pz9738v8DjAtcxmjDGlkKMBACXAGKM2bdo4po7AmpKSklS/fn1t3brVsdYygP9hygEAXMVsNptmzpzplsfaovQcPXpU77zzDmEWKAAjtAAAALA0RmgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaQRaAAAAWBqBFgAAAJZGoAUAAIClEWgBAABgaf8P4r/Jioe8o0wAAAAASUVORK5CYII=", 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", 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" ] @@ -2201,16 +2195,16 @@ "id": "bb448b77", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.608831Z", - "iopub.status.busy": "2024-06-04T23:19:52.608714Z", - "iopub.status.idle": "2024-06-04T23:19:52.752561Z", - "shell.execute_reply": "2024-06-04T23:19:52.752274Z" + "iopub.execute_input": "2025-04-03T19:34:09.982843Z", + "iopub.status.busy": "2025-04-03T19:34:09.982768Z", + "iopub.status.idle": "2025-04-03T19:34:10.071618Z", + "shell.execute_reply": "2025-04-03T19:34:10.071279Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -2250,10 +2244,10 @@ "id": "baeaf16b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.754050Z", - "iopub.status.busy": "2024-06-04T23:19:52.753954Z", - "iopub.status.idle": "2024-06-04T23:19:52.757273Z", - "shell.execute_reply": "2024-06-04T23:19:52.757029Z" + "iopub.execute_input": "2025-04-03T19:34:10.073077Z", + "iopub.status.busy": "2025-04-03T19:34:10.072970Z", + "iopub.status.idle": "2025-04-03T19:34:10.076577Z", + "shell.execute_reply": "2025-04-03T19:34:10.076292Z" } }, "outputs": [], @@ -2281,16 +2275,16 @@ "id": "dc2d10e4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.758473Z", - "iopub.status.busy": "2024-06-04T23:19:52.758407Z", - "iopub.status.idle": "2024-06-04T23:19:52.824936Z", - "shell.execute_reply": "2024-06-04T23:19:52.824706Z" + "iopub.execute_input": "2025-04-03T19:34:10.077984Z", + "iopub.status.busy": "2025-04-03T19:34:10.077903Z", + "iopub.status.idle": "2025-04-03T19:34:10.127425Z", + "shell.execute_reply": "2025-04-03T19:34:10.127113Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2336,16 +2330,16 @@ "id": "01bf456b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.826162Z", - "iopub.status.busy": "2024-06-04T23:19:52.826082Z", - "iopub.status.idle": "2024-06-04T23:19:52.888028Z", - "shell.execute_reply": "2024-06-04T23:19:52.887787Z" + "iopub.execute_input": "2025-04-03T19:34:10.128975Z", + "iopub.status.busy": "2025-04-03T19:34:10.128845Z", + "iopub.status.idle": "2025-04-03T19:34:10.171730Z", + "shell.execute_reply": "2025-04-03T19:34:10.171416Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -2377,17 +2371,17 @@ "id": "9d9b7fe8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:52.889367Z", - "iopub.status.busy": "2024-06-04T23:19:52.889276Z", - "iopub.status.idle": "2024-06-04T23:19:53.700461Z", - "shell.execute_reply": "2024-06-04T23:19:53.700207Z" + "iopub.execute_input": "2025-04-03T19:34:10.173144Z", + "iopub.status.busy": "2025-04-03T19:34:10.173050Z", + "iopub.status.idle": "2025-04-03T19:34:10.940131Z", + "shell.execute_reply": "2025-04-03T19:34:10.939836Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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/JxSu34SlN90mUowMM9lQKFWS7k8ml0OhknafmO7C87XXXsPp06dFqt0TfvGLX+DnP/95IA+JYSaF6GweVCEsK3LM7bRJcRMrOofJuT4dzSWRGO8yseVQPUdGpwANZSXY99JfYDFTN65nHbm2zt2Sg3uEGF1+610BPkqGkZ7Y1FSX6391810O6XVbpPOXN9wG9TglJrEpaZhuBEx41tbW4jvf+Q527doFrVbr0XN+/OMf4+mnn3aIeGZmZgbqEBlmQuks60KbiyghiU4Sc5MJTwRwMygyWu8yMjrr+owAHRkjJdQ4dPC1F4aFpG82MKWH9yElfzYyC+dLfnwME0giYhOgDo+AfqB/7NrNYUh0unuMoIuwxKxsTDcCJjxPnTqFlpYWLF26dGSdyWTC/v378dvf/lak1BUKhcNzNBqNWBhmOohOSk9b4mJGRTZzQiCKGShxSuLTOTKqO1cP7Kxj8TkJOPXJ+zBQOZQb70FqsrDVu1EUyOVJVybD0bdeRdqPfgalSh3oQ2YYySBnnvzlq1BycK8klkrh0bFIyZuJ6UbAhOeWLVtw4cIFh3WPPfYYCgoK8MMf/nCU6GSY6RTZpPpMEp2TLbIZiMgoidGWYfFpg0Vo6DHY24Oqc6e8PuH2KiJRGZGDNlUiulTRMMkUUFjMOPTKcSydnYEtBcmYERcesONmGCmZvXo9Sg/uhwX+C8+5GzdBJpt+WihgwjMqKgrz5zumUiIiIpCQkDBqPcNMt8hmqNRnhmIklCOgoUnVudNuI52uGJRrcSx+FRq16ZDBDAt5x4gFMMuAKz0WVJ6uw5sn67AiJw5PXDsTSVGelWUxzEQRGZeAxdffhNPbHTvWbVCU37l73VVTUVxqOuas2YDpCBurMUwAI5zTNbLpLfYinCOgoUlbbZVIkzuLT3cehp/Eb4RMEQaLQSfuK1TOolIGW/D0VE0nvvnyaXz/utlYnZcYyLfBMH5TuOFaNFdeQcOli16PvJTJ5VBpw7DhwS8JG7LpiMwSwoNCqbkoJiYGtcfLEB0ZNdGHwzA+RTg5suk9zSVXK0EpApqcHs7ic4L54L//FT1tLaPW00xqT9j4xJtjPi5ioTLgRzcWYO1MFp9MaGMyGnHojZdQc+GMx88hG7GwqGhs+coTiEl23SE/Wenp60Xmytno7u5GdPTY5zyOeDKMn3CEU3o4Ahp6GI2GgO6fIiAyC/Dfn15C/sORSOa0OxPCKJRKbPjCo6gqnI8TH74N/eCAy4yALcpJMb78FWuEj61KM71/2yw8GUaCCKdtehDXbga+BpSmJXENaPBRa7QYGMfDcG/Ucnzw2k/E7dWPPguFyjuXEjplG80W/HZ3Of7pDu4FYEK/yz13yXJkzV+E6gtnUHX+LNprqzE0YA1EyJVKxKdnIC2/APkrViMiNm6iDzkkYOHJMH5GOCd8etA0wOHzFdOSGjgCGmTiZ2Siu7Vl1JQim2VStzIG7ZFXfZdJdDrXdZoMOhx69hFxe93jf3NR9wlR93mmtgtV7X3ISRh7iALDhAI0fShv6UqxECaDARaLWaznKV2j4U+EYXyIcPYkxYmFRefEQKUMtu+ADPc9GTHK+EdSTq7b0ZhERXiO6F6XArkc+PQixblDA4vFhP7ODvS0NmOwt9vrhhJmekGCU6nWsOh0A0c8GcYDsWnDllZnsTnxjHwHdhFQmgdvg0dzSkvOgiU48cE7MLup9WzVJMIiUSyDop4X68cbwhpYjIYhVJ07g4qTx9BeXwuT3ftWh4UjOXcmZq1cg/TZhSwwGMYLWHgyzBhQJM3WMESEsegM2Qgoic/mi0Pivqyzm+fCSwxZwMxZQ+bZe11G/LpV0VBAOap7ndLrV28PubxNOKfdqzscxxIGk9qiczjy7utiNCLV8Tm/X2okqS8tRt3FC6I7ef0DjyAubXKVe5hNRrTVVKG9rhZ9ne2iJyYsMhJx6RlIzskT4pphAgELT4ZxE+GkOk6u4Zw82DsJVO20RqdZfErLoq03oubCOQz0dDlMMCJZZnZzOrHVdDpz9IXHHe47C1ajCTBbLJBTp3AQU+oU1S07etDaoSzWuU6r294/WUx98tv/xqo770f+8tUIdQw6HS4e2CPeIzXBkLC2Ts+xWP9nNkOuUCJn8XIs2LQNUQlsbcVIC+cHGMYpwkmCpfjikKgdZNE5eUUoRanpu7QvlWD8g+rWrv3iV6BUqoVFjA3rTCL39Z++QLsPnuS0cuKDd62ik/CwjpOEGi1H334VlWdOIpRpunIZH/z631C059ORzmsS1mazEWazaURMUzS08swJfPjrX6D08H6uaWUkhSOeDOOUVmexOZXS71cjnzY4AuoflFK+7hvfwp7nnxHz26l7l4g29KJbNfqzpe51+/S6LdI5nt1SZly4iMYFi9qL51F29IBf+zj6zmtIyslBZFzoRQlrLpzFgddeEOFp23c2FtRIRnLz5Idvo6+jHctuviOo3wczdeGIJ8Ow6JzykU+KYNPCEVDpxOetT/8Ys1atg1yuEIIkSd/usqudajevLppRdku2xTnaOTcteP8Oyf7m6Nuvj6TXnaGxoDShiRb7EaHOUNTw+HtvIRTHnZLoFNFZD0SnM6WH9qL00L6AHBsz/eCIJzPtYdE5teHaz8BA01dW3n4PFm69AZWnT0BeXo/yPum62tflJyBYkPm3LfXsDyTsGspK0NveiqiEJITKaMfDb7zs937O7PgQM+bMRXRSsiTHxUxfOOLJTGtYdE4vuPZTerQRkSjcsAkPfOkhzEmNFNFKKfinjy7ixSNV0FOXUYC5cvqEZGlkqn0NpVrPijMnRAOUfTOYL9FcipSe370jgEfKTBdYeDLTFhad0xNn8WlbGP8g4fadLbPGbAiilDp1r9PiamqRPXqjBW+dqsO3Xj2Duk5XwzqlgRpn2mtrRjXQkAizLXrTVUFGt+0fc7W/ttpqhAqXDu93W0LgDSRcqU5U1+9/ZJiZ3nCqnZmWsOic3tgaj6juk2DfT2nIjI/ANzfl4/92l0uyP9KCTT06/P1b5/Gf9yzEjDjpvSV1fT0w6q96jdp4+uN3XG7/ox0fONz/3e33OW5gsaCrqRGhgK6vF11NDZLtj2pYmyvKkb1gsWT7ZKYfHPFkpg22yBaLTsYmPm0Lp9+l47q5qXhqc75IuUuRdqcMcb/eiH/5+CIMJlNAaiAl36ddhHQi6WhwPUrW12guNZJ11NUE9JiZqQ9HPJlpOIFIxROImDGtlzjy6R/Xz0sV9Z6/3nUZFa39kojP+k4d3jxZhwdXZUNK1Nowl+t/dfNdDoLMFun85Q23Qa0Y+9SpVKsRCgz2dLtc72s0lwzmB/smdpQpM/nhiCcz5bGPcNoiXCw6GWc48iktOQmR+PV9C7AhsnPM7Wik5v4/3CsW+/GazlAF5ntnGzBkkDbqSaMhw6JG/z3QKJUji73QpNv2j7miv6sT53Z9LIzYJ5SA+G6ylyfjHxzxZKY0nFZnpnvkk+aKt9fViLpDo2EISpUasSlpiM/IhCY8MqCvfeL9t3G5TQGZ1gyLBHGOQb0JhyvasGlOCqQkJW+WsFRy1/ntNRYLLuzZhdqLRdjy5SdcCttgEBEbL3E0V+Z2nwzjKSw8mSkLRa1YdDLTVXy21lShZP/nqC25IASVdSa3XHRdkzUO3Z9ROA9z129Ccm6+5K9fe/ECyo4fRmvanZKITkIhBy429EouPGetXIOqc6ck3SeJz+6WJnz6p9/ghie/B01YBIJNwowMl+vdRWpt0dyxphnRBQvD+AMLT2ZKQ6lTFp3+YTCacPhKO87UdOFScy86B/SQQ4aUaC3mpEZhdV4CFmfGBCitNzFMZvFJUc0zOz4SNjrkKWmL4lkF59U0Nd2vL72IuotFmLVyLZbefDtU6rEtjjyFUszH3n0d/cpImGSjTzP6gS4cfeGr4vaKB3/nMFLTHmfLJZMZKG+R3s6HhHdCRjY6GmpdRj1JjI3qXvcA2ldfZ7uYZrThC48i2Ki0YUjOnYnW6kpJorkKlRqpedJfpDDTCxaezJSlrZJOUKqJPoxJi8VswSdFjfjbkWr0602Qy2Qw23kd9rb2obKtHx9faERatBZPXDsTS7LjMFWYjOLTMKTD7uf+gPZhH8nxxIbt8fKTR0U6fuvjT4qaRyminWTlY3Axu52wiU7ixCtP2q23znG3QX6fzlCHu9RQ9HftfQ/i4//5T1hcjP30B/qMq8+fxsxlK5E+uxDBpmDtRrRUXvF7P3QRk798lZhYxTD+wM1FzJSu7QzLCmwN21SlT2fAT969gD/uqxCik7AXnTZMw+uae3X4hw+K8ce9V2CisNQUYTI1HFEEc//LzwkB6WyGPu5zzWZ0NjVg7wvPOERFfaXq7CkhVBQS7MsZNeXbA0BMUirWPfBwQCL39FlcPPA5JoLMeQuRlJ0rjsEVtmguLWOl2ak2OGfxcvR3dkjyG2GmLxzxZKYc3FDkH306I3709gXUdg56/BzzsM755EKjeP73r5sNmXxqpN4nS+Sz/PhhNF6+5PPzSXy2VFeg9NABFK6/1u/6UtpfpLkfMphggcKhY33FQ7/FiZefEreX3fcrnHrjaev6B38Hdbj7f7OknXITpTeRt5G9YAnkCgUOvf6yKFkQDvYSQJ9FU3kZBnu6EBYd3N8P1fWuvfdhfPKb/4TRqPc55U7R9J1/+LW4LVcqEZ+egbwlK5C7ZDlHQRmv4IgnM6Vg0ek/v99bLkSnqwjneNAz9l1uxUfnpZuWEgqEeuTTaNDj9HZHD0ZfZ3Kf/fRjITJ8xWQwiDQ7IYcZsQbykrTg0LOPjCw20UnYRKct7U51nbbFGfpJzkqJQiDJnLsQt3//x0jMlNYvlGirq8VEEJWQiM1feQIKpcpt5NMbzEYj2mqqcfz9N/HOv/0MFaePex1lZ6YvLDyZKQOLTv85XtGOA5fbfBKd9vz1UBVaenwXL6FIKItPmqHtj1i0x2TQo/LMSb/GKtqT218FqaAs+IZZSQg0FJXMKJgviUizQfvqbm7CRJGUlYObv/V3SMjIsh6PzN/3Zv0bYdDrcPjNl3HwtRcn3reUmRSw8GSmBCw6peGNk3UYK0NuNuhQ8fxdYqHb7jBZgI/Ph8a86ukgPqkzXTKRJJOhrqTI56cr1SrI5VeruHIGq6GyGLHu8Rew7vG/iWX1o886pN1trH70Gbf7pbd3zewkxIUHZyqQyWiATEKzdNqXyajHRBKVmIzrv/FtrLv/EUdbJGG15d97JR/Ug6+/xJFPZly4xpOZ9LDolIa6jgFhlyQFFDHdUdyER9fmQD5Faj1DueazrbbaZe2efUrdeSa3PQ5NJRYL2vyYxy2TKRCbmjoyJ5xE59Ku0zgWt8rl9uqwGJfd645YEKZS4svrcxEsVBqNGBEpFbQvpVqDiYa+n9zFy8Uy2NsjLKT6Ojpg0OlQtHcXjHpHSyuPsVhQc+EMymfNwawVa6Q+bGYKwcKTmdSw6JSOkkZpZzAP6E2o7xxAZkLwjbODIT6bSyJx8VB9SIhPd/OzfZ3JrR/oF2lT+Tgzyd2Rlj9HdMnbxHD2YA2aNSmoCqe6SW8vRMwiWvh3181BbFhgo53kCFB78Tzaa2sdjl8KaF/xaTMQStBEpRlz5onbh996RUR5/eXkR+8ga/7CCTHMZyYHLDyZSQuLTmkhT06FTDZikWTDPqVuMTretj8ty100g1S09U9J4UnQb64ZCA3xGWLpzfwVq1G8f/fIfZKaK7pOQgYLKsMpaunZ8cqE6LTg+1vysDwncKMayWD9xAdviSit1XSfjk/az5T2G6pTf3T9fag6c1ISoU3NZRUnj6NwwyZJjo2ZerDwZCY1LDqlY8BAtjejqXr5QZfbV7/+ZYf7eV8aHV0bNEwdT89QFp/ayCiRNpVqJjeZyMvkCr9qCXMWLXOYfy6HRYjP1KEmnIxZio1PvDEs7uQuBSeN2YzXd+DBPBk2znU9+tFfaHTouV2foGjPrpEaWSmjnDbos8yctwCa8ND0FabmNLNFujn15aeOsfBk3MLCk2EYgUZJgzClRa2YWvWdoSo+yfqHGoKcGzt8msktk4nOZ3+bTVbcdhcaLl+CQTcwIuZoj1mDdUjXNaEmLANVYdnoUMc7jNUMMw0iWd+M/IEqZIWbsPmWHyEQ0Gd1/L03cfn44YAJzpHXMpswN4SFWFttjehyl8oYvqelSVh8kek8wzjDwpOZ3Gn2pKkzonGiyYwLd2mjlPPQKw7pdVukM/v+5yBTjm0cnRU/NdPsrsRnVY30M8Q9Jb1gnhhTKRUzCkhC+wdF9zZ/6avY9ezvhO+jvbBTWozIG6gSC/3iBuVhMMvkUFv0UJsNIvpIc8a3PPZtKFWBacgpP3FkRHQGEhLwBeuuQWJmDkKVrmaqZzVJKup721oRF2I1rUxowHZKzKSDazsDw+yUKJepdqrdtC32QpNu2z/mjFIuQ3ZCWICPmiFyFy2VrGOaJvfMXLpSkn2R2Lr+699BRGyc2wgqrQ03DyLS1C9EJxGTlIIbvvFdMcYyEAx0d4kmGHfQvwO9XAW9XC2qTH2FoohJ2XlYfN0tCGWoLlPyfUrQqMRMTTjiyUxKWHRKz+yUSKTFaNHUrfO7rUIuk+HaOUlQKX2vE5yMTJTFEonOxdffjJMfuhdTtpnc47Fwy40i2igVNFrxlu/+UNRRXjp8QBjdy+UKB6N52311eATmrt+EuRs3+dxR7wmXjhyA2eQY4TPIlKgOy0JtWAZaZeHY9xdrZH/9488jAXpRmzqzvwIRpgGvIsfrH/giFCoVQhlNuPRjSNUS/oaYqQULT4ZhrMhkuGvpDPxuzxVJUm23LEzHdGKi/T3nrNmAmqJzokPbl3pF0XWdnoF5G6WvRaR0OUX95m+6TtSittdWo7OxQdQB0pxvshlKzMrGjIJ5ARWcBNUxUord9hnRRdaV8DycjVkEk8x6oWSiOe227aFApyoOXaoYlEQWIK+/Aot7zgt/UmesHfFmaCIisfzmO5CzeLnftbLBIC49Q4zAdJ465Ss0yz0qMVGSfTFTDxaezKSCazsDy3VzU/HZxRZcbulzWe9JKXVX3esO28iA2xalY2ZyaHbwTlXxSWndax95HLue+S26mhu9Ep8kmKgTfdOXvu5XN/t4ULNJzsKlYgk0tkYrZ+HX09oK/aA1ammUKXE4bjUatWnDEtS9SKQue6IyIldsf237fkQbh2fSK1WIjIsXpQUZBXORMXd+wAW0lCTnzMSlw/sl2Rf9lpKycoVRPcO4YvL8y2CmPVzbGXhoytAPrp+D779xDr1DRq9ntlOKfWZSBB5ZQ0bh05OJFJ9kg3Td17+Nkx+9hysnj4xE4Nxhezxn0VKsuPVu8fzJSp/OiN2lzThV3SkunOg+yci4SBXmpERj7cwErMtPGJmoZIYc++PXoU2TOCrK6e42oVBpoVNosTvxWmxr3YNoyyDmrFmPZTfdgclKRuF88d3bBLk/0O9p9up1khwXMzVh4clMKlh0Bp6UGC1+efcC/L/3i9DRr4fw0vaQgtRI/L9b5kE9zWo73YnPiYBS12vufgC5i5fh4v7daCgrEespkklCTFijD6dUU/JmYd7GzUibVYDJisFkxpsna/HWqToYTdYfq+0nS//t6DPgaH87jlxpx5/2K3FrmlGsL44qRKsmaSTKeejZR1zu/+gLjzvcp/GeFP00yNU4HLcK13Xsg37w6mCFyYhCqcS8a7bgzI4P/doPXchExMYjc+4CyY6NmXqw8GQYZhQZ8eH43YNL8dyhSnxa3CzSle6in7LhDvYvrsnBbYvTp9xs9slK6sxZYhns7RY+jV1NDTDq9VCqVYhNSUNCZjbCoyd+zrw/tPcN4WcfFKOmfWDMhjjbT7dfZ8SrlUBy/Aa0aJN9GN9pt0/I0amOQ3l4Dma780SdRJDPaNW5016XadhDz1t3/8OTqsyACT4yi7PjcAjR09ODmJgY1B4vQ3Rk1EQfDjOBcJp94mjq0mFHcSNOVnWitnNwRICSOXxeUiTW5Sdia2EyIrWh3bkbbKp2NkDW2Y2565InfJb7VKRzQI+/e/Mc2vqG4L1Osp32rgpPk91oWP1gN068/JS4veKh30IdFuOQarffT7ixH/+4KhyFazdistPX2Y4dv/81hgb6fRKfK267G3PWTP7PgfGenr5eZK6cje7ubkRHj32O5ssSJuRh0TmxpMZq8aV1uWIxGE3oGzKKCGi0VsXRzRDucp9KdLc2oe5iEdrra9HX3iZk407lArQaI2DxKWo5+jn2glJhJ0IVSo2T2HTcz4AyEp1hlLKf/ETGJeCGb34Xe55/Bt2tzVdDxeOk16mxbeUd9yJ/+eqgHCczuWHhyUwKWHSGBuTLGTfN6zdDrdaT0s2HrrShvKUPtR2DMJrN4qIgPyUSC2bEYGlWnGj6mox01Nfi5CfvoaWi3GGWek1YJirjpHVNsI94mgx6h9v2jzmLUBksaLJMnQldkXGJuPnbP0Dx3s9QvP9zGPVDoj7YYbKRTCYuPum7SJ05GytvvwdRCVNDfDOBh4UnwzDMJKS1V4e/HKzE4Svt4j5pS/vsaHFDN945VY+ESDUeXJWFbYUpk8JTkqAKsKI9O3H+sx3WN+Y0S70kco7oS3c3fI+Eoq1ZaN3jfxsjYnkVd81FJ155clRzkQMyoLbD/27wUIJqNBdsuQGFGzah+sJZNFdeEd6ret2gmGxFNcKJGVnCDYFsuBjGG1h4MgzDTDL2lbXg/3aXw2A2j2RDnbOipmGd1t6nF9vuL2vFD68vQFSYKuRF58kP3xbThYZXODzerYxBlyqUfHxlGLJ92BOMxWJG4+VLqC0+j/a6GvR1doj12qhoJGZkI31OAbLmLfS4+YcmYs1ctkosDCMVLDwZhmEmETuKmvC7PeVeP+9CfTf+/u1z+I+7F4W0+Cw/ceSq6HRBmzp+XLN3X6DIqL13p81GafWjz0Kh0rh9HpU5h6snvvyEJkKd+OBt9Hd1jBpJSv6cvW2tqDh9TIwlXXLDLchfvmbSRMCZqYXrPAXDhAi2xiKGmcxQg1FnWZff+ylp7Mbv93ovOgnKVDd06/Cfn14ameoTagx0d+Hkx++OuQ1FPGUizT46vX51cTSAt3/MHZSOv7pcFZp02/4xV59rTsLE1XiaTUYcefsV7H3xGfR3dw4f0+jRl7YaTf1AP4698zo+/+sfYdANBv14GYYjnkzIwt3szFRAqu52g8mEX+26PGIC7wskks7UdOHz0hZsKUxBqFF6eB/MxtEz0G0MGY34y99+4rJ20xsDeCmh76IwbWL+PtHc+QOvPI/akiLbCo+f23SlTIxX3fb1b0GlHr8GlmGkgiOeTEjDopOZKuLTEheDtso+n/dxqLwdTd06t5OkKJq3/w/3imWsyB7x6vGakIt6UuTu8rHDPpuXTwSUqc5OCENe4sREPIv3fo7aixe8Epw26HPubGrAiQ/eCcixMYw7OOLJMAwTBMKyIoFWayrUFz650CiEjhR6sblnCBfqurEwMzjeoiRy6bDHsnXqam6CYUgX9BpNV1Ak1ZPIKH0X9y7PnJBayZ7WFpz/bLtf+yDxWXHqGHIXLUPaLHIKYJjAw8KTYRgmxKE0+6XmXklEJ0GWmEUNgROeJDQvNvZgz6UWXGzoQb2YeAWoFDJRDzk/Iwbb5qYgMy585DmdDXVu0+s29Kart+3rOAl3lkm2Gs2x8MV+iT7DpVmx2DhrYvwrSw/tg1XO+wf5oxbt28XCkwkaLDwZJtBYLCht6kFRfS8q2/rRP2QUJ2Cahz47OQrLcmKhZlN2ZgxqOgZdjoV0ND13bKixx1lIWcwWFFU0wrx8huRzta+09uF/PytDZduAEGf2x20wWXC5pQ9X2vrw7ul6rMiNw5PX5iMhUiM6r0kEOafan/74nQDWbvom3KiTPSVKi+9snT0h0U6T0Ygrp49JUpZA+2i+chn9nR2IiCPHAIYJLCw8GSZQWCzYe6kVr52oRX3XoDhZERT5oZsnqjphslgQrlLg5oVpuG9FJrQqFqCBhATX6ZpOsZQ196GjXy++i9QYLWalRGF1XjzmpESNmJZLDTXLJZZ1ed1g1K9z3XDja0MNjZlsaGjC27/4GRZsvg5z1qyHTOb/b++Dc/V49kDlyMfnThfZ1p+q7sQ3XjqFH99UiHCFIuh1pzR1KGOgFlUqbxqtLJgRIcO/3rMQsWFqTARdTQ0wGQyS7rO1ppKFJxMUWHgyTADoHTTgV7vKcLK6c8Rt0L4phG6S6CQGDCa8fboOe8ta8Pc3FKAglZupJMdiwYHLbfjr4Uq09uqhkMlGPn+ipXcIRfU9eOtUHXITw/GNa2ZibnqMpIdATXJVNX0+dbcrldILYbnFjKH+Ppz88B1UnTuNax56DGHRvqfe3ztTh78crBK3PdWPJECHLGb8/MNifGtJrMsn/urmuxxS7T/a8YG4ffsD/4r2yAxYXPTIelKjaTIMorCvFHN7S5GiiMMh2zEZBkbti+ybSKwrLUbM6yvFrTnJiAtXT4hBvNGgF+bwUkK+n52NDchZtEzS/TKMK1h4MozE9Awa8Pdvn0djlzUN6sk5mEQpTZj5ydsX8I+3z8PCjOA0fUwHdAYTfr2rTIyWtMk3e9HpfCFQ3T6AH719AXctmYFH1+ZAZgtVS2itRN3t3gjP9NgwSRtqZDAhxtgzcp+EzPY//A9ufOK7o8QniR0yH+9qbhRRNqVKjdjUNEQmJEIms4o+KiV57pBVdHqL7at4tqgfW+VaaM2ODUYapevT1Kr+IuyPSsegUutSfI4FCclDz35xRGzac+SFrzvcv+Grf0WCoQOpQy3IGqwV4rO3xRJUb9PLx4+g8XIpOhrrxrSb8gfDkGN5BsMEChaeDCNxKvcXn5QI0Wn2Mm1ojYha8M8fXcTvH1qGpCjvOnGZ0QwZTPjZ+0Uober16iKAePtMPbp1BnxnyyxJU+++dLdTSjcuQoXOfoMkDTUk1OIMnQ51foM93dj70l9xwxPfFmn33vY2MUHoysmjLrvN1WHhmLViDfJXrcOvd1W69Rf1pHGH/qkMGsy4OGM9ltV/7lHtotY8hC1te7E/cb0wlfdskpF14lG6rgGecmvL6M5xs9EgPDR7WkmQN8Fk0EOpVosZ5lGJSSOC3B/oMz/9yQe4fOKwqCMNrM2UDAoVywEmOPAvjWEkZHtRI4oarkaSvIVEj95owW92X8Y/3z4vYLWGY9Fc4vvxu2IifVj/crBSiE533pfj8VlJC2YlR+KmhemYaMjw/Z1TdT6/F3vkMCNDV++wjoRNe20VSg7uF1E1suqhrml3goeagS4e3IPPTpaiIXat38dEL1OOOBRAg3B4NlEn3DyI61p2oziqEKVRs2GGrU7V8d+NNVUuh8qsx5Kec8gZqMYKNyn8X95wG9RjNVzJZBjo6cYb//gTGPQuBLk2DDNXrBZ1s5FxifCF7pYm7P7LHzDY2yNUeaBrX81mI2KSUwP6Ggxjg4Unw0iE0WTGK8drx9zGbNCh6uUHxe2ch16B3NUIPosFZ2u7hGAqCPJEFBKdYaf3ICpOmmhrb+cQqs/NRvYDhQg2RXXd2F7UJIl4XZGbMOER6BvmpeKd03U+NWLbRx3XP/4CcvVNUJtdN6ec3fGRECIe+0Bqs0aEnd/IgI7sFYioPuhS8FLa/Xe33zdKRC/oLcac/jJUhuWgSZuKDk0i9MOnt7gIJcLbq5AxWI+swTqxvW1friDR6e4xgcWCvo52tw/rdYPC6ujS4QNYcv3NKFh/jVeNW73trdj5p9+IcZZU5hAsEjOzg/ZazPSGhSfDSMSJyg50D0rTaUpG29svNAZUeLqKbJLozFtZjfil0pyEDJ3dqDnyPupf7EDqF9chmLx+slY4CbiLEHpyEUAYzcBH5xrw2PpcTCQp0Vo8sCILrx6rcak9PTU9V5oNWNQ7PGLRBZ6KThut6sRRotNXmydiMCkf2vZz0PX1epVeJiFdMHgF8w21uOGL30N0krVTndLUn/z2IDo76yTxvfQEOm4LzDj1yftoqarAhge/5JFtFc1TpxGYQnQGaYITfT6xqekc8WSCBgtPhpGIc3Xdo7qlfYWinqdruhAoqnY2IL7txKjIZtLKaiRsWQlZlDQiS5MFZGEngAOofxFBE5+tvUMiaiwF9F3sKG7CF9dkQ6GQS9bdjp11mHV9hlfPvXd5Bs7UdOKSH+UDK7pOQWOWppFEL1dhSK6VzubJAlR3DeHvvvZt7Hrm/0Sq2VMBRh6gKrUGW778xCgRNXv1ehx9+1VMBDRH/eg7r2HtvQ+Pu23p4QPocGOkHygojT93w+YJ8SNlpic8q51hJOJyc69L0UmRNdtiMV6NBNFt+8ec6Ro0oHtAL2mEkxYSnRmDn4jI5pz7hxwWKUWnDc2i65G1Jh2J0SXitYPBxYZuSfc3oDehttOzukNPu9ubB1W4vNM7kaGUy/GPt81DQVrUuNtS1NG2mA1Xjz1hoF5MA7It/mAMQOxiyGhGVEIibvnOD5G7ZIVYJ5OPkaoebuSJTkzGvGu3ilT30ECfwyY5i5YiPDrWrbiypfBpGTPN7gsWCypOn0BN0dlxNjOh5MAeBBMS6yl5s5CzeJnXYrW/q1M4HVATGkVqGcZTOOLJMBJBQtEVtnSuM9Wvf9nhft6XRk9o6dUZECOBX6B9hFOrbUbiUiMil0kvMt2hzpuNrKVNqCsJysuJCVGuos/2An/URYDddq7S7pVtfchJjJjw2e3haiX+7c4FePt0PV45Vi0in66C7O6ijrYmGhvONZPeoIRR8rnpGqV8pGt+7T0PonDdNSg7ehA1RedHCUpRFDpcB9nd2owzOz4ceSQ5ZyYK1m5E5vyFwgJq7X0P4bNnf4cJQSbD8fffQkbhfLcp9+aKKxjo6QreIcnl0EZEYt19D3sU7SRxWVd6EZePH0ZrVYWD0wG9p/gZGZi5fJWY+65UsyMH4x4WngwjEVSXKTX+pL9sNZyDNX0iwpl9nREReWlkzgNZ3JygiU4bsrAmyDopEhn4DvF+vUnSiwD6GvqHQieqo5DLcd/yTGwuSML7x8qx63wd+pWRQT8OqqvUmHWj0u2+2jzR55znJO7j0mZg1Z33i2WwpwtV587gwp5PRWTTQXE7qW+axNNSdQXJeflCwKbOnI1lN92BU5+8h6BjsYia1fqSYmTOX+Ryk7aaapcjQwMB/V0Jj4nD1q98E+Ex4/vJttdW49CbL6GntcXlMZpNRrTVVqOtpgpntn8ovqvsBYsD+A6YyQwLT4aRiIy4MDT36kZFn6hxxT6yZhM52fc/B5lSO6aQTfaxk9pVhDPq2jsxUZDIVceVIW/G+6h4DQHvcldRV5E7Y0kfoO9UJUF9p9QkRGhwS54Wms+2Qy9ToU8ZAYtMDrVJj9tvvmvEVMgruyAvSdK3oV6bLk1XO+C2oY6E25mdH6Pi9HGP9mMTR61Vlfjk//4TWx9/CoUbNkGp0eDEB2+LjvFgNfAQJNiqi865FZ6Utg7GMVCafPaaDVhy/S0eRSYvHzuIY++/NXIR7PYzG/7Dpx8cxIFX/oqmleuw8o67JRnFykwtWHgyjETMTokUDUHOnbP2aVv7P9kkOt11UhOZcWFQKb3/o22r4bSPcMqzbsBEI2o9qdHoSODFZ0Z8OMwuum98vQggMuNdTw+aKHpam3Hk7VfRWl0p7qstBsQb7FK1vtoFeUlufyXqtN41SY0FRXGdPS3P796B6gtnPZ/F6ZQipqk8lGanutFZK9ciNS8fJz58Gw1lpUKM0T9Ze+siGiFpprpFElsSeWiSYKOooDvIhD5QQpjeT0xKKmYUzMOslWsQEevZTHYaHnDsPWsDmOdeotbtLh8/JKZ+rbz9Xp+Pm5masPBkGIlYl584ro+np1DA7to5jifgsah+rQQafYe4nRFdgux1ExvhHEt8pna+iwpH73LJmZ0c5TLY6etFAH0feYnSp7KpwciX7vbKMyeF6Aymz6M7UoeaEWXsQZ8y0ue56bbPeP2sRCRGakeEzsX9u3Hu009gpvfphwAkQUc1ifSZbX7sG4hKTMbmx54QnpnV58+ivb4G3c1NMJvNou4xISMTybn5InInJdSQ4w6lRit5qn3d/Y+IEgNNeLhHdk729La1jIhOX6Ha3LRZc5A5d6Ff+2GmFiw8GUYishIiMD89GhcbyerGvygJpdm3zrX6EHoiOimFTZ3jVtTQLLoV05n85Aikx2jR2K3zO9tO38XqvHho1dKmDMlWqZnEp5fWSpVnT+LQG1ebdyYaOSxY1XUCnyVu9ms/9E/GZLagpLEbBalROPbeGyg/fsSj51J3/tMfW+tyf3XzXS4juiToaN55Y1kJ0ufMFeuiEpIwf9M2l/ukukXJGeNCITZFeh/NlNx8hEX55gVMonOsCxtPPnOKGB9953Wkz54LhdRuAcykhX8JDCMhX79mJr7zmnvbFIqqueped4Y8I2PH6Ga3j3Dm5R4QopOiiZMBdVx44Gs9ZTLcsWQGfr/3it+7oouI2xYFpiHKJj497W6nKNSRt7z3o3Q18UdKEvQdWNJ9DmdifG8ooQuEIxXtOFTejtwIE+ZWnIfUMWaKKJYe3j8iPMfeViFqII16aTxPCXWYe1eEpOxcSaOd2qhohEXTDHvvofKGpitl/h+ExYKh/j7UXDiL3CXL/d8fMyUIvWp5hpnEkN3OY+tyfH4+pRspanr74hnjRjjXPHhFLJNJdNr7etJ7oPcSKK6flyrmrLtzG7BdBNDiLs1O38eWwmTMm+HbCVxqjrz7+rhRqCfff0Ms/np0esvs/stY0n1GjM+U+Rhntumuqn4ZtidfhwaNtFFAW9TTaBjfH5eaaeJnZEr34jIZEjKzxhSekfGJEr2UHLNXrvXZFaPq3Clr7askxyLzuCGMmR6w8GQYibljcToeXJk5Ilw8hTalFOM/3DoPcjdPtE+rk4CzLZMNUetZML6Niz/QZ/h3189BmEo+0t3t1fNlMqTFhuGrG/IQCnQ21qGlojyondjeMru/HNvadiNBNuj1798eqhU1yxQ4mLAOzepkt+K6R6cT96lr3wbdHssgn2pHu5o8G2SQPqtA0ok+6bPmjCkW512zRZLXkSsVmLVqrc/Pb62pdvk7s/9cPf3M6fOmpirPm5OYqQ6n2hlGamQyfGFVNmYmReI3n5ejZ9hY3t2fXWtEziJ8Ge9fngnlsIH2eKKTGR/ZsCG5O1/PsZgRq8W/3jkfEZrQ+DN55eRxkf4N9SkxcfoubK7/GB2aJJyNnI82ja9RPIqbWnAkfhVuatkhPEOd+fFORzN8Tw3y+zo7kJg5fmaCDNHPffaJJJ3t1Fmet3TlmNvkr1gtGsfIg9SfC4zlN9+JsCjfo/TdbqydbDWd3n7m1NhFKXdt5PgTt5ipT2j8RWWYKcjKvAT8eUYMPi9twScXGl2OXIzUKLG1MBk3LUgT0bWpVMs50dC89r9/6zx6dEafBKtSIUOYOjh/IlsaBsZtMGqprnApOu0jTM5RKHskHwU5zucXaehBh5qi2iTaRkcNaZSnbboSTTpybSwvh16uxvmoBVjefVq6A/RQSFJjTsG6a1B6cK9/ETuZTEQzaRrT2JvJsf6BL2LH73+NwT7P59Tbk7dkBfJX+h7tJEwm6S9uTEEu/WBCFxaeDBNAwjVK3LIoXSx9OiOq2vrRN2SESiFDZny41SDeTSqv6cVDI2LTCotOT7GYLfjvnaVCdPriMEDPqG4fwAuHqvD1a2cikNgajFrO1Y8pPsnuR8ooVKCpCM8RZvauRKe3afdydSrmmGUi6mkvqP9p601QKZRi3c8oMumhQb43nd6Ltt2EuuIL6Ovq8C0KKZMhJikFCzZf59HmNEno+m98B7uf/5PwavVEJFM5gDCGX70eK267y+/yALU2DPqB/lHrqXsdPg4loH0yDMHCkwlpdOfqxUmZTs6TnUitEvMzPEt/keicMQ0inIHqbt9d2oLixl63j9PMdtv4TDKVd9VcRP7zH11oxKaCZMxODWyK0JPudpNxdKo5lKkMz4XFT9Fp48CzX8IBF+v/YVhsemuQH5/uuW8qzXnf/JUn8Okf/xeDfb1ep90pxU5RTG98NCPi4nHzt36Aoj07Ubz/c5hNJusgLqfXtpVehMXEYvUd93nUre8J1FTV70Jou/tcx/vMSUyrtGMPaGCmDyw8mZBFRH521olI0FQRn54wXUSn1eD+XQASi0+LBe+erpNkYibV335wrgF/l+q+KSRYKFQqmAwGSaNQgcIgU6JXzI6XjUqvX7095PI2MdY8d3+wdqpnQeVl9C0qPhE5i5eh5MAer1+TXAgOvPYirv/Gt6EZw07JGfK9XLTtZhSu34SKMyfRVH5JNOnQSEqKokbGx4s61ewFi5A+u1Ck6aUiJTcPNUXubeG8gcRx6sxZkuyLmRqw8GQmhfjswfRguohOZ/Ep5SSjyrYB1Liop/UFStMfvNyGb23Oh0almNBaz9iUNLTX1Yxa72sUKpD0KilCPDraaavpdOboC4873HeedEQ1oPH6dmxq3+9SXFOdq6tGI2coYliwdoOX7waoLy32SXSK1zSbRcr8wCsvYMuXn/A6DU51oQVrN4rFH+i9e/rauYuX49TH70tiok8R2Vkr1/m9H2bqwMKTYUKE6SY6A8Wl5l6X0U5Kr9vPabe/bZ9QdE67mywWVLb1oyAtekJrPZNzZ6KjoS6k7ZRsmGTSinSKgMoRPkpI28S1Jwb55EsZnZiM7IVLvHptg06HI2+9MlJH6RMWi4hYkvn/mnsekDQ66frlzGgqL0Pl2dNoq6lEb0e7EIAqjRZx6RlIzZslOugpBe5O7M5Zu2HMpipPP/PEzGwkZvnubcxMPVh4MswEYjNQp851Fp3SUNvRLzw8afyiPbaaTmeqX/+yw31Xk6Vq2gcCLjzHE58zl63yOeoWbFQurI9skUv79Lot0rn60WehUGnG2KMFKvP4pu9jIYMM6x94xOuZ5RVnjkNHjTYSWCpVnD4mJiH5chye0lxZjqNvv4re9rZR9ltka9RSWY7W6gpc+HwH8patxvKbb3dZerBo242oKTqHge4un5uqSGCvvfchSb1QmckPC0+GmcAI57z5RxCTaR0MqI5j0SkFepPF/+JOO+icqTd5d+LVG004WdWJS029Ilo6aDAhXKVAbmIE5qRGY3lOLFRKhVfik1LtabMKxChDd0Ig0KMxPSXaSFFnEyxQeFS7SaJzrLpOmoYUZ+jy6VhEdFEGbHjoMcSled5UZOPS0UOQEqqdPPqOWggyKaHI5LldH6Noz66RqUPuPF9tvx8Swg2XirHpS18f1XClVGmw6dGviaYqg37IS/FJMh/Y8IUvIiohyY93xUxFWHgyk4LBmj5gCjUXkejMn38E6dfMgjxr9UQfzpSCBJ4rqHvdPr1ui3Rm3/8cZEr3oocCXWHqsVPHNCSgsXsQeqNZzBrfe6lFmNYrZDKRqrdxrq4bJks9ojRK3LEkHXctyXA5MMBdl/vqO+/HB7/+N5hCPN0uhxkJ+k60q+OFHZK/kIBN1Ld5La4p0hYWHY319z+C5Nx8r19XPziAnhbXNlb+QCMkMwvnI3P+Isn2efqT91Fy0BoR91Qk0na6/j58+qffCAunuDTHUb10sXPdN76DfS8+i77Odo9KDUj0qtQarLv/EcwomOfju2GmMiw8mUnTYFS1swE519s8LScvLDoDS05iuIPYc1W7aX9aJtHpbla7DYpUOtPcrcP2okbsvdSK9n7XaWDn47Dd7x0y4qWjNdh3qRU/vqkQGfFjG4vb2+yQiNr38l99T/1SCDcI4wtn9l9Bm1qa2eMasw7puiafmnJyFy1DVdFZXPh8F9obamHUDUGutNZ70nz0nMXLRR2iq3Rwp4ejNb1GJsOx99/EjMJ5kqTca4vPjYhObyHxSVZd+196Djd/94fCPspZfNL6C59/itLD+2DS64VFlNkumioirPSbksmQvXAplt10u1deqcz0goUnM6nEZ3NJz6S2VWLR6ZoZ2kOo2hkjyYVFYaq0vw+tSo4sO2FoMJrw+ok6vHmq1poE9lHE0bPqunT4uzfP4T/uWYisBBfidlA1qtYzc94iXPPQYzj4+t+Ev6Mn0S0SBrSQ12P5yWNiCpI34pNEmTosAnrdgMfRtKzBOlyImo9BZZjLqCel1p27111jQWFvqYiiesqym+8U9kdnd34sIsQkiGiogK0Gg8pFqVGrq6kRl44cEGnm1Xd/YVS6WVgXBQKLBbq+XtReLEL2gsV+7YrqNo+887pfFxT0nVJEs2jPp1h83S2jHicxuuT6WzB/01ZUnz+L1upKdNTVQj80CIVSJSKlJN5zFi3xa1QnMz1g4ckwQYJFp3tLpWzy8zz0Cap23uS3+EyNDcP89GhcbOwRJvD++nhuK0yBUmEVTn06A/7fe8W40to3LGH8ewESrYMGM372QTH+8NAyaO1S+mM1GpH4vO17mTj23utoKCt1O8Pdtj45ZyZW3/2A8KOkiBTNH794YI+ofxxr9rvt+TQ2MjwmDqc+ec/j90ZCcVXXcexJ3OTVZ+Lw+jAj1tCFWf3l3j1PLsfH//vvGBrot6aH3QgyW9SOIpvbf/ffWHrDbSjcYD1eet/VF6TxsnR3jNXnTvstPKvOnnQ5Zchb6HMqPXQA8zdtE/WdrlCptchfvlosDOMrLDyZScVkrfVk0emZ+GzbXilGg/rLvcszhZhzB6XWXXWvO0PJ11sXpY80DP1/7xUJn1ApE9UkPjv69Xj+cBW+4TSecyzxSWn3zY89ge6WJlw5dQwtVRXoamoQJvNypQqxKalIzslD3rJViEtNdzCiX3rj7chZtEzY5VSdOwOzmfwaZZDJhy2DLBaRTs1asBiF669FQkYWBnu7cXr7+15ZCiXr27C4+yzOxiz2SXRqTENY334EcngXnT398Xuw0P+8qHUkSFjTnPK5G64V5Qz1JUUIFPSaZAjvL5ePHxn+pfr/qzTqdagtviB8PBkmULDwZCYNk7XWk0WnZ6jjPKtz9ISl2XHYNCcJ+8pa/Yp6fnFNNtJirVYzrxyrRUUbRdA8f74noznFdhaIetH7V2QgLkLjlb9nTHKqEJLeQmnltfc9jBV33IPOhnqRdjYa9FAq1YhNTRV+j+T7aINSqFnzFwmLHW86nOf0X4bCYsLpWBKfMg+bjSyINvRgQ8chhJsHvHpfJIwtlJb3Me18dueHwnKo4XIpAs1AD1kVmURk2R2djfWovnAGbbU16GlphtlshiY8HAmZ2UjOzkNHI01fkOZSiC42SAyz8GQCCQtPZlIxGWo96dhEZJbqA5srWXR6QWJ0Cap25kpyYfGNa2aiumMAVW0DXtdhUvxo7cwE3LHE2uVb1zmAd87UudUyngrM8fispEVEa50ZT3z6A6VPKRVPy3gsu+kO1JdeFF6U3pA/UIFkfStOxSxBiyZFRDOtc9yvNvTY1iktRhT2laKgt8yruk4H/GmeksnQUGb11w0GYqKQG8F5/L030VpTKdLy9mJf19eD3rZWVJw6JumxUOkBXYQwTCAJ7PgEhplmkOi0nLiAOapdWJK0H/PXnGLR6SHqvNnIXmdExiDVevrfTRyuUeJf71iABTM8v0CxNTZfNzcFf3f9HGFET3xyvnHYmTBwkFY6V+veq5LEp3bRDDFW8/LOOkwENOlm9V33++ztSSMvb2zZIZqFUnVNCDP1Q2saRLSxCzkD1VjZdRK3N32EuV42E0lKEDr+bShU6lHRThKiNCjgk9/+F9rqrKl4VxFm+65yKelpa4HFEph9MwzBEU9mUhKKtZ420WmLcMriUij+BVlU7kQf2qSAPqeoa3MlrfWM1CrxL3fMx47iJrx4pBq9OqNoGHKOgNr8NpMjNaLOcnlO/NUHLRbsuUQpe88EidlpHKenozlp75dbrJFydwQy8ukpVBtKhuLH3n3DOkbSS09REqALet3X304nEmZkjLJxKvp8J859tn3Cjom67Q+99hLWPfAwZBKPPp3uUJ103cULaKurQ29bi7h4CIuMQvyMTKTmz0ZiZs60mPLEwpOZdCTmRgKVfSFV60miM+z0HqTPv8wRzhCq9RTIZLhhfhq2FqTgcEU7TtdYJwp1DuhFFDMlWoM5qVFYlZuAJZmxosHGnpbeIfQNUfONZ7Pfa5xGcHozmnNQP36kKRTE56wVa4W/46HXX0JfR5t/c8wlpEcZheqwLHSo4tGtihL1pGqLXpjZJ+lbkTlYJ1L5oQA5CqTlz3FYV3+peEJFp42q86cRnZSMhVtvnOhDmRL0d3bg1Pb3UVt8Xvw7kcvkIxFr+rdTV1qMc7s+EfXai6+/BZlzF2Aqw8KTmXTEzY4VS6g0Go2IzsLpIzovD+hwtKsfl/t1aNJb53KnqFSYFanBqphIzInwrcbR0luJ3krfRiOOB00I2jg7SSze0NB1VVR6MvvdHzyNdjiLT3ExNvxvwxuosaWrucnaWKTXWbvhk1MRmzYDCuXYp4ekrFzc+r0fo+bCWVw6egDtdTW+zfSWSHCejFmCVhf1ozqEoVcZhcrwbJyOWex//ahEWCxm8uvCkbdfxWBPt/AZbam+glDhwp5PkTlvgU9jRqWAGt06GxvQ194mRJomPALxMzJEk9tkigrSlKpj773h4LlrtitlsLf76m5txr6/PSuyCqvvvt+trdVkh4UnM2kJhS736SY6L/QO4I+1rbg8MATFsF+17fRdPajHyd5+vNTQgfxwDb6WmYTFUeFeic723cdRcTwbupTQKU8wSiSmxhvNSaTFeC7YbeKzuaYPqgE1DM0daKvs8ygC2tvehkuH9wszeRKcztA0nZxFS4V/p7Opuj0kTnOXLBcL2TiRtdPQwADkSgUMOh32vvgMAk15eJ7omLe16LjqmretM8pUwtS+RpspOuYjTN51zEvNuV3bHaJfocb5z3bimke+EtTXbLpShtLD+1FfWuzyQiYiPgEFqzcgf8UaqLS+XeAGC5r0dPLD8W3bRhgWoORi0N/VgS1feWJKik+ZJRTyI27o6elBTEwMao+XIToyaqIPhwlRqNGiJykuaF3uJDZtTBfRSfWNz9a14u3mLnF6H0+KyYe3uSM5Fl/PTBI1lOMxdG4nzr0eg47EFRMexbantKkHP3jz/LipdudUetb9zzmk3sfrdqfaU7KA+u622T7/HnXn6pGcHu5WfFLTSMnBfTi742NhOTRWhNJmHk+G6ou23TRqlKI7yEuUIjw03cYVA5pwDERECqEYPtiP8EFrXStFJTtUceK/FpkMKrMBcYYuxBs6oDZbo+r2mCHH0bgVqA3L8ui4HN4bzNCahrC19XOv7ZqmG1u+/ATSZhUE/HVoZvzx998UEXTnLv5RyGTQRkRi7b0PIX12IUKRxvJL2P2X3/v8fJlcLmyt6D1OBnr6epG5cja6u7sRHT32uZgjngzjQ4QzKs56FRo1DUQnXZv+R2UTPu/otd734Dm2U8b7LV3oMBjxk7w0IazGY8CUGFKik7jYcFXYeTL7feTxcaKbrsT9hlnezza3v+Aaq/bTbDLi4GsvCh9OT7BNNCKT+ZaqSmz98hPjRpgoUnXq43dHre+MTkDZzHmoTp8JnTbM7kUsUDf1QV3XB4PRejqSwRb9s3p+0v3sgVoU9l1CtNH6XXSoYnEobg0GlNYSA2+h/eoUGhxMWCPEpzcG9dON3X/9o7DRokECgaK3ow27/vR/GOyzfr/jlmxYLBjq78PndGw33xnQY/MFshs78uYr1slgVFLhAxazWaTpKfMQquLaV1h4MlOCYHS520Rn3spqxC/NHl47tUUn8WZz54jo9BY6nR/o7MNrTR14MC0BkwGjyYyajgHUtPfjZHWXMKEfC4p82ney20dBPZUz1M+UFKnB0qw4+MNYjUfH3nsTNcWjI7eeXHh01Ndgz9+ewbbHnxQnU1cU7/sMZ3Z86LBOr1Lj5IJ1KM8thMxihsXuuTKdCcryHsh6jdBDOWJWZYGTvRAUqA7PEsv8nmJEGXtxJH71SGrdV0h8dqricDkiX5jcM26wWMTFBF2IzN24RfLd6wcHRkSnNzXCtmQtHRsZ6uctXYlQoeL0CTEcwB1DRiOe/tiagv/VzXdB46aemmpZqdmMhSfDTMMud3vRmbBl5bSxSKrT6fF8fZtf+6DTw98a2rE2NhI5YaFbr0RC88Pzjfi8pBl6k+cRMHdNRs6p97HS7DS56NtbZo3qqJdCfBIdDXW4UnbU532SIGipKEfpof0uo0tNVy6PEp294dH49JrbMRBmjUo6iM4BI1TFnbAFN8d717YazQvR8+3Wun+WyaDDoWcfEbfXPf43KNx+9jKURBVgVv+VCW82CnVOb/8AiTQtKTdf0v2e+PBtr0WnM8fefxMpefmIiLWzQZtALh05IMkYU4vFgvbaanQ2NTiMvZ3ssIE8M+mhTl4SnylhBkmMx52ZrqKTeLu5c8w/naZBHXavv1ssdNstFuCtpo4xX0vfOTG1dkajGS8dqca3Xj2DT4u9E51ScdeSGViY6V1H+ljYzOap9rknIRZHD56BsSV9zAjMk++/IRa67Y6zn34Eg27QYR01FR1+8yWHSCjVce689k4MhEWIek0HDGaoirsAIzUZePvObPuSrqt5SK5FozYVgWDO2o2IjPe+fCIUoZrDQ2++Iko2pKKttgqVZ0767YZgNhpxZsdHCAUogktNdlKNMYVMJka4TiU44slMCUZsZCSOfE5n0TloNmNXezek0GEU2KJ0/RNZKYhQyEd1s/edOovqQ8qgd7PrDCb80wfFKGrsGW4o9f7NUtOQqyajcbvYqfZLJsdNC1LxpbU5I6v7dEYcKm/DpaYeMRteZzBDq5JjZmIkCtKisC4/UUxl8rT2s6H0IgYjyzHUko7k5jQoUxrhKyajEVdOH0fB2mtG1tUUncVA99W0In2CR5ddg0FtmIhymoYGcfKrVj/I5c9sh6ZKD/pR+S4dZeNGOVc/6nk3PdWQtqkTMEMn3UUrpUhnr14vGrLI53QqQOKwv7MddSVFyJq/WJJ9lh05ONLE5u+xUSf48lvuhHaCG5Epu+AK+ws6vcn1bcI57U6uB+31EzOpLFCw8GSmDFKLz+ksOgny6NRLmH00WoBL/TosjXa0WLKJzrqwm4LbWGSx4JfbS1E8Ijp9w12TEYlOl+n14RdTmY344uI43HHNTBHVIPP4l45WYUdREwwmMpm2TlOyUdk2gE9LmvHHfVdw04I0PLQqG1r1+JNlai6eh1whhzm5AS3+ik+LBTVF5x2EZ9mxQw4G8vWpOahLc/1vRdZrAJp7cMBNGtzzFPlo6LlXb+vtbjvOlXfeJ6XxqZteyshgRGwcUvPnCE/GqQS9t8vHj0giPKnpprr4nN+ic2R/ZrOwYJq5fPWERzxdYavpdOZHOz5wuP+72+9zuE9WW/rBfkwlWHgyUwqpxOd0F51ExeCQyyol+5S6STfk8jahCHM8wVOcs3xgtPAk6nXrkHNHcGuYdhQ341R1Z1Bfk4QbdWbPHKjEbHMzbl3/MyE6Kbr5i09K0DlgEPWehL3oJGwjO6kU4INzDThY3oaf3lSI/JSxIzxtNdUwm4aNq+3Epymh1qcITEd9rRANlFqntGt7Ld2/eqwXZy0gn77RKXb6TbToYAmQ9/fRF746cvvEK0/arX/cYbuNT7zp9EwZDPLRVlHqsDDodTqvZ7eTAIpPy8Cxd1/HVIPeW2t1lfi+/TVx721rhUl/9QLBX+RyhYg2zsTEQhFcaXcoE+9tKsHCk5ly+Cs+bTPX89ZMX9FJ9BnNwiSeIpX27N3m2lfuwK2OzTRbDr7tcJ/6ZvqMoWGUTSn25w5WBPU1r23biwR918jIxqTsHJQfP4wObQr+62gbDGaLxxqHxGl7nx4/fOc8/u3OBZiT6t7Rob/rqrjuUsWgOj8emuYkvPvx//oUgSGrGDKHV4eFo6etFWbzVaGqU2vRlJwp0usjxzpk53fa2gOz8w/KD+yjnP6gsJskY4vsUeSs6uwpDPa6ttMai5pizyyrAgIJwgDac9PAASqtoKiuP/R1jl3z7S0UGaTBCO4Y7OlCc+UVIU51ff0iC0D1twkzMpGSN1MMTZCC6ETXk9Goe93+4u5Hw//OfnnDbVCP8dqUao9KTMZUgoUnMyXxd6xmSmKvsEyarqKTUIr0qcT7lKBrWwoOlLVi0CB9FzOl1l3NYCdiDT0Oc8LbaqtRV9eIT5Kug54ibl5GkOir0Rst+KcPL+JPjyxDpFblejuLBV3KaJyMXYp2TaLV1ihSBhz08s057NP62RnsRCXREWc9QdpqOp059hfH6COlwR3Fo2zMFLlzKt52ezxo27FqPGMM3aMiezTGUtdnNbefLFAUOiwqGtHJKWiuuBywEaZGp+/GF3z1txx7n6P/YJHQPL97B+pLiqxz0qmmdKTn3CI+I3V4BOasXoe512yBSu3fNKTopCQoVCrRdGePO8sktULp9jGboE6ckYmpBAtPZkoTCmM1JysZWtWIlbc91+562SG9bot0bvjwOSi07u2SKNCVpfVs+k2gOXSlPdCBIUcsFgfRKVaZLTgdtwgGucpr0WlPj86IP+2rwPevn+Py8SsxBTimvJqAtNkaUVNUZLv15B/XM4ADh7820pgTIzNgXccRRJpc1JZRacDhg0jIyIRS4/h9d0fGePWhOqfBx3psdIrcc8aqFaUaT5qOZB/tTMrORdW505hMUOqbjn3DQ19CZGwcPvrNf4p6w0CIz9OffIDUvHxkL1yK8Bj3bgwUGe9sqod+cFCMV41OSkZ4TJw4Vm2ExE1AMhnC7BqLaEoXjfws2vOpeMwmSm3jSe1/pfqBfhTt+QxXTh7H+gce8csySiZTIHvhMlSeOS7JZ69Ua5A2J/CTo4IJC09mysPi0zdmR7g+WTvXbo6s12rcPjayz3DHx3v3visaiyxxMQgaFgvKmnuDJzoBRJr6oHCK8PQpI1ATlulWdJIxvc0jdLxxm3vLWvHFtTlIinIUgm+drMVR1SyXz6H9DQy7CPUlyIHD1tuJA0r0JkbjQNp1YqqPxll8Wiy4sGenOKk6pyfNCoWIJFH3+si6IR1OP3UnAoEtkkmRUPsazxUP/RYnXn7Kow53OUzI0NVb78hk1tGgwyIuUBFDqaFjVShVuPaLX0VSljVLc93XvoVdz/xfQKK2DWUlaLh0UXh7Zs5biOW33DUiQA16HSpPn0TZ0YPoah7dxEbRxZlLV4pSBkk/Y7q4U6tHROfB1/6G6vNnrRLTg3/sFIEd7O3GZ8/+Hhsf/jIyCu09Y72jYM16VJzy3TfXBn0+s1aunXLz2ll4MtMCFp/ek6xWYW6EFqX9Or+ttSnGNjNcg3S7iKdNdAa7m11vMqNXJ50X4XhQajtlqGXU+opwacs43jlVh69fezWyea62Cy8cqfZ6P2FdZiiVZnTGqnEofg22te4eNVLSJhacPR3V+iGxpUJjNxbTDqsIlI1EM1c/Sl3f9vt2fEyh0oyk4um/9ul3/WA3FErNqC526wF6Fu2kme25/VXWWfDUxCGTY+Ud9+LQ6+5T86GEzYooLX82Vt/1BYfoY0xyKm7+9g/FvHBXAtAf7MVi7cULaCgrxZp7HoRao8Hht14Zrot1fUFF0cXSw/vEEh4di/7uTslSD9VF57DkhttQtHcXqs+f8fr5FBUl0br/pb/ixm9932fT9vgZmZi9ah0uHz/sMv2vUSpH1U47Q1FhTXgEFmy+DlMNFp7MtIHFp/fclRqHf7ni/0mLTlN3pcSFRDd7MCOd4vVkcszsrxy1vkkjbcPAkYq2EeGpN5rw611loqHL1iXvKa06JdK6rMKiIzYWlyJniznpnpDQ1Tpm2YA8TAuZnVYlYelsp+T8mLs6TltE0+VjrzzlgfA0Q23WY2FvkbhHUaVrHn4MeieD/FCFRGZqfgHmrFqLhEzbCF9HqN5zy1e+ibd/8Q8B++GTCKWGswOv/FXcv9rtbhlXuPZ3SdtgRKKWorCXTwyH733EAjMOv/ESbnzyaZ+bjpbcdBuaKsrR297qQ1RXJv4drX/gi6KJb6rBwpOZVrD49I4NsZFYGh2Gcz2DLus9KbXu3L0+ahsAcyO12Bw/scbONjRKuViGjOagRDuT9a2IM3Q5dLT+9+dvAHhlVAqd0uv2ZvT2t+2P1lXavb3/ajPDgcttaO93b1XjKo1va4qqoe+syoDUPmt0sTR6Nub0XfZopGRsTydUBj0MlK529bpxWsjbpOlE9w8ysLdgTccxa7QTQOH6a5A2qwBnP/1YNKDYagFDjdjUdGx69Ksej4ck8UlzzCvPnAhK6YCrCF+woNe+cuqY//sxm9HZWI+a4vPIWbjUp31Qk9K2rz6FXc/+Dr2tzR5/LjK5HDJYa3VTZ87GVCSgwvMXv/gF3nnnHZSWliIsLAxr167Fv//7v2POHNdF8AwTDFh8eg5FL76fk4qnLtagx2hyKT7HE52RSjl+kJvqt++fZMhkmJkUgYuNvYF9HeqgtZixouuk5HPf3XXOd/QNIT5Sg+1FTT5FO21U5qiAKiC1bgjIUKEoZQHiZV3Q6HWI626H0s3YRLnZhFlVF1GSv3CkiYnS7qte3GvdoN8AWSuNE/Qc+450SrXbUvFUx2mfard5d9pS9O5N6TWQWUxY33EEKfqrJRDFe3eLaUPWTv0Q+a26gMYxHnrjJWz76pOikcUTlt10m5g4ZKBmowkUhsFAqpGeJADLDh/wWXjaRP9NTz2Nszs+FqUFY9W0yoYfo/T+uvsfEaUSU5WACs99+/bhySefxIoVK2A0GvGTn/wE1113HS5evIiIiIhAvjTDjAmLT89JUqvw3wWZ+PtLteg0eC4+6ZQYrVTgP+ZkIlUTGt3sNpbnxKO0qddnYTYuZLAOYG3nUUQYg5e6re/SIVqrQnlzn9/vzSY+U+qG0LhwLk4t1o7YJF3/789jfl05suuuQOEUGZxdXoyyGXNhlMsBjcIh9W6JUAFpMdjwxJsupR2lxZ27192lytVhMSOPuUrRu/P6jDZ0Y0PH4VEd++RHSh3QZIVDqdZQhcRJS+UVlB46gML113r0HE14JDY++Bg+f+4PosRgqotPyczyayqFdZQ/zT303OW33oVZq9aKKV9VZ09jaMCx4YvS+Sl5+Zi9ZgMyCgo9vqCYrARUeO7YscPh/vPPP4/k5GScOnUKGzduDORLM4xX4jMsK9JhvjXjSIZWjT/Ny8Efa1qwq6NXGMu7m+Fue+ya+Eh8MytFiE9nqLGoqfRq+jnYbC1MxktHKalsCUh6XWU2YE3XMaTqWkZNAzLY3XZOofs8932YXoMRtV0Do6Ye+ZrGJ/FpqQKiumh7rYNf58G0HJwvWIb1J3ZD0zOAKxF5aNCmo1cZCcW5bnHhQVOKLGEKmOM1MKWGASo5jDlRUHfrYTFaIAuy/pnZV44VgyXoVcagMjwHfYoIMWFJa9Ih1tAF09HDyMifNSm62c9++hHyl6+CSuu6kcuZ1JmzsOlLXxONM0ajflK8x4mGBHpnYyOSsnL83hdFMFfcerdYyMyezO5p/5qISMQkJUs/8SiECWqNZ3e31aQ3Pt51bcrQ0JBYbPT0eD8xgmF8EZ9otU53qarpGxGhjCNRSgV+kJeGe9Li8WFLFw519qHTaRJRrFKBtbGRuDU5BjOdrJOcRWdF/e3IfqAQE0FchAZ3LE7He2frJYl6jvjiW4DMgRos6TkPjflqjaW1ptO7FLpHc9+d+Hl1E9QSp/HprcnMgKLu6gxq2zjMXm0UPs2+DooWGq9qFp6YDs+1ALIBE2QDA+L55oxwGDPCYZgXC1Vxl1/ik4zfXb2mc5TTbLgacdbou/Bh3Gb0K60ZN6XKarpv3YcMSosBec2VmCMPQ7g5tJuMTEYjrpw+joK113j8HKphvfX7P8axd98Qc81DuZY1VND1SV+SExYdK5bpStCEp9lsxne/+12sW7cO8+fPd1sT+vOf/zxYh8QwV8WnjZ11aK6ZXNNKgk1umAbfzk4RS6fBiDaDUcQNE1VKxKs8+5MykaLTxkOrs3C0oh1NPUMjc9B9me6UEqNBXlIkZqdEIau/Chc/8rymU0rEOwhTQj9odCs+vYWipCaTAco2I2TqfgdvTtmgEarSblj0FkCldSkAbdh0ubxuAKrOIRgKY6FfGA9leQ8U3YaRSTLun0+lCxYsGSjFF26/T2zb27EfRdFzURuWIVLr1zzx2she9v3hfpf7eeYdxzGhzml9o0yFy5H5Inq7tPsMcgeqQrfa02IRzS/eCE+CLIw2Pfo1YbFUfvwImivLRSMN45qQqU2fQgRNeFKtZ1FREQ4edD+n7cc//jGefvpph4hnZubUGhXFhL4Ilb9zFjPm6yCLm7rF3VIRp1KKZTKiVirwT7fPxw/fPofOAaNX4lNOgjNKg3+/Z4GIntqoL+3CRRfbf3/zfQ6p9t/se8erFPp40JFbIhTWwlrt1ZGA8CCNbyPr/ucgdzoWipJWuXg9Z0N4T6cKiWMaMIlop2F+HMyFMUCnHrKGAch6rSUI4jxvMY1EIRUWoxCA1FlPRvw2okx9WNN5HEu6z6NFk4hOVRwGFVq/qyfodU0yGU7ErkCfMhILeopCVny2VVUKKyOabuMtsSlpovaQeP+//kXY/jCjiYjzzD2A8ZygnDGeeuopfPTRR9i/fz8yMuyiS05oNBqxMMxEkrUmBRqNNfXOTG1SYrT4r/sW41efluFCfffw/Gb32DrFl2fH4ttbZiEm3DG2mOBmpjLNY3ZFhMwEnZsU+lhz352h4zamDfv9KWWirhKDlIp23J8NV9V9cg/T+f4i0u86E+LqBjBnUTJmz9Bi+boIJJhlOHG2FEePnoTBYITapEecsQuxhu5R40bt0Zp1yBqsE4uNJTdbBRXVm+6MXYvDL3zDZcf7GEcp/r8kshBaow6zB8qhl6nQIovAL979s3jsW/d+D0mw1oYqLBOTrqY0+fbf/QrbvvYtaCM8LxGiUZpdTQ3Q63RilCVH9VxDTT+xySkTfRhTjoAKTyqc/da3voV3330Xe/fuRW6utJM6GIZh/CUxUoN/u3M+dpe2iOk/NZ2DQnZQVNMWObQ16+QmRuCeZZlYn5/g0ihdGxWNuPQZ6Gqs98ive0FPMU5oN/of7dTIYUmwE5ZJWihq+t2mz812zUVjNRlRlJS2rXGKjt545x/Rk+DamWS0fZELMWsBBhoHcP/aKMxLt45LrS06h84dL2A2fXB+dl3TZBhKzp9JXAu5LMxtx7snnI1dgAZtGlq0yTCKCUlW4XkifO7ImM77H/oFCodqkWRoQ7Dpbm3GZ8/+Djd887tjdl/T3HSqCb187JCwZJqS0KhTibxEyd4oY+78adX0MyWEJ6XXX3nlFbz//vuIiopCU5P1xx4TEyN8PRmGmV4Mnds5od3sbpHJsKUwBVsKknGltR+XmntR0z4AndGEMKUCOYkRKEiNQnbi+DZwBWs34shbr3r0shm6BtQP1otu8LEm/rjDdnrVZ6nR8MQGcTv9N58CyVooavtdhm89bTIaaz68XOm9gBu1Dznw/tl6ITxJCB149UWrYJDI6qdOOwMd6gRYnOyUvBLI4jNWollLUS/3309dWAYao/OhsBiQrmtEfn8FkvStwUnRWyzobm7EuU8/wbKb73R9fCVFOPL2qxgaoN/E1LRSIqFI05ySMnNQXXTW7659en7BGuu/KWYSCc8//IE8w4Brr3X0GvvrX/+KL33pS4F8aYZhQlB01hxpGLexiMY9nqjqxKWmHlS1DWDQYEKYSoG8xAjMSY3Gipw4KJXuG1nGw2g0o61PL/qho7VKRGqtnc0j5vLJkWLxldzFy1FycA+6W5phcdEyT2n3H2+7Kv5Wd53A7oRr0a2KprOnx69ji8Ya86KAKKfXUcmtUc8W36cE2aKi9tZLNkzGIZgMSr/EJ+mCoxUd6NPpxWx04Z0poSgqj8gXTUn+Yt8hbz8rnj4D+23oszDJVKgNy0RtWBYS9G1Y2XkC0XZ1qYGCBHvJwb2YuWyVmGxkv/7C7p04v3u7NZ0+RUWnjfX3PYLw2DjUlRaL2ld/RGzm3AVIzs2X9PiYIKXaGWYyMtTaB1VyP2RRXB4iBZbeSug7B8YUnSQ43zpZhw/ONaBfb4JCJnPwozxf1w2TpR5RGiXuWJKOu5ZkeCxAewYN+OxiM/aVtaK63dHnMiFChaVZcbhpQRryU/wf6ylXKrDuvkew/fe/goVq/8b5M6gyG7GlfS+Oxq5EQ1i6VRyME/0Uu5TLYJgZCUuiFhgabf1jSglzKTzH8gql/drS6s7pdXt2vPsNh6Yit+LM7jbhLFTprR4/cxE1zZ1o0WajUxULnUILmcWCCFMf4vVdiOuvww8/stpR/ermu0QafTwMMiVaNYnW5iQXpvTe4MmseEq5209YotfsUMdjR8p1WNtxVES2A42YtHP0EFbece/IutJD+4TonCznY4pYDvRQrbUMFovZ4/dNbHjgUSTl5Inbq+64D4feuPp9eAPtTxMegZV3XG0IZKRlcrajMkwAUaWlwYDl6Cs+iUgchTxr9UQf0pTAMpgKS5y1ns+ZqrZ+/HJ7CRq6dSNBGWcTdNv93iGjMH/fd6kVP76pEBnxw001LjCbLULIvni4SjzflWcnzTjfXdqKXSUtWJ4dh29tzhdjJ/2B6jyvfeRx7P3bsyJl5yry6Sw+aZpOdXgWLkTNEz6TziMvR25S4CpeA32qAtBYhOi0iDGPw9sNDddqKigqR80jjmLPXZPReJZGvogz23hLG64E4POn2tCZctPw/HSrL6e1utYCC72JSIoYuvZBdQcJWFfvxhuBbJ+K9wT7bel9WrvyzTgcvwYb2g8hbSiwdZX0O6s8cxIrbr9HRDc7mxpwevv7mEwMdHchY+4CdNTVYrC3e3yxLJMhIjYO6+59eER0ErlLlkPX34dTH78rPgtv5qRrwsOx7WtPedSsRfvtqK9FW201upubYDQYoNJqRNSZTOfJOYAZDQtPhhlDfBpaSqGOq+TIZwC50tKHH79zHkNGi8eZQNqsrkuHv3vzHP7jnoXIctHoMqg34V8+uojz9dbBFWNhs1I6XdOFJ146jX+4dS7mzYgZ+zlmC05UdeBMTRfKmnrR3j8kApWp0VrMSonC6rx0XP+N7+DQa39DD1nVjHcOlcsw29KKh7emojs2W3TZX27pQ0XPIDqMJpjJJilSKaYAQS1Hw9dd1581/uA2h/u5j73jkUn7WBFOG5l3/wG1bz8hbq9+9BlIQafFJvJJblobOUiC2jDJrp6mqMkn1zi+7Y9OESapQKb3SmLUGyFqRS7k85G4lbi5ZSc0Zt/Tv55g0OvQ19GGqIQknHj/LUxG6i5ewObHviG8RS8dOSDEKGFr8rEMG96TzVHBmo2YtXqty6YqGidKE4EOv/WKEKFj/XGxzUlPn12I1Xfdj7Cosf/tUzT2ysnjuHhgN3paW4QAlsvou7Ze7thM+eMzsrDg2q3InLfIr89kqsHCk2GYCaNPZ8Q/flAsRKe3Ju60/aDBjJ99UIw/PLQMWrXCoY7z5x8UoaSp1+t9UkPRP7xfhF/etRCzUl2k3i0W7LnUghcOVaF9wDCqJIAiqKVNfXjvbAMy48Lw1Xu+gbD6IpQe3o++9naxjVxhTQ+aTdaYI0VZZq1ah4J110AbaY20LMy0Tjb5U00LPmjphM+GPRJmWGXKqyf4zAY5modL4OzTzBQ9tAk5z+yLPI9MHoxcDP1AMXIGaka6110jbVqZRCctKx78LU68Yk2xL73/v3H69e+PEqejkcEoV+Fc9AKs7Ar8cIHmyiswGQxoqbqCYCLVFCQSgVQiQOJz7sbNQthRVLG/u1tc2JEBfnxGBqITkyEbpyY6fc5c3P79n6L8xBGUHj6A/q4Ol9ul5c8RTYFps2lO+thxf9rHoddfGv58h7cV2RTTqF9eZ30t9r30HLLmLcKqu++HJmz85sTpAAtPhmEmjL8crECPzuDz2EoSih39ejx/uArfuHbmyPo3TtbiYmOvT/KDjsVotuDfd5Tidw8thUZ1VdDq9Cb816eXcKyyY0QuuZqLbltX3zWIf/iwBLctzMCXn/4p+tpa0VFfg/7ODhGAIZEZn5GJuNQZojbUFWEKayTFGdG9bpdet0U60/7zA8g0dun0RhPkjVaLKGdslkljzYd3ZadEtOqUiO8yoiPWfZORL/ZFY0cmv4qjdvd/d/t9Lr0Xw0yum6rGE8g2wUv/HSsVTygUGpfi1BWUdq8Kz8KingsBj3qe37UduUtXjETxAoqMamhVWHbj7Tj5yft0JeX3LumYG8pK0N/VKdLoNOOcFl+hWfaFGzajYP0mDHR3oqOhDrr+fmGXFpWQiLj0DKjs/r2MBZns7/zj/1rdAaxHO/Z7Gf47UFtyAV1/aMR1X/+OV36rUxUWngzDTAgtPTrhneku0El+kzbrn7GsfUgobi9qwv0rMsQUocauQbx+stavmBfts6VvCG+dqsNDq7PFuiGDCf/f+0W43GyNonqyf5ug/vB8I3p0Rjy9bTZiUrwzpM4L07iMdso1Ya7numu0Do+ZMgFZjwHoN7oVny73M2wob+/5abHv5DYNicgydbcT/torSQWd2GN7u1xWrY4nkD1NxRMmI3l6eg6Jz1rtDMzSVQVUEA70dKH6/JmANhORqKX6xbylKzFz2UoRBTQLj1PpaKutEsJTKiiSGREbLxZfoC75z/7yeyE6vf3+aPve9jbsffEZXP+Nb0Mmm97eoCw8GYaZED692DzupCDPseCzkhbcuzwTH59vHBYc/u2ZztsfnmvAvcszxHjNZw9UCNHpS3SWnrK3rBWzUiJx2+IZIy9Q2tSD09VdKG/tE5FbOu4ZsVrkJ0didV4C0mLDUBDpp6BTAMa5sVCWdAF9rsXnWNhHO231ncSBw19z2M6fznEbrhp6Vjz025EO8qupe4uYpQ59/ShBRI1dA6XFSBpqRZsmccwZ8v5w+o2nnY597A5+hVwGzby1SO5QoPnKZevxetH44g1U5yk1NB1JrdWKiHJkfKKYeGSj9uIFydP2HfV1yF6wBKHCmZ0fYaCr0+fvi8RnW00VSg8dEPWn0xkWngzDTAhna7t8TrE7Q+eCc3VduHdZBj4rafa6XtQdZOtEwjBco8CO4ma/9/f8oSqsyk1ATUe/uE1TkqwTkq42VlW29eFgeRueO1SFxZmxeHx9LpZHR+B0T7/vrpRKGYzz4qC81AVZlwGBxF/7olH7s6srvZq6t6Aydh4KWhyFJ4mCtFkFGOzrxay2K2jVJHv1Wu5S8Z4wXoMSlfO2GlXY9vhTomGGInoNly+h/PhhTAaoLCQmyXXK26AblFxE06SlUIG+L2p0ksIH9fxn2zFr1Zoxp0xNdVh4MswYsJ9ngLBYUNna5zK9PrKJQ4p39DhHh92RYXhzH5p7hoRYlApqHCpv6ROTjEgg+itoqfbz5x8Wo5bGcg6HHp33aS/Gybv0O6+dxXXL0nGS/lq7CVdSaj3jTwdGP0D7Ghr+PBQymObEIv1SL5rtbKvGmw/vrgZ0/do/I3aGCp0xE3EakaFHGYMhuRoa89UUL308WfMXIi41DW3P/E6YuJOfpquopyuB7C4Vb2sc8laMOqMfbiYjv8qsmMWi27m9tlrMTQ91n01NuPvaRIp+Sn38cg/8WoPF5eNHJBPWhiEdqs+dwczl09emL3S+WYYJRUul3nT0V5xGBPt5SorBZIbeZPF5nKOzOCIGDCbUdg5IeJRWUUjCk6Kz0uwPQnQSnpzDbKJ0+8l6zJoVh/JE5fgFBEYL5G1DkLfpIOszOFgpaTUKZKVFo7tf77GTgL3It5f08vBwKJQKKFT+nUYcG3pG1wnqdT0O29qLw05lHFL1VyPRqflzRNczLYVrN6D/+EnsTNoKM3mf+pFyd9U4ZLWTknnVwU/DD+whMbNw643Y97dnEcoo1Vpowt2PuY5O8q5ueTyoOz4mWdp9+kP9pWLJ6nJlcrmIdLPwZBjGJarZy2AoAwydU8/Pk67eT/UM4Hh3P0r7dWjVG8TEkCS1EnMitFgdG4klUWHj2ov4gmJ42oiU0LEbjNJGXWhvnQOB7UL2lOrLnUjXxKMxSuE65U4Dj9p0UFb2UWhVrHL+5nRDJpysphIHC5TkUO8UYXWHrU3HEq8eWaeNNSHW0o9OxPtlPz+eN6a9ZRF1tdtHKYecOst1vb1iWhQ1byy/+Q4MdHVhsPwA9ids8Ft8OjOqhnOcDn5y0JqZNDpqSKMZs+YvQk3ROYQqRr0Ou575nRiKMNTfJ3xpyQpMExaGuLQMxKdnSp5qT8zMQihAvqFdTY0S7s8syiymMyw8GWY8otIBw9Woy1TgUGcv/lTbiia9kcr/KEg2QpvBiMsDOrzX0oU0tRJPZCULESolcrkMCZFqtPfpPUrrEln3Pwe50xQee7RKOSI00naLkjYzmCyjvDonApJ1/WXdmL82HeftJhUJzIDySo+IdI4nAW1RTvFfEqtWVxw3AtS6N7nMgvBEAyLNHbA6aAKLSk8hJ3UAyYjFueiFwrRdBtOICby94KIMc5haDp3B7HeZnH3U0zrh6CqdTfWoOHVCRJPIcHzjQ19C7O4d0B74HIfjVqJbaTMGH+sTsr5nqWtV6TOYmxbt8rEVt98b0sKTaKm8gjf/+acuR1nSpB6Kena3SDGdSYbopGTEpg434U0whqEhmE1GSfc5RIb20xgWngwzjWo99WYzfl3VjN0dvSOnXldBQts6Eqb/UN6A6xKi8J2cFKjGMWz2htQozSjh6c7aRzw2bO/jjkGjCRlx7tOBvhKuDo0/k/SV6AwmLBuUYXNuKv5S14pek0nE8OTlPZC1WyOznsYdSWjatl2fnyjqY2mKlHFYGWpMOiToO5A5WItMXT0UddZv5Npt1nIIeYsaMgwgZ7AGWYO1aNSmoUmTgnZ1AobUUSI1OzLFKTdelCu8fbrOFowd1dBjS7WfeOXJUV3t9OZtxu32RBqdTuAyGUoO7UPeslUiAkfic9G2m0Ut5dw9u7CvsgKXIvLRq7QKQBrRaR3NSZ+iDCqzHpGmPnSpYj2qC7U3uh+PKK0SK3JdW/mERUZBHR4B/Yg/pP+ERUVjsFfaC2Z389OpRlXCV0HhhmsDkmkJHeSYzoTGX1SGCWGmSq2nwWLGP5Y34HSPtQ7Sk8CTbZtd7b3oNprws/wZUEp0QkgU89B73TYY2ftHetRgZKG0uAHpMVo0UvOMBMdI4iwpSiPqPEMBOp5PzjfixRWZ2JYQhYOdffiwqBFlw6LTW2zRURoV+seHlyJaq8KJnZ+gZP9uKNyIDFeQxf0MXQMy9E3CsPvWr/5ICB97+vVGEfVzxha9tM1Hd0Yd5np8IYnGGKOTsLJYhAjqa29FVOLVjva4tHRsfOARrBoYQGtNJcqqGlHTY0SXSQmlRouBunKEddUgTt8Fs0yOHUnbMKAMHzc1701U9J7lGVANT6xyBc32brh0UZJ0NdkRpc+Zh962ZrTWBNY3VEqo/jEhIwv5y1chVKDfM9W4UrmBVETESedPOhlh4ckw06TW88X6dlHT6ctpjZ5zvHsArzS244vpiZIcj7uIhj8NRjQp6MYFaXjuYKX/x0fTgeLCsCgjBofKpfdF9JXOQQOauoeQGqvF8ohw/K7EOobTVxN++m4H9Cb89VAlvrttDlZefyNM/d1iFrW3okGl0WDb498cJToJsobSqqzpdm+wilG72e2GISE603SNMBqGHPwkbbTV1qCruRFV588I4UX+i4RCpUZ8+gwxInHNjetGDMqN+jU4s+NDYZmjkAHXtu/H54nXQqfQ+l0XSuXMuYkRuH1R+pjb5SxcgvrSYkjVnJOzcLGYyvPpH/8HvR3toS8+ZTIh8tbd90hIGazT36mEjAw0V9CITP8vCmRyBRIzczCdYeHJMNOg1pNqNt9o6vTrzyY995WGDqyPixLTdPzFluqVsnLSaDJja2EK3jxZi16d0e/3+8DyTGQlUOQrtKho6xPCc3dpM4a8FHKuoHrPvZfa8Ni6XMSEq7HmrgfEZBoyzSbBYhmjA8nWVEKRKhINNIbQFVqVEtvmpeDjc41e+bdSQ5Hj/at2Rm+6GJtJAvjYe2+KCJXz2EhK5bdWV6KtthrnP9+J/OWrseymO6DSarHitnuQu2Q5Sg/tR82Fs9jauhvH41agWZPqc/MUic6YMBV+clPhuA11WfMX48SH70CvG/TLL5Lml0fExSM1f7a4fd03vovdz/0BnQ11CGXEsX7tW25/PxMJfTfNFeWSNStlzVuA6QwLT4aZBrWebzZ1iEYZFw5GAtOgDnu3PSRuX7vrZSjCXNdSUpDy7cYO/CAvze9jopo3uYumnfFmh49FdJgSkVolnto0C/+2vcTvY3z3TB02zUmGRikT9kOhQp/O2uzw2UX/Te3txScZ19+8MF0opsINm5BROA8XD+wRDTsmo1F8/yTm7CGBWrD+WuTRfPBxaoC/sDwLe0tb0TdkdKmt7FPX+/9wr0/vg4SmLS3qLspnW3/l1DHUX7qIzV/6OuLSZohI1PoHcmC86350NjZgU3MDTraYsL9Vjjqvqy0sSFaZ8IMNM5AcNf70KZp5vuL2e3DotRe9fSHHV7WYsfqu+0e+Cxohmj67EF2N9SHtFUqCzGyUtolHKvKWrMDp7R+4tPvyCpkM4dExSJ9TgOkMC0+GmeK1nj1GEw509rkVnd5A+9jT2YsnTSkIH6NezRPyEiNcdoqPNzt87H1au+/X5CfgvuUZeOOkf1GeirYBVLSFnvWJQiGD3mhCTcfgqGisryb8FLm83OyorqhOctWd94uoYHtdDTob66Hr74dcoYC+IgrL16RgxoocjxtBosJUYl79P3100ecpQtc88ltc33UIWrP/NlckQHV9vfj0z7/BDU98DzHJ1sk8SrUGSdm5YplFgpnGQnYO4ERlBz650CgasVxF621d/SqLAQW9l1AwcBnHnzPiSkYWVt5+r4gKj0XOwqWoLylG1fnTPkc9C9dvQurM2Q7reltbQi5q7wxd0NSVFCN+RiZCDYqIL7nhVpz88G3/dmSxiN9BKJUSTAQsPBnGx1pP1SQRn2X9OklEpw0K/JUN6LA4Ktyv/czPcN004ispURrEi4YlK4+szkaYSom/HSXh6P/UoVAiJkqNxi6dy/fka40s7auy3XVXtVKjQcrMWWIZeZ2dDYiI1Xjdfbw8J16Iz1/tKhP33X0t7jwxN3cfQ4zcRDYHkAJrhFSPfS//Fbd8+wdiFrkrMuPCxXLX0gxUN3Xi5fd2o6rXJCYoUUOS2qxHvKETyfpWZA7WQWG5arff0VCHHb//FRZuuQHzN1/v9jOj9WvvfRAmgwG1F897/V7yV67B0ptuG7XeaDRIMu4xoFgsaK+3mXWFHnPWrBffCdlK+VQvK5Nh5rJVyCicj+kOC0+G8ZaodFjkJJpKMRm4MjAknBVNLtLrI7d1Qy5vE85pd4pzVkggPNNjwzA/PRoXG3v8ntlO5/FbKEXstJI6iZdmx+IPe6+gtEmasZcTDR39/6tvwSKlSvJ9G4yenVBJdMo6uwF4NwvdxrVzkpEaoxXikwS0NbbtOoJutTyyO8bIMJgGjVCYpRuNSkKip7UZxft2Y8Hm68fcVj84gAuv/xnZLU3I8lCA2ITKuc+2Q6/TYelNt7sVnyR8Nz78mGh0Ov3JByJ1PpbQof1Q09TK26lGdYXL/aq1YaPqXUMNKgOgJqhQhUoXyECf6mWpRthbIZ82cw5W3eFb+chUg4Unw/jAYH031NIG7ALGgNlsnQvu9HfSVtPpzIFbHSNjWw46ppeoVrTfhxCqLKxpWKxcFYhfWJWFn75b5PY5rmaHj9qvDMIGaOtc1yP28pIi8fPb5uHg5TbsK2tFUUO330J3orDQ9J04NcwK4NygzuUfcH9qZCOcRjqOJTrnrktG3OxY+EpBajR++4Ul2He5Fa/vu4hmw9WpSFexwBivhcHOn3XHpjtFKnxGUzUKyi8graXOx5lJzi9lER6g867Z4jbqSRx993VhlO6riCs5uAdKtRrxMzKE7VFUQhKiEhMd6mPpdsHaa5A1bxEuHz+M8hNHXHpyUhPRrBVrRaSTajndQZ381efPIPQJ7X+YKo0W2x5/Cud37xAXKR4fr0yGpooylBzci7kbt0xxj9LxYeHJMD7Vejagv6JxUtR6qiT+I0cX+mov92ltxjqLvBnvo+I1IPuBQrF+YUYsbpyfip3FTT6LQTqeb23OF01FzlS09uG9M/XYf7kNpsmqNu2gueuGVGuk2axRwEIm6U6RF19rZGk6U76LkY6u8Fd02lArFdhWmIo1qUq89ev/QocsEu2RCSjNX4C++FhAq4BFIRfCctWLe0eeR++4PjUbdWm5mNFYjTWn9yJc57/5Ohm4XykuhjEpFzqjCRqlHFlxESO/LUq1Use7v1z4fKfDfRLSOYuXYs7q9WIEpY3wmFgs2naTWAZ7utDV3ASjwSCEKzVDjSU27UnKmenW/D1kkMkQOWxvFcpQE1hiVrZ3lhwWi4jokmVXb3sbVt1537iNeFMZFp4M40et52RoNMoKU7ucTkTd6/bpdVukc8OHz0GhdW+XRAnOTK2r6NTYRF17J/WJY6j9EKrtxOfjG3JR1zmIYh8jkV9ck41VeQkO64xGM145XiMm5UyV+k56B5ZEDRAznGKn6GeUEugxSBLxo0avwnTXIx0DTWRcItbceBPePX4Sp9ZcIwS1ZawTM32fvSYo+nRoHozDh7NuQ1ZTJVL7G5E81IYIk3ciVC9XoSosG+URM/H6Phpq4FhfmRKjwXVzU6E4snvMmeRDRiOe/tgaof/VzXdB48Jj1BVG/ZDwTS0/fgRz1mwQjSwkRu0Ji44Vi237zoZ61JcUwWymmenhiEufgch4ipyO/jUkZmaLkZY9rS0hG1UkITZe81Uo0NlYhwMvP+/z88tPHEFkXALmb9qG6QoLT4bxU3wCjQhl5kS4jnK5s0wi0enusZF9Ro5vD+NOfGbjXdRTlsou6vWPt83F7/aU4/PSVpE6H08nUq0mGX0/vjEPNy1wtHaibu9//qgE5+q6hvcTmidar1PsajmMuVEO600pYVD1GCR5jQi1AqvzXI90DAaG+cuxT5lsjdK6i6hbLFA0DELRNACZnkZdDgtwADXqTNSorB3RKUNNmNtbgmT9+Mb/NdoMnIxdCoPc/cVUc/cQXjpaDbllCZaGyZA7UClNet/JTogoO3oQTVcuY+vjT44y4m+uuIzSQ/tQV1rsMtWvjYwSUdNZq9c7RENJjC7YfB0OvX7VKSDUoPefPst6MRqq0Mz2g6/9TQxZ9adZ69xnn2BG4TzEpY49VGCqMn1jvQwzTUhRqzAvQivJP3ZqUlocFYYElbTXrCQ+v7dtDv7hlkJkDdfzkbikelIbdF/4SMqoMzoWv31o6SjRSSeDX++6jPMjojO0oPdjXWReTe6p/OtdqPrTHTCbnMaIxmtg1tLAyrFrZGkZz4pq7cwENHTphAl/sBk0m/DLykYR6XQnOmX9BqjOdkJR0w/ore9YNlx+QIv9h9CiScaexE04Hb0EJjfWNbT5mehFOBK/Zlh0ir25PUb6PdG+TsQux9G4lTBLLj1tr2NBT1sLPnv2d2IyEzE02I+Dr72IXc/8FnWlF93Wl5I11PndO/HBf/0Lqs6dGjUZKSw6dAvTycoqMTu0/ZGpTnas+l6KeD/5/htiodtjcW7Xx5iucMSTYfzE0Nkd8tZKd6fG4Z+u+B+ZpZjMXSn+12HN0Dqm222syE3Aipx40YF+trZLzEjv7NdDJpdhRqxW1CBSWj052rWIIgN0WkIVKiX4wfVz8GlxE87VdY/ZZU9RThJU5pSrjTWjkAPG/BioiqwjIf1hV0mLWJRyGebNiMbN89OxMjcOCj/9Wj3hneZOtOmNTv3rVzG39eDE07eN+Hu6s1qyYRtzeTlyJrpVUdjYccjB3oi4ED0fZZE2v0tPRaR1u5qwLLG/lV2nHASG3uT6NuFp2t2+y/7cp9tRsO4afPrn/8NAd6dDZNQeEsFNmlQ0alPRro7HoDwcb+7pQNLxvVhSkIlVuQnQ1BZhsIea+0KTBVvc20yFCiWH949ZauHN91tXUoyB7i5RxzvdYOHJMH6m2wfKgHCEtq/nuthIrIwOF7PaXZnQUGrduXt91DYAVsdGYlVMhF/HYku345Br8UkRr4K0aLF4g8lkxjP7KyQfwykVFOncODvJusxKxIX6Hnx4vgGHKzsgcyputahkMCdoYUrRwmi062au7oTSrIesz2A1VKWTIDXgRKkg6zX4ONjREaPZggt13ThX2428pAh8f9tsZCX4950T1JXd3dIs0pU0kzs2NRUqtRZ6sxlvNXW6FZ0YMkF1yVfBJEOrJgknY5ZiVdeJkbUt6kSURPqT1pWhMjwP6bom/OL1X7vc4kc7PnC4bz/a05NaUBI31AFffeGM+OxcRdno+64Ny8TZ6IUYVISPGNjbqNVZUH+uAR+cbUSUsQ9LNKlIG2pCKKLW+Fa+EywMukF01EnoM2qxoOlKGfKWrsR0g4Unw0gkPpWdodtoRFfpT+em4qmSGnTqjS7F51hQPWWiSonvZKdIEpVwVevpLyeqOtExIE29o9RQZDM1WoOvb5xpXUE1dxkxUMeqsS9JLsQVxMx1GaCRw2yxeakaYOm9ml6XV3XAor367VH6XDZgFNFRKbHp4Kq2AXzntbP4++sLxDQob/07+zs7UHb8MCpOHXNpB9RTuAS7C1eh310jkcUC5ZVe+NOQTdHPqvAcYeqePtQoxNrJ2GXCH9QWGXVV3nDo2UfGibBaRG1oQJHJRFTMFUaZEkfjVqBeS13w1i/MXnQO72Dku+xThmN/wgbM7L+Cpd1nYF+g4WtTlJT0tLfZGa2FHjQEwBW+RrzlcgXa62tZeDIMM3W73ONVSvxqTgZ+eKkOzWOkNZ2hU3OaWoVfzslArCp0R73ROEOyBHI1hnOiI53psVr8yx3zR1k+XerXWUOUWoV1Gabh69e53Ffd20843Ld5nIoaRxevK0of/fg4qAyAnv/LHSX4u/gUZMmNHlkpUTq4eN/nOP/ZdtGI4Ryts0CGkwvXomTWIrcHaBoahIwap9p6YR6udRTrHW7rcPSFr3qQgjfjXMwCpLU0okWdjF6lFN37MgzJtfjePd9C5mD9iNiwRTp/ecNtUI/hB+oR7j4bmQK7o1fgg79+z+vygysReRiSa7Cm86iD+JxIQt3cnnB14UTYBLs3EW+C7K0G+8hBYfrBwpNhplGXe6pGjT/Oz8Ff69rwfkuXOBW5i36SDKJTwZ3JsXg0IxFaufS1flTrWbUzBjnX+x/ruNTcE1Ki0yb8bl+cjodXZ4sGKmcahqSN0NoHPuekRiFCrURNxwC6BvXQu/LU8gBb+v7jC034j6/OGVd0ktXPnhefRfOVMrf7O7ZkI8py5w4ftOtw7cmv3uhyvW1mu3fIxWjLNnUiasIyRqWkfYWipo1RM5FvaB71GIlO+yiXlLWg56IWokPtqwOBDHVhM3DJMBuFfZcQCpDo1Eb4X84RUAJQfyrD9ISFJ8NMM8LkcnwzKxl3psTik9ZuHO7qQ73OMBIBJXmZoVVhbWwkbk6OFV3xgeBqrecnqNp5k9/is61Pj1CATiap0VqszU8U5vgpMe4jUUMBNLUvaezFT28qRGKkBt97w73pudmgG5nvTlOPXHW/02EOGozYWdSEx+Y5eqY6R3H2vfQcWirK3W5TmTUbZXnzEGxIJDZrktGuSXApOil6evW26wgrYR9ZpChih8r95yFFZMyZVnUiLkfmw2J3vN4jw9mwmUjqrUKUqV+ypih/iEu/apwfikTGuRb6VJpgw5uIt0wmF36e0xEWngwTgC53dVzl8LSe0CVNo8ZXMpLEojOb0WkwCtEUp1JCE4Do5ti1no6jNH1homOd9y/PwLVzkpAQqUWY2rNoWgQVzwYI2vP/fX4ZS7PiJJlRT08/UtGOLxiM0Lqx06L54o2XS93uY0itxbHFG607cxFBovS6LdK59L/eQljpoHW9YWgk0rniod9CobSaq5sMepx45cmRbZxxrNNUo0MVjz6F62k/tm3Hi7BufOJNh/s6hVakvp275gMBRU0vROXAbBiAyWDwq/zg4LNfwkEfhTD5hZJ1k1RoIiIRk+xZ3fBEEZua7rKj3Z0wd454O2M2mxA/w+o7O91g4ckwAWg0AnW5xzWHZK2nKyiNTkJ0MpMQocaA3ipUgo1WJcddSzMQ7sGsc3tiXAk4M5D96KuQDafGTbpu1A7Xdmbe/QcotDEwG3Woef3LqHj+LrdRSnp2r86IA5fbJJvcNGQ043RNF9bOTBz92EAfTm//YIznGvH0+y8Cb76I5c9sh0Izhk0URRblaihUo4/7xMtPeSQQSXQ5IsegQgtzAFKmZGdElxokNNxFLH2NjI2Omr4jUfmB7/WYqTNni/+SV6i/tZkU+SPTe5kbv9VQQalSI21WARrLL0lSjypXKJGWb7Pzml6w8GSYaWqxFEpIUes5OzUK9V26CRmP+ZX1eV6LTiI/bPRoUlnXEBQyDTBc4UAic+QxpWZcI3hnXNW9UnrdhsXoeNv+lOr8WtS8Rd6qroRnxanjMJsCH/XzBxksUJsNGJKPFjn2QtU+wrr60WehULkfIUv1op5EO32NjAUK2/vd1LYXkbpWr4RwbEoaMuctQPW5035mGmRQaTWYvWYDJgNz1m5AQ1mJ3/uRyeXIXboC6rBwTEdYeDJMAGDxGfxaTzKe311Cs6iD20C0PCcON8xL8XmcqUomg8FOHMp7DCPm8eK+8qr4s7/tCe5Ega2m05nq17/ssmveBon6hi7XUeWKM6fGbKHXK11H1Cm9PrL/Ibs6S5lhpO7Svv5y9aPPjKSPnQWi/Tt2rtOkGk/tYDuUsh60RtFMcMdyEncpaRKdY3WMxxp6gtYd/pUHfopLkbNEjaov5Qf278N624KBsETEGzo9FsIU7UvMyhGThhZuuwlnd37oxzuyYNWd9zuM9wxl0mfPFVFP8t90FfUcK+Jtj1KpxuKtN2C6wsKTYQIEi0+IutHP2ntQ1DuIywNDGDSZoJLLkBOmQUGEFlsTYpAVppZEfK7KjUdMmAo9g1Yj9WCwKCMWP7yhwOeO13ClAlvio7GzvXvkmMmX090bsEU/vYlSSkFUmxmppiH0QyUM5kcdl8mI7ubRbg72ndxtUXEuBaa77vXT37t7zNpKVwLR8zrNNyAFJGZpLrxMrnA5UUhyVBFCMDo3R3lafuD8GVIE2CDzrnmQJu2k5OWJ2/Ou2SwmLFWcPg5fWLT1RmQvWILJAtV4rrn7C/jof/8dhiGdzyn3lXfdh7Do6TexyAYLT4YJINNVfPabzHi2thU7SFAN65SRP9FmC872DuJC3yBea+rE0ugwfDsrBel+ik+lQo7H1uXgfz67jEBHOekE9MjqbNyxON3vkZL3pcULcW60qU0TuVyOnrdO9ZxU1+ltlNIVVBdqL1xt+8i+/znInKKqJDrndw+gP0OFrnglop28SIn+ri7RLOFpJ/fpp+7ERGI1j/e/ppC+qUdv24i+S1FouFSCgR7XZu++RsacUXg9+mH845d77OhrZe7GzSP1mFSfueaeLyAiNh5Fe3ZaJ2mNJ8ZkMvG8BZu2Yf5m1361oQwJ762PP4nPnv0tDEN6jy84bI1Jy2+9C7mLlmE6w8KTYYIkPmPiQtffU0rIFP1n5fXoMpjGPKWZhnXWud5BfLW4Ct/LScVWP8XnloJkHCpvw6nqzpGJLd6KyiitChFqBRq6R9vVUET1urkpuGF+qtt58d4yQ6vGlzMS8ee6VusKBcWhAuvxZx8Vtf+OSHQ6R0wT+0xCdHbEKiEzA3lJo9Oi/kb7qNlIHMuQbkSULv3tu1CYlVCf90zMjVWnuebRPyPV3IMNHYfF/ZK+clwSc9pdf8oUVRwvwkridaa5BfPnbQDmzRHr9IMD0PX1QSaX4czOj1FbfM63qJgQZ6NFXLSx1+W0pbHKD8aqT6V3EWPo8UgIU10iWR7NWbPe6VDlWLTtRmTOnY8zOz8acTVwawovhhKYcH73Dlw5dRwFazdg1qp1UKrHOs7QIj49Azd/6+9x+K2X0VxRPq4BPn2X1Lm/9p4HkT5n2L92GsPCk2EYSUXnDy7VQG92FDQYR4CSbPmPyiYYLRbc4I/4lMnEeMd/+KAIl5p6vRKfZDmUFKnGL+9ZKLwv+3QGVLYNYFBvhFopR2Z8uOicD4SR9N0pcage1FtT7uFK68Qei+9RykBBh7RgRsyo9erwiHE7ufsgxz98/NaIqJTbzeZ21eFOj8tpfSYgb9L5LBCt22mwovPSSO3i/N6LqNPOwIAy3O3YzPFEp8qsx4PzHdOl1CxiaxhZe88X8Fl3J9rrarwSn2IkrUyGDV/4krCnaqm6MvL8BH3HiFj25L2PV59K32icsXP8Y5LLodaGY8MDj7rtPidroC1ffgK9HW0oP3YYZccOiXT0WPR3deDU9g9Qcng/1t/3MJJz8zFZiIiLx9bHnxIXF6WH96Ol8srVB+k7HE71UIR0zpoNmL1qHVTasZ0cpgssPBkmSEwWf09fGTCZ8Y/l9WLkuK9mI/9b1YzZEVrkDYvPtu2VXvt7atUK/PPt8/HC4Sp8eL5xXA9LinKSQF2RE4enNucjNtzaBBOpVYl56kFBJsP3clKQqFbitTad6xGYXkQpnaO03YP+T0iic+nslEhkJ4wWmdQc4srb0aFJxa7ek0TleHZKI0/LioSqS09u+y4/F0+Y31OMGCN5xVpRWozY2H4Au5M2wSBXeyU+SXRSF/vmriNYsNo6stIVFMHb+pVv4th7b6LyzAmXHpCj9i2XQxMejnX3PSKaWBJmZOKD//kFTHrrcIQoYw9iDV3oUtHIT/9KPOh9pOkaRZf/mNvJZAiPjsWWL38DUQmj3Qyc6WpqwMWDezw/EIsFgz3d2PXMb7H2voeRu3g5Jgv02WTNXywWshPrqK9DX0cbLGZAExmJ+BkZwiReXEwwI7DwZJgg+3uq8zAlxedf6lrROU563RP+vaIRf5ybDXWc71YjGpUCX7tmJjbOTsRbp+pxvKpDBCBIhIpRlhRlHRYBhalRuGNJBlbnxQckmukpdHL64oxELIsMw08qzsJsq0XwA3qvty5Mw47iJnT0611GgG01pONBH9dDq7LdPk5CqYrsdaRuslHIYJgXB1VxJ6Ajx0xPufpLnNVfTq3EDo9Gm/qwtXUPDsevQZeKLjA82bMFEcZ+rO08im03bhu3G5vE57r7HkbOwiU499l2dNTXjkqjy+UKUR+rVKuRv3wNFm69YSRqSlG1LV/6Oj798/+J+3SEc/rLcCx2pcefgvt3Isecvstu08S2yOvs1eux5PpbPEqFt9ZUYv/Lf7UKbC9szWyvf/iNlxAWFS18QicbmvBI8W+AGR8WngwTJKay+Ow2mLC9rXvsms5BHfZue0jcvnbXy1CEjY7SkWSpHNTjTO8gaKhiYnQJqnbm+myxVJAWg//vlhh0DehR1tSLyrZ+DBpMUCvkyEqIEBE8qWo1pWJeTATuXjwDb52u8+bc7RIqEbhxQRoK06Lx0/eKfN4Pxda2zU3GkqyrnenOUCqRInseYffG7KcVUa3nqhf3jt5eLYdhQTyUlb1QtA052E25QlgnKZT44QPfR9Zgrdvtokx92Na6G2WRs1AaORtDcu3wHHf5sMyzjDQiUWqdBOy8/kvIKijArNVrPa/jLZgnls7GOlET2NFQB11/PxQKBaITkxGfkYX0OQVQqUf/Fin9PO+arSje95m4nz1QjYqwHLRpEl1Gaj1JwdMFCV2U3bXxRlRfOIu22moRdSRIAMenZyJt1mzkL1/tcfe10aDHoddfst7x+Ycrw6E3XsJt3/sJVNrQ+nfJSAcLT4YJIlNVfH7e0TPSLOQvNEXyk9YuLF10PbI7pZnlTunzlXkJYpkM3L8iE/vLWtHaN+RXlPLrG2YiOkyFhZmx+PqGXPzpAJUueAcFvnISI/CNa2aOuR15O6bkzXKoSbTH1sBSkTUbB7U+RLOVMhhnRQMpBmibdTC261zqG7nchGx9LRa2nYfWPHqM5qjtYUZB3yXM7ruMJk0K2jQJ6FLGwiBXQWk2ItbYhXh9J9KHGkWKPXPuQqz/AtU6ep/qjkvLEIu3zN+8DVdOHROlDCSHV3cex66krRhSeFcmYBOdqTFafP2afERqlMgZ7rCmSDVFKskaypfU8OVjh9HX2e5WdIrJVcMuB1T768or1GIxQ9fbg9LDe7Fg8/T1uZzqBGcgM8MwjuKzsQD6ijJYer0XAqFIcd+gZF3YJGAv9FpNxYW/5zojMgZJfDZgukClAj++qRAqhVwIBe+xoFDZjdjKI6g6ewpGnQ63LJ6Bp7fNgkZJ+xx/p7ZNClKj8cS1M6FWjm0/RGKFunblSuWYwiW3pgyzK4p9i4pRR3S0Cn2zomBYlQj9/FgY8yNhzIuEYXY09EvioVuRhNINK/D5tbeiM9rzCw0SoCQuF/YUYWPHQWxp24NrOg5gUc8FZA7VQ6NSYtVdD2Djw1+GIsiThigSSil7G+HmQWxu2wOtif7dedG4BAuyEsLxy7sXCNHp8JhcIcY4+iI6STCWHtnvR6TTfl8WXDpyMDi+qMyEwBFPhpkAJutMd3eU9TuamNun10du64Zc3iac0+6dRhN6jCZEKxWSTTaabMxMjsS/3jkfP//wIvqHTJ6NAqVtZDLk9VdhQdcplNXJUWoyQ6lSIX/laqzbeiPmP7wMzx+qxMHytpG6V1u9q03kUpQ1Oz4cX1yTjaSyISGAPYFqEq99+CvY8/wzonDCVTMNvcTiE5/DPNiPS/kLYNG5nlxEODQgDe/LtkcTCaQoFUxRrg3Q22MT8dHWe7Hy7EHMqfCtzEAuVyI2LQ15S1cgb4n7EYeUZq4+fwZN5WVoq60atlSSIzIuHomZOcicv9A639yPGmKqH1x20+049cn7I2UCN7Z8irPRC1ERMXO4JMD192QVpzLcvSQND67JhUoh7Vz0ntYW9He0S7Y/iuxSo05CpvuaYmbyIrOM12Y3gfT09CAmJga1x8sQHRk10YfDMJJjKDuF8DQSnzGTWnzed7YcXcbR0nP3esfpM+7YcvDtUev+tjAXKeqroqJ377uoPqREXdj0EZ8ETWL6074r2H+5zW2HvsxihkUmh9o0hBVdp5ChGx0dJm9JTXgE1j/wCFLz56CzfwjHKjvE7PWGzkEhPuPC1ULw0kQmqn8lEUuR5nlzNYib7fmkFUq3H3j1hZG6QWeefN+zyUGrXtgzXGlp/0a8F28rz+xHgYfikwQjicSVt9+LiNhYEQV0B/lRlh7cLzwpyTrIVaOOrXmIOsJX3n4f0mZZPT99pfzEURz/4E3xOrbX6lNEojwiDw3aNPQq6VwpGxGc0cYezI004iv3bEFKnPvz6GBvN+pKL6KjrgZ9nR1i32HRMcKzMn12gRiR6Y7KMydx6I2rHqquJlfpTUa38+BHp91lWHn7PaKxiZkc9PT1InPlbHR3dyM6mlwX3MMRT4aZQKbKZCOVnCIt/vazO6J2EhjTNfJJNZo/uKEAX1g1gJ1FTThX14WadqtQJDQmnfB3zB6sQcZgvdu54RazBUP9fdj93B9xzcNfQcbc+bhhflpAjjk5Z6ZoEPnw17/weJqPK1LbGpDQ3oyymfNgoFnvPkYMTyzegKTOZiR0Dpv0jwEJxVV33ovIuMRxo3J7//Ys2mqqRta5qm21TXTq6+jA7ud+jzlrr8HyW25364c5HvkrViNlZj5Offwe6kqKhMSMsgxgcc95sRjlSujlWpH+ToqNwrLrb0L2gsVu99fX2YbT2z8aMbu3CWVb+r3q7Emc+tiCpOxc0d3uymuzv6vT5dhQd5OrbALUhrN5vVwuR3+3778bJrRh4ckwISQ+J6vPZ16YBu164yjpSd3r9un1A7daTc83fPgcFFr39iyRchliXdQUTlfxSWTEheMrG/JEyvmz559BXXk5DUmH0uJ5LZzQqhYL9r/yV9z87b9HTHJKwI6XupITM7NRU0xRT4tbY3nnSJhKRMJkoh5Rs/997F+5DUalyk+rKwsOLt+K23a9JvY7Fituu2d80dnfh0//9Bthlu7xEZC5Iw1ZOLwPBt0A1tzzkM+p96j4RFz7yONC8NVePC9smrpbW2AxmaCNikbCjCyk5s8SFwBjvcblE0dw8oO3YbaLntqPPrUXktT5TrZOBeuuxbKbbhNCc2Q70f0/auaBf4RuMpbxExaeDBMKRKUDhh5MVgojtTjZ3T9qvSvLJLFeq3H7GMVOCyLD3J4wp7P4JCrPnkLTpYt+/fEmkUGeiTd+83sUXkKgoGk2tSUXRLTVHlcdzQSlX+0f6w+LRFVG/pii09mOyZUxPZUhdEfHoTE5A+kto+2VbObuS2+8TUQUx4K2O/LWK0J0+jQOE0DF6RNIysrBrFX+pZIjYuNQsPYan55L9kxndnzo8fa291p6eB/6O9ux8aHHRsRneFS0g2D15ALDPtXujNliTfMzUxPuamcYxm82x0dJlmin/VyXOHaN0HTtdofZjHOffjzmJnSC/8WuV8RCt11BQrC9rhYNly8hkKTMnOWzOCMqsuZI5pZAdbCXcwtdPGCdo73pS1/D3I1bxt1PTdFZ1JcW+/W+iJMfv+dXGYI/1JYUeSU6HbBYxPPPfvqJwwWGK+giwrbYC03bBYZtcfUaNLWJmZqw8GSYEECVloaBtuhJa7GUqlFjbWyE8OD09w9SokqBdXFjT4SZruKz6cpl9HWOP1vbE6jZqOzIgXG3C8uKxMVDLegs814kUapdNKX4mFJuTUiVLH1LUc+WBMea1sj4RCy76Q7c/v2fYsaceePvw2JB0eefSjLhymwyoezoIUiJQacT04Mayy+J7nqDfvSsdP3gAI6+9ap/78FiQfH+3WivrRZ3Y1NTxcQhqVCHhbkVs8zkh1PtDBMiTHZz+W9mJeN0URV05LXo4nFKrbvqXreHYkjfz02FykNz7lBIu5vNFtR3DqCirR+9OiOUChnSY8KQlxSJSK1ScuEpV8hhNvkfX6aoZ1PFZRFFHSvdnlIYjWYAFw/VYy6ZoHvR3U4p7MXX34x9f/uL221sxvKu6IhNdCmQKL3uyoJpTDsm6twOi4BepYbaoIc6PFyMsiToc6C6yPCYsd9bV1M9OpukucihiGnZsUNYtO0mv2yWaEb4lVPHUX78CHraWkY9TsJ/1so1yFu6UthBXT5+GPrBfr9rKOmYz+/5FJu++FXRKDV7zQac3/XJuPPox92vXI5ZK9cF3SuVCR78zTJMCDGZxWeSWoW/z03FP11p9LnR4IHUOCyLjvDqORMlPrv69fjoQiO2X2hEj86a0ib9YDvv0mewNDsOty1KF/+VAkqPuxKd9il1g5vbhHNdnVFvQG9HO6ISk8Z8XX/EJ036yV64VKSovU1PG92ID1tNpzOnn7rT4b6r8ZtGhUoIT/3AAIr27nJ4jKyDCtZdg9wly1x2nbdUVTp+yX6iH+jH5aOHMGv1Oq/FJwk8GlF64v23YTAMuT2m7pYmkdYny6eVd9yPS4cP+C0OxeubzaLkYKC7Swj2gjUbcHHfbmEr5e0FxggyGVTaMBRu2OT38TGhCwtPhgkxJrP4XBcXhZ/OBP69olGYkHvSb20zYiLR+diMsbuJQ0J8WizYe6kFf9hbAZ2RjN0dHrp6G8CZmi6cqu7E2pkJYvoPje70h8E+1w1o//25a1/M3+xztLP58bYHR20z1N8/rvB0Jz4NjY2iTGQ81tz9APq7OtBeV+OV+FSQD6TKvfuBLyjd1L0SnY31OPzmyyg9cgDr7nsIMUmO3pVdTQ2Qy+Qwe+EkMB7kydlYXorVdz8ATfj4JSY2/9Cj77yBKyePevYiFgv0gzocfPV5/w7WxX6bK8uRu3i5SO+7E53e7G/NXfdDG+HZ58BMTlh4MkwoMom73DfGRWHmPA3+s7IJF/t1ou7T1Rx3iifR6TtercTf5aRiabQP87uDLT4tFjx/uApvn673KKprM3s/WtGBS029+OVdC5Ea67qb3xPkEk+cIWQeTiVyJT5jLCdh6E0XF0tjoVRrsPUr38TRd15H1blTI13kYx6XXI7Evk7UhYUPm/VchbrX7dPrtkjn0t++C7lGK35wlrZeHPvitWL96p++CXlEBFRqI2RG96LRdkydDXXY/ttfYcuXnxD+lTYMQ0OSRAudqSstxs4//C+u+/q3ofVgWMqJD97xXHSOIP1xk+cniXUy3D/69mt+749qbjPmLpDk2JjQhYUnwzCSM0Orxq8LMlHSr8MnrV043zuIJv3VSFOsUo6CiDDRvb4mNhIKCZo1giE+Xz5WI0Snt6dxEqCdAwb85L3z+L8vLEWE05xsT4lJShEnemd7ou9vvs8hvW6LdH77mruGfTHH9oT0BhKfVTV9uLKnFMtubEJ4XI81Qk8XS07YR0NJfK5/4IvIXrRU1ALS+xDpZRKhw1FQm3m5XKlC/rJVMM0txKutvaMi564sk6zPV0PTYIS8VQezTn91fYsO9DHQp/Z+6q3IHajC3N4ShJldR+joeEwGPXY/9wfc/O0fICrBGhGmOfTWVDskhV6P7Jk+f+HPuPGJ7zp4ZDpD6e2yowcRCtDHoOvrR9GeT2HQO47B9YW+jjZhik/lGczUhYUnw4Rql3tZA6CafOl2GyQq5kaGiYUYNJuhM5mhkskQ6cIcXioCIT6NRjP+fKAC24uafN4Hic/2Pj3+cqAC394626d9JGRkofLcqVHr3Xkikugcyy+RRkJSk423aJsrkT//CCLyZkEWlwKLpQSd3a3oGjCI6LZWKUes3kBVlqNS8ZmF88VCpufU1EMzuYcG+kWEMyYpWXQzZxTME7V+2XoDXm7t9fi41Oc7IJdrIXMShnTfdmljkilxJSIPVeHZWN51Wkx8cgVFNk1Gg0i9UyRSJpMjLiUVlcNG8FJD4pPGVV48sAfzrtnqZhsTjr77ukcR42Bg/UzNKD95zG97KbE/uRylh/az8JzisPBkmBBlMtd6uiJMLhdLMJBSfHYP6PGzD4pxpXW0Qb49ZoMOVS9bayhzHnoFctXolDoFKneVtODWRTOQm+RdExWROX8hTn78LqSyU8pdssLr5zW9eEiIzvRrZqE6fAE+PtGImuoh6I2OxuwLza3QFjcjOm0tVmTHQm3XKERilATmeJY5KWoVNsRF4VDn6KinK0yDOshUw7ep4QajbxMKlRZDxiH8x2v/NWJ07spPksRUa3UlaovPI2v+YiRkZgdc8J3/fCdmr1kPlXr076fuYhEGe2gSVGhg86+g6LAk+zOb0VxxWdSKqqhkgpmSsPBkmBBmqonPYGIvPptetBqHD6njkf2ACxNxNwzqTfjpu0Wo7RyQ7LjkMhk+vtCIpzb//+ydBXhj55m2H7HMzOyxPfYwM0NmkgkzJ02TtmlSStskbfffttvdbbplTNq0aRtmhskkGWZm45iZLcli+K/3k+WRZckWHNmy/d3XdWaE5xwdydJzXnje4TOvvZlUkzVjFhpLycA8cAFENjv+iM7EldPw99pkfHDmFMRiEazWhOEPVgJzYjtQ13gANZ1SbJ2Viuz4CBg6NDBhkVdNScQj2ck4oeqH1mJ1m+GW9Yux5uE32OW9T9/idh2H//3gkOv2x18u72hQZqDATNWrl6FtdcSnoja7EDu6TVCdqoTFZoPk6i8hvrsNqR3NyK8rg9KNV2YgWIxG1J46gcKlK4fdV3PmJIsKChFdFALaDxLizvPdhYBqbN3NhOdMDrjw5HBCHC4+AxOfhXGfohCXYOzRorWsF9Wvwmvx+dz+aiY6BdB4Q1LuB6s68ej6aX6ZeC/cdgNaystgtrrvzqbUurvu9SGIgFnrr0BEXLzPojN6eT6+e1iJhu7mQR9Td1zSJzHxiSSgXWTDn+tacENCBpalq2BoPu61+IyTSfHD/DT8Z2UTcz8YsjWTFdJLKnZbIFXCp2LmIrNnL5QDNZ+dcck4tGAdemITIbZZYSVfWYeNlSIMutQcNKXm4OSsZZh+6TzmXTgM2Qjd8r5AKf3myjK3wrOzvjZkRCfBuvCtFPcUNgqs6elGMv+am7Rw4cnhTCDxKYtrmfLCkyIsHSYzWgwmJghjZBJkK+WQehBxirlb7P+TKIv7FDj0nlfis6xVhe0XhkbBPKXWs297bojwsZn1Q0aIuqbd1QYzOvuNSIz03SooMj4Bi6+/BYdo+oyfKXbyq5y9frPPojNp1TR875ASjT06r6wsmfh04lcHjfh/axMxN73TLj7VLiUQUeluxejimEj8pCAD/1PdAhM1/gzcLm3VAebLonPlgy8MSa87RzqX3fes0336Iel3g8WK84oczFJfwMXCeTg/f/XgOpnodME28Fmj/8sKZqMhIw8bDnyIWFXgU6VsNis6ByYCDbndahm3EZvuoDrT6ctXs6YoDscXuPDkcDgTglqdAR909GF3lwpqFxN1qQiYHRWGa5NisWyELnkSodnwTnx+cJo8G0WDlkgjUf/aA0Ou17lcz79/qJ8m0a7S+yU8iWmLlrLxiMc/fMe3RhMREJeWwWyCWIe2jzWd/6pPRl13c0D+6b84YMCz1+ciNo5EWsuQ+zQXmj1GQpfGRuIfs3Lxu7o2HOvrh8higbhVN0TwU+2mJ+i+A3+/x+19h/79FRwavPY2M5739iWS+OwPi8D2tTfiyt1vIUYduDg0aIbXEwsxrUooKN0fHh2LGWvWM2N6EUSCxjzDvLCU4kxcuPDkcDghDXXDP9fYiffaez16gpptwFmVDqdUOhSEK9gEpdwwhd/i02CyYH9Vl1ei018CXXXxqrWITU1nXdc0PWYkY1GKcpI4nbV2I2Zv3AqJbKADxwfR2Ro9F++eOuFRYJiNBuz40bfZ5Sv+53fMPsnt46w2/O20FU9euWzYfZE4DM0FN5HQgag/Tcf6n8JMVHT34Dcf7UGT2b+BA0JDc+BNMhl2L7sS13zxOsQB1jtS1PPUJ+9DFhaO+PQMNvOeuvzpZMFKpvrjiYhkpohZY9F7TNFzIes7iTg+p31Sw4Unh8MJWbpMZjxe3oAmPVnzuBedDhw/fTVaA75+sQ7/kZ+GFXFRfonPmk6NW9FJ6XXndLqDrJueZv83vPUw+z+HUu/Skbty4yICm2JEpBYU4trv/gC1p0+i/NB+tHV2oCU5A91xyegPs09/iTQZMCMhFptnzUSWlw09rqJTnL0MH++rtgvYAAteqSb04KUudPcbEe9yDGg7JD5dI6H9lGKnUpMBo/qi+DjMNFvQ5Ka6k9Lozv9fvt0wJN1Oz3Ok4pfd93dIZAqYcyJgSXXvEeqN+OyLisX56Qswp/SYX+sYXJfNitL9e2CDldV0UvMO+Z+Sn2hf29BjM9aRTqpBXXfvg4PG+ilkqSVUw5NIhNiUND65aJLDhSeHM4Ew9fRBHlczJeo8qYv5+2UNaDGahtRLjoZlwLfxZ9Ut+HmRBPOi3PtUyudcAbnhQ8SY3sILT29GxcYMKMQi5IYpoWvRuH2Oo6bTFYfgdECi052dkgOlTIy0aGHsYijqFDN3ESqS8vB5lwpm2NgYUoc8pMvnALzZpMJaHXBvegLSlMNFb1upCvozdnN8hbF7iOgk9lR0eGwk8hkbcPhSJ66aMzyy6dieMxE4jP7qk0x8Dt6WVQRRaTdsbAbWZTyl0107253rQUl0iuVKSMUK1jzkisWgG5wPT1OTPBnYk3C6WDgHM8tPQeKh+ctbrE7Pp4giTXxiJRUCzor3moFtxqVnYtWtdyM6KWXwrrCoaGTPmov682cCF582G6avWBP4/nJCGi48OZyJQlQ6tC1Tp7v92YYONFMDkR/PpZ9l+m3++aUWPDc7DxFOYyHpx/vDzj681tKNdvF03DJNheXGj1Hz4SbsX5OMA70aoFUXtC9HqhtdkB3HoodC8HlnH/5Y1w4jdV8P3GZ1Ewmm/3d3q7CvR42vZCbh2uTYwa56Ep1hJ3dhWkklYrLs0SYyh3eIwG6tkZnDu0uvD27Hw2XCNe1OFkyV7e7FvTtoPyKcIqEUAS0w50CEsCAMggwMo0yBhvRc5DZWCbre8exmp9T60htvw7RFSyASDR/+MGfTVjScPxvQe0FRU5qile+HtyxnYsGFJ4czQaCGC/rpJ/E52bvbK7R6fNTZN6JR+O7Nd7HL6z57CZIwN2btAFRmC15s7sRXs5LZbe1GExOjF/ovp2HfSF6Mm3EMd+JzYK99Yoyo34yjscPrIMkY3jnV7mgiotS6zU2TkTsohb9ttvcp75F4vaUL/2jyvqvYOrD9Pze0o9NkxgMZiWgrUzPRmV5SOSTC6Uxrn3uvSkdNpytf/PSJIdev+qW9FMGBxWpjnfH0Plf1G9j7RKcGaUoZiiKUzDjeFef9IhGa31OGFZeSsN86tB7QU2e7I53uERtgUwQ+4EBMpvPxqYILz3FFBDRcPIeCxe59X2OSUjFv69U4+fF7AW1mxa13QeJl0xtn4sLfYQ5noolPtd0/cTLzfluPx0YiX6Ao38cdfbg3IxF9JjO+U9aAXtPwRog3B8TnNVn2Gdjmo+GQnJmOQ9OG1po5p8+tLql16pp2170+5PkiEWZnRmNOZkxgLwzA/h61T6LTlddauxFfb8KGmmPIX1KH+AXuRadDKApNeb8ej16sH3QloAi1452hBrHrk+OwKSGKHTNXaD/jNUbMbjwP1GKI+PTU2U6i0/U+h/G8A2uEbEh6ffB2g97tZbZel7S7VSRCd2xoND0JGW2lGfHqznZEJdpP4lwpWbUe/T3dKD+0z6d1kysDRd5X3X4fErNyBdpjTijDhSeHwwkpKCK3h0YkCqR1dFYbjvZq8HxzFxOdnvpvSXxi4Df15shjuC16P7Bv1TDx6S+UWVdIRfj2xiK/jOOd6TNZ8Ntazx6jIxFdZ0RSh33EocJ8HJmb2xG/IN+j6CRiwtx3wVP3unN63RHp3PjjX0DioaudoLfWQmrTyZXAmWqtAb+qbcWHHb14Ii8VGW5qUpNKVqP3dCNmo3KY+PQVtnmFGLbwy2lkR02nKycfvWHIdbJeGoJIxDrcJxuUCr908hjmXbHN/f0iERZdcxMi4xNxavsHrEFqtPIAWqcyIopFOtMKpgdpzzmhBheeHA4npKjXG2Gwuk+vD17WX64htPb0Q6Q3wyaXMHXnmnYnffNuWy8a9Sava9BIhN5SsB+32fYB+1cLIj5lEjF+eu1MJEb5593pzLttPei3eLawsRp0aP7mFexy+h92QDwQlSPReX/LGcQW2usrE+K7cC43G+kjiE4iI1bJ9t/k6p/qQVyS6PR0H4MinBGef34cW6nU6vHIxTr8YnoWpkcohwmdlBnrUH78c8zODVx8sm72AE8IHEjH2/IoCDjm1o8EvSclq9YhY/oMnN+9g7ktUGOUSCwZ9B5ggtRmgzw8AkVLV2Lmmo2QKflc9qkEF54cDiekaNbbo3GuOGo6Xdl761eGXL/i9edhSVKS0mPXKXJa2q8fUXS6qxl9I2cZbtPsx22r7OKzVWIXUjW5ssG0+2ip9UHkYqQuSEGNzIY8iwVKyfAGDW+htPcHHb1ei+j8k/2QyOwidZu5lKXVP5vhmIMdh0ZkYI3VijCx5/pGiViMWRnRONPYJ0hnO7kOWGNGt5Oi905vs+HJ8gb8dVYukl1qP6+clYqPz86AWHTRrfik1LprOn3YvsAKq1I6zEaJuted0+uOSOeCP70DscKzUKITnfwIu4gl6yGaOBQsKGKYlJuP3uYmGPU6wWemu9LT6p2VU3RSMlbccjcWXn0D2mtr0N3YAL1GxQRoZHw84tOzkJybB7GES5CpCH/XORxOSBForEhS1w9JQz8sWRGwpIWzyTJ+SSWFFG+mLcEttqO4aesBdLfHQd5nxdn6XLydnep9d32EFOZZcagUmfH7ujbWrf9wdjKuSIj2K8JGE5zUI0Q7HWLTUQl8W+4JKOR2kRcf18VEZ5XEycbIakNFvx5zPdhOObh6djpO1Qc+lYcdkzAJbFHepaMp+kni81c1rfhFUaa9JnAAuVSM726ZjsffMmBF2CU/Ip/2T0ZYGmByeSs8WSaR6PRopzRQNrCqpBiLC76PskP7UHPyGKwCzXEfBkUOlUrc9MOfsRrMjoZa9La2wGzQQySVor1a2AYnq9n9SaEnFGERyCqZxRYOxwEXnhwOJ6SIlg6PBopURmz++e+ZSBKNUk/I9IPVLkBFvUaYp8dQyM6vfaEo2Jv981EoboQoGphj7cCa7DPAfuCUNWHIYx2R0CECK1wC84xYu5nmAFqrFb+ubWV1p0/kp0E2QqSR0OjN2F3WhtONfahsU6NPb4aMOrCVYibe8hotsIVLYXEytL9Vtod2kXFsXjikgzWS4ahzFp0DPp+VXgjPRXlxyE+KQG2X1m3Uk1Lrrt3r7qD3x5wVMSi6vXEooMjnabUOx1RaLImJGHJfUUoUfnRVCf73Y0Ac7r34pNMR2oPbUtVQJ6XgbX9PUFwgL9jVcZEIk8Rg+Y23Y8m1NzPT957WZvS1t+Li3p0QCkpZ9/f2sklU2bPnscWB2WTEq//5fQiJRBr40AMOhwtPDocTUuS7jLoUqU2QlfaSsnGZUTNyPSE9VtxngrSsD+YSEn8ir2pGnS8zpkWjsi4HkhYtqpCEGxMuYsXGMyhujxt8iEwPnKvMZpFQxywdW6wM5sIYe+7VDft7NTBfasGPC9KHRPEcmMwWvHqsAe+cbGJjJukhDr1HjxbpLMgt0+P+nBMwRkvx4G/+9/K6ndbz/PU/HLLejfvfGnKdDkuPm05/V6i7/LtXTMe3Xj0Fm58e5vQUa4Ic1gTfa/okA24HrsKTWJwbj1/dMhe/3hEGq+38iOLTMVk0PS6MvZ7C5ChozGZsP1fDhhYEIj5JxF+dFIswp1IKsgeKz8hiS2dDraDCk/AUM5fK5IiIi2ed5kJticZjcjiBwoUnh8MJuYhnbpgcdTojbBYrpJV9LILpT9sHE58qEyRNlHqP9KpmdN81DwwTaubcSFjj5ZBUq/GObgYKYzthjb4sUdJFaqxOsUdCT4oSmbCykR/h6f4R968dGrx9UouFsUPFlEpnwlsnG9Hbb8RIP/X35Z6AbKYaR5WB+YKSdZU3ZMeH44mtxfj5J2VMvvkiPlkEOFIK87Rov/aRpPEJtRYmmxUy0fAo8bSkSPz+9nnYWZaOpnO7h3S7O4+xz0oIw9Vz0rGpJBmyAYEYKZXi0exk/KKm1e22KbU+rHvdBdqjeJmUTYbyRHhMLISEakgj4zxvLyVvGmr6+gSpM6WBB44xmRxOIHDhyeFwQo5rkmPZNB5pkxYig3VE0ekwMSdrH0+RT0mjFtYkJWxK/7/yrNFyWOfGswhsiSwdBxt60aG2R00rZWmwFtZgeWIpitRJbKMGoxHf+Ml/s/v/+JP/GKyzdIUijiVxUYMNR3qTBQe7OnFVAU0iGlnZyTLVeAFzAL399bOIYrISxhQZ9l37ZfaY1R88B4lSMWJNYqrCe/ufZfkJ+K9rZ+KXO8qh1pkGo7CecESArYkKmPOjWNmDt9Fm17Q7pdxrdEYUhbuPmFLn/ZaZqcDM29FXsR+LyuqxsT0Z7UklSIhUoCApks2Hdxdh3hAfjTMqHT7t6vM56kmiUyoW4f9NSx8S7XQlLCqGdXMbtSOfkHiNCCyS6olpi5ah+mRgc+Odu9qnLVwiyLo4UxsuPDkcTsixKSEarzR0QtWqcys6HfWENLLR0/ScIYgASZse5pzLUU+qJ3QWPI5I54hCTSRCTHwY7p2Ti49PX4LRenl9OxGP+uzL5v4m3WURdWpZLGQuJQTOoiUpKQbbkmJZLv33X1ShPl/pVff4JX3O4OVB0d1rg80puEuvxV3dpDMFLlZFozE3KxbP3L0Qrxypx46LrdCbrJCIRWyfbQMjMan+kCKirLkqMxzWeKVf0WZXOg0mj8LTmZiiVYhSHkZSdTXM+ljI8haO+HgSo9/OTYFUDHzY0cfeF2+GVJLMVErE+K+CDJREho26jayZc1B94qggUUgSg5TKb64oRWxqGhO2zqI6OXcaS49TfWkgIzepez5r5lyWuudwAoULTw6HE3KQtc/V8nC8IpCLPNn3iNt1QPblphZPYmwkoUZipDhCiSN9/eh3Iwydu8UtkstRvWpJGiQSz2KpWyPF1fl5+PhsM75ooiafkRt9RoL2SlLr/Rz0JLkUhR5E8UhEKqR4aE0+7lmegxN1PWz2enOvFmazDZFhMpb63m/V44xV2I5uXz4Rjhnv/dUnYaoAZEULR61j/WZOKhbHROJ3ta3oGRjlafUgOEk6Lo+NxDdyUhAn8+7ndPqyVbh07BCE4sTH7w5eDouKRuGSFShcumJQhC6/5S58/Mdf+r8BkQgyuQKLr7lRmB3mTHm48ORwJig2dc2kntduVhlZXZnNReBRlNOBxcNlwjXtLqKcstHCbJL8hQTI2vgolGl0rGfIdeKOvynkTqMZ3UYz3jjROOL2nSO8I5UWjJr/HoBE1XXJsW5Tz96ilEmwsiCRLa7U1bXhQmffsOPkV7R5gPgBf9ZgiU8MiMlFc/NxsFeDnZ0q5gPba7ZHKGnrWWEKLIgOx1WJMcj2UbRTBDJv/iLUnjkZUBTSHTq1Cud27sCFvV9g0bbrUbBkJeJS07H0httw+K1XfF8h8yIVYfWd9zNRy+EIAReeHM5EIyodmgvNiJxpH5k4WcVnQ4/WrYDylFp32Cs5cGftI9ZaYPVTeJI0i5SImVXOzi7VMDEVaAp5T20XOjW++SR6ggTp1j/8FaYFnmeGk4BKUUiZ8AwWlBL/0NY3fNt+RJsdbO9UoTgizCex7I/4pAamtXHRbCF0VgtzF6AaTmmAE44WX3MTWqsqoe9XCy4+aTKQxWTFkXffQFtNNVbeehcKFi1jx+vIO6/bSyC8SPNTep0649fceT/SCosF3UfO1MY/czsOhzNuyNLSYIleBM0FG2w9/s3rnggYLfZ6QUHx0IZNYofEIC2ehA8989GcZMjFYhj88RIahZo2DauNFALWzU9zRz3YJDE7JhHwZF4aFKP4iPoLWROdVAvUROPEJ50q/LOp0+fnMfGZnwaJrRmmFu8m8DgTJpYgSioNWHQS8rBwLLvxNiYCg0ntmRM4+t6b7PK0hUtx9beeQFJ2LrtOU448CU4is3gWrv3uD5FWVBLUfeRMPXjEk8OZoOLTpKZ6Qt9/QCcKEQopE0euv82UYnYwkpG8W6hzxA9IaqyNj8S6uCj7vonFQyx6hEghd/bq3QoRf0sLCLHWDGuMZJh1Eu37TwoyUDxKM4y/tBiM+H55IyshCAavtfZgaUwEZo5ieu+KKC4FUTMU6B25oiHomPR6Fn0cCyqPHkRG8Uxklsxioyyv+Oo30d3cyBqcOuprmKm9xWyBVCZDXFoGG8FZsGgpohKSxmT/OFMPLjw5HE5IkpcYjv2Vw293J7BGMpJ3ZkNmPD5Xed9441zz9/281MH07rQIBY6p+oel2/1NIbMIJXWEuwmABVJawPyHBnA0yeQo5Xg8P22YUb9QdJvM+G5ZA2vM8aorfCDa7Av0Wv5Q345nZuT4XJ9qE9UDahOQFpj3aaANQTqNyj8X/gEMZjMe++htdvk3226Egnxj3UDH5+i7byCjuAQikWSwzpSbwXPGC55q53A4IcnM9JhAfpeHQNIkIzYM3y9Mw3dzU6AUi0b98qP7ZSLg4awk/Oe0tCGm5TMjw9zWePq7b9lKGSJkEn9Gt4+MU+qe6i0fz0vFn2bkBE10UsT2d7VtbBKSQIYEbqECAvLzpKYfX3DUQ0uVVOt5AuOBpqcTVccOeaztJEH5yHuvs4UuC/GeaFW9aCwrDXhdHI4Q8Ignh8MJSUpSo5AeG4aWXp0gtZ5XzUlj0Z8tiTFs7CI1qXzU0Yt2N+ngOKkEVzJvzRg2SWl3txrnNTpU9RugsVggF9NMboDKKIXg6uRYaPrVOFzTPSzdHkhpwVeK0pAXG46CcAWipL51gzvoMZlR3q9nk6QMNiuUIjHywhWYHqFkx8YZspk63Cd8XacnflXTgm/npmJ2pPfNRv40GglJ5ZFDrI5S6KaikaDt1Z89haySWWO2TQ7HE1x4cjickISExG2Ls/Dbzyo8PsZhJD/yeoAopQwbS5IHbyPPxTvS4tlCwoqiZzqLhTXa5IbLkSCVwgwbXm3pwVut3dBabcPsk0QCpJAphhopFWNzQgwuam1uTeP9LS2wycQ4oNXiivQ4RPohOk+rtXizpRvHVFq7N+jAsSRdbBnY9xWxkbg5NQ4zBmpF327r8dp4XQgaDWZ8r7wRc6PC8L28VKTIZSEvPpvKL/otOp0joEaL+8uEa9qdttdRX+vXNjkcoeHCk8PhhCzrpydhT0UHTjf0ejXJxx0klL65oRARcvdfdyRCXc2/G/VG/LiqCY1602C01TW1LkQUluTHYzmpCJeIMS87FtFhMjanPVAcozMvavR4vKIRv56ehTCJ2Otu9L/Ut2FHl5qJTcfrtLi8aNr3Q70a7O/V4LrkGNyUEofTah3GA4pGP3S+Fv9VmIF5XjYcjYf4tFrMrJnHX0HpqOl05cnt7w+5/ufrbh32mP7ebr/2mcMRGi48ORxOSEc9v7u5CN9/8yxaVXq/xOfti7OwNN/7UX8NeiO+U1qPfstok9ID5/bUOKyIs8+3lIrFuH5+Ol44WBf4dsWAOdUehazWGvBsYwe+mZMy6tNUZgu+V96Aep3dT3Q0t0fH/e+392FnlxrjBdWTUonCjwZEtrfd+g7x2XcRY4JRp3Mb7fRWUAYCbZeOUSDDAjgcIeDCk8PhhDQUBfy/m2fjfz8uw8VmlVfPcfhh3r88l4k5bzFarfjPykYmOgOfpO0eiiKS9LgvIwF3pA4VxNfPy8Du8g409ug8pt2dSws8TTKyZEcCcnt6nbZFs8c3xkeNaD9ksdnwH5VNaNAZfU6V056qLWNXs+gOtnUb8L/VLfjbrFwog+RPGgg0iSsQqHvdORrqEKZPbb0Wcol0VO9QLjo5oQAXnhwOJ+SJCZPjqRtn46NzLXj5SD3UejMkYhEsTuKMmaKLRUywTU+NwsNrpyEvMcKn7bzY3IVmA1V3Co+jRpQac76Vk8Kac1yRScR48spifO+NM9AbLd5OvhyaYo+XwzIQ7XT27nyrrXdE4flmaw9rIgp2lNdfaBypYzIU+aW6s6ci8UnNYq+1dOO+DM9Tm1yxd7kHP91O4k8ql8NsNPolKD1ZJtFjPN1nR4SEzOyA95/DEQIuPDkcTlAx2aw41NuPM2odKjU089rMpr9kh8kxPSIMa+Mika6Uj7oeitZcPScdW2am4kh1N8429qKiTY0+nYmJ0Iy4cBQlR2JlYSJyE3wTnARFOd9p7xlReHkjfhwkyqTQW61M9GUq5CiOVLI579PDlSNGnrLiwvHUjXPwH++eZ6/NlxiVNUEBc0G0vQvIeb9tYHPHKZXu2olOaMwWvNDSGbKi0xdIfL7f3os70+OHWGCFQq2nSCRGfHoW2msvDbndf0Hp7XaBtIKigNfD4QgBF54cDicoUD3Zex29eKm5C31m67Cu8GaDCUd6+9n4w0XR4fh6djIyvRCgFBVcVZjIFiHZ1a2CUaBsMYnNmZFKzI4KQ0W/AXU6A06rtKzesiBciTlRYVgcEwGxBwFKkdovXTMdv9lVCXGHfUKRyM0kI+fLhgw5ROlywGC/zVUU00ur0hqwIHp41PPzbhVM45spFxRK+59S6ZhtVqjVembPnjdMeAYbErz5C5eO6TY5HE9w4cnhcASHLIr+q6oZF5wMvl27wp11zimVFl+5UItHs5NxVVIsxoOzai2zAhKitpMijHt6NNjbo2Ei1Pm1n9Po8EZbDxJkEtyVnoBtiTFuI6CtFgtQGANTphmSdj1EvUaItGaPk4z23P/1IdddrZzotdVo9W6F56Ee36c5jQUUYR68rDe4vexOZFNMl8oGvBWeY8m0BYtxavv7sJgCcy+gSKi77nVX6LNVvGotlBH2Jjah8WfuvbsRwJypAxeeHA5HcNH57dJ6tJu8n7risOr5XV07dBYrbnJpuhkLKjR6t6LTX/FD2NwIbsf1LpMFf6hrx+5uFX6Unz7M0skw0P1sU0phpmYhKtELYJQT9bUY3DyfItOVIVrb6ShrcGXfNQ+MKLLptTS4vD+hUuspU4Zh/tZrcPyDtwMSlN4ax0fGxmPOpisRNNGpbkZYRkxA69FVnBhzI3/O+MGFJ4fDEQwSMdRVTA0e/kYO/9rYiWkRSq/9GIWi34Opt7/ix1suqPX4Tlk9flucPUR8Kt35bopErLbUWfw69mP1B8+xmfCeoEYlGhXqislmg8ZPj9RQxZ3gH8taz9Fsi6YvX43Gi+fRVlMVtAlGJDoV4RHY8MDDkMpGL2HxR3RKVMcROVMEUVxgr0FkrYCW5kREpfPo5xSAC08OhyMYNIaSmogCgeTW/9W04p+zctkkobGCGp7GAxLobQYzfnapmXlQOgRLnlLuVjx5amgi0TlSsxNJg1wPUdlQxV+RTeUNkV4a5gcqPklknlT3Y1eXBhc1OrQYjazUIkIiRmG4EvOiw7AlMRYJTicVVHO59p4vY9e//or2upqAItmeiM/Ixuo77kVkXILXz/Flfr3E1sxEJx2zQJHn079l0LYAJnXz0Du5GJ10cOHJ4XAEgX6AX2rpCng9JJA6jWbs6VbjisTAUni+kK2Uo9s0fC68v+LHV/F5XqPHh519uGagxpVM0EmCCiVJqO6xMHz4/spFIkSIRegPwainvyKbhB/Np/cHX8TnGXU/flvbxhrlXJvnyCWBxo5S7fDzTV3YmhiDr2QlIVxidxWQKZTY+OVHcG7ndlzY/TmFKGGzjpAnEIkgFomZHZNRr2MRTRKs9uiqmHWfUfQ0PCYWM9duRNGylRCJJD6JzvC0MsjivP2bE0Z0sjVF5THxKYsbWi9q6umDobkZJizi4nMSwYUnh8MRhFNqHUuxC4F4wBJnLIUnCb2zGh0TLUKIH394ubmLNRtRt3u8TIqF0eGs8SrQhieKAK6MjUSUGyslirAWRihZpDr0pKd/0OuYFUCpxmjik8QeuTG82trNPqsYIbXvSEJv7+zDkT4N/qcoE/kDnxuJVIp5V1yN3LkLUXFoHy6dPOq26UgeHoHCJStQtHQlwmOi0VpVhc7GOvS2NsNsMrFUelxqOhKzc5GSP80uRF3qMEeDRKc8v4iJwPGAtuu6bXlcDVVfw9B8nIvPSQQXnhwORxDOqbXDoj7+4rD+oQabsUq3r4+Pwist4zvPmhqOjvb1Y1msvQP51rR4HFdpPT6ehK83taUkpmmWuieWx0YGXCIRKogHIrv5YYFFo0cSn39t7MDbbT3ssrfVjfS4XpMFj5U14LfFWchzOmmJTUnDkutvxeJrb0Jvexv62lpgMZshUyiYoIxMSBwiJtMKp7PF2zpMRbpq1EimKG78ROdokVAuPicXXHhyOBMYSkVRVCAUfjAuaQ3DooWBQD/UdTojitxM+AkGuWEKzIsKG9fIHwn3c2rdoPCkBqstCdH4vEsVcNTzvfYeZCjlbg3kNydE4x9NHYL5mPqDQmxP+/dbbG7FnLcim55LNlVC4E587utRD4pOX6H30GCx4mdVLXhmZg7kLidVIrGECU1avGWkiCbVYZLoHM9IZqBw8Tn54MKTwxkFTU8XWqrK0d3YAJ1GDRFEiIiLR0JGFtKKioPmjzca9CPIOkFBKTL7F/R4ojZbBBdsmjGe/70gOgKnR4j8eSt+/IWixZXay/ZNxMPZyajS6VGrNQYkPvd0a3BCpcXPCjNQHDF0pGakVIK70xLxXFMnxgOSX1TbOisyHD+51BzQetbFRw4KdyGwpi2ApfNT6Hs+R/veZvw2cnpAtbf0HjYZjCxNf296omCd5e6hjvOJKzodcPE5ueDCk8PxQHdTA07v+BDNFWXsulgsgXWg+N9xmf7PnbcAczdfhYjYsfeedIhPKsof7x8XORUSCoxsDBvNO4wmvCJAc1Sg0FhLZ8IlYvxfURZ+XNXEGpD8hdaqNlvxeHkjfl2cxTqunbklNQ4HetSsxEEIE31vofhrolyKezISESYW4570BLzQ3OXXeqaFK/CtnNSA94nS3HVnT6H88D50NTawJp7seDPMEW1YoAX2JrofP2kx6HD8Ibtn5qJnP4FEMVTgOyDR+k5bD25LjR+1lGSkTnMhO8snjPg0aaD13aKVE0Jw4cnhuGCzWXH28+04v2vHkJnXDtHpfJn+rzl9AnXnz2Dpdbcgf8ESTFVylAqWJhaixtNBlkBd497wWmsPjEGwtRHC1omagshqiWbJ/6OxEyY/d5PixyarDT+rasazLnZVEpEI/12Yie+VN6BR71t01d8IIIlFmViE/yxIZ6KTIOFJVkjPNnYwlyGLl9umKUVP5KchzA8bJWc66+tw4PUXoe7qsFtbDXwm6rul6JqZhbmaUqAL2JsQ2Oxz6no/1KvBuvjoEUUnmdpH5HuK7k0N0cmZXHDhyeG4iM5Db76M6pPHHDeM/hyrFRajEQffeAmGfg1KVm/AVIRqMc3twq2PomCxMu/tYAKBpiXt6OwVtEbV3xrPnDD3Zt8kgjYlxOBfTV3M9N1fSMiR+8DLLd34UsbQVG+MTMIaX/5S347Pu9WsG36kY+K4f1tiNOLlUrzQbO/y9ka0SgYE9X8XZgyLvt6QEsfKHp5uaMNJlW6wc9y58MLRyJYil+L+zESsj4sa0bTdG6pPHsWhN18ZkLP27nUHZokUHycuxtqIMsyFXXy6sisyy+ttreuugMHcDNMIwtMhOrm45EwmuPDkcJy4sPuLy6LTD058/B6iEpORWTILUw3qjKYGEYMAZZkkNK5I8PyDLDSl/Xrox7GxxgGJuJGaqT7t7INBAL9NeqnvtfXgzrThqV6q93w8Pw2bEmPwZms3qwu1DQhFRwDQMiDNlsZE4uaU2EHrosUxkfh1bStqdUa3Dgf0HJFTQxP5WtL23EEC/KmiLDTpjdjfo0GFVs8isVabjU14ouM0PzocC6LCAxacRMPFczj4xssD14YfY1VULGxiMXZHzABSLiI6pY3dbtQb2f8J/f1YUqXC8YHHWw1DyyKc0+5ruiqQm1MNXUIcYtJGKtEZe9FJYltltsJoszLPUTLC53CEhAtPDmcA8sQ78/nHAa2DfgAPvfUKrvvujyAPG9uRj+MN/UBtTYzFh+29AdcI0mTHq5LGzsOTZpWT/BnL2kZ30E/86rgoj/d/0tEnWAOX1mobMdW7gERddDi6TWaU9+uZmCR7K0qJ54UrUBSuHBaRnh6hxF9n5OC8Rodd3Wpc0OhYIw31iFEKnGyOZlOnfmI0kuQyr/aTOvFvG1GcBY5OrcKhQdHpHrPk8v4y8TnAF1tucvv4k4/eMOT693/7t8HL+TnVOJ2QAXFUHu7OzsF4Y7JZcbBXgx0dKnYS5tzUlyiTYk6UElclxWI2DTUYpwlfnMkDF54czgDnqKZzBAxmMx776G12+TfbboRCKnUbLTBq+1F59CBmrt2EqcZ96Qls4hA1yAQSQLwvPRHJXgoTIegyme3lvOOYaicJtyY+khnHu0NrsaLRMNxc3F8oIlmu0Y9YY0jQ/lA0e7l9oNKokDAhcUnLROHMZ5/AZPDcsWIwWfD9/3gRwItskpU/gwOiZ9gjpMRpZQaqJOmYEwLRxON9/fhNbSs6TRZ24uP6d9tpMmNPjwY7uzUoClfg+3lpHstBOJyQEZ5//vOf8ctf/hKtra2YO3cu/vjHP2LJkqnbhMEJPfQaNerPn2H1moFC4rP84D7MWLNxzKIDoeLnSWnTH05Lww8qGiGy+a7j6IdvTlQYbk71bHYeDEIhhqMQi/CVrGSP99fphG3lpTR4tcDrnIgYdVpWXkP13f7g7UjV3S5ilYT/tABN7gP9nvp7YyfeaOtxW0PrjKPOl7x6H75Yi+/lpWLDKCcsHI4ngn669dprr+Gxxx7Dj3/8Y5w8eZIJzy1btqC9XcAuBA4nQNprLwkiOh1oVb3QdI+RNU9UOgzN0TBWV8CmphFz4wuZnv+0IAMysb35xKfnRofhp4UZrMN6LElRSDGeo8rp1X43LxUJHqKdhCEIHfe6EJzPPtY0ll6A1eLDqFeX94Gin4OLk9B0jFR1LO6Ef3Hk2AxHcIdDdBLefvNZBvb7F9Wt2NujDur+cSYvQY94/uY3v8FDDz2EL33pS+z6M888g48++gjPPfccnnzyySGPNRgMbHGgUqmCvXscDqO7uWmIT6dzet2B0eL+MuEu7d7d3IiohMAMor2BjJTJUJmMlWVxbeMe9cSAtc3fZuTilzWtuED1kyN0R9PZL93/5cxE3JAc53WUmHw3P+1U4Yxai6p+PatZpPWkK2SYERmG9fHRbBKRN+srCFcGVBrgL44KycfyUkes7SQcdkNCEhEE79WxxmCyorpTg4ZuLYwWK8JkUuQmhiMnIRxSL45ZV1MDxGIxrC4nnpRed3eZoprONmv+pN2JcLGIlTCMB8f6+gdFp7/8qqYFxRHKMS2J4UwOgio8jUYjTpw4gR/84AeDt9Ef+KZNm3Do0KFhj//5z3+On/70p8HcJQ7HLWSD5A5HTacrT25/f8j1P1936/B1atyvM2jiU01j9loQKqQr5fhNcRbOanSs4eiUSguVU9MCia7sMDk2xkdjS2IMs/LxBo3Zgr81dmBHp/3E1FkuUDSmXm9Cg96E7Z0q5gd5fUosbkyO89g9TZREKhEtEQ/ZP39xlIq6q5dz4GhkylTK8UR+KhO+o0HHSsgyVEr15vspmkKBijY13j/TjP2VnbAMRG6dbDehlImxeUYqrp6ThvRY90buhLqzc5joJL7yp91uH7/v2i8Pue7PJCv6bFyXEgdlEE4mvGkkoppOT59Pi06P3ZvvYpc91bPSISYv2T/VteO/CjPGYK85k4mgCs/Ozk5YLBakpKQMuZ2ul5XZp8E4QwKV0vLOEc+sLO990TgcfxGJhP8BEFFr9hSHoo1zo8LZQjVlPWYL+swWSCBCqkI6bFb1aNA4yf9X2YRe08jNSw5xRt25LzZ345XmbmxNjMZ9GUluvUFlIjG2Jcfi9ZbugDvbadsPZSYy26PPu/rQbHCJjouBmZFhbEQkjXb0tqyAIp65YXLWXS6E+CSRPmMcU73+ojdZ8PyhOnxwphlisQhWp3IB5yy43mTFR+da8PG5FtyzPAc3zM+A2M2xttmE8zLwZqSqZKC0g6ysxoMDPRp0OUVw/YUyGEf6+tFiMCJNwZuNOBO0q12hULCFwxlrohIS3DYXUPe6c3rdEel8auu1kEtG/vOJHIM0+0QTodQh7alrezSoEeZ7ZQ0wWm0+p8XpZ/bjThXrzn08L9XtLO9bUuKwvaOPCWN/456Uuc5WypkBOk0gujs9YaAb3Qiz1cYM0zMUMrcCyBvIYuov9R0QAorwLomNwERCozfjR++eQ01nP7vuLDrd4bj/XwdqUd6qxuNbpw9LvyvCI9iJp+vf/98eXTck1f6Nv+5jl//w1dU4t3A1KnNnDEm5ewOJTrlYhP83LX3UUZnBgjIFI0XjfYHW80WXmn3Ogw3Vr1Mdu7azGLIiPqd9IhNU4ZmYmAiJRIK2tss2EgRdT00NfJ4uhyMU8ZnZQ6aUjFS7SZDo9HSfg4SMsY/Wh0p3u9DorVb8pLKJiU5/YzW2gQjoj6uamfjc6GJQbzdOT8UPK5r8Wr+jVpXGNjqPvaRZ6+R56dU+2mw4p9FhX48GZRodWgwmJhBipBKURCgxIyqMlQ/Q6wgk6kl7d3NqPIv0ThTIOP6/PryI2k6tNwPFhnG4ugtP776Eb2woHHJ7fHomm8vuisJD6YdSJsHK0qNQSCUoLZoHkdXKjOW9ntRUlIFpXn4ehIY+X/S5srpJrw+pYXVzmXBNu9N6aH1jJjpbSHQuDPr2OBNYeMrlcixcuBBffPEFrr/+enYb1dLQ9UcffTSYm+ZwfCIpOxeKiEiPtZ6+IBKLkZw7bewN5Fl3ezNVv0Gej0klPl9u7mJjHoWI0pBmoaYnqq8kw3NnFkZHMFFK9xPebo8EJy0/K8hEvp8WOdQk9Ye6Nlaf6jr1h4Rmq9HExlhSqj4Q0UkCKCtMPuaWVYHyzqkmlLb433BKYnXHhTYsy0/A4tzLae7UgiK3J50jIYINi88dREZbAw4tXIf+8CiIrBbYxMPFqmTgc7QhIQpfy0pm4nO86DVboHETJXbUdLrisIZy4FpGQGuqHpjcFHRM6ew7jjPxCfrpLtVsPvvss/j3v/+N0tJSPPzww+jv7x/scudwQgGxRIrpy1YL4rtJtkzFK9dirKEGI0s0dbdHw9YzNMswkdFZrXi3vVfQrnN6l39R3cIaLVyhSOj/Tc9ks+KdRzyO9AWao5TjjyU5bISjr5Do+VtDO75f3ogmvd0g3nXUJOFwBQhkJCmJY4rA/ue09CFR2fGAShBaDUbmTmAZRfip9Sa8dLje4/1mowEff/9httBlT9BLpqins9CkiCctvqbNifT2Btyw/UVsOPARchovIbL/sjCmE4QZEUrcnhaP5+fkMeP18RSdhCkIllwmAW3oOFODoNd43nbbbejo6MB//ud/MgP5efPmYfv27cMajjic8WbGmvWoOnEYOlWfW09PSq276153jXam5BeO26z2UOxuF6IZQi+w3ySl62kK0L5uDTa4mQk/Jyocz87KxScdvXivrRctRvOg0KQ9cewNNftcnxKHTQnRfgk5EkC/q2vDJ2469IXonneGHpckk+K/CzNZtHc8oDpdcjigaTmtA8eUkA2Yqa9LiMLmhJhhAu3zi+0wCzLcAehQG3CyvgcLcy5HPeddcRV2/uvySEt3afd/f2ej2/vENhsyW+uQ1dbA7NOu/Ob3AakEdNoSauMlPVlyeWuC78s6hYLXdk4+xqS5iNLqPLXOCXWkcgVW33YvPnv2T7D5YVxDolOmDMPym+4IuR+cicxFjW5Y6lkI6OfyvfZet8LT8YN6Y0o88xalNHel1sDmlhMpchkKIxRIlAXmYUiWTw7R6SsO0UnNKiTM3R0jR8SWDBauTYrF/ZmJ42Lh02My44917djfq3Hr6UrWPGVaA8q1BjzX1IkHM5NwXVLs4N/RwUudftV1ukMiFuFIdfcQ4Zk+fQbyFyxBzanjfk8wondkxa13QRbgZyKYkKCPl0nQ7dLV7smL1GGC7wn6JBW5lKsICa/tnJyEVFc7hzPeJOXmY+09X8aeF59jP0DeTjOyi85wbH7oEUTETqzauVCnWmsQXHQS9M6W9+thsFpH7DAm8UN2MUJbxpCIfbqhPeDXQKLzrrR41o1PIr2ZOuhtQKREgunhSsyJDsMVCd77pAoNNZ/8sLIJugGPVE+DBAi6y2gF69w/1KvBT6ZlsFGilzrsXezOOKfULR4uO04onSHPz7LW4VN3llx/MzTdneioq/VRfNpPUpfffAcSs3IQ6tBIWmpeG+l98BZaBVmDBRVe2znp4MKTw3Eho3gmrnr0uzjw+ovoaWlya7XiLDhJnKYXlWDZjbchLCoGocBk6m7XBrGGjNZcozWgONg/nm74sKOXdekHCknmA70a/HVGTshF2im1/nhFo18WWGdUOvxHZSN+mJUCkxtj/x0/+rbb533x0yeGXL/ql08Pe0y7+nIXtwOpTI4ND3wNR999E9Unjw51o/cAfTfIlAosv/lOZM2YjYnAVUmx2N0tzHALil67ukNwOKPBhSeH44bY1HQmPuvOn0HFwX1or68Z9iNEIzbTi2dg+vLVSJ1WFDo/+pOsuz3YqWGKFI41VNv5UXufIA1TtA4yla/UGby2bRoLjFYr/ruqGSY/RCdBzzmv0ePttl6MFSQ+V9xyJ3LmzMOZzz5Bd1PDwN+1aPDk0zFekxoS8xcsZvWhysiRx52GEnMjw1AQrkCNzuA26umNCT5Bf5VXJcYgOkgNU861nZg4h5fjBVx4cjgeEIklyJ2zgC2U1utpboJeo2aZtYi4BMSmpLIfn1AjFGe3BwL9SFZp9UFJtxP+mrkHQrvJzKY4Ccm7bT3Mm5M67L2dhhRM3mzrQZPB5LFS2tvRjG939rI6Vlez+Cv+53eX12U0DEY6N/74F5C4pNddiQ0fuWwiY/oMtnQ3N6L1UiV6mhuh79ew74ToxCTEZ2Qhs3jG2FumCQAJ6e/npeLrF+v8XgdJzTiZFA9kJiEY8NrOyU3o/WpyOCEI1YlR/edEwdHdLrLJ2Jf4RBafxRFKfNjRF7T1Zyhl41K3KjSfd6nZohSLsDkxhjXn0Hz38YBsqt5p6xFmrrxYhIgoGdR9xhFrNx2Q6PR0H1udWITpKd6F0AatliYZeWEKPJabil/VtPr8HpHolIlF+ElhOrPmChq8tnPSMnHGVnA4HJ8xdnRhorMqLgrKIH1ThYtFSJOPvfB0NNoEA2o2+rijF1+9UIvnmzvdepUGm1MqHfrMwmyX4sKaKCnrzBcCipwuyInFVGdzQjR+kJ/K/EapVtMb6M8wTibBr4qzQqqsgzOx4MKTw5msUK1nh4alrCjqOVGhqMrVSbGCf1nRj+36hOhxqc2liFEwodo9EmwvNXfjRxVNbOToWFLWr3MrZii9Pri4jGZ0vs8VfbJSMDulSIUUK6YlCrOyCc66+Gg8OzMXC6PCB6OZrm+beGAhu66rk2Lw91l5QRWdjjQ7fXdxJic81c7hTFKcaz0neqPRPemJ2NOjQZdAYzMd4uya5PGJfOWMYsotFKTVzqp1+N9LLfhpQfqYiewGvdGtUPR3NCMUUiyYnoCTFV1u10updXfd6+64Z3kOZMFMEU8wUhVy/HdRJpr0RnzRrUKZRs8aj6gpLEwiRmG4ErOilNgYH3xLrkHR2RzNprDRdxhn8sGFJ4cziZks4pN+AH88LR3fLa+HyerbhB+PHblJMX7PVQ+UTKUMYWIRdAJPZHIHHavDff34tFOFrUljY/flj33SaKyel476Zg26+o3DGo28gYLMM9NjcOWsVIH3bHKQoZTj3vTxiwRz0Tl14MKTw5mAkLWLurMDXU0N0KlVLD0WHhuP+IxMRMYlDIlsTRbxSRNS/q8oC/+vsgkai9VvYUMpYKrrfChIHbnedtJTA9BHHb2CGHl7w18b2tlIyrGYXBQhFrO0rUXA0YyxSin++/pZePLtc+jTmXwSnyQ6cxLC8cNtJaFje8Zx28WO6HQuOic5XHhyOBMIk0GPyiMHUHZoH7S9PYMm9oRjylJ0UjKKl6/FtMXLIJFKh4hPmMowkSmJDMM/ZuXhLw1t2OWHCTYdKRKdvyzOYlHU8YRGWNLc8rGi32rD7m41tiYGP+qZF67Azu7h04H8Hc1IUHSaLHx+fctc/Oazcpxvsp9wjSQ/HR7wa4uS8bV1+QiX85+8kBadUVx0TgX4XyGHM0FovVSBg6+/BK26b4iZvetYT1VHB46+/ybKDu3FqtvuYZ6DkwmqM/tBfjruzzDixaYu7O5Rs1nfI+GYD07pdZoDHlQbGC8hq6M70uLxcku3MLZD3kw46taMifCcFRkmaKo9VS5lopNIilLgf2+YjZ1lHXjrZCMaurX2efRODVs0FpOYkR6NWxZmDpnLzgkduOicmnDhyeFMAGiE38E3X4aIfmJHbe+136/u6sD2v/wO6+79MtKnz2C3UaeoLNk+93oiptudodnp389Pw7dtKdjVpcZ77T2o1A71eiSolnJtfBSuTY5FQYhZwNyZHo8zai0uavSC10S6Qusv0+owVt6rmQrZiAbyvghmcjVwhtLlG0uSsaE4CTWdWlS2q1HXpWWjNcNkYuQmRqIkLQppMWM/CpXjn18nF51TB5GNZreFKCqVCjExMWg4WoHoCTSSjMMRkuaKUuz85zP+PVkkYqM9tz78bRb5NLW0QKI6jsiZIojiUia8+HRFZ7Wy2esqs4XVUWYoZEhXyEK6ro86iL9+oW5YPWSw+GhhAWSi4Ed8d3ap8FRNa0DroHctQiLCv2bnB200I2ecI57lGmgNxVx4TnBUGjWylhShr68P0dHRIz6WRzw5nBDGqNPi4BsvMuHk1zmizcYakfa//iKu/ub3B2s9Te1lkMcJL/qq+u1WLFqLDXIxkK1UsKagsRINYWIxZkROnCgXvad/rGsbk1S7Ayo5kI2BDl8fH4UvulQ4qdL6LarpuHwzJ4WLzkkIn8U+deHCk8MJYS7u2wWDVutRdBrMZjz20dvs8m+23QjFQDORM1QDqmpvRdWxwyhatkrwfazVGfB2aw/zAKRaS9FATSWV2VF6l64vjg7HTanxmB898WZbB5N9vRqc1ww3TA8WNAFKMUbRXzpZejwvDd8pq0eLweSX+LwpJRZr47gqmWw4WyfxLvapx/hX2HM4HLdYLWZUHDkwrHnIP0QoO7h3UMBSraetpy2gNZptNjaS8eELdfhsQHQS9J95QHQ6rp9QafFERSOeqm6B2jxWSeXQ5922njH9EqYa17EsO6BGsF8XZ2F6hPe1tY7pOfemJ+ArmUkhXSbB8R3u18nhwpPDCVE662th1NobgQLHBlVHG/p7u9gXPX3hay7YYK0/7NfaaP73T6ua2EhGkpGjeVE6pOaebjW+VVqPbpMZUx2qQ6Vop3UMv+wXxURgrKFu9N8UZ+HhrCREDrgJ0PhFVxy3kRXT70uycHf6UD9azsSHi04OwVPtHE6I0tXU6La2k9LrDowW95cJd2n37sZGRMYlDtZ6ai4cRyQOQ5y9zKd9+11tG472aX2uTSQB2mI04QcVjfjTjOwxaXIJVaq0l2eVjwWk4cbCSskd1Oh1Q0ocrk6Owb4eDU6rtCjr16PPRE1g9qk508OVWBMf5VN0lDNx4KKT44ALTw4nRNH29UAkEsNmG5qadtR0uvLk9veHXP/zdbcOuU5G8/0DpvOEv+LzYI8Gn3UNNwf3FoqO1uqMeLm5G/dljN+IvvGm1WAas22RvL8+ORbxA16Y4wWdaGyIj2YLZ+rARSfHGS48ORwX9P0atFSUobu5AZrubpamDouKQVxGJtILpiMibmzMqINjdDZ0pa7iczSLJYq+/qWhfcQtWHR67N581+CIRHcTaWgvXm3pxjUhIIacu/J7TGZWX0jp4WCPlrSNUS87NXqlyKW43w+RT3W8x/r6cV6twyWdHhqzFXKxCLlhCubVuTIuChEhYMbPCV34OEyOK6Hxjc/hhAD9vd04s+Nj1J45CavVwvwv6X+CXT5iv5xeVIJ5W65GfHpmUPcnPDrabTc7da87p9cdkc6ntl4LucTznzQ1KSmjh6dah4jPmfaGI0/i87hKi3ajMPWZ9Mo+6ezDXWkJGC+aDUY2tvJQrwbNBvOgFCTxSQboK+MisS05FilymeDbjh0DiyASnbSdnxdlQuGDkLbabHi3vQevtvSg12xh9ZfUMOaA0uQfdvThj/Vt2JoYi/vSExDJLY84LvDJRBx3cOHJ4QxMBjry3huwms2DXeQO0el6uaWqnC2z12/B7I1XsHR4MCDDd/LgdMVd7SZBotPTfQ4S0t2Pz/RWfH7S0QehoFd2oEczLsJTZ7Hi2cYOJp5ILrn22ZPGajCY8EZrD15r7cENybG4PzNR0ChoQRBrGR0zzGdEKPFEfhqSfRDO7UYTflbVjHKnGlRn0el83WAFE+57u9X40bQ0zInidlmcy5BzhifRabNaoO/vZ3XsivBwiMT8xGWqwIUnZ8pzcd9OnPz4Pa8f7xCmZ7/4BJrebiy/6fagiM/E7BxI5QqYjcI0oUTExiMq0XO61RvxeVrV7zG97vh/37Vfvny7fui+u6bdyQOU0rnSMexebjEY8Xh5IzoGIrcjmTs57nu3vRdH+/rx1PRMn0TcSCTLpEiTS9FqvBxpFUpw0sSmW9PisSUh2qfO8DajCd8urWdRTm+hR/aZLXiyohE/K8zAwuix75znhDYO0alT96Hq2CE0XDyP3tYWZhlHiCVSxKamI2vmbBQsWoawKF4DPJnhwpMzpWksPe+T6HSl+sQRRCcmY9a6TRAaqUyBgsXLUX5ob+BeniIRpq9YPapAdp5sJEPbEOFJFkgacoV3g6Om05V91zww5PrG/W8Ni5yRaEkYozrPTpMJj5U1MGHlyxGlx5IJ+vfKGvCHkhzEygKPzpAgvC4lFn9t6Ax8XVQCopDi+pQ4lESEoTBc4bMVEZ0A/L/KJnZsRrPHcoUdSxuYxdazs/KCUprAmXhpdlMPZUjSYDGZcPaL7ewk3z5NbegHjARod1M9epobcPbzTzBz7SbM3rAFklEyOJyJCa8K50zpcZSH3nzF7jMTAGc/+xh97YHNpPbEzLUbIZXJPe4jpdape50WT2l2Epvh0TEoXLrC6+3aFMNT8hc0OkzkJhv6sft1TZtfwsoR2aMo6R/qAjPed+bKxFgmusUC1HL+V2EmrkuOYyNK/fG/fK21G3U6o1/HxiE+aYjAr2ta/RvvypmUXex6cRQ++tOvcGHvF+wEeqTPBt1Hjzm/+zN88qdfsQgpZ/LBTyc4U5aKwwdg1PUH3D5Owunczh1Ydfu9EBpKOS254VYcePX5gPZv5a13Qyb3vqZQ19QHuUsfUrUH30lKr69+/x/2ywYjDt7y8OB9K17/CyRKBbvsrrudBFfMGDWl7O1VswlKgUDic3+vhqXdlwhgxh4mEePx/FQ8Ud4Y0HoeyEhEllIeUEf/6y3dAZ8CkGg9rdahvF+P4siwANfG8YVWgxGH+/pR0a9Hi97ETgQS5VIUhiuxMCac/T/WolMnm4Ev3nqZNW769D1rs6Gvow07/vpHbH3kO1CE8fKNyQQXnpwpCTXtlB/eJ0hkhs7Q68+dhv6aG6GMiITQ5M1dCG1vD05t/8Ae+fRyn5n5PIm/m+9ESn6h19uzp9sBzYXmIf6e/RYrE4pWL9PsxMFbv+4xzU5QynqsTOTfaul1u/++QjL5rbZuQYQnMS8qHN/OScHvBiKpvn4ir0mKwU0pcQHtAzUH6TyUUfgTff2oo5cLzzGiXmfEs43tbKADxbnpK8IRtRb32313n2sCisIV+HJmEuZHh4+ZX+eJLz5hotOfUiF6jqanC0fffROr77gvKPvLGR94qp0zJdF0dUKnErBD22pBR101gkXxirWYu/lKVvPkTRqVzOIVEZHYeP9Xkb9gic/bczdWkyx1hG4Byg+zR0PHoqGoTCvMeEqKep5S6QQd+3llUgx+PC2djZSUeCnu6P14KDMRj2YnBzxa8oxay9YpBCR6TqqDU5bBuQydNL/T1oOvXaxlNmekNenz7Vwqwa47Tcp6oqKRlYrQyNtgi8623i7UXzjjUXTSBLZH3nudLc7T2Ias02pF3dmTaKkoFXx/OeMHj3hypiRdzYGlNl0hn8/upkZkzZgj6A8LfeGWHdzL7Js8fYGTyBRBBCv9mNhskIdHYPqylShZtR7yMP+jG67m8tlhJW47wMkk3rmL3bmhiFLw7lLsDjYljE33KqV+haayX4+lsd5HuLUWKw73aVCu0aNOb4DRakOURIJpEQrMigzH8tgI/GNWHl5u6cL2zj7ordTtfznATdqSmrFIIK6Oi8TdaYnIDvM/ve56fPyt7XQH1cJqzBbu7RlEnm/uwkstNODCOxzfHh919KHDaMKPCzIEc5NwN5no4odvsu+mQBsjaR0X9+9GWlGJIPvKGX+48ORMSQz9Gp/S1l6vUyBotOWht15Ga1XFyF/elE63WiEPj0TBoqXIKJmJxKwcZk8iBM7ic17hWQApwx7jSVh6mlrkzGKB0tWjUa83DjNBDwSSU3V6I5Z68VgqUXihqRMfd/ZCb8WQ/aCf/WOqfpht3UiSS3FXWjwezkrClzISWRSyQqtHu8EMK2yIk0pRGKHA3KhwNllJSGgikdCoLVx4Bovd3SqfRKcz9NGjtPw/GzvxUFZSUESntq8X7dVVAa+brd9qRUtlGXRqFbdZmiRw4cmZkjBbIYE7b0kg+gMZKfe1t6GntRlmgwE6tZrZjphNxoH7RxAFA6/BqNei9MAeJOdPE0x0uorPsMpjuDmmHG/Kpwuy3sXR4YgaI2FiEqh+0YE9+jj6Os+ptfjf6hb0mC7bNzmLX5vTdYoS/q6uHTu7VfhhfjqWxUayZSyQiIX3UZUFYZ0coNdkYelyh2erP9Dz3mzrYZHzQGpxPY3D7Gqoc/t455Q6TV1zd5lw59DR1ViPzJJZfu8rJ3TgwpMzJYlK8H1u9WjNSpHxvq1T09OJ8oP7maGyyRBYKpjZlIhs2PP8P7D5q99AUrbneev+i8/FWNi8H51JF9GojEWVJD2gdd6cOjYz74lwiUTQ8wxKS4ePMqP8pEqL/6hsBGleX+KJFzR6ZuL+u5LsMZtjn62UoUtAI3uFGEjgHoxB4cOOXmgtnk3IyGXC0fA3UtaBPr0vt3TjvwozBB+H2dveOmTksIPHPnrb7bocY38dkD2c60k9WdZx4Tk54N8MnClJfIawc9apHjPBy3WSSC07sJd1qdPlgM3hL6+Yic/9r76Aa77zBDOgFxL6YcmzrUR+9RcQ5fQCSgwTn/Qj56573fUHb218ZFC7a12ZFq4YcUKRr9hGaYwia5ufVPkuOh2ilqKfP6lqxu+KsyAOwlQnldmCzzr7cFzVj/J+AzQW4VLttLcFYf55iXJG/575oL1XsCa5I339rN4zyUfD/9FmsFvM5IshHFTDTib0nMkBF56cKYkiPBLJeQWsE10I4Uc2Skk5eV6l1fe/9gLqzp4KeJvu12+FtrcbZfv3Ytb6zYKvX56ejoWmtVBd2glMcy8+RxOdqQopHskeXisaTIojlG5nsvsL1Wl68kUkcfCbWuoc9t+6ifazrF/PupZvEjAyTN3MLzd3M7N4RzOR0FbvtL6NibwWLxg0G0zo8WGcqTfvFQ2GWBcvE0x0EjKF0u1giN9su3FIet0R6Xxq67WQj1AiRCfoMsXYOGBwgg+3U+JMWUpWrBFEdIoc4yjFo9crHn3vTdSdO41gQsLHPmZTyBjfZdJysrEqdz0Sq6XI0veiwNLs9ZdNhlKGXxVnIXqMm05oeyvjIgWxDKJ1bIqPZubv7rio0TETdSG6xCkVKpT1TbvRhK9fqGPrpLpSWxBEJx3ecLEIG+K58AwGVTq9x/T64KK/POiBLjvf5+4EimyWfMaU7lF0EnGp6W6/W6l207E4C0267Hyfu++02FT/SgI4oQePeHLGBYOun9kP6dVq9msVGZeAuPR0wdPDI5E5cw4zVm+vveS3AKXao/DoWGZdNBrN5RdRefQgxgLqAO1sqPcqCusP2bk5iJFuxOnGfbBl9EISBpSL3Uc+SWLS0b0xJRb3ZSRC4WcTVqDcmhqPfT3COA/cmOrZsJ3saoTqoFdbrDjQo8G6AIUceY5+p6yB/R/MYZa07q9nJ49a/8rxD7UH9wFPQxycrc0Id2UwVHYhNPGZ2fYBFgIVVtP3bELm8DG+nIkJF56cMcNiNqP29HGUHdqHHnc+miIR0gqmY/ry1cgonhn0GjFa/4pb7sRHf/gla+7xXXxS5ZEIq267B1L5yIKZUkVH339L0C/jkXdNhK6mhqAJTyImMwsrJOsxq/sQSqP0iEUbjlgvp9Dp3ctQyLAqLhJXJcUgVSGM56S/0AzzW1Lj8GZrj98pcHpNd6cnIHeE+k4ayymUbRMJ2LNqXUDCkz5vT1W3MNEppFenKyQ1Kaq8eYy8WaciUsFHONBnTOS7dVJHNBA9cukReRo3lJ4TwMdTguxZcwPyJOaEFlx4csYEssI48PoLUHW0exaUNhtaL1Uwz7bUgulYftMdiIgNbBTgaETExmPzQ4/is7//GSa9zusvSWbaLhJj7d0PICk3f9THkx+nprsTY4VYJGHTmQJF369Bd1MDtKo+exo1JhbxmVmDs5Mp1RaN5VjRdhzr01Ww5EnQqcxidkM0JzpsnKKbnrgvIwGXtAacUml9Fp/0+pfHRuL2EWou+0wWQWvwSMBSrWcgfN6tZqn/YLMiNhJP5qfypqIgkq50X4vpaYjD6g+eg0Tp+SSJmt8yPazTG7/OkZixdgObXOQJSqm7dq+73a7NiplrNni1j5yJAReenKBDNY37X/334PWRIn4O4ddWXYmP/vB/2PTgI4hPF7YD3ZW4tAxc8+0ncOit19BcfmFEw3bHfbEpaVh5692ITfWusabhwlm39iLBw8a+sP3BajGj5tQIkWkACVk5KF6+Brlz58OoVKCjTAlJ2QVIEk9DFx4FUUw+xJnZSJlWAKlsfCOdztBc+J8WpLPmn53daq+8EB3z3bcmRuMbOSmQjCCsVBbh39++AIQs/a293tIVkOfjaMdGJga+kpWMqxNjuOgMMgXhSrfvpSfLJBKdIw1xoM+1pya5QEQnkZiVi+KV61B+cI//WR6RCDNXb0B8Bk+zTya48OQEFYpgkuj0Nd1Cj6f09+d//xO2feNxRMQF1/MxLCoG6+97CK1V5UxwtZSXDheJIhGbCkSlADmz53nVTOSgs6FuDEUnk50s3eUrPS2NzI6JPPNGEhHdjQ0sgn30vTcGPUhjJHJkpmmgSGtBp6kUZ7+QsE7UwiUrMWv9ppBJlcnFYjyZn8bMs//a0IFWo9ltTabjtnSFDF/LTsYSL6Ys0XOEJpA2rCqdAXX64NjQZCik2JIYiy2J0YJPUuK4h2pnl8SE43ifVhCHhkSZBDNHMZD3R3Q6mL/lanQ3N6KjttrnE2H6/iHnkTmbr/LpeZzQh39bcIIGpa4PvPaC3xOC7OLTgINvvswin2NR85lWWMwWivr1tragv6ebibiwyGjEpWeMWsvpCU1PF8YSOna+RgkaS89j74v/hG0gCT1iZHrgR8TZ+L7PYoS1OQzZNhsS09SIjKdpJGa0nvkM1aeOYeWt9yCtsAihwsq4KJYePqnW4mhvP0tpd5jMLKKULJMyC6alcZGYGxnm9WePyguEHM1JEcWcEVKlo1Gq0Qke7fxBfiqWxEQigjcQjQs3pMTjSJ824PXQu3ddctyIPrGBiE5CIpNhw5e+ggOvvoCGi+d8GlOcNXMuVtx6FyR8EMGkg7+jnKBxYe8XrEbQk4Ch8WmOSRbk7+bWRsNqZWn3xgtnkTVrLsYKGjtJwk2oFM+YNBS57L8vjUUd9TXY++JzsLLItP/7qrZaUN8Sjpheu1iKiTUwEdpp6sXOfz6D1XfexxoFQgUSlAujI9giVCqfGo/8sqhxA0mCosjRU6GeqNUZmf2TUELYsU9cdI4fC6LDsSo2Eod6NW6jnt4McaAoeppChhtSYn0eh+kr5FSy5u4vo/b0CZz4+F3oNWqWLXK1e3OUItE89oXbrkfOnAW8dGOSwoUnJ2gd7BWHDwjjkykWo+zQ3jEVnkJDEVOTPrAmEV+OV+7cBV6ntmkmPEWm7YbPgSsUEp/qfvvlPl04sikSmKYGIo3Y9/K/ceWjjwW9bnc82RgfxRqYhNB69NO8Ni7K7+fraZSqwOc8eg+t8WTLQ4KbpjbRX32sVILCCCWLHnMBISzfzElBmVaPHqPZ55Q7nTJIxcCT09JY2Ukgfp3eQu9/3vxFrCacMiuNZaXobKiBrq+PRUHDo2OQmJ2LjOIZyCRHEx/KmDgTDy48OUGBvDGNusDTQZejnlVsfaFSJ+grVBuq7u4UbjzmCFC3/ax13k8tKj+4115S4EVk+tfbboQqORPNqdnojEuGNpxEkQ2R/X1I7OlEZnMN4vs6h0VAB8VnlAkHXnsR2775PRaVnYxsTozBv5o7YQjwraaf3tlRYcgO8785S8ncF4TNtSudXPjpM7OvV4N323pwXnP5xMp5k6lyKa5LjsWVSbHc31MgYmUS/Hp6Jh4vb2TjVa0+fKZkYhH+uzADRV40FQkNCUpKodPCmbrwbwFOUCALHoq8uRMxjoVGpjmgy873uYN8KScqVDc6FqLTUdAfnZTs1WNtNgvKD+7zuhTg4w03Y/v6G3G+eAGaU7LQGx2H3uh4NKXm4mT+DPy/5/+CR957HU1R8cPEp6ElColSMSS6RtSdP4vJCk1JejAzKeD1kGB8NMDRorlhcsG9Ox0epi0GI75b3oD/vtSCUifRSThvss1oxt8aO/Hg+RqcVAlzMsqhVLkcz8zMxRUDvqkjTeVy3DczSom/zczBnKhwL/06hRm4wOE4MzlDDpxxR9NNFi6iYYEWR+TMFcfMXgfu/N3GokGHmmUouko2Qv2qXvYLSpOJ4jMykZJXAJnSvygB1TUe++BNGHXB9VPMnbsQxavWef34nuZmaOl1joDVKe2lioq12wuJhp5U2MgY3+m2HWuuxdzaMsy/cIR9CoZEPtM1aDmxHXlzF2Cycm1SLGtYIjN5f083vpaVFFC0kyiJDBO0sShMLEK2Uo4KrR5PlDVAT0aQAyUBnnBsv9tkwQ8qGvGtnGRcleS5tpDjPVRr+1heKq5LicUH7X3Y26OGxjL0E6cQAwuiInBdShzmR43eKBdoQxGHMxpceHKCQlCaaYIYMdSpenFu1+e4dOIwLCaT3SB+YEoI1T5StFIilSF/wWLM3riFiVFfuzvnbr4Kx94fueg/IEQiZrjvSz2dpyiyI+psFYuxa94a4IM32HWL0cBEpgOJwoMVi0iE89PnQ68Iw/KTu9iRdBafCrTB3F3Bjgt7eFTwJiyNB/Qe/L+CdPy0qpmJT29xeIZ+OSMR1yYHPjyhIEyBHKUM9XpTwAKUomZbEmNY579DdPpSX+j46/1dXTtipBLmKsARhmnhSnw7V8lEPb0/bQb7+x0rlSJLKfP6O4GLTs5YwIUnJygoI917SFL3unN63RHpfGrrtZCPUvOnjAjOD1XNmRM4+vbrMJuNg+lw+t/1h9piNqHq+GHUnD6Bpdffgrz5i33aTtGyVag7e5p1kAcl7W6zoaWqAvN9jEy7M7YfEpn+4M3BiycfvWHI4xY9+8ngZauTtZLjcnl6LqI6ijC7oYJddxafPUcbEZ2YBMiaIc+ffOKT6iv/pzADb7b14F9NnazJxzKK4IyXSfC9vDTWuSwEJDhuTUvAL2taBVnfNUkx+FVNK/Q230TnkH2iWuHaNsyIDOP+nwLDvC/lMrb4ChednLGC/9VzggJ1LbszTHdnmUSQ6PR03+A6gzC9onTfTpz4+D2vH0+C0Ww04MDrL6K/rw+z1m3y+rlsxOa9X8aOZ/4AVWd7UMRnb2sz89ikbXmP/x3Hxx+60u3tzgL1OIBf3PFlRGrVQ8RnUkcqpFGFkLA6sopJKT7JI/HW1Hisi4/CJx19+LRLhU6jedjRzw9X4JqkWKxLiBJ8zOim+Ch83tXHZr4HUu95T3oCGvRGnAlw/Cbtgs5ixcst3Xgk27taZE7wsfW0sS52S3QxF52coMKFJyco0MSJkUZP+kpEfALCBZ7b3nD+jE+i05XTn36AyPh45M7xvlaRZpxv+dq3cOTd11F39hSEhozvqVTAW6N7ZWQUazByF5nev3gTGtJzYDEaB4Xkgj+9A7HCXudqMehxyiUC6onSgjlYfPbA4HUSn7b4ePYDZ8IiGJqPT1rxSVAE6r6MRLb0miyo1xuYt2a4RMSadSg6Gswo2BN5afhGaT26TWafxSft2cLocNyWGs9qNKniN9CpOfT8Tzt78aWMRN7pHkLYFFmAMBa0HI5HuPDkBAUa10hjJWlOe8DiUyRic8GF9AIkY/tDb7/qYvzi+34dfed11nREpsfeQpZQq++4H3nzFmH3838XfIq2Lx541DTlrh7XGh6F5rwSFrFzbhoi0emurnP+n95hR9KdQCWqxFIsPH8YYqcoeNTAGFR34nPwtUxCEUpWOLGysbUFi5dJ8dviLPywohENXtZ7Ov4yVsVF4vG8VBisNhbtFOrTqrcCZ9RaLI/1fbQrR1gozW7q6YOupQ+IEmaYAofjCX6qyQkaszdsGWzQcQel1ql7nRZPaXYSmyTqChYvE3Tfyg7sHhj3GMDPqM0Gk9GA0v27/Hp6ZsksxKQEZpfjCh0rX0bMJWRmuY2OdiSmDGkiGg2JQjlEaDoEqmMxyWToib5ssUQenjHJl187iU+qK6P6MmO5xr5UV7AfRI5wUdenZ+bgjrR4NtaT3l3RCD8KURKaaZ+KH+XbjcartHpBT5FoHyq1YzNUgeNdbacQZvEczmjwiGeQ6WtvRVPZBdY93N/TwzqkI2LjkZCRiYzimYhNmbx/5DHJqZh7xVU4tf0Dv9dB0bgVN98JmZOoESIdXXHkoCBlALSOqqOHMHfzNr9mCidl50HV0TFsfJw/UF0nTf/wdZxdwaJlKD+8b8jx6IlNgthmHWabROl1T81EI4oSmw3dsYlI6O0YMJGeMywy64h8Dk6b7KQoctmkj4AGC/rbMcMGKZ3+DZxE0EjP+zMScWNKHHZ09rGO+/J+/aAFD82aL4lQYmVcJFbHRbLHO2h3qU0NfP+AVoNJ0HVyfIM3FHHGAy48g0RXQx2rH6QJPuxLn1KWAz/sZK7ecOEME2Q0T3vBVdcjyUfBMFGYsWYj1F2dqDp2yK/nU/c4ma+PBDXTaLo60dvWArOR6htliE1NR2R8gtsmm56WZtDI/2kAAFpgSURBVBi1AzMdBcCo16G7uYGJSF/JmTPf72Pj7jiQj6evzFi7gXXrU9OUA6NUYVcGIrtl0tLnd7Pbj9zr3iPUtdvdFfLyNA5EVklkT1++yu3jnH/4SJJoW+iCauDOydn9LiQV/XrWwHROrWWNQFTPSfI+UylnU5DIbHx6hJKZ3N+cGs8WwlFuMVI5y4Blp7AEY50cr+CikzNecOEpMPTjf/bzT3Bu12eDX+LsS92pjs45stTZUIdPn/kdZq7ZiHlbtvnYjRz60DFYesOtiIxLwJnPP2a3jRZppKYkqVyOZTfewepEPaHp6WTz4Em4uTNml4eFoWDxcmZjRNt30N3cGNBrGr7DInYy4Y/wTJ1WhOjE5MDHaYpErK42a8Ysv9LzNNvdWQCLWcOR/zW1w+tARRBbLGw/6T1Nzp026jrsEVBAa0i338AjoB5p0hvx69pWNraSUtjUuOSA3sk6vRGNBiM+7OhDcYQS38tNHWJO7039NFk9CQltkupdOePXxc5FJ2c84MJTQEhgHn77dVw6fshrE3WH2Liw53Po1H1YfvOdk1B8ijFr/WZklMzE2c8+QWPpOXZsyD+SHSNWbyZi9ktk0j5t4RLM3rjVY8MOdWGX7t+D09s/gg1Wj4KNxGjp/t0oO7AX87dsQ/GqtRCJJNCrVW69K/1FLBJDr7FbBfklzG+8DZ/97Y+B7YTNhqU33Obz/HOKRh94/QV01tcOuT1a3QurGzHi6tvpqZlo2O6JRIhR0yQoGwqXrvR6/4b8IKalQVsxNAIqi2uDOFvY+t+Jxs5uFfPWdHzdOItOZxzd7FRX+bWLtfhWTgozhPeWAoFne9N+FkWM/bzwQNFarKzelSLKJpsN4WIJpoXLkROmgFTABsixwCLiNZ2csYcLTwEpP7hnUHT6Q/XJY6zmk9LTk5G41HSsvefLbEpQ66UqVvfqEGxkS5SQnoXUgukjjqWk+sy9L/8TjRfPe7VNuxG8daDsoRqr77w/kEDeCPi/UuqKn7l2Ezv58JeCJcuRNWO2T8/paWnEZ3/7E2uQciWhu90eknLB06QiT93uzsSz+k4xDr/1Kq7+9hOQynwfBykrWghtS8tgBDQcZZDhMERxKVMyArq7W4VfVLf6lLF2CFAycSe8FZ8UncwLk6NOZ/R7DKgzFOscbWZ4KHFercXbbb041KsZtJNyGP8T4WIRtibGsPGVNEd9InSxA1x0csYeLjwFglKlJz/xv4nGwelPP2LdztFJwnY7hxJh0bHIm7+ILb5A0dGDb7yMxtILfm23ofQ8Dr31KtILpwsW7SRoXRED1kD+Mm/L1cx/s+zgHp8tnvLnL8bS627xaXs6tQqf/f0vTHS6ixjHqboQre6BKjLWrQD1BZHNitT2JiiN9gYkTU8Xzu/agXlXXO3X+gYjNAMR0HCKgLZrplwEtNlgxK9qfROdrvy+rg0lEWFez4S/LjkOv6uzC9ZARSc1MJHN00SIcD5T347tXaphHqbOfzlaqw3vtffig45ePJiZhOuTYwW1gAtKF3v0QBkLhzOGTK6c7jhSum83q+/0BM2+fuS919nimIPtDup6v7DPP3ueyU79+dOoPXNiSL2sT9hsqDl1DPp+4RqLHCQEOFWJfqAWXXMjVt/5JcjDlKOKPXo82SAtv+kOLL/lLp+8O1lJyDuvwaTXeSxToK3PqDzj8+twuz2RGDOqzjrvAMoO7IPZFLhTNYuAGortS0sx+qtbYK0/zH5cJ7sV0+9q2wKaRETQnxITr17+TW1KiEKmQsYEWCDQx/ue9ESEOj0mM75ZWocdXfbyjtFOV+l+kw14uqED/1fTCqu/31VBhNd2csab0D/dnADQD+ilE4cFs+epOXkMi7ZdL6iF0ESHUuxH330zMMN3hggXdn/GJiH1d3cJsm80USk2VZgvcGq8SSsoQvWpY8zySdU+fMZ2VHwiS61PW7SMNRT5SkddNZpKRy9VKKgtRVXeDHTFJg4xkXfg3O0+UrQzs6UW6a11Q243G/VoOH/O56j3aBFQUwWgudAMRdLkjoBW6ww4HeDoSodQKuvXo1yrR3HEyKUSBPl5kpn8d8oaAtouWTrleBllHS9MNiub1NSkN/lVWvBFt5q5BzwcgmNBeW0nZzzhwlMAupsaWZpUSJHV1VjPOp45duovnIVBSzO9A8XGphblF89EdU+3/9FTp8hj8QqaqiRc8oAmGxWvWMsWMrnvbWtlVkdUE0mm63R/IJQf2s8ipKN5h4ptNqw6+jk+3nATTFKZW/E5mugM1/Vj2Yk9wypgqbmrs6FWEOHpGgE1taTbvUA7oyHtOYkIHJ504nNHRx8kosv1moFAXfA7OlVeCU+iODIMT+Sn4qlq+0mRr6Ls6qQY3JIi7PjbYPBSczdqdMaATnPfae/F8rhIzAuRWlZe28kJBbjwFIDu5iZ77shFxDin1I0W95cJ16k91IDR09zIhacT9efOCDb7ndZjNhgQnZAUkI0RrScyNp7ZNY0EnZRQmUBTeSkTW1RfSZHXiJhYJGbnsJpeWtx1pFPU21uPV+pQp1KEzsZ69LW1wGq2MEuphKwcpORNQ/bsuZBI5WyggbeG9dGaXlyx5z18vvoaGOQKr8WnyGZDhLYfV+x9F2FGndu62J6WJgQDdxHQSBwevH8yiNBzGp0gotPRXX7Ox+jpuvhoREul+L+aFvSZLKOmoCk1T1+RD2Qk4qaUuJCsfXSm02TCay3dHkWnRafH7s13scvrPnsJEiqPcQP9tfy5rg1/m5k77q+Z13ZyQgUuPAXAqNUySx0r8z68zGMfve328U9uf3/IdRoZ6QxZCxl0WkwlqLu9raYK3Y0N0FGnu0jEZnnHZ2YhJb+A2f0IIToJWg+Js3V3P4DtT/8ONtFQn1XvoHdJhJW33+OxO5tqfisPH8TpHR8yk3lX4azqbIe6u4u5GSgjo7Dw6huQO2eBzz9QJJ6PvfcWmisusm3Qa3HU7GlVvVB1tOPS8cM4+t5bmLZo6RCjeG+I7+vEtTtewZH5a1GXOY1FMj0JUMd9hTUXsPDcIcjMnjMBJkPgNZ5eRUArgL6L9utS5eSIgNbrhT12jXoj+8z48tlbEB2Of8zKw2stXfioow9qi5UJLbFoqKiliOrauCjckZbgdRPTePNxR58g3vb0116nN+GCRodZ4xz15LWdnFCBC08BEEskgg/goFTkVBkpevaL7ag/f4aJMubtyY6mvZaTbpPIFbD4KJZGQ9vXg7j0TKy790Hsfv7vTCR6K2xJ3Ikgxpq7v4TELPfRSKNOiz0vPoe26srB29yt3xF5pPT/gVefR8P5s1hx611eWw1VnzzKGoUc63a3DUcHP9VVkuWXP1BH+tojn6KrPAnl+bPQmJ4LvYt9EqXVs5uqMb36PGLUPaOuk4YEjAUkPh2QCO2vtotPBxNRhBqFOQcbhD4hNF5T5qMtWIREjAcyk3BPRgLOq/Wo7Nej1WhiU47IfqkwXIFZkeGImWBG8ft7NIJYRhEkvA/2asZdeBK8tpMTCnDhKQDRiUluU5e/2XbjkPS6I9L51NZrIR/B6JuEQlRiEsYSEj41p46jo7YanU31LBVNs8djU9LZ/O+8eQsRnSRckTxFV0r37WT2USQ0HYLJnc2R0KJzYAfYf+lFJbjyke/gwGsvspGbFPHx2OE7UE5Bk4ZW3nYP4tMz3T6M6jI///uf0dPa7PP+0CjV3c/rsOH+r4xqBl955ACOvPs6xhKatb7i5C7gJKBVhENP9aY2IEKngcJ4eXb7aNAJhqfjN5YRUInNnoafaOJTLgYMAopPkoU0091faKb7/Ohwtkx0jFYrGnRGt+n1wctOEWfny4Rr2p2ivuX93v9tBANe28kJJbjwFABKB7vDtXbTAYlOT/c5SMjIxlhg0utx6tMPUHX00IAdFAmvy79oOrUardUVOLdzOxNpS66/GZFxgdmgkLA79t4bqDhyAOMFWRGd+ewjJripTIKaXKhkoqmyDL0eag/J3H/6ijWYtmDxiKLw2PtvMdHpT2kAHZvWS3S8P8Xczds8Po5qRY+89wbGk3CDli3+QCcYiVk5GA+GREBb0qG5cHzC1YDmKhUoZx1UwpAVJh/3GsRQoctkdluz6qjpdGXfNQ8Mub5x/1vDHtNi9GyhF2x4bScn1ODCUwBoDnh8ZjZ6mhq89sPzBH35k8AZi4gnzRff9fyzrL7yskhy3X+KRtpva6kqxwe/eQrLbr4DeXMv/3j7CkU6x1N0ElTneHHPzsHrLNVus0EREYkZq9ezKK9Bq2WRyPCYWMRnZHkc4ekMHSNKfweEzYbzuz5D9qx5iEvLGH631YKDr784cnQ2xCHhnzVzznjvxsA8+EXou9g8oSKgs6LCUKUzCNbVPivSu472qUBQ/qLG8c+U13ZyQg0uPAWiZOVaHHjthYDXQ0KieOXaoEcfSHTu+NsfYTGbvI7M0eMsViurRbRZLMhfsMSvmk5Kr4cC7tL6hn4Nm+8eHhOHVbffi6Qc30YwXtjzhTDd9yIxSg/swYqb7xx2V8PFc1B1dmCiQp/t6ctWMfEZCrAf44EfZOcIaCiLzysSY/BWW68g66JU8NYk72e2T3ZipfaiA1etSN3rzul1R6Rz9QfPQaIc+bOcMM41rry2kxNK8MlFApE7dwGSc6fZu4rdQKl16l6nxVOanZ5Lkbb8hb4LOl+gGkSKdPoiOl059NYr6PWlhnGAs198OuLJv7cTnoIJiX/qBv/sb39k1kPe0t/bg9aqcoEGCVhQe/qE2w50Mpb39DkLdWi/yXB/9qYtCEXox5kiQ5oLNjYBKVTJC1NgYXQ48/IMBJJDsyKVKArnwyochEvESJUP/46m2s3BxUlo0mXn+9xFlMn7lMPh2JmYv14hCBmIUzeyTBnmlyig58jkCqy89R5BzcjdcWr7h9CrndPr/nHgjZd8SvVSSp/8LL31kBxP6NhYbVbWmU5NR97Q2TB0Oo8QgwR6yCPWeb9sVjZ5SChrqbGEPuNiqRRr73oAUlloRDu9EZ+OJZgjOC02Gw72aJgv5pfO1WDbiQpcdaICt5+5hJ9UNeH9jl42M9yZb+UkM1ETiPakxMp3c1MD3v/JxrLYyIBFvXNEmU4SOByOHS48Ba71vOKhR9lkGV/EJxOdyjBs/sqjiEoI7vxiaqapOnow4LnyJHzI5N7ZLmg02mouTSzBxPwwraye0hux3NvaJLANlgg9LUOjypruLkGnZAWKtydJ9BmXK8Ox+cFHWL1sqOMQn30X09hCIpQaNIIhPg/2qHHP2Wr85FIzdnWp0WQwsXnfJFi6TRYc7u3Hn+vacduZKrzU0gXzwMleqkKOJ/Ls6VN/NBI953u5qchQTgxvzbFkW1KsIPWzdIwTZVIsiYnAeBDMkyUOx1+48BSY2NR0XPOdHyBrxmx2nUYTesIhTjOLZ+Kabz+JuLTg28vUnj7OInlCQPtfefSgT3Wl7oQZiVvH4jrhyfm+8YCEcndzIxvZORomA1mwCFebKxKLYDYZhrkQhAIFi5dj4wMPs8YrwtOJluP23LkLce33fujR9zRUxSd1wNNCIpQaNIQUnyQgf13Tip9camECk3B3ekN/raSByD7p+aYuPHqxjnVeE6viovAf09KYvZK3ETp6nEwENvZyQ8LoDXNTETK635wQxUoR3EEpdepep8XT1CIMvG8PZiZCPA6OAfQ5pcYiOmlCFO9m54QOvLkoCCgjIrHmrgeY5U3Fof2sGYTqKl1HIWYUz8T05atZXedYWZm019UI1g1Noqy95pLXj2cTidzg74SnsYLEU/nBfciZPX/Ex0lkMkHbV+k9sq9z6LACoZHI5bAYjaN+LphxvkiMBVdeg+kr7A1w1z/+H2guL0PN6RPs804RWbZOqQxx6Rls7GvB4mUsG0BQtLantQl97W2wmk2QKpSIS01nM+hHOkkLle53Q/NxqrKFPB8QRfnWeOaM1WbDz6tbmFE5u+7l8+jdqdMb8Z3Sevy+JBtxMilWx0Uxo/bf1bXhpErH0u8ULXXFcTvVdH4nJxXpPNI5Ig9nJeOESuvVSFB30CnXstgIrI+PwnjgEJ28m50TanDhGUQoukML/Zj393ZD29drt+eJjUVEbMK4+OZ1keWTm3S3v3Plae44Temh8oLRCEbtKtkfUSc6O5a0formisT2UgKBrIboeFFdpdlkHHGiUGxyqttOef83bENM8tAfjKiEJMFm1juIjItH8Yq1zOKKyicIR2TaYTNFAnjagqUoWb1+SDmISCRhJ1C0DD7eamUi0vnz3dVYj7KDe1F35hSs1uHRazoRK1iynJ2IOUTqZBaf77T1YN+A6PQVSgF3GM34ZU0r/qcwgx1nSrs/VZSFGp0BOzr6cF6jQ63ewCYcUTQ0W0kThMJwRWI0pvFGIq+IlErwi6IsfLe8Hv1mq0/ik77piiOUrBRiPP1ReTc7JxThwnMMoC8e+jENhR9Us4cZz4FEHSma643wjIyLGxiHKcyEJ2LV7fdBKpehs76OmbY7xKGqo42JHcHmu9tsrN4yKdtzqjgxW1hDdBKYCRlDyy9IAMYkp/rlKOCJvrZWnN/9Gdbc+SX2PnY11UPd2Q6rxQpFeDibMER1md7YH9HJhUhy+QSDuvJPffohyg/uZWLUU60sfYbIxqr8wD6UrF6HgkXLEJkQz4TtZBOfrQYj/tHUGdB+0FE8rtJiV7d6SLqcut2/mi3chLGpTk6YHH8sycYvqltxsV/v1mbJGcnAe3NlYgy+mp0E5QR1n+BwggkXnlMMGoMp/DqHpoM9kZCR5VYIBjbhKZOJpaTsoT/+ZBcldCOTXqMa8X6KRlLZRFdD/YjNW95AIo3qhKnpzJVpC5fixMfvChbRJSgav+Nvf8CmLz+C3DkLBFknRcLZ6NCBSVCjNmjZbLDazLiw53O2iKUyJGZkYdriZciZM9/r+fVjLT5lcW3sdlFcilci9P323hHfOhrN6JiSQ96RnmoISQS92trNUrl86lDwSFPI8ZviLHzaqcIbrd1oNJjYsXeuqXWUNsyLDsftafGYGwJz2TmcUIULzynY/KRV9w0TLf5GHeVKJUt3e0NyfgFLqbrWu/obDUzKzfcYaQ3KD7EXpQIzV2/AnpeeC3hTJNJKVq1ze9+0hUtw5rOP3Xp8BmYfBez6999w7WM/hDIyKuD93/mvv9pHh/opkKkGtKO+Bu111Tj5yftYftMdyCyZhVASn1pyHWixTzxSpI8eAaVjsb2zz6+awWHromZBnZFNMCrk6fOgQs1BVybFYGtiNKp1BjZ7vV5nhNFmQ4REjGnhCsyIDEOy3LuTcA5nKsPzAFOMpOwct6KMIouOxVloOqKOjsU1rZqQ5X1jFEWsqI5PCPNzEkoly9d4vD8iNk5gayMgMjZu1MdkzpzD6h0DeY303MIlKz1OTSKxvejqyycKQkFRWjopOPpu4DPgL+zdhc762oCjzg7RatT2Y/fzz+LER++GzJhQf7reKVqmEcKnZwD6yyvV6ARbH2dk6LuOamSvSorF17KT8c2cFHw5Mwnr4qNDSnSS7yzvZueEKlx4TjFy5y0SsO7RimmLlvr0nFnrNzM/R+ZcHcCEJ4qeZs60W1Z5SusL2egjlkgRm5Lq1Q8TRebIZsifLm16bdTlvWDbtSM+jo47pZ89HUd/oc9G/YUzbLRpIF6xZz//RNj9GhCbpft34fT2DxCqvp+jiU9q/vGUXh9cnOqw6bLzfa5QurdGRzZeHM5Q0cm72TmhCk+1TzGoIzmjZBaay0sDmiBEAksZGY3smXN8ep4iLAIrbr0Tu/79rH/bFYshlcvZDPORuuTTp89gEU8hxCdtM6N4htdCktLUW776LXz+3NOsycmXWsyEzGxsuP+rkMmVox7/lbfeDRFEqD17EkJCr5c63Bdfc5Nfz68+cSSo06ku7P0CqQXTkVY4HROt8UjvMn3IgaOm0xXHPHAH5BvpDJVH6DyskzP1oBMeU3sELNHFXHRyQhYe8ZyCLLn2JoilnkWUN1FHikCtuPkOFgn0lYzpM7HylruYePJ5wpNcgc0PPjqqQwB5qVJEUAhvSIoCTh8hre8Oinhu+8b3MXPtpgH/yxEikyIRGyW54MprseVr3/TKIYCgY7/y9nuxgo6lgN2z9Hpbqsr9fn7dudNBTYfTCceht14OydGro0U+FULNYRxALAIU9A+Hw+FMEHjEcwoSERuPlTffhb0v/9PvdcxYswFpRSV+Pz9v/mJExMXjwOsvor+ne6Bazb1YcfhWJufmY/nNd3ptSzVvyzbUXzwLi9EScLQzJb/ALweB+VuuRsnKtag6dhhNZRfQ3dI4OPJSKlciPjMT2TNmI3/BEq8F55D9E4nYc0/v+MjuEysQ6o52WMxmn10QSAy6jvkUGirxoNfaWHYBWTN8i7iPd+QzR+nekoq6153T645I5+oPnoPEw3PYY21Abljozr3ncDgcV7jwnKJkz57HomWHXn8ZNthNv0fDMdlm5pqNmLf1moD3ITl3Gq75zpOoPnGMmYuztLTbx+WjeMUaZM6Y41O3OgnsJdfejENvvuzX/rH54mHhWHr9bQF1yVPqnWpbaSHRZDZQDZ8IUoVCsO57oUdp0vts0usg8bG7neo7rS5DB4IBvTfVJ4+FpPAcSXxmK+UIE4ugoxy5E54sk0h0jjaSkYzKORwOZ6LAhecUJm/uQsSnZeDAGy+hu7Heo8G3I+KojIrGipvuCCjS6YpUpkDRslVs0an70N3UyEZr2k3345l5uTsvS28hz0uDth8nP37P3ojjZQqYiU5lODY/9CjCoqIFTRMH8no8rzgIq3QygveWseo4p88jdc2HMu7EpzgqD1ckxuDD9t6ALZXoLU9XSLnw5AwZk2nosAHCfWVxOILDhecUh6bgXPn1x9BRewmVRw+hrboKWtXllK08PByJmTmsi5oMzf2p6fSWsKgYZBTHCL7eGas3IDoxGYfefAVGXf+o88hJ1KQVFGEZdadHxyKUoQjjyY/fFTziSf6szH3AR6h5bLSZ70JB41rNJgM7eZlI4vO65Ax82EHjcwNbNz39ltTxGb3LCc1u9v7qFlhEC3hjESek4cKTw364kvMK2EKQiDEZ9ay+Tz4gJCY6ZDx+3fd+hIojB1FxaN8Qce0M1XJOX7EWmeTFGeKvu6WyDPte+bcghvzO0OtOyPTen9V1pGdUUgpUAdgx+YLVbAFCxz7RK/GZkQ/clZaAF5q73GpPSq27dq8PewyAkgglrkzkoS3OQDd7Tx/M+gXMV5bDCWW48OQMQ6ZUsmWyQfWas9Ztwsy1G6Hp6WJpfUO/hkU5yWaK0vr+NPiMB83lF+1jQSmyKHB0kdaZO8//sZmZxTNQ2tku+MhSV0gYk7VWKEGNYz2tTazBikaG0skbZRXi0jMBJ/F5e24hzkeH4bRKB1+PEjXGR0skeGJaWsifHHHGEFM6N4znTAi48ORMOejHOio+kS0Tkf7ebux5+Z9BEZ1UBytXhtnN6f2Epi5d3LsTwYYiq8Es/fAFdVcHSvfvwaUThwddC5jFlc02WHaQXlSCkhlzkUIlCajEf+UW4n9EETjU1+/1dqjqNkkmxVPTM5ESQpNyOBwOx1tC41ubw+F4zeG3X4PVbBZedBI2GxZfexMbb+ovFD2etnj5gJF8cKKeJOrSphVivCGXgrL9u3Hq04/YZefX6/rayRu1uaIUhUUzMX9xPCJQiZ/kFWJHXAqeqW9Hv9XGhKW7I0a3U2zzuuRY3J+ZCKWAvq0cDoczlnDhyeFMILqbGlhtZ1AQiZA7ZwFy5wZeI7boquvQUl4KnUYVFPFJ6yxYvEzw9fq0DzYLDrz2EmrPnPDu8QPHoaqqFKr2OKzZWoJIVOKKfBHWzZuG3d1q7O/WoFyrQ6/Z/liFGCgIU2JRbASuTIxBvIx/ZXOGw7vZORMJ/i3G4Uwgqo4d8mh7FSg0/nTFLXcKUjdIllEbv/wwdvz1jzDqtYKKTxbtLChCXBrVTY4fxz98z2vR6Qwdi3ZVD/btKMe6bTMHu923JOZhS6Ld1cFotbLIp4Kme/E6Ts4I8G52zkSD52s4nAlE66VKwUWnRCrDkutuweo77xe0ZpJZdT36HTZ/XjBEIra/S2+4HeNJ66UKlB/c4/fzSXy29XTi4gW92/GacrGYpdO56OSMBO9m50xEuPDkcCYINMJS1dUh6DqlcgWuf/w/mYE/mdsLTWRcIps/v/SG29iI1EAgESYWibH2ri8hIjYO4wU1Cx3/4O0Rj5fBbMYj773OFrrsaT1nTx6GGgUeZ7tzOKPCu9k5EwyeaudwxgGz0YCO+lr0NDcyI3RqHSFhlpCRhYTMLLeRR7NRHxTrJH8mM9H+Wy0WZmc0WpRUJJIge/ZcnN/92aBBv78iec3dDyCtYDrGk86GWvS2tQg3+rOmCrPmup/tzuFwOJMNLjw5nDGEjOsv7P4cl04cgdloZMJDNDDv0mqzMmGpiIjE9GWrULJ6PWSKy36qYqnwf64SCVmRjw7NX2+4eB41p4+hs66WTUwiaP9pKlRqQREKFy9HbKr7yMux99+Gtq83oFpPmm2fmJ2D8abx4nmIxRJYBSh5oONRf/405m+9xu1sdw6Hw5lscOHJ4YwR1SeP4uh7b8JiNg0KMPrfNYZJpvbndu1A5bFDWHnr3UidVsRul8mVUEZGQa9RC1qHORpNZRdw+O1XWWTWNWJJl/vaW6Hq7ED5wb3IKJmFZTfcysafOmivqULtaRJUgaFXq3Fh9xeYd8U2jCedDXVuRadzSt1ocX+ZULicQKi7OtmkMHfjNbn45IwE72bnTES48ORwxoBzX2zHmc8/8frxJOhIaH3xj79g1R33IWe23dA9KTsXjWUXBOkSp6hdYnaux/tr+nV448hRlGt0UC+9ihlJhms1SOxpR3pbA5K6WgZitbS/lsGJSu//+n+x7t4HkZJv99ksO7QvoBS7A/LJrDi8H7M3bGETgcYLTU+329sf++htt7c/uf39Idf/fN2twx6j6+uDLEnJxSfHa3g3O2eiwoUnhxNkLh0/7JPodBZaxP5Xn0d4TCySsvOQN38RGi6eE2S/KGqXN294J2xFvx5/qW/DxX49RFEpQBRgG+iu7o2KQ2tKJs6WLEKMqgeLzh1ERmvd5X22Wln95xfPPYNND34dCRnZaLhwTjA7JRpD2VZdyaYAjR/CG/fbnNbJxSdnNHg3O2ciw4UnhxPk8ZZHP3gr4PUceO1FXP3tJ5A1YzbCo2OhU/cNjmL0B4pAks1RfEbW4G20vhdbuvBSc9eAuBINCs7LTxTBOhDnVEXF4ouV21BQW4qlp/ZCMhD1ZPtltWDfy//CytvuEdT+ifa7q7F+XIVnREws+t1EPX+z7cYh6XVHpPOprddCPkoDVrhTaQLBxSdnVHg3O2eCwu2UOFMCs8nIpv5QtIy6yU163Zhs9/yuz+zjLQOx3bFaoenuZJFTMo9fftPtAYlOghqalt1w2+Vt2Gz4c30bXmjuYsblDnHpDotBhyP3rsPh+9azy5dyirFr+VWwiiVD1kcNSBf2fAGhUXcKaynlKwmZOaxMwRWq3XQszkKTLjvf50pEbDwz3HeFxKclmsQnt1ricDiTBx7x5ExaKOVbc+YkKo8cYLZFrmItKj4R0xYtY6MXqWlHaEx6PS6dPCZQmlmE8oP7ULRsNdKKSjBjzQZc3LvT77UtuvrGIR3on3T24f2OPr/WRVHRlpRMnJy5lKXeh5ikX6qE0FiDNP/dWyjaWrp/l2AR3MwZszze74h8whSkMakcDoczxnDhyZmUtFSU4uCbL9s9Mild7CZCqO7uxJnPPsK5XZ9i0bbrUbBkpaCTYtprL8FqNgm0NhtUne3Q9vWwCBnZ75CwK92/2+Prc8XR4LNw2w0oWr5q8PYOownPNLQHtnciES4WzkVO0yUkdbcN3i6E5ZBrpFYRHo7xJLWgEJHxidD0dAXsq0rvR9HSlYLtG4fD4YQ6PNXOmVRQVPPczu344p/PXLYdGkEc0OMtJhOOvPsGq0kkv0qh6G5uYGLPFUqpOxZX2x3n+9yus6mB/U9Tc0hArr33ISjCIwZucy+aHftAgnXL176NklXrhtz/XnsvTFbbqKl1WoyqHlgN+sH76DLdT4vVoMW54gVuty0UJGTj08d3Rjs79lddO+LnilLq1L1Oi7v0OluPWIz8BUu8srQydGh4up3DoM8AfRa0ndG8m50zIeERT86komz/Lpz5zN5B7msdZP2FMzj4hoQ1xAgR+dSpVCxCZxPKdkckgk411MMzq2QW0p74MerOnsKl40dY4w35hDqQypVIyslF0ZIVyCiZOWzKkMVmw8cdvfA2Lnnq0RuGXD/pcl38713QKiMQru8fjOjRNoUU9Ml5BRhvsmbORe68Rag7c8KvelsSncqIKCy6eujxcwdvNOIME50txbybnTNhCYrwrK2txc9+9jPs3LkTra2tSE9Px913340f/ehHkMvlwdgkh4Oelkac3P6B/yuw2VB75gQyps9gtkWBI1zankECx80qpTI5pi1cyhabzYL+3h5YTGY2zjI8Jm5EEV2vN0BjEbBmUiRCR0IqS7k7kIeF2ScdBdoQJRYjJb8AUQmJCAWW33g79BoVWqmO1YfXRq9DrgxndlPyMO/KBsZLfPZbrKjS6tFntoDaqdKUcuQo5ZAIWJLC8RHezc6Z4ARFeJaVlbEGgL/+9a8oKCjA+fPn8dBDD6G/vx+/+tWvgrFJDgdH3nljxPspfe2INpL1jacU6NH330DWzNlsNngghMfGDfFnFMR2JyZ21LnokXHeC7MqrcHt7ZQ6d+CcWp/761fY/2e+ewf7f8Gf3oHYeayn1Yru2MQhwpOOA9WoGvr7A+rGp+jpnA1bECpIZDKsv++rOL3jQ5Tu2zW6Sf5ALS4NAVhx692IjEvwaXtjJT4NVit2dqnxfkcvLrn5fMhEwKq4SFyXHIcZkcO78TkcDmfMhefWrVvZ4iA/Px/l5eV4+umnRxSeBoOBLQ5UKlUwdo8zCaHax86GWsG60WvPnGLd7oGQkJHpVoh4ErwO250R15l52XdTqIgWVWG67uXxh650+3iH4HRAolOicBIfViuMToKdTUfKzEHGpq3Y9a+/+b+jIhGmL18dEml2Z2iC0sKrrkfO7Hk4v/tzNJaeZ+LSbrdEUUHbYINVbHIqSlavR/6CxaxO1B+CLT4vanR4qroFrUazxwYAkw3Y26PBrm4NNsZH4eHsZERLh9tLcYJV20lpdl7byZm4jFmNZ19fH+Lj40d8zM9//nP89Kc/Hatd4kxgKHKm6e5CV1M9NN3dbFQjpZQD9bdkiESoOX0sYOGZlJvHaizNRr0AuyRCbFrGkBnoQkApU2Hn8Nggdkrdk+iiCF/G9JlYcOW1OPnJ0DpWb197asF0LLzqOoQqiVm5WHfPg8xFgU6AepqbYNTrmTCNSU5hRv3URCRE7XCwxOennX34TW3boOAcqQDDMvCh2d2txjmNDr+anolUBS+jCha8tpMzmRgT4VlVVYU//vGPo6bZf/CDH+Cxxx4bEvHMyhI2wsOZ2FCTyqWTR1G2fw/62lvZbWSq7mk6jnN3uGsHuTNDIo02G7oaG5iIDUQoSGUKFC5ZjrKDewSYU25D8fLVEJospcyt8Fz07CdDUu2OJiJKrdvcNBkNPlYkRrSmd/A6Ce+sWbPZ5RlrNkIeHoFj773JBOlox8RxIlGweDkWXXPTsMaoUCQsKhpZM+awJZgI7e95sEfNRCe9t74YYNFju4xmfK+8EU/PyEEUj3wGD17byZkk+PRN/uSTT+IXv/jFiI8pLS1FcXHx4PWmpiaWdr/llltYnedIKBQKtnA47uhta8GB115AT0uTvV5ugJFGMvrbQU7m84Z+TcDG8jPXbWJCmWaMu2tAcdjujATVDsampLEUrdAUhCkGEsJDGZI+d5NaX/r8bg87K0Jiz4CPp0iEklVrmAAf3N6iZUidVoCTn3yAhgtn7V3vYslgOprZL9nsc+rj0jMxf8vVSCu8/H3CEZ4ekxm/qrWfxPmDQ3w+U9+O7+fzFDCHwxFQeH73u9/F/fffP+JjqJ7TQXNzM9avX48VK1bgb38LoL6LM+Vpr6li3pxWy4DIFCKlPgpCmJ8rIyKx/KY7sOeFv/v1fIr6kRhbeevdLLIrNJFSKRbHROBEX79PkS632GyI6lchvqeD7TNNhpq17orh24xLxJo7vwSdqheNpRfQ1dwAdWcni26S0Kfa2LSC6UPmyHM8+3tC1hxQup3GpOos7trg7Fh0euzefBe7vO6zlyAJUw5/DIDPutW4OjkWJbzhSFB4bSdnSgvPpKQktngDRTpJdC5cuBD//Oc/IRbYSJozdVB1tOOLf/2VzTz3tYYzkA5yuZv52f6QNWM2ll5/K468+7pPdagk3mhZf99Xhoy3FJobU+JwtM/uuxkoJZVn2N86WTmtvefLrPPbE2HRsShcuhKFgmx56iFErafGbMGOrr7ATzpYvTDwQXsvF54Cwms7OZORoBRNkehct24dcnJyWF1nR0fH4H2pqaNP6eBwHJAv5cE3X/JLdAbSQU4jEQO1U3KGBFZkfAIOvvESm6jkzWshj8dVt93Don/BZEF0BNbEReJAj8atABkxtT6AiFLjvV2YXlvK6hw33P9VrybycMZXfB5X9cMokI0rNRzt7VXje7ZUiLnPZ8Bw0cmZrARFeH722WesoYiWzMyh4+0E6TrmTBkazp9DZ70wNkneQinttIIiwddLtYrXPvZDHP/oPVSfOMLqGEeCfC93/ftZVudYvGoN8+gMFt/ITkF5vx6dJvNgx7K3iGw2SM1mrDmxE8XLVmDelqshkw9Px3JCT3xW9OshFQFm2/D0+uBlvcHtZcI17U4itl5vRG4Yr9UPBC46OZOZoAhPqgMdrRaUw/GGskN7RzfmFhhqVipatjIo6646dgiXjh/ycqqRjXXxn/j4XbRUlWHN3V9mU4qCQYxMil8XZ+Px8ga0GkwjWum4ik6F1YxHLL1Y+43HWE0rZ+KIzxaD+xMNR02nK/uueWDI9Y3733KzThMXngFi62njopMzaQl9fxLOpIOEXW97G+saJ1FJE1xoIo+rdZHJoEd7zeUJOIHibQc5RSbj0oZG6oWg8sh+nPj4vYFrvoUVWyorsPelf2H9fQ/6bT4+GslyGbPE+UdjB97v6GMjEi0j1PORYFkeF4lv5aQiTsa/Siai+LTYPDcV+YuVZ7U4HM4I8F8LzpjVajaWlaLi0D60VVexSJ4z8vBw5M5ewCKNjkaanubGMd1HEr5SqRzLbrxN8HWrOtpw7IN3/H4+peWbyy+g8vBBFC1fhWARJpHg0ZxUNg7xg85e7O/WsPS7MzFSCZbEROCapFgU+9FIYjGZ0FJVzqZNqTo72HhdRVgY4tMzkJxfgJgkXhs6Vv6esTKJ21Q7da87p9cdkc7VHzwHiXLkaCZ9PjiBpdlNPX1UnDPeu8LhBAUuPDlBh4zeyX+zu7nRY9rcqNWi8thBVBzZj6JlqzD/ymvQ33vZiDzYUBSR9m3tvQ8iPHrkeej+cPS9N0es6fR2jvyJT95Fzrz5UIRF+CT6m8vL2DjHjvpaaPvouNqgjIpBYlYOMqfPQOaMWUMM2rPCFPh6VgpbVGYz2ox28RkvkyLBz+gmRbBprGTl4f0w6nUD9lA2VvctFolROWBflZyTj9mbtga9qYoDFIQr8Gnn8NvdWSax25UKj/cRlLOYFs7re4Wo7eRm8ZzJCheenKBCYmfvS/8cFF0j1Wo67qs8cgCtVeUoCsKkHk+iUxkVhTV33I+k3Ms+tEJGO1svVQiyLovZjOoTx1Cyap1Xj284fwbHPngbWlXvEKN2wqjTQdPZwRqdFBGRbKRl/oIlw0oeoqVStgTqw7r/1RfYSMnLn4XL+2K1Xb7cUV+DL/7xFxQsWY5FV98YtLrWyYy2MxqQlY2abp8XFQEbLruOBIJ4QMiGSbh1nhCik6LXHM5khAtPTtBoq6nC3hefY6lUX2oaKQKm7u7CxT07g7ZvDhEmkclRtHQF5my6EjJFcCI1dedOCdcgZbOh5tTowpME6uG3XkHN6eODU57cGeI7bqN620Nvvoy6c6ex+s77BO1Kby6/iN3P/529r6N18js7X1w6dpil4smaiYtPX9PtgKG5edRaz+wwOWZFKnFRo/e6ocwT9PzrUuICXMvUhItOzlSCC09OUDDp9dj/yvOwMqHhe7MBiTSdRiX4fpE/Z3hsLOJS0uxp5hmzgyY4HXQ21LudtOTXHHkaHdraympkPc0up/toUlJL5UCdnw/NHvScL/7xNDY9+PUhoy4DifbufuEf9s+Bj00nJEA7ai7hyDuvs8lNnOA0Gt2fkchmrXuCUuvuuteHPIYqEhUyrIvnrgaBdLFz0cmZCnDhyQkK53ftgL5fHdBoy8sRQnfTxP2Lcm79+nfG3PKnr73NrX+tv3PkrVYzq3+NSkh0+/xzOz9DM4lOP449HfOuhnqc/Ph9LLnuFp+fP2RdNisOvP6SPcrp5+fANhDhzZk9D5klswLan6mGt+JzTlQ4rkuOwfvtfX7/ldHzHs9PhSxIjgtTBS46OVMB/i3BERyzyYiKIwcESS3b6w0DF53UyJI9e964+EwOzpcXcp3WoVFRB72tzTi/e0dggt9mRcXh/awuMxCay0vR1VgX8OeAPgOnP/2QD5/wU8hYokl8RrOomie+kpWERdHhXrnLOiMaWB7PS0VxBB+VGVgXO4czNeART47gtFRVsA5mIRgUGyRAAxAeJF7mbr4S44EyIgLavh5B58h76mq/uH/k0Zbeds9TTeqFvTuRnFcAfyk/tE+Q2lb6DPS2taCzoQ5J2bkBrWvKRj7V1CHd4vkxIjF+UpiOZxs68W57L4tIjPauUXo9QiLG9/NSsTSWp9gDqe2kEwNE8y52ztSARzw5gtPdWD9glSMcYhoXGcD854XbrkdUQhLGg4TMbJbmd4VEn2NxFpqOOfKOxRVlZBRbXDEbDag9fUKQSDOto6nsAnRq/yIxZOHUWl0p2MQpErDkdMDxH4qqkdAZSXx+PTsZv5qeiVlR9rpn+osjn0+R02VCIQa2JcfiH7PyuOgUQHRSVJqn2TlTBR7x5ASpplHYEZcz1m7Axb072Xp9FTMz125i3qDjRUp+ASqPHhRkXSToU6cVur2PfFJdjfkDpbO+Flkz58Kk16Gx9AK6Guuh6mxjXfNkwRSflomUvGlIzMkbYsOk6uiA1al5KmBsNmY4z/GTqHSvutwdNZ+/mp6NBr0RZ9RaVPUboDJb2HlfukKGwgglFkVHIJzbJvkNF52cqQwXnhzBIGFQemAP6s+fEaQu05nYlFRs+dq3cOC156Hq6hw17U4RMrFUisVX34Rpi5YO86YcS0i4ycPCmG+mIHPkl7qfI9/b0uK2JMHf7nmK0nbW16GlqhJVxw/DajYN8QKlY9pw4Sw7EYhOSsacjVuRM2cBu13fr4GQULpdp1Yj2J/f+gtn0NXQAFVXBzvJCY+KQUJWNjOzzygugYgi71NknGaWUs4WjE+iYGpYJ0XzLnbO1IMLT07A0AjEM59/jIv7dg3MERe+CUQRHsFS1tu++QRrfCk7uBf9vd3sPnsaW8TSuyRQpHIFE5szVm9AROz4+wpKpFLMWn8FTg7Oafd/jnxSdh6Scqe5vd9o0LPjT8dBiO55muJdenAvE7uOKLOzFyirvx0QuRTh3P/q86g9exIrbr7L5yYVbxCJg3PyQNOcjr3/JhOeFFG2v1b769L29rDbyw/uZROt5m3Zhrz5i8f1RGYsxSdHWLhfJ4fDhScnQExGPXY+9wxLyZIIcRU9QhGXnsn+l8hkKFm9HsWr1rIO7q7GRmh6OmGz2ljHenxmFhIyswTxoBSSklVrUXv2FJs/71fdo0jEBPbyW+70KHqkNMpSwBIH2k/v99Uu1JrKLuKzv/0RK++4F0JCgjAqIVnQdZJwPvv5xzi367PBY+o8TcmBQ2zT9KeDb7yE2rOnsfr2eyFTKie0+JTFtXHhOYZw0cnh2OHCk+M3lIr87G9/RndTffA2IhIhOiFpmA0SRfbi0jLZMhGgFO36e76M7U//ngkYX8QniSJ6vWvvfgBR8e69O4mY5FS3lkOBdM/7Cr2u3vZWnPzofcjDI2DU9guzXpsVCZnCvdd0nI69/xYqDu8bvO4tLZWl+Owff8bmhx4RdMJTKHW5c4SFi04O5zK8OpzjF+ruTrz/6/8JrugcYPqKsZnZHmzComOx9evf9skSiNLr1MSz4YGHkT59xoiPpWivu85/f7vnAxGfzRUXEZ+eyfZfCOhVZc2YA6Gg+fQO0enP66PI9bH33sRk7nLnCAMXnRzOULjw5Pg1e/uD3z4FNTX5BBGK9IVFRiN/wRJMFsKiYrD5K9/AkmtvYTWDzjWqDhzWS2KpjDUSXfvYDz12srt6e2YUzxRM7AWKitwNhBgiIBYjs2Q2wmPsxytQyCLq2Afu6169hV5X9cljaK4oxcTtco9mgoiLz7ERnbKihVx0cjg81c7xlbbqSux+/u9DmkyCBaU/V9xyZ9BnqY81lDYvWr4KhcuWo6WyAu211ehuaoRRr2WNSLEpaayRKovmyCt9mwYzY9U6NJWeRyhAJQWZM2ahqfRCQFOH6LkWswlnP/8Eidm5TIR7mlPvDRWHD7D1CSGIz+38FOlFJZho8EajsRedHA7HDheeHK8x6rTY98q/Bffo9MTCbTcgrbAYkxWq+yTRIqRwSckvZBHimtPH3UYbvemeFwqK3EYnJkOV2M6i435HP202tFSWoaWqnK2D6n2LV63HjNXrfBagJGKFGudK6+ioq4Gqow3RSSmYaHDxGTy46ORwPBMaOTnOhODM55/AoO33GL0iv8hH3nudLc7ekb5GkahzffnNd6Jk1boA93hqsviaGxGVmDzuKXcr1UK2NGPzg48gKj4hoP2hz5xDLJJH6OkdH+KjP/wSfe2tPq1H090Jg8Aeo+11NZN+ljvHe7jo5HBGhgtPjlfQ7PWqY4cEG4HoDhImWTPn4Jrv/ADTFi7FZIQEVH9vD4uS0f+BpKA9Qen5K77yDcSl+jj7WXBvShsTidRUdeUj30X+wHsqyDhVmw2qznZs/8tv0dPS6PXTelqaIHRUlxqNJjJMfIr4nHAh4KKTwxkdnmrneEXjxfPMKD6Y3PD4jwVrIAklaIxl/YWzqDp2mPmd0kx1B2R2T3WLBYuXIXvmnIBqF52hdPTWr38HF3Z/jnM7d9hHjXoQuSwSabOxqUO1Z04Isv0h6x4Qw8tvvB2Fi5eh4tB+5mka6HhPOgkym4z44rlnWAOWPCx81OcYdfqAtjlsH2CDSS/sOsezy10eV8PT7X7CRSeH4x1ceHK8orOxbsi4xEDHMbpClkGTUXS2XqrEwTdehLavd2Cq0NCIMYnQtkuVaK0qZ69/5a13szpNISARO3vjVhQuXYmq40fQcOEMS307BJ9YLEVsaioyimehcOlyVuIgpPAk0RmbPLT2MTErly3LbrqdpcnP7foc9edOBSQ+qfzj+IdvY8Utd4/6eGreEhIRRGw061Sa5c4ZDhedHI73TIJvTM5Y0Nfe5raT3d9xjK62SYlZOZhMUHTx/K5PceazTwbGiNpN0N0/1n67TqXCZ8/+CXM3X8VGbAo1llEZGYVZ6zaxhSbzGLRatn+K8PBhEVYSvySSBcFmQ3xGtpubbazjvbe1FY0XzgW+mQFro9kbtiIqwbPBPhHjIoQD3rbNipgJ2FjkCm808h8uOjkc3+DCk+MVwUyzkxDJKBrZHH2iQSluEp2Ety4Ajsed+exjVgdJQlFoaL0kRD1Bc8gv7v1CsFre7FmXTd8Nun5UHj6IisP7mfAUEoquVh49gAVXXjfi42JS0lik12oNLM3v/NmlEa2TAS4+fRObjmYsKlHgopPD8R4uPDleIQ8PtzefuNQJCjGOkVK8efMXYbLQUV+L0599FNA6qGs7dVoBS0uPJUVLVjDhKUij2IzZgyb59edP4/Dbr8Gk1wWloYqEcktlOXDl6Kn2nLnzUXvmpNu57L5CEeKk3Mkjzrj49HESESONi04Oxwd4VzvHK+LTMgdTxkKPY6QUqUw5OUziSVQdeuuVgNPk9PxDb74SFJE2EhFx8Zi17oqAO9yp7nHhVdezy+e+2I69L/0TxiCJTge9bS1eNSwVr1gjiOgkSlauY36skwlniyU+2Wgo1vrDQycRDSwcDsd7uPDkeEVybr5gP9bOUbH4zGzMXLMek4W26iqo2lsDTlXT86n5pr2math9JK7UXR3sfp2qV3AxN3vDZiRkZAfku7ns+luZiC0/tJf5vzKCLKLpmJHt12jQVKiiZavcnkj51DiVmo7pK1ZjMsLFp3vRydPqHE7g8FQ7xytSCwoRHhsHbW+PMCsUiRAVn4gN931FGF/HEKH61DH2eoQQ6bSe6lPHWZc7pagvnTyG6pNH0dvSMqRGkcogUvMKUbh0BVILigISVAQ1HG184Gv44Lc/h06t8n2/RSJIFUrmVXrio/cC2hfft+3da59/5TVsVGmfHycJJDqlUjlW33GfYPZXoQhPu1+Gi04ORzgm77cmR/Af9NkbrsCRt1/z+BhfxjFmlczCspvvgCIsApOJzvoawSLDtB4ayVh1/DCOf/A286x0FzU0arVoKD2H+gtnEJ+RxSyZYpJTA9p23dlTfolOtt82G/a99E/mTzpW41UJuVLp9Wx7mVyJTQ8+gp3PPY1uMpX3MhpLopO2semBhwM+xhMBLj656ORwhIan2jleU7BoOVLyCwIexTj3iquw9p4HJ53oJMGl7uwUdJ0UNTz81it20/kRxJEjakeTeT76/S9Rd+6039vU9HTi+IfvIBCsNivaay8FddLVEEQixGfm+FRb6zDZn71+C/tMjxQtdXzmqWGKzOpJ4E8VpmLanV4jCU4uOjkc4eERT47X0I/6qtvvw6fP/B79vd1+iYoFV16LGWs2YnJiHdMInzvoPbHBin2v/AsS6YPILJnl8zrOfv4pm7Me2I6MbVMUbS93zgKfn0ap8rmbr0TR0uWoPHoItedOQ93RNqRuljrzM4pnoGjZSsSlZWIqMpUinw6xaRPbR7zqWvq46ORwBIQLT45PhEVFY8vXvoU9L/6DjX/0Bkc0adHVN7CmjskKdTeTNVSwR4t6hQ04+PqLuOaxH7L3zFvIa1Moq6GxRKZQInfefL+fT/Pk52y6ki1U0qDrszdtKSIjJ11k3l+mgvgk0dlf3QKzfgEQZX/fZUUF471bHM6kgqfaOX6Kz28yISkPt3850zhN1+ioIz1JzTFXf+vxSS06HcSlZSA0sMFkNODkJ74197Rfqgp4hvp4sOiaGyGVKQRZl1QmR1RiMqKTUrjonEJpd2fRyWyS0tLYwuFwhIVHPDl+R/eKV65jYrLh4nk2b7yzsR4GrQZikRgxqanMkidn9nxEJyVjqpCSV4Cuxvqxq20cAdoHil7SNB9vo55dzY3sJMLdeNRQhE5wMktmI3/BkvHelSkd+XQw0SKgDuFMU4icRSeHwwkeXHhyAoJq5HJmz2MLByhYvAwX9nyOUIHSxdRoRKbp3qDt66Ms/YQhragEq26/V7C59hw/xKdJM3Bj84RKvztqOWFKh6HDBouIi04OZyzgwpPDEZCohCRkz5qHhotnQyLqSYKsu7Hehycg5GE1wxBj3tZtKFm1dtJNDppo4lNrsF+XdGhCvvbTXYQTUelAtP31cDic4MOFJ4cjMEuuuxmtl8ph0uvHfOSlKyR+e1qbvX58REwcQhlFeCSLKhctXckmI3HGF2exFuqNRzzCyeGEBlx4cjgCo4yMYj6lX/zjaZpv6Zv4pJSxwGLV4kOzUEJmVkh2tJP3a/78xQiPieNp9QlY++lgrMSoa9MTj3ByOKEDF54cTpCajDY9+HXsfek5GLRar9Lujqk4Rm2/oPuiUIZ7/VgaECCcJRQJxMBENB2TxKwczFq3OeBRoJxxqv0cvHNsakBZZLN9qBsBj3ByOKEDF54cTpBIzp2Gax/7EU5t/wCXjh9h03woWucsQklYUUSUnACmLVqKeVu24e2nfiKYFyh1qMdnZvnkhzltwVJUHjsoQI2qjQnZ9ppLfpUc0LGi5rUVt9ztUXSquzpw6cQRtNdUo7e1CWajGWKpFLGpqUjKycO0BUsQm5oe4OvgBFL7OUgnOSuUuY2ECgVFNjUXbLBEFw+9g0c4OZyQgQtPDieIyMPCsfSG2zD3im3M2ohM97ubG9kITKlcgfj0TDbTPHfuAjbC0SFYWy9VCNKcRLZItD5fmL3xCtScPs58QP1N+4vEEsSlZ2Dd/V/BvhefQ3NluU/rcgwdWH/fQ4hKSBx2v07Vi6PvvYmGi+fs4t3pWFmNZnacydaqdN8u5iO77Mbb3a6HExzciry0NGgrqBhUFbTtsshm9CIuMjmcEEZkG+/uhxFQqVSIiYlBw9EKREdGjffucDhjQsOFM9jz4nOCrIsM/m/6wX9BIvXtHLP2zAnsf/V5v7bJIpVSKa76xvcQk5TKDOkp6lu6f/cwkejp+RFxCVh1+z1IzModdn9zRSn2vfQvmM1Gr0sYaFl+0x3Im7fIr9fEEQ5TS0tQ189FJ4cz9qg0amQtKUJfXx+io0f2jeYRTw4nxKD56mS6r+7qDDjqOXPNBp9FJ5E7dyF0ajVOfPSOTw1PJPAovb/h/q8y0UlQunzBVdcjPj0L5/d8jr42u/AYbBJi66dNWFnUd/qK1ShZvYFNEHInOnf9+2/21L2X+8Tm11utOPDaC2w7efO5+BxPuDDkcKY2XHhyOCEGpalX3nI3tj/92wDWIUZcajpmrF7v9zpKVq1DZEIiDr/5Cow6LROGnjdoF6exKWlYeevdg3WVJBApenpx7070tDQN7hs9lsnGAQEZGZ+IwiXL2TZJqLpDp+5jkU5fRKcrh958BfGZmYOimMPhcDhjC28T5XBCkISsHCy54Va/nkvCjiKHa+/+MhOxgZBVMgvXfu+HmLNpC7OJct4GE5ADxCanYumNt+PKRx4bFJ1Uh7nzn8+wSGOvk5coi0C6iMf+3m6Wjj/89qvM/9QdR999k6XXA7GbssHKxGcIVxhxOBzOpIZHPDmcEKVw8QpIpXImxqwsXeydv2ZMcirW3fugYAbrirAIzN6wFbPWb0ZfWxu6mhqg16iZ8IyMjWcRxIjYhCH+mpqeLux45g/QaeyNJKMJPUdJQc3pE+hpacbmhx5ljVkOqOyApkGNhMFsxmMfvc0u/2bbjVC4KTGg7VDjUWdDLZKyQ8vgnMPhcKYCXHhyxh0SJZ11NWivq2aiw2TQQyKVMQGVmJWNtMLpHtOvkx2qR0zKzcOxD95GU+n5gTS1vR7SAdVUUvc6WSHNXLsJM9asD8rxotGUFM0czZ7IbDLgi3/8hYlOX2tU6fG9bS2suYp8UB02StUnj3jVmOQNFAWuOnaEC08Oh8MZB6bmrzknZARnzaljOLdzB/NjdKRuSVxQ9IyscijKR53ZJSvXYMbqjczcfKoRGZeA9fc+xKKItWdOoauxDr2tLWwikTI8AgmZ2cwyKWvWHLcNOWPNmR0fQ9Pd5Xc6m97/tupKVB45iKJlq9htbbXVgohO+/otaK+pEmRdHA6Hw/ENLjw54wKlave/9gJaq8oHb3MWFvYaQHtqmSb5nPl8O2pOncDqO+9DXFompiIkQGet24RQRqvqRdmBPYLUUJ7e8SGmLVrGuvJ7W5o9ptcdGC3uLxOuaXd1dyezeZqqkXQOh8MZL/i3LmfM0alV+PSZ37OGEq+x2ZhY+PSZP7AUrDt/R874U3X0kGDz5o06HerPnWblBmaj0e1jHDWdrjy5/f0h1/98nUujls0Gi4kLTw6Hwxlr+LcuRxCorq+roR7dTY0smkniIzI+AQkZWYhLT2f1gYTNZsGu559lotOf+j+L2YSd//wrrn3sh0O6rDmhQWPZBcFS4lR60VJZxoQnRT0pQikkZHLP4XA4nLGFf/NyAk6tXtjzBZtFTmMgqTbTLjJtrOGFCI+OZabg01esQcXh/ehurPd7eyRqqPno6HtvYM1dDwj4SjhC1E5Sc5hw67Oio76WXY5NTUNHXc2wx1D3unN63RHpfGrrtZCPEM2kkyJ/jPU5HA6HExj8m5fjN5dOHMGx999iUUhHlItq+2w28zBxeurTD1Fx5CD0/eqAt0vbqj9/hnlDjtZhzQk+1GFP70fpvt1eWz55C4ue0/z6nHx0NtQNi6a6s0wiSHR6uo8iqbQ+DofD4Yw93ECe4xdnPvsIh958mUU5vUqt2mwsvW7xUKvnjyVO5dGDgqyL4z/kRkB+nfte/he6Aohkj0b+wiUCdrVbkb9wqSDr4nA4HI5v8Ignx2dI8JEFks8IOC2GIms0t5szfnTU1+CLfzzNIt7EiCM1/SQiNo79T56u5OfaeqkyIAFK0c6YpBSk5BcIuJccDofD8RYe8eT4hKanE8c/cN9JPNbQNBuKuHLGHlVHu110mi6XWQgNGeMnZl92L1h6w63sNk9Qap2612nxlGank58Vt9w1ZMoSh8PhcMYOLjw5PnH280/Z+EZPkK/iI++9zhZnj8VgQZY7nLGFnAkOvP6ivbY3CFFOB9Scllkya/B6ZFwilt9yZ0DrXHj1jYjPyBJg7zgcDofjDzzVzvEag64ftWdOCt5AEggiMY9cjTW1p+3TkwKZmT4qIhHCo2KQUVwy5ObcOQtYA9uhN15motebaKt9zKiNic7iFWt83xcOh8PhCAYXnhyvabtUKbiXYiDQPHdlZOR478aUo/TgHpaqFmI6kUdsNiy+7qZB/1dn8uYuREJ6Fg699TKzWHLMqnfXgEYnSVQfuuLmO3mkk8PhcEIALjw5XtPd3Oj2R97fsYWBEpee4VaYcIKHTt0XkA+rV4hEyJu3CFkz5nh8SHRSMq746rdY5JU8ZNtqLkHV2T7YwBYZn4jk3DxMW7gUyXkFvKaTw+FwQgQuPDleo+3rgw024cYWBoJIhNy5C4VbH8cruhob3N4u3MmHCBnTZ2D5TbePui8kJml0qmN8KkXjLWYzM4bnozA5HA4nNOHfzhwfoKiRKGTS7NMWLB7v3ZhyaHq63c5iD/Tkg9VhApiz6UrMWruRpcl9hcQmF5wcDocT2vBvaY7XRMTGur3d37GFgbDwyusgU4YFZd2ckbCxUw8hqztJdBYuXo7pK9cgJilVwDVzOBwOJ9TgwpPjNfEZmW472v0ZW+gvIpEYaYVFKFy2QtD1crxDGRnltqnI35MPEp2z1m3G3M1XBWmPORwOhxNKcOHJ8ZrU/EKIpVJYhfTndJO2HYnkvGlYc9cDTIByxp6E9Ey3t/t78kF2SInZOYLtH4fD4XBCG/7rzfEaSm1PW7BksB4vUKYvXw25QjlqxzFtj5a5V2zDxge+BqlcIcj2Ob4TmZCI8Gj3JRf+QDWZSdl5gq2Pw+FwOKENj3hyfGL2xi2oOX3C46hKx9jCkSARGZeWgUXX3IA5m7YyO5zywwfQ39M17LHy8AhMW7gERUtXISohUbDXwfEPijRPX7Eapz/9MGAfT/oc5M1fBHlYuGD7x+FwOJzQhgtPjk9QtGvp9bfiwOsv+PV8im6SF+jKW+9mHpyK8EjMWLORLfp+DXqam2A26CGWSRGbnIbw2DjuwRhiFC1dibIDe6DXqN2KT29OPgjqXJ+1fnOQ9pLD4XA4oQhPtXN8hqJUC666zn7FB1FIES5KrW740lfZNBlXlBGRSCucjqxZc5ExfSYi4uK56AzRkguaBBRoxHPRVdcjKp5HsTkcDmcqwYUnxy9mrN6Atfd8maVJR635HBCPJDa3PvIYUvILx2YnOUEjragES667ZeCa7ycHJavWo3DZSsH3i8PhcDihDU+1c/yGRhom5eSj/OA+VBzeD4NWw26nVDpNOKKOZSImKQXFK9Zg2qKl3OB7ElG0bBWUkZE4/NarMBkNg+/3iE1iIjEWXHUtpi9fw6PZHA6HMwUR2QLNlwURlUqFmJgYNBytQHRk1HjvDmcEyN+zt60FXY2NrPZPJBYhMj4B8elZ7H8uMiYvOrUK53ftQNWJI7AYjQMRcLvNvAgiWK0WdjKSM2c+5mzcgqjE5PHeZQ6Hw+EIiEqjRtaSIvT19SE6OnrEx3LhyeFwBMFk0KOtugrdTY320ZqwISwqGgkZWUjOL2A1vBwOh8OZ2sKT5z05HI4gyBRKZJbMYguHw+FwOO7gzUUcDofD4XA4nDGBC08Oh8PhcDgczpjAhSeHw+FwOBwOZ0zgwpPD4XA4HA6HMybw5iIOJ0QxGw3oaWmGqqMNVquVmfXHp6UjMiGR+WFyphY2mxXttdXorK9FX1srzCYjpHI54lLTkZidh8TsXG5bxuFwQh4uPDmcMcBqMaOp/CI662rR09oMk8EAmUKO2NR0JGXnIn36TEik9j/HtpoqlO3fg8bSc27HUpJpe9HS1ShathJKbjM26bHZLKg4fAAX9u6EtrdncFKYzWpjl2tsx9nnJCohETPXbsa0RUv4iQmHwwlZuI8nhxNkwUmCoXT/bhi1/cxInQzVHTiuUzSzcOlKqLu7UH/2JEQ0/cnpca6Q4JDK5Fhy/S3InbuQR7omKeruTux75d/obqz3+jnkmbr6tnsQFh0b1H3jcDgcB9xAnsMJAfo6WrH3xX+ir6ONwlbCb4DEps2GmWs3Yd6Wq7n4nGRQicWnz/wBRr121HGkriclYZHR2PLwtxARGx/UfeRwOBxfhSfPx3A4QYDGh3769O+g6mwPjugkBtZ7Yc/nKN23Mzjb4IwLJqMenz/3tM+ik6DH6zQq7PznX1nEncPhcEIJLjw5nCCMjtz53DOsjtNX0eAvpz79kNWOciYHpz75ELq+Xr8/P/Q8irSf27lD8H3jcDicQODCk8MRmFPbP2ARJyFEp8FsxiPvvc4WujwSJz58J+DtccYfraoXlUf2u20s8+kzYbPhwt4vYNRpg7ezHA6H4yNceHI4AqJT96Hy6MERRacvYtJbaHutlyqgptQ+Z0JTdfQwVWoKsi6rxYLqU8cFWReHw+EIAReeHI6AVB0/AoxTux41lTRcPDc+G+cIRnNlKfPsFAKSr61V5YKsi8PhcISA+3hyOAJCP/KBigbnKKjR4v4yoRjw/XSmq6khoG1zxhf67PQ0Nwn2maB0fWdDXVD2lcPhcPyBC08OR0C6mxrd3u6LcHjso7fdruPJ7e8Puf7n624dlm5Xd3b4td+c0MBiNrHFFX8/E4RRqxNwDzkcDicwuPDkcASCokvU0e6OQISDT/sQ0LM5441IJAnCSoVfJYfD4fgLF54cjkCQgTvVWQbazf6bbTcOiYg6xOlTW6+FXDLCn6xIhLCokY17OaENjU1VRETC0K8R5jMBIDKem8hzOJzQgQtPDkdAIuPioe7qHHa7L8LBXe0mQY/zdB9B87kTM7MD2HtOKJCYnYumsgtDBg/4/ZkQi5GYlRuU/eRwOBx/4F3tHI6AJGXnsR97V0gcOBZnoekQDo4lEGi2e1phcUDr4Iw/ObPmCjbtiqLv2bQ+DofDCRG48ORwBCRn7vwxm1bkmuaPSU5l0TLOxCZn9nzIw8IF+UyER8cio7hEkP3icDj/v727i4kyu+M4/geEQeTFly5SCjhCIbZYpciLldaqMWJrtLpZ4YK0YiwpCRqNFwhe6I0JJpJogq9XSlCjW4waLjB6oyZbDVblQhPcGqtQWRFpZQarwALNOZYNrLss6sx5huf5fpIn4wwDz9+cmXl+c85zziPwAYbaAR+KT5sjU6ZOl/92/+d7rzzzPlQv6HgmHKl9Zf5ujQ4bmNhCQkMle/Wn8sXnJz/6NZG7rsA/E5YA4APR4wn4kDrIL/y0cMzQORwc1Paxw+tv9xkkyZnZ8pM56R/9txAY3BlZkjQ346O+SKTmLOI1ASDgEDwBH1PnWf7s10uN7EsFk9jkVMldW2hkfxBj7ZpX+Ef5cdqHDZO752dKzh8+83ldAPCxCJ6AH2T+fo2k5uS9veOH4e/hCUxpv/qNLCv+ix6ehf2WVlr6pz/LL1euluDgkO+ctDaS+nnIpFDJXvOZDq1BwQyxAwg8nOMJ+IFa2ihn7XqJdSdL08W/Sn9f79gzlYOCJDTUJQtWr5M3PT3y4MZ1ee3pfrs2aFCwXhhexdfBwQH99LiUNPnFshUS604x95+CcSo8pv92ucyalyFf3vxCHt66IX2v370SkVr7My13kaTm5ukJRQAQqIKGfDEDwk88Ho/ExMRIW9OXEh0ZZXU5wAfpff1KHv29Sf7R9DfxvHj+zs+jf/SJ/DRnkaQsyBFXRKR+bGhoQDof/1O6nv5LPJ0dMjAwIK7JETI9PkE+cc+WyGkzLPifwGrq47rn313ysuMr+bq/T0LDXDItLl4ipk5jYhkAy3h6vJKYkybd3d0SHT32hUwInoBB/W/eyMvnX8nXvX0yKSxMps6Mk9DwyVaXBQCAkeDJUDtgUGh4uF5kHgAAJ2JyEQAAAIwgeAIAAMAewbO3t1cyMt4uhNzc3Ozv3QEAAMCpwbO8vFzi4+P9vRsAAAA4OXg2NjbK5cuXpbq6ety9o2om+8gNAAAA9uC34NnR0SElJSVSV1cnERER4/qdqqoqvXzS8JaYmOiv8gAAAGCH4KmWBi0uLpbS0lLJysoa9+9VVlbqNaCGt7a2Nn+UBwAAgEAPnhUVFf+/hN/3by0tLVJTUyNer1cHyffhcrn0wqMjNwAAANjDe125qLOzU7q6usZ8TnJyshQUFEhDQ8OoS7ipS/6FhIRIUVGR1NbWjmt/XLkIAAAgsFl+yczW1tZRE4Pa29slPz9f6uvrJTc3VxISEsb1dwieAAAAgc3yS2YmJSWNuh8ZGalvU1JSxh06AQAAYC9cuQgAAABG+KXH89vcbree6Q4AAADnoscTAAAARhA8AQAAYATBEwAAAEYQPAEAAGAEwRMAAABGEDwBAABgBMETAAAARhA8AQAAYATBEwAAAEYQPAEAAGAEwRMAAABGEDwBAABgBMETAAAARhA8AQAAYATBEwAAAEYQPAEAAGAEwRMAAABGEDwBAABgBMETAAAARhA8AQAAYATBEwAAAEYQPAEAAGAEwRMAAABGEDwBAABgBMETAAAARhA8AQAAYMQkCWBDQ0P61tvTY3UpAAAA+A7DOW04t03Y4On1evXtz5dlWl0KAAAAfiC3xcTEjPUUCRoaTzy1yODgoLS3t0tUVJQEBQVZXU7A8Hg8kpiYKG1tbRIdHW11OTCANnce2tx5aHPn8dikzVWUVKEzPj5egoODJ26Ppyo+ISHB6jIClnqRTuQXKt4fbe48tLnz0ObOE22DNv+hns5hTC4CAACAEQRPAAAAGEHwnIBcLpfs3r1b38IZaHPnoc2dhzZ3HpcD2zygJxcBAADAPujxBAAAgBEETwAAABhB8AQAAIARBE8AAAAYQfAEAACAEQRPm+jt7ZWMjAx9adHm5mary4GfPH78WDZt2iSzZ8+WyZMnS0pKil6Ko6+vz+rS4EOHDh0St9st4eHhkpubK01NTVaXBD+pqqqS7OxsfWno2NhYWbt2rTx48MDqsmDQ3r179bF727Zt4gQET5soLy/X10iFvbW0tMjg4KAcO3ZM7t+/L/v375ejR4/Kzp07rS4NPnL27FnZvn27/kJx584dmT9/vuTn58vz58+tLg1+cO3aNSkrK5ObN2/KlStXpL+/X1asWCGvXr2yujQYcOvWLf15Pm/ePHEK1vG0gcbGRn2gOnfunKSnp8vdu3d17yecYd++fXLkyBF59OiR1aXAB1QPp+oBO3jwoL6vvmgkJibKli1bpKKiwury4GednZ2651MF0sWLF1tdDvyop6dHMjMz5fDhw7Jnzx593D5w4IDYHT2eE1xHR4eUlJRIXV2dREREWF0OLNDd3S3Tp0+3ugz4gDpl4vbt27J8+fJvHgsODtb3b9y4YWltMPd+VnhP219ZWZmsWrVq1PvdCSZZXQA+nOqsLi4ultLSUsnKytLn/8FZHj58KDU1NVJdXW11KfCBFy9eyMDAgMycOXPU4+q+Os0C9qZ6t9V5fnl5eTJ37lyry4EfnTlzRp9Ko4banYYezwCkhtPUicZjbeogpAKH1+uVyspKq0uGoTYf6enTp7Jy5UpZv3697vUGMLGpHrB79+7pUAL7amtrk61bt8qpU6f0BEKn4RzPAD3Hp6ura8znJCcnS0FBgTQ0NOhQMkz1loSEhEhRUZHU1tYaqBYm2zwsLEz/u729XZYsWSILFy6UEydO6OFY2GOoXZ0yU19fr2c3D9uwYYO8fPlSLl68aGl98J/Nmzfr9r1+/bpetQL2deHCBVm3bp0+Vo88dqtjufosV6vUjPyZ3RA8J7DW1lbxeDzf3FdhRM1+VQctNUEhISHB0vrgH6qnc+nSpbJgwQI5efKkrT+gnEi9d3NycvSIxvDwa1JSkg4mTC6yH3UIVhPHzp8/L1evXpXU1FSrS4Kfeb1eefLkyajHNm7cKHPmzJEdO3bY/jQLzvGcwNTBaKTIyEh9q9Z2JHTaN3Sqns5Zs2bp8zpVT+mwuLg4S2uDb6gVKlQPpzpvWwVQNctVLa2jDkyw5/D66dOndW+nWsvz2bNn+vGYmBi9Vi/sJyoq6p1wOWXKFJkxY4btQ6dC8AQmELXOn5pQpLZvf7lg8MIeCgsL9ReKXbt26RCilli5dOnSOxOOYA9qKTRFfaEc6fjx43ryKGA3DLUDAADACGYkAAAAwAiCJwAAAIwgeAIAAMAIgicAAACMIHgCAADACIInAAAAjCB4AgAAwAiCJwAAAIwgeAIAAMAIgicAAACMIHgCAABATPgfCWLwLsYxSF4AAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -2441,10 +2435,10 @@ "id": "29ad59f1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:53.701878Z", - "iopub.status.busy": "2024-06-04T23:19:53.701781Z", - "iopub.status.idle": "2024-06-04T23:19:53.748985Z", - "shell.execute_reply": "2024-06-04T23:19:53.748785Z" + "iopub.execute_input": "2025-04-03T19:34:10.941574Z", + "iopub.status.busy": "2025-04-03T19:34:10.941456Z", + "iopub.status.idle": "2025-04-03T19:34:10.991970Z", + "shell.execute_reply": "2025-04-03T19:34:10.991684Z" } }, "outputs": [ @@ -2487,10 +2481,10 @@ "id": "e104aa54", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:53.750319Z", - "iopub.status.busy": "2024-06-04T23:19:53.750237Z", - "iopub.status.idle": "2024-06-04T23:19:53.772699Z", - "shell.execute_reply": "2024-06-04T23:19:53.772472Z" + "iopub.execute_input": "2025-04-03T19:34:10.993468Z", + "iopub.status.busy": "2025-04-03T19:34:10.993370Z", + "iopub.status.idle": "2025-04-03T19:34:11.016882Z", + "shell.execute_reply": "2025-04-03T19:34:11.016600Z" } }, "outputs": [ @@ -2601,10 +2595,10 @@ "id": "ea2c4d2e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:53.773947Z", - "iopub.status.busy": "2024-06-04T23:19:53.773873Z", - "iopub.status.idle": "2024-06-04T23:19:53.782386Z", - "shell.execute_reply": "2024-06-04T23:19:53.782167Z" + "iopub.execute_input": "2025-04-03T19:34:11.018318Z", + "iopub.status.busy": "2025-04-03T19:34:11.018224Z", + "iopub.status.idle": "2025-04-03T19:34:11.026905Z", + "shell.execute_reply": "2025-04-03T19:34:11.026685Z" } }, "outputs": [ @@ -2707,8 +2701,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -2720,7 +2714,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/Ch10-deeplearning-lab.Rmd b/Ch10-deeplearning-lab.Rmd index 10b78a2..494c3f0 100644 --- a/Ch10-deeplearning-lab.Rmd +++ b/Ch10-deeplearning-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Deep Learning diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb index 395f2a5..4ebd8ad 100644 --- a/Ch10-deeplearning-lab.ipynb +++ b/Ch10-deeplearning-lab.ipynb @@ -899,357 +899,357 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch 0: 100%|█████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00, 4.03it/s, v_num=7]\n", - "Validation: | | 0/? [00:00┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃ Test metric DataLoader 0 ┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│ test_loss 107904.6484375 │\n", - "│ test_mae 221.83148193359375 │\n", - "└───────────────────────────┴───────────────────────────┘\n", - "\n" - ], "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│\u001b[36m \u001b[0m\u001b[36m test_loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 107904.6484375 \u001b[0m\u001b[35m \u001b[0m│\n", - "│\u001b[36m \u001b[0m\u001b[36m test_mae \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 221.83148193359375 \u001b[0m\u001b[35m \u001b[0m│\n", - "└───────────────────────────┴───────────────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "[{'test_loss': 107904.6484375, 'test_mae': 221.83148193359375}]" + "[{'test_loss': 107904.640625, 'test_mae': 221.8314971923828}]" ] }, "execution_count": 28, @@ -1431,7 +1414,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -1953,27 +1936,16 @@ "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃        Test metric               DataLoader 0        ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│       test_accuracy           0.9620000123977661     │\n",
-       "│         test_loss             0.15120187401771545    │\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.916100025177002\n", + " test_loss 0.3469300866127014\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] }, { "data": { @@ -2288,7 +2249,7 @@ "outputs": [ { "data": { - "image/png": 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0g+dENn+zmddN2mYWkJGeN3soptjH8wGavVcatO4vuhs5sL96mzZo2R8ft/nzSFpalvmjZHJ8DzAZ4zxxd+2u8/lWG43/c3O4qc2A6owTmrZbOP/19/tOGaMRJkYcDPGYG7u4UUWc4XzI5m8+x8hz10cnWSv1BT7mcIzz5Xe/hwlw89ztf6+98Tk8r26H3oH9s6oPn4YzbsgcfvK+i+/BHod+0RBCCCGEEELMHD1oCCGEEEIIIWaOHjSEEEIIIYQQM+epPBqD0bbF6aFmc2sHdW2f3UCN7Wc3MSGTmZsCZ2FhCeJlStDXW+hCvHoRdd+NOmvUXL+Dn2C8R1piv0Qt8R5pBHd3MdHU3BImdzEzq7VIR02a94Q08g1KJsdax7v3XG1jTjrCpUU8j1YTtYtkBbBRgrrF772JyRPNzD5570OIL/dQK1ueSBCU52efLGjieZYdJZ0JqfhagXVai1x9cky63DRFjWszxM9EpHlk70JRocGP6cRWN7CMlbfRA8TJ9vYL9BC9uYFa9t0Y+4GZWTrGxJfXFlGjPx7hMdormPhyPEVt7XzTrbvRAZ7nEo3d4X3Uva58/jWIk4J8IalbefUGnjd7nbaGx9eRVCSDOgvCKLQwOjyvPMd6ylKsZ06YZGZWI80xa8fTCc1PpLOt0TGLin7u1bDevDEWUiOvUY10z/z5hMZBm9rJzOxCA31qO7uoq9/dR0/Y5i1M6DpPyac6FX1wvuPO93/eGKahpenhPLRL6wonBfMzV6Ndp4R8l1vYtnPkk1zpom9qoYv9t9Nx+wIn+Srrj+9PcYImDsqxa70mjRkf5yszsyJgvx0nCCUPBt2N8BpcVsjbaxFeR5c8flFIyfXIQ7HUweu4fgHXVzOzOiWefeczHCcf3j8+ZlXS02dNmqWWHt2LFM4CiHV+4cJ55/OtNiUgpXZjfwW3S5ZjXxmR18HMbEq+tVody+xREsDRGMt0kgLSdUVOgluzNMY+yefJPt3xBM/xYEh+vwp/xYj603BEa0WO58D+ltPWnsO/cUJE+i3ihIcjCCuSxf4Y9IuGEEIIIYQQYuboQUMIIYQQQggxc/SgIYQQQgghhJg5T+XR+N733rUgPNQQ9vuoud3eRo22q98za9Ee8Jcuo857cRm1dLU67dk9Qr1iv4+aaDOzJMU93ttd1LxnpMs/KPCYOw/QH1FOsYyLF686Zbbb6NGI6fltaw9zedQi1GF2aK/yeh01hGZmddLG7uz1IT53ATV9cz2s6zd/+wcQv/U916MxGWDd1AzbY3n+2DNTspD2DCj80Iojn0RM+tSc4qygzdvNrKD30HbulhSoX85Im9n08PNRxXP69U9QN7l4C70NW7vYnwZjbLePt7A/jqmatzfQM2RmduUy6uO3+qiPbzZI7076z2yK59hru96CMSUVuXrlAsSDAMfZvSnmZMlS1KR2I+yfZmbTAjWmkwPK13BCz5sEbv6Is+DTjz6w6Cg/wbkV8muRj2qSuD6Sgt7DeTICD+s5T7Efx1PyFbWx3s3MYpoXC86bQjpcNs+xD6ReQx2+H7n9I6ehcI68bJ06tveY6uY3/+JvQvzXfuNfd8qo1xrO3/68cbHTtsbROnp/QHp2GqOR57ZTJ8Cxf3EB26VO83qakd+C8hPVPFdLnodY7t4QxzWv2gc0yU3IgtFpohY89V2fWsz+FI9yWZFmPqO5PElpTGRu3dWpjIDWkzEdY5dE8K8s4+3WcrciXxHlJri6hON78+C4vqfm+pieNVnqWXZ0nZzqoSR/YrPhzk2cPqkgDwZ/hnsXN5PV6sYUdGKeYVvOk+dnY/0WxDF5PEIP550Dc9eeHnlkA/LO+TQ2uxH6zVYu4H3l5ibmqTIz+/gzvDf97re/A/GlObyun//FX4S4bOF1TCqMSCF5MtifctL2lRVP7tPVLxpCCCGEEEKImaMHDSGEEEIIIcTM0YOGEEIIIYQQYuY8lUfjw48/e7THcEEaeI+0XAuLlFvCzC5cxH2je4u0t3CM2vI4w2NOSM9+/y5qy83MBkPMi/G1r3wZ4vMrqC2v1/GYi8uYy2O8jYLRMnH1oU0SHo5pv2PeEjn3sNqHIyyj3cI8B2ZmAeVwmCSo1d/dR03f3BzuYX3zY8yRMez3nTLqNdQNbvexLhuNYx1iXrHP87NmmsaWHeVfCCgXSW4pvdf1kPCuz6yjZA9HTOL1IWnZf+2eO3zCj7AP78foz9nZRQ/H5hS1mz/8+FOIS8rt0Wpj/zQzS0iTWlKehHaTNKZ99IEE5J+423c1qJ+7juNmYwM9WWOq79VvoObUp75VD926Y1tNVFB7xMfjqsjdvfTPgnt37jzaP/z2jVvw2sU5zPvTzV2fWtDAtkmm6EPhvc3jMb4eUFt6U9cHkk+x/eq0j3xnDvvQAeW84Bw13WX0jNVbbu6EA/IWxTmOx6iLdXOugXPclz7/JYgbdVffzXNOwF6TPwd4aWzeUS4rtsoE5CFYjtw5+soC9oVz81jPnLdlfQ/73yDGQTpwRPNm5mO/3+7jWA2M524sMzEc920aM6G5Ze7TdO/THv+cx2BqdE4+Xlfhu3XnnDfp7gd0DjH5RRcGOC5X5lzPUZZiuUvzqP1/+eLxdYwHZ/89cTJOLAkO6y7J8XoSwzqcVNxdRlRnJeXjIpukkwtiSnnQPn7vA6eMS5cvQ9xbwvvOeIhz5mAT5z/OwbJZ4r1WZ9n10NbaeJ4Tytm1t4/3AR55gC5cvEan4HpPvveDH0G89v7bEH95FT3PCw2c5z/3Sz8PcRy44yine4eCFqTkxJo2iSsSif0Y9IuGEEIIIYQQYuboQUMIIYQQQggxc/SgIYQQQgghhJg5T+XRSNPkkUejQfsdv/76axCfP097zJtZk/bDNh81jBtbGIe0F3YUzEM8PEAPgZnZysoViC9dug4x56zwSFdYp/39I9JZ7vfdPAY5aVIXzq9C7NfxOrZ2+hCnCZ5DmaMO0cysVsPz3u1Tvg8f85r8zM+iBvpf+LWvQ/zgwQOnjH3S5pe0x/VweFwXRXH2eTQmxdjCIyF/M8f6KKkr+76rHyzI15EZ761OelH6/Bu7tBf2vaExG5SnZTwl70iJ57mxg/rQzS3sXyHtE54lrjdhnjTMS3Poh2hRPpo61d3NB5sQ10K37jiHzedefgHiEenDN2mP/6hD+3PnrtcpJa9JkrJX5LjPpXb2/c/MrNmaszA6HM8ptwVdU1LRVgW9p5bhdbTalLOC9MABaWb93N1Lv0lzxXBI88kBZjJgv4wX4Xw1obZMyDdiZpZRfhDeL79F3pK5eZzLeX6rwv9z6MlgLiw2rdU57CPpDs75UR3r5/q86wFYpNwNGY25jPbRH+Y4543JH+E9QR6DlHJYxFSGH+E5FQX216UOvt7sutd1i+4deK73yBvAxslGjdaPiu9gk5hyjJR4zIRy3mRURp/yAt2tVdx+0dSbFaSZPzFf8FxyFnieb96Rjj+kOvXofiGKKvoG1SuvsVyH/duYj+nd3/8WxHfu3HbKaPzsz0K86X0CcTCP/uFsjGUuL+O90xLdz9Xqbv9Lt3FOfTBB//Dtdbzf2t3Ce9fLL74I8Vy355Tx1tvvQuwb1m9R4Bz6R3/0FsSL13HN7l3B6zIzm9BYZatrfGJsDznhzWPQLxpCCCGEEEKImaMHDSGEEEIIIcTM0YOGEEIIIYQQYuY8lUcjCLxHHo0XrqOm7Gd+Gvfo9So08gcD3Ht/m/TpRY7a4Hqjh69npI0rXV3v/Bx6Q3rzuLew76FOcjpF/fGDB+sQp33U3s3XKvR5JB1u0j71KWlS79+9gx8gL8rq6nWnjAH5Ud5+6x0so0BNYJu03l/98lch/mt/7V9xyviHf/93IN7ZRq3scHxcRlmefR6NXlGz8EiHuNRF7XpMG3DvFK7PJfKwv3Qj9DI0Qmzbcoxt//J99F/sDLHvmJkNKYfK/gg1j1sHeF77Q/R5hCHqXidDPIeL59wcK03S+gbkX/BJ+PvhBnoy7m6g1vvnXrvolPHai+jBur6Idbk56EO8s4f99R7t6T8fuuPIy/A9J/NmmJllJ/S76VPoQ2dJWWZWHvkkImqrToTjPGDBtZmV5DNg38EB6e6NyogC8tvUK3TQGfa5qIbzS0B+CH5/Qe0QNvAcr1695JTJV7pBfWw8Qb9NQNdVq5/u0RBm11c61jnyYM21uS+Rh6yib+wOcL7ZG2G7FAF+pkbWhjb5DX3P7ePsY/RK7F+985jjKSEfgpehT7JO+WiWFt2+MiSf4wH1aUpdZRGVmZFXyqsYu/UmVgaPkyIk30cH57hLPYwnqVvG1gGuYxOaA0+mOZjQ2nIWeN2GeUf5Pzy68YkopY9fcXrsa9vcwnvCu+s4b3zyA7zP2Xj3fYh/5ZvfcMqoTbGMjz/7DOIXvvbTEF9fwfmsSb6jEZ0T5xczMxv18V7gd298BPHbN25AfPE8eonH5P85qPAfD/t4j7dPuYu8DMfyxcu4jg+pj3cKt/85ni16z8m8Gk9zB6hfNIQQQgghhBAzRw8aQgghhBBCiJmjBw0hhBBCCCHEzNGDhhBCCCGEEGLmPJUZvNNtm3+Uien69Zfgtd48mlRH477z+T4ZRG9+hknnfEr65RVoCounaIzNKhLW9PtY7nSK5u9GAz+zvY3GtfuUWCUjw++owrTo7WGZe2M05Vymurp0Cc1HOSXhyjI3IVarg+VevorJVvYP0BiUTPEcbt7ApDVf/PwbThlslv7D72DCl7W7x6aoovBsH71Jz5y2H1h0lCRojupsZ4LtdL2NSXnMzJotTBK2V2I9Dw3dbOdv4MYAe7fw/WmJhnszs/6ANhugBH27lPxsdxc3RAjJYrWygOc810UDu5mbGGpKbd8kU+jWNo7DL76IprHXX8LEPmZmL6xggs69ATb+Bx/hBgfJHPal1UtYxnzo1l3QwmsrKAHUYHJsZE1CTuZ3NmRpYmV5aJALeb5q4DV15tw+mMbYP2ptMr42KUFjgdf5wmWcO86fw80uzMw+/fgmxA8eoOGy3sTzrHVx84ommcdf//yrEP/CNzAhlpnZHB3j9s01iH/7t34P4iJgMzglc62ArYt/HtP3+VaYf7QRx/Jcm16jpJgVZk9OxlgGtHEEmbsjSmraa5BZvKIRPvrwBxD393BN/dI3MHns8uorEDfJkO4nOGfu3ce+ZWY2V8f+V2/SidFGDXGC1xmXnLjQtbrmVJ8B1V2ry5ssUHJDwz4fp24ZkxzXi4SS4oXB8WeK4PQxM2s2hvs28g/PYdTHtav/Ga4Bo3sbzuendG/04Q00at99gPdj2QA3T2mRAf/ePbyHNDOrefgen9bU+598CPF+SQlKD7C/9Qe4QYc3ddee8RgN6J9u4b3DA0qSunkfr3OdNiHizQnMzAKaM/MA+9d6gvcvdZ/qYZ6SwUYVvzOQWb8eYZ+u+cf9s0iffAMP/aIhhBBCCCGEmDl60BBCCCGEEELMHD1oCCGEEEIIIWbOU3k02q2G+UcJozodSkqXsE7cTah1cIDJgjKyWMSkX55O7kNcr6NOfL7nahRXzqEuuiyxkL0+ehlYIz+d4HW0mnidZYUs0o8wEU+t3YV4n3T75y6gXt0jLd0P3vq+U8brr6NO+o03MGHi5ibqda+SlnuXkrts3L/nlHH+HPoBfu3Xfg7ie/ePPRpJktrf+m/+J+cYz5J72cDC9LABbjqJ7ihp3Rh1wWZmjSkluAnwMx3Sw+bvon50Yxc1kUnkavD3KSFfUpBONyKNc4ivXz6/AnFBSbjK1M2C1OlhH2WN6c4OjsWXXsS+8/JlTHIZNNxketsHqBndoUSWn61hUqPalR7EPdIz72R9pwxLcCymNEEk8fF1ZOPnk7Cv1exYeKRzTzMyKbWw7TNKYGVmNp1gv+1PsB4L0uaWHsavv3IV4q98DRMpmpmtkn/rt/4B+iNi8q0NB1iXQ9L+Rrfw+6j1TXfuWOjhWLh0BeefpYv4+oC8SlsPcLy2W66Hp9vFedVIs/zngfW9kbXSw/YoKNljneav+Za7WHXobwV5EdoRHrMRog7bozKTgWvUG25jW053Mb7xve9CvPBNnL8uvXIN4v0HfYi/8we/65TZ6qJHtLWACV27lOi008OkgXNdnPPGiev/TEk23yL9+oSSBo5jjLOUbrcK93veBiXkpOnf4snxHJPFZ+9T27x330ZH5sxbH6HX4aPf/gOI/Z2+8/mA+s/mPvoFNylhabtJvj1aT9/89necMpoB9ukwID9ORj6kCSViJB9JkuNc1fYqjEmUgHbgYf/JIyxjfx+vc4M8HXOU9NnMrNfr4THpPEaUxHYxxj6f5fh6OnbH7uY63nO3OnhP2J47jovYTYr849AvGkIIIYQQQoiZowcNIYQQQgghxMzRg4YQQgghhBBi5jyVRyMIPfOP9Lv1GmrSUtIWTyZuLohaDfXpS4sXIN7YQC1ng3JWrF5G/XqSuBrFz3/hZYg9Q13a7i7uKf/665+D+KaP57Cw2IO41XGrjHX5r7/xRYh/+EPUMoZ91CUuL6MeeWWFtMhmlheoEwxpD+TlZdRAhz6e5yJpqAcHeA5mZr6Hf2ON/AsvHGv+pvHZa+RHZWHBkaa4RnuSe5TToPTdZ2jaXtt6PvYvfwM9Atk6xoMpaTkrNpEfTVH3eBDjedzaxDp+gfw6vLX1HuVxqYWuLr1OXpOM4gdb6J/wfByHm6SHTyau9nLzAV5XkqC3YEK5PBLa77wRkRac9so3MzvfxpwQkylee5Id97mk/nzyaNT8qYX+0VxHOtxBgN6FTnHZ+XzexHr4+ONPId7fwbovcnz/jdu4b/wf/eAdp4wvfvELEAc11CR/+gl6j9ao3w9HWLecr6igtjYza5CvZ2kJNfKLpC9uNNBv9/a770P88ivoIzIz+xv/7v8S4pdexvxED/ObPIT9BH8W2B4X1jzy9LF/p5Fju+1u4Lg3MwsN558swwln8RrmtFik/FhJifPP9hqup2ZmtPTY4gKuPeNtnDs+fe+HEF8+j/r0B+uYN2O66+ZnGG7itSbkp6h3UOt/6SXsO5/7ylcgXphz89Ps00GXuuh36R+gKcvzsCJSWjLDiu5Zq9FnyF8w8I8/FJZn37/f/f3vWPNo7N6/cQNeiz9Dn0E3c+8RSlqEI/KZdAusY5oubbCD/XdKOVbMzOZoXc5znFNL8nZ1cuwb9RLHRGhURsX8l/jYF2Ja70Z0P8L3y4MDXF+L3L1/Dnz8jEcDzTmrMXkGKcfcjRu49piZfXTrFsT1Fnp/z60er2mjkVv3Pw79oiGEEEIIIYSYOXrQEEIIIYQQQswcPWgIIYQQQgghZs7TeTSCwIKjfZ69APV37CGYTN09ekPSjkcB6tquX8X9s5tN1NJdvoT7/Q+HqPU0M+u2m/QXLPPcCh7jhWtYZjqivckbqF9vdtwcAw820INR5Lh/9uIi6tyKArVzOemwL5zvOWWwhs8C2sObcnWMSc83HPYhDml/dDMzn3TzH310E+L5hWMtd1qRz+FZU89SC46Kzannpjnt/+677TSgfCX9EnMavHQf82xkMWrTxzHlOMgqfEg+amq3t3G/7IA2Rmdt+3wT2yVJKZdE4epep+SHSmkP+D5pKeMh6qpHgz7EIWm/zcw6NBb9EN+zO0St7XnaY94jXeuo4jru7m/heZBhZXJCj5uy4PmMGIzuWHDkk5lmpC++hOdUlO70WjOsl5I8N0Nqy4Q279/dxz772W3URZuZvfn2xxAvdnFOvE85dQZj7C88T5uPY4u9EGZm0xTntAH5PO7S2PJDbNuI4mHs6n/zwu2XJ2HviM95Nui83aswc1Tv5PPwHn6o6sNnQBDULDiap8sUvTVba+9BfOPDbzufP9hAL4zfwPVw+frPQvzSq1+FuNFGz8YnH+K++2Zm6wPyGdA8OkmwXd57H/vwB0M8b5/26w8Ld25v1fCYAfWFgvycn779RxD379+C+Jv/8l9xyuiSh6yY4jHnGji2C+4jLexL44pcQDmNNV5nvRP3Cl7uegWeNR98/7tWO5rbU1pXWkNsp5U5vO8xMwvqOM6n5Cvq1vD1oMC+s0M5gGLfnROiOnkWp7QeTtFL06a+UqPv3wvy4nlOw5olHvmjaN1OQ7ovpfuEknJ1xKMKH0iTcguRByiiNbdX4jhpphivrbv3z9sbeG9QeOiHGpzwjFb5sH8c+kVDCCGEEEIIMXP0oCGEEEIIIYSYOXrQEEIIIYQQQsycp/JotFutR/rkOMb9jKMINWfDUd/5fL+PmrB0gvq6CxdQ/7lKcZGiJqxekSthMkIN88Ii7sndm5+HuKQ94i+tog5zRDrKubarO7x2GXMhDA9wT++VRfQ+bGyiXnk07kP8YN3dJzygvBGtOmr+Dvqo8Ws38TrNsO4arPczszzHMl59/fN4Die02/FzyKNhQXD4z8zSBMtPStJnV4ioWwX2l2aE7dK5j30nTvEYvOd8RnlGzMwC0oe2m+hDaodY7x6dd076z/kO9t/B1NWus/8mibGty5T1nhiPx+z/cfdnz2iqKMjHkdJ5hxcxj0KnRnvpV2jwp7TfeTLhXBnH55Ulz8ejMX+pYeHRXvcbezgHFj6e76B0NbBxDc/7wjXMmRNSU21t4TGSBPXF07HbB3f38TNbu30sg3TgUZ307TSvZtR/qnK5eL77N/gMabNffvEqxH/1r/0mxL/6q99wjnH9hesQ8wjnXDpOvg/2lnjuHFHweyhXwcMy8vLs9fFmZu2gsFZwWPat9zH/xP27b0O8fg89dmZmY9JVD3Icx99+F3Os1D7sQ1yWqANnf6GZmRewV4Z8j3VcQ+sBasf3NnE+anB/zXBONDPzed2nXAqrlA+rTt64W3cwB07/b/93Thlf+8VfhrizjOt+UcO5PSU/lu9h/4zN9Tlyzppsiu1TTL3K/58V2XRkfn44h+U5zmWjBNug1nZzkYQRnfMu1kFAx+QhOk1x/tvz3XWg3exB3Ghhf7lzA3MR+ZSLoxnhml3wvEI5M8zMYsO5f1jidYW07tc4YVaJn08r0kRl5NfLm7gme12MG9TnN8mn5J3HPHZmZhepwsdj9GyUJ/uw9+S/U+gXDSGEEEIIIcTM0YOGEEIIIYQQYuboQUMIIYQQQggxc57KoxHVmhYe6XNZD8s6zKp97gvaO//a9cv0DtS1zc2jjnLjPu6zH09dIVtJWrmNLdTjJeQtme+g3u7BBpZRb6AXYuro3c3SnHTUpLPc3UNd4foDPKfVK6jTHkweOGW066T/pP2nKd2HxaQ7HNKe13fvoU/EzOzchesQ1xro87i9dnzezyOPRlyvWdA4bK9GiDpfPyNNZF5xfrTvtD9CvXK0jdrgMfkOFpdR05hX7KUfNfC85nMcJ6MJ9o2c9gkfkyY3DHEM1Gpu/pPhELWxE2prVs9HIY7Vbhu9KtPU1f1nVJ8laW3DJh7Dv4J954A8GbzPvZnZlLwjSYFlpslxnE2fj0fDm4vMO/LhhBnODWELx+Q4cnMJ3X2A/qvVlUsQv3ERdd8vTzG+/Wkf4k/ednX4jQa2RYva16dsEfO9DsQ7Azzv0QCvq875KcysIC9DzP4lD9vyZ37mKxD/2//GvwbxYAd9bmZmOY1x9gcEdF5RhLFX4lisGr8BrWOsgn+YQ8T3zl4fb2aWHNy3MD9sn4/efwte68e4du2l7jkuLq5CPIhx/au3aG/+EH1TfoF9I6y5evWcciHUQjzmvOFcUAtwHp6rY3+NKGfUtMTXzczGlMfn/gDX+a0RnvcS9Y2FNs5X61uoTTczy777XYh/+pv/EsSNRTxGQetNTn6YIHJ9kl7KORvwOnb2j9ftKflSzoJ8FFt+lEOJ15naBOfkwX7F+XGOJg4THNNZjvdOrQ55HciPYWbWpG6/2EWPRtTC3FaFj/NKbwm9wdsH2AZBy/Xp9uneoT/FcdNrokcxLOjWu8CTnlbcv+yNyYe0jNcVLuB6tPAi3q8cNCgnS9vtfyvtFyG+fxcnyfREDhK+53oc+kVDCCGEEEIIMXP0oCGEEEIIIYSYOXrQEEIIIYQQQsycp/Jo1Gp1C4/0liHpLlkX3FtA34GZmdHewpevnod4OkGdm+ejXrHTxTLm5ly9uh/gZzYf3MHzrKHm7O49PKfBPmrrFgLU1u2s4X7bZmbb26jnXFhAzXO/34eYfSTDMV5Hp8M5MMwCY60/6lpXL6D29vYt9IHsk16yt+jucb21g76Ng+F9jPePy2S96Vkwnu6b7x1pgn3UI84FqBUuQ7dvjGiP6OYu5c3YxzoNQ+xvSyvYX+/fw75lZlZrYr+vkw73YIT60Dim/bP5HFucu6Ni73XKKZHSMfMMX6+TRyMib4RXkSchJU06+ymCFbzOood1tzfFMVIr3DJaHrbhpMA+dvIUnpNE3hrNrkX1w/Nc4TmuuQ7hNrW1mVkUYL8dt3YhLkr05CwuYp+7FKJnY+0GjlEzs6U5PK+f/hXMh1N4OEce7OCc1xhiO+zuUP6jvtsH2xGWubSyAvG5RZxHr197HeLPbq5B/P53/rlTRr2H2unvf/ARxLwGvXjtBYiHB32I79677ZSxch7rtz2POug3XnjJzMwmFR7Es+D2J39ojeZhH5pm7EPAduwsov/HzOzzr2D+klfoFmCQ4fzTCCimedXz3YG4PUbtfkZz2iL5zOYoXqBcVQWtNQdjnKfNzPYbeB7blCdqTNp/zsO0RefsV/iQtqjc77/7LsTtHnpkCprP+Ii10NX6hyl5Yqbo17y7cTze44lbD8+aT27ftPAoZw57ohZ9HH9blPPHzMyjvpCT1/eAvHtj8npNqf/5Ff1vSG1bHOB8FZHHYm4R44UezlXdCzgHbwzc/Eh1zvEW4LgKqY8XGfZp38fXg6brfRpRfS5QDqUvLKO/4mIT7/ESysn08Y0bThmtBt673r6N79k4cc/DY+hx6BcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDlPZwaPjs3gAZldyhLNLYuLrqG53UKj494eJ8VB006njYaYJhlktrYw+ZWZmU+JwyL2BFNyFj9E81G9gc9e0xgNw2XpJgkMyIAex5QILsK6qlN2vTRGg2hckYzs/DIaIe/toBF5MEBTVK+H5vClFTRuFaXb9JOYErpEZARsHF/nYcI+NGM+a1qJb0Fw2D7dFrUzmf84wZyZWZ7hZgPzm2i8S6aU9C/AMsZkBqxKWpjso+GKk9/xMUpKHFWLsP/t99FQHEYVCbLI7P0wqdhDfKOYnNQ5JVsrAjeZXsGeOzKNBitoqNuakmFugu8vzM2WVqPp6EJnET9zomqy6Kmmrpmx+WDHwtph2efmrsNraYJtM91354ppgv0hG2JdxxmO2/EebtDg1XGebZ136yGkjFVbAW5gkcV4jJ0B9skxJRidxBivXnE3knht6Q2IlxbwPT5ZYUeUbOzdDz+BeH0djbVmZmu3MTnhPpm7721g3X74KR7zg3d/gMd7z52/Wotoah+SX/ff+3f+QzMzG5Px+qx4953ferQZwfxiD177la++BPFk5PaNSzVsh89/DjcKqDdxwVy7g5uK1ENc11t1d9ONO9u4wcGUTuP8OTTYd9g4S3NNmpGpOHfrfm+Mc+Ae9fEx7V2yP8Rj9Pdwnn2wjtdgZnbzAW5YcGsD719abezzQUgJ1Uqa60t3Q5ULTezDSw3aLOREYsKTydPOinvDA/OPDNyctPluitdz88BNClyn9atG/cejtWlvSInwyIAc+HhPaWbWpq/PqUvbmBJA5pS5M6YNVwpK4jkq3HZrz+Na1ZjD+zWf+njHx77Qo8/3czdpbjzGvnB+HjfcaBjOXd/67e9A/OLL1yC+/RnOj2ZmIfXJmMbuyaSpnED1cegXDSGEEEIIIcTM0YOGEEIIIYQQYuboQUMIIYQQQggxc55K6OxZaJ53+JEJJYup1/GZhROzmLk+jpWVcxD3+6jpu3cXk1EtU2KVLHO1cnxenBAtCvEz/H4r8TpYhx84pg+zdgt1qzs7qPf0PDzm5SuoldvcxjLurt1yytjbJt8G6QobdfKekM71w4/fh3hjq++UQYe0lOo3O6HBZB/AWVC0muYd+XS8OvpaOGndbuom1WnuY1Km9hq+Z5+OUZvvQRzn5F0IXL9ERn6HISWC2t9D7e+E+lOzQUOypDJ997uBCWl1E0py5IX4GZ/ijDJJ5aWrveRiOWFeRB6NcooaaK66NHATLSWG9b+2i2N3sXOcSCnLnk/CtGScWP5wHLTxGvZ3qH+1sI+auR6bhH1RAVb0YNyHOEixf/RecuejkvrxwRjbIj3A8x7vYX8ZDHFOrM3jWJ+Wbt2PfLz2JvepDM97Yws9GF/q/SzEd7cxGZ2Z2Xe/jZrjr/zcVyH+6DZ69j5dxznv1j3yeJBPxMxsRN6udIBz4P/09/+Hw78/RbKqWXKwd8vCI5/F5holih1jwshf+fpfcT5fo/lp8723IX71dUyk+PJKD+KUEm02KhKmdS6hdnx7iPPsgod13KLknz7dO/gdHDONOTcRoUfzCd9/jMg3uTPFuWVrD5O0fbTgJtP74OYmfoY8ftMRXmeng75Bn4xu8023D10n+1PHw3EzPiGaj/Oz/5643pt7lMxwNMIxPyJfwYi9NWZWTikx4givIaL1MM6xnTLubhVehgH5Ir2YYkosODJsx8CwkAmtp0Xk1nu3i56MFt0TZpTgs6zh2nDpKt4Tzldc13S7D/EcJc0OIhxH/X2815hrYgLT11dcH3W6gQki5154FctYOvaSTCYT+7t/6//jHKMK/aIhhBBCCCGEmDl60BBCCCGEEELMHD1oCCGEEEIIIWbOU3k0egs9i440xmGIWk3e/zjN3D3kd3ZQU5rH+Jl2G7VztRrGRU7vb+E+1WZm4xHqAmPSQBfkOxjto1a40+xAfDBA/d7FS64+dKF3AeK7a7SP+JT2qR/dhnjnAM9xd9fdwzvtomb02mU8j2mC9f3Zp1jGnTXUL8cVOSDYdVFQm/ontIvPxaMxHJqXH3bZ/gHqqzPy3qSh27VbB7RHtIfa35L2OZ+QttOaVEbm5pvIyN+Qk0eI9z5P6fXQx3ZmCTTv8W1mNp7gMXPSmYch58nA6/RJ+FoP3e8fCvKKpHRiBemoc9K1GtmpiqLiOw661mmK42Z9eKzvLaZPvof3LKk3Wo/yaGQ+arKHMcZF4Opsl3o4bgvyLhwckIfHQ69CO7kI8TzlfTAz649xTuM5MEOZtKVTbJx6G88pmsfrKFN3bG3GfYgHI/Sp5QGWEWXYX3antyDeK909+P/5d1A//MIrqDk+mGCeg08/+wDiJEaddOOqex2ju9jPu5z7af8wr0SWPJ/+F5aJRUfz1GCMc+C3vvu7EE/Hrgflf/XX/wbEqxfOQ9zfwTruLqPfokZ5OJLEXefbTdSfFxmO9fNzuMb2WqjLL2jc5wXWdd1z6z4gH2RJuQ665A+9NI/jZpvyHFxfQs29mdlX33gF4o/ex1xWN25jn+0PcRz6CZ7DS1cxZ46Z2eV5HBejPuaROHmfVeWDfdasrlx4lEttl3KPHIzwXOOJ2zfYpxvQOs25OXxaRxq0RgQR3iOamVlE/Y/mHr6PTEbklSGPB+e64vXVzKygvFIe+WcCaqoh3b9MaT3zfLdtlyKci84XOFe9kGL9NweUi+gH6HF7uY0eIjMzn/LIdZYxv0d26fKj/7NH53HoFw0hhBBCCCHEzNGDhhBCCCGEEGLm6EFDCCGEEEIIMXOeyqORZckjXWCjjlryFm2cHviudm6P9pC/s4YaR84hsLSAWs4sQt3a9rar493aJX0nicOzHF/3SMsZFH2II/KJzM27+ryDffxMPKW9r8eopXuwgdq5jLSWyyuoizMzW72IWtqC8gjcvHML4tEYdfutBuoWUxZqm1lBIvmA9JIeeTQKFt4/Y4qsMDvyRcSkuyzpXFNz9ygvNtEjdH8X631+HvXIno91NiGt+6hCpz2ZovYypf2wa3XUI3P+j8mY8rqQT8YL3dwdfo20lpzjIiI/BbVbSJ4MsqqYmdk0xf4UdPE6gvOoeS6pvzXqqC+1qavfbfh4bX4dx814eOyXyivO8SzIxomV6eF5re3fgNeSGvXBhjvGuiX+7cq5L0Jcb9A+8hvoEWuSpnm+6WrJh2P0dQynfYgbHWyL5c9hPZceVu7uXZpDPdeb1B+SXpve0+rhPNqmPnhjA3NexKFbxqjAsfHOex9CfOVVnCMXL+D60VjCsTnO3fZphriuBS08z2bncMynz8mjMR8kFh3lWvGa2Fcm5Jv77OYt5/N/9+//I4j/5V/7DTz+HM6B0wm2g5djfTRqbh6Xgx30GZWUC6HuYf/joTwZ4udDypHB85WZWTHF88zJU9age4/iAMfIHF3XhRXsS2ZmLyyhJ/TVBZzjHryGdffpLcxrcuc25uEIPRwzZmaTKZ4ne19P3iM9B4uGvXDxqtWOcl1cPY91tDfAdtvcwOs1MxsOcMwlCd+f4fVm7OUrOV+Yew+SJdRnaV5u0BrqkSetpL7A6UqiosqjQXlZNu9BXKM1OKfrPvgQ7xPm6u4C981lTLLyM13Mg1E8wPvphO5Le1R3rb7rBd4iP8rmxx9DnI6O52AnB91j0C8aQgghhBBCiJmjBw0hhBBCCCHEzNGDhhBCCCGEEGLmPJVHo9moWVQ71LfNkUa73aL9kHP30HXKvcF5MzyP80mgxq9ZRz3ycIj7BpuZ3bpBukDaH7sMsIxuG3WW53s9iOe6qPO9dRN1l2Zme33UHYYR6nwzEvkVHl73pdUFjC/jXvlmrh/lLvlbMsoPUq+Tlp9eH45cDXTu5IWgfAtQl2cvEM390MojHX+N8kk0SDbZqri+5R1s61ob23ZvhLrdeIz9azrGXCTjA8ybYGaWpOi5CEIcJ34T9cmej/2zoP4a1Mi3ELraTS/FtvWobQrak3s8wfMuqd2LihwpnIujhVVpRRM/E5JnJqf2arFnw8w6AR40ydAXUnSPzyEP3RwVZ0FWZo9yyKSkH87H5K+YuvrhQYJ9atTE+Wpl4QrES92rWEaBWuDBjqvzjmrYnhG1v99AbW1zEftUuoF+m3yf+sOyO7ZaXfQJpTSXpwMsc3eI11F0sO7q5nr86uewf/xw7W0so4V5DpYXcR6+H2NdBbHbz8+v4PhLaN7s1s4d/n36fPpfL8ysduRR6PVw3Vh++TLEReDmmQpD1HXvbOPa1QjxmEmOdURyd2eMmpllpN1uRdhf+qwlp7UqpPmrNke5Yjz3+9GoRvMmeRuMcg5YQWVQ7oRw6I4rzh3WbON5rbZw3X79HGrq3+rhGv7uh+gxMjPrUw6R+Sa2Vzw6PkY8dev+WVOab+XR99Mh6fkXezjeOi0359iQ8pLt7KFvansH16aUzHgB+XOyipwWOeW0yAaUR4PySHGyKl5jy4J8khXrY2k4n5Ue9icvwPkszqketsjbWWHAWWrj3y4uYF3cJ69mSZ6ONcpzkuVu//lsivcvBwWOq5Xa8bo9jZ+8/+kXDSGEEEIIIcTM0YOGEEIIIYQQYuboQUMIIYQQQggxc57Ko7G6esHq9UOtWVmipiwmbdferqtxvPHpZxDv7qM+9PwK6hEXeqg563RQA7i06OabWFnE88hJj+eRdeH8OfR9zDdQS7e5ibp8zoFhZpbS3s/tDpZZa6CW88r1FyHuLaBe/d69NaeMBxt4HiVpTpst2pucdPvNGukOKx4xE9I21puoiZ7vHbdPnuf2/o8+cQ/yDHmlbFtYHjZgk3Jc5D7WxyLleTEzW/kc9pf7N7Get9buQ8weoOmY9KNpxR7epOccT1C7mdOzfUl6ZJ8OGaakN63Q/SeUi6PMSG9Me3iXlMMgp75UIYE2n/YeT8d4jNY98n3M4VjOqMy4Is9J5qE+OfDIv3JCt1qUrk/gLJjGhQVHbdxsY9v2t1B3Ox6RTtzMfLrscRP/MNi+i28gj89Pf+EvQPzO/X/mnmSd6jHAYyTcJ4eo5S9SHFsLc6iDXllEb5OZ2Zh8Z9MB+p1shHpeb57mq0U8xwrptaPD9+exT41pDtjYxTlzn/wxRVmREynAveU90vIH/uF1cX6cs+JgOrLoaL2JKa/PIMG1qc7GNTO7cgnXu509vF7OD1KjfDgR5aPodV2v1asvX4O4STlRJpRvoSQfTLuJ6/6EvAiZ7+bgiSLyiFK+GZa8FzQmUsqvle6567wZ1k17Ba8z4jInlLdlH/tfMnZ1+HztA/I0jPeP8zPEsTuHPms29/YtPFoLanW+ByE/RY2MfGa2uEw+Dlonmi3sX7t7lJuIJobxyPUJcN6VlBJLlXQMtu/ECfavgOad0HN9khnn3qA4oemiKPEPpVHOqIp7iz75HN/bxbxgn9WwD2fn0bO1ca8P8ZcWyftkZktX0SPYmMf5onkiz5NvT74G6xcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDlPZQb/5JNPH5muOm007ayunoPY98l1bWZ1StL1wnX8zOVLGM910WA4xwn+CtdsaQUlRIvQ9BXSMYxMpR4Zbi6E5yFeXMEkKGZmsZPwDM1CSysXMF5Gg83aXTTJ+xWtcpHqtySTYqeDBs0mOXr5kJPENQoOx2iCmpDZ8KRxOc9cs9Kz5na6Z8FRQruITLJNcvnvUlIoM7P3b70L8f4tNH+Pd9GgVQ/IsBph36nX8BzMzOp0HhGZ0gcDMgiSMdIn07TH7Ri63w2UBfa3hNxtKbWVF/AxKCGRXzGuKAFW6ZPBeAeNaP4uGh9LGod+rSLxICXuqhl+Zrt/bH4rphVu4TMgDwdmR20QttCMWaP+0OCdJ8zMJ5PfwQHW08Emmhvvkyl1pYUbSbzy2hecMt67i4nB9u/dg9j3OLkq9vP9AzRyryxjErdG4vaP8Dweo9bCPujnOHbCOeyDbQ/nrzv3XTPuq69+DuK5RTyPJMG6e+8uJkRrdmnTDs/dTGRCrmGfxl98lPQqi59P/5vrzFvtKGve+hiNwusPMJlskmG7m5lNYhynX/upL0O8MIftkCXYX+fn0Kx8+YqbXHZ+Ht/DQ31uiTbqoP5otH62aP7y6+4c6PRITqpG7VjQHFmfYP8dD3BtMDMb7eO4OtjAZJuDQR/id959H+LP7mP/bNSvO2UUhvcKzRbOKecXjw3W0+nZf088v3zOoqN5bnv7Ab6Y4XzXbbrrYxpjn23R/diLHexPly7h/dbBAX5+b9dNmjuZUNK5PiVJHeIxnAR9KfaNJMb7oqRwTfycv9jj9ZL6I+1dYAH1R06UaWb2EZ33nX289pv0u8GluRWI71OS0VZF0tGlfdzEqeALO5HEmDeheRz6RUMIIYQQQggxc/SgIYQQQgghhJg5etAQQgghhBBCzJyn8mjs7u5aeKRnazZQS+eRnvr8uVXn8wuLqD8Ma6jPu3cXdfUHlODGK1AX2+1ioikzs/EB6s480k1vko7y9h0s0yNtXJ2S8Pi++2zWpqQzK+dQG5flqGW7s3aTjol6vitXrjplbG+hPnST4pASqgWkw9/bR931/hC9AmZmDzb7EO/28T1zc71H/y+qdIrPmO1kaN6RgSUfUNIm0lYv1l19fLMkr0KI/adBuvFaQMegvpFP3cRRKSVpGg0pcRnp9nkMsJaTtcWceOrwj3hdQej6H6AMSrTDeuWCz8HcRIOcFCnewuSbtU3Uj0YX0JeUDFErbma238e6Koy9Jsfjv4zP3iNkZhbMTyw40si359E3NdhDDXZYdzXK0TzOT5Mcky6tUsIkTpS4MXwH4lqOc6qZ2evXvoHntfM/QuwZ9o8m+W2aBWmtlzGx0/i2q18vU5ybly/iHFZ42LbDfew/+1Ocz5YvuktTEuMxbnyIyQ23trAup008J7+Nx2wU7vrhUxKyNMF+XR4l2sqy5+PRmFpsxcOx2MJz4CmvnLga6nGMuvp3P/gWxHPk9eu1cG3b2EAvw511NylbTkvkQ0/JozIouWwUoafDo+SPzm1KxfzE82ZAiXqzFMdds+FRjHV1+8b3nDJ2tz7Fs/JwvKe0Jo4mWOZiA/tW4GHfMjMrEuzDrcU3sMxa79H/JxM3Wd2zpj3ftdrRmhXSvdUHH6In6iJ5u8zMFubQnzOlMd1tYV85t9SDeGkej3mB/K5mZgeUCHpISQ8P9sknGWM97h7wvRIl+qzwBhc5ti3PD07s8fqFxwyb7rj6mO6HQ0oKOKBxMr1xG0sgr9PNXbo3MbNBSIkEp+S1PHGI9CnmQP2iIYQQQgghhJg5etAQQgghhBBCzBw9aAghhBBCCCFmzlN5NBYWeo/yaCzMk+Z6ihq12+voQzAz+/iTGxCvXEAfh0+6tXiCOu8rlGej5rta9Bp5Ku5v4n7sdzY3IJ6Q1junfcOndE6rFzCvhpnZpdVrEO8f4HnfvotaYtbAs+S0WaHP8+lakyme15D0oo0OaiGLAp8py9Jt+ojyRLRpL/y57rE+sigK29lyNX7PkiD3zT8SAC+eQw34+Q5q1ed81P2amU0nqH8dG/aN/j3ch/5gpw9xMsXPh4Vbh4FPngvyyvD+2mFIeV/Ik5HQvvfs5zGzR/uaPzoGbY+dZdTfyJNRlpRHw92V3nlPMkZ9ZrSJ8TTE/CBZD+sl6Lp9PEqwj3sp5Vo4kYujyDPDHb/PhlojeuTRCBqU1yfqQ5yPXQ31dA/rsd7CXA7+BWybxR5qxzsdbNwf3f4tp4yXzv86xC9f+zmI7/T/EM9hmfwRdfRk+CQnbrTdXELjHHXRSY1yOuyjgcDz0MeW1HDOnOR4TmZme3trEHe6OOa9ENvD9+mY1Gcvdt08GnmCc9rGJta/3+kfvQ/b6ay43GlZ/SiPxBKN+4Q02NMM5xYzM5/8OJtrP4L4fk577Ue4jqQJziVZ4M4VQ/JDJBl62ZZ76I0JIjzPLMdjplTX8cjd/7/RwPn+4kW8t1inXDJxgrNHq4nX1ay7/rtGHfvCHHnheC73KddTnTyCVlAeCjNLc/QPbD7AOaTWOB57k+mT5zGYFfWab7Wj/jca4PW8eP1ViD+7gZ4NM7MiRw/QMuVcKUn3zzkt6pSAgnMvmZm1z+O9af0KrjWTCZaxtYNzze11vF+r92mcpe7YHwzYy4X9ycmrQfdjPq3JnL/HzGwSUY4uyk2Vj/BeIT3ANXhlGefcRfK/mJldehnvsc3HupqcyOWRJO44/HHoFw0hhBBCCCHEzNGDhhBCCCGEEGLm6EFDCCGEEEIIMXOeyqOxtLxktdqhTmxAexOnddTHhoG7h3xGGtKNDdTI/9TXvghxfxc1aevr6K/w8gqdLMmid7f7+BnjfBOos2yTZvCFy+gFuHb5slPkOEFt3MYmauPqddQIRpQ7waNmaLVQF2tm1myi/nOO9MWjIWoEA8oBsUAa+VqFBrUzhxq+lNorP6GdzfPczHCf5mdNUI/Mrx/WVTpCLeutEe1xPnb1q8EN1F+HO+Rz2cP+GFEfZu/M5ADHgJlZVpL/IUeNYxDwHvGoc+T0JB55I3zf1aS6YH/KWf9JuWB8o/wgrCU2M7osi1P8TBHjG6J9fD1dx7rKr1ZcRw37eEDnmcXHfbZ4Tt+RlN7QyiO97Z3bONlMJ3hORe7WY5iiRvnC6ssQj6eYo2JvgnPLQg89Ypm5ZfzovTchfvHCl/AcOnjMnR3ywhhqnHvzWGa04M67Owlq/ff3+xBvrZP22lAXvUhelemeOwfmTRzzXobHSCnPkmU4dlpNzAmxOO/metrYxn47SdBX2C0O+2hZPJ88Lq9fvGKtxuH4zih3iU868LTCa5XQmhmQ5j1OaF0oydvn4fvHmTvPZuQjczyJGeXtoc+nNP9MQyzDzUFgFkTYp0vuG8kmxhmO3YMRnuM0decXf4xnulNQzq4C+2ejhnXVrWMc+BX3L2Sw8wK89rw89ilNn0MuoVdeuvpoHfzRBNfcosD18o3XP+d8fmsTfVa7u5in4foF9Ag0yAcznaK3psjcOmSvTERN6TVxDe6RZygr0ftVIx/IAefwMvd+azzG9a5Gfqo4xrHLOeOqVreExlFOd++NOTyHosBzaLTxqKsvuveyCys9iOt17GNJcjxu4vjJPUL6RUMIIYQQQggxc/SgIYQQQgghhJg5etAQQgghhBBCzJyn8mj0+9uP8mgkY9RhXl69BHG3g3uxm5m9cP0FiPf6qGnMpqh9u3rpIsR3StSEDaauVi45II0j7a0ekEZ1aQ71eS9eR830XBu1wvc30CdiZjYYob4u4+2yKZ5M8LzbDdpHnPYqNzMbpqgpHY9RkzoaYhwPsG7btOf/tEJfF9C+zHXa33xneKyPzCv0588arz80r36o2Rx42I6ph3VW89w6bNdRs1hO0NfSbpGvKMVrLBJ8Lq/X2W9hRnYIC2vo+aFtwq0kfwRL7j32U1TkjjG61pKGtR+QPp4103QO7NmoOC3LSRvrjbB/kpTbihvkNam7+nHrYX9L6VLTE5VXpM9HI59mZg+3QC9GODe0qF6HqZtnpt1EL0K7hfPP3v7HEGcjbP/7O3cgzoduf/DH6CXaGuB+9u1aD+KuoSa5U1uAeOqh3n2Y9Z0yt3dwXvRD9Hk02Hc2Qj3xdAfn7XbPzQER1rDudvqUq4M0650I398LcT2Zb6Enzczsfo75n5IM5wiLj3wez6n/nTv30qN5ajzEdccjt0Ozys5FZqsJacUn5IfgHE8R+Q6S3F2Dowa+56Gv8yEFrcFJRvvxU+6hlHxuzYp7i5TWo/0B5lBZXcL7kzwmjxmVUZQ0UZtZQQv53hjH9+4++gc88qrklFeD1wozsynleAhCrJuTlr04Ofs+GFhmgR2e4+WLOMY3HuD1jysu8PyFKxDv3Mf8Jp98hh611QuU0yfEdYbbxMwsovUuTrGPeqf4HJcW0EdXr+Ec22/TnGBmB0O8FxgeYFuPKaeSZzi/FW3Oreb2v5T6U71BeTQSPK/pFMvcH+OY2Oi7uYrKANtseQnvX5rt47opPXfs/zj0i4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDlP5dFI4oGVxeFHPI80ZrQvdbPuHnphHrW/zQbq73pd1IMFtF92SvtW31p3/RL1APV13Q7unb7aQ93u5cvXIWZZ4UeffgTxzi7ux21mFoWkQSUd7HhC+xlTXg22EwQVz39RhGUMU9Tnch6N6Yj3ZSZNveeW0Wph/Se0T7ud0AhyboazYBgW5kWH58D5JeoF6ihrI9L9mplRroeA9rb2yBtTkibSQtJAh26umJL0x0XGm3jT+2mf+pz2UW/QOTrnZGZZStfqce4N2l8/RR02a4lD3+0bJJu20sO+4fl4nn6I4zAK0IsQpNjXzMwK8iqNS0qKE578jKthPRPi4FFugZjy5/gx9sHsvnuN859Hn8D+Hubc6e/1IQ5L9BFMYtTV1lPcd97MbHEF80NM5z+AuJa/iHGEc+SQcg5MDignQeqO/WEfx06njm3Z7SxBvB9hzpqwRjlpFlyfkHlY3/UINcJRD98eFPj+qE5+p8AdSwdD1N37GV7HfONQY575qZlhvZ4F5y6+Zp324frR38U6LMgvUffcObBJeTOCCL0z0wTbdmPzAcRxjOtKqyJfFv8loqaMM2wX9mZyLqGImqnbqVh7avimxXOk7acJLBnR/EWLcFhxZ5TQPNuf4jo+nOK9RUGJDhJaCyrSFVl/D/vfdMpz+4ljehU+t2fM5vr6I2+iZzjfzeGUb5/cdO+VFpZwbmr3cP7amqLv6qM1vMdbXEIvxNJChV+H1oaYvAs18hlFAY0Juv+ab3GeM7pQM1uiPGUj8u/193Dc7NP9SY18IDH5K8zMfMopUqd8IEPqUDXy2I7Iw/b+B584Zewu4zw9N4fXsbDQe/T/NKm4x/ox6BcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDlPZQZvNpuPEvYN9tDAlVPCGzaimJnV62hmaVCCtHYLzS3r99cg3t9HU8/BwDXMnFtGQ9aVa5gk8Ny58xDv7WESkxufYsKmrW00w3m+a0RrkonaJzNtjRK7ddpoYOrUMa4yaicJGnjjGK/dMQ+RUexggHVXZSPLKOnRHhlTmyeSFxbs2DsDOuPS/Pyw3Mw4MSMlQ6r4fFzDv+YFJUcicxsnA0omaGL0KgqJ6DyCnBJDcb159P4I237KyYYi13yZOq2JcWMex0SNXg8C3GigNd9zyrAQz8sjY21jEZM3+ZToMqPchsF5twd6PWyPQUxJJ5vH55VPEtu177jn+azx8ke7N8STxxviu6togDcza5CB7/49TMAX1vAz7SYatUeULO/iyivuOUZYb0VJpnQyrY88NBWzOTfZp8Zruh0/G6BBMhnie/xwB+J6Deezgoze8cSdX7KYEopSXVkdP5MUaKzNWpgc7MbBP3XKKOq4HtRDrLt7a4drUsFZWc+IduecdTqH59Si5I95hnU6Hvbdz9O47M7jZgN+gG29sIp1uLODZl2vImFfs4ZzQ0BJwPISPzMZ40YAe7uY+C2jhH67D3ADBTOzSYzHCMgcnpd4DDbWtjp4K1RWbDbhezRP+lhXvXkcq0mC5zAe4zH9wE222aV7B06MGvjH5zmZnv2GGFEQWe1o85vRCO8pQnLQX7mMG1+YmRUeGuijCOcNn9aZ7S2sowebmNAvjt1xeOUS9mmP1rskxbmGCen7d17nq3LmPtyg4SHzcz2IexQPxjgG9ijB8nCA5nEzN0nyZIrjvdvG/ldr4NwV0iZEnTl3fapT/W9toqF/NDzucxlnH34M+kVDCCGEEEIIMXP0oCGEEEIIIYSYOXrQEEIIIYQQQsycp/JoFLlnxVFyr1YbtXV18iF4FclkgoATqaAOLc0xod9wjBq0dgtfv1qhgX7l1dcgXujhZ9bX0XOxtoaa03jKiVTQa+L7rnaYNe6cwI8l9AEliGHt3WDQd8rY3sakWXt7qGPlY9QpYWKTNITsI6n6m0d6vZMJi8ry7D0auR9YeSSQLEk4WVDsVfTsoE31XiONPV1TQMmtuqTBjUeu1rNGHo3QOGkTJaGkpIj8ensRPUW9FVf3mpEPJKB2bJAu2yefR07a2qAiW1VJGufhAHXS0xH2x0kf/VUJJQ8rdyrGUYLnOW5g3RxMjvXixXPQJ5uZBbXskf6720QNbH0Oxbu9OewvZmZj8kqNc6zHK+dRX1zEOJfsUUK1cYkaejOzYorzaiO8BHFCiewmlISt4aNnLKT5bbDleuNWz13BzzSwjJz6XJFgPJigZvmrV7/hlHHrPUzetb6Nc/dLX8S5P69jHxluoRelb7edMmoLuOYY2VN2bhy2X/EcEpYeEhz9MwtpHamH5P2jJIlmZkEN2zKI6AJp4uy18JgLyzgfpVNKtGhmHiUOqzdwPkpz7D9jSjbbW8S+Mxqib2Y0h3ONmVlOfrq9gz6WQec5mtIxd3EMFCX1AzMbDykJLvlSV89j8rmC5uUJrRdehUej3qD7qJDm9pP+zeDs1+AoCh8lD2ZPxpR8oqsXcC4zM7uzjvcxZUAJMXs9iENKVMw3Uzvb940ZDXBcv/LSVTxmje+/sB1z8h44CWw5w7KZWc5eGkr+S2NiaQnvXReWcZyNR+7aMRqhj2NnF+M4pvs1WiITWjP9LtetWRRi/7t29WV8/cQ8zol/H4d+0RBCCCGEEELMHD1oCCGEEEIIIWaOHjSEEEIIIYQQM+epPBpZeqyQu3juArxWlqhRG5KezMzMPPY/oI4tJx1bUENfweVzqIFcIr2omdkBlfvBBx9BvL2FmuiyxGct3yfdK51DVrFv+ME+ajdrNdQfcw6MnHT4TXo/ax3NzHZ3cB96PiaXmZHusN5ALSS/38zdv7w0bJ8wOtaUPo88Go25ZfMbh+2TpajrnY5pb/XU1fCXCWpiI8pfUtJe7GmK/dUC8l/USN9sZgnt+W4N9B2EXSyzTjrrqIH9rdvDvfLNc3W9HccTRHuxk/ekpCwjaYza4cGOu0/9eA+9TdNxH2I/YM8MldnEuvOm7nccaYL1H11BD0RWOx57RcX+6WdBK2xacORdChZof/YS+1yZuRrY/h7VbR3nq7CJ+Uj2dyk3AuUWuW83nDIW6ujJ8MmrME5xnMd9bJu9IWngW3gOUUB5Oczs/AX07N3r45xIqROs9PAP/Qc4bt6/7/on7q+jx2IwwTIWL2F8bhXXqAcPUB/uhegrMTMb7GCujXC5D/HKK4dtmieFrf+B8/FnTppMLY0P+11glKsqwP7o+a5PsiQtee6Rzton3xoNM9ar12h9rITmAvZTBNSf5nuoX48ol0mtiXOomVmH5tVL5I9IUixzuI/j8M7aBxBvbaH/x8ys28T6vrd5F4+5hv1rvoPrQ0Q+wjR1fT77Y/xbFuM46TSP63sSn71PLcvyR/kT2KMRkZ9iOnX9Y+dWcJ64cw/rLKzhetlq9yC+uIplpBXrwD6tVT989xOIP/959B206lgme4fZbxxVGEBzGldxifdwtRqu20GA5x0FlHum496fddroN16Yx/hggMfcH2DfGU9xTAwO8B7KzGx7E9ujR56ZpeXj9SlNn/weUL9oCCGEEEIIIWaOHjSEEEIIIYQQM+eJpFMPtzI9uRUnS3c8kk55tB3m4R9pi0uKfR9jZ+tPKjOOXRlTEuN7WP7CW5exdKrgnczo507eRtbM/SmYU7PzZzjOsse/buZup3hazFvq8s9//FNf1TF4C9uTcqmHr53FNrcPyyhO/FRcUN9gKY2XunXoJVgHRUbynoyun+qoIMmRV1WH/BluS2prUqeZT30nY/mW55bJ2/KeLp0iKcMpY8TMvQ6+TrNTpFNUt17Fr/4FtZlHbXpSrVckh6+d1TbLD8vJT2wvm6c0pmgOzPyKuSLja8LzzxKaC7ifU58tKuQXOeldMqrsnD6TU70X1FbG76+Yn1KScfB1lAXWVek9vsysrOiDBR+TPkN1lTqSVd7O2inCct4mO8E3+UdtnCdnN/+dLGd4YovUgMZcSP0trFVsMR/R32gLc+Mtzunzocevu2UwvCX8NEWJZE5rNm/by9vCTmJ3W/GS5CwpS6do3I1GtMXuBM9hMnXvXzxD+cuU5qeQxkUtojHBktUK+TFLknMqIzghbXsonTrLNfjkPRfLsxO6H8uq+gYpf3mMFnRLGgbYTklCZbBU2dztanm75YTWu5BOk18veO4qK9Z9+ptPskWf12SafHySTlWtwdzOVHWWpnxfmVJcPjY2c+uOj3FyS9uH/3+S/ueVT/Cuu3fv2pUrrp5ViLW1Nbt8+fIzLUP9T/w4zqL/makPimrU/8TzRmuweJ48Sf97ogeNoihsfX3dut1uZSI+8eePsixtMBjY6upqZfK/WaL+J5iz7H9m6oMCUf8TzxutweJ58jT974keNIQQQgghhBDiaZAZXAghhBBCCDFz9KAhhBBCCCGEmDl60BBCCCGEEELMHD1oCCGEEEIIIWaOHjSEEEIIIYQQM0cPGkIIIYQQQoiZowcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDl60BBCCCGEEELMHD1oCCGEEEIIIWaOHjSEEEIIIYQQM0cPGkIIIYQQQoiZowcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDl60BBCCCGEEELMHD1oCCGEEEIIIWaOHjSEEEIIIYQQM0cPGkIIIYQQQoiZEz7Jm4qisPX1det2u+Z53rM+J/GngLIsbTAY2Orqqvn+s31eVf8TzFn2PzP1QYGo/4nnjdZg8Tx5mv73RA8a6+vrduXKlZmcnPizxdraml2+fPmZlqH+J34cZ9H/zNQHRTXqf+J5ozVYPE+epP890YNGt9s1M7OP3v/w0f/LLIH3xIP7EA/7GJuZ/fB3fgfiG29+G+JBcwHifWvjAXIMy7JwyghDfNpeWepBXI/wkn1Sjy0uLkOc5iXEQRQ5Zbbm5iFutObwnOo1/IAXQFgafUNQuk+HaYoXn+QYT+MU4vEkptcxHo0xNjMbjsYYjzFOThSZJLH9t3/rP3/UH54lD8v4e//j71m73TEzM/5SZXkJ2y2O3esbjUYQhyH2hVoN2zYIsR2CAOOyxL5hZlZQn2w36hD7dOJ8jLzAz++P8Dq4jczM+TahVsMygxLL9H2Mp1Osl7ykgWZmtbABcUHnmeUZvk7903l/5pbB7cHfnHknXh+NhvZX/so3z6T/mR33wb/6l3/eokdzCJ2fx+fvjuPCsL1L58tB/ENIbXX92lWIv/pz33DK8H1s/2x/A+LzEbX3cBfi/f0DiCc0lsYxzv1mZjlNxSn1h6TEOa+gfs/jAD99CI/psTM/UZkpzd0B1ksY0rxs7rySF3jMh2MnTjL7f/5X/+zM+9+/+dd/49E85Xyz7GMjJElFO2VcJ1gHYYjtFNJ6WYvw/VnujuOM7g1KWs8CH8uo1fCYEa2X3bkexOPJ1CkzTSYQ87DK0pT+gudUbzXxeJnbA8MQ+0+e43UW1FfiBPtrPMFzDGpu//MDXIM8Os+TrZckif23/+//8kzX4O//7n9vnc7RfVmBdcprH8/5VfC4Py1+Ejz6CB+D5x7zsA8H/H7nNrnqnPBanXmdr6vgm1l+e1UZfJ9Y8RZ4+Sepy8e/5+SaNhxN7Jf/tf/wifrfEz1oPJzQut2uzc0d3kSXKd3I2gA/k9FDgpm16KarQZNYQjfxNY9u6mnsVz1oRPSg0aBJ67QHjWYDb6hCWkGDyJ0cWk2cpBo0aYV1vO5ZPGiEdKPm+TjoS7bf0E1PXrg/f6YZLVR8M+iuKWfyM+rDMtrtjnUePWhgud0uPtxFkbsY8Y3faQ8aIT9ohE/woEGTa7uJ/elpHzRyD6+jNOw7Zu4D0NM+aPB18s2VmVktwj7tPjhg/8ufyYOG+5B/Vj/jPywnikKrzfBBwx2G+IcoePx81mq1nDJ8uqHOUmy7ToR1n+X0/hjL8LzTHo7M8gLfE2b4puCUBw2Oqx40PFrM84zqm7oUn3cQ4PvDyv6EMc+TPHbOuv/VatGjG/PTHjSqbhoyn9op4DmP6uiUBw2/4kGDVRSnPmhQn67RelmnNZn7mpmZ7+G1c6twuzkPGlSmH7jzrPuggcfMC17XadxQXf1xHzQevecM1+BOp23dP/EPGo8/xp+VB43TquYnq8snf9A4/tvp/U9mcCGEEEIIIcTMeaJfNB4ShKEFR9948NNrFNHP0hF+C2HmfjNBPz5YQN9KRPRNbUHfZBQV38p79ETGT1Ihfd3i8be99C0YfxESVnzTEdCb+BvmiL45c37B8Pibcvf5j3+8KejKQpJ48TdP/GtFGJ7+jbJP3zx5J35O5m8NzoJ6rfbomyeWUezuovwjq/jpO6Wfz/kne/4Ghn8K96ldw8AdPvytwZR+5udfTdpt/OWPJW/7+/t4vIQlAK5MiX/KDOkXNJYZvPl9lDB6nvtN1Je/+DP0HvqVjrsDlel+E+y2z+YWyi3nuosQ11vH9V3xReqZMBoklkSH9eN8RxpUfQ+PON8ynfLzNlfrD3bfgfitN99yysjp18/leWyrv/GvfB3iyws4DoIpXtlSg75lpV9MDsGxkdE3diOSdhb8SzHN7Xnm9vMkwfGWdToQxySV6k9QWjVKSd5X8e1dg35192o4lvy5JTMzm0xdWdJZkMTJoz6SpiRRovc2K37tCkP65p9/TKeFhn89KGnOq1oGwhrOaTyvLiydg3j10jWI2/TrdIfaeW93zynzkZzniMl4CPF4jHLBuXmUaQ9H+P6q9aNG6+P7H7wNMf/ixr+SBCHXnbvO56xBpDUYbwSerynbbfqzvyeoxunUELKqoKD7r4TGQEmSzDJ3r5PnkinJ5PhWu92iOdf5NdJtW/cXCuctp7z/6X/RcOeHsvL/p6FfNIQQQgghhBAzRw8aQgghhBBCiJmjBw0hhBBCCCHEzHkqj4b53iPTgkfaQd4mLwhdj0azjVrLWkSad9I4+qxfpuOxJtKswjvAOx+w2YF1ko4Z4vGejcPz9E6JqQjSBHo+ezRcH0hBLRXQtQcBa+LZT0DtFVbtqoGFRBSf3CmIfSlnQZqmj3wWfK6sqa3S2LKmkD0avNPIeIQa6GTkbpnrwNVCfocwwjJ4Z6/BALXCwyFtZ1uxwwNvLTsY4A5wdfIIDQfoZ7mz9gnE0yl+3sys18MtnK9cfhniwGfPBvd6rIe1ex87Zbz19g8g/qmv/hLEq5dfePT/PD/dD/EsmEzGlqaHbcgeJt59rgretYPHPuuHecLh3bpi0p6bmcW8tTWfFjVNvU67/1HbjRIssx5Vzbuka6ax1qjzjoI0P/FubFU71rBeOCVPFfnQptRHYtopManYppfnlSnNxcPa4esh7xpzRiRZYuWRj5B9Bwl5UMx3l/eFc5gLIaUtcIsUPWU5VXpnfgXiOdp69hD8zPw8zh118sEsLKAXqzePcauF/otr116sKJPPAPtfRLtnce8a8bbhFfrzm7c+hfjcuQv0DvzMhNpnTLr9OGYdv1tuGPz4rfErx8gzxvO8R7sMuTXEPjz386fp+p92B60n8gnQHHuwuw3xb//j34E43cU1uNfENhhP3LXn3i5uCX5zqw9xf4qfeeWlVYj/g3//NyFud9xdW0tn2/mn88Q8Wd06NzBP8d4fj37REEIIIYQQQswcPWgIIYQQQgghZo4eNIQQQgghhBAz56k8Gn4QPNKxszwwrKEno95w9/Cem+9BzJnCB7SHdMi55E/JgWHm5sFgH4eTZ4MPQRfGXoQKa4OFnF+BtNscG+1Fzn6XomJ/bSezd4maP9ag5rTXc05axqgiMzP7PHjf7/BEnoCiIp/Is6Yoy0d7skecRZ78FsMh6izNXD0n+zj8gveYJ99LQHlceM/zimNm1E4e6ainCWfQJj085Z5pNFzv02nXFbEHyLgM0qFX6P6/++bvQJyQlvvCOdwLv8ix7+ztb0L85g/+uVNGmqJmeWd3HeKFxfOP/j8Zk6b6jDi/svwoM/iFORrHpFmtymB8WkZszjmQUj6JjPpcWnN9IUmG7TnfwfOq1ShfEWWS/+7HdyD+waeY36RLXjszM5/8EVsPHuA5LGI+ildevg5xs0FzjbOvvJtzxHkLnQMvDxn1WTcvuNmVC5hfISdb1nffed/MzOLk+XiETq7B870lfNHRYLvrSHMeteHLHfRPtMh/w/Mqe7WWF5edMnLKP9RsYf8qcuzTAfuUfD4HbKm5OTwHM3fcuJnAkZj8ORFl/R45eRDMekvnIU5pnt3axHGSUN6mBucNi9zbr5z6MK/jBeQxOHufpGfH3cynexKey3i8mp3uEzjdc/EEvgS+B6TzHGygR+NH//MfQNylDO+9l3FtswqPxsaNexDf3kOfY073oZvkoxyRF7PddT0aTvq1UzwSP0lW9dN+ezjZflVZwn+yowohhBBCCCHET4AeNIQQQgghhBAzRw8aQgghhBBCiJmjBw0hhBBCCCHEzHkqM3gQ+BYcGZ99MiIVIZrGwgqTYquNhsCQzVGUkCTgBE1OMiv3HDnhFXvCPDaYc4I+53gYcxI7MzcZHifLc0zWAWffO90MzgZePg2+ipy83imZv0M+B3MN1mzc8k8km/OLszeDN5t1a7YOzdBs9qvXObGR2/9yMu+xWS+eoEHQ8XpT1+Fka2ZmXCtlzgZhMn/TObH5ks3f/LqZWUGmY6+On4morkaUj6/uYz2srvScMnb2MCHRt7/9TyC+cgmTaI2G6KLt97fwHAPXzF0n0+dkhIkFDYz1zydh2oWF7qPz/IXPYcKuWon9p8qKx3MDX0dOAzdOsB5TSjpXlVispGMU1P4dMqHyWCroFKc0H0WFa6P2aUOMnQzjZIIjo0vNH8ZkICaDuplrfozq+B6/gec1HuGmBrsHWEa3YgpbdZIZ4jGvXT00hk4rkv2dBVlWmHeUBLSgjSbYnNxo4XprZnbhHCbcu3DxIsS9Ls6b9TrGvB62W+48yxtaBLSQ83mOx2i8dtcu7PPDAc5FZmaNBppneZxx3+E1mNfXKHT7eG8eNwrIUuwDg4P+Y+MOGe8bDbfuNjdwA4yD/h694/g68vTs+6Dne+Y97AQ8UVBy2mrfNhu1KVFnya/Tp5/E38y3ePSH/e19iKcZJZikRNIezW1VeRIzjzaBof7UiHBN9hPcmGJnG9v5/CqO05+E0+u2ykz+bDYY0C8aQgghhBBCiJmjBw0hhBBCCCHEzNGDhhBCCCGEEGLmPJ1Hw/ceJbArQnpG4eRuFY8wzTbqKHOfdJKUyIdedvwXVRoz/gsn9TvNWeCzZ4OSkviOG8KMJHwWUVa/KEJdvUf60IAS9mVViXjoPaWXU0w62JQSD5IuO6zIPBjQhdTIs5GdaJ/TEiI9C9rtpnU6h7pWTtDEHo1Go6pro0YxjlH7m+ePT5bEr5cV/h7W2KcJJVijhH18HawvLchbk3tusiBHe0mjYELjajxCjWo9xHNoNd1km/191JAODzDp0e0MNad7u1hGI8L+9sILl50yRiPUanMTdtrHbezZc9LI56kFR03iUb2v7eE5bfaxTszMIh/fw8nJuP8kE+yjNRp2va7rZeiSVyHwKLFgiW3hpTTHkb+CE5JWJTN7sIkenJR9IvtoDHr37Q8hvnYNvQJvvHbFKaPBidu66EEIaNHJMlxvPB8TrrUr1qhegL6OmLwjL71wyczMxhO3bc+C4XBqYXRYtwXNP8kU2+XqS67O+9Iq+orY2sbrdkT+wRZ5Mua6bvLGaYLzzWef3YT4u3/4hxB/9DG+bqRn/+av/DzEv/zzP+WUmabUxz3y77APiZIK8r1Fp13hMW3ieS1QAmJOXvjuu9+HeP0+JsIcDHCONDM72N2AeH8XE18mJzxbvJacBZ7nPbrvYo9iSd9bV62Pp/kEXF+BcwR8f8U5sieD18P9B5g81qgv8P1cSB7GonTXnp/7F78Bce02JvD79Ps/omNgIffuYrt/7ouvOmX8ce+4TkuW+CzRLxpCCCGEEEKImaMHDSGEEEIIIcTM0YOGEEIIIYQQYuY8lUfjUCXmHf/3JD6J6TJ3n/waab/HpKNMSTNPtgTzPdbIV+wh72jcyatAskHWuXqkmQ84J0bFo5nH+0eTj6Og5znWTLM3wvfcQjhvSWmoK8zpwmt1PIeYdPph5Ho0IvJx1GsYJ8nxeeXPwaMB+tBT9Iae514f57AIAtR8R1QnXAb7KdycCO57phOs94w8F1V9GN+P7TydutrwJEHNKOfaKEs8h40t1IP2D1A/v7PralBjyhuwvIL67ynlIJmbm4O4Wce6TRL3OrhPhpRop9M5OV/gNZ0VG1v9R+NkY6+Hr22sQTyZuhrqeo3GKY197rc18mLRFu8WJ24ZaUy+NMpJMSXvUUp5C+ab+PpLi+Qxq/h+6gLlm+j10D/RprxKEY295WXsLysLbvs2aPKukbcozcn/RK87/r2K64go/87GAI/51vvvmJlZnDyf/pckEyuKw7Jzmmta3R7EL7/yOefzSz3Kk0Fzhe9huzRozZ6fx3aKE3eueO/99yF+9w//AOI/+t3fhjhroXdm9TX0YAQBem3YM2Rmlhd47zAlbw17NHwfr5NzYVWtL37A/gEcJ3Pk2fjqT6G35OID9B3duPGBU0anhdd6+cp1iOP42EMUT2P7B3/vd51jPFNKe2STOM1fUb1En7Zu8+u8Pj6+DaqKKMgvdpP8EzfJS7c7wv716QC9NcOKOfcvf/1rEMcb6K3hG89Rgid5+xauHbPwUzxtXf74v/2Y4z/Fe/WLhhBCCCGEEGLm6EFDCCGEEEIIMXP0oCGEEEIIIYSYOU/p0TjGUWeRXjQej/gdjuliTDkGJlPaY75OPgTyLjg5CMyM5MeWch6Cgv0T+HqWodYzJN9Cla7XqQ0KeY/ugPSgrA/Nq/ShTnoP8n1QHhOfrjuknBhh6GqM+byc+ET7ce6PsyYMH991i9ztG0WBnSOJyT+R4z70XAZ7Mqr6X0Z7m3M+E+4c7GXgOt/awvwEVbBONaJ8A3mJfeGDj1EbvL2Jno25jrs3fqeNmnseV6Mh1h3X1dwcHpOk4YfnSYO3pLF50teRpM8nj8FkmlqWHZ7nrbu78NpwH/dnnyPfgpmZF+Be/J6P/omAdOAeTdEl+Yzyir30yXJhJeXMSQvs9wnlJJlvY5+9Mo/xXMfN3VFvYF4UztNTC2v0+uP33I88nIfNzOrkbSszvI4yffz++TmNf86RZGYW+nieOeeqONr/Pimfz3d05y9dtuho8CQxjoHL116B+Ou/9E3n89029r9kivUcUQ4L9k0VOfaVe3dvO2V88INvQ3zzLcybMd1H/+bgAI9x/spViD/36nWIOb+TmdmU7h24aT3ynvg++8FoPalYg50+Sp8paB4O6BgL5G+5cM7Nc7KeYntkOV5Ir3N8jOlzyeVS2EMPKuerqMqb4fK0Hg32sGGZheM7MDNe++kzCc0TI4optAb7jSuKvP7iOYg/W0cfSHBhEeLba32Ip1P2blbV5el55PAYj33ZKi/kiXwcT49+0RBCCCGEEELMHD1oCCGEEEIIIWaOHjSEEEIIIYQQM+cn92hwvgrSCh/s9t3CQsqbUaD+a0x79ddJr5gHqJ/NKiRmGelwp5SHIKY9lYsSy5ySR6NW4D7iVlZo1uhv+/sHENdpq/H53hLEjTpqN+sVGtQ6aUqjOuWEiHk/c3yGnNB+59U5SEj/yHkjgrDy/2dFWR7rDk/LP+F57vlNJ0OIDyh/BF9vdw73NK/Xsf+Nx26uGOe8StI4nyJj5ZwYaYo6dPZwmJl1yFMRU66Nd95/E+K7D+5CPNfGPt5bRL2pWcU+9ZRHgPN7+JQPIiBNfm/B9YEcHGD7rJPOdXPz2BMxrvKAnQGFV1hxlNuiVcdrSmo4jocVOtvpAdZjnOB1OHvRs0a2fAKdbk4+oA7OFV/5HOmByW+zv49te3eP5pY+u0DMYtKLd7vo6fHJs8F5gObmML9Du8LfktNcztcekL8iZD8M+Q1qbhF2IcTxu7+Lc8QP3z/cU5/z4ZwVo/HYoodzAmvJG3i9Uej2jZC8U1NnQsI49MnDGOOc1wzdPBoLLazYfcrr0rrwEsbUEC9evQhxRAbFnT1cX81cz2CN5urATvFJRuyFcsfuZEq+RvZkUN3WIjyHPfKk1mo4RszMOt0FiHd20fc1PLHm8Dx/Nhwn0ngyT8aTHO8kp/kOTssN4R6D12Qnbxkdg71gQUl+sYZrMNymeeI3/vJfgPjvffoxxO9Qfjf23HpVecqcSz3NP3GaScP9PHtgHnvEp7Bv6BcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFznkpon41Tyx7lX0CNY7yPGrUHa3ecz/cWUPvN2x0PaS9s9n3kpHdPeF90M0tS8i6QHi+KaP922kM+ijBu1Giv9gpdYka+j9/9vd+H+PZt1MSfX8V9wpdXLmB8ztXIt7uoaW+0SOPOngnaSDzj/CEVGlTWPEfkFZmczDtxyh7Oz4I0jS1JDvWRc3Osb8Xz4X3VzcyGoxG9BzWu7HXodtGjwXgV+/AHPrbDlPapz2jv/34fczFwj/Ypr0Lpue3GOVQ27mJejLffeguPSTkAmi28zlGF/yEKKFcHaUibpIn2SQPdIl1rkbv5H1hnvbXTh/jgxBwznrj+mLPADzzzj7TvF5fR27LYRu/VNHGv0Se9cEweHM4l4sDjtnS9AgmZ16IG+h+8knxrpKG/sIw5MZqtHsTsezMz29vr42eaWGZB+QDyHK+75VFujqxC/03FFvQ9WUm+oJLGDn/eS9zxm1M/55wjwZEvpwhO0z8/G5LxxIro8EK686jnnyNfTJ5ibhszMyNfUSPCem5SM9QibjfyWi3iOZiZvfGlr0DcnsN8ETXqT+cu43p4/sJ5iEdD9G4NBu7YbzbRn8KeoIBuNlx/j5N4wymjQZ6LOMG53fFo1LEyGzQmBkPXJFSr41zcaGKbTk76DCvWn2fNsUPjj3OEE5HjteTXTztaxRtOuTU5ranHE7yXbZA3+I2vvuwcs033Dv/ot34Xyxxhn+V7V/biVXnvnD+dUjen5dmo5vGeGTjkUxxfv2gIIYQQQgghZo4eNIQQQgghhBAzRw8aQgghhBBCiJmjBw0hhBBCCCHEzHkqM3h/Z9/yo6QzBXkQ0709iLfvYbItM7P9HXxPTIbdcYpGlIwS5KQeFppXuX7IIDUmQ2ZtTElu6JhRgAavThPPMS/cJDleiabQ9fsPIH7/vXchTsnoOL+AZrkJGYjNzHb7WHdjqrud/v5jjzEh41qr6SZM42Q4EZnfwvrxdSaJe47PmiiqWa12eE4hGaBT3jiATNdmZpzrrtNFc978fIdeRzMfmxA5QZhZlekVDZk1Sk7FxrSCNhvI7fTEYJlhW2zv3ofYo7bq1CgJZUHn7LtlRjTgm5TgarHHyQ2x7ji5XRy77VOv4THOX+hB7J0w4Hr+8zHjpqn9WBPezgDb+sO7u857IjK852TcPhhRAlGaE9MY+/l8m9y75jSnzbfwmD/zeZrD2tg/rp7DxIN5SYnvnBLNioI2+iDzLa8XXIdJRvNs6RrOy5KWq+Lx7sicNunIU9pspKgoI6Vj0mYir7x0xczM4iS1f/o733c+/6zxgsi84HAOmVtYhNeiGs2JY1wzzMzCLr6nSf0noA1APPouMkspcWyAc6iZ2fUX0Sx75eo1PIcalunTuB9NsV1GlAyyau7gBLJhiO8JA7wu3pQjpXavzpfG/QX7l8dJSmkcdikxajydd8rgcZPRphnFiT5dOv3/2ePZ8fg/mxn4FPN4hVuca+U04z8nk/1Lf/0vQfzd3/oWxOcuuhsg3LpxC+I52oBjixIqx/ljTNZmP1Hl8nWeltT4SY5R8Y4T/5MZXAghhBBCCPEc0YOGEEIIIYQQYuboQUMIIYQQQggxc57Ko7G1uW2TI49DPEFdb3mAuvCt++vO53NKNjUYYmKUSYqv7x9g4rCM9GPdLidtM2u1UAdZo0RilKvM0oQSqFHiwS5lMDrn99wym1jGuXOYcOj119+A+Df+jb8O8U/93C9BHNXdRD7DEZ7X/QfbEH/vB5iU7ftvvQ3xH775XYizioRYUYjXwR6NxROJBfOKhGvPmigKHyURHI8rklGdwGeBrLnJG0O6Xn6dfSh8zUWFjjwIsb8sLqKOmnWTJSXuGY4wOVWeUoKtlts3dvfQD7X+4FOIl1bwnPb7WHfTCepHe3TOZmZBgedVUKK4hV4P4jrpxccj9BDNzeH7zcwKn/TiBeuuvcr/nyXDwcDCo2RgCfmC1jdxvnr7ExyjZmaDKbZ3fw/H9ZDawnNMPPj6Us/1Wl2/gvNPnZKXZRm2XUbJ8350B+fujQP0Jh0MXX/WaID9o1VjrxJ9hq6r2ca5vDPv6td5ztrtc5/E65ySx88nTfFLl90yri2SkcvQn5LEh+eQViT7Owty8x5po5ttbPvBPnoypsOe8/nwwjLG5HXLUvRDPNg6gDigRL1zHdcjFNGaW2uzj4PaKcE+vb+P7TomzwZ7Ow/BcdUhPwR7xjJKOjk17FuB5+rbsxjrxo8e7/vIyJiUkUdoMMCxb2Y2JS2/T8ni/Oj4OvyqpJbPnGOXxmka/Z8sYRzC66XjQ6j8kHMiGNIxGw1sx6/83Fchfv8770D82Sc3nSL/N/+L/wjiD97E+7FNSvw5ZdMaJ4J+gqrzKvro44/x9EYQTkp8MvZ9nit/PPpFQwghhBBCCDFz9KAhhBBCCCGEmDl60BBCCCGEEELMnKfyaNx/cN/arcM9r/Mp6ijLIeqR75O208wsTVDrO0lRtzslzfsOaRgnOeoXL3iuRnFxDvc4jkiXFpO2P0053wTqMF++jp+/unTBmCDGY/6ln/4axFuvfgniixev4+ebqAOOS3ef8JLyEPikO0xI272xvQHxfkwa6QqLhTflcrH+sxPPpYWzMf6zpygKK458PsMh9j/W4LbbuDe7mVmjgfXM++w7+/CTlr3TwWOmidv/fNqAvUP65IMD8mDQIfZ2SevexTLZc2Rm9s4PcT//nT3U2L96FXMcZDl6CfY3cKw2Jm7bvnD1MsTxuA+xRzkJ5kgjvTSPuRmKiu847m/hHJKT5t47Md69irF/FpzcRz5LcbxcO4ea+X/Bw3o3MxtPcA4sS+wfEeVZCX2KaT6Laq6Yt97Euu3UsM80QjzmlLxx79zGueP2Fs5va/fd/CDbO9h217vUviHltCB/1OrV1zAO3KVpwhp4D30d4zG+/mAb5/KcEowETWwLM7MLbexzGzt9iO+tH9ZFmp29R83MrL+3a8GRr2JMvr05skuM9necz4+H2CfrVCdvfvdtiP/m3/4tiF9/FXNk/PXf+HWnjOXzS/QXbHuyatqY/J7DIc5Pg/0+xKOh623IaL5ZXuxBzF4Ux2/HJ1W6c2BOuV4aLZybE/JMJE7eFsq7UfE179rdO/QXyi124hziqZvT65lzcgKkqYctGT9BGge3uFNyQ1RZGfg8PMoP5lMOpmYH+06ng3NA2ML7hl/4lZ9yyvzkk1sQ/9P/6Z9B3Mgof1LOPiPsO1U5ME63bfBnTvsd4fQjcv37vl/5/9PQLxpCCCGEEEKImaMHDSGEEEIIIcTM0YOGEEIIIYQQYuY8lUfjwf371mweaor9FHWSxRh13pt9V/+ax/iehDSPKe01zHk1DmLUVdYHbhlXyHMxnND+xaSr9gPUSL92FfV6v/5LqM9bKFC/bGZ2+9t4XUUfy7h8bgXiRtSHOGlQ7o4S9zo3M/NIS9tP7kJ87+77GG9hHJIpo95EzbyZOUlGpuRXsZN5Jk7bw/kZUJblI+0ia2pZL1ipcSS9Ifs6nD3lc9o3nfwXc/PYV6oIAuwLpYf9k6XBnB8ijPA6PvzwXaeM738fPRrzPcoPQte1tIQ+Ji9ErfHBwNX+bu7gHv3zHdT55ynq+Ot0HfUG1tVOH/NqmJm1mjgWA2qf+RM5I8Lo+eTRiNPc8qPcJ6yRXqAcJ3PX3HwkcYZ1nZFJh60n3K+5D1aZraaU/6Ue0H7nNDYK8ioV/Dpr6mNXv35vF9t/PCRtbwPbMibP2EGGeZgK380X88mNNYhDH+fmuRZ6ZDb30KPA+YnyxPXQpDkeczzB63o4h8wiR8BPwmi0b8FRe95bw/38Fzvoc6k33TwhnDNnGuO4/vD9DyG+9QmuIy9fX4U4qGF9mZn55AHi/sX5szY2sJ36lEcjp77Sarjfjy4s4rU2SVfPngv2aAQ0ztLMzdWRF4//DOejGVPeDV6Tej13flhawnX5weZtPObo+Bx4DJ05tMRWrbmncZoH4yeh5JwUnGOFfI//yl/9CxDzGvyf/O/+fYj3DnDMmJk1ySPUo/51P328B4PvPar9E09XN6fOUJVFPJv7Ov2iIYQQQgghhJg5etAQQgghhBBCzBw9aAghhBBCCCFmzlN5NLZ3th7lIiintJd1grrK0RS1rWZm8RB12WlC2uCMNI8kQSxT1HBniSsyG8S4x/viPOrxrvSuQBxkeMwrl1DfntZRs3bfd/MYrF26hnETtZdJgPq8yV3UsNZHmxB/LUctspnZwgA1zDXSDr+wgNfxqz/3OYj9Eemu/Z5TxkYfdYVvvfcxxPmJfcKdfcfPgCRJHulS2V/h0abko5Hr3wkCfA8fY0I5Dj76GPXJ/T72rXrD1ZHz1tKdLm1uX2CfzTM8hu/j+2+voUb3O3/4LafM/X0cV+0Was+3NlFTmpL3Zm6uB3FYc6+LtZs5eQvyhLwopMtOyF/V7bgeoQs0VvfH2B7d7rEG3/UqnA1xUtjDLdHjKeq4WxHpvCum17zA+YO14klCe++Ttpz7uatHNpvGWDfN6PFtV+T4+vk51Le3AuyTq+SlMTP7/Hn0/TTqeO3NJvkjKEdNk8bS9Uuufv3L5zBvhlEf6zSwbidTPKec8hOxz8jMLKN+HJfskTmM/efgUTMzS7I984/O6d59HMcvXsNcN+051+sXNqgOPWynr3/zVyB+9fXPQ3z1BVzrOnNuviL2ZHDCiJK+32w0sd0udXD+8j3KaxC5a3Cb5pNaDftsSTksaiHn6MExkxXkazIzI98Qj9XRFPN/pCn5p7jPVMxh117APCUhDbVPPz324/nl8/BonEykQa88kW8J6+DpLQFcRpUXk95BhSwu4xh441d+GuJ7d3G93KWcZPcfbDll3lnD+7NXFnEOfW8NfUicQ8X1aFRVzB9vzvGeIG8GVx636cnwaWxq+kVDCCGEEEIIMXP0oCGEEEIIIYSYOXrQEEIIIYQQQsycp/Jo7O3uPNK15zF6MoKc9kWfuHvxT6ek3cxQB5lmeDoF6Xg90pYvdXHfdDOzL34edalf+gJqTFcX0aNx6+M7EN/YQG3nb32AOt9X3iCNq5nFS/i3nQi1svdDPKetCdZVZ+0BxNfSdacMb+ttiHcfoI/jc6tYF69cuQTxvdt9iH/4masz3N1A30c6Rb2yndDKFoW7l/6zJgwDi6LDcyhyFAhOxniunDrAzCwK8bl6cxPr/fd+/3ch/vDDdyAuStTcco4DM7OSNPVzXdIfr16EuNPB/d/X11EPem8TfSHDEY47MzPPpz26Dc+BdbBkJTCf8tdYhe6/Tbp81mGP6BBBhPuKLyxgLpkwchtod4DX2uc8Od6JselRgWdEkqSPvD451cGt+6jR/v7NvvN5j+a0OME+lZH3aUrvH8XkhanwSk0oV9D1lR7En3/lVYjnW3iMr5HWP6R+HpauODegAccerpjmi7x4/L7yVTpwrm/2pzh1UaJOuqB+HiduroQheYniFM8rOarbNHPzl5wFQTC2IDism2a7B6+tXroOcWnuGGM/jpvbAde7dhvXw6LEPn6w69YDe99qlLOp1sR2udBGn4dPvhHWiXNeIDOzWg3nWZ89F+SX8KhPs98iz935hXX0Q/L08dzMXpSC+uv2wQ2nDAspr04Ny5imx3mUEvaAnDXsMSH/YZX/ojzFY3G6z+NJvKHkK6C42cD+99/91/8A4t/8t/4yxP/sH/8I4l//l77hlPitb+HctPODNzGm22H2HXF/fDJO82zQ609k0Xi8RwNNGqcf7yH6RUMIIYQQQggxc/SgIYQQQgghhJg5etAQQgghhBBCzJyn8mgMhgOLk0N/QR6jVtPPeU9pV+MY07bPUYp6u4h0r/M+6t6+9DLq2X/zr/6CU8bP/+JrEC92liAe3j2AOIlRAzlu4B7e4xFpzbfdvatrm7cgvtREj8WlS3hO6wVqUofbeN3NFOvWzGx5HsvtdVDjlwcoAry1hXtBf/Iuejo+W3fznGyPUefqSJ5PaL+rtOHPmiiqWxQd6iun1FdyipukAzYzywvso9/5wz+A+K23vgtxEGL/e6iNfkgcuz4V9q5MxtjfDg5Qx9tsoPdhewf3256SRtwP3O8Guh08Rk4JaG7fvgXxK9dWIW7Vsd03d7HvmJmVBU4VO3uYR2ft9j2Ir5FefLVF++0Xrj4+pjljGuO1N5vHXqg8fwqB6AwpSjPvqOsnlGNh5wDnkp1d9JyYmZ2fx/mkpHkzCijHTg2vc75Gfomwqh5wblhdQM185HFeDeyzrG8vSOqbVIx9j3Nz0DjI6Dpzer2gQqqml5N5fA6PQXMA+aMy9lFQGRVWE8spf8L+Ps7F/f3D68g4V8QZkSeZlUfz0JDy5yQJ9r92x81FEpJPbXiA43jKXgWPvDU5rhs7266fsN7EPn5u9QWIPZpXW030OHqk/Q8jHBNh6HpPHGk/51KgPv/wPuYhvH5U+e8y6l/jGNfcjPxUAZ1nENJ11NzruHX/H0M8HZP3dXCccyupWH+eOZ73qLJLGkDsvygrPQTsn6Ax+dQn5H7CGdf0lnMX0S/4SgP9rf/93/5H+PqL6Ovd2MC8Z2ZmX//GVyB+k/04/9n/CLHvYbvW625uGJenvefi9z/J7wrchvyqB9GTol80hBBCCCGEEDNHDxpCCCGEEEKImaMHDSGEEEIIIcTM0YOGEEIIIYQQYuY8lRn8YHhgtSNjVjYlMzgZT6axa/YMyA3eoQRoq220npy72IP4l3/tCxB/45dfccooKeHS4IcfQ7z3HiZpm99EA91rXTKxt9BUvLTpXlc4RIPclQiT4fWSW3iM6ALE3xugSey9vms2Wk/QRJysoxFwvI6m4zVK/PbxFl7nZuwa0cYldgdO5hScML8WT21M+uMTx4XVosNyfR/PNYyw3Te3XJPi97//hxC/9fb3IObEd49cv49e5wRMbsKkBiW2iyeUACzGY6Qp9ifPw883amyqdTcj6LTRfFlvYH96sIZ9YXMTX1+gyx6O3M0Ipgl+Zu1+H+Jba9tYxi4e48I57K8ri7ixg5lZFOK1z3Up0VetfuL/7jg8C/IsN/MPx8WIkpK+cAHPd3WJDPBm1qhhv43JQFrQmMszTtDH53N6wr5mE9suM3x9SAlE0+TxicCKoup1MnWSmztykrDhObGtMK9IGpnRnMNzUFE+PoljXuKc51ckfjNKpNVocXK5w36bPqdkaWkcmH9klp4McIz94bf+KcSLNH7MzFbJmN1q0xij5HnbWzh33LnzKcT9XXzdzOzy5WsQew1MAthooPm7pERvXTrvKMJ5oSqnW0Lmbk6omOUYT2nstpt43VENyzQz6x/08Ri0eYVHfZzXqDznMYGmZDOzufaLEG8++IcQN06M5SrD+p90nKbjxqzK8vfY41V0Bv4T1dP6Hbw3+PxP/yzES4s9iNst3Ezj/AXcMMjM7B//I9xY5sJl3oiB7iXo1XjCm/OcbqQ/3Tr/x98w5fQEik/Gn76eKoQQQgghhPgTjx40hBBCCCGEEDNHDxpCCCGEEEKImfN0CfsGBxYdJZ1J2aNBCZi82E0I9/I86l1/+oVLEF8g2fbSEurDriyjfnbn7XecMnZvo5ehdoBljjcxQVFjEatgIcXEY+N30dvQr6Fez8zM8/FvQQu1m/X6VYivvooXut5CvWhZ9pwypgkmWfvt2+9DvP0hauT9BOuuz0kCOQuXmcWk6SsoQVF4QjftVWionzVB4FlwlKAsTbHObt/5BOI3v4eaSTOzW7fwPazpjiiBUkDJ8aIaad1z1ycQUUK1KQ2DTgf1yawlnkzRy1CjhG2e53prOh1KKrmAGucywbG6u4ceosEYT3JSusmD5ufxmJ12k17H64ooMVhZYl1NxjhOzcwO9nGsvfz5r2MZc8e6V997qqlrZsRp/EgbPRyhLjzwsX98toF91MxsxEm+KPFbSv6KmLwAOenZq/LGxXSMi8vYP776OUr6x8mi6JiRh+9nr5KZWUEfKvjESOqbkl59Sslb4wr9cVLgdU2mWP8T8gVOEjyH0Rjj84vYXmZm5uN6cfMezqvx0frHSQ7PiihoPep/7Eu7v30D4v/yb/2nzudXr1yH+NU30PfYbC1DfONTTPS6t4nr40svv+qUMc2w7Ta2MAnp8hJ5F8jzU9DawongahXJzSZTHFeDAXoWY/KHcplxinPP/Z23nDKSmO4V5r4EcUBznh9wxltKhpa519GqfQ3iXoc8W+nxPU8QpGb2Q+cYf6Z5WpuCuT6DvU1MpPrg9zBR76/+q78E8btv433Dt3/vTaeMz+7gmvp3/i76bMdTnC8a1DVSmru8iuuqToD4bJlVWlz9oiGEEEIIIYSYOXrQEEIIIYQQQswcPWgIIYQQQgghZs5TCZ37w5GFR3uPT2lP6YUMNWarmatfv055Gr5y6TLEF8+jZnE0uQXx/j3U1h1suPv916aoYY8CLLO7hP6IaYxazjLFz6f7eB1l19Vdz19B/8O5169gmS+9AfG+h3r3v3AV4/4dzP1hZvbf/IN/BvHubdQETkhHPfFRXXdAe8inrtTfMsrpkBrqWP0Txyifg0djd2/DkvSwzd988zvw2o/eexvifh/7iplZvc55QvD6RiP0DYQR1ulCowdxt4vadzOzKWmFPdLtNjrYx+N99EeUAWo56+QJyir2789pD/l2Az9z7Sp6hNbpuu/dx9wyqe/uIc8pRjoN7PMvXMF9w9t1LKNOXpONTSzTzKxWw3Fw8QLtx3/Cn1LlVTkL6s2GBUca+cEI23p3H7Xon6y7PrXdhHO1nLaP/Cm63Ip95xPqI3Nz2J6RUQ6LHI/xwRq2zXSEvqHQc+dAP8DryMiDMYqxX2/t4rw7SPHzvdWXnTKaLRxv23uoq986wPN6sN3HMkboz/s3fhFzFpiZtch78f5HtyAeH82znGPorKj1xhYc1fUcbdVf83CcTw7cc1xfR735cPIRxGnegzgwzPkUFDhHrt36kVNGSV6aF155DeI2eco8H+ODIZ43+5g6FflBSvIcxjH7eSgfDQ27jT30PL6/9v91ymiG1yF+NUSPaRqjR6bmncfPz2P75KV7jxRPyKPn4/3K8srxMaeTs88l5Nmxbp+9M6zor87BUD42dI/BH/ceF1Z+pqQcFovn0Yd0/Rd/DuK338Q+/YUvoQ+J75PMzP7JP/ojiD/+FD0aJfk/C87X00BvWJU7wq1vys1RnlJ3T2ZoOe00fiL0i4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDlP59HY37MgONKW0d77Xox6WX+KsZnZjQH+be6dzyBeIullka5D/At/8RWIV15wtZrTPsajW+jjyDYp/0eMeuZpRBr766h/P/9TS06ZS5fRazLuo0Z+/AA1zwsL+Hy3u41+i/d++3ecMtZ+eAfPc0J+FpLfsWcjpxwQOe1Bb2aWJKg9LEjTl+bHdfU8PBp//x/+91avH9bt2l3UwzbqqHFsddw98vOMdLrONVC77AwpRo33lauY26SKTgf7QquF2syDA8qTkFI+gjqeI+evMDMz8lxk5BNZWupBXAtegjg1PKf1rT2niMEAPS8rtAX8515EPfILlxbwnGKsy/1911+1cgHPq0Wa/OmJpCTshTkr6rX6o/wqowT7k1FulyvLrLs160yxPde3cR4dkJehJM1sjbxXVV6BnLS6AfmzQmrvCc2Bf/dbmJ9o7QHOTyttd+wvd7GfL89h29Ub2GEmGV7nA0qrsmMbThkrSysQN1s4/7cLHL/4brPzS3hOvZbbPsM9nAO7PHaOLr0oCpuOXQ/Os6Z3PrYwOmzfyT5eb72G68x11+Zi9+7iGhxRTpTFJfTObG9hWxfUHzcpR4aZ2e4e+R59vM1oUS4hz8c+XFJOlpxydKU5jTsz82m9G5H3crOP/enmrXch3tl9G+Jaz/WQRXNYxtbwt/EN8U0IB/fxOncT9AZYDd9vZjad4t+We9iIy5eO5+bQd/16zx50acAr1AbV9winif5P8xGQD6HieIWxDw7DFnkYswneC80t9CDeeNCH+Cs/8zmnzH/3f/1vQvz/+M//fxDfXMM5NORcRAF7V9w8PXytJftVjMcR1oP7q8ITGFwcyh/z/8ejXzSEEEIIIYQQM0cPGkIIIYQQQoiZowcNIYQQQgghxMx5Ko9GmkytONKW1Wmf9F3SuR1krj4vIUnX4GP0Hcz7qL2cC1FPOvRQI//6ly86ZVxcwb2tgxXU4U4MddQ+WRXmz6Gu0l9Erfl2xd7Va797G+LsJmonw6ADcdH6ED8/QP3o997Fvc7NzA5i1FVPKSdJ4pMnoYaa6HRKXpSJ69FgubcXkObyxF7lz2Mf+c9ufmBRdHidkylqcBd6qH+t112PRkB5NDLqo4GP2k3fwzpbX78LcaeN7Wpmtkh5WkKqwygkjSlpMSeUm2GBtO7LK65HKI/xM8kEx00ak3+ljTkwOnN4zrU+jjMzs6TAukjJk3XpHI6T+S56STY2cF/xLHW/4+jO47WFlCrjZF2GwYw2+H5K6vXaI5+aH+EYqFP/GYzdMbbUxj64QG1xMML+MKb5xqf+4vtuPhGf5obLTawr38Nj5uRtKGkuySL0Qmyn7ty+t4f94z7NL1cX8DwvLeD8dG4Bx1IQuUtTXmCfq9P4vb6I5x2s9CD2aP/7ludex7CGdfHlL6MvcDg+XKOyLLON+9inz4LA8yw40lH7pKe++BLOAxeuuBr+xhxe891PsZ6nYzrGRayP/ja2W6PhrgObm7ge/vAdPEZnsQexF+I5nK/jnNfp4Pw0mpChx8y2d3ANvXUXfUY/+uif0vu3IX7xFfSYle7Qtb1dyjM1ojnt4D7EO9vfh3hEN0ALPbf/NXs41vICz3M0PNb6Tyeujv/Zc8KjcWoOoJ/g6N7pHjTndE75Ex+hQXlc/uif/C7EX/rlX4T4k4+xP9/5EO/fzMy++o2fhnhxCeezm3fR9+hkIKHrHvb3jZlM0RPWpLWj1cJ715LuPQqqS/Y1PUv0i4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDlP5dEIrLTAO9R5JaQXS0krPAxdbV3hkf44xmPMkw68VaAm8tPv4TG//x7m2TAze7mDf1u9iHq8z//CFyE+fxW1/d0l1L3dXUNt3dv/3N1fu7WP1z5P++tPC7zOj4foL/g47kO8kbo+kEFGnowQtbJeEzWBMeXEGA5R38x6PTOzIMAy2KPhlccx7+F8Frx49dKjPBobm+iXGA7QV+B7rn/Cp+tLU2ynKEIvQ7uNca2Gnx8MXa3w6iXMrRGGWIcN8s402DeSYjvlBfknItd7EtXxmMkEc1RMxhhHdfQSGI27eoU+vszxb1FE/S/CYyak+985wOsK6nhdZmZXr13D95BJ46SOlTWtZ0UQNSw8Oq96iGNspYN9rs31bGajGMXfKY3Tyz2sZ59yPxwM8br7IzenwMGUxn5Bfq2c8myQn+mXv/gCxH+J9p2/tOSOrYUOjpUGKaML6g9pjuc4ySifTOb6C9hTlST0mZzzL2DdxORNKXP3ezZO0dAMsA825g/rIk2fRw4Ds6hWe5RH44Uv0XrZwfqJcCkzMzPPw7Zcv4Pt9PqXUOf9wst4kE+zPsRp7I7DC6v4tzu3MV/WRx//COKM+oYf4Dmev4jzwsaDj5wyb27+EcQHCeYtCCj90OIKeiEWzuO8vb3p+h+2btMak7wN8cpF7Cu8nkYF6u79wp0DI499gjhvHuwd11U8PXufpOd5j/JleJz6gfNXVObM4NwbT+fJeLL3P/49XfJuff3SL0H8yceYo2v1Et4jpvfcsf9b/7f/AuL1W5hfpk7f6XvkMcwzPMd//E++45SxtYX3G715nIe/8MWrEL/yKualiho4R1fdwp26qp6s/6dYg/WLhhBCCCGEEGLm6EFDCCGEEEIIMXP0oCGEEEIIIYSYOXrQEEIIIYQQQsycpzODB8GjZFUpOYFydpaQWdTMrE+m6DhDw9WIzCVdn5JbJWhmuVthGLx70If4jRgNSivzeA7+ApozJxfx9Tt37kGcZ24mn6Kg66BkcvdirKsPBmjwuk1mpbG5ht+YkmiVLTTpTSiJVv8ADcAF+bQqE32Rea0g49ZJU9XzSNh37dJ5azYP+8ClC2havPEpJn/cG2E7mrnmb48S1rDR2qPrXzmHCeW2tjCZkpnZ1uYuxKsXViBuUBntBo6TuS728SzF/tbfdxP5rPR6eExK5FNmWBfTEZrKmjU8pyVKGmVmdjDAsZnShgXrD7AuCrKV7Y7wOi5fxnoxMwtCPI+Cxnf9hCk55Gx+Z0S90bDwKMFYTP2HN2hIInd+yo0NyzgXTFNsqwYZLOfnsH9MA4zNzDamfYhHBfbjjEzSdR+dsn/xpz4H8UVKGjnfcr+fymiCub+Fm2jc2cR++2Af56cHB9gn9yuSHW7vY13FU3xPmzYgCamuW7S5w8vXMbmrmVl/jHP32t0+ltE5nHey7HkkSzO7dP281eqHfT+hRJ1e3oc4Gbpz9N1Pse26czhOJzH2jfffxvlscRnH/X7urvP5BJN3Xr92BeK5Gpqgd9bfg/gPNjHZ3vff/B18/xZuBGJm5kWYLO/6aziHnV/A9WKXkuQGdeyv8Qj7mplZvcS/ZXQvkNH9SZvG1SjpQzyYYN2amU32sIwXO5+H+PaHx/0uTZ5DHzyRr89xgztZ6E4/HN9HnHZX4bxeWQYlKKV7nf272ParP4+bDVyjc+rRuv/Om5iI0czM7uD9R5M2rjhXx3E3oo0ottfRPH5vw02aO87wutY2cQ69udaH+Ofp9V/4RdwIqdap2C2CKNm8D/GT3wPqFw0hhBBCCCHEzNGDhhBCCCGEEGLm6EFDCCGEEEIIMXOeyqPR7MxZ+DCBEfkSapToqPTcQ++gpNTiFI+xQ/HQUFsXG+pDOz4niDHzA9QTfzQkfd4PbkN8IcCTqnVRd5amqHPrdt1kVVPygeyO8ZgfjVBvt04awiEl6Ul816PhtVFjSrYP2x+gtjinuvQ91i26z5j8nlNS5zz21WdBzS+t7h+e1dw81kd+BRMuTW65Ot6DIbZLvSKhGpRXx76zsIDa4t1d1y9xm8pthjgOXriK3oQe6d9XlvC6xtS3drZRy2lmFpCm9IWrqD33C0rQNiH9MXujungOZmajSQ/iW7du4XntYx8fkdbbJ8/WykXUbZuZ5eQlyDPy1JzokeyfOSuWewsWHflsYvJT7OTYVlnF/DQp8ZoKTupFc0GaY1uFJXoZzs+5fXhhjnxBNXxPQEno8hLP81sf4hx5QJ6ycewmCdymhIzb5MEYJ1hXYQP16zH7SHK3fbnc8RjXg2JKC0yC5/TGtUWIr1296JRRBthPozqOhYfWpAp74JkwWetYFh22XznXg9fifWzntbfdOToeYlt/5StYJ5nXh/juHfRe9XpYPwtLbhk3PsC2O3cB31NvYP8aJZhk14/Q+/DeW9iuYdlzyvyLv/7zEK9eRb9E2uhD/Lf/JiYRnI4wieB06I7dvE9rZoHrdG2C1z2/iHV1uUX3Dql7L9FdPg9xvI/rQ+Ftnvj/2Xs0SvOsPFr7PboHcC0a7j1GRRrnx7+D7lPK4vR536P5jNe3rdvou713+x9C/Cv/wb8F8WfvYoLI7Y8woZ+ZWVnD/nZhjrx4NGWOqemmffTrDFuYJNDMLHGuHa+LPc//7NvoffIj/PzXv/E1p4wgQN+G04ZK2CeEEEIIIYT4k4IeNIQQQgghhBAzRw8aQgghhBBCiJnzVB6NWq32aA/59sUL8FqPtKzr9zeNubeL+vJmg/aAD2hP7ww1ZXdI6NZM3eekHsWTKR5jL0bt8G0P9aDeLsadAHWYKztufoamh1rMtQlqoNcK1Fnu0/77JX0+7Lh5DGLSKu730R+QJlg3JennigA/HwUVHg2KC/qLHxxrAJ9DGg0LA9/Ch+edoz57aQ61hQtVdRijhtG5BNLUh6FPMfbXS5fcffjHlC9iSPuxk7XJ5rpzFGO7Did9iNnXZGbHdXJEbw7HYj3EujkgP09IOUemiavBX+iiP2W7jed99x7uY9+kPC+vvPYaxCsrqEU2M2u0cJz4Pvbh6WR64v9unoWzoN1sPcq30qjhuJ1Osa0Dc4X8nDdl7KH+fEpzWj1CL4NXog5/FLv7rTdq2J7LbazXgHK5TKlP/dY7NyG+v4v9JS1dD1mS4rV6tMd+UST0Op4j672tdOenPMfz9Gh/fC/Eusk8uk4Px29eYbSo1bGuFpZQR+/lh59J0+dj0mhNEqtlh9e99BJ6ce7ep3Yu3bXq/Hlsl3bUg7jmX4Z40sScA/v30U8RRe5CUAuxrYd7H0PcJ11+SPlNzs3jvPqVr+LxBgfYH83MFi5h20eLlNdlvQ9x4FGeqTHW1UsvuPlBtm5j//v4PYzrC3TdMbZPmGJ/TGP0W5mZbW3je8Yl1n95Yk5ME9dH8szJs2PDASfnKmhM8OvmeuucfFycV6OkMU/H4zwPh3/EctmDxuv6D377OxD3ruEYeOOXfhrinbrbN0ZN7MMLMeWMo3uLGlUVezjWHrjzek65Nwqqq8C5X8Hb+//hH6JnY3Ee13Qzsy99FXMolSH5cE4WmT95/9MvGkIIIYQQQoiZowcNIYQQQgghxMzRg4YQQgghhBBi5jyVRyMKo0e6r/kOarC7pK8Ottz9/qcT2kOeJF6NGu1LTRpoj/NqJKiJNDPbpr/VSePXpv3ZE9K3lyWWsUtl7seuNncpwjL6dF3DnPY7Ji1xk/bXTn23WXb3Ubs/GZNGvXx8ngwv4GNWmSzob+TzaJ7IAVBYYa5S9tkyv3TO2q1DzXq8jx4gn3wFV8+7eRoCyrGysYf616BG+njy54xGqKdvNF2tekb645T07/0D1G6ukAa8RfkFrNzDc6rI/XFuaQHibhPHTbPFuRmoD1M7T6vG7gA/025iXe71P8FzoD69uoR5Tpbm3X3Cm238zJRyL+zsHJ/XcOjqm8+CKIwe5dHgtqhHqNMdjVAHbmaWki+g28Zj5KR7nUzoOoMlCAtzdbaTDPdkj2lskEXM2PYzmOL798l3VJSuP6YsWAeN8w37QgLy39RDft0dW5z/o9XGsTI/j76hiPa2X6qf7uspSzzvZgvLiI7Gd5K6PqazYPn8ktVrR+dYoCeo28O1r12nucTM3njlVyH+5V/61/AYXexfWzuYR+PtN/8exN9/5392yvDDOxCP9si3toN1l8Z4HR+OcP7x69i32m76E6t33oR4gTwab/4B5k649jrO5ddfxXZvN932nWtin93fx/kqbOHY9pt4j7RxF8tMElx/zMwyGv/nL+G82T2RXyGJUzP70DnGs6Qs40djpCTvQ0H3WgXnszj8FB3vtPiUXCGVtzHkT6XzyPgcaEL8nb/5dyCOOJXExJ3XB5xDiU47aOI8v0T3z415nKuuL5J/2cwy8mgYzaHsi/M4PxYWaWnuXkdR4Fj1qf7LE3XL730c+kVDCCGEEEIIMXP0oCGEEEIIIYSYOXrQEEIIIYQQQsycp8uj0WhYdKSlrZH+NSAPQFhz9xrmvdLTBDWNeYa6yCRFzWmLfCB1zsNhZiXpeKe8n3GGurKkwHMKPPZg4PEij3dyNmvR49rER61cRseodfA6igjrrj9w3Q+DAe6rXJIOkTXNIXk0QpJLTqmezMxK2uu5UaKm9NoJsWJWmLmZUp4tFy68aJ0jbeMWeWmGGWrZP/95zNtgZvYqtd0Hn7wP8b0HaxDnCbbD1CcNru9qUHlfeZaQ9vdRP79MWsy5OewbDdJ2tupun+90KNeCh+flkW/Ep7MKKL504ZxTxrvvfITHJA3+5cuoJb6wgh6Mc+TR2N129aFLAWqeH2w9wDec8FfFsZsj4Cyo1aJH3jHWwIbkM/ADd64YUV6VjOa8GvvUAvQdTCZ03Z7rZSgjzFEypVwdI9LElzkeo9nGPCznyNsUVewjX6e/sX/FI99ZRHMkz1dF7s5PCXl2YspbEo8x7tD4XJjHcyhD12+XUpt6Po+dw/NkD9xZ0bi2YvXGYXtt3MT5anCAPj4/cD2M3TnyTl3DvnK+h56MF6+jRvu1N/5jiK++jTkGzMz+8T/8P0N8f3wDz4t8SS+9gqaLhPxgW3tbENcvuf1v2sdx9PE99D8sXcR5c/ka9r96m8Zu6Xo09um8WnPojRsMcZyFIdbd3CLmBxn0XYPBuTbOve06e+GO25jvn84Cz6+b5x+2n2eU48Lj+HQfqJtGgz/zdHk3qj4T0fqX0z1fTO2WkN/szf8WfUivee644rbPyefmRziflTT/NWn+fGMV+5aZGduDC0rK1SBv5qVVXINfe/06vn7tmlNGGNLY4vvdE7FfMX/+OPSLhhBCCCGEEGLm6EFDCCGEEEIIMXP0oCGEEEIIIYSYOU/l0SgsstwOtYyFhx/NyDNQpz2kzczqDdQbJumU3oHaON7/PU1RM1+vuTkFmqRp99tY5nSKOt+Ccgr4tOdySefQ8l3t8JTqYkD6zoz0dz7psIe03/4B+THMXCki68NZM+w78nDa5zmq2KeeNH/nDOvmhdpxXaZFYd93jvBsWV65Yt3uoX58OsFzG45Rx5uWrpfh2vXrEF+99hLEe308xqef/BHEP/zRWxCPpq5GsZ5iPU8p3wl7C0Yj9JbUSMs5R5rqsMJbE4Tk16G3eNyn2QtFY+LSJdQSm5ldXF2B+IObuC/90iJqi69dxzwm7TbOBw8euHvI57SfOXu0Fudd3epZE4ThoxwRp43BKHKn1zDi3Cw49jPKzxCR163ewPltTL4EM7MpeS7KGvahnPwPPnnnLl1B7W43pv3Zzd3bnn1ARvuvcxl10m83Q9LMm7tHe5c8Fg0PvSTsr4vIX1Gr4etFhYa8oIk28rCuHub/CHhP+zNiWmxYWRzWw8E+Xt/OfTz3TtedK3749rcg3t79IcSvvYhzwfklHNf1hd/Ez+985p5kDc9j+Sr6sw7u4rhOCrwPWLmIvqRLn38Z399wx9W0j+Pow3Efz+Eytn2nh8d4cBf7W77vlnHzBv4tTnGd7nQox02M47C32oP46qsXnDIGlLck9foQ1zvH7ePFp+SYeCaU9sgDwfcYpetJO40Kyysesnx8rgjnHMzNm8Hz9Bd//ZfwA7R+7m/1IW7SnDznu/PGtQvYv87TPBLTiYYt6hsr2Hf6U3f+WyRv75UrOK6uvYjr9jIdMyJ/Z1mRr41zLHHdnYyD4MnnQP2iIYQQQgghhJg5etAQQgghhBBCzBw9aAghhBBCCCFmjh40hBBCCCGEEDPnqczgL776ZasdGUo27t2G1/YnaEpMzTWidefQ5LXXRzNTnlEiFDKisCF6GruGmZiOETXQTMnmyqJEUw75ZC1L8RxGFflh+G8cp5TIK6akWweUoC/PK8yW5NI5LWFURqapjJPL5W7dzed48dfJeHrSLF4UbrK6Z03UalutdWhsPX/5BXitDOYhrtXRAGtmFpIZKqIqXJrH/hl5aPa7cwv7fJa5iRXnqY+XizjEvAJNY+MRbRwQYX9rcBtUGFg5QV9RcCJMfL1Zo8RlnBSw4SbEungek2ptHeB5nltFIxrXzebOLYiDEI1qZmaDAZrdekto/j65+UNac5MmnQW+7z8aewG57kNKYlg1RgJ6T0iJ7YYD3BwgScmkSsmomh130w2PkkXxaeQFHiOnnSO6EX6g5eN81ApdB2adNjFo0vTfpGReNSdhH8WFO7+dlsyroDVnmmM/5uSs49jdECOmzT58MqOGwdFmKEFVorBnz8b2utXqh3XTuYwJuVYuvgLxcBuTg5qZ7e6jefvBu7QBxib2x5ULuAlEVMPP7w9vOmXwmrmwgn00bOD6t30PjdwT6juLXfz8xs07TpmdWg/i83TeZYnXuf4pzh/bt6jQ3B1XkwMcVyWNg/kO1l0e4djdjT+F+O4Hbh+/exPr8xxtwtE6kdA1TZ7efP3Hpiwf3Yg5XnCKq4zelfn1HoN7jNNK5RTLZiXdOy1fvwrx4hdxU5g2bdBR0hHvbrvr/vY97F87eziP93fxmC8uLkL8lc/h5imtrrvxyYULmFyz2aZEvQEbtwOKfYrdBvL9H2/+Zk4z8sNxn/ytQgghhBBCCPFk6EFDCCGEEEIIMXP0oCGEEEIIIYSYOU/l0fiZb/6L1jrSyN/46Efw2g/ffhPi/Y0d5/MF6euaLdRB5gVqaqdTSkaVc+KoioRLJEieTjAZUJrgJddr7OFAHT8nyLLUTdI2ogR9CSVCiXM8p/0RalRzOqZfIX7zOCEfxSXrrkkMmZFufzHHczAzu0568XaAGub7ybHOMCvOXqMcRoGFtUPdYW8ZNf6Zh+de5BV9o+R6Rg1jSXW0MIf62C9+/qcg3jtA3aWZWXuuh8dYQF0leyzufPYRxDdufABxHGPfCgPXv5NR0smMfEphiX0lJC3n8jLqRee76DMxMwsi1IPPnfsixov4+h9+5x9AvHdwH+ILFzDZmplZp4tjL/DZ81D9/7PED3zzjxIV8Zj0SN8aViRECmhc+uTZYN/HmOavJGH/jdsf6nWa4yLywtE5NEO8ji9fxLHEvqLA3DmQv7EqM5rD2F9X4nXGlAAv9lyPX2zkdSN/HfsCc5q/UrpO9yrMIg/Hm0f17R2tYdzWZ0V7rrRa/bAyL1ygRJ10Tjtvu1ryg13sT2GIvqgix7ljGu9BnJNvrbtYkTyvgY09mWJyzgYlyztHdrqgje3YX0eviV/hEbIOTgh+jNe5u4dx5KG+3Vnmc9en9upXKMGej/U7mGAS05K8bsMdPMfBhptsMyqwz7ZKXOfq3vHrnlfVg88O1zN1Oqz5/0mOQUesKuSxZVA+P3vldVzL+n3sb+kU+0570U20uLSM619Oa3KbEvRdON+DeHEZjxnWcVweHhPncU4u6rG/zM2+R+FPMod5P+b/j0e/aAghhBBCCCFmjh40hBBCCCGEEDNHDxpCCCGEEEKImfNUHg2L5sxqh4LKN77ys/BSrY1+i33aD97MbG8PtW/sf0gS1MIVJKZLprgvdaW+j7wDvqPPQ+3cJKUyaGv1iLR1ec3VbsbksUjpHA6GlGOE8hoEjtatan/jU/Jo8B7J9HKTRO2v1N0yLtRR47dDWv97J+qS9/M/C5Kp2cOt71POuUJSdd6n38ys08K2q0fYtgXpKmst3Mv61dfRo2Hm7sPPz+6sJw5o//0rlzH/RFDDY37/XfQ+WcV15aRvLxLSWZdYOZMEx1GYol4589wcJI05zKMxP4/+lQ7lD/nGL//rEG9sr0Fci7BMM7PVy5yLA681jY+vwwuebuqaFXkYWP4wZ8QpeX6q9nh3ofmK8lE0fNLqBughqMq5M4mx/YMS/VjsbfMN54aIUwqQ12SSVMyB5LlI6TMp+SkmdNoFeVU8z21ftoUV9IeSxdfkh+IZL8xdow/Pmz4l23m45jyPPEJmZsNRadHRuCjX0fdkDfQILLyIuYXMzOoLOKft3Md1+mAb16o4xobqnUdvlddy+4JFOI+WE2qnHMsIqI79gnJh1fEcGouufj0MsT3SPs3lBeUOmuK9x7mrNAZq7tjN6P5kf4I+1Ji8mss1qv8OjpFgsaIPdbA+83wT4jI5MTcn7th/njheiD+2/6LiGOwzqPgMe4E5z1SeYTuSNdVWL+LaZuzFqyq1xNwcRmtsTvMFezgKWqOLirXDJ/+ez+fh+MYe78moap+fzLdxOvpFQwghhBBCCDFz9KAhhBBCCCGEmDlPpD94+BPLZHz8M2sW4E8/0wn+HJqlJG0xs4J/5qefuosip5fxdeen8Yqfftyfgx7/811p/HMfnyL95FXxkzn/jSVf7k+Kj3+98jydz3Bd8Psf//mMP29mKV0Hb2F7clvMh1urzeLn0dN4WMZgcLy1YppR/5vSNrC+e30lbW9bD0k6VTy+T4/GvCXwE0ingsdLp4zOaTxm6QL+BOtXSKcmtAXqmH5i9ehn2SlJq8KCfmIN3K2P8wC38k1Lkj7Rz7Yn5wozs9GIPh+57XOyfc1c6VR2QiowHB4e/yz638lykhN1F9DPzEV5+s/6TEbnn9OHMtqmOUlOl06l1L4JSTomMc8dWGiSUhlUxXHifj+VUB9LSa6Q0nXGLJ2i93uee12nS6dYZkGv0/F4bjdztwt26pf6wVn3vzQ5PufEmeMev56amSVU8SnJb3yS9foJ9Y0pvj9mDZy5dZhNWTZCaxkdwgvxD3zOzhxqZgVJVFPq4xyXCcZJzHXnjt485fMiaWdCY5U7OU7l0JbHf+S/0ZbZJ9rrYdud6Ro8PDGPO/dvVMc/gbz6VPnVE0mnWDJJY5q2PvZoHAW0TeyTSadwzjxdOsVb1WIZQebOsW65Tyudwlerus3pyqnjNwyPLAFP0v+88gnedffuXbty5cqpBxN//lhbW7PLly8/0zLU/8SP4yz6n5n6oKhG/U88b7QGi+fJk/S/J3rQKIrC1tfXrdvtPjOziPjTRVmWNhgMbHV11TWmzxj1P8GcZf8zUx8UiPqfeN5oDRbPk6fpf0/0oCGEEEIIIYQQT4PM4EIIIYQQQoiZowcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDl60BBCCCGEEELMHD1oCCGEEEIIIWaOHjSEEEIIIYQQM0cPGkIIIYQQQoiZowcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDl60BBCCCGEEELMHD1oCCGEEEIIIWaOHjSEEEIIIYQQM0cPGkIIIYQQQoiZowcNIYQQQgghxMzRg4YQQgghhBBi5uhBQwghhBBCCDFz9KAhhBBCCCGEmDl60BBCCCGEEELMHD1oCCGEEEIIIWZO+CRvKorC1tfXrdvtmud5z/qcxJ8CyrK0wWBgq6ur5vvP9nlV/U8wZ9n/zNQHBaL+J543WoPF8+Rp+t8TPWisr6/blStXZnJy4s8Wa2trdvny5Wdahvqf+HGcRf8zUx8U1aj/ieeN1mDxPHmS/vdEDxrdbtfMzP7d/9dbVmt1q9/k4RNN5VMv/Ynf4nzi9DecWoZZSS+fdszTKJ/gHae/5/GnUPH58pR3nPKH099vZiV9xomLR/9PxkP7L/69n3rUN54lD8v4v/xn/0drNhtmZhb5I3jP1asRxJ2Ge17bOxhfvXId4igMIG7OLUB858EuxP/k93/fKWN/eh/ipeUc4ovdHsSNMZ7n+299CPHLX34N4s6lc06Z3/rOH0B87+ObEL+0uIRxVId4cacP8aXLF50yhl9chfjb2wcQ9+/gdY733oG4V9+G+Ode/6pTxpdf+jrE9z9Yh/jWD99+9P9Jmtl/8ne+cyb9z+y4D/4f/uv/1BqtppmZhT6OXK+ZQTxNYuc4dar7MqV628d47x4ewy+xjy5fn3fKaHUaeF4pTvOF4VgJToxrM7NxNsZzbOJ1Rh4e38wsy/Hafaqbcojrw9o7d/GcpjjXFAWek5k560FE8dw1PK/GMtZ1uk3t1XYnwSYOFes28Zgba4dzQDyZ2v/pP/rfnnn/+8Fv/551Ox0zM/OojgMf+0ZeutdXnrL8FfSZMMS+w2ubH2CZVfB7yoL6RlVbPw7PfX+e5xVvPCbg8+SKeIIbAS4jCLBPFyW+nmU4JnK6zqii7soC65c/c/IchsOh/fxf+NUzXYPX1tZsbm7u8Fxo3vD5HvCZn9VPCo+Lx59paXidzj1k5TGoPzpj8ZSb4bLiFwKnWO7z/Jk/fgvwPeDJ+/qDgwO7cuXKE/W/J3rQeHjwWqtr9Wf6oPHUTyKnlvGn40GD3/8n/0HjIWfxM+rDMprNhjWPbvIiHwdZu00PGs2mc5zJFONOpw1xLaIHjW4HyxjiTV+9gTcyZmZ1q0HcaOJ5Nlv4mWaJNzL/f/b+K0iyNM/yw/5Xug4dKSJliazK6upqLUdjd2dWASRImpHEG55gNGBJmvGR4AvNgF0DCJAEiTUDQRC7WCNIYHdWDDhid2Z6emT3tO7qruqqysrKSh2RIT3CtV/Fh4iMiHO+2xEZ1Z4RPT3n11bW+Q93v+K73/3uve7nfKcS437Uavj+esPdr7iCn4loP5xlUl2PcBho0vLMzAra7koN9zOu0EU25m3A8aFec9fRamBb7FD71mJ3uDqtn/Gfrqdar1m18bQPHv2gYZF7sage86CRU12hm3y/wDao1uvOOmqNn+xBo6DdyGkb4o/yoJFjW1Sq2I/5BvejPGhU6aGgWsc6oP3w6iUPGvTwUaNxpFof0Cadbv9rNZvWau5eg50HjWACDxp0oxtGP/mDBm/XcQ8ax149/6I+aNDno9Adz5wHDfpM2X6e5jV4ampKDxp60Dj2b8wzPWg8JYoCi6Lyj3is0SrdIK6P2UB63X378etwX38e3Z9v6k/2oPEsDy/cUb1jHzyOe9Bw18nD91EPGnnq3ig+b9o7mzZMdm8eVlfeh9eWLr0OdVTBG2Ezs85wC+qkh9/Kz9LFaKaODyJ2Eb/uvLhAX3+a2cPvvo1/oItNso1POwsR9seLL78ItUcPQ29+gPttZrZJF7jqdfz1IZ9ZwNfP48+cSznu95WL7q8mW+dwO84XH0Ad9tpQD/yXoe6vYX958/v4a4WZ2ctXsa0qFy5BfXfr4BekYUJ3w6dE7dyW1Zq7N5txA9t9o4v7FNbdm7CcHsDMw/HUD7Df+ts4rvoePlhEC/jLnplZfR77WIVuiLIUH3ZGa2Oo51t43m+FK1A3Y/dXFKNfWgLaj/aDDtTf+d6/hDrZpJtRZzRyb4IDwz7w+alPQP3K6/irWWdEfaaaOOuoXMD1hiG+p3/vnpmZjQr316pTwbP9yx6Pz85NadkXQ3zTT6/ztYtvlo0fII/e2lI8w21Iabv5Gs039Flecu065lcRbhuffv3xveMfmI5rb37QcLaJbj1Kt5l2jdv/8DYct8/PG+f+4LgvN+2n5OHj2G9d+eY6o1dLNCj04Oqd8KbfvR0r+ZLA+Qt9CfUMnzgpz/Kg8Sxo1ikhhBBCCCHExNGDhhBCCCGEEGLi6EFDCCGEEEIIMXFO5NEI/N3/diFTmH+8oZn1XY7e64T+rGfwm7vvcVb5k+vYmDINH75+9DpL7BMOjnfoOH8TezpKNtFnzSUZQQ5Lfo+23j0f6lPNg1mnttDo6QeoO1+jWZHMzDzSjS9Mz0A91cfPDFdRc5/PLkL9+o1XnHXcfR89Gmtb96DuttFMmk6h2fTquWtQP/wAZ+d50sbZm8zM5mZwdqzFa7idF5bQk3HpCvpAXpqmzzdcg/HKh7ehrsdTUG8HbaivXUHPzLmbPwf1o9vfd9bxZBPbP4pxPx4ODs16lk7+vH0W6o3M6s3d3r+VPIHXggj11NMlkwV0UvQJ5WQOrzUu4DKaeCz8GPuwF6L3wcxskLXxDxFr3tGz880/+A7Un3zjJtRXP419tF7iPUlTGtvJzDhI0NMw6qxBvbOBvpKoxPjPevV8hJ/xfTwfpy/gxCXZAD/faLj7MTeN/Wp2Co/P8HrbzMwGPZpZ4pTwfX9/znrW6HP9LBp5x6h9jO6evZj8/jLSlPwRfF1xJgLAOqHj7rFB0cquh+TncUzrZOSmtnsW02ues0fj6GU4ZumSdeTp0Ybyw585a4/GcfvzU+HHeCawjdMMx9RkjNelUbLqLKEw9LkFPk4k4xmOI2GAY2oY4fuD0L12+DQJR0G37z/N7a1fNIQQQgghhBATRw8aQgghhBBCiImjBw0hhBBCCCHExDmRRyMOfIvDp88mpJN8Bv2h69Fw3nBkfWxy+DO8x13FM4TlnRCeU9l9/bh1lsyhTMs8dt5lfv8zmDR47npeR3HIh5OdqOdMhsICK/ZyB16+8TF4rVpFn8H2JqYbm5ldOI/+h9bsHNS+4WdCaoAKJYfPk8fDzGx+HjMotim7o0fZHRlFdZynzz++8wDqCxV3nTdexLaYnsP3LFy6CvXFKy9A3aweHcplZvbSq5+CuraAWR2/de+fQx0GuGOf+Qx6NF6+ct5Zx3CAuv3tHrb3+RsH3oHReGz2R991lvG8aTXq+6GJj++TRp6yZcK6G2w3GGDuRRyQ14h0uHGMPoOIwha9vCR9PKAAxiqOzcEY/TWtHM+D3/uHmHh/89YVqK/dwP5kZvbg/iOoVx6jl+jOu8tQ11I8tnPnUKO83t501hGElCkSUVZOhpkXHExZreP7gxKJezyi6wXFSFy7vpvt0uu448tpkGe55Xv5DXyWsg+h7FvE4oTz4PvsyThmnbt/o4BIx0tCoYAcfMfBeLTNHCJoVpLFYUd/JsvIF8ILLPOBHGN8TBPsf45nhq4n3LZmZiHle0QR9uHD2R2nFRb5Fx0n/4QycPpD9GJ2u3egTsc4lqUpXqfMzIYjvK57Hh837BtRiN672Rm8JkcVHJPNzCoVzJWqxOjnKzy85v409Q79oiGEEEIIIYSYOHrQEEIIIYQQQkwcPWgIIYQQQgghJo4eNIQQQgghhBAT50SW3igoLA6eGmsoyOdoH/fe39hAflxg39HvLzW7HGPuPtYsfqyF5nizOJtpjwvoc53dZWbwo9/BhifHtuYs4Ph1OP7xQwby3D99q1GS+Raku8/G4z4G361voDnz4qJrWF26hEFkRRV3cDxHITtkWqz6aLYqtt2wNI+CdqZmMHRuZgpN6y8vXYe6XsdteOnV16C+fBmNuWZmL738EtTNKTYQ43ZXI3w9pLChnB2wZtZqTEN90cP9zBM0Me+QOW6QkAm+xIkbVXE7F2q4nX/zf/o/2f93r9e3/+i//u+dZTxvMs+3bG/iiwuzN+A1yuuybooGaDOztMAhdyHC/uA10aidTdGx88jIPT3vrKNa3YA6SrehbkZ4rD7+2nXchnUMo3vnzzCs8dt/8I6zzp0naJhkQ3qfGqdJ48e1K2hsbEVuJGjaxD44oBkpxj00XNYpRLDjk5F+4K7Do0lNVrZWoB4lu/vZ755NYF9hB8NyxqZpNmaXXIQTMiw7BnIyKPv50Rf2InBvIXKf/4bHPoxwXF39EENN/+wrX4G6RaGVF69ed9b54BEaeuMKGv9few0nzLhEy+ApFVLXHm5G7R1S04QhTarB1+RnMG/ze0InMPHg9TJT/GlyNmbjZ7j/oolusgwn4Njq3oK60/sQ6jzDc74ocPwMPZqEwsw2n+CkLysrOInL0hUct2tNPHbRkMzkYxzrzMzCPgbENht43W/V0FAehrhOM5644fRCb/WLhhBCCCGEEGLi6EFDCCGEEEIIMXH0oCGEEEIIIYSYOCcS+VWCwip7Ho0TZu1Z2Yc4M449HI5f4iOs4zg8J3iQPRvP4p8gTwbvp/OBY//gQutwFKTHmDiODwks2Y8j6rws7eo5M92Ytlp9LyztHurQhzuoZR/H6DswM/MuUl1FreXdNQziaW+g7vLiND6XP1heddZRIT3xS9Ov4uv0/psvXoeasqusNYtazetX0RdgZjY3jzp/1l0HpGX3SZqeU1/IghJtd4q+jUpYg3q+uQD12n3U8Rc72Fb1zA2a8+jEufACBix6h/wtO52u8/nTIMuK/bCvVgv9NHmObRSn6DswM2tGeDxbFazTBOsRtYlPHaTRwONgZlahgL6KodfFBqjT73fQ75SO8XWfxoHehhumV6Pgwc11PD+vvIjeovEAPVUJDdxxxd2vgjwWg21s7zu30Sey8gjPXz/C4zE1jeeqmVmd/pRk+IfxnkfsrMLS8jx3guCewuN1XurDO9o3wLUb0EdhfGWhubR9VQo6vf3OD6D+f/3n/wW9/h7UOz0817OSoLsReU+qVeyPn/nMZ6D+3/xv/3dQz1zAAFEOrzUz87ltqC153D2urdljY2Y2Ho+PfM/hZZQd37PkuZwRzi5SCGLJSrMMx4WNjbeg3ulj7Qfotwp8/PxggOvsDNzxL8+x/3Xa6N/sTeG9xjjBHWvv4Hg4N+8G2rZ4GKdx3M9wPxpNvFcIQrxGl/l03dN5MkdVv2gIIYQQQgghJo4eNIQQQgghhBATRw8aQgghhBBCiIlzIo9GIzCr7ckQHXUX+ytKpF2Od8ExemDJT0FOdIOTmeFyrJbW8YUck7tRAqs5j7NgHOeWcP0Ux7/HzcnA0qN57PPCfcb0fPyQ76z0YBnBGXg01h/f39fepn3U+NcL1FJ7HdQ8mpltPr4LtV9Hb8POGHWV7zzE/ID37qDme1SgZtzMbGoGNfbNAnX8cYH6Ty9HXWWR4Jzd861zUA96+LqZ2UqG292gLI56OAP1kHTAQQW1xfU6+l3MzLw+fqa7gvPWLw7wePQf45zfO9/4Nm5jxHN6m1WmsO2iFzD3JK4d6FzTxG370yAKqxbt5VCwp2QwxPna6zW3HaOATAAp675pfnWf9xPfv9Nx9cLxCPv+fBP7w8Zj7Me33sZ+/vj+Q6yX70Odp6gNNjOrkFfk/GX01/DhfryF233hEvonhin6RszMHq1in2t3sC0uz1yC2iMtfxhiW1Yq7JgyIzuBNWIURo+S3WOajc/mO7rA9y14ul+cuUA5DmmJl4P7UxRRtggtM6csoYL6n1fiM4jo4vNHv/U7UP93/+9/APXd23eh7g9wjByzN8K5MJl1u+jjYL/E17/+dVzm4D+E+t/5O/8e1NdvorfOzCxJj/aQlnkuDuN4aEqOD+eY8HvSQ165NHXzjn7mcG7A/KNfNrP25h2oV1b+HOqogtfQag3HrpQOY0Zr2Wij/8zMrL+N/W/Qw2vB8n3yfaR4za5P4zizOLfkrCPwyK9jtM4hjtMF5X1MTeF++uZen0oS2Erec3L0i4YQQgghhBBi4uhBQwghhBBCCDFx9KAhhBBCCCGEmDgn8miEgVm4l6ORs3TLI71h2fzaHj7X8LzUjOvhON4H4h/nsTjOsuFEd/C84s8S3kFtwX4KO7oum9/YtWAcraXzSM/HatCocHXWVxv4mVaIyxwlB0vp+W4OwvOms7Nj4/HueqshzpPealE+Qex6AOI++iE+/CHOp/32Ns7f3riIc1nnPcpNWHfbsB6gDnJEfog4xHr1/i2oBzsPoH7tjc/i8sz1nqxR/sfli1eh3oxQ79lOULvpLaOe9GLu+h82v4u5GCvfw7nwc9Lxv1hgW8f3cRuzCLfBzGw1QO1s7WX0ClxYWDxYX4lP4DQIg8jCYHfbux1styTFc6IzRB2umVkUkU57hO2QjfC8TjP0EXGmhXnYzmZmmWE7bm7hdiw/xLyJZIjn/doKZp4UNC/95WvohTAzG6ToPfnYG5+Gut/Gfv29H+G5t7JBuRtLbgbJu3dXoO71cbuWLqOnp97A601/SGNWQRPTm1mS4PmZ0rjabEyZmZmXn+jSOTH8wDd/z3/Amn/2CJSqq9knyVkPzvuxDXmZMQf/mNnjWzim/ZN/9N9A/f233sZlkG8kYa9DBV/v990xkPNl2J/TI838D76DnrGv/+FXoL5CGT6766jSX7i1fnzmRRnsqTFzjyF7Ng5/puzzP2uUJD1AlY53nHcsP/4G1L0+XrtaIY65eYH5EuxtKAI6R9jIZWbdAfav7R3crp0tvAPz6NC9+DKOXdPNOWcdEXnMzMOxKvfQezcYfQh1ZYT3L9XKi846vMK9Lk8C/aIhhBBCCCGEmDh60BBCCCGEEEJMHD1oCCGEEEIIISbOiUR+RZFbsZelwN4F55mlRJ7oOR6Lo+ed9j3WXTpb5K6D5/nm2uPXSTvnLI9fP35eYfZD5I6dgnXW9HKpD4S2gzwxZXNyHybOUdN+re7OwX2pRnN2J/ie3iEtbe6ffo7GME2tSHb1kbMzU/BaQfrFtOQRerGJmuztAeoo3//gHtRLCS7klz72BtQ5S3bNbJjgsX3SR2/C1hpq1Yf33oR63MOsgOk53K/P/cqvOOusoVTTmjWaw3ttGeqHD/ADa9/GbcjfRK+KmdnSJuYaXKKTsRVTBksNMwpYczp9/SVnHevku8h7qMX2hgd+BG90+h4hM7P+cGC2d0gyQ+9DRpkoo6HbCftjnPt81Md9boXoNapQxomNcJmB764j9bDdQspR8RL0U6RjPLaDPno4PArBuPrix5115j56k1bX0efx+CF5eF5AH1FEIUm3bqM/x8xsbmoe6iTBdWysYr18F/t9a448VnVXax0FqFEOSEzd6e9es4rB0deu50UYRvvZFzlnI5FxsuxbxIz9gezzcPyEiB/gceq12846vvI7vwX18kMc87IAxzQeu5tN7J9JTr6ZkXvc+OagUsXB2fF9FFj/2R/9IdRf+MVfclZx47VPQD3mZToZJHh82G9RxnHX8cOvH/fen0X4zmhr447znvV1vJ6FFfQujBMcB6IM7wviKva/kM6zegM/b2Z25foLUJ+fvQj19kYblxljn79y5WXchtj1j3H/Kjy6XpJ/s8jY04b3THHk+uACGv8mFKOhXzSEEEIIIYQQk0cPGkIIIYQQQoiJowcNIYQQQgghxMQ5kUdjLsysHu7qEtMMxVsp+y1K5pBm7wHXbmwGeTpy9lu428hPTq5ng97PuRnPkNXBUFM4NQvdODeDpZasmy1bBrcN71mdPBTTFaxnYvcZk1XHY1rHTv9AX97r9+y0GQwSy/b6QJfmxG/T3OoLl845n69ewvmyb05jTsavFujZ+MN/8TtQf+2Pn0D9+Td+wVnHzBIus9FEPefXf4AejMEG6kc9Q1/MiObnbtbc/IhaC09jjzrgwzuoY731j34f6pxyE86N3HVcPIf7da6FGlL+RFJrYD2PeRD5RTwWZmZ16oFBA/Wi3fUD70C3e/r9b3e9q5bZnv7bo20oUNsbG3oKdt+DxyoM0B8RZKg/Hw7w9dgjfXDh6tVTGlCGFLXx1ncoE+XuB1DPzeCx66bYn7Y6bnbHdgd9HbWIMiy2sa0+/2mcN/6d9zB7gc9vM7M56udLs6g57jzG/JDxFp5L0QL2pz5lZpiZVca43lnqg1Fl12sUj9GDdFqk6diSdHe7fZ5XP8C+UOSuj4RH/cD5DPk+6ANZguPsV3/rf3DW8a2v/RnUQ/JDeBn2H74JyekavFhBv8WNi4vGPGljDks3Z/069oUxjZG3PsBz4Gu//1VnHS9fRx29Vclrws3NHlNq/azEY8FeEuc6f7guvU/42cKxt2Z4XFce/8j5TL+P19gadbDuEM97L6L+GOJxoRgNazVx3DEzq8/h9bByhcdlPPZ85GPKRAmiujGcK5cVNEZSn88yHHOHQ7x/GdXcDJJ6bcb52yTQLxpCCCGEEEKIiaMHDSGEEEIIIcTE0YOGEEIIIYQQYuLoQUMIIYQQQggxcU5kBh9sPjBvuGtGjCpofokqFLJTYuarxGisO86YzWFUfkCmV881QvIy/GPc3GynYqM2ZbU4gUfln+Hnt+NCATmIpSyIEJeRpWhoGvQxCCzNMUysnaDx584Ovt/MNellKR7DJxsH4XNDdpieAheWlqy6F8TUaGGwWXUKDVq93A0kXKOAvvHKJtThB22op+9iGz15hCaz3/3+XWcds0toQp89j9uV9tA0ayGae7cG2K7ry1h/+M6Hzjqnp7BvvPPt70L93u8+wvffQ+PkFJlKz81juJ6ZmT+N5/tWEw3DlUUM/wlmKVyNltmOXSNknpKhP0TDbfdQgF/aR5P0aRGHuVWi3W3PHKcsjm/V0DUMhzzi+hTMNMJ6kGE7JQGZWjuuKT5q4rjY3aAAx9vYh7whmghnKNgyIOPsvQe3nXV2aDvmGjRZQBf78fffREN6cx7P5+sLOPmAmVmFphxoGn7m8Rqe35013KZXPo19sIjc60fo45gX5HhM65XdYxqMyIh9SozGI4v31u2TmTiI0DSdl5iFqxQUlpH52JmIhMqQrsl54k4csbWNpvw+vSckk/RsgPU0hX1+8up1qF++6oZ9fvAYAx4/WMH68SaOuxsDHGtGdGJ+7c/Q0G5m9qVfwMk/bnzuM1CPMzw3CwpQzOhmwuObFTOLK9jfMgrNPXx4OND4LwPDQRvq1TU3XHaU4lgT0MQNXoH9MQqxL9QqHHiK51W9guOOmVm1itfDgAKVfQrC8308DwO6l/W9MjM4TdSQ4EQyfK9a0H6Ox3i/MxxhmLCZWa16if4ymT72l6+nCiGEEEIIIZ47etAQQgghhBBCTBw9aAghhBBCCCEmzok8Gl//w39q8Z5GdXYGA7ea0xiiMxi4YSCVCurScgpf8Un/Wamgti7wKTwpdnVstTpq3usN1MjnpJv0SR9aa8zR66idS0tCkIIIt7OgZJUkIb8KJfv0d1A/Oixpu4DEstvtFajXN7FO+6iTHfYo0KgkcC9LUQ+akwZ4dCjMKklcD8Tz5pd+6Zf3A/AyauNLFOLUp/YwM/vun/051Mn75Hd49z6U8zu4jnh6Bup7jx446yjaqHv0HqCesxficWzP1KAeRNh/Vx5h3/nOH7/lrNMfY+Dera+hlyR7hHr5izGeE9U69vmFaxRMZWYjSi3aqWDbvPLpL0NdmZvBuo5DzWgbfSNmZtuPsO36FALauHqwzCw50dA1MSpByyrB7jHrk+Y1DnAcqNZxvDIzG45R/xv4+Jmc9jmqk6aefCGeX9IOGX5m0EbNciXFY7c4j76imSaOq+89wmNVLfE/zZ5DT86DB9gHL12/CvXGDnrIGjOXoS4KN0yvVcH1Nmh8anax7dor5FvrkMdj2m27qRz3vZ5jW3q2u12BnY1Hw/f9/etklrFHA99b5k7kMT5wTEMIO6k65L+4feeu85mtDrZ7b4THcjrGMe/Vi+jveu3KRag/de0G1OcXlpx13ryK/evuCvbZDx7i2P719zEg8q0NHEPf/eB9Zx2/9Vu/BfW/+/GPQR2TT5Wvn47/pewAsaeU7lcO+2743uVng6ODiLt0H7PTxxA6MzdMekj9L8xwmXkNr7HjMftXsZ2bFXdc9+h+JKfjGHnY56vxDK6B7m2DEo9GlqMvMUl69DqOf+y/8n28DqQpXr/M3HHXo+3+qPws9lQhhBBCCCHEGaMHDSGEEEIIIcTE0YOGEEIIIYQQYuKcSOi8/PBti6Ldj6yg5NE80pgVuTu/dhBSLgZPQ+9oynDzanXUiyWJK3IMSKg6RfkKIXkuAtI5tqZQr8xzXQ9Hbj5IXEMNfEG6wuEQ9ciRj9uQjdCT0e1Q1oKZ9SgnY5ShXi9PWQ9K3hJq26zk+NRC/MwSZVV0Dul5x2P388+b2Zlz1mzttvVojPu/fOcO1O98/Y+dz//om9+A+hcvoC58mua2Hs2jD2nU4/ngcX5tM7MR5Yt0KfdgNUMN5KMB1kUD+2NAGt1kHefONjObzrBPXjHUMI9ncY7vcQXr6k3UGlduvOqsY7BNmSMx9pWZ1z4BtV/F/a54qO22PnoVzMzyLdT1BwG2d5rMHvq3+/nToChiK4rdsS4ISL9K/orNnWXn8xntU+Th2BGSzyPlXCAar+LY1dBGIc3pTrr8Co1PS4vob+KMHp+05TMl+RMZZSVcunIF6vPXrkNd62BbLSzi/O2rK/ecdYQR6Ydz7PdhDXXN2Rj3c+dxG+qrc7jfZmZX6zNQ+wGOq9vB7jENg7PxCAVhYMHeOO0V3BfwGpwmrs+lIH8g+wi4Zg/Q7VsfQP31P8cx1cxss4PXu84I+8bleezz18/jNbdG3swtymCZc2N+rFHHPl8j32QzxrF6mryc42XM3bCgJMvqGP8EezCOq8soyBXDvtXD9xacv/WzwdH7NBjifVCWufch3M4JjU18HJMR3ksMyN/K38ePYvf+zFI8T6IA+1tEngufbr0rAd6nlv0GMBrhdvE4XdC5a5zHRtenJMXz1Mwsz8nPEsqjIYQQQgghhPgpRQ8aQgghhBBCiImjBw0hhBBCCCHExDmR0HQ0GliW736kEqEe1C9orvaS+bk90oyNRim9jto5lsH6pF9MBq4GtZug36HXwXmX45jmO85wG6aamL8Qkuej10M9n5lZ5pOuNcB1pLSOyCN9XgXrdt/VzvW7qE0sWKsdoiawXsHXo5jyQsydp3mhieLXa+fOQ71zKEeD56Y+Db76h39stdquZvDxPZzn/M63MSMj2HR1lPMt1OVO0/zsr34c53O/vY6ayOVvvAf1uHDn0x4FqIPcIZ10mySo2wPUmacD7F+NFI/ThQuoZTcze+kczjs/foxzwqc11Cuf/9LPQx0voBclO+eKoKfGuK9TpIlu0Hk16NE25DjfedF2c07CLnmZ5mdwu8JDbRu6eTanwcroR1bbG/uKBD1MUzGOFXGI7W5mFgbYB4OAxtE+fQaHVfM8GmvG7ndFcUyesRGOHednUQ9caeGxfPQI+32VxvpRQhtlZmkFl1lUsG3ev4N+lV4flzEYkZ49ddfRuIRZHVXKpAlpjn2fsi5GA3z/8rI7zlbGOBbPN/B4xNO74098BjlCZma+55u/5/HzPPTKHJvbsPf5k3wmII9As4l9yw/dPJEB+RjZszhdxWsV57Ysb7Sh/uABXtMz3/XGVWvYR7/5w7ehHnNmF91cBORPHOdu243GOJZzJgm3BHtOGfZjmJkVdI/EPozDx4uP3V8GxmP25rmejiLnezxs04z8rP0BZYrRItn365f4d4KQPMoBee/Io9Er0PM4DvGc8Zw8EbNRgp/Jchyn+f6a+05G96GpX+KTLJ6P91a/aAghhBBCCCEmjh40hBBCCCGEEBNHDxpCCCGEEEKIiXMij0aaZmZ7PoqUNI/NGuomF+dQ329mNtdA3e7y6iN8A2nj2OaRjFFjVotcfejCFGUGcL6Ex/NUo66tQnq8uRrW12n5ZmZ90gCOCtzOIsR1Nqqo5+uRRjAt3Oe/WoTabp80fLNTpAmkzJJeD7dhrunqXOencd96CWY2eIc0p152+h6N73/nW/tzxd97+/vwWnOI3oZP0bz9ZmbpGHWQKxu4f69cx89stFEb3Kljm6a+63MZ9LFdRkNcZ0K5LAXN8b0wi8u8soBz/deqru5/q4/bOcpQe7lwbhbqV15/AWp/FvtOXinR/o5wu6dSXGf61h9CnYxIL98iDfQaz1Vu9s73MAvlwRPUOL9y9bP7/+4Oj5+T/nkwTgbmJ7vbFRboS4jIJxX7rocncXSz1F/Iu+DleGw80swPB26uT0rHZnV1DeoxNV27j8sY0OGfWsCcgzByx6fVPmWIUHbCHGXSPHz0A6gDaqtGxb00VevY3tWQMyGwz7FFIU3x/XfuPXDW8eZ3vwX1dIGN8bGPXzUzs16PdN2nhRft/meuByAM2V/h5p1wDktOXgTHV0C5Gw3yrDRmeP5/s4hyVmI6DmSnsIvz5HWqoUeMo6uaiasj3xnimNeaxmWyt2nUIX8P5WuNUnd8efQYfWX9IZ671SYuw6PcgsKwLtPhczYHd+LDuRqcsfGzCR63lLIjOHfIzKzIcRzOcxpTqU29nM4TzuGg+4ayr+fjCp83lL3hkycjRd9cQHk1vueeu4VHuRkeZanxfWdB9xrsXSm5zBdlfXIC/GXoqUIIIYQQQohTRg8aQgghhBBCiImjBw0hhBBCCCHExDmRR+NXP/dZq1Z2BZYP13GefD/DZ5Z+iYZ1HKP+/I2br0BdIR1lMkbd28XLL0G9to2aXDOzuRnUNNdjFIQmpPG7cwd14WGETTJHGQNR5OquPfJ5tKbQT7E4OwN1SjkZH9x+F+rgqpuVwPpvI/3t7Dxq+UPa7x+9g7kTUy3UsJqZvXgV8xh2NjCLIhkeaB37Jdrw583999+zcM+4M00Cw6uzeJzGHXce/oByDrZH2L9ukWdjjc6O+jXM2dh8gtp3M7NehtrLHuViBBnqJKMct2HOx228PDUD9c4maj3NzJbJn3LpAvqjrlyh4/oB9reLb+B5WCvx74z72Da2hv6qfAX7SljF8cCvYAbC9g62k5nZ+jb2qQc9bN/L4wM9bn98Njkasd+wir+3HQl2kKjA836n7Y6BnZTakTw78ZDm989QA++x9pcF7GaWG/aHwQi3Y4s8Gbfu3IP6ytWrUPMYOBy5WUKzzRrUUYLb6ZHW+ud/7stQLyxgH/3ut9ArYWY2plM6K3A/2m1s28VL5OFjbXaJxv3O3Q+h/tGffw3qqV/f/f80PaMcjSAwf89PkJNXoawvMHEFNe2syHZ8LTTO1pro47t86Yqzjne/8z2op2id8+TnbNFWvPLCi1AP+tjWnRXydpoZH41Pv4T3ChXKPvjK99Aj5pPtY6rhXh8/+wXss7UW3muk5G+hWDDH/8K5B2ZmZAlyc00OeUmC4C/j98SUtVbi0chSHIsKalTPaWQsOXeDP8/5KWZmGXWgwMNzcTTGsYmPaxzhNnue6z/mfWffUcb7aewZwmtLUbg+ECvcXJJJ8JexpwohhBBCCCGeM3rQEEIIIYQQQkwcPWgIIYQQQgghJs6JPBrnr1y12p6+cukizq0eJahby2LXy/DBCuoi8wZqf6fOo7Z8lOEyw1nMAzh3HrfBzBzvQuLhsxTPIR/H6G2o1FCDmjVQhzkqeTbj6Yi3SOP3/g76Qjod1N/1Gp+BughKDgvpif2QvCeUK5B3se3yxRmoY2erze5t4XZFAerDd8KD/RiFrk77eVNNxhbu6RA/fhl9LP0n6BHYGbk5HwtLqAPfTrAN3lpG39Fj0lmuddtQjxNXEz0mYW4aYl0hD0aTsmBmSEfZpA574fySs84R5bDEtMwh5Sgkj5ahzvt4Xl569bKzjjjHbAZvm2qa09tC1M56VerT1ZIcl0voDajM4HYczmIIK2eTY+Dlwf686yn1n8EO1T3XR9IvcL7/rMB+WhnNQF0b4RgXVHF880uyEoaUKVAn/8TsBexDCWl5r73yGtTbO9g/hiNXo9wZoEa5Po1ZCJfP41h/lXwgOx1shz/56p8467j9PnpJ6jR3/WiM25CTvyAnXXTou+Nsg7JxRkP0091b3/UHZGWT0J8CeZ7v71fIQVMl7z2Ogt7DvgKj62eTPGOvvfYxZ5l/8Bu/CfWNF9ADdu0S9ulhB9s4b6F/K+/jcWUtupkZydWtQvcjdfJDzJFHsVHFa92rn/mCs46/+tf/Fv6BMrdy8mC4FiDOMXDHh4Cu/XlGOvxD68iys+mDPw52nDyL2p+9CozneDIob6LEZhD6eA9XUBaM0XXdGSeOyTIpStqdfRsZrcNyzvwhvxg3VuHeW/ictUE77xVHezKMfB+cXVS2zEmhXzSEEEIIIYQQE0cPGkIIIYQQQoiJowcNIYQQQgghxMTRg4YQQgghhBBi4pzIDP6bW7MWDfaMhQUGcOVkIgkKN3Akn6GaA0hWKVCEHoOCLTI8+25YC1tZAnJk5fRslXloWox6uA3TO7iNoePaMQvoTwUZz7KMmxlN7VkFzUVjNiOZmePro7C5IRmUYjq0HFhUzV1DZ0HG44hMd73k4PURu+pPgVcuXLTKntH5cgMNrht1NMAGSxiuZ2aWhNg7Hj5+DPX6MtZrPiaE5X2sqyX5WD47AKm/hNRZahSQM0sm/+YQj/N05Lb7kI5F+wmavW9tY8jfzNwM1A/XV6C+fetHzjquXcTPXJzB9m9SSGVIkypYhHVn866zjtkmLuPFN25AHcdp6b9Pk413U6vWd49xbR7Pj9UIJyQoMtfs2aij2Xh7B42uO5toso8iMjiTydWvueY9Dmqansexet1Dc3dM4WSP1zFcamcHt2lzww2N7A5xO194GY/37BxOutFP8f1rbZyIYX3DDWULCtyOc9QHI49Nm9hWcchGUrft+PysRGTg3Qui/Cnz4ZqZaw7/aIZ1akMn2wzHtxdeedVZwoXr16H+4Ycf4DKGeBxv/txnod6mUN1hhn0lreFxNzMb0wQFmwO8Hoxp7L+7RecqBRH+2r/xbzrrmKI+PBjhdtX8o+3PrvnbHcvZ/OzR9eOwGfxZzP7Pk49i/j4xfP2McRKRMHInFTGajCfP8Zrq+RyceHSAX5ZygN/xYbEeXde9iAJMydSepbjMsnUEwdEm9ZAmmvE93O+czN9+4LadzwbyCaFfNIQQQgghhBATRw8aQgghhBBCiImjBw0hhBBCCCHExDmRIGvgtyzdCzTKSB8WULCPn7r6wyiiMBp6vSBvQkABJPx+J1jFXJ2qT1q4lHTTvM6IZLscmDMq0VUGpOsNSKvp+UfvN0u58xL/BMP+Fja0VAz9BB6FixUlgX1G4XIF+1EOa/z809fIZ92BpXua9e0xBra9+PJLUK/lrn/n1v37UG90cRk7BWpu+z7WMYXy5KMSjS0d64i0wXyeeNT/uhuoj98aYhBeZQGDz8zMpkmzPNNCD9Cgim0xbqCW8z6F7yVDN4yxGuEyOhsYqnX5IvoApoyCz9bR/xJsuOuYSbFteusPoN65dfDvbv/0AyPNzN7+zTv749gLX8LQucY13OfWDPlUzCwjz00xxP5SreKxDMh3MB7ieV3EbjskIZqHOExvdX0D6ksXMfwyIf9El7xJ79++46yz2cTAsyr1++XH6AMapLjdt959C+qFadfjd2Ue27dLOvswwPauU3BlSKdrXjKW5wGFxAbYJ6vR7hiYnpFJI4wii/b2K6XjVJChgsP4zMxY+Z0kuIwwpDaj62mS4/s//slPO+v40q/8CtT/z//Hfwl1o4LL7NA19/wceobWKKixVZtx1pnQNXeQ4dhemcMx8f3tNtT/+v/ifwn1p7/8JWcdfEvjsYn0GNxsOtfVkPH9CX3osGeD/Rs/bZSF8fHfuGbfiUfXyzDEMSCK3DG2IE9PmFX4DVh7eM7zvVHGno+SwEj+zt6ncTv1sfb5uAbsG3H9Y8f1H/d+jW7vC/RohD56Is3MfPJ58Do/apfTLxpCCCGEEEKIiaMHDSGEEEIIIcTE0YOGEEIIIYQQYuKcyKMRW2bRnsqTtebGGjRHDequjCXuBWUQcCQB+xJifoOZeTwPOL2elugG4XXSCI7o/WVTZaek1QyctkAy2g/2u6QlHg3WCaY0R3JEnoksxf1IyV/QT9115FXU57FKujM60H6PR2M7bTbS3KI9/eTHv/gZeC2toXbzW1/9uvP5IXkP0gC1nKw/nI1pvvYKtrnFJXNdb6OefdbnOd+xNySkB306T/9TevT6Tgf9FGZmfh/njJ9tkWeDdP8bA9TwR2PqK7nbya+8+DrUjRjP5o0Ht6G+9f73oL56fgm3ed3tP+NtPD7hFLZ3duvg9Wx4+v3PzGz91mML9/IXOPPiwsuXoX7xcy86n/dauN08GlUauM9FRjpcGjt8ngPezCLDdUQeaflpjDt37hxuQwU1zRcvXIS6TT4iM7PVJ+jB+O6f/ynUI8o1uHAOPT1L59B7dPkq+l/MzNpP0OfjU3bC1ByOAWWZR4cpynIMyDNVi/B4BPFu27DX79Qo8v1wC9aK+7S/Pgc8mau5DgIc5T2PRn3azzzBc9QLaEw0s//R//h/BnWb/Fjvf/drUG9srUF99QJ6hq5dwb7Qf+j2Pz6ScxfQk/G9u5jL0gnRU/TFn/9lqOOmm9WRU1ZMQKdeQs3NV9iiYC+nq8N3Id9NkR769xn1wf31H+1dyFN3+9iDwfcl/DrbcD267wkD12eQRR38A+VFFOyv8vB6mNORy8grHJZ4a/jYFnRPyNlGfB7mdH/n+yVjE/k+cqr5ltzLOUMOx/W4ghlyu+vA97g+m49m0tAvGkIIIYQQQoiJowcNIYQQQgghxMTRg4YQQgghhBBi4pzIo5GlY/PTXa1ZFpCWixT9fknGxYjnSKaMC54zeUzvJxmbjfkPZuaTiSIk7Zzj4aBF9GlecZpG3UIWwpmr7Xdqej9LZ3kvxiUZJCPaLtbn5ST3DEifl9IzpRe6ORPDhPSSGfoN0kPtnZV4PJ4381dfsEq8q9FcfOUmvFato3az/XtfcT6/ubkKdRyyCQjb1MlkIU9QXGL3maV56Kep3Xeo3UiCbwmdE13yOtVLdK8N6uO9Ds4hH9M84r0hvl4jb87CEvopzMxm5lDP2e+iP+HuGuqsQ9IfJ9Q/OynpaM2sghJ7yz30GuTdg7bLz8AjZGa2szW0YK8fZNu4DcM2HuvtDTxnzczmrqOmePEytqs3TfsV4jnYrFI+ycBdB+fwzFbQ/1CtotdhdRXPi/EYt+HJE3ydc4LMzIoUdc6tCmrcP/8x9PhUWZ5OmRA7m21nHR69pxnjOV8hXbRP1wcnd6DEwxHT+TvVwOPVG2ybmVnin40+Ps8zy/LddbPXhj2O5fP9U65PwE480pqTnj0I8POjkr4wd/061P/zf+d/BfW/+u8wJ6PoYy7LgweYn/PKlVegvvwSeobMzHYS7LOPttEzdPsRjk+P17eh/t730FN2+fo1Zx0e+VV89liwn4Da0vO57Us8GuyJOcLTwP6G06Cwg91Ms6P9FWlS4jWlbeYcF36d7RQ53ejEsevRGCXkd6Brqnk4NpWfJ4fXiccxLxk3Ajr3CvpMlvI28TJ5rCrJ+KHxzI1xoWXyva+P9wG1Eo+Ge7d6tKf5WdEvGkIIIYQQQoiJowcNIYQQQgghxMTRg4YQQgghhBBi4pzIo2FFtvuf8ezOZo6toETaFfAc8AXr0vD9bPPgucuLEh2bT7o03k7W0rEkLSENfE7PYmXzGwc0n7WjHidfR0ybUOSsS3T1vzyfsWeoiR5RKEmQo7ab/QWVwtW4hwnOdT8kTd+OHWgbkwlp906CV62btzeP/Zvvvg+vff7zn4D6wnVXx/voCc6lXmTkK6LskZj0oBH1plaJxvZcjG3GuvEuaYn7pG3fyvHzfozb2ArddSYhatW9CE/rgj7Ty3EbphcWof7kL3zBWUe1iduVFLjO+DxmMczP4zz1C5fRJxBPu/0n7WxCHc7ifvR7B+fFqMTHdBrktZp5e8d9NMRz7PHKMtRbO3hszcyW72K7LVxZh/rl19GDUb+OmQJ5jGPFqOuOFaMeDmozTTy+r9xAzfudOx9APT2NGvpkjPsxHuF+m5ldvnge6p//3Ceh9jPMUkhGONZ0d9A3NBhhbWY2yrDfhuQz4/nty/x0h3E8G2ZWjfB8a1RwDv5073UeT0+LJEn2de2s0edtKjtDcrq2hCHP/8/XaBw7hiNsjw/X8Zw1M7u/2ob6CdVZhcaGFubPZG08J9658w7Ul0s8ZNt91Povr+My/Cpq+bvkDfj7f/8/h3q6hdtoZvZLv4xZGwm1pXtrQBr5gDMiSjIJ6B7nqJwJ9kScBnme7fehjO6V2LORjl3/WEo+K8ejkfG9EBtfKOemjtcVM7NO/y79he6FyB/h5XS9JF8kWzhKYqacY+lsN7VV4ATaYOl7Jf4dNgzze2i84yyPajwDdaXi9vHnhX7REEIIIYQQQkwcPWgIIYQQQgghJo4eNIQQQgghhBAT52Q5GsnAnk4FXWSorfNIL+aF7qKrEeo7C9LK9RLU8fqOp4NyOEqek0IfNbYRz/NNGj9Hk8r5FLSfaUl2h9E6eP5sNp9wToZfoC6xKPFoDGlu6BH5CXJeZ4xtzccn9dzjw/NN9wz15KNDn0n909eHPl5dtSja3aZRjjryr/7p16EOa7j/ZmZeBfcnS7HNYtJncxZJhYSU1ZKuEJKmNvSxTQPSRCekY90ek9eGtqksR8MS1LMPKV9gutrC16mvvHAZNc+XPvaqs4on9+9BXVRxv86T7n9mDjXRrUuoB+2lmOVgZrb9+D7UixfQW9AYHCyzGLj+h9NgmIzNz3b7QUT9adBH38FOe8v5fJ42oO53cMyLgg2oF+ewz9VJpztYdfvDo3uYEZDN47F78Spq4u/cQg18Rv6Il65h/3hnu+2s8/VXXoa62UKvSafdhXplBXMN+uTR4DwLM7PG3AzUPs3TH4Y0dlOd0fUjK9GQe5Qd1IzxXOlUdpfpn5FHyKzYD3/KsqOzPMLIHeM5G4ivC5wN8e1vfh/qr/7Jt6CuLlxw1uFVZqF+/z3MyciX34X6lz6N/q65K+hLerKB59FGH/uSmdmwj22xwLk/a3gOPNnA/peO8Dz8b/+bf+Ss4xrlg1y5dgXfwHYCvi8g3X6Zx5SzUdiHcdiHcxY+oSIvrNjbD2db2bORuufXmPxeWc73PnxfQVkxdA7Xam7/q8TozxyNsf/5AbU7HSfOzXAPU0mOkPO3o+8BC+4s3Ja+e+7yfSX7Qni7Ax+vNc0GjvucJ7K7TF7nZNAvGkIIIYQQQoiJowcNIYQQQgghxMTRg4YQQgghhBBi4uhBQwghhBBCCDFxTmQG7wYNC4NdAwkbm/iJpeq5Zr6tEQYujWkZbFQzMruwAbrwXENynVY7Q+bJGhlldyh0i8PyxhQoM0hcA14QsgGdQv7Y9D5EQ5RPRqG4xOQVsNmeTHuO6T3AbRqTcXBr5LbdKMP1RmT4blYPGdGOCcN6Htx+/30L9sLS2lsYEPY5Cuy7vDTjfH7jxg7UH97CoLLRGNuoQYb6kILxksQ1JHfJoJpwHw4pVIf645AM6Dv0+ajEDD4mY9mQkqPaAzQpD8ij16fjvvp4xVlHQufu2jqalh93cR2NBTRjfvf7b+M2rWDbm5lFHoZ/tSI0sc9ePAiBC3tuaNxp0Ov29k2YHPjGdZq5Vrqsg0bWag1N04MeTT6R4LFJR1gnbq6dXZxHk+p777wJdb2OJsCXXnkN6h++9UOoG3U0FV68dt1Z5zWaDGD14YdQv/Xdt6B++QJODvCJ169CPehgfzIzGw/wmA/6eP4NKUCTrw/jMYWUlkxYwpOBVCNcZm0vuJInfTgtPM83b6//BQEbu/G9ZXluec6TuOD+VmLc3zEFLf7mv/wnUM9duOas4+d+4VehTjqrUBddNHd72TzUrRqe98FFNPwmrsfYxnXc2e0Bnmd37qIheDzGvsT3BW+++X1nHb/5m/8/qP+9//XfgZqviHxd57DE3IkTdjlqjPHO4Bp8FLytZYGC/Df3PWz+pgkenOBid9KXehXvDcYjvFZ5Ht4HBCFtE00AxIl9rvF7d0toJUe+zK/zsSwz+vPhZo96XmCAabOKpvhaFc+jsrkkfLrnC0pCTT8KP109VQghhBBCCPEzgR40hBBCCCGEEBNHDxpCCCGEEEKIiXMij0Z/7Fuwpx2LSWtepSCyceEuup2gRpbVX0GGulf2PuTkS8gdlZpZQnq6Aekia6R9iykYZYN0v2lGIU8lz2Z98lx4FIbn6AppGyPykWSBq22k5rWEggMj2qwhBeEEHrVDSShgy0M97lQFtdy9Q93FszPQKGdDs73QvNXHT+Cl738LPQBf/rnPOh//lV/+a1DPz2Ig3AZ5E9Iutsegj3VCAX5mZhXSfXMzj8g/kVOIpVHoTkK+kD57ccwsIh15OI26f/Y2TTVRE73dxfPyg/cx3MrMbHFhBur2FupcZxcWoG62cB3f/Nof4zbtYKicmdnCPJ5H8QzWlYWD8WBUPTqs7HmRppn5e8fwOPlqYa5PrT+gYDryb7UoIK5KoXO8znrVHWevnkMt7nSE7bi+gZr5a9dfhPoChbDdvo369p/75S8669zaQd39+++8D/VLSy/hOq9i4OZw3IF6dcf14HS2aHyq47nTmMKQSBp2nQAxxxNoZhFdc9yAu6cHYFJRVifD84N9f15OQa8c6FWk7nUko+uZT+NRRCF/A+qvvW30UX14+7azjvvv/gjqm69+HOpPvojhjsMY/Toj8qW1qugR8mqoRTcza1exLb5KffbtD5ehrpGHtKB7jyR3r2/f/POvQf3Lv/DzUN+8eRPqmZkZqDNaB/tBzcwKur/gGob/M/Bo+L6/7x8I2BNFJxy/vvv540Ln8P2OR4Pue8Zjtw3TEfkee3gf40V47Zqaxv4UxXxfyaagknZnjwXVnn90zW3lh27b8ToKClQOA7zmNuroe/MMvU/ctrt/pABF+3Hj38nQLxpCCCGEEEKIiaMHDSGEEEIIIcTE0YOGEEIIIYQQYuKcyKMRhbmFe3MOR6QtHpP2czBCPa2ZWUICPNa/8rzSGc17HpEvJCrRAMaUN1Ejrds4xWV2xridi03U/VZIw7radffryZDmJqfNcvR3pKVNaULjUeLqQ2sx6QhpvzKa171PGQ9NMnE0o5I5/nNc5oByALz0YD5qb+i2w/PGCyvm7bVlkeFxevRwDeqv/h56AszMrt+8DvXlK0tQf/JjqCWeaqA2eHUV1/Hovutl2FnB9/Q72E4BzVM9TXN2hyQ5dTJavBJfEi2zR3PCN+ZQA31xDucZX7yA/oqpKXy/mVm3jVrtMMdzd6GBx2PcxnYoxtgf6/VZZx0LF3G76uewbp6/tP/vvH76/c/M7NKlpf3z+cGDB0e+18tdn8FnXkOvwt/4q78M9YuvYqbFOEK/zZg0skuXMK/EzCwe47zx04s4dlxfxP7RbOAyL37qVag/fRXnY59acPvHxhr248bHcT9WHzyC+p337kLdH2NuRj5yx8AW5Xk0ZnA7goiyg5ysi6NzT8zc+et57E73xmYeb0+LLMsdn8VTeFs5Z8PM9a0MKUfq1q1bUP/Tf/rPoOYxMAxcH9LaGvoh1tbxM7fex/Fm+SZmcfzKGzgOn5/D/tqlTB8zsx/dRa/Ib/3Z16HusD+UtjvnTC/fbbvb5Ef59/8P/z7Ur72Gff6Nj78B9Ze//GWoX3/9dWcd44zGDD5ghz0NJRL7581hjwafK3w/V626GRdFQVkiI87VwNcL4xwNfD0tOQ8HFBTV3sJj3+7i+HjpMuUEXUKvl+9RXlbJNZh9uIznH52bwTeNee7+BuD5FXoPbmcc4/1MFOO1oaBlesHp+Rz1i4YQQgghhBBi4uhBQwghhBBCCDFx9KAhhBBCCCGEmDgn8mj4Xm6+t6upG9Mc3WPyXwwzVzvHerrMeN5l1E3GnC/B7y/RxcUxfYa3a0xzItPr0zXUg1ZoG5a7qCU2M2tWUDuX0GaNyIPBT3es7/NLvCc5ZW+kOe87vu6RPreX4Db0SwSeMfk+SLpth6ewdlZ/Cvjmm7+XXZHS+nle6s1tnCvbzGz9Wz+E+v1bH0B982Wcd/pzX/wC1B/7HNaf/8VfcNaxsYZZHE/W21APttHrkGzi68NNnKd+NMT315s4J7iZ2dJlzD3w6Ry4sIia6KvkfdheXYd6bR23wcxs5wnmJHjUAYIt/Ey9QF3slRvoTZi/eM5Zx6WXcT9al/G8Cg/NCx76Xefzp8FnP/tZi/c8MNPkZXn8CH0Iv1rSP/71f+1LULcqeKzaO3i8B31s5yIkL8wYj52ZWdJ7DHWUY1v5AS6zt4WZNGGAmuU50qsnG5h5YWa2QFkulRaO5cMmLiMkTfJiE9uyVnG1/2TRs4Q8fJvbeH2pTdG4y7Lokq/ZAlpJSr6O0XB3nUl6NjkueZFbzmEDe7DfIo7dsWJlBTNU/sE/+AdQf/WrX4V6eRnHs8DH6yN7G8zMQrpm5qRpf0hj5D97gl6nb/8QM1gW51B7vlOSnbDWxv3a2sHaD3C7OeCIMwXYG2BmNhqhVv/hw4dQ3314F+rf/r3fhvrGP78B9d/7e3/XWcfnvvA5qId9PNdGh7yRY9qe08bxntIJxpkZZub6G+gtwyG+niR8rKn23PtMzobJKHvj8UO8N7j7IWauvPwK5lFcuox+wkbLHZtiyjMK6f6r4KZw/GGUkVG494D9Lu57FJJvdw5zwfj2vvA4x67sd4bn89uDftEQQgghhBBCTBw9aAghhBBCCCEmjh40hBBCCCGEEBPnRB6N/mhswZ5GnjVmrM+LWBBrZmEVNWW+jzo01klmOervCvI6WES6SzPzKAuiRnr1iESBic95FOjhSEgrxzphM7NWDTV7PZrnm3WsAbUd+y9c/Z6ZUVtk7Pso+chR6wxL3u/nPKc1bnd6qLukJ+s6EyFIRxYUu+0QF9geeYB9q5uXzCFPPpbODmrXv/vdt6B++937UF+49jLUn/sE1mZmH7+B/ofPf+JFqOcWLkH9+Al6fn70w+9BHeaoJ/3kZz7prHPpCuYcFJSrEZH3qeJh29RWUKM/LsmK8Wju/pDOm2qd8mcogyQib0ncwHwIM7OQ/Arm03Z40wf/jFwPzmlQqTSsspdp8/Nf+jl47eolPA6vXLvufH7ryXtQP36MOu/+CL0KRfUKrr+B5+Sg52YKpHT8aqTNDXlcLbAPjhOcZ54kzqXfThU0ho3JPxF6uE2NOi6F59xP2ehmZtvkj+t20JPQmCGPTxX7vZejpj3w3Hn+Q/Kpjeh6EWS7251zo5wSnuft53+w55FzQTZKvFb/wX+AvoCv/P5XoM5IAx8Y6dE9vmaT59HM8ZCwfy6m8cfIP3FvcxvqcYRjyZXr6HUwM9sY4VhedHG7i4J77THH7xkOr08mn4qH1yA+Hnfv3oX6P/qP/2Nnmf+X/+v/GerZacxKOLzMshyY04Tv+Ziy7atW8DoQ0HWEc1n6AzyunKMRhK6XoVLHdTSn0GNRq05D/fAB5qPcvYseoQZd21rT2B/NzC5dQX/ExYuYYTE1jX0jrnBuBp4T29uu9yQMcXx7/TW83lQqM/gBxxdC52WJSY3/Nqk+pl80hBBCCCGEEBNHDxpCCCGEEEKIiaMHDSGEEEIIIcTEOZHQfpSMLNjT5QU0VzZ7I5x5g83Mz8nXQULI/Ji6EvK8za6QsiAdGr+HXR1Vmv99qo5aumYF9XkZ+ynMbKuH+uM0IB8IzbleI2/JiLS2ae7OE+7TMyFvR8bzgrMXhZqq6kobnTmuhyOsw0PHw8tOfw7vrfiC+XvzU7cS1G7WQuzKQYnGNqNt5jYtClxGp4tt2L61BvX9x6glNjN760foPfjiGzgH/Esvo2djO8G+UMR4XD/9qc9A/eKNF5x1Gh9LPi/ovMtI1r/wwnVcXEmOi89aWFpmWNCc/aTf5fEgLcnAGZM+PDTUJ/t2eB1no09ub21bvHf+BrPopxiP8Dz+4z/7Y+fzU3XUtM/Pt6BuTuEc7kmEeuKggv2rm7mZFkPyebR3cHyKBuhtiNh4VrAfh3xuXkmGRM7HDo9vPcZzKy0oM6mCuucgcMfZfh/bd3YO+1ythWO3pdjRq3Si1ALXo5GSRnxEbfXUm+RI/k+Nwp4aCKKIPCh0nH7nX/0z59Nf/UPMyfD5Os4nKtV81nol56HzHjrX+dBm3Ja0Td0B9nHW7ZuZ9Xvo2SpRnx+zlSfHyd6g2iPjJB+vN7//prPM73zrW1D/zb/+a1AfzgVjj+tZw3r+0uuIk7VBHo0Qr4dxXDmy5rwKM/c4LJzHceOlIfanLMP+dO8+buM6ZWEtP3aziz54H3NbanU81o0W7ldrCseuqWkc95cuvuas47OfxRyvC+fxPXFEHmjq8/4xvurSv03IiqZfNIQQQgghhBATRw8aQgghhBBCiImjBw0hhBBCCCHExNGDhhBCCCGEEGLinCx1rcjN9gJTCgrwMgoJ4yCWXciUmlEIHZl4cjKiTTXQQDNPIWBmZvMUBHZhGs2WEQUOsRF7THV/jAbiYVISkEX7EZAhvUbm8HFCpmT232RuWEvmmNnIkMmBWenR4Sx+yaEf0zoGIzSu5slBnY1O3ww+evGvmB/tGjjztTvwWuyhGbA5xtAxM7NsTIGQtH9pRsZHCpIKazP4+dnLzjo262hqfefJFtTtDhoA55to4JohM/D9d3GbNh89ctYZ13E7IwoqY5NY5OM5UqlRmF6NTLVmVmvifjXqZNTmxEgyi3OiZFCWMMmLcPp8cug1NyjsNFhb27Qw3G3fgiZ5aFaxXbMdN/hwtrkA9VwFg568xjmoBxXsD9bA8Wyn505IsNIhE6pheFQypkDHhMZuCsOsUrrnVM0dd33KdePQtrCC403MplAyYdcDtw/mIQZvDQcY4OfFbA7H/W41Z6ButHB5ZmbjJrZ3tTlH9e4446dn0/92r8G7bRuQEfbD+w+g/vV/+s+dj6fUZ9mw63g/PdeUDy+XBHp5zrWFruu8zJzDZ3GZfJxXN3FSDrOSIDdnYhmeLOX5U+R8P4P7nRbudf5P/uhPoP61X/krUEfxwXkShXTS/QXgOMO4aw7HvlShyXkadTc8r9XCyTCmp/Acnp/Fc/ziZQxFXXlyF+qtrTbU/aE7AUdO92w+hYHWazipx9w8jvvnL2AY39LSdWcdc3M4sUwc475z2/H4ENAYWzaZAE/uUEzoTNEvGkIIIYQQQoiJowcNIYQQQgghxMTRg4YQQgghhBBi4pzIo5HnidleMJ/H2jpD/WFeEsiVsq2D9HqsD6tG+By0SB6Nhaarz7syj3q8hRbp2Gi7E9LWPVzfhLrXQ0/GWtcNC2r30a/QqlLoXxX1emsd1Pjx014YuLrX7hDXwfrPSnS0XpM18Unmhm7l5OMY8ZYd8izkJYFaz5vGxZf2A8v6MR7nza0PoJ5OXX18JcBjGcTYF3LDvpDkFKSYkCZ87LZhxWag7k+j9rJxHo/bOR89HDU6J7IBbkMvw20wMxuT16RKvg+vTvr4Oh7XCnk4ahQKZ2ZWC7EPx6T/dOWefP5TXZq3R+MB+am8Q2MMjzenxWc//bl9nfDVyxfhtWuXUEPrJa6Of0zjR04a5OqFq1D7AR8LPHbnFq8561hbQd/GaIShc/Ur6C2qkQcjLnCsCQvypZXo8n0KIY35+kB1RJ4+2gSrxO7YPl3Q2D3GcyOkfhzWURc9tYD+l9Yc+mX2NgzKax0cRy5e3D0+w9HQ7Cu/737+OZMmqaV7/cqjRvuDP/gDqG+9f8v5vOPJKLlOT56TheXxNoXkRajV3fFpax3706S05Ycp86PAOo9pS96m0HOv2bfvoM/mzod3ob7xyvX9f7Pn42cBbmLHW0reLvZwmJlVySs31UJPxuIijrHXrr8OdZL1oB6TTzdNXZ8uH9vAp/EwwutnRMGDnh9SXeZ9OtrfElEd8DI9vkifXuitftEQQgghhBBCTBw9aAghhBBCCCEmjh40hBBCCCGEEBPnRB6NwXBsfr77bBLHpNEmTVmeuTkL4wQ1tUuzOI/5/DTqcj0PdW+NCureBiVZDgXN3z4eo056nONntvuox/tw7QnUT7Yxn2FcosvPaE71zQ5qokcV1OPNkWejR/sRRu5hGdB2bg9xHVlOmQ+kz+O2S3N3Du8habmrpIMPwoPn0ixzdYrPm3zlR2Z7Wse0cQNeG1ZRH59s3XM+PzPEbI0aNXMQkScgw/43Ssmfs+mu49EY23BniHN0N1ovQf2JN16D+oUZ0qDW6LjGbt+IaEciyiwIY1yGT1r2mDTQrPU0M6NT0XKnLsvNOYSjX3b1zB5971EUPC/4wRhT5GfzHckv/trfskZjN0OE5PyW0NCQJO4+Zg3U/KeUKdDLad74kDJSSJZdbbhZEK+98Xmo+zRWsPeILWGNKvcH8nel7hjIat8o9Kmm+fHp/bwNUYnnzA+of1An5M1i30h1Cj0bAWXBmJm1SDd/6eYncZ173q8+jcenhR8E+34XPqW22m2oOcvEzCxgnXbBpfOHoymReR/rVeDXWZfv5G3hgW3TfpqVeEJ5ExztP2/48Xp1/syx/hbn/Xg8/NDNipk7j9eLddrXG3BN/tnzaBwPtnmZb4bzIdibENBgE8d4P2Y2TTWus/yw83tSepUGJ9rsgraR/ctm7q8CznsczzN/4vQ8GYx+0RBCCCGEEEJMHD1oCCGEEEIIISbOM0mnnv5EmB+a5isv6KOOdMr9WS9P8eekbEQygiE995BeYzygqT1Lpncb0k/acc5TluJnhrRMXkc6pG0sk06NUEbE086lXPNP/iwBy9zDkpGsKac6o3XwdGmph9ud5iX7QTIzXqYdmlru6facxvSI+/0vOdT/xnic8gSPU14yBV1GUxln9EtiRvuSZdgfc34u993pS70Uj2VGUqrxELe718P+2qUpK4PsGaRTKUmnxvRTccTSKWybmKYIfBbplHPUg2OGko8kncJlHv7Je2ens/ee05ie82A9/d6BfO6jSKfG46OlU2OaFjGjZbB0ajRy+/mgj32MJabHSaf8HOVDCb0hfQbpVOJIp0gSSO/nbQjLpFP+0dIpvuR4JDvLSXqVlUiLxjSNeL+Pcski3T2fB4Pd8/a0+1/30PTIBZ0vI5qGs2zbWBrF7zmxdKqEk07zetw6eHl5ybWLZUk83hw73e1HOI4nb7uj297MLCUZdp/uRzqHjn+3d3p98Ok6dnZ2nL895bjpfye0JVS763SOCx+WgiRtx67jZ0U6dfzvCs5E1Ecc46d94Vn6n1c8w7sePnxoV65cOe5t4i8hDx48sMuXLx//xp8A9T/x4ziN/memPijKUf8TZ42uweIseZb+90wPGnme2+PHj63Vap3SU6v4aacoCut0Ora0tOR80zhp1P8Ec5r9z0x9UCDqf+Ks0TVYnCUn6X/P9KAhhBBCCCGEECdBZnAhhBBCCCHExNGDhhBCCCGEEGLi6EFDCCGEEEIIMXH0oCGEEEIIIYSYOHrQEEIIIYQQQkwcPWgIIYQQQgghJo4eNIQQQgghhBATRw8aQgghhBBCiImjBw0hhBBCCCHExNGDhhBCCCGEEGLi6EFDCCGEEEIIMXH0oCGEEEIIIYSYOHrQEEIIIYQQQkwcPWgIIYQQQgghJo4eNIQQQgghhBATRw8aQgghhBBCiImjBw0hhBBCCCHExNGDhhBCCCGEEGLi6EFDCCGEEEIIMXH0oCGEEEIIIYSYOHrQEEIIIYQQQkwcPWgIIYQQQgghJo4eNIQQQgghhBATJ3yWN+V5bo8fP7ZWq2We5z3vbRJ/ASiKwjqdji0tLZnvP9/nVfU/wZxm/zNTHxSI+p84a3QNFmfJSfrfMz1oPH782K5cuTKRjRM/Wzx48MAuX778XNeh/id+HKfR/8zUB0U56n/irNE1WJwlz9L/nulBo9VqmZnZr//Gb1uj0TAzM9/DJxh+yi176nX+VvB7aBn+0esIgsBZhx/Qk1VRUIm18XbzFjn75T65ObtF68hzrvNj3o+vl23ZcV8qPMvxYJy2OWIber2u/e2//kv7feN58nQdv/vuP7RGq25mZne+/wG855/+w9+Auui4y3n4wSbUcYjtfP36Rajfe+tDqLs7Pahff+OGs45/++/821B/8Ze/AHVuuIw4HOM6tnHDv/2nP4T6zW/cc9Y5GA6gbsxUoZ6anoV6egaPWaPVhHpjfdtZxw++9SbU5xfPQ724tAD1rffeg/reBw9xHavuAeIzy+2OB+d7mmf2jbvvnkr/MzvogzeuLViwNy5Vojq8p1ptQF2Ja+6CAjoPfax5/ApDHKKjMIY6Dt3xiP/k+7gMz8M68HGZBY27xbOM7f7R44vPY32G5954NIK6k2YzrwAAozNJREFU18fzxMxsNMRzJctSqFOqkyQ5cpvK9iPLMqgLw2Vake+/73vv3Tr1/nf5+rX9bw+bTTxvz5/Dc7Lw3OtIf9yHenp6Guoowr7x6MEjqGuVCtSvvvqqs45KjO/hbzsj6m+VmMarKWzTd955B+qHD+8766zVcRnz8/NQLy7i+BSFEdS9PrYLb4OZ2fLyMtSjEfWvHPtTc2oK6m6/C/X7H95x1lGjY7p0aQnqVu1gjBmPx/bf/tf/8FSvwX/3f//vWrW6e3yrMd5/+Xw7WXZ+GfXJgkv8g3Mf49yhufA4UNBYE3h030jrSAocAzK6Hxul7n3SaIzjV7frjl+HiWM8B5Ixjm28PDOzqSk8V6eaeP0x3m7ab27bUucEv8Wj8fDQOD4cje0//E/+q2fqf8/0oPH0YDcaDWs0dk8EHjxO40HDf6YHDfrbT+WDBh+8v3gPGidZ7k/Kfv9r1a05tXty1Rt4YeELZIHXETMzC6lvhHTTF0d8A4bHOqB9jUL39GnU8eZyagovHDm1YRzigOLTsW/QBbRawQHKzO1P/J5aFS/89Rous07b3K+5g1wcYYMet45KjO/ntuJjYVbyoOGcBu5nTutn/KfrCXx/v18E/FDA/aukf/zEDxoRP3iUPWjQMulBw3ceNPBYHf+gUTIGnvRBw6cxkPo93wiamWX0xcBxh56XyZReo3gZzp3QycfVSfB0Pb7v7197+foX8hhY8qAR5tyf6DzlMTA8uk/zDZOZWUxjQ+DjMtwHDRw7qlUcn3gdZecVb3dM40+FHpC4f/HNKb+/bDv4cllkNLbT+6OU2qVkP/gYRrQMbluz070GV6uV/bH++AcNd5zIDM/7Ez9oODdb7ramGW7XSR80wmMeNLySBw3erOO+5KjQcXWHT3cdfM2t0jX3tB809t/yDP1PZnAhhBBCCCHExHmmXzSeUhTF/rfex3/7/SwLPPpl75hfH8q2Iaefvo/75cWRMR29SeaVfEvE8HYd11a8Tc9i7OJlHvdUyftZto7jJF2H18Hfop8G/rgwf7S7TQtTi/BarYZP94OB+41Cd7QF9dI8/vT4K7/6C1CvruPPn0PDn+zf+MInnHX8a7/2V6BOCpQ1edRuIX2DPTuHP0NeXEA5TpDiPpiZVQrc14+98ALUC+dQEvZo5THU6Qj7zuqTJ846wgC/ZUzoNOgkKD0I6JvSwKNv83L3G+uwwt+u4jFtt3cOtvn40/C50KxN7f9yEZM0qkbSqWqVfto2M4++IT7uFww+T/n1kh80nG/HPPo+Kc95rMBtcr5FDD/Cr9XONtE4zD885/iHNHYvTXmG/XxI39jx61lKsif36zpnHZnzazMu4+n1hSVWp0WlUtn/JePSpUvw2tISymzWN1adz09P4y+s9Tr22SereO5PkZSHv+kvu7Y9lVc/ha8rQUFKBerDvR5KjPgX1ytXSvTg3tHXXP6GmeUe/Itct4vbYOYe80Ydz+/tNn6G38+/1Lx686azjv5oCHVK2725cSD/TcZHf2v+PJhfmNr/RfzSBZSn8XEtkzkdd9/Ax4HHDW5T/iVqdx10cSBFiUfbWdA6c1rndgfvA1Y3d4xZ6eDffFYZ0LHf3FqHmuXPL730krOOubk5qCP6RdPLWTqKygSWPTYa7vUpozGy8Ek2Nj6o+wPsq0ehXzSEEEIIIYQQE0cPGkIIIYQQQoiJowcNIYQQQgghxMQ5kUfD87x9La6j43W0dWU6XtbwsX/iZDNZfRStMHOch+Oj4Ho0SEucn8zD8SzvebaZqw54Fh/IUfpwnuXjNPiNf/R7Vqnuav1vv4XTvL5N092efwF1wmZmn/4bOB3tG29chfrlL+L0kK0/x3186Txqoj/3i2846/BpZghvhLrdH373G1DPT+N2Fl3Uaq7deh/X+Qpus5lZi6anjcivkg1QP5p20E/xzjs4je9O39XRpjTTxvoGToHrNbCtWjOoJ02yu7hNhbuOEflqsh5uZ5IcfIa19KdFJaztezTCgHwnbDzIS2YnYb06z2xGuuaAxsSAZz0qaYc8ZR0zea/Yo1GQ1pdO7SA+ftYpZ1pYGvMcvTaNV6xFH9J0o2Zm3S5OiTzo47kyHqdU4zKPGxN334P7kTszuWTPvKznwec///n92YwWFnDKVscLwTOcmZlPHsMnT9DHEdMMZVdfRD/EVrsNdZlXpddDTfvcLE6vXaVZpriPb2ziNOSej32lTFs+JG/DcR6NPvWvWg19INvb7hTfPOXtwjz7BPEzwxFOWerT8aiG7ix6W9ttqO+v4vG5fvkgyyLnqWJPhXzvP3fWqZDGO9cjZc4dJ8+c5t6X8HEkv0XZdYBvTXgY5vsx8qgN6Ljdv4vXx7UN1yf5ZHUN6pT2nfezP0T/REb99dY77zrrGNEU4Dxl8+IiXnMvXzgH9VSdZkEruYVLuW3Io5Ee8kIF3vH3rU/RLxpCCCGEEEKIiaMHDSGEEEIIIcTE0YOGEEIIIYQQYuKcTGhfePvJqIWjP6aMizKvAz3W5B7Pb8zJr5wMfryfgt9TItDD97NHg97trKM0TZbzPWiNBa/zaD9FmR/DO4Eebvf9J0+wZR3hUfkez+LxmDS/+//93f005u4Oam6bpNv9uV/8uPP5pRs4z3wcYLv3U5xD/su/inNZ5zTX/41PoGdjbym4jgpqf5MCdZL/5X/xj/HjOzQX9g7qMpuU6m1m1iKfR4f0x8srbahHY9yP9gA1qcOS+c8LSludmUU96AZpmrOUtmFjGerByJ0DPiWNPXu4ikPzn3Na62mRpdmhKfux3Rztb06vm1mY4pCb0zlXJPQ65WZkPF4Vrg46HdPxHGI9HvFncBsqNfIZ0VWiTJfPGvhjPWXHfH4wGBjDuvrhkBPsec59PB6sGU9KNOQjajufvIdP58PPJ5Ej9REYDIeW7rX/ysoKvNakzItqSbp1v4t+rTrN789z9bempqDe3EJ9ellf2KL39Mmzceki5vosktekPuTtxuM4KJm/Px/gezjvYzjEzzyhrCDOIJmfx4wIs+N9OWGAJ8pUC7dhag69Kg8pz8jMrE7ZHPUa1jPTM/v/Ho/d8eV5UxTefjo0+80sJU9USdiRF9F4R54z55zNyj1SB59318FJ9OwH9Cl8qL3Vhvrh4w2oHy9jX+H+bGZWpDgWVSl5PknwWBXUNv0+vr66jDkbZma9Lq535Qm+5+VX8H4lpGsLeyoWZ6eddXjZ0ddgHGOf/R5Qv2gIIYQQQgghJo4eNIQQQgghhBATRw8aQgghhBBCiImjBw0hhBBCCCHExDmRGTzwQwuC8o98pDC9gMye7LtmwzEbvUvWUTiGcX4DGbGdbeT6+LAqNqD7HKpFy2RT2XEGqF3IOP+T5wqemMPbfRaBVVHmWbhnVA4zbI8Rhbvlfdds7CXYrmubaPq6dgUDmOZmZnD9IRrz5qbRKLm7YjSFFRn2jVff+AzUG//Vv4D6e3/2HtRsHu4P0cxpZlb4FDLGmzRmAzH1VzqnC98NkuJlPlpGI6oX4zJrNayHQzT3RiVG1SSjwD4yF/qHTMvFmYRVmfV7nf2QvSDkyRKwT5aFWkZk7uawspBMhGHIEzQcbwYfkfG1SwGN/R720TDAdTam0FRsFDTW7WIIpZlrtnUmi+Dxil7ntkoSd7/6bAJ2Jt3AZVarOBHD1DyFSCbuGLFOYXFxhG3zwvUXzMwszVJ7+J3vOJ9/3qyvre0Hp7I5/hOfwADReQrNNDObncbQOTbO8nVlh471DI2JU1PuGMjbxab1RhXP/VYTJ7Pg48ITobCx1sys0cBlPA01fAr3z4iOK1O2X0yfrjls5I7JaM/3EmxYL9tODqlcXT0wJpf13+dO4e/+Z2Y5XYM9um3xA/deie/xCmdaiKNxx5WSGyGPJ+fBa8VOH9v4R+/dhrqzg9eq4RCPc1lQ3fwMGqt9Cr5s71AA5IBN17jNtaobOOzRrBw7bTw333rrHai7O9h3ok++BnW94l7nKxW+PtG97KED6PHBPAL9oiGEEEIIIYSYOHrQEEIIIYQQQkwcPWgIIYQQQgghJs7JPBpBsB/qdlxg2zN5NGgZx+r1OHilLLDP53A81GL6pEc+LsjumbwQ7KnISTNfHO2v8H0K7Ctph+MCEp33O+tk3eLx2shnCfk7TbJxYd7e8c0TOs4BanJ/75991/n8X6vgZ778tz8J9doGan9/87//NtSNIa7jS5d+wVnHwyHqtkMKBbt680WoX33xKtS3vvcB1O1NDOnJSXtsZlZtoZ4zZY0z+VXGhtvEjqC45PuHhN4VzGBb1qZxu/qbqB/1CtKCZyXjA52rEe1qcEgTelaBfd2dzf0QN9ejgdsURW47hqTdZX8XexXYE8cBcn5JYNV4hB4Mx6NB4VARNbQf0PhFh2q7TXpjMxsMcJ18eahRoOblq1egvnjhAtTjEv35zg76k7KUx3rcj8VF9FxVYtTEp5nrA9khXXNGwVrTU9M/dvtOgygKLNwLPWv52KbNOnpS0tTdxoDOsZyuVY6PIMb3L1KQXdl15NxVHNNY553StWtE3qzBGDXyayvopVtfxQA1M7OZGfSenDuPoYC9Po6jFfJPcNhhWRie0zbkNam1cJkB+bEG5L949OiRs46tddzX2SZ6ReJD9y/eGQyBvh18O80afY/GKs93NzDP6drEHiEe3/gekQajtCjxs9LYM6Sx6b1bd6B+8hiD78iSZmGO/dEvuW3OR/ihqI5jUauBHo5aawbqDvl91tfRK2ZWcn0MGvQ6tuXGJi7zEQX8XbmEAZJm5jwRuHF9Xum/j0O/aAghhBBCCCEmjh40hBBCCCGEEBNHDxpCCCGEEEKIiXMij0ZRFPuaTM5RYC1d+fTGHChBr5PmK6BpfgsPNbWdnbazjjRFbSVrUleWUd/JmlzexirpMJtN1IKamS0snoN6Zha1b8580qxrLbgtXd2ro3Y8Rp95nAOjzH/xLL6Ns6ReiS3c6xRdymXICtR+bj12Nbbf+v0f0l+wEdMuamyXf7QKdYsyMf7kN//IWUdKmRYrH96H+nO/+AWo1++iFrMRoM46nMKTIPfd45bS3/rk58nIm+LT6zlp3cfmarujFg4VSzexz89eRO32nTfRa7J1dwvqsrnwWWPvZkYc+vfZWDRsNOzve8NIgm1hSOdPXjaPPB5PztGwjDJNaBAM2PzgToXuZGuE5LmoV9kXgnVM+5VSf6mWeE8C4/wPXMjNj92E+qVXXoZ6MMDzuUoaejOzND0P9doq6tlZVl+nZfA1KhmjdtvMrEbZBt0xavsHvd06SV1/x6ngF7v/mdl4jJr/fpc8LFU3pyGOUSvOvpdaDccfzqd48gSvn2WZTy36zMsvoi9tSLlR3R6ODe/fwjyAhx9SZk9JdszWOo6Bgz72pzGts1pDf8sbH/+4s0yG82OcewHKvOFr1Hvvvgs1t6WZ2UwDl/mpNz4B9eH8j+FwaP/D0Zs8cQ7fA/L9Gpe+VzI4+Ud7R/nGxV0H3SPyIGxmgxGeFx/cvgv16soabhJ9PqXzqtnAcyIZutfHkAZNjqIKQzIcUo7L4gJeP6dL7jPbW3iuttttqPu0XZub+PqjR7gf2zcuO+uoVHC7gpAyt7zyfx+HftEQQgghhBBCTBw9aAghhBBCCCEmjh40hBBCCCGEEBPnZB6Nvf+ZlWjn+L0lcn+W47E+OSXd9oOHD6B+550fQH3v/m1nHWOag5tl0xtPUJ83ojnnY8opYM1qpeZqhxeWcN7wT37281CfpzniG3XUh9brqGkta1qfxNgFa7upwX3nAPBCS7I6fso9GpVLoUV7OtjXvoCa79EYtcK3v/Ge8/kwxLnS43QG6vXH2DcqpKssaA7wH759y1nHi6+jpvb+A+yPP/i//TrUoyFqh8McjzPP2V2UiPL7NKe/T3ONj6mv5AXPVY51xieNmZ2/hp6MxUuoKc1Jgjo1j22ddVFXvbOM3igzNxOC9d+Hq7PK0bC02D+V+FuakM4x9sKYmfkhbjdrYIMMPxORGSWkYxX47hBexNhHOLuDx4IwZH8FZy3gpyuhqx/OUjxWC6Q5/vhN9GRcWFqC+t69e1B3S/x3U1OYKXD5Ino2lh+j32lrHeeNr9NYXuYv4HG0SPD6UN/LAxmf0Vd0SZpY4ZX7JDvkIWi1So5T2T4fgpeZ0NjCuvCya0aFfC7sjZmdxrHhwft47P0xzv9/4wpeP8typnLS/nd7OL50h5zzQn4Kart5ygsxM6vTddsn/9SIzt31J+jx29rA/nn+HPZfM7OYtutpbstTgkM+kIh0/qdBYQd3DnwcCifTx71W+d6JbjnddbClLXP7wv2Hy1Dfev9DqPs9vCYb+XdadbzH63fJD1riL5wnXxJdYq1D/dGjMbZP/c8ds80CNubSucfndkBtt7aC/XHliZvVceEc9nv20PiHQkYc7/ER6BcNIYQQQgghxMTRg4YQQgghhBBi4uhBQwghhBBCCDFxTiSYy7zMMm9PB+bMmczz7bpGg4A+1OmiZuyHb34X6m9+8xtQryw/ouW5FDnq1IY0V3pB858HJKYbkp50WEW9aFji0Whvt6FefYya09b0DNQLi6g5/ewXvwT1xaUrzjp4/v2CnhG5uT2PtI2kvU2TkrmgaU5q1tayfve0ufnLr1mluqttTBdRB7y1inNM37Abzudfu/4S1BcWUSc+3sL9q38KNeHJNvaFwnc1svceYZ9+QLpxr4v6Tt/DZbDONSVfSJK7c/9XKC8gCMgDVKWcjSHqQTPSfuaxe2btbKHGdOPbuJ9xA9e5Rfs9FWJbRhzWYGZ5RvkPNKf36JA29qw8GnFc2R/b4hDbNQ45j8JtR97vMMI6Jt11JcY6ps97gduOPi3Dyd6gwSKi8z4KyHDDguMSWMs7PY3a8iigTAda5oULeC62tzBbwcwspbHbJx3zpaVLUCcJvn9E8+t3uq5PaExa/qkm+gnqe+N/WDJ+ngYzs7P72vzpS7RtNA6Uafj7/f6R7+FrALc555uwt6GMDmV1JB0cGzLSr9+4gn2hWZ2Belxy7u8MMe9kfXMb17nRhton/TpnWlwgX6WZ21YBnXsF5RhUIzyPOBuh7Pgszi9AnbNv7ZAXcczBMaeA73v7nj726Tq+3TKfLo1F7PHhcaSgaxMPRavrmKVjZvburTtQj8a4jN4OngP1KmcV4VjV38Fxw3O8KGZ5in/b2sb+16dxpd7Cc9fxxWXuOooU+5dX4L1u5ONnpivkgaaMuUcPHjvreOUl9BvP0HZ6hzyD3rFpbQfoFw0hhBBCCCHExNGDhhBCCCGEEGLi6EFDCCGEEEIIMXFO5NHw/N3/nv4bIL1YmX7r0aP7UP/xn/we1G+//SbUowFq4yKaY7oo0bGNR6hDS3huYdrwOEY9Husqh6SL6+24ut6QtHF5gtvd66JGdXVlBeouvf65z3/RWcf1lz4GdVyheZuPycDgrAQLjz/0vMzD9VlkbnR7mY3T3eM3buFxGYxw/5bOXXY+/8K5F6C+9z3Ucm48wHn3pxuoM/d6qFfub+JxMzNb+xCPbUzazYw0qJzbwhrVMUnBOVfBzKxRQX9ERhrSPp2bPVpoQsuM6JwwM9tZaeNnSO/enCad/xi13J0Rnje1mruOok56XLI4zNRn9v+dZZnZE1ef+7w5f+mKhXvz51fJL3FcbWZWqVaoxuPP4xH3j4jqoOQ8DmkMiyL2XpEmmfIAItKe58nR2Qpm7njAvo/WNHp0anRutaZwGxrNWWcdy8t4bvW6qMuvU59q0Nz27TZu43CIfdjMLGxQPyYd/dPcpTQ/Oo/ieVGrVC3a8+289NKL+CLltoQlHqHhE9xn7l/sw+O+MTMzA3Wn414PuS9wVlV7E/1dLcqnOL+APoVahH0nLbm38GgoHtMY1yNvCnud2I/I43DZ39hPNxxw3gL5Qenzl8lTZGa2SPvO7Xu4bcvOw+eN5/nm7d1DOU3kHJYSbxcHYTh+B/J90P1am/J1bt3+wFlFf4D9zfOwjzdq7Lui/krjQjXmXA3sS2Zm60/QU5bQuZjnuB/VEMeq1hRlqZU4kFt18nUEOB6mKWXFJDhGbdG964fvuzl0L1y7CPUnPo73nYfHB/YPHYV+0RBCCCGEEEJMHD1oCCGEEEIIISaOHjSEEEIIIYQQE0cPGkIIIYQQQoiJczIzuBeYtxcc59h8PDSe3Lr9jvP5P/j934X6gw9u4RvIRBaQaZHNezmZrXaXgWUU4LPU3DSaDGcpRMejbdiksKFs5Bpg2JCZkkndJ9NWRvWd996GensdTT5mZjdeRePOqx/7BNTzC4tQV2poLvLJfMSeLDM3JMbLf3xQ1xl4wa3XS22c7m3TFm5bbQv7yuCOa1Lc2MF29SlrqtjGPpz2cRmDLpqP63NueGNjhMd2OMaVcAAWB7jFlRouMKBArcA9JnUydI5pG/IMD1aNQgITn0ItQ7ePV8hwHqe43Y0A2yKt4DZ0x2iUDDzXTHvxKgZ1jTJcRnHIfZimZ2PGvfn6G/uhelUyWVci7IO+V3aSUMgSHbtGC42vcRX7g0/9oSz0q8wEfBQZTZjBJtYhmR+HibtfrSkOI8O2CWg/a80ZqNlYG3vuflXqFLRFhvKUAszY1JnQuVc2oQWbowMKTBw9nRwkP5vv6CrVysE28rWO+kLZ6J0k2EbkkbZKBa8TfM3l9llcxOuOmVmrhX1hRCbpLo1HlSpeq8KoRjWOLXnqmvj5aFQqHFqJr3coUO3yVeqvgXsOsfmaQ9a4v/U6OPbXKrgf83NzzjrYcM7Xi8PtX7aNzxuvyPcD6wrqgDxBUG7u/RkHOx9n/k7pGnD39odQry3jxAJmZvUY+09O1/GIglUHHDpJk6NUIg4udlZpgz7dm9LYH1X5Go3vf0KTNCRZyXWezs3Ip/vlAOuYTOwxhU1na2vOOh7ew7DpV27ghBP+oeu87z17/9MvGkIIIYQQQoiJowcNIYQQQgghxMTRg4YQQgghhBBi4pzIo5EXheV7ulbWHw+GqIP7/pvfdD5/+w76NjhUhzWncYi6Nk6I4eApMzcUh0ObZkg/2iRNPIcADiLUzg1Grj40o8+MKSDNCtTAs16UJIDWfvLIWce311GL+N6PfgD1uSUMqPv4pz4H9UuvYPCKTzpGMzOPjBucDXcWIX2HOf/CFavUdvvEyiq20fY99F8sjDDcxsxs5Q7qD+cvzEM9u4Sa780PcR3T07jMyzfOOeu4Tn32N34dQylDoz6do37Zo6CeZoP6Z+7qXrMCzyMvxHOgTv6c2RTPgZC0n/2q639IcxwqMta7V/AzfsjeKNRyuwp8s+42+qHGtI70UHAc+6BOiwvnz+3r2GPSSAd0wnQpXMrMbMChXhT8xt6qGh27tMBjG3ruEJ7TedzvY7DdmLwMAwpGZa/DsI/bXKeANTPXgzFdoj8/TEba7CnyzlUzt58XFCQ46OI1p72GYySHz1U4/HAaz3czM5/8dp0+6rdHex6HcZk/8BSYnZ7Z7387HTxfqhT22Cg5TnNzGAiXUTtvbKBuu9fDvlOj/nnhwgVnHfPzOK76dE32qM+3N5ahjui8Sijbc20Lg1XNzAIaViNaZ5UC+VK6f+mTWWWbPBxmtu/NekqF2rug/sZte/XqNdxm3/2el30gA/J9pIeWyUGIp0JR7Bs0Pfagsa2gxKPm0Rjpkc6fvVory0+gfvgAr8lhyS0sH+vpRRxbNtY2oe5s43Fif4Uf4DalJddg3u5KHftGvYX+iJC8KFmCdadHvhEzazXwfL44jefZ9jbuV0oe23yE2+2XBF+yzzCnc9U/9LpXcv/949AvGkIIIYQQQoiJowcNIYQQQgghxMTRg4YQQgghhBBi4pzIo5Gl6b42m+Sy9mT5MdSrVJuZ1Uj/ZTQXfjIibbiPureA52AukYj5JBRkHWRE86IHtE0Dmmc8iPH11pSr/aep7Y1kvlar0NzspGX0jffTWcW+N+YpowT1yY/uvg/1VruN76e2fvV1zOEwMwt83E7HknHoD6xJPA22VzYt3psfvebhtiYhzVF+Dn0IZmaz3H/IV3Dl8iWo39xA30drFpc5NeNqvC+eR9/GC1fQm7D2CHXVc03KOyEdcEo637DidvoqZS2QrNI6HdRZN8iTkVP/S4auBjUgz0Vax+2MG9j+VcpV2Nrcgnpzs+2sw0Z47lVj9k8d/Dsrm8z8FOh3dywb7/a9MZ2n6Rj11J1t3GczM8/Yy0LZG6R7ZU/GTh912axnNzOrkReh10P9Oc/VT6UzRrbmMNuDtehmZn3KSblCfibez4JWOjWP59KAAx7MbIM0yBnpvWPq1z3q913yG/C1wsxsahb7sUftG1f31sFGu1MiDkKL9y6+PZr/nyXxldjN+WE/zmCAy+h0cXzyfWwj9hBwXzIzq1Zxvezp2WpjHtHqww+g3k5wG7Mq9q2kJMelQl7LnHyR8+TVnGrh+/vkhfjmN12P6auvvAJ1q4FjXJ3WcZ78K/MLqKkva7sB5XY5/exwfzyLHA3P299uz/GYUH6Yk5lhjg+3oHNwNMRrwK33sW/sdPEcjn23j1uO41e9hsd6h/JPajXsn70h9p3tbfIplRgM4yr90cebwOEAx32PfJUxZaxM1d2VeAV757BuVnE/0hwbezhoQz3ddD1cNTp3nWN8+KbwBJ5d/aIhhBBCCCGEmDh60BBCCCGEEEJMHD1oCCGEEEIIISbOiTwalci3yl7oQ0Lazg/exYyM/parT66TmcHzSUNGelB62UKeC7tEI+bRMtKcdG0p6T1J99YfoTaY9aXV1NVV5in6JRoV0ji3aL9JmzkkfeiYtOpmZhEJcKdrqEceFajXXFl5CPUffZXyHKqutvHGSzehZhvG4e0u7PQ18j/6/W9auKdZnZtFTXd1hF25mHP1q6++9jLUWYzH4eLV81DvXEe/RX+MOsvtHbePX1iYphr17Z1V7CsLM6jzjUhr/OAxzjFf8ITxZtas4TzhgwH2H5/Ok1qMbZWM6LzaQU2qmVlGfXhqAbebMyXaGzjX/dYT1GXb2O0/rOkNqriv6eiQrjUvMTKdAh+8945Fe34DbsfpFmq0K7E7vHLOT0z7OD2FyxjReZaShywduu1YJV33FGlxFxYwS4HzFoYZzc8fHj1embk66LnFGdwmWgf7KVLKTHmy7uYY5B5njmDbTcXohxqRH+HBowe4zpIsjIiOB3tLKnv76bER75TI0tSyvXONj0MU4TZxjoiZORp5zosYUU5Ui3KnQjJnlmWqsEeDczRqNFZcoDyTRoHHhX1IFxex/5qZ9RK8rq/1cL+iKo1XVRyXOffn8QPsK2buvueU5VOtHO1N2djAMfDcOTeHaYq8bf0h7ldwqC1Kj+9zxvO9/Xssj/wVrqXTHaP5Mx6N+SsrmIWzuo5tVpD3IUndzKc8Rf/DeEh+L/J2VchD2yefSETZWLWa+/18he75Oj26h8vxMyHd3NYjOkfCEi8m+TN3KHeKLwUF3TMmY2yHhZKsI87N4Byd+iFvU34Cn65+0RBCCCGEEEJMHD1oCCGEEEIIISaOHjSEEEIIIYQQE+dEQlPf9/dzKVbWUYP96OEjqLPM1c6Nx6QdJ89FHJVMUHzE+33f1Yixboy3YzRC/XFIWtuA9KB9ms89Jf2fmVlMGuYwQm3maIxaOdbBDUeoSR2PXO1whcI1PJLwcWtnlL+wuoJa/z/+A/RsmJn1O6gHvXb1JdyGQ1rGIWlHT4PaINj3aLS31uC1yMe+s1Civ/6QdODWRO3liHIQXnnlGtRpFRt92HfbYNzHZSR91Di2prFvBKTNbM2gRv9SgHOxp8+gSfWof0XcdygbplagXr5J+RVmZsk0tu+4gW05SvG8evFj13EdpB/vrLaddfg0n3ky4gyRg23wzihH4+HdOxbsaZTPLeK8+FeWUHO9MO9qYGOehJ1OZJ+yDxqUD8P5E2VOlXM0X/+5c+g9mp/H12PK3eiPsM96NL7FsTtOV8hzwXPTcw5QRHr2h4/QU7a1hZkZZu5YzZeYuI7bMDU/A/Wlq1fwAyXTwOc093xB4+jTnJMy/flpkKbp/jWKr6ddytXwPffyPk3ZPwEd2/4Add+s0ebrY9l1nj2IIenNp2s0Vz95mzLKn0lov4pqyfz/pF/n47NO17ZRF7f73CX097x844azjvPn8TziLKn75OtYWXkC9bVreD3httz9I97jsN/l8P1LWZ7N88azQ2OOx6/R2FR2jtDfBgO8brz3HuaBFQW2R418L0nX9YtxDllO1yYaqiynQLaALIoFjzMl18eQjmWziWNkQufJTAs9Qi3yi1Zjt2945M+M6H75wX0cQ0cptk2Vsq6adK9hZrZI2S+tJuYhpZClphwNIYQQQgghxBmiBw0hhBBCCCHExNGDhhBCCCGEEGLinMijkSSJjZNdPfgm5WR0upgPUKbeikhjW1D2Q0hzWbP+M0lQi16mEWs2UHd28SLqKnMS3HU6uN08r3iTNGqsdzYzG1CmyA7NoTwYoD40pf0Io6NzNszMSDpsEbVdTJroSoz6vP4ObuPDux846wg8FC9OT6GW+/LlSwfrD4/20zwPwmptXwvZ6eD+dQts40cbrnZ4lubgvnT+Bahv3UeN7b/xyVegPncV22N9ue2s486bt6FOKS+iMUW5LE3UTQakzZyeQ011OnY9QoMetsWIdK9j8p7U6jhXe4X08s069nkzs/AiakrDF/E8O3cFtZ3eEM/lh8uPoZ4NXf9Cd4x6+CLATh8kh3Jc3MN7KnQ63f0sjGoVz5e4jtrdhSV3rKg1sR3bOzj+dEiPfvEcHv+pGZ7/39XyLi0tQT0zizkrrO0fkA8trrDeGPtsxl4nM0uoj7X7uB+bW6j9j2uUL0PtYO4qLKB9rTaw/Suk/b9QvwT14kXso0XmjrN3bt+BeqvdhvppdkF2Rl/RTU1N7/c79k+wZjsp8RNyRlOV2mx2Fs/9Nl0PI/IMTU3hcSyHPBsxieRpXF7fbEO9uox+0BcjNwNq+gJma6x3sL892cG2CKj/TVO7+CXup03yDZ2bR08W9+GdHdyGBt2bcHaMmdkWtXeD7j8O+3CSks8/b/KiOPBbHSPRL7s/88iTtry8AvXaKnovGzMzuIAI+04+cDPHeMN8yqyoNui2l3xK0xkep14P+05SkgHFkRKc+eNRHQT4gakWHudi7Po/2XPRpet+h/JCFi/g/crLr2KO2IUlHA/NzGpVuvaT96QLYR3yaAghhBBCCCHOED1oCCGEEEIIISaOHjSEEEIIIYQQE+dEHo0sS/f1uaurq/Bav4f6sCpPVmxmBWlsPeMMC5wnuO3oY1nn5uqTWzQ/8dVrV6GemkJ9Outc33rrLVonaumGQ3fe5oLmYfZou2LKX0hzbKtxiutYpPn5zczmZnG/RqTHSzJcBut16zU8HoMh6vjNzO7dRX3y/fv3oF5aurj/b26X02A0Glm217bJkPTHPs2bnrn71z6P/eu1C6jhnq2i5rZB87sPSWdeSfEYmJkNB6jTbcyjdjgg/02zhXrjao2yPei4JiX7NSY9Z5/0yebhMprkC+l0sS1jz/3+oVrFc+/6xzCTwKuhl+DBfdTaXn3jJtTLb77nrKMRUU4JaWsHWwf7mZ1RjkZ/nJu/dwzHa+hTu30ffShh3Z3vv9HCYxXTOMl7xd63adIsV6ruOOuT1+3xMmYcsdeNteL1KnpNMspu2dx0My7YhxZQHkCHcgxqDdRW12q4znOLmGuwu0y8XlSqeK5k1M+rpOf26Xu19VV3PzyfcwxwGfu+wbOJ0bBKJd7PLKlT/+Kcke1tGgfMLPDxHGtNU4YFnVecjzIzjd4q9jCauR7DEfnKltvkZejjmBbP4Ji51MC6togeJDOzuxt4P7JG+UUp7cdGG8enmO6E/BL9+YDuFeZnsC186vPcdlGE/bXsGrq9gx6N1jRe94uPmGMwKbJ89z8zM958j9vMd0+SpMDz/vEy+iJp6LIq3TMGFbyvqZZkQdgIxxrOybAKHofhEP1kKV3/PLoumYefNzPr0/1Yq1mhmq4F5LcY97GtxkPXezKi8Wu7i30lpE589Rp6UK+9gLlodd4mM8s4h45zu7wf8+9j0C8aQgghhBBCiImjBw0hhBBCCCHExNGDhhBCCCGEEGLi6EFDCCGEEEIIMXFOZAY/DIc8ZWTUrpWYxPIETV++j8aegtwlUYzPQaMxBbEErhlqp4MGzTt3MJju5k00pc7NoaHr9ddfh/rRIzRSlhmwGg00wnpkJspzNFsOR9h2YYTvX1hww8zYbM/7xc6cgIyBLTIOBiWG3x4FD777w29D/cL1A2N9v+8aoZ83ST6w4ul2e2gwjAI0OMdV1yR2f6MNdf513L8vfR77xtvffxvqqRq26ZXz7nG6SsF17Q6aupI+BWaRMS2O8ZwY0XHPspIQLgpL4/40O4dmSvPxXK2gD9eKthsWtECTLGRkjPzg3Q+hfvEKGu0HdLxWt/DzZmYph8/l1KcPhVQWxdm4cXu52VOva97HfXr3g/tQX7p2zfl8UqARdnaGwqKmcSzh0NIRTUbBZnIzN3SUzd9xjH2OjdgcqpXScSg897Lh+bjMMRnMqxQoOkPBg60WTtLBk1mYuWMvX3OSAtfJRvmQtrvXdfsgm9o5lCraM4bmhTsRyWnQ7w/2TcTcZuvrGGzH1wwzswqF5VUrOIaNxnRtIgN+TJ8vMzTzJC0f0LXqvbs4yYhHZtwaXS+vXkJT6wclJv5bD+9CvXQdDeNVumw/fPIO1Hfv435fXHAnIzh/0w3gPExBbcEBxWmCY3+l4l6jZuj898lYnxwK20zGZWF1z5fCDs4INv17BZmoA3eMbm/i/RlP+NNocPAwjis87EcV15hd0KQQKbUTZec5Ibg+jTM13oaSoE+PrtuWYR/O6VAt0CQMFTLOZ4m7jhFdcxO6j1y6dBnql29gQF+VgnlzToE2s5zM4M4xPlSe5AqsXzSEEEIIIYQQE0cPGkIIIYQQQoiJowcNIYQQQgghxMQ5kUcj8IN9/eUseRtYP1vmZYgrrA2msCAKJ0tT9HRwCNRo5IaXDYeosb19+zbU1Srq1F599VWoebs5FIk1a2Zm01OoqwxD0tmTli4IOBSJQmhKdIfDAWqzm03U5/J2sU7baL/K1hGStnbtCYbpvP/+uwfbUxJc+Lz53GdfscpeKM3q/Q147fF91CcPE9RImpltjfFvHQoe21p+CPUXPvsxqG++eh3qW7dRa2xm9uJLL0JdjbHdu1vYZ3N61k8yfH+vh/0546BCM/MMRafnL6G+eG4BfSPLqxgs5+Xsd3H151sPMOAqmsf+d36M2/D6FO5Xu4X9bWeWfCNmtuHj8enR+X84Icov0cmeBkP78drULoUsLV687LxnbhrHk5gSqmZnZ6He2cHQNQ5UY7+FmZlPXhfW8vNnOOgtTSi0iTTNFTb1mJnv4zLYW8Lek/l5vH5waGDZOOtTmN5whGMQ+0RiCkjjNEQOa92Fztcutv/TAL/xGejjzczanR2rjHf3a+ki+hA693A8GqfuNoYUPsbX3DDE66NHl3HPCWkrOxvwb3cfoHdpMEaf0rVreJ48+BDfv0LX8E7X9Z5MTeN5MzuHbZPutHELyQM0pHP3/Lzr0XiFAtAS8ggtzs7gB8gbdff2Lahfegk19GZmF+YwrDel63bjkEcmPP28PvPs0NGl9bNHxcvc68iTx0+gTimsmD1EAR0nj8aqsHA9QgV12oLHFroPWGhQiHOB19zRCI+jF7jfz7Mn1Ct4PCP/Dt9/ZTiWVSvuOmp0S1OrYPtevIgeopkZ3C/ur+zHMDPzqL/55OU9HDjM4cNHoV80hBBCCCGEEBNHDxpCCCGEEEKIiaMHDSGEEEIIIcTEOZlHIwz38xlefAH1iu+dQ31Ykbhz8TdpjuSA5ujOaWtqNPd6vY46uI0N1OmbufkO58/jdrFWuNNBvSjPC37x4kWomyX5IOzriCLWK+Oc8evr+PrmJu5HXCFRtJnNzKAG9cIF1N1z3sfKygrUrCkum6eeJXcBzYO9vHLgYSjzxzxvPv2xV62+p5EevoCaxn/x//ltqEcD16PhR6g/9klDOqCMi51t7MNvv3MH6vVN9DqYmTXnzkFdpYyCNMV5xAsftyGMj57rukjdvtFo4jouXMFtKArSuRb4/u4O7nd/213H6iZ6NBpV1H9+7uPoTZnu43l0e20V6icJat/NzII51Oeev4B65UH74JgnSWb2fWcRz53UzLw9cXJI50uni3kA62vu+PSpj6Mn7Knn6Ck8PnGuA9cBz99uZhXyoXGuAfsf+PU4xtcHA+wftbrr0agZZXHQvPCLlEvAHjHeL/ZsmLmZIbUGrnNMc9dHAbbNxioejzIfCPtZev02LjPey9Gws8nRqNWqVtlrh4JE8mPKaSj1Ezo5LewXxDZmj0ZB4xH7e8zMcvZn8nb4uM6Ccn9a57CvhDQuxy33GjxP3iaPjv1OB+8LOOeloL5T+CX6c/IIcT5Wnc676SberywvL0P96L7r8XMyuKgpZ6dn9v99Fj5Jz/fN88u/n2ZfVpmbbaeN91t18ulWq3SPSOME73OSup7FmDot55nUqrTMAd7LeJS/UyH/WZq7Y1NKfqiMDGHOuF0lb14L93swdq+PffIRzZAn6NrVK1Dn5Mng8YJrMzOf89ioP4aH7lcC/9nHQP2iIYQQQgghhJg4etAQQgghhBBCTBw9aAghhBBCCCEmzok8GlmW7evw5hdwHvyXb+Cc0Pc/eN/5PGdSFKQHG49Rbzc1hd4G1rmVZXU0Gqj5+8QnPoHb+TJuJ89Tz3O1z8zMQD0aubrIguZMbpKGlLMQzp1DDX2lgu0S8fzvZpZmR+sxeTuvXEG9HntXyrS1RYGavtTRoB9kc5zFPPJ3Vp9YdS9zZNjHnJCAchtmyY9hZmYFtmvUxHafm0Jt5mob/RQ7D1Ff+qkvfNZZRWvxGm5XgJrvnU3Ugw6oHR0vDR8nzz1ulSncr9lzeN7cu4MZI2trmB+yvIavj3dcfWijhm1TJd/HrffQv/LWd3AZWwmeq2X68eE29vFLL+P8+luPD45Hkrg+ktPg8F74pOMeDTCX4Vt//jXn80uLqJFfPIc+lHa7ja8vol59gcZdP3L7Q0j+Kx4n2V/Fx8KnPsc+kNZsiU8t5znZcZnVKi4joLZjnxF75XaXSPph2nc/xbrXweOxuoo+oVqtJA+E1uH5rMfebcvhGXjUzMxmpqb3s6B4W2PKb+LrrZl7fUwp64GvCyHpsPm4lHlpMupv3FY7dD3sjXDMq5K3oULezLBE+++ci4bbNRigf6qg/c7JX9Du4/vNzPoZfiYjXwf7khrk5wxC9mbiOGxmlha4jJQ8NIfHg7PwaPieZ/7eeMHjBu9/OirxTwR4HQkDfI9P9z4++Xi75IMbdvGabGa2MIXtntE1tVYjzyJ93c75Eh5ZEfLc3a/Mw74QkNdpaQnv+R4t41j0wd0Poa6XZHVUaAz9OPn9Zubw2lJwcBDvl7MGM592NqLzPzrk242CZ3980C8aQgghhBBCiImjBw0hhBBCCCHExNGDhhBCCCGEEGLinMij4RUH82p7pOW6eHEJ6q21J87nA5ovOyJNKU/r2+2xThLfUJZpwZrTNEHtHOsaOZujQVkfrRbq3VdWcC5sM9fXwdrYRgN1+rUq6WRT0rQOXP3vcIRtsbWFc8Kzr+Pnf/7n8fO038vLbgbEygpmcRSknc0PzRWdp6fv0djMMouz3bbOmqgwvPi5G1Cvvukep6UpzER5eO8+1FevYxbEJz+N/p6VZcySOH8VPQRmZm++dQvqR7fQqzRso7ekOY3zv1cow2BMOtdxgl4bM7Nrl1+BOqAsjvv3H0L9ePkB1L0h9q3ZBupLzcxaVcqGIQvM4xDPs6yL+1E37J/Tnqtz7ZApqHsft2v7wYHmnrXLp4VnB9pWj8ajgDTLD+7ddT7/O7+DeS+tKRwbWCO/tIR99gXKL3rjU59y1rFIOQSspS7zth2Gp8nn6dJZD29mluf4IV7nOMMxLeZsF9Z7lwiIC9IYd0ifPaBzhXMzkmP8CGZmMR2Pag33q9vbXadXoqE+DYaDodne8eOcqQuUGcV+DDM3P4lzM7hNODfD51yNkr7EXsr2VhvqDnmZUvJb1SrY4TjnpRi7Y8eAvJMpHx7KPogz3O6c7meSkqwE3m7HK8KZR9Q27G+Zn0d/lplZle4V2tvYxw97Ss8iy+owvH/soUoztw3ZGZCTJ6XTwTG/NY33eDXK1Rh13OsA+2h9Jz8CPzNKsR27jj+HxqaS+IjrL6I389JVrOuUz9P/2rehvnsX772uvIL3M2ZmN2/i3y5cxvOdx0zOyeDrVUmMhvHQHoZ4Ih3OVgtOMAbqFw0hhBBCCCHExNGDhhBCCCGEEGLi6EFDCCGEEEIIMXFOlqMxziwb7eopY/JbzJHecHoetedmZmPSOMakF2VZrkdaugH5DIq8bC59FI+/d+sdrN9/D2pHp0u6w8uXUYd/jnSwZmY+TcT8ox/hOi5fxkyLV19FTT37K+oNN0cjruAcyT4Jp7d3MPNhTDrFFs1Nniy4x2drHX0NYYDzzA8PPZcWLOQ+Bdqb2xbFu8ercQP1yWOa43y5i+1hZuZlpO8kD8raKnowNslP8WgZfUff+NY3nXXw/OwF6ZWnGjNQVxp4HLMh5k90+qgfXbzsHrfWIs7R/dY796C+fx+3ezBAn8cC+USqJdr1jW2c8z2g/ay+jL6Abgf18bZO+SAVVyDanMbxYGelDXUjPujDaXo2ORqBHWiRAycrAseeuTlXg93vYzusPLnjvOcw9+6jdvedd25D/WSD2tnM/tqv/VWoL1/CMYzniWetNedweKTTLdP2hhFpr0mvzl65bcr1yUnPnaeuDj+n8T4hrX6nh2Nel3I0UtLdV2LXi8T+upy8JM29DJE+ndenxaMHDyyOd8cx9hMu0jjA/p9ngb0yYYjjU0jjfhi716oPPsRMgJ1tHEeTBM+B3iaOeTPkPRmSZt4vydDJKM9j7GEfr5Kn6+oUjnlDkvonJT5JXkc2xP7HmQN8bzE7i+vkvC0zs6mZOajrDXzP4TwsPygxCzxnsjy3bM9rcpzXKy8ZKLbJV1XQLSiPPex7iaj/NepuFk5Onot9Y/EegyGeu+Mxvj8h/yl7cG/evOms89XX8G+cf8VN9eXPfhzq125chXqaMuTMzGo1vL6wB4N/NqBTwPyixPhGBAH5Ovg277CJo8Sr9+PQLxpCCCGEEEKIiaMHDSGEEEIIIcTE0YOGEEIIIYQQYuLoQUMIIYQQQggxcU5kBn/v1ttWq+2ab9gfOKJAuWTsBrqlGZq4wjquPiBzSZJQCE8Xl1mvU2qYmVWrZPDz0MDV66NBsCjQCBTFaEbKCzIYdtEUa2Y2oqCo99//AOof/OBNqP/4j9HgxSFK8wuuiTSOcL/GZKirVMhERUb66Wk0BhbmGuqmyDzoB7jOXn6wjrMIC6rPBxbvhTlNz2Ab9hIK1VlwzVRZfQbqfhdNYU+6uE+/9ftfhXrlCZqqw5LJCL70yU9B/Ytf/gLUP/jB21DfuYPheb6HJtlz5y9B/cLLrznrfHB/FdfxXZwAYWO9DXWtSiZ/OpSbW2jeNDMbkLFs+xYa58/V0EQ2X8HjMT2DkxEs912joJ/heDBfRVNoxw7OxSQ4GzN4JYz3DbNz09jHztEEGDXnnDTr99AM6QTd0Rg4IPN4r7cC9c4f/JGzjo1NNIj/2q/9GtQvv/wy1JUKjqOeY2pFw29QMhEEm4g5PSqrYAcKQhrbE+qEJWFfIw5Mo7bKMrp+kFm8SuF0cYmRmQ394wT3tSh2+2h2jBH2eRF6voV7Ds21FTzvPTJ7Pr1WHyam62OfTPls8J0jAzNPwrC+hf3RzOztt9+CmoN5Wz7W4x08rt1aG+rNTdzPuZprot6mcN8xmVqnqA/PX8T9Wt3Bzz/edq/zHQoezDp4/eD+ND07g/U0TuiSZe4YxsbjkNru8Do4APA08D3P/Kfn9jFe4LJAt4QmhQgjPN8aLbxOZDQu7LSxr/jseDaziNab5zgOdLt9qunelfr45Ut0DabQVDN3bHGPDfbHBbpWzFPNEx+ZlUyWRON0RpN8eD4H9tHnS44fj+0cQotecJnBhRBCCCGEEGeIHjSEEEIIIYQQE0cPGkIIIYQQQoiJcyKPxvrGo30Na+HoC1EflpZ4NIZ91J01qqg/nKZQucJQoz0aoyayLDAmruAutaZQzxnG+HqTAnFuUvAK6/fKdL0z0xiyM0/hhW+/jbp8XmZO2trtbTdsjrW0rImenUX958IiblOtTjrrwNXXNUm/2+2hPjJPD44XhxSeBl/6G29YrbG7jU8eUButovbz81/+pPP5+7cwyO7f/Ld+FeqVdzEM7fYdDFOrTKGeNHQzxezDJw+hXniEfeHJCmp/h9vYxrV51FVPtTAIr73hBoXdeR9D3bY30GMRRhQeRCFcq9Tf6g1X291ooIY098gztIzjQfMCnrv9DL0JVQqmMjPbXMW2qfm4zMNbdfpRVbtUq9V9bSpr4IcU4ra95YbpcTCdo4mlY8Mhcxy2t7nhjrN/9qd/DvWjBxjE+Tf/5t+C+gtfQB9RpYbHNqGgskrFDbrj8FVOqPI5eIsUwwW9PmbjkJmlPRwDN7aw3w7ofJxhfwGFTFbYz2dmFfLoFRQcmCa72+mlZ+PRmJ6b2W//mAK8OuRT2NxyryOXLqPefGcHw/I+pLC9115DT1hEbfb+XfQjmpk9fHQf6pcuYWBtlTxBPbqXWN/E8avfxv0KR27bjzlMr8B6mq7RszX0I27u4Pg0ztzzapUCXWdoDOySZ2NnG5c5Q97LekmgYkTnlkdetOyQLzA7A49GURT7913O/RfVQeDeXhbHbDN71BIKZ1xZIW/gIt73mLmBx/0BHsvRmMYasoMtLOA192Mffx3qBt2nmrltwV4bt62cBUDJ4Y9ly+S24v7A6yz848csn+4LA7rP8w9tuH+cSQc+J4QQQgghhBATRg8aQgghhBBCiImjBw0hhBBCCCHExDmRRyMICwvCXR2YR1qunOYw90q8DJ1t1IM+WcE5uFsvXYN6ZgY1jAHJ1jod1ECamWWkrQwpC6LKORkZPmvdvYN6d/ZGfOazn3HW+eorr0B9+fIS1LOzON/+8jLud0r6UvZwmJltbJCukDwwUYyNMzWFOkLO0Rgn7jzNvQH+rb2NWtkiPtiPJHE1rM+bcD6zqLmr2ex8A+dWX38btZs3P4tZAWZmNo9t9PnPXMSXP3sV6nfffRHqf/6v/gzqtKQJWhfwWH97FTMtBmuo4812UAO+8AnURLdHeExGT9z53YeUtZCQvj2s4rm5OcC2Cuv4+guvXHfWsbaJ25HT0DFVkL+K8gcGPvmSmiXfcazhdqR9bKvkkF+BM3lOi/yQ7nW73YbX0jG2e5G7WRAR6WpD0t36NMjxXPs5eRmKEplsQU27TXPPr6/jef397/0Q6s9+Dv1N5y+itnyq5WbUsGeMdcxVygHqUlZLt4PXhuUH6KcyM7t/Fz1Tj1cw12ZnhO3987/wC1BfuXIZ6rBkHvicxdOUxdHb2+7+wB0/TwM/DM0Pd8+9qRnUp/O2D4aun4vhbAee///dd9+F+sWbN6BuTrt69etX0QdyjrxMNfKWrJBI3qecDYrEAJ/CU2Zm0I/jUQ5BLSKPIgWBNch/99lXPueso1nHtqqPSTM/xrZ78ASv8z7lF01fuOCsw6f7JieK59D44ZXkVDxvkjTd92yNR3QBpPGOM0DMXF/UmHxTfC/E3t+YPLZFieWjyOlGscA2zQrsP5Ua9uFP0T3e4kX0bHglVgc+bwK+WSX4yHke+y/KPs8+D84uoqXSy+zZKIvBYI+GR1vqHcpfe3aHhn7REEIIIYQQQjwH9KAhhBBCCCGEmDh60BBCCCGEEEJMnBN5NCIvssjb1bvxnL4sKStKlpxPo47tPs3Bfe8eahrZ69Bsoc+g3kBdpZlZt4da3zwLqEZl2f17OOf39773bXw/aaJrVdd70qrhfm2s47z1O23cpnoVt2lrC30gvR7ql83MAsoUiGNcZ5NyMuo1rJMRrmNzw53j//Fj/FuS4UGcP3dQ52egkd8crtkg2N2vuQD7wnYX2+zdP/+B8/nPfhH9D40G9oXFGcx+yJLzUH/sOno6bt1HjbiZ2cU30Gc0dw59H09mHkD98PuoO69+agY/76Mu+OHX8PNmZmur6FeZncZsmHGAeuXz53GZN15F7fp8DdvBzCym+dxv30afR5s0qjtblL0wj6+3pl2ha3MWtbKVHDXA/d7BMs9gCnkzMxuPRvt+hDFpxdmTEZaIWEMyUHArsPcko/HHJ2FtVpIl5JE+OKB19nbQL7O2gv3nw9s4Lt//8D2oZ2fdDJQFygio1bEPVUiv3W+jv26njZkPyRDHKzOzkATZ2Qg9CB/cwnOJl3nzJmYkzZDHwcys1cRxJaNwjoePdsf2UVISonMKJEmyf+3lPBOuOc+pjKkp9Nt88pPoz3nvHfSYpXQtSzttZ5nzVTz2zRi3qyB1d0A3D+wb8evobeiO0HNk5mr/2b5Qp0yHJEE/VYv6ws1Pf9pZR6eL/a23gv0risin1MJxuL3dhvpSideEtf08hFT9g7Y4iySXIs/3szBG5EkbD/CcLcvRiChvp9vDNu1TVo4VeA2Yp9yMQccdJzbW8TOej9vR7uLYc+PVl6CeW1iAOmdvg7NGF9cPcXRuEPsleJwvWzMff/4Er7PUlEG43hH8zOEojmeI5fixyxVCCCGEEEKInxg9aAghhBBCCCEmjh40hBBCCCGEEBPnRB6NOIitsqeRZw2Z5x//zBKxZpR0alub61A/eoSejXoDfQdxxdWchRFuR046SJ/0oBeWaI74GdR6bu+gDrPTw5wNM7PvfRffw/OzN6dR0zxH7VBvoE62ve3maAwz1CLWaE7ueh317bMzuM4hzSve77shEAllijSncG7ywxEA/kkmUZ4Qudn+LM4eyaunL6Medrskb2LpCmovL12fgfr+Dx9D3V9Hzf1SC9v40hc/5axjcBk3bHwDj1O1jqdcfA77Y/MGztk9foRa8LsPUYduZjZLnoqlS+htWnwF9/viJ3D+9rUOnmeDB21nHc1l3O46ebK6O+iRSen1BnmbWoOSDpTiudpu43mQHJqnPjmrHI0s39et0pTjFoXYRpyZYWZWDck/Qcpa1sQ62l5W5pasIyKt/vlz56B+9dVXoX7xJcyLYT9YUeDYcf0y5iSYuf65lOfHT/BcalCWQj7GepC7Hog6+UBW19BbsjA7Q+vAdmhvoK+oT9kdZmYhaeRXHqIPa2Nzt5+n2dmYhIqi2Nd/1yifgufyZx9l2d84tyCkPjw3PQN1ZxnbfNhz25ADhoJF1OWnrF+npkyo79Qi3KZGw81xMd4POk/qtIzNbhu3kTwaXuYK0GM6r4JF7I/jbbxGe5TZZU7Gl3v7FdD4wFsx7B2ci8MzyLLyvMA8L9j7N/Uv6n/9vuulGZAnY/0Jnl9xFceBNKX8JmrDYd/NKhoNcPwaUa7UuaUZqG/ceIGWQOMfZXl4npsPwla5Yz0a9Hn2gZQZQZxrA73unyjZotyycewSPuJNoH7REEIIIYQQQkwcPWgIIYQQQgghJo4eNIQQQgghhBAT50QejSwbWbqXQ8H6roh0azwftJkZS0bPn0ft+PQUas3HY9TnmVcc/bqZ9fuoxxv08D3VCmoAZ+ZQdz83j1rjy4Y6zNEYNYZmZuM+ako9D/XoURWXWW3M4Ptj3K/zFzF7wcws2EBtrEe6wTTHxk1ybP/huDiyNjOrN9HnUKW5yQ/re33WZ54CufmW76131ED9/l/7t34F6pX3McvEzIxiGuzJw3tQ3/0QP/P4PcwVyQs89heW3DyBjQHO0d2kHIwp2oiQMiyyBI/L5hP0/5y/iL4ZM7PXr6DGdJTgNqSG9e0H2Jf6VcrE8fG4m5kFNN9+4xxqZdsPUf8e0njQ28T++v63ML/GzGyqwEEl6eF5VTn0vUhyVkEah2CfWuCzv8I9xzLyKvA4WSGNMmt9WVPvl+hk52bwPH79dfRkvPrKK7gMzt3wUKN88QLmx8zOun0wjnHM43niE9pv9mzkGY7bydidH39zHc/HCmngP/4a7meVPAyscWY/gplZlbxv4yH2weW9bUjPqP+FYbi/3bw/nIlRdg1mHwf3p/Y2eq2GAxzzCspKmKP2MjMrDI9LSL4Do/N81EMtfxphX/J9XF5UuPtVDPA67/dxu8fkqRk4/gHcpu6a6/Ez2teQ7iUC8qHNncP7m1aO5w17uszMMs4josyHdru9/2/2XZ4Gnu+ZtzfmRHT+eTR2jYeuf4L733CAx2HQp/Oehjf270xNuWPR+gb6Pjgz5dOffgPqCxfQF2keXtscq1NJfoR3jNeOTRzeMf4GHvfLOM6zcdwyS1dxbNZG8WP+fTT6RUMIIYQQQggxcfSgIYQQQgghhJg4etAQQgghhBBCTBw9aAghhBBCCCEmzonM4MNh356GmXDwDxtuyox2bE6LyORVr6PZqknmZA7fu3//gbMODvkbj9AwdYnCzLIU19Hv4jqCgAIAC9com1G4T2+IZqJOH823XQorm53DQK2pWazNzNY20aS302lj3cOww/UtNEtz23e7rqmd25uP8WHzkWN2OgUCz7dgzwwek/l98wke96DErHn3Nhr87i3jsc49NJotfoxCnAo0933z9i1nHVMvXIH6wR+9CXVB5rdf/LkvQf2kjQFYTxI0wM5dJUe7mWUhnnutGCdVYMPnEoU5tmPsG70UTX1mZvHH8D1XPvExqN/6E9xP28ZtuvsE92Nl2zUyjmmCA79JoW6Vg21Izyiw73BgHvmdLUlwm7ISs5xjII8o/Ik+kpOJtaB+Xa2449GrN1+G+sUXcHKJJMFzP6eJIeZncPKKgtp60HWDuKzG4yb2lxGNw+1t7Oc+HfvZBXeihWoN22rxwnmo+wPs5+MxB5odP2ZxCNkFul7cW9kdy5P0bPpfmib716Q+GWfn57HNIjJVm7mG0IyOrWNQLiisjDroVB3HGjOzQYLblRRkCs5xHaGHfcWv4HZ7dA32xm6YY0iTwMxFeF70KJyxzoGRBa5j7a57bzF7GSfuSFNsmwaFVoY0mcp4NKYat9nMLG7gPdDaJo6b2zsH+1H2+eeOn+/+Z2bsyffJHN6YdYMVz1/GsNiE+lMcYpvxZAQZXcsuX3EnzpmnQOSZGdyOmzdx0ggO6eRhwncm9XAnI+A+ystw8vg4wO8ZAvuOD8g7+nV3chL3/U7QoLMjefm/j0G/aAghhBBCCCEmjh40hBBCCCGEEBNHDxpCCCGEEEKIiXMij0ZeZJYXu5rOnLRyrP4ajVx9Mvs2YvJohMHRoX8eaTlnZ10dL78n8HE7ZmdRd886Vg6WCgNqohJvQuChJtUPUY88GKCmdHUNdZfLFMrW7blhVds76NHIMtzOmPSRm1uoSR2SnrMszGl6BsNvmi32yBxqyzPwaGTrqWX93Wfj8SbqXb/+4Teh7m65bRhUcH+mL6HnYuk17BuvvohBZTt32vh6jFpPM9fDE6xjuy8tvQj151/D8KDbd29D/Wj+PVxezdWGR/R9wWxMIUbkIbpURc1qhXTa3/jgA2cd9TqeBy0KHqzXsI9HY1zmpSZqj/3MHXrWB+gr8pqkcQ4Plpmmp9//zMyKwN/v+xzaxv6Lkrw+q3GwE7W9F2A7+obneaWK7fbyi9eddVxZ4hBIPFeSFOs66cLHIzx32huok94sSXqKyGvC2t6E/HWrGzgGJmMen9zvwOYoKDCnttvZ7tLrOMaFIQX4lXloqFtutnHf797dDfXkYLXTIgxjC8Pda9bqEwzJXFnBoLJz59DDYmY2NYU+At6PTheP/U4P23QmwjYtStowIQ8Ft6lPfaPRxLHEa+H45NOlKiwJc6yH2Kdr1DeKBgWOzuG9QzLGdlglD5GZWUR/q1NIZUZ9/Dg/zAa1rZnZZpev23gv0e0efMb1ID1/0qKwdG+/enScKefTCs+9x2jOoX9iasBhx9S/QmzjhTkcAy5cQM+HmdkCebeaDfIscjAsBQt6Hp3bZQO5w8nGg+MC/crur9zPcHmMv+IjXDJ5jBwd8iWNUnk0hBBCCCGEEGeIHjSEEEIIIYQQE0cPGkIIIYQQQoiJcyKPRhB4FgTlQi9nHuASQRhrLZ9mcjwlId9HRlkJnOvgiALNbGYWdbjsRchyWseY5vgmTVpB72ctsplZGKOWvE5zQVeruJ1j8oEMaK7o8dDdrynSuPN87y3StbJnY5ygFrLMo1EjrXbC+x4fzD3u56c/j3zwgW9BdXe/kw3c/2Ed27w1jX4MM7N7dx5DPdgmD1AH9z98grkbVcqK8UskslkH3/PiImaifPHnvwD1+XnSnfcWoR5+8fO4zT03fyLqY1t0lzHnoLuK+/H9bz+CuphBDevHbrja7hFpt5/soMZ+4TXcj04b1zm/gUNNc9UdH5I7eB6EFXpP/UCvm55RjsG5ixf2x6HxEI/1mM7jUd/VkofkPWg1se3DiH1puJ8XL+Kx4VwgM7OxkzOAWtq5+RmoGw3MFIhoG2OqWYtuVjI2sxeF5p6fnkE/VHBEZs9TqjU8P9ub6G3jXAP2aIzoeOWc72BmNKzaO++iX2l7p7v32WfRbU8e34vM93bH9nv33KyHwzRb087fugN3/DjMgF7vk7dvukoa+rKvKuk4+JTTkvnk4Yip/9H1lOfr9wM3H8SnLpmTL60yjX68WhM9GkUfPZD9dTejYryFY9os5X0EHnlQKePmMWU9cUaGmZuNceUK5jJtrB3kZSWJmyfyvHn8eM2qe/kga+QRsvxZTAB4XLbJV9XZwWtXpYJ9w6vgPu/cx2vZ7hqwM3D8xHHfrvO9lbP8CdgD3fvlZ+BYX+xJl1mSo0F/CwrOGjs4/wfDZ89x0S8aQgghhBBCiImjBw0hhBBCCCHExHkm6dTTn3lwitSi9D1HwXKdkJ5zihyXwT/Hc83TxT3LOvNjpyXkbcDPp6XrxJ+beBW8Tp5Cl6eeHY1cTc6YpCL8895oxLIAXufx0imPJBIh7Wt2aJHDvZ/NPtJPgCdkv/8ND/ZhOML9GZHszi85zElCErYRfmY0wHYf9LEu6PUElTK7fyPZWxTgZzpd/NAO/VTMr/d6eFyHfffnSpZO9Ye0H9RWA5bW0Ovh0O1/I3oPt38WYoOPSJKY8SpLpqfl6WKN5VHpQV97Kp06jf53eD2Hz2U+r1lOU7Zt/J6MxjwePDyaWjGhc3JUIp8I6PiylGBI44tPctiUxgGu8xLplHeMdIqnXhxQHwtI31AmnSpozBvQfgxpv1k+k6e0H88gneLx/unxe/r/p93/Do/zaepu/2H4mmB2/CScPGUqXzfGAS5hWDLFKvfJjI5tRm08pqlpc0eSQdKpxN2vgLeDxvqCXo+obficYKmxmVlOy+D2HQ5RdsbnOsuieBpgs5L25s8cej3d+/epXoMPtVMR0jn6EaRT3O4jbhM6IYfUN1LuTCbp1AkWWLKEZ5dOneQe0Cue4V0PHz50tIJCmJk9ePDALl++fPwbfwLU/8SP4zT6n5n6oChH/U+cNboGi7PkWfrfMz1o5Hlujx8/tlarVfpNk/jLR1EU1ul0bGlpqcQIOlnU/wRzmv3PTH1QIOp/4qzRNVicJSfpf8/0oCGEEEIIIYQQJ0FmcCGEEEIIIcTE0YOGEEIIIYQQYuLoQUMIIYQQQggxcfSgIYQQQgghhJg4etAQQgghhBBCTBw9aAghhBBCCCEmjh40hBBCCCGEEBNHDxpCCCGEEEKIiaMHDSGEEEIIIcTE0YOGEEIIIYQQYuLoQUMIIYQQQggxcfSgIYQQQgghhJg4etAQQgghhBBCTBw9aAghhBBCCCEmjh40hBBCCCGEEBNHDxpCCCGEEEKIiaMHDSGEEEIIIcTE0YOGEEIIIYQQYuLoQUMIIYQQQggxcfSgIYQQQgghhJg4etAQQgghhBBCTBw9aAghhBBCCCEmTvgsb8rz3B4/fmytVss8z3ve2yT+AlAUhXU6HVtaWjLff77Pq+p/gjnN/memPigQ9T9x1ugaLM6Sk/S/Z3rQePz4sV25cmUiGyd+tnjw4IFdvnz5ua5D/U/8OE6j/5mpD4py1P/EWaNrsDhLnqX/PdODRqvVMjOzV6+fs2DvyeWXfv4NeM9MM4J6drrpLOf6uRbUjXgItR9W8AN+DGVhAdTrO2NnHY8G81C/+sYXoV5bXYX6O9/5DtSzs7NQByE2UbfXddbZaDRwO4sC6iRJoK7EuJ/5ENshiNxvDB6uPIB6pzegbcDtvrh4HmoPN8m2tnecdQQBHsNqA49hEFf3/z0aDe3v/6d/b79vPE+eruM/+8/+71ar1UrfUxQ51ONx4rxnNBpBzd/MRBHuPx9HrtM0ddaRJNgnff/ob3+CAPt0nuN+8DoGvb6zDN6POMbzhpfB+xFF+P5+311HTH222cS+wd9opBltNy1zNML+a2YWhtj+3DaHz6PRaGT/p//0PzmV/md20Ad/7W9/0aJod0zgQ1tdnIJ6lLj7GFRon+g8vDq/gMuwDNdRx3X4qdu/ltceQZ2HeCxuXH4V6g/vPoR6nPegbrSojw5pMDEzS3A76k3sU/4M9uvuCM/P0KP+FHScVUQhrnc8xHUMh3h+R1XsT2McZm2ng+83M5tq4XjvU58cj3ePRzJO7Tf/4bdOvf/98P/4d61V3R2Hi+0n+KYMx57UsH3MzL5B18O3LpyD2vvhn0L9wZ/+PtQPV+m4VOvOOqp1vB6O6ZpZDLBOUxpXEzwuRYZ9x7mYmZnRtSs8dK0yMyt87MM+vX/23CLU8ZWXnFVMV3G/zjdwGcNzl6C+sL4J9S/ffxs/P3bvJcIGtmcS4Hk1WNve/3c3SewX/vFvnOo1+M6d2/v/5msVw9eZZ6EocLwr7OhlZGnm/C0dYP+pVPHaVYR4reLrDMP7edx+m5XdO+Dr/KPQR/mV6KTt6/7q4K6TN+OoXyo6nY7dePm1Z+p/z/Sg8bQRAt+3INhdcSWmm9IK1rWKO8jVa/i3RowHzA/pMz51EA87RN+9l7RqgQMMPwR06UaVb8q4U/LNT5K6K61WcZ05dQA+WNUKvj+j94ex2wF4O6NxeuTrlQruB4/NfONoZhbQvlZoOwOqzT7aCXJSnq6jVqtZve5e2Mzckz8I3IdQPg7P40Hj6flxsE5cB6/zuAcNfkh1Lroly/xJHzTKBlLuL/zA5zxo8DppmV7JzcJxDxplF4TT+hn/6XqiKPyxDxoRjYm5544V/KBhMQ7BFRpHC7LR8et+4O5/RMvMaZSvVPF4x/T+gj4QV7iPllzguA9W6Ia9gsc/phuI0KP3B+6lKYpovQXtJ92kRLQNfF2ORu75y5/hL5oKO/p8fl48XU+rWrWpvXOvGNEYTg+dScmDRp0eAvjLJK+GY/zTvv6UkNrDIvc4hTSO5tyGvAyj8San/sY3RKUPGvgZ3k73QQNf57E/qrjXx5iu8xV6kC1qeG2qVvGLhibdE7VKjk9I7+EHjZDGGLPTvQa3Wi2bmtr9suOn40Gj5Ms+uo+sUp8+mwcNrPmY/UV80Dj4zPHbLjO4EEIIIYQQYuI80y8aT4mnlvaf/m4/op83k22oL10o+UaAviV49QX8yYW/6UjpyZGfbqOW+ySatvH38bu3b0Hd76MsIPJxmRX6BqFep184fPcpcmZmBpdJ346sra1BnaT4bfsgpW8+au4vB9Oz+M0Tf6Yxzb/EYNtN1fCbrKhEntWnnxy7Oxu4DQsHciyvcL9JeN5kWbb/TTk/aWcZfRNS8rTP30TwZ/hbeP5WjN8/Hru/moxGJIMLjv4VhX99YHnXs+wX/+24ZXDbjUb4E/52iazu6tWrUPMvZtx2/Dq3S1byy0yej6nmX6kOzvcsd38yPw2yIjO/2Pt1o8KSM2xHbnczs4B+6j/XmIN6aQYlj/fWHkPt0zIrJVLCxSWUgfQSHPPCOvaXmFSucYhjxWCIcplq6H6r6lM/n1pAiVdaGVKNxzbkbxVLJGGVALc7H2J/GfWx345T3M7GNLZLteSbuN4Y2yom6drTX5/TMxj/zMzi+csW7/2qm3l0Du1sQfn2ldeczz84j22Qf/O3oX7vT76K79/ANs06+CtdbeC2w8UmypcTOtarWyijrPGv71X6RaOgX2py97pPb7EKqR94TByOcBvOn78IdUzXPjOzbo77vlJFz8LCBl7n1xZRzvyVxueh/swGShbNzF4Yt6GeGuN25ofOk3Fw+mNgEAT74/DzMKDnBS/z6F8GfM/dBp9un/jaFJOs7rhfF3g/y37ROO4XjEn8osHv4e3g13mdx/1y8yzrPMxJjr9+0RBCCCGEEEJMHD1oCCGEEEIIISaOHjSEEEIIIYQQE+dEHo3WwvmDmWFqqH+9dA01jlOkPTYza55HTeM4pqlASUKWpKjrHdC0eDsl03D6tJB+B70j7NGoRvj+lKbdHJNnoyjRhu9s4zR2PAtVlqI+dKqJ+mWPptrjaRzNzKo09e/CDOo/5+ZoWl46tIMxaVTH7tSOXoht0ZhGD01wqK2C7OR6v5+UMAz3/S/H+Q6c2VHs+Bmf+HVex7P4Jdifw7NOsV6UfR68DtZhjhN3JqOYZ0yJePYwmr2Ntnt9HX0AW1ttZx0vvYTTPfIyne2k/So7HsxxU/vCe0s8HqeBH3j7Mz3x9Kl98jLUSqb+rETYbudaeN42I/RcNGNchkfjW2u6ZBY28jdsPkC9OevTcw/HggbNRDROaYaewP1+iqfGTgI8PjybX4tn1ivI07Pj9hefZvHyLKHXcZ09mgLcYvKqVFx/C+9rQr6hZE/rn4zPxiOUBlVLg93t9lvohbhbn4b6B6+4Ho32h9+G+kdf+ZdQf3j7PtQzU+gZunDlBag7bby+mpmFNEY1mng9jK/inPvbO+gDyTM8BlXyFyYl536/h8eWp6+tV3AZYQX7fIt8ln7JrdHgEY6TXoLj05M6TdH84A7UO4voj1mhKenNzK7Qer+8g+tcqh+6JpfMrPi8KYpi//rxXGadMvYdHL1MntbezMyj8WlM/sCcbhtjmoXPO8Z78Cw+yZN6NJ7F78DLOK79j/N0/KSUeRB/HPpFQwghhBBCCDFx9KAhhBBCCCGEmDh60BBCCCGEEEJMnBN5NK6/dGV/3v+AtOfXr74IdZ65c61vDfAzOz3UckYRZSOwpizHZfb6JTrKIerGkhQ1pM0m6igblI6YkLY8JR1aUJKEOhqi3pNTlDnNukmR7WlG2R0lR6XVJA08+UA8ziAhGWHCGRK5q3/PjfeVMx4ONOijEo/H8ybPsx+rC2T9YZmOkjWLCe2Do5OkRThzSpfoQwNHa0nZL5RBkGV4HPjzPvXPrGQubPZkcGo3p4v3ydu0sbGO2xC45y77jniZfFz49fGYzxE3FfejaHpPm2qtsp+8XfikJyZ98Sh1z5FGA4/NpfPnoJ6mseNaA/NL3n/4IdRbXfSHmZk1pnGMSwd4LNob6HV7srwM9YtNHMvPL6IXwC8ZO2LaryEl/IYVzkgibxItsijcHKaMshHGlGvgUyLwVAXH3biC7dIp8fjlCR7D6WmalH/PxzYenY1Hw3xvP5L+HmWufHcR+9K4QG26mdnmt/4Q6vWHK1CHhl6GShXbrNnCOo7c87haw78VdJyccPGI2phyDnwa8/zU3a96lXOkaJyMKEeKM3B66K8alKTGV2hfvSdPoB6lD6AeNiiRuoOeobkpCrAxs8E8+ji2l16F+m92D8bq7tBth+eN53n718HjchueBcfLQNfL47wLZX4KJ3+JOtyI2o2vn5w8/1EyL07qyXCXWeYDoc08Zrs+Str4ST5zkvfqFw0hhBBCCCHExNGDhhBCCCGEEGLi6EFDCCGEEEIIMXH0oCGEEEIIIYSYOCcyg7/+yg2r7hlNe2SeunwJA/ui0DU6cUCaFRTYx0YgMlfutNH4mBuaGs3M/AiNZwmZVOtklK3RZrKJ1Q/xWazM71R42Iy50X6SiTGhILKwQFNis8TgFEXUVjUyFzXIMBdxSBsubzAiA56ZDce47zEFWtUOBXn1+xSGdQpkWb5vOj4uoK8s7M0xfeVHB/BxPSQTWZkxnU3OxwXwsaGKzeLHm8ZcOCyP94O3yfNwHbOzbpAUL5Pbl+vjzOJl4UHudv34ff0oRrdJUKlVLI73QiM93N5GA83HaUmmZVilkEgylPf7GF4Wz2G454AM5qtkSDUze7mBZu5WTIFn5IFenEUT8dw0rtM5bwo3KGzkYx8LyIw7HOO50+1SuCEFqnke1mZmqeEyAsoqjGmykGoFQ2N7Xdzxftu9fnBCmN/EurYXWhrlZzNxwTvTU/uBim/PzcBrI8MxuftnGMZnZrby1ptQx2R+b55fgLpHY97jZZw44uUbrzjrSEZ4bHfWt6D2Yrzozi+gqX1MMwOM+zihS6XmXrsCDkqlay6PNjzmDXq4n2nJhX6qjtfU7S5uZ4OuubaM4YfDOvbprZlPOOsIUtyujRlc5tdaLx8sb+BOZvDTzk9qIH8mwzOfwwFPsIJwO9ZowoPjjN0fDb7XoFefYTIbn7eD25YN5cdNblPyt6OOl8zgQgghhBBCiDNFDxpCCCGEEEKIiaMHDSGEEEIIIcTEOVlg36UXrL6nM1xeeQiv1Sh0pxKjvt/MrNJA/fogQe1lp411TJ4ODtkp+u5zUuHjLrGMvj8mTTTp8wrS7XO43jhxtf8eBUXlTvAbiuPCgHVwqMu2vqsdZp1qVMF11Guos56KpnGbUgqxmXKDltKcfQ+43fXpg7btVk/UdSaO4ztIjw6MM3M9AMfpQ0cj1MN3u3hcPkro3HG6xuO2sczbcJxfIq6gzvepx+ApN29iKFSl4p67vO/HBSTyfnBgX5q6x4f9KEf5cI4LcnpejIeZFfnuMRyPevgi+RQa51yf2jjF8WZzhO2ajvD1qIvtdv4SaugHD2kbzGxIQaizMzg2ZDX2P1BIG43TGQVTeiWhpaGHGvecvCRj8gJkGa5jewN1/WFJMGWf+lDI51+E7d/rY9sUfF6Yey6GEa43pjFxobF7PRh57nXgNHj7wgWrNXe3Ie9gQFzvz38H6nf/+KvO57urFPAYoR+n3kR/1tQ8Xv/4etmcmXHWMVfHPvqDbVxng7wKERklfboGc5je3BwGSJqZTc3i36pV6uPUH4c7uE3tLgX7loTpbW3he85fwHW+ePMNqH/71/8J1PXBBtTzs66/quNjnw4C7NNr1w/WOSrxgP20c9z1kcf1jxICyCF+PvlxOFCZx4mYPEDs/yyHt5Pro72+HvuTS24THB+Hkx98XFvxOo73aBz1ujwaQgghhBBCiDNFDxpCCCGEEEKIiaMHDSGEEEIIIcTEOZHQ/tHKitX2ciiGA9Q81ps4t/r6BupHzcy2d3A+7TTHZQx6qFeuxqiVu3LlGtTjvERjRjI130M9elhDzenCNOos0yH6JSq0DX7FncN7zBPTUx2RltvbXMG6cw/qIHb3K66gILMw1Aj7Y1xnbKiz90l7nHuuRr7ZpLnsfZrzvzjQYYcFHrvTIAj8/SwW1m6OSL9d5tFIOAsiwZo1h4PB0VkhZfrRXo/0niU+jsOw14Hr47bBzPbPyR+3XT3yV1y+fBnqT3wC53N/9GjZWcd7770HNedqHLcf7NlwshmsbA5vfP2wfvesPBp+4Ztf7PbBYQePTX2GtL3uLppP59QajTfbHRwj4xFpe6lbj9AaYWZmG6s4Fly+dB3qLQ+3e9Ch7I4B9tmoinWSuzkaeYLLqEf4Gc7k6fdxR+4+vA31+RnU+e+uFxt0p4vn2vQsBmsUPjZOkeOYOd1wvUi1Juq3Q/J+hXvhKGnqeqVOg2L5LSsau+P06lf+Mbz21g9+CHV1Fa8zZmZRgfvcpetjXEfPxs2bL0H94AGODYP1x+5GXr8OZYt8jsMEx+qFWcw7CSlb5sK5RagvUWaXmVlniMdp1MXz6NJ1vHfY2sJ1Rk/wfiUduD7JWh37V72GfaXbwfPu/Is3oX7p5mtQv34DazOzH37/G1B/4yFmcdQPZXmMhqefZTVpnDGfXucsiGfxbHhsXiCPhpHnIgyw7m+uQT1DOUN54F57fJ+uh/wGOu/8AseiLCU/WeJ67yoVHBNzuqfLKL/NyylbzSc/Mue9mVlOnjTnGnyoLb0Sj9uPQ79oCCGEEEIIISaOHjSEEEIIIYQQE0cPGkIIIYQQQoiJcyKPRntn24bjcm1+Tlq6JHN1vAkJiusVXP3c4nmoWY831cS5raMSEfTW2v+/vTdrjiS7s/z+7h7hsSMCO3JBrpXJrK6qZrG7STY5Pabukclko4d5mxfpI+gD6UV6kUwySWaSSbIxG0oamTSSWupuLk2yWGRVsXJHZiKRWAKI3Vc9AAXgnOuJAIqRSMrs/MxozD8ifL9+r3vFOfdsQR1Qvke1iRrUWhXXkQaoneP5jYc0z72ZWYnmVK9XcJneK9RZDl4+grpZxXNVWUVNqplZaw7320jrHZGWeDxBDfRcG/0XccGcyzlpGasNXCY9pUsOCnSK75o8O5krOqe5/XlO6KBAw8+ujZR8BOzhYJ8Bz6dd5L8Yj7GNs1eE2zRrVKd5HYpkkaxjTVNWiOJC18nrdO3aOtTb2zTXvk3PyZiG679wrw+f37POzbeYXn0m5F5s+ZERbG4edbfVOexrugfbzvLNBuq8q23MLdh8g3P1JxnqwLc2KU8nxPWZmTUWcJ3jMep9Bz3MQGo38Dwv1PG4Rin2+dwnmpklEe5HNqBradheOlW8/jfW0SsXD932FQ0oX4Gzczz2reHn3pjyRRqcJ+LqtTPK3tgZHHpRosn7ydH47X/9nxzn4PzuEfoKrkaYN5EUnMM35DlcaKJ/otdFb8PdDz6Cen0d/V3//X/1nzrb2OthGw04v6TZgbq1gGPbgDyMkzEu/2LD9d/V5jA3qntA3qct9JbUWnjtqx189hiV8L4zM/MO8D6KErxPOD/rBz/+S6jbS9egXriG/bCZ2Q3KJ/vZ57+E+uWpMSeeXL5P8g/FHQewzsnd4PgAXBPHObZJvgMaeipVvCf6lJeyT/lurXlsa2ZmOfnHnOMif5iXoQdovIO+kK1N9ESamS2t3IK6PIfPiZUGekks56wrftwvytHg+vw+jLPQLxpCCCGEEEKImaMXDSGEEEIIIcTM0YuGEEIIIYQQYuZcyKMRhiULw8NF0gz1nz3SZYYVd9Wra6ghq7DOn3wHnEmQkc7t5vpVZxs+CfB29rr4hQjXub9P+035Eksd1I82y64oMCK9+vYLzMXovkBPxpyH2sqQMgUcXb6ZhWWe257yPChvgfNEclpnNHY9NHt7qGstkzZx/tR855yjcBmkSWLpkd6XzxFrCQPfnSOaz8GE/BQJaYl5nexL4O8X7kfg7sdZ6+DjYi9MlrttI0lxHXFM89Cv4rzz6+u3oPYDbH9RQQYJU6c55dmLUnRukCLtJ/4tSd7uA8nek0kjz+Njv0pzDs/BPmnkY88NuZhM8Nrs7qGnLI4pm6OFWvFGB/tVv6AHDxt47kcxekXCMl6rSlijukI19jVR5M7fv7yyBvXTX2MfOBzgPpRXcRtzIbbzcebeN15KORo72D/tkbZ6jrI7qtTOJ7Hbvqrk60jpXvKCwzaaXmAO+Vnyu2evrFQ6PDel7g58NvDxOm0GrpY8LJM3oYXf4ZyX337xe6hrIbatUgW9DWZmozGes/kO+m9abdxmQmNREOI+5hO8r54+xvHUzKxWx2Xqc7jNrdevof5k/ftQX72FmRae4+gzm2viGJx6eP8/3kAfyNYb3Ob+HvYHi003x2X1GuZ7XCcb4O+ffnX87+Qc/fQfG+z1c3Ix+MHlPCYMYpoPhMdkHktqdE/s7uBzUV7QNhq1Dv6Buq8sQU/G1mvMDapS9oef4DbNzLZpGX8f13n7Hj6rpvTskPvocQ5899z6BX97KxfoAvWLhhBCCCGEEGLm6EVDCCGEEEIIMXP0oiGEEEIIIYSYORfyaJR8s9KRRnU4QJ3uXBM1ku0W6sHMzMYp6dAaqKM0msPcr6GfYoe06E0jn4KZrd28A3VYfYnLhKjBHeyR3jilPJAe6ix3t3G+YzOz3gCzNYb7+J2moQaVpts3r4Tve6yTNXOzEnyaszskXbUz/zHpEMdDV2c9or91yaNRq55oUicjV3/+ronj+DhrgY+fKfK5TBMVFi9zse+z/nPaPNTs++B1OhrWgsPOsrOzOZaX0Rs1TzkL7KcYDd2sGF4nn38+bq75+0Xnjn0/Z/k80jP8G++SNEvNzw7PxcEQNbLs0WivuDkNPl3vLuUWNFuYaxCEpGku47Wpt90soeYCeZEOcD/bHdQgtyukmR+SN2mE16Hhu/kxbbq+DxZQa/50FzMfGjke5xunDbta4Qn9rVXBPo/tczXqEwO6/8djN4dgskX9WozHvn7l0JMQeO8nRyOMR1bKD8+1Tz6WjQyvS5UyMszMWjUcfFJ6BKg2cJlXL55APSLf5Nw8jeFm1mqQN6ZB3oYENe47m9g2rt+4hcvX8D7a2XHH4O4eeoDu3b8H9bWr6FO7df8+7lPGeQ5uv92hYx2StyQlf49PXVxYxnvkGXk6zMzKMWYYrXU6UD9+/cXJPk71wV0uTnZEgY9u6vjGngzv7LGteHydktXBWVb03Fmqovem0cR9fvX0N84WG9RPL652oA6of9t7/hXUWYZ9UbPjPtu2F9CTnGZ4Xwz38Fm3SuO8V+Jn8uk5Gu718d7y77PRLxpCCCGEEEKImaMXDSGEEEIIIcTM0YuGEEIIIYQQYuboRUMIIYQQQggxcy5kBs/S1LIjA1I8QeNwMplQ7ZqFex4GilQW0LBVaaC5JSQDjU3QiNY118y3UEKD1J07N6CO32DYz34XDTTbZDTLEzqugqC6UQ+PtUEmxIU6GuwqOR5HXiITvD/dZMMmnSw7O0yOA/+addeoyv5c38dj9U6FxXkFwXHvmjRNj83T04LwioxofF7ZoMzLTDMwFxnReL+mBhRNwQnwm2KCN3MDIFdW0QxeraDRrDfBcKBSyTUYdyhkyw/ODtfkAL9p59bMrELm3loNTaWn2/Q0k/27orvXs1L56NjLeM9Vl9BsFxcY1ssUGpdkeJ48Snoa0aQLQRn7isacex/3x2hKf/3qBdQf3/kU6nsrH0DtD7HNpdQH9g9cM242wL/daOJx1OZxP6MYr/X8Ipp1f7bhhrJ199AoW/KwT6uT0b5M1ycNsM0UZAJaNsI/Vg2vV907vMaB937C0pJxYnYUxJWX8Jw2SnhfhxzqamZl6vcpj9Aq1Hcst7B+GWH7G43dSUVqNby2UR8nPHi9icbtKMdzHoZ4XduLy1AvtNw2P+5TWGMD9+H2g4+gTnPsf76gYMJqA59VzMxadTyf2zSRTJn6pDkyxXMY2usnXxjTqeKYu7R+Hdf51YmJOPYu/78T53l+4TEMl8cGxxOZOOZvWv5cZvAp33HHIl4HntcWBSs+67om/u3Nn0KdrC9BHXpYVyLsP7Z62EcPx+5kSotXfoR/oJC/rz//B6ivfoATHqxcx3VmhvfI4d+Ic5nvp6NfNIQQQgghhBAzRy8aQgghhBBCiJmjFw0hhBBCCCHEzLmQRyPPEsuzQ530aIAhUL0q6r2qobvqQbUD9aiEgSKVEINSJhNUjLVbqJtsV1wNtD/ZgXp783Nc5/OvcZspHkfdR+3cIEGNdLMgBMkv49/qFdL1ZqhRTQe4zpR0+KxvL2Ja8E1KQVODPh5nZx41g2ZmddL0pqSf9E8pJv0LhLXMiiSNLUkO2xVLBVnrWaQjTVPyO5DPoEVhabyOCfmQinwGfF34WvIyJfLnjEao+2VNqx+4/gmm0UQt5vISXmsnlIdCksqhu40KtQ0+N3wcHh3nZIxtvuj6sDb7rNC/i4Yrzgo/NPOPTk9CiVzVOu5/f9h1ll9Zwz6vF6G2fEJhZhUKPq0GeB0O+m64YsPH73x481Oo//TGd6FeCrF95BH5uyrYYEbm6odffIX64L0SHlc1x3a9snAX6uYt9NLtk8/EzGwQ435E3AfRflsZ209EGvnBwPURNup4feIeeo8G/cNlouj9eDTiyLP8yGuSUNBYhW7sJHYD3YIq9nFNCugLAu7X8RwuL2Jo3ctXrl9nTNuNIrz2rIlfXkAPWYn6uKUmfp8y7MzMLMlWoV67jf7PUgV9Hb/77LdQb7zEYN7bd9xxPicfZIn6qytreByPP0efUUD9RafqjqFz5LmKUzz/pVN+qeyPLLDvPOQ5HzN7MthHid92PBpFzyFTfAU8Nnk0FgXUT1iO9/reBvp5zMzefP63UGevsY/c28fn45VF7O9WbqEP6U13y9nGy8fYZpstfF72oi7UW0/wWbdB/qzaEvbBZmZpjvde4Jzv4n9PQ79oCCGEEEIIIWaOXjSEEEIIIYQQM0cvGkIIIYQQQoiZcyGPxrDft+xIQ7zfxTnNw5C1nDjvvplZTvM+b/dQuxmk+Pl4TJrIedRENnxXp10nDWA0RG+CP0Htb9VD3f3SHGrpRmNcvlJHXZyZWWcV54CfDEg3TXPhT1LyE9Bc+gHPLW12nB9xvAzleSTkPwhJZz8gLXep5GqgS5S1kZMO/mD/RHc9GLkZJu+abnfPxhV33naz6d4HM9cXENE5ZI8A5zqUaY55viZmbn4Jewl4P8/yIZiZmUeT/ecFvhBqLm3KvGjSvPMeaVAnpKEumhs/JX0y52iE5NGadh7Ys2FmFpEHhrW0p/0u44LlL4NbD9YsrBy2rX6M+v1SGfe3Xuk4ywclaqdN0guTv8uozXnU5uo1dy70e/N3oP6nH/w11PE+3Qd0L3Pv4/t4L5XNDaDoH5BHjLKDPvjOx1BXF1FTX6vjvfi9W+vONjYH2KZ+v0+ZRzQczJUpx4A9Cz23DxtSv1gO8Pq83N40M7M4du/9y6BcqVrp6N7rLOC44/nkSSnIsuLIgMVlGrsOMOPCSti+MhoPJ2O8B8zMQmrDGY3JzTblYiyit2E4xGuwsYHH0S7wf969j23+yvpNqJ++wqyggxFev04T19mpu2NwicbpVoWeR8gTtG2UTxRie2x08B4wM6vOYd/99PFTqPM0OfXv99MG34aTGeX0JK4HKMsoqyqn55yc/RaUH1aU50VjJI89jr+PxraInhG7ryhrbfO5s8nhS7xv9oboURsk6Jt7+AK/v/WsA/X972G2kZlZ3v8S6oMJtrd2E/1lQQXb2+MvMevjXgP9VmZmpRr59eh856c8G/kFXBr6RUMIIYQQQggxc/SiIYQQQgghhJg5etEQQgghhBBCzJwLeTR2t19Z5UjHngxRY+ZFuKr9N65+q1dHndq+h9o3v45zD0ekqe37qCmrokTSzMxqg2e006hxXChTFgLJHBPKPchS/P7Dr3FuYjMzC3DO46UF1MqttvDc5KQ953nGg9jVDscxaphL3tna/jBEbW3fUEtbpJHnaINkjPux++Yko2Q0wf25DPIsP9ZXsh50qg6zAP4Oey7YwzHN41G0TmcOb6p5m/y544UI3Ubv0XcWFhagZq8JMxgMzqzNXN0+7/dwiB4gbo+cB8LHaeZ6YDiD5PQ22TNyWaxcW7BK7XA/q328r1P2tpTc8z5J8ZjqbewT2aPhkUa57mOWULPseuEerNzHZSh/ojvB/R7usy+B/DgJtvNxirlAZmZ3f/wDqBdv/CnUcRP16Pv7mFsw7H6GK3TyHMxy8oZw3gt7SYyaSDnBdd5YcjXye2MccxLyDQ2Hh/dGnLyf9lcKq1YqHZ6HjDJXmm30xSzOuxrssILtLaR7Lq9j+xqN8L7u7+JYF41cr9+EttFqoq+RrQXDCHuXgx6OTZ0r34G6sYTjq5nZyjLtd4L9TS/Ca1+jPrEUofekEboZFXmAfe9iu4Of77/ABSiDq4ebsOfbT5xtXLuD9+7TjU2oD+KTcxUnrgfiXeN53nHfzeMhd+lFCn72VPC44I40vJbpvgDX54F1RuPKsI9emv2DLtS+h+35yqc/drb5dO8J1Ac7+Bwa0HFl9Pz14veYuVIuuz7Ja/fx3hvTuSjf/RuoW8vXoH6zQd6nX/+9s427H/811Alli522F8cXaH76RUMIIYQQQggxc/SiIYQQQgghhJg5etEQQgghhBBCzJwLeTS8PDHvaI7iyRBzNOIh6hfjmrvqKEFfx8On/xfU82trUFebqA/rZpQFUWDSaOyjh2KRtullqFMLKNsjIG15h3SvXz3EuYzNzL5+9Cuov/ddnDO+soo5BjHrfilnI8vdeeobTfxOmaSN1RrqYJMUNaa1GuoMWV9pZjYaojaf97O7c3Iux5GrYX3XeL53rP1njwDnUXBt5vonijwWZzHN02Hm5kcw7F3gdZ7Hy8DUazhf9soKzkvPHg32Ppwnk4K/w+uYTM7OVfE99hC59y63yWnn8n2w19+1MDns23y6VskAtb7NGnplzMyaHfRUDHMUbmeU29Ak30FlTDkbe26ujzWx7U/IT5cnqGePRzjne5ZhPzA2vNZbBd4TnsO9TF6HR0/Qg8ExPOsd1BNXPNcHEpDvbJXmfC8Ztqnt56hpXqTxhPt6M7PhFm63Wce+e7l+eK9FUWJmD53l3zV5llt+JJTmTIvNHl7HMvktzMwW23idhpzHlGF/U6XMnUpI5zBwteQe3ceNZgfq7dfoZfAo64r7lp2NJ7i+guyY7zy4C/X+Dj6fVKmfHe/gtTvYxG1cu/0nzjZWW3jvlkZdqH/zm19A/XwL+4PPHn4B9cZrbJ9mZgvzP4d6QH6BZn5ybpI/shwN5hxDl3kU7OLZ2Z5Gpug5hvM72Hc77FOfS+dxvoM5Lx75YVP/U2ebw018Lnz0d5ShMsL7JBth/+hRhtyrZ5gRZGY2Jh/R3CL5+QLMyag08Dngags9W08fuXkgqY/Psjc//gjqcvWkPwxiyos7A/2iIYQQQgghhJg5etEQQgghhBBCzBy9aAghhBBCCCFmzoU8Gn/yycfHWv9KiLq31RWck3xt7Zaz/Ovfoe7s9ReoWexvoaa53kKN6bCDGrNRE3M3zMwWWqRTbaNebzRErbkX47sWHpWZ75+dV2FmltPc+IMD3IdulbI5xqhtG/SxrlRRB2vm+gky0iEGIWVzDCjXgLSOUYHHIo5Qq5ikuI2wcrKN9BzzWc+a8WhseXa4T9O0m+fR9xddy7M+53UWbYP/Nm0b7H3gLIlyGZcvyo9gD0a7jVpi9qtwTkZ3D3XpeUEGCR/XiDSn/Dl7MFLyDLEu1sw9dl7H6XPFWt7Lotvbs/AoM6hCbTCgY6xXyU9hZqUqdrkcvdEI0BPQoOyHeB+vzcG26415OHgCdfsD1BxPUuwbujuPcQUZaoGDEu7DOHXzGXpPsW9/OP4/oA5XcJmVe/8Uaq+OeuLdPdejsbODXpPGPJ6rPMN+Nh5jnVTJY+Vzb29WI/1/hf5bXH6UKfJNP3TZNMqelY+uh0dBIQllR5RL7vAeUMbF93/wfagnpGf/5U9xrv1KBe/Jdgv9YWZmE7q3t9/gdRv2UL+ee3ifcD5IvYP7/OBDzNUwMytzBlQX2+P2c/Ru/uzf/gRqr4p9ZvxT1KqbmV3/8ne4jRHeJ//qf/s/oW428fklJ28TZ7SYmXW3MTej0uhAXfdO+pg4eT994B/CVM+Fc1/lZ1Tmhn+ZWUzPSpzxVKL7IiSPo+djG/fJo7HkX3W2Of7wr6DefI5tfvNz9O8EIxyDy3zcA/fe3XqJx9Xr0ji+i9uIBjgeXb3yAdTzHfQ1mZl19zHbrvEMz03z1DN4n4NhzkC/aAghhBBCCCFmjl40hBBCCCGEEDNHLxpCCCGEEEKImXMhj0ZYqx3r2a7fvAOfXV3FedA7C+vO8n+zgBqxBx/iHL2PXmxA/ewl6sX6L15DveO7WQmjO+jb2IlxPu18iP4JL8Z1jEij+uwNaj2fPHvmbLNGWuwx6e9GI9QlZhFuI89wH8YjN9+BdfULdZwP3fdRnzsk/ahP8/GzV8XM1fiGIerzFhdOvCPDsatvftekaXqcXcFaz2m1mTvnNnsb2BPAn/PyRfkT07IgeL+m5X/w95PE9TZwngd7HTirY38f74HtnR2o+33MmjEzG5HOlecmz1hjWsLjyFLKnCg4Dp4/n7W0p8/FefJF3gV5dvg/M7PBBK//+iL6EOpN1JabmY1zvLcbZczBqI5pmV08J94Y22SrjtpyM7PHL59CvdzBNrhI+R7RCK/3q23MOQgreB38MfbLZmYlunYxtYdwsQN1u43HXSb9+qDg3urMoScjoLyhfWqjcYxtbkT3ycKi6/GLKXNkvoHn9+Dg0FST2vvJMBjnuSVHfUxOx3NtFbXjowIf063b96D+Z//+f4DLUDbH3Bxep/1dbCuLC26Oy//yr/811HmO+8m+tWYDr2uNfCAf/eUPob5165azzb/7H/9bqDfI6/B0A9t0bHifrVxF38fODvaRZmYbXzyB+tdffAX11i56Tz6+jxr4O7fxGWl3p+tso0n3RamG43wyOmmff4w5Q6cpzLigv7nfwfqb3La3fGyTifusFFG/zON6icZYznjyyTPk0efVmptPs3zrU6jv/gX5cPfx+W23949QBz7uc567z1d5TM+Zfey/ogka/gYj9Fftb6IXb/0jdxst8nGE7Cs61ecmBXlvb0O/aAghhBBCCCFmjl40hBBCCCGEEDNHLxpCCCGEEEKImaMXDSGEEEIIIcTMuZAZfGPjpVWrh+aQVgXfUbKMQp3GrklnaQ7NkrevY0jTX/+TT6GekIF0bxcNWoNtNHqbmY1f/Qb3+eHnUJcpkq8RotnqoEfhLmU0wzQKQgL9hMLzDPd7TOFmGQV7NSpouu71XDNuWEMjEBtlAzLfJmQUHB7gOuPYNTN6husYkmE88E+u8Why+Ua0PM+PzWPTgvCKYAMxm495nVyzqZrrb/Zx2ndOw8ZtXj7iiQPOsQ42sbNpcHd398y6X9D+IgqX4v1gE/uEvs+GusLgS+fY3T7kG4qCCy+Delg/NkePaReaLexLFhbcYLudAwyiCxI8D5NN7Cv8fZocIESz6MhzTdMv9l5B3R1jAGgYYfvwfezTHj7FfjYPsQ3fuI39tpnZuIdtKO7jcQQ7GBqYUDDUxs5zqF/vPHG2EZbxXFxZQvNzm8zg3W2se2R0rmRoEjUzqzSwL2a/Y1A5vNeC9zQZQaNePQ7i29mjwKwqto1K2T2+sNmBensHJzt59eQJ1EvLOKlLrYZt42Bny9nG/btogv77n/4D1EmORuxGA9cZHeB9//olto39F25g2nYX+6z60g2o5wa4zpT7GpokZmMDt2lm9uzRl1APacKCFrWdKpmQI+qzOst4T5iZpTHeN+MB9hdr8yf3chRf/oQsZ/FtIiy5z/doJZ6dPSYkBWMEt3uPx3G+dWls4v/8nrNB3dz7qtHGPvHmR38BNfePX0VoDk/3MFCyRO3AzCwmk3tC++3ReNSjMNfePralLPups40HizhWNJeu4H5WTozwEx8nPzgL/aIhhBBCCCGEmDl60RBCCCGEEELMHL1oCCGEEEIIIWbOhTwa1XLVquVDfeX+HgbidJoYYhL4qFc0MxuTr6BRQwFsycPgnkYH17nY7kCdrbpa4acRhlXtvUI98miAevXBiIPHcJ/qDdS9zrXdgKxV2q9STtsYdHEB0tbV6riPlcy9LKyzz1LWDVKoFoXORBHqrLt7rr4uzykcjgKv0lN+g0l0+Rp53/ePtf3VKup82ZdQpO/nYDv2FbBedMghdXQN2OPxbbbB63S8CxzwV8LramZWr1P4GXk2OFjwDYVQ8j4XBS2lU7wmfJzsTclzrP2CsM1pvpvT68w5IPCSCIOyhcHhvdZoocY6iLFNbj5yvS61AP0Q4228D8sx3sdZBTWzowl+/puvf+FsY2WV7uMJbuP5BK/3OMP2szPEa7W+vIQbqOI+mZkN63i/RQlqkHsDPBePPvt/of7VEwxUG5bde6sc4n6XfOyL7yxhEGH5A9QXv6EQ09fkxzMz86lf8Sk8rh4eXr9g8n708XGS2zdqeA664zsiGbs+uo3nGDj7r/4H9FjsvsHwztVVvPZXlnHMff6l2/66PexvVtZuQb10BX0fi008x36GbWnrDYYo/uR//omzzcYC3ovL129D/eYVei5qFTx3mYdtvl52+6IW+TNv3bmF6yDtfpdCduMtDBzuLKw528gi3O487cedWye+r3FBWN0fO9OCdXlUSOOzx9yw7I6HAY0jCY097NnI2bNB4cc+7WPOIYJmFgb4nfYitsf1j74H9WSA99nuIwoy3HKDoYc72H8NYwqVpOfGJMHn54Mu7nclxOcAM7MbQ/RxJOyhOe0FLimwTwghhBBCCPEe0YuGEEIIIYQQYuboRUMIIYQQQggxcy7k0bi+ds3q9UPvxbMRzgu8v4t6r6Qgp6FWRd9GTBMa98eov5vQHPFhiXT4Q3eu4X4PdWutFulYSdudTVBXmRuu89kz1HYGoes9WVtdhXrYRf3d/j7qDMMKatuaLVwnZ2SYme33UJ+3s9OFulxBnXUc4XGOhqjn7Pfd+fdJqm8eiRe94EQPGSeXP498mqbHfoJpXohWy9WRs2+Asx76fTzHrAfl77O/wsz1N7BfYpqXgY+LfQu5uefdmVuc9qvb7UL9egt12QPSErOnw2x65ojRfvk+1qUS58C4c5GnlC9zlndkWj7Ju6JsTQvt8Fg++uDP4bPSGI/pJWUSmJnV2bvmoe57j879o1eo6x5MulBfuYb3vZnZ+ir2eS+fo8Y9bOG12B/heZ9bwfyP+aUO1NnQbR+tOn5nmPNxYnvZ38Pxo9nEPm9/4PpbStT/b27hmFNPsY2uLuJ5aM5jPd7EftrMbD/CY4uoXc/XD69X2Xs/Ho088y3PDs/lyjz2cd0+njP2H5qZxZRN9eoJ9nk+ZRA0KthWJvPogznouV6/3V1c59L6fagX5rDNDw6wLZQq+PmbNxtYv8Z7wsxs8codqD//HPOz9rYwt6VDno7VVWzzV65dc7YxIV/Orbu4zQo9G/zsZ/8IdZs8NZa4HosR9cXra7iMd+3myb8L+uk/dqZ5NKZlVfF4ep48rVKAy6ROWAfvkxPmgdvk3A0z8z16xgtxm/NXMdflxsc4dliM99FuF70SZmalkMZgfByxdExjbIj9ZVjD9rY3cp+fx+T9DSico1w6qSul8/t09YuGEEIIIYQQYuboRUMIIYQQQggxc/SiIYQQQgghhJg5F/JoXLu2bo0jnWE07MJnO9ukm8xdDbVHcw3H5Jdg2WtAc0gHNKd5kUr7oIcax3SC26jS/P1BHes0xW30XtD82nVX+9+LcT+jFOshzVuf5VinCYrt/FLBPM2U58FzQ08o86E3QP3m5i7WvZF76SPazyDBC9JsncwxP4kvP8cgy7JjzSb7JVjrybkaZq6ek70I7G2Y5sngbZq5Hg1eB/tveB28j6xR9VK31Scxai97B+hTevUSMwq2Nl/h93uo7S7K0SjTfvNxsIeD5xoPAp7jmwSmBXD2Bn06dfl3Qc1vWcU/7CN+8Ok/g8+qyTzUj+q/cpbffPkE6p99/fdQf/4lfl5fQN33rQ9Rv/6du26uT76D5/bFLraxGuUU7Ed47Zauol7dq1EfmuM+mJkNyedhPunHPexn+yPsa9rkCxlkrv68RNfcJy31JuUVeSH2mTwffjt0+whuVj751EoZ/v9lM47MkqNtDym3YTTG+/j2gz91li8ZXqdnT1Gn3Z7H63D3Ox9CnSTkYfFcj9D8MrYPL8f2uEvPCv0D1Ke3F7GvqJO3c2EB99HMbG8L+7iYxq6E/BUvX6D3cp/8LbWymxEwmeCxP3n8BOp2C497ronnJqO2lI3wWcXMrNPGHJfGh59AvXX9pOYx/7JxfHLfwrbJ419KN2Ae8Eop06JgDPYDfrY5O6sjpydJ9mi447w79uT0zOaT16FaxbbRuXIP6mSM98jOM8yDMzMbDylHiHwhkzfYHvKUvcHYfoOK6+EqVTr0B/rC6WeDgueEt6FfNIQQQgghhBAzRy8aQgghhBBCiJmjFw0hhBBCCCHEzLmQRyMPSpYf6d/CBs6nfaWG3oUazb9t5s6BnGakOaPdqZdQm1mlufijAh3l/gD1dqURLlPzUZfW6+Fc6l4ZdbudNZz/+HXP1QQ+3kZtXIP0yL0xZVoMUBfbbaN+r5y620hZX0zzzrOufnsPj/PJG/y8N3HfMZMIdYLzAa5jYW3uZHuRmyHxrgmC4DiHYlomBnsGiuDvTMtmcPwSBfpQ/g57Gfg6FfkhTsOeDWeObzMb03zYLzZw3vnHjx5BfbCPHo5p+1y0n3zu2L/C+52QZppzNYr2g8/v6QySs/0b745odGBefrjvBweYT/F6G/uSL77E825m9vOf/x3Un3/9a6hby6jr/u73UcvrNciLNXQzBeoTnHt/5GGfNt9EX0eIsnCzKt0XJdT2DmK33Q9CvP5RE78zomu5+RJ1+Ut17Os5v8HMrEkevWoDl+nFqHl/+gbzYuZrlKvRQk/NIZRzQ54o/8hX6GfvxyOUpSPLjvZx3McLt9rBMXh53vXvJJRdtbSEfUctxM93drGvSCP8frVO2RBmlkzwOxnp1f0U21OrhOe8GqCHaDTEthKP3fyJpQW8lrmPfZg/wuPoki8t46yFgrHAIy3/LrUvHj9LZbyXe7TbnbqbyXXlkw+gnnz8Y6hHw5NzGSUXenx79/BYVuDZmDbeeTze8edO5kXRfyun7KkpY25u0zwZ080nGe8XHUeZfCP1OfQZLd3+GOrF+5SzYWaVxatQB9QHPfoMs2MG25gds0B9dKszZ0xzDjPhcnqWPe29ZB/mWegXDSGEEEIIIcTM0YuGEEIIIYQQYuboRUMIIYQQQggxcy4k8oui6NhnEVZQH+rlqJ/1PFfjmJPWLRqhVvPNCDWPMdkAmglqHmt1V2O2eucjqF99hXrjehV1q9091JNOUtSkRT4e55v9XWeb/R7u6HwN39/SgDTTlEHx/AAvQ2Xgzq/drOAyzRr6OgYJ7ueXz/G4H2/i8lHueixCHzWmH9xH3Wt7+UQDPJ5M90DMmjRNLU0PfT2s53e9DAU+l5TyTGgd/DkzzW9hhj6CItinxPW0bI9qlQX17jLPnj2DenNzE+oBtS/OHCk6Lj43rlZ2Wn12PojZYf9y1jrC8KSPmXat3hX1dtmq1cNr9vPP/i18tvMSdd9Pv8C5/c3MtofYx333x+jBqDVobvSYMk928FqXa24WxGIV+5ushX3cKMP95DZboiaW0yixP3Hn709CvJ6xkWeKrm1GXriELmer4+rX23N4XK93sS+OPFzJgO6L5cUlqBcXXY9GvtuHmjNmKs3DPvAi+uRZcuvPPjq+D17/9mv4bDDA49997Oa49DPyqZBPYLiH7TMy7J9KOV7HeOzm4TSaODY1QmxfnQX0dy6Ql+Hrx7+Hmr1PYa3jbLPewHH9zTYehxdhW5iQnyKhLKxqUQ4T+cqqVdzvEt04fepn2/P4+Y1PMSPDzKz+8Xeh3jRc5trv/5/jf3O///6hjIsCHx33+1M9G1NyporGecvP9lRM82J+G6btFx+nE+HTQW/Ehz/4d51t9Pb3sKYclbi5BvXO738B9WIV+8vqnNv/NeauQO0HuKPeKc+G5539rAPrOfc3hRBCCCGEEOKc6EVDCCGEEEIIMXP0oiGEEEIIIYSYOXrREEIIIYQQQsycC5nBewddS5NDMxh7cMIymcF915DD5pFaHc1UuYfr2CJD15ePMLinWnUNW4820PDy1UNcx1KLQv8iNMdtdTHY54unaKx9vY3mQDOzOoUczVXxONfm0RyX+x2o93Zx+TXXB2nzVbxUnuG52thDg9MLChYc+3ick9g9jkYTA59u3nsAda16sk2/IDTpXeN53rHJis3AbCQ+j+FrmhmcazZ8FQXbzc/Pn1k3ySh52uBsZvbw4UOox2M08oaOi8xsRIF9r16hgbjfR4MrHwcb1fhcFi0zzfzN32fDcVGg4jSD/+nP34Wh7zyUwoqVjoLjHm18BZ8lYzymxZt4bc3MwiU0rdaaeLNHPbxW0RDXOYg5PM9tD3kH+7hRFU2DSYDn1acwqUoL+4GEQuuqFdcEmFGQ5IRCsCjrzPZjvLdG1J/MV9xz10/wONISGSzp3vDp3krpuIsmxBhP8PxmdG9M4ujo/y9/Mgwzs4//w//Yqo3DPuTlL/8WPtv72/8V6nwfx0szM0vxmDe7OF4GNInLwhxel2iM7bM/dM3gzQ6GkS22cJvlDMfYzS72X30Kgx3F2J6Dutvm93t4rCVqj/Pzy1Dv0Tif+XicYdsNOyz1qd0HZGwmM/SVFTTWLl3tQL3+4ENnG9sL61DXv/ot1A+++vnxv4fvoQ3meX48XngcZOdxMF7x8mfVjGP25rLIDD4lYI+X4Xuc92naWPf2/Xj75yUaDwOaLKV2/bazjrnl61AfDPBebHVWoO7fxHV4Ewx3DQsmrqm1yAzuh1SXCv89Df2iIYQQQgghhJg5etEQQgghhBBCzBy9aAghhBBCCCFmzoU8GvFkbPGRzpU11x7pYTnQz8zMo/eaZgO1wM026tmv3kT98u++fgz1f/Ff/jfONl6/wXCfIGdNKupBU9I8v3yNy29sbkM9Lggo6uaoN36VoXZyawf9EUuk3UxIU1iO3SCeu8sUrhKgv+WLJ6i1PaDjDufwUt9eQ/+Fmdm9m7hfq9evQn3zxolGcDAcmtl/7qzjXZKkqQVHAXYcZMeaff7crECbScuwb2CaZ2CBgqfMzG7dugX19XXU3E7zR7TbHdwH1ohP3Pa3t9fFP9Ay6zdwn3gdm+Tp2NvDe6AIDhos8qucRZGHy/F1lLCPyU/p/nP2AFwSSe5ZfBQIdUD+mTjG81pOUUNrZjYZ4r2dB3geEsNjHpOvLQiwX00DV6/+JkcvQ3UJr83cKmro44iOI8Pj8Og+KBVooJsUVjbK2NuAdY3kwb0ehpsNYlc/nCR4PtvzGNjarGKdxBQAGZJHw3dDH/0yHluthOd3PDo8t+/Lo9FeXrda6/A403+CY0JtEX0IW//df+Ys//ophn5lpLOul/EcPX2CHsVqCz1mtRqObWZmr15iUOVkgOcwJE9QrYP7vXwbteU7/c9w/ZtuEOa9+9+B2mk+FOY438Znj8cvMNQ0Z1ORmZVL7BsiXX2rA3U9xHti4SoGqpWWcHw1M4tp3LpOPrDWKU+Nz4nGl8Bpj4bbDUz3Mkwb/6Z9313ndL/EVF+Isw8XC6M9zzaZEo1/AbW3vMCLGVBfVKvi8/E8PU/3l65BPaGg1brnPkvU2hhq6vFz/qnxygvO9qWcRr9oCCGEEEIIIWaOXjSEEEIIIYQQM0cvGkIIIYQQQoiZcyFhdZ6mlh/Nw52SBi2mudbLnvsO06ijvrNcQp13TvMw87zMv/rsc6hfvEZfgpmZkeY0Jq1wxNK5HPez0Uafwv051Kz19t1t7m6jjyNJUBtcqqKONfXwuKukC27NodbYzKy1jPvVvnIL6ps0p/fKd1Bbt7KCfoJb6wXzNFdR41cLUJNarZ1oBLOyqz9/1zz8+uu3egHOk3HBnotpWQzTsiA6nY6zTLWG3pmA2nilgjrLOmXJrF1B3S5LPVnLbma2vo5f4mPnejhErebcXAfqr77EudvNzF6/Rg0z+zzYT5FRf+DMVV7gseD99DPUVZ++XkVZH5fBqzdbFlYOr+neAc7dH5Rx/5MCGf98E3X1OWl1X/Yx9ydsoM77ansV6oph+zIz89vYxpbSDtTZHOVs7OK9nB9g+6iRpj6auNduzqdMpIT8T3Sv1erYt+wfYLsepa5/otXBbZSpzc1RRk2lQtlDPumgM3cbaRmPrXeAeUOto22kpffz3+hqr19ZvX+4TxPyNI7v/gXU9R8+cpff/gnUb3ZdP+BpGhU8R56P53w0cMeBK3X8zhKNf0Ma/yZ0jocH6BlabuF4mMXudfOovUyGuI4u1ZUy3ldXrqKXrruL96GZmVfD/Vhawb66RM8eYYBturOEzxJ9umfMzJI9zDrovEH/XHQqY4T9HJfNNB/CeXx0F82suKgXovA7VPK4zs+dzHk8Gk6uFGX48CZ8el7OC7qXMh1HuUTP3DR+lul5O80xZyPI3AGqEpInkPo5z88L/z0N/aIhhBBCCCGEmDl60RBCCCGEEELMHL1oCCGEEEIIIWbOhTwagX/4PzOzgHMzaN5f1qqbmc3Noaa0TJpGn/TKgzFqhV9uom4yy9251lk2lrHtw8P9LpM/4tqV61CvX0VdZZbgPpmZvdpEXeWIsjaqDdTKDXtd3OcxHted9Y6zjSu370P9J9/7EdQfNXHO5CTH4+R5mqOCPIYoovnzyaMwmJzoeQeJu/y7ZjgcHuv4Wc+f5Xh8XsH82sw0fSh7ODg7oijTIqT7Yn4e23yTdOT8/Wka1HrBvPUsQU1Jr8w1H8e9Dz6AulZ1df//5t9gG/3qK5zf3ZmrnP0wvMKCy+NoZYnT2yjKSbkMBv2eRfFh22NPgJMN4rs+klYb5z5nnfW1NexvynXsVxfm2lBXPJ7b3yxOUXdf8/E76RD3qx7iPiXUdfO9VS7QKKd0hVPaLZ5znTX1Sx30hy0sYm1m1mpi+2hVcL+D4OwclphyBwLfbW8R5WPsDdE/EB1lcbyvHI1PNp9b68jX9XWKx+MvoX8n+rN/z1l+5Sn6Nl7+5H+Hup/TWDXE+7o6xro5516nIZ3nx8+fQp3QdRpHlCnQxJyXYRd9kdEE/RZmZkn+EuoKed8m7Bmi7JhrN3HcX17DzAszs89/+TOouS++voD1+g30faSLuI2DzO3Daq/Rk1Hdwjo45fErar+XieN9OEesguNlsLPHu2lZVkV+CfZgTPNL8HNnUYYK7mPR3+j5g7bpXKuctkl5SWnByfQpr82j/QxC7JPq1L589lVn7rMED8G+z+cif8u/z0a/aAghhBBCCCFmjl40hBBCCCGEEDNHLxpCCCGEEEKImXMhj0ZYqVp4pIsNq6iBrJImskG+hMPlUW/MfgqfdGysefbPMd9xmecrTvE7rQbqeh/cvwf1D//8e1B/cAe9D5a5uuuDHs4l3h/QnN00n/GrDdTJfv1r1Mm26q72sr12E+rGPOZqpD7q7aII15EnqO8rOdo7syxAjV9iqPHLvazw35dFs9k89hd8G40+exWm6T/XSKd7584dqD/5+GNnG/fuYXtaWES9MXsy2C8xLdsjz1ztJmvP+dzwcUdRTJ9jzVp3s+nZHDFp1jk3g3X+RepO3u+zPDTvy6OxvLJslSMPS6uJ+nTnHCRuX9GkPpBvo9tL6OnpjtAjkFMfWeTZ2adshFaI38kyanN0bYbU75ZDvNYLdTfnx6PsjUqA30kCag8jzrTBbVbK7tBUJh0z67snpLuP4jHVeD1WF9APY2Z2Yxnv+THlL0yO7rXsPbW/xd7Q5o42Xe59DZ9lNNZ1l3DefDOz5b/651Df3UZ/4ZNffQH1Vg/bdKmP9VJBXlbYwPa1P8L+p1bDz+sNfHbYPehCvdPdh3qh5fqSWjXqj8i/ksWYaVEq4TriBM/dLvlCzNzMkEaK+zW/hG1ncg29bwch+qsGO25Wx+0vf4XboG2WT41JpejyfUJ5nr81u8L1W7htw/FL0JibcbYNexmov2S/xdFGztwmk2VnezWNMy4KDp+P3XPuC/IwOrtEzyIFI6R7GLRN8iw7+0DLB7577pyxn5+xT+24VxT28Rb0i4YQQgghhBBi5uhFQwghhBBCCDFz9KIhhBBCCCGEmDkX8miUqy0LjzTBJdKWh2GVatQim5l5pPfinAZzNLhIvYbrrJRcHRvJiW2pg5rnP/34I6j/5t/5K6hXFjq4AhYFmntc7TYu42rL8Tjnm3juSt4e1Fnq6n9X1h/gOkuo98wmpGVMcb8TmlPZ81wfSClA/0pO2v1KcKKfTILL92ichj0CrPVk70PRMky9jlr27376Xah/9CPMLrl2jfw7BesIaJvOPOJUs5+CcbWdZrmTJ4Ptb1o+xWSC+9jvu1kx+6STZj8Vn27OMEhT9mhMn4P7bVpgs+m623fFwkLHqrVDfXeDfGplznYpmI89GqBWPCSda4f8XBHl1WQ1yi+quHlFtTF6EQLSPfslvM/DKvZpWzu43zXytc01O842s5xzlPC4+hlqzcMq+UL28LykIzcrIQ7w/A765F+h42w20ScyGqFnY1JzNe53FtHXMCKPxpvhgZmZjb33k6ORTSaWHt17TfKc3HmI2Ta9Av16dgd9ZXf/xX8Edbn6P0Ed/AKzIza38ZzvuVYG81Icc8fkJdjv4zldX8fvB4b9T508HR0a0w+3geNbv4/tae/gAOoytflgdwfq7a1NZxvLC3hv3rqNnozsFmZdPU+p/f3yH6FeG20721h9+nuoq20c58vVk/GlXJDTc5lMG8sKl+GnOiqdaA5nG9ymZz8O8DbOk8nl5l/ZmbULe1MKPBr8vOw8DJAvl30h9G3ODzEruoZvr70L/E6hXzSEEEIIIYQQM0cvGkIIIYQQQoiZoxcNIYQQQgghxMzRi4YQQgghhBBi5lzIDJ6ZZ9mRGSQ/wyRy+AXXzMKBVqUyGhs9CisrkeH0Rz/4C6gbDdcIGdB+3Lq5jvU61gsdDN3y2MBJhxVHrlHbp/A71xiEy9RqaK68fRcN6kXGoUYDg9+S5GxTVJaRWfGM4JXjr7AZn4zwpwNeAv9CTWcm5N7JbgfUNu5R8OL9+2jMMzNbWUGjZ7OB177mhE6isXtuDs19Qck1nDuGLYID+djw7AYYkam/YKIA/lvRd3AfcZtsnF9dveosc+/+h1BH8WdQHxygWdyjyQc4WInN4UXwuTh9rt6XGTwaj83zDo17/b0ufHZlBc2hUYEZPKd7bK6BbWrYJVM0BW1mNTzuqOA0lsvYjiscbEdtjnLerDOHBtQ6mcEbZTeM1avjZCADCv/0EjT4drfeQH2lgeF5iee24d0RGsozD8/NeITnbkhG7k57Aer9Hhqbzcxe072QV/B6VYOjPqH0foy4Xpabf9QncHbn4j5OKvLRb3/nLD+hfjG58ynUd//lMtTLH2OA7cYv/gHqwYun7jZoconJEK+LT+NhHKNRu1rHtjIY4PdHBae+TBMFlAIOrMW2EJawvTbK2F47f3LL2cbcBzghS3Yb+8SDBNe59X+jkX7hOdb3cmzPZmYdnmCihcb30ikDb6nAzPuu8TzvuO+dFi5bhGNQzs+uv43h/KxJRM6zjj/086J9yGi88wN+Rjz7PBTvCG+UPv4Wx3Gu7X6L7+oXDSGEEEIIIcTM0YuGEEIIIYQQYuboRUMIIYQQQggxc7610D4M0V/BYWjsxzAzS1l/Tj6BssfhZrj8nVs3oF67gppoMzMjTXOjhrrJEgWJBXa2JpBDTfgYipbhgDQOYSsFeO46c6iJL9a+sQeDww/x81KZ9+HsfT5cJ+oIeb+nhcm9a6IoOt7HBw9QL/v9738f6nXy4piZLS6iDrzRQK05exXYI8DHH0WuWJivPa+TzzHfN/w5b6Nom9O0krzf3FZqdI+sra466/jkk0+g7vVQV/3ll6gHj2PWpOK59H13n6f5V0632ffl0Uj93NKjfR+nGKa308UArqK7pURdrt9EDXaPQuiGtI3FFbw2FfJumZn5FdS0V8grMib/Q059SSnHfWKvFntAzMxKDfI2GPb/SULbaFCIYIYBalninj2PxoswwGUWr+D9fdBDb0Ctht6TWgXbvZnZhMegEP0p33iLUu/9BJb6lpt/NGZ5ztiFfc+VPfRsmJl973P0VpVv34R6cwnbV+kv/znU9ft/BvVk67mzjXgH/TcHr9DH0ZigN6FF/U/mYd/y4ilex8UFt3+KcurjPPSarH6A40H92m2o527ewn2Yc0MBByH6qfaorw+ePYP67h4GKN6hgL4rDdfrVJtHH1EQ4n0VnOoTg/NL5GeG7/vOuPgNrjfV3cFpY9W0IN5zhQJO8XVc1AdyHp8I/4mfz9zjvtg+mRWcC/qdgG230871267jtP34hqAgEPSt2zr3N4UQQgghhBDinOhFQwghhBBCCDFz9KIhhBBCCCGEmDkX8micnkOZ9V/DIc59nRfMsVwiX0cYojYzmKD+PCiTbpd0bRXPfU+axGOoB1RXq7hN1tDz3MTBOaTg07TlrJFn6Vya8Ebc48pJg5rlrGEmTaDxPkzXFLtzP/M2s8J/Xxarq6vH3qAf/vCH8Nndu3ehrtdRW23m+ifi+Gz/Q6WC7Y/bSpFfgr1JzSbqcHkfpvli+PMiXSX/jbcxGuEc8hEdd4Xus7k2atnNzO7dw/n3U8rqiCa4jUePH9I2SbNf4PeZlily+vNvM4f7LMgroeWVwzaY1SgbYoL+imbV9QDwMUWG5zGrUl+Ap9Xmy6jh9quuljwpo68jMNzP0GN/BN4r5Rz17b5xv+D670Ye9u2cNxSklGNQxXWMhliXKV/EzGylhbk23S6e75qP56J9/TrUrJseU86GmZmX4L0QJngvNSuHfUCQ4Tm+LNI0sSQ5PLc59cE85uacnWRmS+Tb+HO6Lr97g36K3zbQD5EtYRaRrWMWkZlZ6Qpey/kH6J/zY26f5AGise0OdvUW8phtZiMa36IY11GnZ4VJgOvYpT5wUtC3+3u7UHd2XkG99jnmZCxuPoK6VSVf6xLeZ2ZmVqFsMfLTJaeye5ICH+y7Jk3T4zFqWh/8bTwazLfZhuexr4PXyePMxfx+RWOwuxtnPwMyKeVOnc+LQvtBJg0+d7zOon2a7ts4WUdS4KN7G/pFQwghhBBCCDFz9KIhhBBCCCGEmDnnkk598/PUaXnU1J+0iqRT9FNfxD+vl2gqtzJ+n9fo/HRkZpOCn8NPkyT0E/1FpVPn+Dlw2k9U06VTRdHwb5cxHX2Dqj9cOpWQxCY9tc3BYFC4zLvgm22cliWxHKjfxykTs8z9WS8M8dr7Psoi+FBYGsVtpU9TkR5y9rR1LGtypVS433FM0pqC++qi0ilHMlYmmUDu3lffXO9vYKnkJEI5BEvIvpF7HNd/oHTqRD5yOXM8frOdyejkuKIxSRcmWE9yvA5mZjkd02iI5208iqjGdQ6HKAX1A7e/S1OWpuAyiUfroCYV57jO80in0glez4T69ijFbUYj3MeMjjMpudc1oB0dj/FchbTONKCpo6mtjEeu/Ilm/rWc2u3IT46WPdz2Zbe/3nh06m8klUq4du+xNMLz3A9JAo3KHRt7eN9PBtjPxgX9EZ+znNoLS6c8Gtl5OmVqfpaV3ceWiSOdYhkZ9sssnUrL1F6LpFMj6vPGeJ+MaZkRnf+AzotF7n1Upr67xNOknupXh0f/vswxuNc7GfMuQzp13v06zf8/pVPYFoqkUzz17x8qnSriItKpb9rCea6pl5/jWxsbG4W5BEI8f/7crpMWetao/Ym3cRntz0xtUBSj9ifeNxqDxfvkPO3vXC8aWZbZy5cvrdVqvbegLPHHRZ7n1uv17OrVq+cKfvlDUPsTzGW2PzO1QYGo/Yn3jcZg8T65SPs714uGEEIIIYQQQlwEmcGFEEIIIYQQM0cvGkIIIYQQQoiZoxcNIYQQQgghxMzRi4YQQgghhBBi5uhFQwghhBBCCDFz9KIhhBBCCCGEmDl60RBCCCGEEELMnP8P8FM3n03dSWcAAAAASUVORK5CYII=", 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", 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" ] @@ -2583,7 +2544,7 @@ "outputs": [ { "data": { - "image/png": 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", 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-       "┃        Test metric               DataLoader 0        ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│       test_accuracy           0.4269999861717224     │\n",
-       "│         test_loss              2.45865797996521      │\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "
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-       "┃        Test metric               DataLoader 0        ┃\n",
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-       "│       test_accuracy           0.8450000286102295     │\n",
-       "│         test_loss             1.2167928218841553     │\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "
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"────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.8450000286102295\n", + " test_loss 1.2167928218841553\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] }, { "data": { @@ -3914,7 +3853,7 @@ "outputs": [ { "data": { - "image/png": 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", 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2mzp06OD+YGzcuDHqcayhQ332ByXaC6Op/f1uTgQA2VA/OZaBAwfWNdyxkz+bVmInN1bT0qaH20keQUsAQLyF6/q3NuAYnp6d7gjOZVeGLdTg97txHCKSo48+2lOA3/ZL9GuFWIp/qHGZAiwYGe0Ps6237akwTc9qIFiqsqUV24ddKz5rRacTyepTWEHb1157rcGJzhtvvJHQcQBAKps2bZqnpjsWsDQ03AEAtJSdi1syxoMPPuiWze10bV1/rSmFJXOcfvrpbmnX49ENOJ340Z08k/nRRAnpLR71beOFWErLkXGZAuwFlS7T9KyLlZ1AWBFZG88111yTlJTiiy66SDfeeKP7pmKvvfZydRpsyn0q/B8BQCqwrEkvTXeiTTUBACAW+2zQVNabBRO8BJDs9pZ11DgjyDKobH22B+gyafp7PGRKhi0yP/uWWErLEbhMEekyTc86mJ999tkumNqjRw/9+te/jmtzoEjsfr/++mudeeaZ7o/Veeedp2OOOYY/4ADQjPrH9gWZ/a0BAGSn1k7Pbm3Q0e7fgg1NTWO0dfYZ6NJLL3WBu2w+zyc4B6R/gJ9YSssFguk+2T1B7BfK0mqtmGnjD4PW4MBS0gcMGOC6tCLx7JuKvffeW6eccorrzuUXnlsA6cpr/WTbL9H1k5Ga5zNIDzyHSJVMSQs62nTu+rdvqracnUtH+kDs9W+VTQEmcAdkHj5vZ2csZU0zz2WocYm09Nlnn+nf//63PvzwQ7399tv6xS9+4V4UVhMHADKdfedoH/bGjx+vUaNGuaV9qKs/NdyCkYcffrhycnJSvn4yACCxwpmSjYOO4UxJL7UlLbMpUtAy/Ldq6dKlURvFWWaUF173AwBkXiyFwCXSkn0Qv++++3TwwQdr6NCh7gX34osvum8KACCTWQ0aC0pahsqUKVP05JNPuvfDI444QrvuuqvbHs50sSY79h5pwo16wstUqp8MAEicWNOzjU3PjtVgx4+go03n9MLrfgCAzIulUOMSaalfv36uKxcAZBP7QGn1eyoqKtz1cIZl+MPl559/7gKab775pgtIpkv9ZABA4jQnUzLa9Gw/go6W+W/TyS3Ts6lAani6ue0HAMjOWAqBSwAA0oQFIaNNuTMLFy50gcpw3Ur70Gc/U8cSAODn9Gw/go5WtsRqatr0dNu//nHCX65ZV+BUbhoBAIgvpooDAJAmpk2bVjfVOxLbbtmVAADEc3p2OOhoGmfwNyfoaI2ArPt43759G6y3oGesruQAgMxH4BIAgDRhU73rN+Bpim23/QAAmctKhFiTtgcffNAtY9WjbCpTMlK5EFtvUwm9TM/2K+ho+3366aeu0dwDDzzgltYsgqAlAICp4gAApAGbPrdixYqY+1nGpdWxBABkJuv4bc116teptEChZT96CfT5PT3b7tPqL1spE5tebpmaFvRs7vRu2z9aTU0AQHYi4xIAgDSwfv16V0csFsu4tOY7AIDMDFpawLFxcx37+2DrbbsXfk/PDgcdTzvtNLekJiUAwC9kXAIAkCIs68Ua8Fgty3AH8LFjx7rGOh07dtTzzz+vgQMHqra2tslGCPZBMT8/33UMBwBkFpsObpmWTb3/2zrLlrz00ktd9qOXwKFfmZIAAMQTGZepxE5CZs2Sxo+XRo0KLe16EycnqcK+UbUTpLD+/fu7qSXR2EnVE0880er79us4AJAKqqqqXIBy+PDhmjJlip588km3tOu23rbvvffeWrRokQ4//HB3m3CjnvDSgpalpaUR65YBANKXBRgbZ1o2Dl4uXbrU7ecVmZIAMobV+rX4yYMPhpbNqP2bDMRSvCPjMlVUVUnFxXZGYp9Aba5faDlpklXQlkpLpe7dfb3LkSNHatOmTXruuee22mYnPJaxs3DhQg0aNMjzMV977TVts802vo7zD3/4g3tRLViwoMF6+2a4u8//JwCQDPZh07JeKioq3PVwA57w0tbbdsvG3GuvvdyyvLzcdQ8PZ2ba9HB73yZoCQCZyc59/dwPADKGlcm45BKp/pc7O+4oTZxo6eW+3x2xlMQicJkKLKPSgpabP7C6oGX9pa237WVlFhr37W7POeccnXTSSe6bW6tnU9/kyZN10EEHNeuFZnr27KlE6dOnT8LuCwDiyQKR0TJkbHqgbbdgpWVfWnDSlnYBAGQHm8rt534AkDFByzFjtp6parXhbf2jj/oevCSWklhMFU8FFpC0D6yRUpltvW0vL/f1bo8//nj34rjvvvsarP/uu+/03//+V6NHj3bTRqxot9VWs7pqD1radRSN05sXL17svm1o37699tlnH73wwgtb3ebXv/619thjD3cfu+yyi6655hr37YWxsV133XXu2wr7oG6X8Hgbpze//fbbOuKII9ShQwdtt912Ou+889xjCTvrrLPcY7rtttvcCZ3t88tf/rLuvgAgWaymZXi6dyS23TIsAQDZyepP2gfkSJn1tr5fv35uPwBIWxaAXLfO22XNGunii5surxdeZ5mYtp+X43ks00csZbuExlLIuIy3228PXWJNE7cTkFgvkhEjmp4ufvnloUsz2YfgM8880/3y/va3v607CbIXmmX3WEMI+9leDF26dNHTTz/tpiLuuuuuOuSQQ2Ie35pHWNHv3r1765VXXtHq1asb1HAI69y5sxvDDjvs4F4w5557rlt35ZVX6tRTT3X13CwF+8UXX3T7d+3adatjrFu3Tsccc4yGDBniUqxt6uT48eN14YUXNngzmTlzpnuh2fKjjz5yxx88eLC7TwBIluXLl9dNC4/Ettt7GwAgO1n9yYkTJ7ru4XbeXr9JT/g83j70UqcSQFr7/nupUyd/jmXvkzZ9vIkYQpMsWOdhujaxlI8SGksh4zLeLLJvKcrRLvbC9BLZt/2aur3dRwudffbZ+vjjj900xfqpzZb2vPPOO+tXv/qV+2W06P1FF12kn/zkJ3rkkUc8HdteHO+//77uv/9+7b///u7bgj//+c9b7fe73/3ONZSwbxisVoTdZ/g+LOLfqVMn98Zg6cx2sXWNPfDAA9qwYYO7r/322899W3DXXXe57CQLCIRZHQdbbzXi7FuS4447Ti+99FIL//cAoPVWrVrlTipisfdBq2UJAMhe9kH20UcfdVk89Vkmpq237QCA+COWclzCYilkXMZbly5SoxOLJjMu16+PHbzs2LHpjEu7jxayXzr7Rb/33ntdVyuLnFsdteuvv959U2AvDvvFX7ZsmTZu3KgffvjBpSF78d5777npKhb9D7MofmMPP/yw7rjjDveit3RkyyqybyWaw+7LXtD1i9kOHTrUfVPxwQcfuG8qzL777tvgW2j7xsC+mQCAZHj11Vfdt5WffvppzH3tvdG+qQUApLdw3WJrjmDnoja1uzlZkhactIZtrTkGAKQsizfUm6YclZXTs5mpsTzzjFRY6O2+PSKWsn3CYikELuPNyzTuWbOk4cO9vdji0IjBCsvaNwB33323+4bA0pet4cPNN9/spqPYlBOryWC/yJaebC86v8ybN09nnHGGq71g6cmWuvzQQw/pL3/5i+KhTZs2Da5bSre9IAEgXmwqn30Ta3Uswx3AbfqIvc/atJFw0NK+DbX3o6bek+wkwU6M7NtWAED6mj59ui655BLX0KF+tqSdczcnW9L+LtgHZQDIODbt2mt37aOPDnUPt5moTSWC2bFsu+0Xhy93iKXUKhGYKp4KLBhpRbQjvZBsvW2P0wfWU045RTk5OS5F2NKDLeXZfgnnzp3rvs21D9gWgbcU5w8//NDzcffee28tXbrUfRMcNn/+/Ab7VFRUuDRqqwthnbd23313ffbZZw32adu2rfvGItZ9WdFZq88QZuO3x7Xnnnt6HjMA+KmqqsqdvAwfPlxTpkzRk08+6ZZ23dZb7RgLRto3qG+88Yb7dtOEG/WElxa0LC0tjdiQAQCQHkFLq09ZP2hpLBvH1tt2AEAzWKxk4sTQz43Pk8PXreFNnDLSiaUkBoHLVGAvqNJS+2Qauh7uLBte2nrbHqcPrFb3wKYqXn311e6FYR2jjP3iW+cqe0FY+vD555/foMZBLEceeaTrcDVu3Dj3QrC0aXtR1Wf38fnnn7tvBiy92dKcH3/88Qb7WL2GJUuWaMGCBfrmm29cinVj9k2Ddduy+7JacVYw1r75sGmV4dRmAEh0pqWdsNh7qAk33wkvbb3VwHnsscdcRqZ9G2vLWbNmufcyq1NjS7tu662uDAAgPdkHR8u0rN9QJyy8zrJxYn3ABAA0Ytnqjz66dYk+y7S09XGs/UssJTEIXKYK+0BqRV1t2vi4cdLIkaGlXbf1cf7AainOlhlkKcbhOgpW6PWAAw5w62wqihVzHT16tOdjWoTeXjjr1693nbOsM9UNN9zQYJ9Ro0bpsssuc1lHVrjWXtjXXHNNg33sg70VsrUMpZ49e+rBBx/c6r6sVsTzzz+vlStX6uCDD3bfWv/4xz92xWMBIBks2GgnGZE+hIZrnL3zzjt1Uy/sG1rLxLznnns0Y8YMt7TrZFoCQHqz9/vGmZaNg5eWXWP7AQCayYKTVn5p5kzrNhNaLlkS16BlGLGU+AsEm/raD1tZs2aNqxlgbegbFzu1DkwWxR4wYICLVCNz8NwCaCk7wbBp4eEMy6bYVHD7dtMClECyz2eQHngO05N9WDz99NNj7mfTDU877bSEjAkAko3P29n53K5p5rkMzXkAAIgDa8QTLWhpbLvtBwDI7G7gtr+f+wEAkC2YKg4AQByEG+vE2se6jAMAUps1zrFaYTbdzjInbWnXvTbUsSCndQ+PVPrD1vfr18/tBwAAtiBwCQCAz6wD+LPPPhtzP8u4tMLXAIDM7gZumZkTN3e+bRy8DF+fMGGC5wxOAACyBYFLAABawEpEW8dvq2VpxbFtaV34brrpJp1wwgmurks09uHUMmsKCwsTNmYAQPK6gZ944ol69NFH1bdR51vLxLT1th0AADREjUsAAJrJOgcWFxe7Wmc23dsyJ205adKkBvvZh9Cvv/7adfmrv58t8/PzXWYmHcMBIDO6gVvn2Fjs70L470dLamUCQCaiZ3TmCfr4nBK4BACgmX+E7UOnBSNNuAFPeJmTk6Pa2lpdf/31+t3vfufWlZeXa+rUqa4Rj9W0tOnhlmlJ0BIAUpsFF/3cz1iQ0kuQEwAyXZs2bdzy+++/V4cOHZI9HPho48aNbunHF3MELgEAaIaysjKXKROJBS1N/cBkUVGRuwAA0gvdwAEgfiyo1a1bN/flvunYsSNf7GeA2tparVixwj2fXhqWxkLgEgCAZpg2bVrddO9IbLtlWBKsBID0Fu4Gbo14mpr2Zh+wbTvdwAGgZfr06eOW4eAlMkNOTo522mknXwLRBC4BAGgGO6mKFrQ0tp2TLwBIf+Fu4NY93D581Q9e0g0cAFrP3ksta93KKW3atCnZw4FP2rZt64KXfiBwmULsPKiszLJ57IOx1KuXNHasTTG0F3OyRwcAMHZSFa5jGS3j0vYDACSfdfxuTTOccDdw6y5ev1GPZVpa0JJu4ADQeva+zJdAaAqByxRRVSUVF1vnQvvAa9k6oaU1qLWZJ6WlUvfu/t5nrJTd3//+9/rDH/7Q4mM//vjjGj16dAtHBwCpKVbQMpxxaQ14AADJNX369CYDjpZF2ZyAI93AAQBIDgKXKZJpaUHLzQ1qXdCy/tLW23bLxvQz87J+98OHH35Y1157rT744IO6dZ06dfLvzgAgzdn0wKuuukr//ve/o+5nH2Lz8/Ndcx4AQHKDljbFu3FtSqtXaesti7I5wUu6gQMAkHj+TDhHq1hA0jIta2qa3m7rbXt5uf9FcMOXrl27uizJ+useeugh7b333mrfvr322msv/e1vf2vQ2v7CCy903zbb9p133lk33nij29a/f3+3POGEE9wxw9cBIJ2nGZ533nm65ZZb6taF39vCnfLCSwtalpaW0hERAJL8vm2Zlk011Amvu/TSS91+AAAgdZFxGWe33x66RGPTxO3zbRPnVQ2MGNH0dPHLLw9d/PSf//zHZWDedddd+tGPfqQ333xT5557rrbZZhuNGzdOd9xxh2bMmKFHHnnEdYpaunSpu5jXXnvN1XabPHmyfvKTnzCFBkDasA+zZWVlrnO4Ndex97JTTjlF//rXv/TYY4+5fSwg+fe//90FMsvLy1338PC+Nj3cMi0JWgJActmU7vrTw5t6v7dzV9uPLEoAAFIXgcs4W7PGpqP4c6zvvw9dmroPv1l9y7/85S9102cGDBigd999V//85z9d4PLzzz/X7rvvrsMPP9x9QLeMy7CePXu6Zbdu3VzmJgCkg6qqqrr6ZZY9aXUqbTlp0qS6bMo2bdq4QOWpp57qrhcVFbkLACC11C+J5Md+AAAgOQhcxlmXLlLfvrEzLtevj51x2bFj0xmXdh9+WrdunT7++GOdc845LssyzD7E25Ryc9ZZZ+moo47Snnvu6bIqjz/+eB199NH+DgQAEsQybyxoWbG52LC939VfWjMeC1paprm95wEAUpuVM/JzPwAAkBwELuPMyzTuWbOk4cNjH+uZZyy7R3H33XffuaU1oDj00EMbbAtP+z7ggAO0ZMkSPfvss3rxxRfdVMojjzzSFTkHgHRj08Mt0zISC1zapUOHDgkdFwCgZazjt3UPt0Y8TdW5tBlDtt32AwAAqYvmPCnAgpF2zhSpFKStt+2JalDbu3dv7bDDDvrkk0+02267NbjYlPGwLl26uOmSFuC0ruRW/23lypVum2UmUewcQLqwmpbh6eCR2HabJg4ASH32ZfvEiRPdz43rDoevT5gwgVrsAACkOAKXKcDOnUpLrRNt6Hr4s3N4aetteyJ7PVx33XWuS7g14fnwww/19ttvu2Y7t2/uNGTLBx98UO+//77b/t///tfVs7S6luFuuy+99JK+/vprVzcOAFKZNdcJTwuPxLbbfgCA9GC12m02UN9GdZss09LWh2u5AwCA1MVU8RRhtSvLyqTycskSeuyzca9eUklJKNMy0Q1qx48fr44dO+rWW2/VFVdc4bqJDxw4UJdeeqnb3rlzZ91yyy1avHix+6b64IMP1jPPPKOcnFAs3Br7XH755S4b004WP/3008Q+AABoBusIbu9l0TLFLePS9gMApA8LToYbr1kjHqtpadPDybQEACA9BIJNFX3BVtasWeMa06xevdpNka5vw4YNrt6jTaNu37590sYI//HcAtnB6vWOGDEi5n6zZs2iizgy9nwG6YHnEAAAZNO5DBmXAABl+xcUll0ejWXm5OfnqzBRxYYBAAAAANS4BABkr02bNunkk0/WzJkz3fXw1MFwo57w0oKWpaWlWzV4AAAAAADEDxmXAICsZPUsx44dq6eeespdt1q+L7zwgjZu3Oi6h1sjHqtpWVJS4jItCVoCAAAAQGIRuAQAZDwr51xWVqZp06bVBSS///57PfLII2671bC1AOaQIUPcdepYAkDqfMlEYx0AALIXgUsf0eco8/CcAumvqqqqrqOsTf2urq6uW1rA0paPPfaYhg0bluyhAgDqmT59ui655BJ98cUXdet23HFHTZw40XULBwAAmY8alz5o06aNW1r2DjJL+DkNP8cA0u/LBwtaVlRUuOsWpKy/tBqXe+65p4499tikjhMAsHXQcsyYMQ2ClmbZsmVuvW0HAACZj4xLH9h0lW7durnph6Zjx47UQsuAYIcFLe05teeWKUlAerLp4ZZpGW0K4jvvvKPy8nKmhwNAirD3Zsu0bGrmi62z8+xLL73UfTHFORoAAJmNwKVP+vTp45bh4CUygwUtw88tgPRjNS3D08Ijse3WjIfAJQCkBvvCqXGmZePg5dKlS91+lPkAACCzEbj0iX3zawXDreGDTT1E+rPp4XyLD6Q3+zIpWtDS2Ha+dAKA1GGNePzcDwAApC8Clz6zQBfBLgBIH5ZxaV86AQBSgyUD+LkfAABIXzTnAQBkpHvuuUfPPvtszP0s47KkpCQhYwIAxFZQUOC6h0eqGW/r+/Xr5/YDAACZjYxLAEDasjpn1oDHalnadG/LnDzllFP06KOP6t///nfM21uGfH5+vgoLCxMyXgCAPL03T5w40XUPtyBl/SY94WDmhAkTmOUEAEAWIHAJAEhLVVVVrqOsNWcIN+CxD7GTJk1qsN95552nd999V3PmzKnbL7y0oGVpaWnErB4AQHKceOKJ7kso6y5ev1GPZWJa0NK2AwCAzEfgEgCQdiz7xoKWFRUV7nq4AU9NTU3dPhaMvO+++3TmmWe6/cvLy1338HBmpk0Pt0xLgpYAkJosOBn+gsoa8VhNS5seTqYlAADZg8AlACDt2PRw+yAbjQUrd955Z/ezBSeLiorcBQCQPixIOWzYsGQPAwAAJAnNeQAAacdqWtp072hsu2VYAgAAAADSE4FLAEDasene4enhkdh22w8AAAAAkJ4IXAIA0k6PHj1i1qa0jEurZQkAAAAASE8ELgEAaeWHH37Qhx9+6GpYxsq4tAY8AAAAAID0RHMeAEDaWL9+vU466STNnTs3ZjOH/Px81zUcAAAAAJCeyLgEAKSFdevWaeTIkXr22Wfd9Q4dOmjgwIHu53CjnvDSgpalpaUxp5MDAAAAAFIXGZcAgJRiU8DLyspc53BrrmN1Kk844QTddNNNmjNnjtunU6dOeuaZZ3T44YervLzcdQ8P72vTwy3TkqAlAAAAAKQ3ApcAgJRRVVWl4uJizZ4922VPWp1Km/Y9adKkun26du2q5557Tocddpi7XlRU5C4AAAAAgMxC4BIAkDKZlha0rKiocNctaGlqamrq9rFg5ksvvaQDDzwwaeMEAMRm7932JdRXX32l7bffXgUFBe6LKAAAgOYgcAkASAk2Pdw+5EZjwczvvvsuYWMCADTf9OnTdckll+iLL76oW7fjjjtq4sSJOvHEE5M6NgAAkF5ozgMASAlW0zLcXCcS2271LAEAqRu0HDNmTIOgpVm2bJlbb9sBAAC8InAJAEgJ1lwnPD08Ettu+wEAUnN6uGVaWumPxsLrLr300gYlQAAAAKIhcAkASAnWETwnJydmxqXtBwBIPVbuo3GmZePg5dKlS2OWBQEAAAgjcAkASDr7MGtBy9ra2pgZlyUlJQkbFwDAO2vE4+d+AAAANOcBACTVpk2b9Itf/EKTJk2Kup91o83Pz1dhYWHCxgYA8M66h/u5HwAAAIFLAEDCsiqtc7g14bE6lTble/To0frrX/+ql19+uW6/nXfeWZ999pmbFm4ZluGlBS1LS0sVCASS+jgAAE0rKChw3cOtEU9TdS7t/du2234AAABeELgEAMRdVVWViouLXV2zcCDSMijrZ1m2a9dO9913n0499VSVl5e77uHhAKdND7dMS4KWAJC67H194sSJrnu4vV/XD16G378nTJjg9gMAAPCCwCUAIK7sg6sFLSsqKtz1cOfw+l1lLZj50ksvaejQoe56UVGRuwAA0suJJ56oRx991HUXr9+oxzItLWhp2wEAALwicAkAiCubHh6rg6wFM8MBTQBAerPgZDjL3hrxWE1Lmx5OpiUAAGguApcAgLiympbh6eGR2HabGk6WJQBkBgtSDhs2LNnDAAAAaS4n2QMAAGS25cuXx8ymtO1WzxIAAAAAgDAClwCAuHnttdf0yiuvxNzPMi6tCQ8AAAAAAGFMFQcAtKrxjtWwtOng4Q7gY8eO1d57763f/va3uvfeext0lY2WcWmdwwEAAAAACCNwCQBokaqqqrrmC+EalracNGmSq21Wv2t4x44dtWHDBtXW1m51HNs3Pz9fhYWFCX4EAAAAAIBUxlRxAECzWRalBS0rKirc9XANy/AyHLTs3Lmz/vrXv2rJkiUaOnSoW2fBzfpLC1qWlpYqEAgk5bEAAAAAAFITGZcAgGaz6eGWaRnLlClTdMIJJ9Tdpry83HUPD08rt+nhlmlJ0BIAAAAA0BiBSwBAs1lNy/D08Ehs+9NPP10XuLTgZFFRkbsAAAAAABALU8UBAM22fPnyqEFLY9stsxIAAAAAgJYg4xIA4Llb+EEHHaTrrrtOH374oaeMS7sdAAAAAAAtQeASAOC5W3i7du30ww8/eDqO3c5qWAIAAAAA0BJMFQcAeO4WHg5atmnTRrvttptyc3ObPI6tLygocI13AAAAAABoCQKXAICtuoXX1NRE3W/y5Ml69dVXlZ+f765bRmb9pa0vLS2lWzgAAAAAoMWYKg4AaHa38JkzZ+qMM85wgc7y8nJNnTq1rhamTQ+3TEuClgAAAACA1iBwCQCoY8HH5nQLt+BkUVGRuwAAAAAA4CemigMA6ljGZHi6dyR0CwcAAAAAJAKBSwDIosY7s2bN0vjx4zVq1Ci3tOvr1q2r22fs2LGeMi7pFg4AAAAAiDemigNAFqiqqnLdwq3xTriGpS0nTZrkOoQ//PDDOuGEE9yUb+sGbl3Fm2rQY93CrfEO3cIBAAAAAPGWthmXd999t/r376/27dvr0EMPdd1tI9m0aZOuv/567brrrm7//fffX88991xCxwsAycy0tKClBSNNOKMyvLT3yNNPP91lXlrNSusGTrdwAAAAAECypWXg0jKDLr/8cv3+97/XG2+84QKRxxxzTF2ziMZ+97vf6Z///KfuvPNOvfvuu/r5z3/uMovefPPNhI8dABLNOn9bpmVTGZRhGzZs0Isvvuh+7t69u7uNTSMfN26cRo4c6ZZ23dbbdgAAAAAA4i0QtFScNGMZlgcffLDuuusud722tlb9+vXTRRddpKuuumqr/XfYYQf99re/1S9/+cu6dSeddJI6dOigadOmebrPNWvWqGvXrlq9erW6dOni46MBgPiyWpZTpkyJWrvSMiotOHnPPfckdGwAEovzmfTHcwgAALLpXCbtalxu3LhRr7/+uq6++uq6dTk5OTryyCM1b968Jm/zww8/uCni9VnQcs6cORHvx25jl/r/sQCQjiwb3UvDnUhZ6wCA5OGcFAAAZLO0myr+zTffuOmOvXv3brDern/99ddN3samkd9+++1avHixy8584YUXNH36dH311VcR7+fGG290EeDwxTI6ASAdffnllzH3sYzLXr16JWQ8AADvsv6c1MqczJolPfhgaBml7AkAAMg8aRe4bImJEydq991311577aW2bdvqwgsv1M9+9jOXqRmJZXRa2mr4snTp0oSOGQC8soofVn/SpoSPGjXKLe16uBLIxRdfHPMYlnFZUlKSgNECAJojbc9J/Qg4Tp8u9e8vDR8unX56aGnXbT0AAMgKaTdVvEePHsrNzdXy5csbrLfrffr0afI2PXv21BNPPOGaT3z77beu5qXVwtxll10i3k+7du3cBQBSWVVVlesYbs13LGvSApC2nDRpkgoKClwHcAtI/ulPf9LHH3/sss4bs/dU6xZeWFiYlMcAAFBmnZNaYPGSS6QvvtiybscdLZtAOvFE78cYM8a+nWu4ftmy0PpHH/V+LAAAkLbSLuPSMiYPPPBAvfTSS3Xr7IO4XR8yZEjU21qdy759+7oP9o899pj7sA8A6coyKu19rKKiwl0P17EML219+H3ulVde0dChQ93PFtisv7SgpQU4A4FAUh4HACCDhAOO9YOW9QOOXrIlLTvTAp9N9RANr7v0UqaNAwCQBdIu49JcfvnlrvvtQQcdpEMOOUQTJkzQunXr3PRvc+aZZ7oApdUECn9gX7ZsmQYPHuyWf/jDH1yw88orr0zyIwGAlisrK3OZlpFYPWDbXl5erqKiIre//Tx16lTXiMdqWlo2pmVaErQEALRarICj/a2xgKN9qZabG/k49retceCz8bFsyrztN2yYP2MHAAApKS0Dl6eeeqpWrFiha6+91jXksYDkc889V9ew5/PPP29Qv9KmiP/ud7/TJ598ok6dOmnEiBHug3u3bt2S+CgAoHWmTZtWNz08Ettu73cWuLTgpC3tAgCA7/wKOEZpoNmi/QAAQNpKy8ClsQY7dmmKNaWozz6kv/vuuwkaGQAkhmVNRgtaGttu+wEAEHd+BRy3397bcbzuBwAA0lba1bgEAMh1ln3vvfdi7mcZlzYlHACAuPMr4FhQEGrmE6mMia3v1y+0HwAAyGgELgEgRRvvWPb4+PHjNWrUKLe067bealUOGjRIH330UczjWMal1bEEACDu/Ao4Wv1L60Aevk3jY5gJE6LXyQQAABkhbaeKA0Cmqqqqct3ArbFOuIalLSdNmqSCggLXTMdq+Zrc3FwXzLSGY43ZNusYbvsDABB34YCjdQ+3AGP9Jj3NDTieeKL06KOhZj/162ZaYNSOYdsBAEDGI+MSAFKIBSEtaFlRUeGuh2tYhpe23jIuDzvsMA0bNkxvvvmmhg4d6rZZcLP+0oKWpaWldAwHADSvM7jVi3/wwdDSrjdHOODYt2/D9RZwtPXNCTjavp9+Ks2cKT3wQGi5ZEnzg5atfUwAACBpAkH7lIyY1qxZo65du7q6cl26dEn2cABkKJsOPnz48Jj7WUDy+OOPV05Ojgt2lpeXu+7h1ojHalra9HDLtCRoCaA+zmfSX1yfw+nTm85wtCzKlgQLrXu4NeKxmpY2PTwZU7v9fEwAACDh5zJMFQeAFDJt2rS66eGR2PYZM2a42pfGgpNFRUXuAgBAiwN8NsW7cU7DsmWh9c3NlrQg5bBhyqjHlCrBWAAAsghTxQEghSxfvjxq0NLYdsusBADAFxaQs6zEpiZihdddeml6TbH2+zFZELR/f8lmRZx+emhp1219S8bG1HUAADwhcAkAKdAtvKamRtOnT6+rbRmNZVzadHAAAHxhWYT1p1I3FehbujS0XzY+pnDmZuPjhTM3mxO89DMACgBAFmCqOACkQLfwjh076vvvv/d0HLud1bAEAMAXNvXZz/0y6THFyty0WtKWuVlcHHvauN9T1wEAyAJkXAJACnQLrx+07NSpk2u605Tc3FwVFBS4xjsAAPjC6jX6uV8mPSa/Mjf9nrrOdHMAQJYgcAkACVBWVuYyLW1KeDQ333yzPvvsMw0dOtRdt4zM+sv8/HzXUZxu4QAA31iTGeu0Helvi63v1y+0X7Y9Jr8yN/2eus50cwBAlmCqOAAkqFu4ZUtGC1xacPLDDz/Utttu6wKd5eXlmjp1qmvEYzUtbXq4ZVqma9DSPpOVldn/hWS9haxM59ixkjVDT9OHBACZwaY4T5wYmq5sb8j1swLDb9ATJqRXB22/HpNfmZt+BUCZbg4AyDIELgEgztavX+8p27J+t3ALThYVFblLJgQjq6pC5b8skcSSR22GvC0nTQolu5SWSt27N++YAAAfWbDLgl42nbl+ZqBlLVqALx2DYX48pnDmpgUGm5rmbX+YbHuszE0/AqB+1tsEACBNBIJWeA0xrVmzRl27dtXq1avVpUuXZA8HQAqxt1HLkLSsynB25NixY13Q8emnn9bFF1+sJUuWxDyOZVyOGzdO99xzj5LNS/AwUjDSlvWDkXYsu52V92wqdmufrfLzQ/e3apW3Y9aNszaosjsWatrf16pydVv16rpRY3/RWUUX769ATr0oJ9FQwOF8Jv3F/Tm0N2p7E7bMPwui2ZtvugfBWvuYwlmOpqnMTS9ZjjYGm84dKwBq5wuRxma1LG1aeCwzZ0rDhimhMvH3BgCQEucyBC494kQfQHM6hdvSpnyvXLmyWcebNWtW0rMsvQQku3XzHoy0i9fPWdde6+2Y9vmuaskqFQ/+TLPX7K88bVK18pSnalWrjQq6LFTpgp3VfUA37xFWIAtwPpP+eA6TxIKXjTM3rUZmc7JRWxsAtUY8VtMylgcekE47TUn9v7EgrE3VT8dMXQBAXBG4jBNOEgE0Zm+fFmS0TuGxpoHbft99950WLFjQ5L5W/9Ia71jmZrxqWHpJOvSSHXnQQdLZZ0vnnx/7PvfcM3RfFjuMxuKI9hnn009jH9MSTgoLgirq/pYq1uyrmiaqnuSqWvld3lHZyoEKDB+m4NwKldUermkaq0r1Ui9VaqymqShnjgJDt0RDScxEpuN8Jv3xHCaRH1mFrQmApmLGZaSam83JRgUAZJU1BC7jg5NEAE1lRw738AHimmuu0XXXXadVq1ZFzM4sKChw3cK7xynzz2vSodfPRPHQuHdCJEceKf32uAUaftngmPvOGnGLBj1zo4pVqtkq3DozU+UqVbG6z3pCVYOKVFwc1OzZAeUFalQdzFFeoFbVwVwVFARVWhogMRNpj/OZ9MdzmMUBUD+mm/spPJ5I3dITPR4AQEaey9CcBwBayGpahgOPkdj2L7/80mVRWlAyHt3CY2UJ2nYLWloWpQkPN7y09bY9fIycHKm2Vr6xzyoxElLrgqherFkjV9MyFIRsE/l+tUlTXuqrjzRDFRri1oX3Dy8rlO+CmrPunaziD/NVMT8nlK8ZDH3ACi8rZteqeEStyiraNMhO9ZSd2Zw0TlI+ASDz2R/GlmRExqP7e2uySO12kYKWxsa3dGlov0TX3AQAZAwClwDQQhZ4jBa0bNwpPB7dwr106164MLQ91meW8vJQrMxL0HKbbaxbevR9bRzjxoXibrGyOMOZn5GmqNfXt29QlQvXRw1amhq10dQfTla12kbZJ89lYp52/1eaHeV4NcrV7Pm5Ki8LqmhYYPP/+9bZmZMmNcrObE479ebsCwDITn52f29tbUoLdnrhdT8AAJpgqSUAgGayKhvffvutq00ZjWVcWlZlfMYQO5Ny6FDpwgtjH8viY1OnhhL8YiVa2L4Wd40V4LRxlJSE9o2WwGHrbft118UOWpqLLw6oV6fvXcZlLNGClvU9olNj7mP3N/XWr0P/7yM2uSzMLVmZgUbZmZtcx3NPqa52QK9pseHsGlvavP7x46VRo0JLu071FwDIfBZUtKLQVsvSGvHY0qZjNzdoaZmbjTMmbRq6rbftsViGphde97OTAPtbZk2IbOnlpAAAkPGocekR9YSA7GNvjza126aEh6d1jx07Vvvss4/Gjx+vJ598Mqmdwv2uRzlypHT55c3oAP6bTaqYl+OyERvLVY3yh9SqbG5oanWkDMX69SNdp/Kh3o5ZdttrGn7lwTHHmRuoUc3mgGLrBTWy1yu6/O97aPhJ28bce9Zf31TRZQfI/siWqWjrxkAqk5vYd/fdoRTWs87y9qQPGkSXdLQY5zPpj+cQKVOb0s+am3QmB4CssYYalwDQelVVVU020pk0aZLatGmjTZu2ZPvl5OSoton0w3CncKtf2VxeSh3atubUhozGjmP3Ec6OjNZVPD9fKioMqjRYrGJdrdkq2KrpTb7mqjR4kwJ62mUjdleVyoLFKldAUzVOleqhXvpGJZqiwmBQAZVK6ub5mEX/7yAV/GlhzK7iu580SFMm1zQZCA3LUa16tl2lFRu7qDbKn0UbR6/KRZp20iLl6KwY+27S1L9UalBuDxXXPLZVY6BJGr+lMdAvf+mtO5EVH73lFmnFCumNN2IXLKUmJgAgnrUp/aq5GakzeTj7s7mdyf3o/g4ASB2WcYnYVq9ebX9J3RJAZqutrQ0WFBQEc3Nz3es+0qVHjx7Bhx56yO1r1/Py8hosbf3KlSubff92k4KC0PzhvLyGS1tv2994IxjceefwHOPol7328rbfrFne7z84c6ZbUSsFZ6kweI7+HRypUre067be3eCZZ+w/NHTD3Nym79jWDx0aDE6d6u2Ymwe68pOqYEGXBaHxaWNQqt28DLr1tn3zMGNe/nroA97+j1ToxhR739pgN60M7qwlwVxtavpha1OwQGVbHpefl/CTGfqFDj1f55wTDI4cGVradVuPrMP5TPrjOUSrPfCAt78ltp8Xjz0WDO64Y8Pb9usXWh9LdfXWt61/CQRCx7L9WjoWu+5lLACAlDyXYaq4R0zLAbKHTe0e7mG+9GOPPaYTTzzRTSn3q1O4vSNb1mOkjEdLuuvYUfruO++ZlGeeKS1eHDuLsn6Sno3DmvVY3ctwxqfVq7TkUbeP1VScMsVbumenTt4H7OUBWcefe+4JjbM2qPI7F2rq39aqcnVb9eq6USUXdFbhRfsrkBOo9/8ZVE3N1s+Fxabz8wOa9YdZGvbjHNdlPGIGpypUduIdOvfp0Zryw6kxmwN5NWvncSpqUyF98ok/7dzDT7r9H61axbRyNMD5TPrjOUTCas1YXRiv3cBbmuXo51giZW6GT26am7kJAIgLpooDQCtZTcvw9PBIbPszzzzjApd+dgq34GG0DuAW12pODNAegsWwopVFtKClxa/qx1gDCqooGKrMKFlX9F5ScKykIqm6Rlq+3Pscdb+CluEHVL9Le05ARZcMVtElTe9uj8keW3FxIMJjD7jtOd2KVHrYcSqeH2maeoVKD7tJgUef1tjjb9OkZ7wFLa1aZ22UaepuSvm+N6noig+8fXCzD4OxurPaA3vvPalfv9AHyfD/V6xp5V7qEwAA0p8FFa1+ZKzalLafVxak9BrkjEdncvt7ZzUym3o8ts4e06WXhv7upeO0caa/A8hiBC4BoBHLmowWtDS23fZrrlixIa91K7fbTvrTn6T775defTV6JmU4S9LuN2oWZViok87WUc5Jk6S995asvucuu8QeqB20d29p9Wpp/frY/zmWSmr7RZsIEC7G2QyWUBj7sQfU/Zn/qGxUscrnBDQ1ME6VwR7qFfhGJcEpKjw8qMCMUHS36FcHq+CZ8pjZme0O3E8vvh69iY8FRxd920fBwj4KFBQoOLdCZbWHb93IJ2eOAkPzpd13Dz3p0f7f7f9ozZrQB1IvH4LsPyZSZNueczIzASCz+FWb0g9+dSb3q25nKgYLaVwEINvFe+56pqCeEJA9zjnnnLo6lZEutt32a45YtSPfeisYHDjQW9kpK1Xo5ZjNLrEZqx5l+LLLLt5rLdr/U3hQkS62fcSI5tdv9Js9/vCYw/Ug7Xr9epC1tcGVhx0bLFB50/U1Vea2n3N2bTAvt9bTQxo8OBj894S1wcNj1Oz0XLRz1KhgMCcn9n62z9lnx65BatupiZkROJ9JfzyH8E1ralP6JVzj0mpZtqbGZSLqdiajVqbdX1P/N7bOLtTuBJCGqHEZJ9QTAjKDveWVlZW56eDhepRjx47VYYcdpokTJ+qcc87RokWLPNW4tFqYXqeHx6pdWZ99mR9tn0ZlHmPXo6w/iFhTgb3Wmtpvv1Dtytdei1040y5e61dde23zinEmS1WVgjGyM2ct7O7pYTdkf5Ij1+IsmxVUYFiRt/+j446Tnn029l0OGSLNmxd7P/vd8KEcApKL85n0x3MIX6VCVmG4NqVpKvvTS23KTKyVac9N//6RM0nDU/qXLPH+nKXC8w0g661p7rlMvCOpmYJvt4H0Zx2+I3UAb9++vVuOHz8+ZldxW2/bbT+vvCbKeb00O+nQa2qmZd/Fyra07baf12N66SoezujzPYU0mLTsTC8Pe++9g8GDDmrm8+71/8hLpqsNYo89vGXENjPDGKmJ85n0x3OIjNTa7E+/Mjf97nLeGl5PHm2/RGaR2mO3+7TsVVsm4v8CQFafy+QkIpoKAKmQaVlcXKwKy1TbXKOy/nLDhg1uOXnyZH3xxRcqLS1VvmWubW7EU39p6217uGO4nfnZF/3WaHvUqNDSrtf/ot6SHL18oX3AAdJBB0Xe19bbl+OWTdmMBx+qX7j5sUds0mL7WdOdWCmhtn3Fii3FI+3BWgroyJGhpV239eGaiOEOOZv/P13KaP1l/e5AXo+ZCmy8loFoqa8zZoSW9bJXvTzsuXNDNUqPPTZ2EqndzrJq7f8gOKtMs/76psbv8rJG9Z7vlnbd1tf9H1k2baxiqfZc9ugRe79GTZEAAPCVZTB++mkoI/KBB0JLyyT0mtkYrttpGv9BbU7dzubUyow3vxoX1c8ibfzYrB62rbftXth+lgVq2a2nnx5a2nWvtweAFqA5D4CsYNPDZ3s4yfzXv/6lftaNefNtysvLNXXq1Lpp5SUlJSosLKwLWkbrY1O/p4nFfGLFA03fvtKUKc3rAO7hwUc/wa7fpKVPn60L9UdrkBMO3sWaQuytQ07zjpkGvD5s+y+NVbjFnv+nnw715vnXvwKaO3fwlt+Pb6VJl0kF0+v10bH/P/sljDWt3Br+WPTUa5d4r2UHmrMfAAAt7UweZkFOm8bdVCMbC1p6CYL6GSxMlcZFfnVcjzSFPhz8TNQU+saY/g5kPGpcekQ9ISC9jR8/XlOmTInaLdwyKseNG6d7wsUjW1m3sn65wXPPle67z3vtSs91K72wFFCLhsbqRG13bkElLzWiqHfoKy9PkRdblQHdXIuzbE6OpgXOrKvFOTZ4v4oOrw11Sl+40Ntzbux35KqrpPPOazqyXj9aHymq33i/MIKcCcH5TPrjOQTiGMjys1amXzUuLTDY1Ed2rzUu/XhM8ai36Qc6rgNZcS5DxiWArGAZk9GClsa2237xSGS0+ItlYUa//1Bw0vekQ3tMXqcCe83Sa9ZcdcTi5ffDi/q/c/ZUVqm7igNlmq2A8lSjauUoT7WapLNVEAiqVAF1j/Wc12fRVQsshj9ARSo7YB+SvJQnCEdYvaYuG7I9AQDxyty0vzkW+IoVLLT94i08/d2yGRvPhmnO9Hc/skibM4U+3gHdVM8ABeA7alwCyAiWPG5dvi2zctSoUW5p18NJ5TbNOycn+lueZVzafl5ZPCRcrzDyMUNZk+HYkK+1K72w2p1ffhl7v/D07+bUo4RvvPx+HH64dMwx3mthbiltGrpBddAOHti8DK13pU0V4zm3O775Zqlbt9B1C27W1kaPnFqKsS0jBULrR1ibU4PVApz2n2WZIxZEffLJ0NKuu0htVeg2XvcDACAetTL9Ep7+brWE6rPgqdfAnB9TzuMxhd7OBeyLzgcfDC291FTyOv3d2PR3r8dszVjicRwADcW7W1CmoIMjkJ7dwm29bZ85c2aTHcIbX2bVa9dtXaGtWWL9ptF2PdxM3NZ5afZo+4XGGaeG2ZEG+tRTweAuuzSzZbW3jtnwn5ffj+b8znltRlr3tMd6zisrg8E99wwGc3K8/07F6lQ+ZEgwuNNO3rumeulOX1PjvYu91xd7BuF8Jv3xHAJp0OXcb63p5O1Hx/VU63Du53j86rbu13FSrXN7Ko0FWXsuQ+DSI04SgdRUW1vrgpO5ublNBiJtvW2vqanxtJ8dz0sQ6emng8G+fb3FZiwGErd4YLSBNg4IRTphbSqQg6SI9fth18NPb6zfObtEesqb+v30FLvzGjn1ehkwwNt+9kBGjPC270knNS9iG7dvFFIT5zPpj+cQSJBMCthY8Mz+ljY+MQivixVc8yP42XgsTR3Dy1iMPSde/tbbfvEei5/H8TMA6sfvr5/BWKAeApdxwkkikJqak0npJTPTWKAmVsJWc2Iz9RMZfeVloO3aBYOFhcHgnDlZFZzJVM3JovQaYxw8OBj86COPvx5eIqeWkdmjR+zMTDvOXnt5z+C0QHys+/Z6sQ8RAwcGg3feGQzus4/37Mx4ZGYmONuT85n0x3MIIClZpK0NftYPgEb7++wlAOpHxqVfY/HrOH4HUlsbcPQzGAu08lyGruIe0cERyIxu4faWV15erqlTp7pGPFbTsqSkRIWFhQpsrl3ktfmiaddO2rSp6ZJ/W3V4bg4vjUWa2yXS11blSIbmdrL32qncSmVZWapYxwyUefyd++tfpcsui72f/Q7ff39ooNHqQFl9WqvvZYX/k8HGOWhQ87qke9Hczus+4Hwm/fEcAkhKx/VIXbz79QvV/fRSb9Ovru1+dFz3ayx+P6bWdm6P1LQofK7vpTZqPLrIt/Z3z0+pNJYstaa55zKewpvg220gRR1//PGeMi5HhgtNeuAlqcwSsSyRcfnyZiYyesmu8jJ1denSYPDgg5s/Vx1pz+vMZq/JCM3KHN6c5VubkxucqaLgOfp3cKRK3dKu2/pm15n0OlCbKu4l27N3b//qcIb/c88+u/l1M/3ImI5DCQfOZ9IfzyGApGrNFGS/pnj7kQHq11j8Ok4qZZGmWk1TP2VqLdIMP5ehqziAtPXRRx/ptddei7lfc7uFL18eO0vNvqjr2jWUtGiZaPZl67hx0siRoaVdt/UNkqW8dDqO1WF57tzQN6D27baHx+5uZ9mVyBj2O+Xld85Lp/L995cGD97SQDxawuO//x36hr1qygwVdXpdwzVLUzROT2qkW9p1W2/b3Q28dqf3MlDb/qtfxX5hWurzVVdF7npe3223hf4DYrH7fO89713Sw+y1bE/K+PHSqFGhpV239caerOYeEwCAZLO/y5Y5eNppoWVzMtX86HDuV8d1v8bi13H86Nxu5w2RsiSNnYPY7BXbL95jaZwB2nhcli1r6217ovg5FtvXPpPZ57jTTw8t7XoiH082iXsoNUPw7TaQHNYsx+pYnnPOOS5r0pZ2fcGCBcGOHTt6yras3y08WsKjJYk9/HAw2K1bHBIZvWZXvfyyf5liLRooMomfncrtS/rjjgs1Fs/NrY3wa1zbMEnQazcqLwP1+hpqTran125He+zhLQ37Zz/z/njOOMNbHVCfX7+cz6Q/nkMAacvPJj+tzXrzayx+HcePLMdUyv6s/38T7eSyOc93azIcU7EWaWsfUyofJwaa88QJJ4lA4sVqppOfn+9+bt++fTAnJydmt/BosQTrEWIBmbg13fF6AtC2rbf9unQJBs86Kw4DRabxo1N5cy8t+pXzEuT0Ok/e7/n0+fne9rPg5QknBIP77x85cGontJ07e//PbEaZCy84n0l/PIcA0pofTX5SbSx+Ni5qTQDU74BjKgRj/ZrinYrBWL+mrT+WYsfxgMBlnHCSCCSWBRot4GiBx0gByYMOOih48cUXBz///POY3cJjJWs1vlhcIVIyVItKz/kdHbJgRpJq5CGzeD2P69nT2371kwTj0jDbaxanl/28voasxqXf0d3m/mf6hPOZ9MdzCCCY7R3OU3EsfhyntQFQPzNa/QjG+pUB6keGY6plo/rZQT6QQsfxiMBlnHCSCCSWTQdv3hTwWvdz/Snldt3WN+dvzL77BoMvvBAMfvttM5vuxOJ1Pq6XyGr9YIbXzDIgAq+xOzvH9Zp4OHhwMPjxx2nyq+nlNeT1DaRr19A3Hl6CnNtsk5SMac5n0h/PIYCMkEqNTVJpmm1rA6B+ZrS2diyZ2HDIjwCoX4+pOsWOE8dzmYD9k+w6mxnZrh1Aq4wfP15TpkxRdZRmHNZ0Z9y4cbrnnns8HC/UBydabw+rLW5NTiZNCl23d0frizF1aqi/jTXiKSmRCgtDPUXq2I7WaGPatC07jh0bajoS3tHLAKxxydFHS888E/PxuCYfdvxmDRRomvWFsp5QVqvdfg3t1zS8tL441kfHmv54+TX2wl5r1p/HXjbhX1EvL6O4ifUasu02EGua1VQznfADevll6aijQq/PWI4/Xlq9OvYx6/8n+YDzmfTHcwgAGS7coM+a31hTn2hNDJtiDWIuuaRhExpr7DlhQuymRX6OxW5rDWus+U1TYSc7v7FmSkuWRD6mnVNZ45tYZs4MNYuK51j8Go9fj2lWih0njucyMfqIAkByVFZWRg1aGttu+3nx5ZfeOoWvWLHlerjhcTg+2KyIj0U/99lHmjMnFPGxCEw4Ihr5AUlXXCGtXRs7mGEBlWYNFIjdqTxW/NvLr7EX9Rtm269ttJdR/cBp3MR6Ddl2G0Sk6G64S7pd33XX0Os+1pcUvXtL998f+5h8+QAAQHZ2bm8pC06Gzy9aGvz0Yyx224kTQx27w18Eh4XPbyyYGm1cfnU492Msxv4fLcAZKwBq+7V0rF73+yrFjhNHOUm7ZwCIYOPGjVq8eHHM/SzjspdFVzYnTNmXRZYRNmpUaGnXa2ulhx4KBWViHy8UrKkT6aDhP1K2tJMCCzKacKAivHz33dAf+nC2VrQTBltv220/C1ZY0CI8qPpLghmIc+zOEphnzAgtG2c7evk13n9/ab/9Yt+f/UpbkDTWy8jW2/akzw8JR3ftPcBSs0eODC3tuq0PR1YtuhvrWxLbblFhr8cEAABoScDxtNNCy5YELf1gQdRHH5X69m243oJ7tj5WBqgFXb3wsl9rx1I/AGoafx7zGgD16zFtn2LHiSOminvEtBwgMSyDcsyYMZpt3xB6MGvWLA0aVBQxaalr19BsTK/qZmB7mTu7cGHz0uq9zsc1TP9GivLya2yxtyefjH0si9Ndfrm3l1H96ggpzeu0cp+ngHvF+Uz64zkEAKSdlk4592uKtx9j8Ws6vl+PqSbFjtMMTBUHkPLs+5KysjJNmzbNBSota3Ls2LHuzWv06NH6/PPP3X6BQMBdai1tspHc3Fzl5+eroKDQxQQjZWvVD1puu61dD6qmZutggTUvz88PhGZge0kBO+44qVOnracabH3gUNE+G6TX+bihB8/0b6QkL7/Gdj0czIyV4Wwvj5ycUHZ0tH3tvuzlkNRamF54nVaeEoMFAABIgJZOOfdrircfY/FrOr5fjyk3xY4TR2RcesS324A/qqqqVFxc7DIqbaq31akMLy0YWbM5Q2n77bfX/fffr+uvv77JfQsKClRaWqqFC7t7ytb685+ln/90lYoHf6bZa/ZXnjapWnnKU7Wq1UYFXRaqdMHO6j6gm/cCxV5ZWpnNvQWyhNeXkO33l794y860oOjjj0ujR3tLWk66FM2a5nwm/fEcAgCyjp8NhzLtMU1PsePE4VyGwKVHnCQCrWdvN0VFRaqoqKgLUNaXk5PjsisPOeQQPf7449phhx3cbcrLyzV16tS67MySkhIVFha6bMxQl+OgqqsjBwLycoNu6uo9i4sUnFuh8tqhmqoSVaqXeqlSJZqqwpy5CgwaKF1zjXTrrdIrr8TOpGxqGuhWd54XmjfrofM5kCmaM1v63HOl++7z9nLq2VNaudLbDOyUz8xMEs5n0h/PIQAgK/kxxTtTH1NNih0nBqaKA0hZNj08Wu3K8JTwP/3pTy5oGRJQMGjTpbdMma7fG6dyefSgpamukSrfXCa9OVu2Z5HK3aXhnUtasEA66STvb+pW66P+N1LRmnAAWaQ5s6Wb06l8xQpvncoHDWpel3KCnAAAACnOjynemfqYclPsOD4jcAkgYaymZXiqdyS2/eGHH9ZRRx0VsQmIBR8GDJC22UY6sO/XylMPN9074jFVrV5vPufvg7GBHH20ZN3PY6WVucKZQHbxWtI13Kk82svI6oX/8EPoy99YmZlnnRU69mefRe9SHs7MjPY+01TPLAKcAAAAQOIQuASQMDbVO1rQ0th22y9WfxxramaG1byjah0Z/Zhq46aDe7LXXtIJJ0g33hh9PxvImWdGTu2iCQfgqceU1+xMq7gQK8HZfPpp9O3hzEwr42N1OK1mZrQ+XOEg56pVzcviBAAAANB6BC4BJEzPnj3r6lhGy7i0OpYWKIgyq7zOspUdVaByVShfNU28peWqWvmqUGGH/0kbYnQAtyjE0KHSDTdIc+Z4y6S0qIvXTuEA4tqp3Cs7zgUXhO7HS5DTxnbttd4CnLzkAQAAAP8QuASQED/88IO++uqrqEHLcMalNd+x4EVeXvT6lbm5QW3btUaTvjlJxTWPabYKt+oWbkHL0tyTFBg+THrmGW/1KJtToM9rWhmAqGK9jLzWwtx/f2nhwuj72Mv4u++8jSsnJ9SvK9oXKfXra4bH73VaOdPPAQAAgMgIXAKIu+XLl+vEE0903cSjyc3NVX5+vusYfuutsZvuWLCgcru91f3Db1Tm2u0Ubt0tXOUKWNLkFVdIa9d6r0fptUAfgITwUgvTXsK77y698070zEz7DmKnnaRvvgldorHvWhYtip3tafdvbxU2Tq91M5tTXxMAAADIRgQuAfgmGAy6zuHWhMfqVNqU70MOOUTXX3+9li1b5vZp166d+vfvrw8++KCuUU94aUHL0tJSBQIBtVtj7YN7uK7iUZvudN/kPuEHKipUVNNEt/BwNMOiCc3JojRkUgIpw2sitGVb3ntv9GPZ/v/4RyjQOGVK7ICkZUXGmqJuwdT//EdauVJ6803p88+33FdT08pnzYpex5fp5wAAAACBSwA+qaqqUnFxsWbPnl0XiLQMykn15nb27dtXTzzxhA444EDdccdC/f3va7V6dVt17bpRv/hFZ1188f7KyQl9Qr+o832ariu8Nd3xGpAkixJIa15ewl4zM21/C0jGmn5uxxg4MNTRPFbwcsMG6fHHYx/P3qrs/ufObd70c6A+yxb+4Yfm365TJ6lDh8jHjFYKOpqOHaVttml6mwX0m3o9etG+vdS5c9PbrGnWpk0tO27btlLXrk1vW71a2rixZcdt00bq1q3pbTbxw94nWsLeu7bdtult69ZJ33/fsuPa+2YP+564CevXey+r0ZSePZteb7+3a9a0/LjbbRcq49GYPWf23LXmb4ydPjZm7/2WId9S9ntmv29NZfR/+23Lj9uliyUENL1thX3/30K8R4TwHhHCe8QWvEf49x5hv+vNEoQnq1evtv9+twTQUG1tbbCgoCCYa0UnpSYvnTt3Di5btiy4cmUwWFBgb2fBYF5ew+XhhwfddnfMnxwbLFBZMFeb3LbGF1tv22uPHxkeRDA4a1YweM45weDIkaGlXbf1ALJKtPcZW1/3PlMbup6bG2z6fSY3tP3ll5ve3vjSvr23/bxebMz2VuYnzmfSX/g5lFa36PfqrrsiH7tHj5b/vv7+95GPu88+LT/uBRdEPm5RUcuPO2ZM5OPatpYe18YUiT2Wlh7X/g8jsf/7lh7XnvNI7HelNe9hkTzySOuOW1nZ9HFnzmzdcRctavq4tr41x7VxNcUeR2uOa/+PkbTmuLxHhC68R4QuvEdsufAeEfTxPaJ556NNxKEBoHlserhlWtZE+ap07dq1+vDDxZunRgabnBo5d25QxSNrFZx4hwKzZqpUxa65jrGmO/Y5LbTUlqY7vXuFbhxOtbrnHmnGjNCS7hZAVmdm2nTsceOkkSNDS7tu68N1I8PTzy0D04S/RQ8vwwnbw4aFsjgtm6Eptt6227fHtq9f7L0xVudzAAAAIJMxVRxAq1lNy/D08Ehs+623vqrZs23OY9PBxGAwoNlzAyqf+5iKtEHdtSF20x2bIwoAjXgtUeu1goSXihR2fdddpTlzYtfN7NtXstK/0abG2fFsLAAAAEC2InAJoNWsEU+0oKWx7Yve3l95gWpVByO/9eSqWlM7X6CiteWu8Elg5croTXfCXcABII5BTq8BzrFjvdXNvOyy0CUae1vluxkASHMLFkhfvSdtv3309H0AQJMCofntiGXNmjXq2rWrVq9erS5W5RRAnTFjxuixxx6Luo9lXO6+7Ry9V3lojKMFNXLfTzRj6hqpf//IKU524mcpTuE5nwCQAuysygKgsZoD2bR1m1Yeaz+/u4pzPpP+ws/hxx+vVufOzX8OabwRQuONEBpvxLHxxlNPSb/9rbp+9Z7abi51pB13lCZOlE48MaMbbzSF94gQ3iO2yPr3iKxuzrNGu+7q/XyUwKVHnOgDTXv11Vd11FFHuddILEf3mqf/c4HLyJ/CrYbluD3m654PCkIr7C2KLuAA0oidvHr5zsXrfn7ifCb98RwCaWD6dPtmf+tP8OFz10cfdcFLAMhGzT2XYao4gBZ78cUXNXr0aK2zr/Hq2FzLsZKsMJt1lZimnJw52nffC/XC2wdHDVqaarVRyXbPSCpoXqE6AEgRXqeVe90PAJBGLHXwkkuaTjuydfbmfumloW+umDYOADERuATQIo8++qhOP/10bdo8/+KQQ47Re+/dpLVrB0tuOoy9vVjdy/Hq1GmhHntsZ51y2BdasHJnNx28qQCm1bfM1zwV7t2K3HUASAFev3PhuxkAyDCWRv/FF5G3W/By6dLQflYzBAAQFYFLAFFZNYmysjLXOdya8PTq1culdd9+++11+xQXj9Y33zyq778PFxNp02C5bt0gnXNOQPf86UPNuGCyXtaPNUcFblp4tfKUp2qXaZmvCpWqWIEzn0jCIwUAAABa6auv/N0PALIcgUsAEVVVVam4uFizZ892zXWsM3hOTo5qrVLwZj/72c90xhn/1pFHRp7qUlMTcF8qfzdgka7TdfqDrlO5CjVVJapUL/VSpUo0VYU5cxUYSqdwAAAApCnrHu7nfgCQ5QhcAoiYaWlBywpreeu6pVVv7m62JWjZt29f3XPPPTrvvJy6hhKR5OXUaur9ta4Cpk0SL1K5ivIqGnajGLq5GwWF3QAAAJCOrLuadQ9ftqzpOpd2nmvbbT8AQEwELgE0yaaHW6ZlNMuWLXP7VFYWRQ1amuragMuudG66STrsMLpRAAAAILNYw52JE0Ndxe28tn7wMnyeO2ECjXkAwCMClwCaZDUtw9PDI7HtU6dOVYcOsbtKWB1LmxKu006TrrxyS0cKAAAAIJOceKJ1sgx1F6/fqMcyLS1oadsBAJ4QuATQJGvEEy1oaWz7Bx901aJFsY9nzXdKDl0s3XMPWZUAAADIbBacLC4OdQ+3RjxW09Kmh5NpCQDNQuASQJO22247BQIBV+sykpycEzVv3p9VUxNeY/tuHZTMzQ0qPz+gwrKbm9oMAAAAZB4LUg4bluxRAEBay0n2AACkng0bNuj999+vF7S0Kd3/llS6eWnX+6u29iHV1LRzeww5ZJMO6/yO+zlPm1wQM7SU8rd5S6VTVpFoCQAAAAAAPCPjEkAD69ev1+jRozV//nxJ3TYHKwslF4S0twybPj5eUrl23nmyPvvsPJ1+elD3fvYTtV1XpnIN1VSVuEY8VtOyRFNV+N1cBcblW8cfpokDAAAAAABPCFwCqLNu3TqNGjVKL7/8sruek/OUamsP3by1TaPlUG2//RDdead0fKcyBY4I3aZI5e7SQK1C9X3Ky2nIAwAAAAAAPCFwCWQpmwZeVlbmuodbI57u3btrwYIFeuutt9z2Dh2O1fr1Q6McIVfz5+eqSxcpMHWatRi3bj2Rd7ftU6cSuAQAAAAAAJ4QuASyUFVVlYqLizV79mzl5eVt1T28S5cuKiq6T88+6zEWWVkZfUdj220/AAAAAAAAD2jOA2RhpqUFLSsqKtz1xkFLs8suuygY7Ok9Ftmrl80rj76zRTltPwAAAAAAAA8IXAJZxqaHW6ZlTU1NxH1synht7dfeY5E77CDVWiHLGFHOkpIWjhoAAAAAAGQbApdAlrGaljY9PJrc3Dx99NFib7HIA96Rbr45+o65uVJBgVRo3ckBAAAAAABiI3AJZBlrxNPU9PAt8lRT8zd9+GGhh1hkUIX/OF3auDG0snfvzYfIa7jMz5dKS6VAwJfHAAAAAAAAMh/NeYAs06tXL+Xk5KjWpVNah++xttZCmpIek3SJpJ/U7d+/v/Tpp1uahoeXoVhkQIG1T0rHHy/16CHXzWf+/FDHHit+afPIbXq4ZVoStAQAAAAAAM1A4BLIMrvttptqa7tIKpVkWZWbNr8VWBbm+M1LqU2bWt1/f45OPVUqL48Si+y+kzRnjmQ1M9u1k4qKQhcAAAAAAIBWIHAJZJF33nlHf/rTDZKetvnbm9e2abQMKBDYpP/7vzwNG+bakKsoWKYiTQtlZQZ7SjVnSBru9nW6WCAUAAAAAADAPwQugSyxYsUKjRw5UuvWHbg50zKSXAWDuaFsyqoqqbhYmj17yxxxazV+772hueJPPSV1757ARwEAAAAAALIFzXmALLBx40addNJJWrJkyeaaltGa84RilFPvD4aClhUVoZXhhj7hVuO2fuRIl5EJAAAAAADgNwKXQIYLBoP6+c9/rtmWNSkrQ7lTzGRri1FWvv9tKNPSaldGMnduqAAmAAAAAACAz5gqDmRYkLKsrEzTpk1TZWWl6yDepk0bTZ482W1v3769jj76R26GdzhxMlLGZa9v3tsyPTzajta1h2Y8AAAAAADAZwQugQxRVVWl4uJil1mZl5en6urqumXYfffdpzfe6KkZM6Ify25Sst0z0ofRp5SHUjMrfXoEAAAAAAAAWxC4BDIk09KClhWb61GGg5XhZU5Ojvr166e1a0/RLbdEP1ZubqjvTmHPxbHv2KVm9vLhEQAAAAAAADREjUsgA9j0cMu0rIlQj7K2tlaffXa0zj3XWoWH7LDDlthj/aUFLUv/tkyBp56MfccuNbPEh0cAAAAAAADQEIFLIANYTUubFh7ZzyX9q+7a//t/0tLPg5r11zc1bpfZGtn7Fbe062Wzguq+X1/pnHNip2YWFEiFhf49EAAAAAAAgM2YKg5kAGvEs6WWpTXKGSvJpnBb/clHJf26bt8rrpBuvqpKgeHFKpo9W0XhBjzf5kmXVUvTC6TSUum226Ru3SydU7Ip6OH9wkuXmlkqBbZkcQIAAAAAAPiFwCWQAax7eG5uD9XUPCbJMiA3bX55WzBzvKRXLUVSgwa9rZtvOlaBYcWhYKQJBzzDS1tfXBwKWP75z1ZAUyovD3UPt0Y8VtPSpodbpiVBSwAAAAAAECeBoHX1QExr1qxR165dtXr1anXp0iXZwwEamDlzlo44wio/5Ef4PsKCkq/p5Zc3anggKA0fHvugs2ZJRZa9CQDIFJzPpD+eQwAAkE3nMtS4BDJAbW3B5kzLSEnUtn6IAoFCK4i5pRNPJLbdMiwBAAAAAACShKniQAa4+up3JO0tqU3EffLygpo2LaBhNt27rh5mBLbd9gMAAFmlpqZGs2fP1ldffaXtt99eBQUFyrWGfAAAAElAxiWQ5l555RW99tpnUYOWpro6EIpFtm0b+6CWcWm1LAEAQNaYPn26+vfvr+HDh+v00093S7tu6wEAAJKBwCWQxlatWqWf/vSnkpZvbsgTIxbZdpX08suxD2wZl9aABwAAZAULTo4ZM0ZffPFFg/XLli1z6wleAgCAZCBwCaSxn//85/r0008lTfOQcSmVvPwzqaoq+kFtOlhBQahrOAAAyIrp4Zdccoma6tkZXnfppZe6/QAAABKJwCWQxsaNG6eePXuqY8dO9tEieixy329VuOap0IpBg6TDDgv9HG7UE17m50ulpVIgEPfxAwCA5LOalo0zLRsHL5cuXer2AwAASCSa8wBp7Nhjj9XLL7+tYcO66fvvtwQa8wI1qg7mKC9Qq+pgrvLzgyot3U6B8kelm26SnnlG6tZNKi8PdQ+34pdW09Kmh1umJUFLAACyhjXi8XM/AAAAvxC4BNKAZTqUlZVp2rRpqqysVK9evTR27FgVFBTpiit669tvQ/v9uOtr+s3qK/WASlSpHuqlb1SiKSoMBhVQqVRcLI0cKeVsTrYuKgpdAABA1rLu4X7uBwAA4JdAsKliNtjKmjVr1LVrV61evVpdunRJ9nCQRaqqqlRcXOymZ+Xl5am6urpuueuut+vjjy9z+/Vu860W1AxSn9ovm54rblPAy8rIpgSALMb5TPqLx3NotSute7g14mnqo0EgENCOO+6oJUuWKNfOKQAAABJ0LkONSyCF2YcHC1pWVFS46xasrL9csuSP6tHjZQUCQU3bdGrTQUtjxfStLpVNDQcAAKjHgpETJ06sC1LWF74+YcIEgpYAACDhCFwCKcymh1umZaQunrW1Vfrmmx/r6SGX6ci8sugHs+Y7Vs8SAACgkRNPPFGPPvqo+vbt22C9ZVraetsOAACQaNS4BFKY1bQMTwuXrBblWEm9JFXaVgttuu3bL51uaZjRD2bbrQkPAABAEyw4GS5PY414rKZlQUEBmZYAACBpCFwCKcwa8VRXd5KssY4KJW3a/LK1DMzxkspVXV2sFbazTeWKVrLWMi6tczgAAEAEFqQcNmxYsocBAADgMFUcSGE9e1qgcYak/M1r2liEst53DvnK0wzt+v366EHLcMZlSUm8hwwAAAAAAOALApdACttrr/MlFURJjs5TtQq09Nt9oh/IpngVFEiFlrUJAAAAAACQ+tI2cHn33Xerf//+at++vQ499FC9+uqrUfe3Toh77rmnOnTooH79+umyyy7Thg0bEjZeoCWmWRlLNz08sjxt0lSVSNtuKw0atHllXsNlfr5UWhq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" ] @@ -4190,32 +4129,21 @@ "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃        Test metric               DataLoader 0        ┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
-       "│       test_accuracy           0.8505200147628784     │\n",
-       "│         test_loss             0.7480539679527283     │\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "
\n" - ], - "text/plain": [ - "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n", - "┃\u001b[1m \u001b[0m\u001b[1m Test metric \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m DataLoader 0 \u001b[0m\u001b[1m \u001b[0m┃\n", - "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n", - "│\u001b[36m \u001b[0m\u001b[36m test_accuracy \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.8505200147628784 \u001b[0m\u001b[35m \u001b[0m│\n", - "│\u001b[36m \u001b[0m\u001b[36m test_loss \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m 0.7480539679527283 \u001b[0m\u001b[35m \u001b[0m│\n", - "└───────────────────────────┴───────────────────────────┘\n" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " Test metric DataLoader 0\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + " test_accuracy 0.8462799787521362\n", + " test_loss 0.7731857895851135\n", + "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n" + ] }, { "data": { "text/plain": [ - "[{'test_loss': 0.7480539679527283, 'test_accuracy': 0.8505200147628784}]" + "[{'test_loss': 0.7731857895851135, 'test_accuracy': 0.8462799787521362}]" ] }, "execution_count": 91, @@ -4255,7 +4183,7 @@ }, { "data": { - "image/png": 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", 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" ] @@ -4621,7 +4549,18 @@ "execution_count": 103, "id": "72ab9a66", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70820/3890359531.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " Y_t = torch.tensor(Y[mask].astype(np.float32))\n", + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70820/3890359531.py:4: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n", + " Y_t = torch.tensor(Y[mask].astype(np.float32))\n" + ] + } + ], "source": [ "datasets = []\n", "for mask in [train, ~train]:\n", @@ -4803,27 +4742,16 @@ ] }, { - "data": { - "text/html": [ - "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
-       "┃        Test metric               DataLoader 0        ┃\n",
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-       "│         test_loss              0.616382360458374     │\n",
-       "│          test_r2              0.4150230884552002     │\n",
-       "└───────────────────────────┴───────────────────────────┘\n",
-       "
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diff --git a/Ch11-surv-lab.ipynb b/Ch11-surv-lab.ipynb index a97dbac..b7a861e 100644 --- a/Ch11-surv-lab.ipynb +++ b/Ch11-surv-lab.ipynb @@ -1,11 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "id": "bd75cc44", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "id": "13a32f39", @@ -43,10 +37,10 @@ "id": "80c238e6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:54.685557Z", - "iopub.status.busy": "2024-06-04T23:19:54.685198Z", - "iopub.status.idle": "2024-06-04T23:19:55.549394Z", - "shell.execute_reply": "2024-06-04T23:19:55.549100Z" + "iopub.execute_input": "2025-04-03T19:34:12.018813Z", + "iopub.status.busy": "2025-04-03T19:34:12.018724Z", + "iopub.status.idle": "2025-04-03T19:34:12.943735Z", + "shell.execute_reply": "2025-04-03T19:34:12.943404Z" } }, "outputs": [], @@ -73,10 +67,10 @@ "id": "a236fb17", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:55.551082Z", - "iopub.status.busy": "2024-06-04T23:19:55.550950Z", - "iopub.status.idle": "2024-06-04T23:19:55.640426Z", - "shell.execute_reply": "2024-06-04T23:19:55.640185Z" + "iopub.execute_input": "2025-04-03T19:34:12.945440Z", + "iopub.status.busy": "2025-04-03T19:34:12.945303Z", + "iopub.status.idle": "2025-04-03T19:34:13.005265Z", + "shell.execute_reply": "2025-04-03T19:34:13.005006Z" } }, "outputs": [], @@ -106,10 +100,10 @@ "id": "4dd4ddba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:55.642112Z", - "iopub.status.busy": "2024-06-04T23:19:55.642009Z", - "iopub.status.idle": "2024-06-04T23:19:55.647994Z", - "shell.execute_reply": "2024-06-04T23:19:55.647761Z" + "iopub.execute_input": "2025-04-03T19:34:13.006753Z", + "iopub.status.busy": "2025-04-03T19:34:13.006648Z", + "iopub.status.idle": "2025-04-03T19:34:13.012550Z", + "shell.execute_reply": "2025-04-03T19:34:13.012293Z" } }, "outputs": [ @@ -144,10 +138,10 @@ "id": "ae9435b1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:55.649419Z", - "iopub.status.busy": "2024-06-04T23:19:55.649331Z", - "iopub.status.idle": "2024-06-04T23:19:55.651992Z", - "shell.execute_reply": "2024-06-04T23:19:55.651800Z" + "iopub.execute_input": "2025-04-03T19:34:13.013818Z", + "iopub.status.busy": "2025-04-03T19:34:13.013731Z", + "iopub.status.idle": "2025-04-03T19:34:13.016497Z", + "shell.execute_reply": "2025-04-03T19:34:13.016291Z" }, "lines_to_next_cell": 2 }, @@ -176,10 +170,10 @@ "id": "a6e94032", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:55.653141Z", - "iopub.status.busy": "2024-06-04T23:19:55.653065Z", - "iopub.status.idle": "2024-06-04T23:19:55.655301Z", - "shell.execute_reply": "2024-06-04T23:19:55.655136Z" + "iopub.execute_input": "2025-04-03T19:34:13.017537Z", + "iopub.status.busy": "2025-04-03T19:34:13.017456Z", + "iopub.status.idle": "2025-04-03T19:34:13.019991Z", + "shell.execute_reply": "2025-04-03T19:34:13.019789Z" }, "lines_to_next_cell": 2 }, @@ -210,10 +204,10 @@ "id": "afa05da1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:55.656326Z", - "iopub.status.busy": "2024-06-04T23:19:55.656246Z", - "iopub.status.idle": "2024-06-04T23:19:55.658308Z", - "shell.execute_reply": "2024-06-04T23:19:55.658119Z" + "iopub.execute_input": "2025-04-03T19:34:13.021104Z", + "iopub.status.busy": "2025-04-03T19:34:13.021020Z", + "iopub.status.idle": "2025-04-03T19:34:13.023271Z", + "shell.execute_reply": "2025-04-03T19:34:13.023062Z" }, "lines_to_next_cell": 2 }, @@ -267,10 +261,10 @@ "id": "34c7c534", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:55.659492Z", - "iopub.status.busy": "2024-06-04T23:19:55.659420Z", - "iopub.status.idle": "2024-06-04T23:19:55.846265Z", - "shell.execute_reply": "2024-06-04T23:19:55.845461Z" + "iopub.execute_input": "2025-04-03T19:34:13.024340Z", + "iopub.status.busy": "2025-04-03T19:34:13.024274Z", + "iopub.status.idle": "2025-04-03T19:34:13.214355Z", + "shell.execute_reply": "2025-04-03T19:34:13.211396Z" } }, "outputs": [ @@ -286,7 +280,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -333,16 +327,24 @@ "id": "5e742ebe", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:55.849368Z", - "iopub.status.busy": "2024-06-04T23:19:55.849075Z", - "iopub.status.idle": "2024-06-04T23:19:55.960684Z", - "shell.execute_reply": "2024-06-04T23:19:55.960431Z" + "iopub.execute_input": "2025-04-03T19:34:13.217705Z", + "iopub.status.busy": "2025-04-03T19:34:13.217547Z", + "iopub.status.idle": "2025-04-03T19:34:13.319409Z", + "shell.execute_reply": "2025-04-03T19:34:13.319126Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70612/380004221.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " for sex, df in BrainCancer.groupby('sex'):\n" + ] + }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -378,10 +380,10 @@ "id": "4106e397", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:55.962264Z", - "iopub.status.busy": "2024-06-04T23:19:55.962134Z", - "iopub.status.idle": "2024-06-04T23:19:56.004551Z", - "shell.execute_reply": "2024-06-04T23:19:56.004317Z" + "iopub.execute_input": "2025-04-03T19:34:13.320825Z", + "iopub.status.busy": "2025-04-03T19:34:13.320720Z", + "iopub.status.idle": "2025-04-03T19:34:13.369010Z", + "shell.execute_reply": "2025-04-03T19:34:13.368744Z" }, "lines_to_next_cell": 2 }, @@ -491,10 +493,10 @@ "id": "8962942d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.005821Z", - "iopub.status.busy": "2024-06-04T23:19:56.005710Z", - "iopub.status.idle": "2024-06-04T23:19:56.022741Z", - "shell.execute_reply": "2024-06-04T23:19:56.022512Z" + "iopub.execute_input": "2025-04-03T19:34:13.370379Z", + "iopub.status.busy": "2025-04-03T19:34:13.370240Z", + "iopub.status.idle": "2025-04-03T19:34:13.390186Z", + "shell.execute_reply": "2025-04-03T19:34:13.389904Z" } }, "outputs": [ @@ -584,10 +586,10 @@ "id": "d923fe3d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.024051Z", - "iopub.status.busy": "2024-06-04T23:19:56.023976Z", - "iopub.status.idle": "2024-06-04T23:19:56.028225Z", - "shell.execute_reply": "2024-06-04T23:19:56.027998Z" + "iopub.execute_input": "2025-04-03T19:34:13.391470Z", + "iopub.status.busy": "2025-04-03T19:34:13.391389Z", + "iopub.status.idle": "2025-04-03T19:34:13.395931Z", + "shell.execute_reply": "2025-04-03T19:34:13.395714Z" } }, "outputs": [ @@ -690,10 +692,10 @@ "id": "43b23849", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.029525Z", - "iopub.status.busy": "2024-06-04T23:19:56.029451Z", - "iopub.status.idle": "2024-06-04T23:19:56.055622Z", - "shell.execute_reply": "2024-06-04T23:19:56.055386Z" + "iopub.execute_input": "2025-04-03T19:34:13.397443Z", + "iopub.status.busy": "2025-04-03T19:34:13.397345Z", + "iopub.status.idle": "2025-04-03T19:34:13.425936Z", + "shell.execute_reply": "2025-04-03T19:34:13.425693Z" } }, "outputs": [ @@ -838,10 +840,10 @@ "id": "20f95f4d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.056935Z", - "iopub.status.busy": "2024-06-04T23:19:56.056860Z", - "iopub.status.idle": "2024-06-04T23:19:56.059439Z", - "shell.execute_reply": "2024-06-04T23:19:56.059190Z" + "iopub.execute_input": "2025-04-03T19:34:13.427427Z", + "iopub.status.busy": "2025-04-03T19:34:13.427328Z", + "iopub.status.idle": "2025-04-03T19:34:13.430341Z", + "shell.execute_reply": "2025-04-03T19:34:13.430045Z" } }, "outputs": [], @@ -871,10 +873,10 @@ "id": "18ef52a9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.060778Z", - "iopub.status.busy": "2024-06-04T23:19:56.060701Z", - "iopub.status.idle": "2024-06-04T23:19:56.065090Z", - "shell.execute_reply": "2024-06-04T23:19:56.064856Z" + "iopub.execute_input": "2025-04-03T19:34:13.431840Z", + "iopub.status.busy": "2025-04-03T19:34:13.431760Z", + "iopub.status.idle": "2025-04-03T19:34:13.436751Z", + "shell.execute_reply": "2025-04-03T19:34:13.436503Z" } }, "outputs": [ @@ -999,10 +1001,10 @@ "id": "dc826bbc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.066333Z", - "iopub.status.busy": "2024-06-04T23:19:56.066235Z", - "iopub.status.idle": "2024-06-04T23:19:56.073007Z", - "shell.execute_reply": "2024-06-04T23:19:56.072802Z" + "iopub.execute_input": "2025-04-03T19:34:13.438051Z", + "iopub.status.busy": "2025-04-03T19:34:13.437925Z", + "iopub.status.idle": "2025-04-03T19:34:13.445359Z", + "shell.execute_reply": "2025-04-03T19:34:13.445106Z" } }, "outputs": [ @@ -1141,10 +1143,10 @@ "id": "867045f7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.074287Z", - "iopub.status.busy": "2024-06-04T23:19:56.074217Z", - "iopub.status.idle": "2024-06-04T23:19:56.079015Z", - "shell.execute_reply": "2024-06-04T23:19:56.078796Z" + "iopub.execute_input": "2025-04-03T19:34:13.446788Z", + "iopub.status.busy": "2025-04-03T19:34:13.446666Z", + "iopub.status.idle": "2025-04-03T19:34:13.451983Z", + "shell.execute_reply": "2025-04-03T19:34:13.451782Z" }, "lines_to_next_cell": 0 }, @@ -1302,17 +1304,17 @@ "id": "9fa60bac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.080399Z", - "iopub.status.busy": "2024-06-04T23:19:56.080330Z", - "iopub.status.idle": "2024-06-04T23:19:56.151344Z", - "shell.execute_reply": "2024-06-04T23:19:56.151104Z" + "iopub.execute_input": "2025-04-03T19:34:13.453343Z", + "iopub.status.busy": "2025-04-03T19:34:13.453269Z", + "iopub.status.idle": "2025-04-03T19:34:13.508384Z", + "shell.execute_reply": "2025-04-03T19:34:13.508148Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1345,16 +1347,16 @@ "id": "f8c1a98e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.152821Z", - "iopub.status.busy": "2024-06-04T23:19:56.152701Z", - "iopub.status.idle": "2024-06-04T23:19:56.230052Z", - "shell.execute_reply": "2024-06-04T23:19:56.229811Z" + "iopub.execute_input": "2025-04-03T19:34:13.509713Z", + "iopub.status.busy": "2025-04-03T19:34:13.509616Z", + "iopub.status.idle": "2025-04-03T19:34:13.575866Z", + "shell.execute_reply": "2025-04-03T19:34:13.575533Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1390,10 +1392,10 @@ "id": "cd1ed9ac", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.231448Z", - "iopub.status.busy": "2024-06-04T23:19:56.231371Z", - "iopub.status.idle": "2024-06-04T23:19:56.253732Z", - "shell.execute_reply": "2024-06-04T23:19:56.253504Z" + "iopub.execute_input": "2025-04-03T19:34:13.577381Z", + "iopub.status.busy": "2025-04-03T19:34:13.577276Z", + "iopub.status.idle": "2025-04-03T19:34:13.603522Z", + "shell.execute_reply": "2025-04-03T19:34:13.603251Z" }, "lines_to_next_cell": 2 }, @@ -1479,10 +1481,10 @@ "id": "d53335b0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.255073Z", - "iopub.status.busy": "2024-06-04T23:19:56.255003Z", - "iopub.status.idle": "2024-06-04T23:19:56.284350Z", - "shell.execute_reply": "2024-06-04T23:19:56.284045Z" + "iopub.execute_input": "2025-04-03T19:34:13.604965Z", + "iopub.status.busy": "2025-04-03T19:34:13.604889Z", + "iopub.status.idle": "2025-04-03T19:34:13.639892Z", + "shell.execute_reply": "2025-04-03T19:34:13.639595Z" } }, "outputs": [ @@ -1623,10 +1625,10 @@ "id": "582c992b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.285647Z", - "iopub.status.busy": "2024-06-04T23:19:56.285576Z", - "iopub.status.idle": "2024-06-04T23:19:56.288228Z", - "shell.execute_reply": "2024-06-04T23:19:56.287999Z" + "iopub.execute_input": "2025-04-03T19:34:13.641344Z", + "iopub.status.busy": "2025-04-03T19:34:13.641249Z", + "iopub.status.idle": "2025-04-03T19:34:13.644691Z", + "shell.execute_reply": "2025-04-03T19:34:13.644397Z" } }, "outputs": [], @@ -1661,10 +1663,10 @@ "id": "f3367dad", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.289419Z", - "iopub.status.busy": "2024-06-04T23:19:56.289350Z", - "iopub.status.idle": "2024-06-04T23:19:56.295467Z", - "shell.execute_reply": "2024-06-04T23:19:56.295239Z" + "iopub.execute_input": "2025-04-03T19:34:13.646236Z", + "iopub.status.busy": "2025-04-03T19:34:13.646138Z", + "iopub.status.idle": "2025-04-03T19:34:13.653905Z", + "shell.execute_reply": "2025-04-03T19:34:13.653638Z" } }, "outputs": [], @@ -1693,10 +1695,10 @@ "id": "fdfd2789", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.296977Z", - "iopub.status.busy": "2024-06-04T23:19:56.296905Z", - "iopub.status.idle": "2024-06-04T23:19:56.300297Z", - "shell.execute_reply": "2024-06-04T23:19:56.300080Z" + "iopub.execute_input": "2025-04-03T19:34:13.655350Z", + "iopub.status.busy": "2025-04-03T19:34:13.655251Z", + "iopub.status.idle": "2025-04-03T19:34:13.659296Z", + "shell.execute_reply": "2025-04-03T19:34:13.659041Z" } }, "outputs": [ @@ -1805,10 +1807,10 @@ "id": "1a9b68c8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.301604Z", - "iopub.status.busy": "2024-06-04T23:19:56.301530Z", - "iopub.status.idle": "2024-06-04T23:19:56.305801Z", - "shell.execute_reply": "2024-06-04T23:19:56.305262Z" + "iopub.execute_input": "2025-04-03T19:34:13.660480Z", + "iopub.status.busy": "2025-04-03T19:34:13.660411Z", + "iopub.status.idle": "2025-04-03T19:34:13.701074Z", + "shell.execute_reply": "2025-04-03T19:34:13.693437Z" } }, "outputs": [], @@ -1852,10 +1854,10 @@ "id": "d2118fdd", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.310515Z", - "iopub.status.busy": "2024-06-04T23:19:56.310277Z", - "iopub.status.idle": "2024-06-04T23:19:56.313678Z", - "shell.execute_reply": "2024-06-04T23:19:56.312612Z" + "iopub.execute_input": "2025-04-03T19:34:13.732130Z", + "iopub.status.busy": "2025-04-03T19:34:13.731547Z", + "iopub.status.idle": "2025-04-03T19:34:13.737683Z", + "shell.execute_reply": "2025-04-03T19:34:13.734951Z" }, "lines_to_next_cell": 0 }, @@ -1883,10 +1885,10 @@ "id": "68304ba6", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.316852Z", - "iopub.status.busy": "2024-06-04T23:19:56.316648Z", - "iopub.status.idle": "2024-06-04T23:19:56.419919Z", - "shell.execute_reply": "2024-06-04T23:19:56.419384Z" + "iopub.execute_input": "2025-04-03T19:34:13.746538Z", + "iopub.status.busy": "2025-04-03T19:34:13.745498Z", + "iopub.status.idle": "2025-04-03T19:34:13.870126Z", + "shell.execute_reply": "2025-04-03T19:34:13.866238Z" } }, "outputs": [], @@ -1912,10 +1914,10 @@ "id": "becf4cd2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.423676Z", - "iopub.status.busy": "2024-06-04T23:19:56.423359Z", - "iopub.status.idle": "2024-06-04T23:19:56.434279Z", - "shell.execute_reply": "2024-06-04T23:19:56.433259Z" + "iopub.execute_input": "2025-04-03T19:34:13.875564Z", + "iopub.status.busy": "2025-04-03T19:34:13.875279Z", + "iopub.status.idle": "2025-04-03T19:34:13.886526Z", + "shell.execute_reply": "2025-04-03T19:34:13.884362Z" }, "lines_to_next_cell": 2 }, @@ -2020,10 +2022,10 @@ "id": "e480331c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.437399Z", - "iopub.status.busy": "2024-06-04T23:19:56.437090Z", - "iopub.status.idle": "2024-06-04T23:19:56.442236Z", - "shell.execute_reply": "2024-06-04T23:19:56.441064Z" + "iopub.execute_input": "2025-04-03T19:34:13.891617Z", + "iopub.status.busy": "2025-04-03T19:34:13.891299Z", + "iopub.status.idle": "2025-04-03T19:34:13.898541Z", + "shell.execute_reply": "2025-04-03T19:34:13.896872Z" } }, "outputs": [ @@ -2056,13 +2058,21 @@ "id": "a194c96e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.445459Z", - "iopub.status.busy": "2024-06-04T23:19:56.445197Z", - "iopub.status.idle": "2024-06-04T23:19:56.618294Z", - "shell.execute_reply": "2024-06-04T23:19:56.618033Z" + "iopub.execute_input": "2025-04-03T19:34:13.901959Z", + "iopub.status.busy": "2025-04-03T19:34:13.901236Z", + "iopub.status.idle": "2025-04-03T19:34:13.997840Z", + "shell.execute_reply": "2025-04-03T19:34:13.997575Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70612/1365832640.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " for center, df in D.groupby('Center'):\n" + ] + }, { "data": { "text/plain": [ @@ -2075,7 +2085,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2108,13 +2118,21 @@ "id": "3c9eae8a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.619764Z", - "iopub.status.busy": "2024-06-04T23:19:56.619647Z", - "iopub.status.idle": "2024-06-04T23:19:56.725906Z", - "shell.execute_reply": "2024-06-04T23:19:56.725634Z" + "iopub.execute_input": "2025-04-03T19:34:13.999260Z", + "iopub.status.busy": "2025-04-03T19:34:13.999157Z", + "iopub.status.idle": "2025-04-03T19:34:14.134838Z", + "shell.execute_reply": "2025-04-03T19:34:14.134512Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/16/8y65_zv174qgdp4ktlmpv12h0000gq/T/ipykernel_70612/957073325.py:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n", + " for time, df in D.groupby('Time'):\n" + ] + }, { "data": { "text/plain": [ @@ -2127,7 +2145,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -2164,10 +2182,10 @@ "id": "bb8a37eb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.727330Z", - "iopub.status.busy": "2024-06-04T23:19:56.727224Z", - "iopub.status.idle": "2024-06-04T23:19:56.739716Z", - "shell.execute_reply": "2024-06-04T23:19:56.739485Z" + "iopub.execute_input": "2025-04-03T19:34:14.136408Z", + "iopub.status.busy": "2025-04-03T19:34:14.136300Z", + "iopub.status.idle": "2025-04-03T19:34:14.150882Z", + "shell.execute_reply": "2025-04-03T19:34:14.150594Z" }, "lines_to_next_cell": 2 }, @@ -2271,10 +2289,10 @@ "id": "e3299d19", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.741062Z", - "iopub.status.busy": "2024-06-04T23:19:56.740984Z", - "iopub.status.idle": "2024-06-04T23:19:56.753655Z", - "shell.execute_reply": "2024-06-04T23:19:56.753435Z" + "iopub.execute_input": "2025-04-03T19:34:14.152239Z", + "iopub.status.busy": "2025-04-03T19:34:14.152163Z", + "iopub.status.idle": "2025-04-03T19:34:14.165607Z", + "shell.execute_reply": "2025-04-03T19:34:14.165368Z" }, "lines_to_next_cell": 2 }, @@ -2381,10 +2399,10 @@ "id": "b9004e25", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.755032Z", - "iopub.status.busy": "2024-06-04T23:19:56.754959Z", - "iopub.status.idle": "2024-06-04T23:19:56.856726Z", - "shell.execute_reply": "2024-06-04T23:19:56.856494Z" + "iopub.execute_input": "2025-04-03T19:34:14.166793Z", + "iopub.status.busy": "2025-04-03T19:34:14.166721Z", + "iopub.status.idle": "2025-04-03T19:34:14.264677Z", + "shell.execute_reply": "2025-04-03T19:34:14.264450Z" }, "lines_to_next_cell": 2 }, @@ -2486,10 +2504,10 @@ "id": "1780384e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.858260Z", - "iopub.status.busy": "2024-06-04T23:19:56.858163Z", - "iopub.status.idle": "2024-06-04T23:19:56.951866Z", - "shell.execute_reply": "2024-06-04T23:19:56.951640Z" + "iopub.execute_input": "2025-04-03T19:34:14.265906Z", + "iopub.status.busy": "2025-04-03T19:34:14.265824Z", + "iopub.status.idle": "2025-04-03T19:34:14.362449Z", + "shell.execute_reply": "2025-04-03T19:34:14.362216Z" }, "lines_to_next_cell": 2 }, @@ -2594,10 +2612,10 @@ "id": "19f93f96", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:56.953169Z", - "iopub.status.busy": "2024-06-04T23:19:56.953096Z", - "iopub.status.idle": "2024-06-04T23:19:57.089908Z", - "shell.execute_reply": "2024-06-04T23:19:57.089269Z" + "iopub.execute_input": "2025-04-03T19:34:14.363727Z", + "iopub.status.busy": "2025-04-03T19:34:14.363654Z", + "iopub.status.idle": "2025-04-03T19:34:14.527687Z", + "shell.execute_reply": "2025-04-03T19:34:14.525353Z" }, "lines_to_next_cell": 2 }, @@ -2715,8 +2733,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -2728,7 +2746,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/Ch12-unsup-lab.Rmd b/Ch12-unsup-lab.Rmd index 258ee24..1e720ff 100644 --- a/Ch12-unsup-lab.Rmd +++ b/Ch12-unsup-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Unsupervised Learning diff --git a/Ch12-unsup-lab.ipynb b/Ch12-unsup-lab.ipynb index b24414c..336638b 100644 --- a/Ch12-unsup-lab.ipynb +++ b/Ch12-unsup-lab.ipynb @@ -22,7 +22,7 @@ "In this lab we demonstrate PCA and clustering on several datasets.\n", "As in other labs, we import some of our libraries at this top\n", "level. This makes the code more readable, as scanning the first few\n", - "lines of the notebook tell us what libraries are used in this\n", + "lines of the notebook tells us what libraries are used in this\n", "notebook." ] }, @@ -32,10 +32,10 @@ "id": "1f7a6c07", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:58.245147Z", - "iopub.status.busy": "2024-06-04T23:19:58.244639Z", - "iopub.status.idle": "2024-06-04T23:19:58.980175Z", - "shell.execute_reply": "2024-06-04T23:19:58.979888Z" + "iopub.execute_input": "2025-04-03T19:34:15.517565Z", + "iopub.status.busy": "2025-04-03T19:34:15.517488Z", + "iopub.status.idle": "2025-04-03T19:34:16.192207Z", + "shell.execute_reply": "2025-04-03T19:34:16.191872Z" }, "lines_to_next_cell": 0 }, @@ -65,10 +65,10 @@ "id": "1e14c3f7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:58.981864Z", - "iopub.status.busy": "2024-06-04T23:19:58.981743Z", - "iopub.status.idle": "2024-06-04T23:19:59.038480Z", - "shell.execute_reply": "2024-06-04T23:19:59.038262Z" + "iopub.execute_input": "2025-04-03T19:34:16.193845Z", + "iopub.status.busy": "2025-04-03T19:34:16.193668Z", + "iopub.status.idle": "2025-04-03T19:34:16.247772Z", + "shell.execute_reply": "2025-04-03T19:34:16.247507Z" } }, "outputs": [], @@ -102,10 +102,10 @@ "id": "9d155861", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:19:59.039866Z", - "iopub.status.busy": "2024-06-04T23:19:59.039756Z", - "iopub.status.idle": "2024-06-04T23:20:00.015887Z", - "shell.execute_reply": "2024-06-04T23:20:00.015393Z" + "iopub.execute_input": "2025-04-03T19:34:16.249169Z", + "iopub.status.busy": "2025-04-03T19:34:16.249050Z", + "iopub.status.idle": "2025-04-03T19:34:16.591244Z", + "shell.execute_reply": "2025-04-03T19:34:16.591005Z" } }, "outputs": [ @@ -577,10 +577,10 @@ "id": "98cd6d32", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.019290Z", - "iopub.status.busy": "2024-06-04T23:20:00.018842Z", - "iopub.status.idle": "2024-06-04T23:20:00.022851Z", - "shell.execute_reply": "2024-06-04T23:20:00.022397Z" + "iopub.execute_input": "2025-04-03T19:34:16.592640Z", + "iopub.status.busy": "2025-04-03T19:34:16.592559Z", + "iopub.status.idle": "2025-04-03T19:34:16.594802Z", + "shell.execute_reply": "2025-04-03T19:34:16.594568Z" } }, "outputs": [ @@ -613,10 +613,10 @@ "id": "93783538", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.025039Z", - "iopub.status.busy": "2024-06-04T23:20:00.024890Z", - "iopub.status.idle": "2024-06-04T23:20:00.029072Z", - "shell.execute_reply": "2024-06-04T23:20:00.028691Z" + "iopub.execute_input": "2025-04-03T19:34:16.595936Z", + "iopub.status.busy": "2025-04-03T19:34:16.595867Z", + "iopub.status.idle": "2025-04-03T19:34:16.598335Z", + "shell.execute_reply": "2025-04-03T19:34:16.598132Z" }, "lines_to_next_cell": 2 }, @@ -656,10 +656,10 @@ "id": "1f89db32", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.031071Z", - "iopub.status.busy": "2024-06-04T23:20:00.030915Z", - "iopub.status.idle": "2024-06-04T23:20:00.034379Z", - "shell.execute_reply": "2024-06-04T23:20:00.034033Z" + "iopub.execute_input": "2025-04-03T19:34:16.599631Z", + "iopub.status.busy": "2025-04-03T19:34:16.599562Z", + "iopub.status.idle": "2025-04-03T19:34:16.601880Z", + "shell.execute_reply": "2025-04-03T19:34:16.601672Z" } }, "outputs": [ @@ -710,10 +710,10 @@ "id": "dc04eed1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.036290Z", - "iopub.status.busy": "2024-06-04T23:20:00.036153Z", - "iopub.status.idle": "2024-06-04T23:20:00.039673Z", - "shell.execute_reply": "2024-06-04T23:20:00.039402Z" + "iopub.execute_input": "2025-04-03T19:34:16.603007Z", + "iopub.status.busy": "2025-04-03T19:34:16.602942Z", + "iopub.status.idle": "2025-04-03T19:34:16.605582Z", + "shell.execute_reply": "2025-04-03T19:34:16.605366Z" }, "lines_to_next_cell": 0 }, @@ -740,10 +740,10 @@ "id": "a350773d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.041302Z", - "iopub.status.busy": "2024-06-04T23:20:00.041187Z", - "iopub.status.idle": "2024-06-04T23:20:00.042988Z", - "shell.execute_reply": "2024-06-04T23:20:00.042688Z" + "iopub.execute_input": "2025-04-03T19:34:16.606705Z", + "iopub.status.busy": "2025-04-03T19:34:16.606635Z", + "iopub.status.idle": "2025-04-03T19:34:16.608125Z", + "shell.execute_reply": "2025-04-03T19:34:16.607914Z" }, "lines_to_next_cell": 0 }, @@ -769,10 +769,10 @@ "id": "f4fe8410", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.044752Z", - "iopub.status.busy": "2024-06-04T23:20:00.044629Z", - "iopub.status.idle": "2024-06-04T23:20:00.048289Z", - "shell.execute_reply": "2024-06-04T23:20:00.048020Z" + "iopub.execute_input": "2025-04-03T19:34:16.609197Z", + "iopub.status.busy": "2025-04-03T19:34:16.609133Z", + "iopub.status.idle": "2025-04-03T19:34:16.612202Z", + "shell.execute_reply": "2025-04-03T19:34:16.611999Z" } }, "outputs": [ @@ -1214,10 +1214,10 @@ "id": "605a0ca8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.049772Z", - "iopub.status.busy": "2024-06-04T23:20:00.049682Z", - "iopub.status.idle": "2024-06-04T23:20:00.051822Z", - "shell.execute_reply": "2024-06-04T23:20:00.051552Z" + "iopub.execute_input": "2025-04-03T19:34:16.613377Z", + "iopub.status.busy": "2025-04-03T19:34:16.613307Z", + "iopub.status.idle": "2025-04-03T19:34:16.615349Z", + "shell.execute_reply": "2025-04-03T19:34:16.615157Z" } }, "outputs": [ @@ -1251,10 +1251,10 @@ "id": "963374b1", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.053341Z", - "iopub.status.busy": "2024-06-04T23:20:00.053256Z", - "iopub.status.idle": "2024-06-04T23:20:00.055003Z", - "shell.execute_reply": "2024-06-04T23:20:00.054717Z" + "iopub.execute_input": "2025-04-03T19:34:16.616457Z", + "iopub.status.busy": "2025-04-03T19:34:16.616391Z", + "iopub.status.idle": "2025-04-03T19:34:16.618136Z", + "shell.execute_reply": "2025-04-03T19:34:16.617904Z" }, "lines_to_next_cell": 0 }, @@ -1280,10 +1280,10 @@ "id": "8d158f66", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.056542Z", - "iopub.status.busy": "2024-06-04T23:20:00.056453Z", - "iopub.status.idle": "2024-06-04T23:20:00.058672Z", - "shell.execute_reply": "2024-06-04T23:20:00.058391Z" + "iopub.execute_input": "2025-04-03T19:34:16.619275Z", + "iopub.status.busy": "2025-04-03T19:34:16.619209Z", + "iopub.status.idle": "2025-04-03T19:34:16.621134Z", + "shell.execute_reply": "2025-04-03T19:34:16.620875Z" } }, "outputs": [ @@ -1323,17 +1323,17 @@ "id": "0ca1c80b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.060083Z", - "iopub.status.busy": "2024-06-04T23:20:00.059998Z", - "iopub.status.idle": "2024-06-04T23:20:00.163601Z", - "shell.execute_reply": "2024-06-04T23:20:00.163039Z" + "iopub.execute_input": "2025-04-03T19:34:16.622221Z", + "iopub.status.busy": "2025-04-03T19:34:16.622159Z", + "iopub.status.idle": "2025-04-03T19:34:16.755562Z", + "shell.execute_reply": "2025-04-03T19:34:16.754910Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1373,16 +1373,16 @@ "id": "4266c73b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.168170Z", - "iopub.status.busy": "2024-06-04T23:20:00.167869Z", - "iopub.status.idle": "2024-06-04T23:20:00.278302Z", - "shell.execute_reply": "2024-06-04T23:20:00.277775Z" + "iopub.execute_input": "2025-04-03T19:34:16.759475Z", + "iopub.status.busy": "2025-04-03T19:34:16.759140Z", + "iopub.status.idle": "2025-04-03T19:34:16.846839Z", + "shell.execute_reply": "2025-04-03T19:34:16.843547Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1420,10 +1420,10 @@ "id": "abf3dd55", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.281501Z", - "iopub.status.busy": "2024-06-04T23:20:00.281202Z", - "iopub.status.idle": "2024-06-04T23:20:00.286262Z", - "shell.execute_reply": "2024-06-04T23:20:00.285205Z" + "iopub.execute_input": "2025-04-03T19:34:16.850831Z", + "iopub.status.busy": "2025-04-03T19:34:16.850327Z", + "iopub.status.idle": "2025-04-03T19:34:16.856281Z", + "shell.execute_reply": "2025-04-03T19:34:16.854840Z" }, "lines_to_next_cell": 2 }, @@ -1458,10 +1458,10 @@ "id": "2b86e577", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.289493Z", - "iopub.status.busy": "2024-06-04T23:20:00.288828Z", - "iopub.status.idle": "2024-06-04T23:20:00.294507Z", - "shell.execute_reply": "2024-06-04T23:20:00.294014Z" + "iopub.execute_input": "2025-04-03T19:34:16.860472Z", + "iopub.status.busy": "2025-04-03T19:34:16.859984Z", + "iopub.status.idle": "2025-04-03T19:34:16.869910Z", + "shell.execute_reply": "2025-04-03T19:34:16.865759Z" }, "lines_to_next_cell": 0 }, @@ -1496,10 +1496,10 @@ "id": "5ffec7f8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.297422Z", - "iopub.status.busy": "2024-06-04T23:20:00.297193Z", - "iopub.status.idle": "2024-06-04T23:20:00.304441Z", - "shell.execute_reply": "2024-06-04T23:20:00.303923Z" + "iopub.execute_input": "2025-04-03T19:34:16.873863Z", + "iopub.status.busy": "2025-04-03T19:34:16.872328Z", + "iopub.status.idle": "2025-04-03T19:34:16.881845Z", + "shell.execute_reply": "2025-04-03T19:34:16.877715Z" }, "lines_to_next_cell": 0 }, @@ -1537,10 +1537,10 @@ "id": "427cf5d8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.307404Z", - "iopub.status.busy": "2024-06-04T23:20:00.307167Z", - "iopub.status.idle": "2024-06-04T23:20:00.417318Z", - "shell.execute_reply": "2024-06-04T23:20:00.417075Z" + "iopub.execute_input": "2025-04-03T19:34:16.885679Z", + "iopub.status.busy": "2025-04-03T19:34:16.884446Z", + "iopub.status.idle": "2025-04-03T19:34:16.958609Z", + "shell.execute_reply": "2025-04-03T19:34:16.958311Z" }, "lines_to_next_cell": 0 }, @@ -1573,17 +1573,17 @@ "id": "4336fd05", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.418792Z", - "iopub.status.busy": "2024-06-04T23:20:00.418713Z", - "iopub.status.idle": "2024-06-04T23:20:00.490050Z", - "shell.execute_reply": "2024-06-04T23:20:00.489809Z" + "iopub.execute_input": "2025-04-03T19:34:16.960239Z", + "iopub.status.busy": "2025-04-03T19:34:16.960156Z", + "iopub.status.idle": "2025-04-03T19:34:17.006588Z", + "shell.execute_reply": "2025-04-03T19:34:17.006345Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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MCripY01bLDt+PEDO89E9GsmP4QEAUCpYiQYAAAAADjucmqFRn6/RjGU77fMWNaI0rk8L1SgXqva1y9kpnXmHDJgVaKaA1q1xZQejBgDvQhENAAAAABy0fNtB3TN1hbbFJ8ssKrvr/Lq6+8J6Cvh70qYplF3UqJKd1mmGDZgeaGYLJyvQAKB0UUQDAAAAAIeGB7w2e4NemvWXfVw1KkQvXddcbWqd2OPMFMzMFE4AgHMoogEAAACAA8MDhk1bocVbDtrnPZtV0WO9GisyJMDp0AAAJ0ERDQAAAABKeXjAw5+u1qGUDDs84LFeZ6tX86ry8WF7JgC4MopoAAAAAFAKDqWka/QXvxc4PAAA4PooogEAAABACVu27aDuzTs84IJ6GnpBXfn/PTwAAOD6KKIBAAAAQAkxAwPGz96gcYUYHgAAcG0U0QAAAACgBDA8AAA8C0U0AAAAACiF4QG9W1RzOiwAwL9AEQ0AAAAAinN4wOe/a8byY8MDWtaI0ksMDwAAj0ARDQAAAACKwdKtB3XvtOXaHn/UDg+4+4J6upvhAQDgMSiiAQAAAMC/kJGZpfGzN+rln/4ZHjDuuuZqzfAAAPAoFNEAAAC82Msvv1zo1w4dOrREYwHc0fb4Y8MDlmw9NjzgiubHhgdEBDM8AAA8DUU0AAAAL/biiy/me75//34lJycrKirKPk9ISFBoaKhiYmIoogHH+XzFTj3y6RodSmV4AAB4A4poAAAAXmzz5s25jz/88EO99tprmjRpkho0aGDPrV+/Xrfccotuu+02B6MEXG94wKjPf9eneYYHjLuuhapHMzwAADyZT3Z2dra8SFJSkiIjI5WYmKiIiAinwwEAAG7CG3KIOnXq6OOPP1aLFi3ynV+6dKmuvvrqfAU3V+QNnxFcb3jA0Avr6a7zGR4AAO6ssDkEK9EAAABg7d69WxkZGSecz8zM1N69ex2JCXDV4QHVyh4bHtCqJsMDAMBbcLsEAAAA1oUXXmi3bS5btizfKrQhQ4aoa9eujsYGOD084Lo3F+rFH/+0BbRezavom3vOpYAGAF6GlWgAAACw3n77bQ0YMECtW7dWQMCxyYJmZdoll1yit956y+nwAJcYHvB4r8bq1aKq02EBABxAEQ0AAABWhQoV9M033+jPP//UunXr7LmGDRuqfv36TocGOD48oFXNsnqpT3OGBwCAF6OIBgAAgHxq1aolM3vKDBrw9yddhPdZujVe905bwfAAAEA+/CsAAAAAKzk5WYMHD1ZoaKjOPvtsbdu2zZ6/++679dRTTzkdHlAqwwNe+vFPXfvGQltAM8MD/nd7B93btT4FNACA80W08ePH27udwcHBateunRYtWnTK17/00ktq0KCBQkJCVL16dQ0bNkwpKSmlFi8AAICneuihh7Ry5Ur9/PPPNjfLYYYKTJs27Yy+J7ke3Gl4QJ83F+qlH49N3+zdoirDAwAA+Ti6Pt8kY8OHD9frr79ukyqTNJnGtevXr1dMTMwJr//www81cuRI2/S2Y8eOtl/HTTfdJB8fH73wwguO/A4AAACe4rPPPrP5Wfv27W1+lcOsStu4cWORvx+5HtzFZ8t36tHPjg0PCDfDA3o31hXNGR4AAHChlWgmGbrllls0cOBANWrUyCZYZvuASZwKMn/+fHXq1EnXX3+9vaN58cUXq2/fvqe9owkAAIDT279/f4HFrSNHjuQrqhUWuR5cXVJKuu6dutz2PzMFNDM8wKw+o4AGAHCpIlpaWpqWLl1qtwfkBuPra58vWLCgwPeYO5LmPTmJ1KZNm+wEqe7du5/056SmpiopKSnfAQAAgBO1bt1aX3/9de7znMLZW2+9pQ4dOrhcrkeeh387PKD7uF/02YpddnjAvV3radqt7Zm+CQBwve2cBw4cUGZmpipWrJjvvHmeM1L9eOaupHnfOeecYydGZWRk6Pbbb9d//vOfk/6csWPHasyYMcUePwAAgKd58skndemll+qPP/6weda4cePsY7NCbM6cOS6X65Hn4UyHB7zy0wa98tNfysqWqkeH6KU+zel9BgBw/cECRWGa3Jrk7rXXXtOyZcs0Y8YMe7f0scceO2WD3MTExNxj+/btpRozAACAuzDFqxUrVtjiVZMmTfT999/b7Z1m5VirVq1cLtcjz8OZDg8YN+tYAc0ODxjK8AAAgIuvRCtfvrz8/Py0d+/efOfN80qVKhX4nkcffVT9+vXTzTffbJ+b5M706Lj11lv18MMP2y0CxwsKCrIHAAAATq9OnTqaOHGiW+R65HkoCoYHAADctogWGBho72jOmjVLvXr1sueysrLs87vuuqvA9yQnJ5+QPJnkzDBL/gEAAPDvmHxsw4YN2rdvn32cV+fOnQv9fcj14ErDA0zx7PMVu+zz1jXL6sU+zel9BgBwnyKaYUaeDxgwwDaxbdu2rR17bu42mglORv/+/VW1alXb78Lo0aOHnfLUokULOybdJHjmjqU5n5NgAQAA4MwsXLjQ9iXbunXrCUUrM2TA9DgrCnI9OG3Jlng7eXPHwaPy8/XR0Avq6c7z68jfz6262gAAXISjRbQ+ffrYUeqjRo3Snj171Lx5c82cOTO3Ae22bdvy3Y185JFHbAJnvu7cuVMVKlSwSdUTTzzh4G8BAADgGUwT/5wJnZUrV86dznmmyPXgWsMDWqhVzbJOhwYAcGM+2V62Nt6MPo+MjLTNZyMiIpwOBwAAuAlvyCHCwsK0cuVK1a1bV+7IGz4jFG54wD1Tl2vZtgT7/MoWVTXmirMVHhzgdGgAADfPIRxdiQYAAADXkbOF0l2LaMCny3fo0c9+12GGBwAASgBFNAAAAFh333237rvvPrv10kzGDAjIv3KnadOmjsUGFGV4QJtaZfXCtQwPAAAUL4poAAAAsK666ir7ddCgQbnnTI8y0/3jTAYLAKU1POCeqSu0M+HY8IB7LqynO85jeAAAoPhRRAMAAIC1efNmp0MAijQ84OWfNuhVhgcAAEoJRTQAAABYNWvWdDoEoFC2xSXr3mkMDwAAlC6KaAAAAF7siy++0KWXXmr7n5nHp9KzZ89SiwsoiNla/OnynRr1+d/DA4L99XgvhgcAAEoHRTQAAAAv1qtXLztIICYmxj4+GXqiwRWGBzzy6Rp9sfKf4QEv9mmuamUZHgAAKB0U0QAAALxYVlZWgY8BV7J4S7zuZXgAAMBhFNEAAAAAuO7wgFl/6dXZG+zwgBrRoXrpuuZqWYPhAQCA0kcRDQAAALmOHDmiOXPmaNu2bUpLS8t3bejQoY7FBe8cHnDPtOVanjM8oGVVjenJ8AAAgHMoogEAAMBavny5unfvruTkZFtMi46O1oEDBxQaGmp7plFEg1PDA57o3UQ9m1VxOjQAgJejiQAAAACsYcOGqUePHjp48KBCQkK0cOFCbd26Va1atdJzzz3ndHjwAolH03XP1BUaPn2lLaCZ4QHf3nMuBTQAgEtgJRoAAACsFStW6I033pCvr6/8/PyUmpqq2rVr65lnntGAAQN05ZVXOh0ivGh4wL1meMD5de1jAABcAUU0AAAAWAEBAbaAZpjtm6Yv2llnnaXIyEht377d6fDgoRgeAABwFxTRAAAAYLVo0UKLFy9WvXr11KVLF40aNcr2RJsyZYoaN27sdHjwQFvjjujeaStyhwdc1bKa/q9nI4YHAABcEj3RAAAAYD355JOqXLmyffzEE0+obNmyGjJkiPbv368333zT6fDgYcMDPlm6Q93H/WILaGZ4wCt9W+j5a5tRQAMAuCxWogEAAMBq3bp17mOznXPmzJmOxgPPHR7wyGdr9OXKXfZ521rReqFPM1UrG+p0aAAAnBJFNAAAAAClYtHmeA2b9s/wgGFd62nIeQwPAAC4B4poAAAAXt4HzcencAWMZcuWlXg88Ezpfw8PGJ9neMC465qrBcMDAABuhCIaAACAF+vVq5fTIcALhgfcM3WFVmz/Z3jAmCvOVpkg/q8IAMC98C8XAACAFxs9erTTIcCThwcs26nRn6/RkbRMOzzgyd5N1KNZFadDAwCg5IpoV155ZaG/4YwZM84sEgAAALiEJUuWaO3atfZxo0aN1KpVK6dDghsOD3j409X6atXu3OEBL17XXFWjQpwODQCAki2iRUZG5ruj9Omnn9pzOROcli5dqoSEhCIV2wAAAOBaduzYob59+2revHmKioqy50yO17FjR02dOlXVqlVzOkS44fCA4RfV1+1d6jA8AADgHUW0yZMn5z4eMWKErr32Wr3++uvy8/Oz5zIzM3XHHXcoIiKi5CIFAABAibr55puVnp5uV6E1aNDAnlu/fr0GDhxor82cOdPpEOFGwwNqljPDA1qoefVjBVkAANydT7ZZWlYEFSpU0K+//pqbWOUwCZa5SxkXFydXlpSUZFfRJSYmUvQDAACF5g05REhIiObPn28nduZldh2ce+65Sk5Olivzhs/IXYYHXN2qmv6vJ8MDAADuobA5RJH/VcvIyNC6detOKKKZc1lZWWcWLQAAABxXvXp1uxLteGbXQZUqNINH4YYHjL2yiS5vyt8XAIDnKXIRzSznHzx4sDZu3Ki2bdvac7/99pueeuopew0AAADu6dlnn9Xdd9+t8ePH5/a+NUMG7rnnHj333HNOhwcXk5icroc/yzM8IDZaL/ZheAAAwHMVeTunWW1mkqhx48Zp9+5j/2BWrlzZJlf33Xdfbp80V8UyfwAAcCa8IYcoW7as3bJpdh74+x+715rzOCwsLN9r4+Pj5Wq84TNyFb9tirPDA3Ylpsjf10fDGB4AAHBjJbad09fXVw8++KA9zA8xSFIAAADc30svveR0CHCD4QHjfvxLr/3M8AAAgPc5o06f5o7kzz//bLd0Xn/99fbcrl27bDGtTJkyxR0jAAAASsGAAQOcDgEuPjxg6NQVWvn38IBrWlXTaIYHAAC8iG9R37B161Y1adJEV1xxhe68807t37/fnn/66ad1//33l0SMAAAAKAXvvPPOSW+gPvTQQ6UeD1yD6f7yvyXb1X3cL7aAFhHsr/HXt9Sz1zSjgAYA8CpFLqKZ3mem0ezBgwftGPQcvXv31qxZs4o7PgAAAJSSoUOH6pprrrF5Xo7169erXbt2+uijjxyNDc4ND7jro+V64ONVdvqmGR7w7b2ddVnTyk6HBgCA6xfRfvnlFz3yyCMKDAzMd75WrVrauXNnccYGAACAUrR8+XLt2LHD7jr44Ycf7JTOli1bqmHDhlq5cqXT4cGB4QGXjpurr1fttsMDHrikgT66pT3TNwEAXqvI66/NdM7MzMwTzpuEKzw8vLjiAgAAQCmrU6eO5s2bp3vvvVfdunWzU9ffffdd9e3b1+nQUMrDA1768U+99vNGZWdLtcqF6iWGBwAAUPSVaBdffHG+yU0+Pj46fPiwRo8ere7duxd3fAAAAChFX3/9taZOnaoOHTooKipKkyZNsgOk4B22HDiiq19foPGzjxXQrm1dTV8PPZcCGgAAZ1JEe/755+0dykaNGiklJcVO58zZymmGCwAAAMA93XbbbbYn2ogRI2wLj1WrVtkWHmZ75/Tp050OD6UwPOCyl/MPD3jm6mYKY3gAAACWT7b5F7OIzIQmc4fSJFZmFZrplXHDDTfkGzTgqpKSkhQZGanExERFREQ4HQ4AAHAT3pBDNG7cWB988IGaNWuW77zpjWYKaybvc2Xe8BmV1PCA/3y6Wl+v3m2ft4uN1ot9mqsKvc8AAF4iqZA5xBkV0dwZyRUAADgT3pBDpKamKigoqMBrZkpngwYN5Mq84TMqbgs3xWn4tBXalZhihwcMu6i+bu9SR36+Pk6HBgCAy+UQZ7Q2+6+//tLs2bO1b98+O2ggr1GjRp3JtwQAAIBDTE4XExNz0gKa2YVgkkp49vCAcde1UDN6nwEAUHxFtIkTJ2rIkCEqX768KlWqZAcL5DCPKaIBAAC4l8qVK2v37t22kGaYHmjffPONqlevbp/HxcXZQQMFTWiHew4PuGfqcq3ccawwaoYHjO5xNr3PAAA4jSL/S/n444/riSeesH0xAAAA4P6O7+6xZcsWpaenn/I1cNPhAUt36P+++F3JaZl2eMBTVzVV9yaVnQ4NAADPLKIdPHjQTm0CAACA98i7+wDuPzygfe1ovXAtwwMAACgK3yK9WrIFtO+//76obwMAAADg0PCAbuPm2gKaGR7wYLcG+uDm9hTQAAAo6ZVodevW1aOPPqqFCxfafhkBAQH5rg8dOrSo3xIAAAAOrzI7dOiQgoOD7ZY/8/zw4cN2UpWR8xXuNzzgxR/+1IQ5x4YHxJYP00t9mjM8AACAM+STXcQGF7GxsSf/Zj4+2rRpk1wZo88BAMCZ8OQcwtfXN992zZxC2vHPXX2wgCd/RkW1+e/hAasYHgAAQLHlEEX+V3Tz5s1FfQsAAABc2OzZs50OAcU5PGDJDv3fl8eGB0SGBGjslU0YHgAAQDHgVhQAAICX69Kli9MhoJiGBzz06Sp9s3qPfc7wAAAAHCiiDR8+XI899pjCwsLs41N54YUXiis2AAAAAIWwYGOchk9fod2JKXZ4wH0XN9CtnWvLz5epqgAAlGoRbfny5UpPT899fDKMPgcAAABKT1pGll788U+9nmd4wLjrmqtpNYYHAADgSBEtb58MemYAAAAArjc8oE/r6hrVoxHDAwAAKCH8CwsAAAC4+fCAp65soksZHgAAgOsV0ZYsWaLp06dr27ZtSktLy3dtxowZxRUbAAAAHLBhwwZt3LhRnTt3VkhIiC3a0LbDNSQkp+mhGav17ZpjwwM61C6nF/o0U+VIhgcAAFDSfIv6hqlTp6pjx45au3atPv30U9sr7ffff9dPP/2kyMjIkokSAAAAJS4uLk5du3ZV/fr11b17d+3evdueHzx4sO677z6nw/N6ZnjApeN+sQU0MzxgRLeGev/mdhTQAABw1SLak08+qRdffFFffvmlAgMDNW7cOK1bt07XXnutatSoUTJRAgAAoMQNGzZM/v7+drdBaGho7vk+ffpo5syZjsbm7cMDnp65Tte/tdBO36xdPkyf3tFJQ86rw/RNAABceTunWdp/2WWX2cemiHbkyBG7vN8kXRdccIHGjBlTEnECAACghH3//ff67rvvVK1atXzn69Wrp61btzoWlzfbtP+w7p22Ind4wHVtjg0PCA2ktTEAAKWtyP/6li1bVocOHbKPq1atqjVr1qhJkyZKSEhQcnJyScQIAACAUmBujuZdgZYjPj5eQUFBjsTkrUwfuulLtuv/vvhDR9MZHgAAgFtu5zQNZn/44Qf7+JprrtE999yjW265RX379tWFF15YEjECAACgFJx77rl67733cp+b3QZZWVl65plndP755zsam7cND7jjg2Ua8clqW0AzwwNm3nsuBTQAANxtJdqrr76qlJQU+/jhhx9WQECA5s+fr6uuukqPPPJIScQIAACAUmCKZeamqJnEbiawP/jgg3aAlFmJNm/ePKfD8wrzNx7Q8GkrtScpxQ4PuP+SBrrl3Nr0PgMAwB2LaNHR0bmPfX19NXLkyOKOCQAAAA5o3Lix/vzzT3vTNDw8XIcPH9aVV16pO++8U5UrswqqpIcHvPDDn3pj7kZlZ8sODxh3XQs1qRbpdGgAAKAoRbSkpCQVVkRERKFfCwAAANcSGRlpdxugdIcH3DN1hVbvPDY8oG/b6nr0coYHAADgagr1L3NUVJTtiXG65qfmNZmZmcUVGwAAAErR5MmTVaZMGdv3Nq///e9/doDUgAEDHIvNE5n8edri7Rrz5bHhAVGhx4YHdGvMqj8AANy2iDZ79uySjwQAAACOGjt2rN54440TzsfExOjWW2+liFbMwwNGfrJaM3/fY593rFNOL1zbXJUig50ODQAA/JsiWpcuXQrzMgAAALixbdu2KTY29oTzNWvWtNdQPOZvOKDh048NDwjw89H9Fx8bHuDL8AAAAFzaGTVaOHjwoCZNmqS1a9fa540aNdLAgQPzDR0AAACAezErzlatWqVatWrlO79y5UqVK1fOsbg8aXjA8z+s15tzNzE8AAAAN+Rb1DfMnTvXJlYvv/yyLaaZwzw2dy3NNQAAALinvn37aujQobaVh+lza46ffvpJ99xzj6677jqnw3NrG/cf1lUT5uuNOccKaGZ4wFdDz6GABgCAJ69EMyPO+/TpowkTJsjPz8+eMwnWHXfcYa+tXr26JOIEAABACXvssce0ZcsWXXjhhfL3P5YmZmVlqX///nryySedDs+Dhgc0VbfGlZwODQAAFJFPtvmXvQhCQkK0YsUKNWjQIN/59evXq3nz5jp69KhcWVJSkh3dnpiYqIiICKfDAQAAbsKbcog///zTbuE0eV+TJk1sTzR34Gqf0cEjaXpoBsMDAADwlByiyCvRWrZsaXuhHV9EM+eaNWt2ZtECAADAZdSvX98eOHMMDwAAwPMUuYhm+mSYvhgbNmxQ+/bt7bmFCxdq/Pjxeuqpp2wz2hxNmzYt3mgBAABQYkyLjnfeeUezZs3Svn377FbOvEx/NBRieMD36/XmL/8MD3i5bws1rkrvMwAAvK6IZhrOGg8++GCB13x8fGzvB/PVJGIAAABwD+ZGqSmiXXbZZWrcuLHN51C04QH3TF2uNTuT7PO+bWvo0cvPUmhgkVNuAADggor8L/rmzZtLJhIAAAA4aurUqZo+fbq6d+/udChuxdxAnrp4u/7L8AAAADxakYtop2osm7MCzVtlZmVr0eZ47TuUopjwYLWNjZYffS8AAICbCAwMVN26dZ0Ow63yPDM8YOSMVfru9732dZ3qltPz1zA8AAAAT1TkItpNN91k+5+FhYXlO2/Goffr10+//PKLvNHMNbvt6PLdiSm55ypHBmt0j0bq1riyo7EBAAAUxn333adx48bp1Vdf9eobo4XN8/q0rq6PFm/T3qRUOzzggUsa6OZzGB4AAICnKnIRzYw7NwMD3n//fXXo0MGee/fdd+3AgQsuuEDemlgNeX+Zso87vycxxZ6fcGNLCmkAAMDl/frrr5o9e7a+/fZbnX322QoICMh3fcaMGfI2J8vzTEHtpVl/2ce1K4Tp5esYHgAAgKcrchFt0aJF+s9//qPzzjvP3q00UzpNovXCCy/olltukTcu7Td3Jo9PrAxzztyHNNcvalSJrZ0AAMClRUVFqXfv3k6H4RZ5Xo7QQD99fmcnhQfnLzgCAADPU+Qimrkj+eyzzyo0NFSPPfaY/P39NWfOnNxVad7G9MbIu7T/eCbpMtfN6zrUKVeqsQEAABTF5MmTnQ7BrfI8Izkt007jJM8DAMDz+Rb1Denp6XYF2tNPP62HHnrIFs+uvPJKffPNN/JGprlscb4OAAAAroE8DwAA/KuVaK1bt1ZycrJ+/vlntW/f3k7kfOaZZ2whbdCgQXrttdfkTcx0puJ8HQAAgJM+/vhjTZ8+Xdu2bVNaWlq+a8uWLZM3Ic8DAAD/aiWaKaKtWLHCFtAMM7lpxIgRWrBggebOnStvY8abm+lMp+p2Vini2Bh0AAAAV/byyy9r4MCBqlixopYvX662bduqXLly2rRpky699FJ5m9Pleea8uU6eBwCAdyhyEW3SpEkKCws74XyLFi20dOlSeRszLGB0j0b28ckSrOiwQNuYFgAAwJWZHQVvvvmmXnnlFQUGBurBBx/UDz/8YKewJyYmytucKs/LeW6uMzwKAADvUOgimlnWn3dJ/44dO5SVlZX73GzxHDdunLxRt8aVNeHGlqoUmX8pf7kygQr089Ufu5M0bPoKCmkAAMClmS2cHTt2tI9DQkJ06NAh+7hfv3766KOP5I1OlueZ5+a8uQ4AALxDoXui9e3bV7t371ZMTIx93qhRI7uts3bt2va5SbLMoAFzx9IbmQTqokaV7BQn01zW9MYwS/vnbTigwe8u1terdisiOEBP9m5st8ACAAC4mkqVKik+Pl41a9ZUjRo1tHDhQjVr1kybN2+2fXC91cnyPFagAQDgXQq9Eu34xKm4Eqnx48erVq1aCg4OVrt27bRo0aJTvj4hIUF33nmnKleurKCgINWvX99lJoOaRMqMN7+ieVX71TzvXL+Cxl3XQibH+mjRNj09c73TYQIAABToggsu0BdffGEfm95ow4YN00UXXaQ+ffqod+/eXp3rFZTnAQAA71Lk6ZzFadq0aRo+fLhef/11m1S99NJLuuSSS7R+/frcFW95me2kJpEz18zkqKpVq2rr1q2KioqSK+vepLKe7N1EI2es1utzNioyJEBDzqvjdFgAAAD5mH5oOe06TCHLDBWYP3++evbsqdtuu63I389bcj0AAOAdfLILuaTM19dXe/bsyU14wsPDtXLlytztnHv37lWVKlWUmZlZ6B9ukqk2bdro1Vdftc9N0la9enXdfffdGjly5AmvNwnYs88+q3Xr1ikgIEBnIikpSZGRkbY5bkREhErTG3M2auy36+zjsVc2Ud+2NUr15wMAgDPnZA7hrko71+MzAgAAZ6KwOUSRVqJ999139pvmJEGzZs3SmjVrcpfeF4W502imeZo+ankLdV27dtWCBQsKfI/ZXtChQwd7Z/Tzzz9XhQoVdP3112vEiBHy8/Mr8D2pqan2yPsH45TbutRRwtF0Tfh5o/7z6WqFB/vr8qZVHIsHAABg1apVaty4sc3DzONTadq0qUvleq6U5wEAAM9XpCLagAED8j0/fll/URrmHzhwwK5aq1ixYr7z5rm5+1iQTZs26aefftINN9xge2Ns2LBBd9xxh9LT0zV69OgC3zN27FiNGTNGruLBSxooITnd9kcbNm2FwoMD1KV+BafDAgAAXqp58+a5uw3MY5PPFbRRwZwvyo6D0sj1XC3PAwAAnq3QRbSc/hhOMjGYBM/06zB3I1u1aqWdO3faZf8nK6KZu5+mF0feO5RmG4FTTAL6eK/GSkpJtxM7b5+yVO/f3FatakY7FhMAAPBeZvKmWfGV89idcj1Xy/MAAIBnc2ywQPny5W1yZHqp5WWem/HqBTFTmkx/jLzL+c866yx799RsGQgMDDzhPWaqkzlciZnm9OK1zXU4JUNz/tyvgZMXa9ptHXRWZXp3AACA0lWzZk371az2Mqu6Hn30UcXGxrpFrueKeR4AAPBcvk79YJMEmbuLpq9ajpw+a6YXRkE6depkl/XnXRX3559/2oSroAKaKwv099XrN7ZS65pllZSSoX6TFmnLgSNOhwUAALyUKV598sknxfb9vD3XAwAAnsexIpphlt9PnDhR7777rtauXashQ4boyJEjGjhwoL3ev3//fM1ozfX4+Hjdc889NqH6+uuv9eSTT9rms+4oJNBPk25qY1egHTicqhsn/aY9iSlOhwUAALxUr1699NlnnxXb9/P2XA8AAHgWx7ZzGn369NH+/fs1atQou0zfNLOdOXNmbgPabdu22SlOOUyPCzMhdNiwYXY6VNWqVW2SZSY2uavIkAC9N6itrnl9vrbEJavfpN80/bYOKhvG3VYAAFC66tWrp//+97+aN2+eXUUWFhaW7/rQoUOL9P3I9QAAgCfxyS5o/JIHMw1nIyMjlZiYqIgI1+lBtj0+WVe/Pl97k1LVrHqUPri5ncoEOVrjBAAAbpBDFKdT9UIzw5HM9ExX5g2fEQAAcC6HOKMqTUJCgj7++GNt3LhRDzzwgKKjo7Vs2TJ7V9HcMUTRVY8O1fuD2+maNxZo5fYE3freEr19UxsFB/zTWBcAAKAkOT2dEwAAwKN6oq1atUr169fX008/reeee84W1IwZM2bk62mBoqtXMVzvDGyrsEA/zd8Yp6EfLVdG5j+NdQEAAAAAAOAM/zNpEHvTTTfpmWeeUXh4eO757t276/rrry/u+LxO8+pRmti/tW6avFjf/7FXI2es1jNXNZWvr4/ToQEAAC+wY8cOffHFF7ZfWVpaWr5rL7zwgmNxAQAAuF0RbfHixXrjjTdOOG+2cZqGsfj3OtYtr1eub6Eh7y/Vx0t32OEDj1x2lu1FAgAAUFJmzZqlnj17qnbt2lq3bp0aN26sLVu2yLTQbdmypdPhAQAAuNd2zqCgINtw7XhmDHmFChWKKy6vd8nZlfTM1c3s40m/btb42RucDgkAAHg405rj/vvv1+rVqxUcHKxPPvlE27dvV5cuXXTNNdc4HR4AAIB7FdHM3Ukz+jw9Pd0+N6ujzHJ/M3r8qquuKokYvdbVrapp1OWN7OPnvv9TUxZscTokAADgwdauXav+/fvbx/7+/jp69KjKlCljcz/TDxcAAMCbFbmI9vzzz+vw4cOKiYmxiZW5M1m3bl3bH+2JJ54omSi92KBzYjX0wnr28agvftfnK3Y6HRIAAPBQYWFhuX3QKleubCex5zhw4ICDkQEAALhhT7TIyEj98MMPmjdvnlauXGkLaqZHRteuXUsmQmhY13pKTE7Tuwu26r7pKxUe7K8LGlZ0OiwAAOBh2rdvr19//VVnnXWWHRp133332a2dZgq7uQYAAODNfLJNp1gvYvq5mUJgYmKiIiIi5C6ysrI1fPoKfbZil4L8ffXeoLZqV7uc02EBAOA13DWHKIz4+HhFR0dr06ZN9gZp06ZNdeTIEVtEmz9/vurVq2cnc9asWVOuzJM/IwAA4HwOUeTtnEOHDtXLL798wvlXX31V9957b9EjRaH4+vro2Wua6cKGMUrNyNLN7y7Rmp2JTocFAAA8QJUqVXTdddfZ7ZumgJaztfP111/XqlWr7IABVy+gAQAAlLQiF9FMEtWpU6cTznfs2FEff/xxccWFAgT4+Wr8DS3VNjZah1IzNODtRdq4/7DTYQEAADc3ceJE7d+/X926dVOtWrX0f//3f9qyhYFGAAAA/6qIFhcXZ5e4Hc8sd6PhbMkLDvDTWwNaq3HVCMUdSVO/t37TroSjTocFAADcWL9+/TRr1ixt2LBBAwYM0LvvvmsHR1100UWaNm1a7rABAAAAb1bkIppJqGbOnHnC+W+//Va1a9currhwChHBAXp3YFvVrhCmXYkpunHSb4o7nOp0WAAAwM3FxsZqzJgx2rx5s833zDT2QYMG2UmdpqUHAACANytyEW348OF68MEHNXr0aM2ZM8ceo0aN0siRIzVs2LCSiRInKFcmSFMGt1OVyGBt2n9EAyYv0qGUdKfDAgAAHsJMXv/ggw/03nvv2efjx493OiQAAABH+Rf1DeZuZGpqqp544gk99thj9pzpnTFhwgT179+/JGLESVSNCtGUm9vp2tcXaM3OJDts4N1Bbe2WTwAAgDO1detWTZ482W7r3L59u84//3wNHjzY6bAAAAAc5ZOdnZ19pm82DWhDQkJUpkwZuQtPHH1upnT2fXOhHTbQ9awYTbixlR1CAAAAio8n5hB5mZukZoDU22+/rZ9//llVq1bVTTfdpIEDB9obpu7A0z8jAADgbA7xryotFSpUcKsCmqdqXDXSDhsI8vfVj2v36cGPVykr64xrowAAwMvccccdtu+Z2XFQrlw5ffPNN3Y6p+mP5i4FNAAAgJJW5CLa3r177QSnKlWqyN/fX35+fvkOOKNd7XKacGNL+fv66NPlOzXmy9/1LxYZAgAAL/Lrr7/afrc7d+600zgvvvhi+fj4OB0WAACAe/dEM8v6t23bpkcffdTesSTBch0XNKyo569tpnunrdC7C7YqMjRQwy+q73RYAADAxa1atcrpEAAAADyviGbuVP7yyy9q3rx5yUSEf+WK5lWVeDRdoz7/XS/P+kuRIQEafE6s02EBAAAAAAB413bO6tWrs03QxfXvUEv3/b0C7bGv/tDHS3c4HRIAAAAAAIB3FdFeeukljRw50jabheu664K6uSvQRnyySt/9vsfpkAAAAAAAALyniNanTx879rxOnToKDw9XdHR0vgOuwfSqe7j7Wbq6VTVlZmXr7g+Xa/6GA06HBQAAXMyVV15px7ob7733nlJTU50OCQAAwDN6opmVaHAPvr4+eurKJjqUkq7vft+rW95bog9vaa9m1aOcDg0AALiIr776SkeOHFFERIQGDhyobt26KSYmxumwAAAA3L+INmDAgJKJBCXC389X465rocHvLta8DXG6afIiTb+tg+pVDHc6NAAA4AIaNmyohx56SOeff77tezt9+nRbUCtI//79Sz0+AAAAV+GT/S+mBKSkpCgtLS3fuZMlXa7CbFeIjIxUYmKiy8danA6nZuiGt37Tyu0JqhQRrP/d3kHVo0OdDgsAALfhqTnE/PnzNXz4cG3cuFHx8fG2XYdpC3E8c85cd2We+hkBAADXyCGK3BPNLPe/66677DL/sLAwlS1bNt8B11QmyF/v3NRG9WLKaE9SivpN+k37DqU4HRYAAHBYx44dtXDhQu3fv9+uRPvzzz918ODBEw5XL6ABAACUtCIX0R588EH99NNPmjBhgoKCgvTWW29pzJgxqlKlim1GC9dVNixQUwa3U7WyIdoSl6z+kxYp8Wi602EBAAAXsXnzZlWoUMHpMAAAADxjO2eNGjVssey8886zS9yWLVumunXrasqUKfroo4/0zTffyJWxzF/acuCIrn59gQ4cTlWrmmU1ZXBbhQYWuT0eAABexVtyiISEBE2aNElr1661zxs1aqTBgwfb393VectnBAAA3GQ7p1nKX7t2bfvYfOOcpf3nnHOO5s6d+29iRimpVT7MFs4igv21dOtBDXl/mdIyspwOCwAAOGzJkiWqU6eOXnzxRZvjmcM8NufMjVMAAABvVuQimimgmaX+OdOczAQn48svv1RUVFTxR4gScVblCE0e2EbBAb6a8+d+DZ++QplZZzxjAgAAeIBhw4apZ8+e2rJli2bMmGEPk/ddfvnluvfee50ODwAAwL2KaAMHDtTKlSvt45EjR2r8+PEKDg62SdcDDzxQEjGihLSqGa03+rVWgJ+Pvlq1W49+vsY2FAYAAN67Em3EiBHy9/+nzYN5bHrimmsAAADerMiNsEyxLEfXrl21bt06LV261PZFa9q0aXHHhxLWpX4FvdSnhe76aJk+/G2bokIC9GC3hk6HBQAAHGBadWzbts3uNshr+/btCg8PdywuAAAAV/Cvu8nXrFnTHnBflzWtrKSUJnpoxmq99vNGRYYE6LYudZwOCwAAlLI+ffrYIQLPPfecOnbsaM/NmzfP7jbo27ev0+EBAAC4fhHt5Zdf1q233mq3bZrHpzJ06NDiig2lqG/bGko8mq6nvl2nsd+uU0RIgD0HAAC8hyme+fj4qH///srIyLDnAgICNGTIED311FNOhwcAAOAon+xCNMGKjY21fTDKlStnH5/0m/n4aNOmTXJljD4/NVNEe33ORvn4SK/2bWlXqQEAAO/KIZKTk7Vx40b72EzmDA0NlTvwps8IAACUfg5RqJVoOdM4j38MzzOiWwMlHk3TR4u2695pyxUe7K/O9Ss4HRYAAChFpmjWpEkTp8MAAABw3+mc6enp9m7k2rVrSy4iOMqsJny8VxNd1qSy0jOzdduUpVq69aDTYQEAAAAAALhPEc30xEhJSSm5aOAS/Hx99GKf5jq3XnkdTc/UwMmLtHZ3ktNhAQAAAAAAuEcRzbjzzjv19NNP5zabhWcK9PfVG/1aqWWNKCWlZKj/24u0Ne6I02EBAAAAAAA4olA90fJavHixZs2ape+//972yggLC8t3fcaMGcUZHxwUGuivyTe1VZ83F2jdnkO6cdJv+vj2jqoYEex0aAAAAAAAAK5dRIuKitJVV11VMtHA5USGBui9wW11zesLtDUuWf0m/abpt3VQVGig06EBAIAS8Ndff2n27Nnat2+fsrKy8l0bNWqUY3EBAAA4zSc7OztbXoTR52dme3yyrn59vvYmpap59Sh9cHM7hQUVuQYLAIDb8oYcYuLEiRoyZIjKly+vSpUq2YFDOczjZcuWyZV5w2cEAACcyyEooqHQ/tx7SNe+sUAJyenqVLec3r6pjYL8/ZwOCwCAUuENOUTNmjV1xx13aMSIEXJH3vAZAQAA53KIM1pK9PHHH2v69Onatm2b0tLS8l1z9TuUOHP1K4brnYFtdf3EhZq3IU73fLRCr17fQv5+RZ5PAQAAXNDBgwd1zTXXOB0GAACASypy9ePll1/WwIEDVbFiRS1fvlxt27ZVuXLltGnTJl166aUlEyVchtnKObF/awX6+Wrm73v00IzV8rLFjAAAeCxTQDPDowAAAFAMK9Fee+01vfnmm+rbt6/eeecdPfjgg6pdu7ZtNBsfH1/Ubwc31Klueb3ct4Xu+GCp/rd0hyJDAvTwZWfl65sCAADcT926dfXoo49q4cKFdgp7QEBAvutDhw51LDYAAACnFbknWmhoqNauXWt7ZsTExOiHH35Qs2bN7CSn9u3bKy4uTq6MXhnFZ/qS7Xrw41X28QOXNNCd59d1OiQAAEqMN+QQsbGxJ71mbpaZnQeuzBs+IwAA4EY90cykJrPizBTRatSoYe9UmiLa5s2b2dbnZa5tXV1JR9P1+Ndr9ex36xUREqB+7Ws6HRYAADhDJp8DAABAMfVEu+CCC/TFF1/Yx6Y32rBhw3TRRRepT58+6t27d1G/HdzczefW1t0XHFuBNurzNfp8xU6nQwIAAMXA3BzlBikAAMAZFNG++uorZWVl2X5oDz/8sD1355136u2339ZZZ52l//73v5owYUJhvx08yPCL6qt/h5oyefZ901dq9rp9TocEAADO0HvvvWf7oYWEhNijadOmmjJlitNhAQAAOK7Q2zl79eplJ3LedNNNGjRokOrUqWPPX3fddfaA9zI9Uv6vx9lKPJquz1fs0u3vL9WUwe3UNjba6dAAAEARvPDCC3awwF133aVOnTrZc7/++qtuv/12HThwwO5AAAAA8Fa+RemRcdttt2nq1KmqX7++unTpYu9KHj16tGQjhFvw9fXRc9c00wUNY5SakaXB7yzWmp2JTocFAACK4JVXXrE7C55++mn17NnTHs8884ydzv7yyy87HR4AAIB7FNGqV6+uUaNGaePGjfrxxx9Vq1YtDRkyRJUrV7Z3JxcvXlyykcLlBfj56rUbWtoVaIdSMzTg7UXatP+w02EBAIBC2r17tzp27HjCeXPOXAMAAPBmRR4sYJx//vl69913bTL17LPPavXq1Wrfvr2d0gnvFhzgp7cGtNbZVSIUdyRN/SYt0q4EVisCAOAO6tatq+nTp59wftq0aapXr54jMQEAALhdT7SChIeH68ILL9TWrVu1bt06/fHHH8UXGdxWRHCA3h3UVte+vkCbDhxRv0m/afptHVSuTJDToQEAgFMYM2aMnbg+d+7c3J5o8+bN06xZswosrgEAAHiTM1qJZvqgmclN5513nr0rafqkDR8+XFu2bCn+COGWypcJ0pSb26lyZLA27j+imyYv1qGUdKfDAgAAp3DVVVfpt99+U/ny5fXZZ5/ZwzxetGiRevfu7XR4AAAAjvLJzs7OLuyLFy5cqLffftveiUxLS9OVV16pwYMH2+2d7iIpKUmRkZFKTExURESE0+F4vA37DuvaNxYo/kia2sVG2xVqZssnAADuhhzC9fEZAQCAkswhCr2ds1GjRlq/fr1atGihsWPH6vrrr7c/ADiVujFl9N6gtrruzYX6bXO87vpwmSbc2MoOIQAAAK6RNOYki+bxqVCYAgAA3qzQRbSuXbvqo48+YngAiqxx1Ug7bMBM6/xx7T6N+HiVnrummXx9fZwODQAAr1e2bFk7LComJkZRUVHy8Tnx32ezccGcz8zMdCRGAAAAtyqivfzyyyUbCTxa+9rl9NoNLXXrlKWasXynIkICNLpHowITdQAAUHp++uknRUdH28ezZ892OhwAAADPnM4JFMWFZ1XU89c0073TVuid+VsUGRKgYRfVdzosAAC8WpcuXXIfx8bGqnr16ifc5DIr0bZv3+5AdAAAAK6DxlQoVb1aVNWYnmfbx+Nm/aW3f93sdEgAACBPEW3//v0nnI+Pj7fXAAAAvBlFNJS6AR1rafjfK9D++9Uf+mTpDqdDAgAAeXqfHe/w4cMKDg52JCYAAAC32s5p+mT8+eefKl++vAYNGqRx48YpPDy85KODx7r7grpKSE7X2/M268FPVik82F8Xn13J6bAAAPBKw4cPt19NAe3RRx9VaGho7jUzTOC3335T8+bNHYwQAADATVaipaWl5Y48f/fdd5WSklLSccHDmST9kcvO0lUtqykzK1t3fbRc8zcecDosAAC80vLly+1hVqKtXr0697k51q1bZ6ezv/POO06HCQAA4Por0Tp06KBevXqpVatWNrkaOnSoQkJCCnzt22+/XdwxwkP5+vro6aua6FBKur7/Y69ueXeJPrq1vZpWi3I6NAAAvErOVM6BAwfaiezsOAAAADjDlWjvv/++unfvbvthmBVEiYmJOnjwYIEHUBT+fr56uW8LdaxTTkfSMjXg7UXasO+Q02EBAOB10tPTNWXKFG3dutXpUAAAANx3JVrFihX11FNP2cdmMpNJsMqVK1fSscFLBAf46c3+rXXDxIVauSNRN761SP+7vYOqR//TjwUAAJSsgIAA1ahRw/ZAAwAAQDFM59y8eTMFNBS7MkH+emdgW9WLKaM9SSnqN+k37T+U6nRYAAB4lYcfflj/+c9/FB8f73QoAAAA7l9EM+bMmaMePXqobt269ujZs6d++eWX4o8OXqVsWKCmDG6nqlEh2hKXrP5vL1Li0XSnwwIAwGu8+uqrmjt3rqpUqaIGDRqoZcuW+Q4AAABvVqjtnMf3RzNNZ6+88ko7YMCYN2+eLrzwQju16frrry+JOOElKkUG64Ob2+nq1xdo7e4kDX5nsS2shQT6OR0aAAAezwySAgAAQMF8ss24zSI466yzdOutt2rYsGH5zr/wwguaOHGi1q5dK1eWlJSkyMhIOxwhIiLC6XBwEn/sSlKfNxfoUEqGzmtQQW/2a61A/zNaOAkAQLEgh3B9fEYAAKAkc4giVyU2bdpkt3Iez2zpNP3SgOLQqEqEJt/URsEBvvp5/X4Nn75CmVlFqvcCAIAztHTpUrv7wBzLly93OhwAAACXUOQiWvXq1TVr1qwTzv/444/2GlBcWteK1us3tlKAn4++WrVboz5foyIunAQAAEWwb98+XXDBBWrTpo1t22GOVq1a2bYd+/fvdzo8AAAA9yqi3XfffTahGjJkiKZMmWKP22+/Xffee6/uv//+kokSXuu8BjF64drm8vGRPvhtm577fr3TIQEA4LHuvvtuHTp0SL///rud0GmONWvW2C0OOb1wAQAAvFWRBwuY4lmlSpX0/PPPa/r06bl90qZNm6YrrriiJGKEl+vRrIrtjfafT1dr/OyNigwJ0K2d6zgdFgAAHmfmzJl2d4HJ7XI0atRI48eP18UXX+xobAAAAG5XRDN69+5tD6C0XN+uhhKPpuvpmev05DfrbCGtT5saTocFAIBHycrKUkBAwAnnzTlzDQAAwJsx7hBuY8h5dXRbl9r28UMzVuub1budDgkAAI9i+qHdc8892rVrV+65nTt32qnspi8aAACAN6OIBrcysltDXdemusygznumLtcvf9HkGACA4vLqq6/a/me1atVSnTp17BEbG2vPvfLKK06HBwAA4H7bOQGn+Pj46IneTWyPtK9X79at7y3V+ze3U6uaZZ0ODQAAt2cmrS9btsz2RVu3bp09Z/qjde3a1enQAAAAHOcSK9FMs1pzxzM4OFjt2rXTokWLCvW+qVOn2qJKr169SjxGuA4/Xx+90KeZzq1XXkfTMzXoncVatyfJ6bAAAPAIJre66KKL7KROc/zbAhp5HgAA8BSOF9HMVM/hw4dr9OjR9s5ns2bNdMkll2jfvn2nfN+WLVt0//3369xzzy21WOE6gvz99Ea/VmpZI8oOHOg3aZG2xSU7HRYAAG5v1qxZuvzyy3O3c5rHZmXamSDPAwAAXl1Ey8zM1KRJk3T99dfbO5OmAW3eo6heeOEF3XLLLRo4cKAdof76668rNDRUb7/99iljuOGGGzRmzBjVrn2s0Ty8T2igvybf1FYNK4Vr/6FU3TBpofYmpTgdFgAAbuu1115Tt27dFB4ebgcMmCMiIkLdu3e3K8qKijwPAAB4dREtJ6EyCU7jxo3tHcW8R1GkpaVp6dKl+bYJ+Pr62ucLFiw46fv++9//KiYmRoMHDz7tz0hNTbXNcPMe8ByRoQF6b1Bb1SwXqu3xR9V/0iIlJKc5HRYAAG7pySef1IsvvqiPPvpIQ4cOtceHH35oz5lrRUGeBwAA5O2DBUx/iunTp9s7kv/WgQMHbDGuYsWK+c6b5znNbI/366+/2pVwK1asKNTPGDt2rL2TCc8VExGs9we301UT5mv93kMa+M5i+zwsiLkZAAAURUJCgl2JdryLL75YI0aMKNL3Is8DAADy9pVogYGBqlu3rpxw6NAh9evXTxMnTlT58uUL9Z6HHnpIiYmJucf27dtLPE6UvurRoXZKZ1RogJZvS9Dt7y9Vakam02EBAOBWevbsqU8//fSE859//rntjVaSyPMAAICrK/JSnfvuu0/jxo3Tq6++aicm/RsmQfLz89PevXvznTfPK1WqdMLrN27caBvN9ujRI/dcVlaW/erv76/169fbBrh5BQUF2QOer37FcE2+qY1ueOs3/fLXAd3z0Qq9en0L+fs5Pj8DAAC3YPqWPfHEE/r555/VoUMHe27hwoWaN2+ezQFffvnl3NearZ6nQp4HAAA8jU92dnZ2Ud7Qu3dvzZ49W9HR0Tr77LMVEBCQ7/qMGTOKFIAZdd62bVu98soruclSjRo1dNddd2nkyJH5XpuSkqINGzbkO/fII4/YO5emsFe/fn27Uu5UTK+MyMhIe7fSNMqF5/n1rwMa9M5ipWVm6drW1fT0VU3/dcEXAABvyCFiY2ML9Trz7+qmTZtO+zryPAAA4A4Km0MUeSVaVFSULaQVFzP2fMCAAWrdurVNsl566SUdOXLETnEy+vfvr6pVq9qeF8HBwXaYwfHxGMefh/c6p155vdy3ue74YJmmL9mhyJAA/af7WRTSAAA4jc2bNxfr9yPPAwAAnqTIRbTJkycXawB9+vTR/v37NWrUKO3Zs0fNmzfXzJkzc5vQbtu2zU5yAoqiW+PKeurKpnrwk1Wa+MtmRYUG6s7znenlBwCAO8rZrPBvbkKR5wEAAK/ezpnDJESmN4XRoEEDVahQQe6AZf7e5a1fNunxr9fax4/3aqwb29d0OiQAgJvylhzivffe07PPPqu//vrLPjfbKB944AHb9N/VectnBAAAnMkhinzrzyzBHzRokCpXrqzOnTvbo0qVKho8eLCSk5P/bdxAsbr53Nq66+8VaI9+vkZfrNzldEgAALisF154QUOGDFH37t01ffp0e3Tr1k233367XnzxRafDAwAAcJTvmfS2mDNnjr788kslJCTYw4w9N+fM1CbA1dx3cX31a19TZs3l8GkrNHvdPqdDAgDAJZkBABMmTNDTTz+tnj172uOZZ57Ra6+9lm8yJwAAgDcqchHtk08+0aRJk3TppZfaJW7mMHcrJ06cqI8//rhkogT+BdPLZUzPs3VF8yrKyMrWkA+WavGWeKfDAgDA5ezevVsdO3Y84bw5Z64BAAB4syIX0cyWzZxmsHnFxMSwnRMuy9fXR89d00wXNIxRSnqWBr2zWL/vSnQ6LAAAXErdunXtFs7jTZs2TfXq1XMkJgAAALedztmhQweNHj3aNp01o8iNo0ePasyYMfYa4KoC/Hw1/vqWGvD2Ii3aEq/+kxbpf7d3UO0KZZwODQAAl2DyOTNRc+7cuerUqZM9N2/ePM2aNavA4hoAAIA3KfJ0zjVr1uiSSy5RamqqmjVrZs+tXLnSFtS+++47nX322XJlTG1CUkq6rntjof7YnaSqUSH6eEgHVY4McTosAICL85YcYtmyZXbAwNq1x6Zbn3XWWbbvbYsWLeTqvOUzAgAAzuQQRS6iGWbb5gcffKB169blJlc33HCDQkJcvxBBcgXjwOFUXfv6Am06cER1KoTpf7d3VHRYoNNhAQBcmKfnEOnp6brtttv06KOPKjY2Vu7I0z8jAADghkU0d0ZyhRw7DibrmtcXaHdiippWi9QHN7dTeHCA02EBAFyUN+QQ5vdbsWIFRTQAAOBVkgqZQxSqJ9oXX3xhp3EGBATYx6diRqED7qBa2VBNGdxO176xQKt2JOqW95bonYFtFRzg53RoAAA4olevXvrss880bNgwp0MBAABwOf6FTaj27NljJ3Caxyfj4+OjzMzM4owPKFF1Y8ro3YFt1XfiQi3cFK+7Plyu129sKX+/Ig+uBQDA7ZkJnP/973/tMIFWrVopLCws3/WhQ4c6FhsAAIDT2M4JSFqwMU4DJi9SWkaWrmxZVc9d3Uy+vj5OhwUAcCHekEOcahunuVm6adMmuTJv+IwAAIBzOUSRl9u89957djLn8dLS0uw1wB11qFNOr13fUn6+PpqxbKf++9Uf8rL6MgAA2rx580kPVy+gAQAAlLQiF9EGDhxoK3PHO3TokL0GuKuujSrquWua2sfvzN+icbP+cjokAABKzcKFC/Xwww/rgQce0MyZM50OBwAAwP2LaGZ1jlnOf7wdO3bYpW+AO+vdopr+r0cj+/ilH//S5HmbnQ4JAIAS9/HHH6tTp04aN26c3nrrLV122WV67rnnnA4LAADA/QYLGC1atLDFM3NceOGF8vf/561mmIBZ5t+tW7eSihMoNTd1ilXi0Qy9+OOfGvPlH4oMCdCVLas5HRYAACVm7NixuuWWWzR+/Hj5+fnZ508++aTuv/9+p0MDAABwvyJazlTOFStW6JJLLlGZMmVyrwUGBqpWrVq66qqrSiZKoJQNvbCuEo6mafK8LXrg41UKDw7QRY0qOh0WAAAlYv369Zo2bZotoBn33XefRo0apX379tnp7AAAAChCEW306NF2xZkpll188cWqXLlyyUYGOMisuHz0skZKPJpuBw3c+eEyvTuwrR1AAACAp0lOTs43icrcIA0ODtbhw4cpogEAABS1iGaYu5O33Xab1q5dW5S3AW7J19dHz1zVVElHM/Tj2r265b0l+vCWdmpaLcrp0AAAKHamF1renQYZGRl65513VL58+dxzQ4cOdSg6AAAA5/lkm0kBRdC6dWs9/fTTti+aO0pKSrIDEMyE0bx3XIGTSUnP1MDJi7VgU5zKhgbof7d3UN2YcKfDAgCUMk/OIcxOg4IGR+Vlrm/atEmuzJM/IwAA4HwOUaSVaMbjjz9um8w+9thjatWqlcLCwvJdJ2GBpwkO8NPEAa11/cSFWrUjUf0mLbKFtGplQ50ODQCAYrFlyxanQwAAAPC8lWi+vr7/vDnPHUvzbcxz0zfNlXGHEmcq/kiarn1jgTbsO6zY8mGaflsHVQgPcjosAEApIYdwfXxGAADApVaizZ49+4wCAtxddFigpgxuq6snLNDmA0c04O1F+ujW9ooMCXA6NAAAAAAAUMKKXETr0qVLyUQCuIHKkSF6/+Z2uub1+fpjd5Jufnex3hvUTiGBfk6HBgAAAAAAStA/ezOLICEhQc8//7xuvvlme7z44ot2yRvgDcxWzncHtVV4sL8WbzmoIR8sVVpGltNhAQAAAAAAVyqiLVmyRHXq1LGFs/j4eHu88MIL9tyyZctKJkrAxZxdJVJv39RGwQG++nn9ft33v5XKzCpSe0EAAAAAAODJRbRhw4apZ8+edorTjBkz7LF582Zdfvnluvfee0smSsAFtakVrQk3tpK/r4++XLlLo79YYwdsAADgzjZu3KhHHnlEffv21b59++y5b7/9Vr///rvToQEAALjfSrQRI0bI3/+fdmrm8YMPPmivAd7k/AYxeqFPc5lBte8v3Kbnv//T6ZAAADhjc+bMUZMmTfTbb7/ZG6WHDx+251euXKnRo0c7HR4AAIB7FdHMqM9t27adcH779u0KDw8vrrgAt9GzWRU9dkVj+/jV2Rs0ce4mp0MCAOCMjBw5Uo8//rh++OEHBQYG5p6/4IILtHDhQkdjAwAAcLsiWp8+fTR48GBNmzbNFs7MMXXqVDtgwCz7B7zRje1r6oFLGtjHT3yzVtMXb3c6JAAAimz16tXq3bv3CedjYmJ04MABR2ICAABwFf/sySyk5557Tj4+Purfv78yMjLsuYCAAA0ZMkRPPfVUScQIuIU7zqujpKPpemPuJo2cscpO77y0SWWnwwIAoNCioqK0e/duxcbG5ju/fPlyVa1a1bG4AAAA3HIlmlnaP27cOB08eFArVqywh5nQaaZ1BgUFlUyUgBswxeWRlzZUn9bVZQZ13jN1hX75a7/TYQEAUGjXXXed7X27Z88e++9aVlaW5s2bp/vvv9/eQAUAAPBmRS6i5QgNDbV3K81hHgM4Vkh78som6t6kktIys3TblKVatu2g02EBAFAoTz75pBo2bKjq1avboQKNGjVS586d1bFjRzuxEwAAwJsVuYhmtnA++uijioyMVK1atexhHpvEKj09vWSiBNyIn6+PXuzTXOfWK6/ktEwNnLxY6/cccjosAAAKteNg4sSJ2rhxo7766iu9//77WrdunaZMmSI/Pz+nwwMAAHCvnmh33323HXn+zDPPqEOHDvbcggUL9H//93+Ki4vThAkTSiJOwK0E+fvp9Rtb6cZJv2n5tgT1m/SbPr69o2qUY9UmAMB1/frrrzrnnHNUo0YNewAAAOAfPtnZ2dkqArPqzEzjvPTSS/Od/+abb+x0zsTERLmypKQk+zuYOCMiIpwOBx4uITlNfd5YqPV7D6lGdKg+vr2DYiKCnQ4LAHAGvCGHMCvRzAABk9PdeOONdjunO/GGzwgAADiXQxR5O6cZHmC2cB7PTHEyiReAf0SFBmrK4La2gLYtPln9Ji2yhTUAAFzRrl27dN9992nOnDlq3LixmjdvrmeffVY7duxwOjQAAADHFbmIdtddd+mxxx5Tampq7jnz+IknnrDXAORnVp69P7idYsKD7Iq0ge8sVnJahtNhAQBwgvLly9t8zkzkNH3RrrnmGr377rv2BuoFF1zgdHgAAADutZ2zd+/emjVrll2R1qxZM3tu5cqVSktL04UXXpjvtaZ3mqthmT+cYoYLXPvGAiUeTbdDB94a0Nr2TgMAuAdvzCEyMzP17bff2qFSq1atss9dmTd+RgAAoPRyiCIPFoiKitJVV12V75wZgw7g1BpUCtfkgW1041u/6Ze/DmjYtBV6pW9LO80TAABXYlaiffDBB/r444+VkpKiK664QmPHjnU6LAAAAEcVuYg2efLkkokE8AIta5TVm/1aa9A7i/XN6j0KD1qtp65qIh8fCmkAAOc99NBDdoCU6Y120UUXady4cbaAFhrKdGkAAIAiF9Fy7N+/X+vXr7ePGzRooAoVKhRnXIDHOqdeeb3ct7nu+GCZpi3ZrsjQAD10aUMKaQAAx82dO1cPPPCArr32WtsfDQAAAP9isMCRI0c0aNAgVa5cWZ07d7ZHlSpVNHjwYCUnJxf12wFeqVvjynrqyqb28ZtzN2nCnI1OhwQAgN3Geccdd1BAAwAAKI6VaMOHD7djz7/88kt16tTJnvv11181dOhQOxJ9woQJRf2WgFe6tk11O2TgiW/W6pmZ6xUZEqAb2tV0OiwAgJf54osvdOmllyogIMA+PpWePXuWWlwAAABuP53T3Jk0TWbPO++8fOdnz55tl/6bbZ6ujKlNcDXPfrdO42dvlNnN+fJ1LdSjWRWnQwIAeFEO4evrqz179igmJsY+PhnTdoDpnAAAwBOV2HROs2WzYsWKJ5w3iRfbOYGiu//iBkpITtcHv22zEzvDg/11XoMYp8MCAHiJrKysAh8DAADgX/ZE69Chg0aPHm3Hnec4evSoxowZY68BKBpzZ/+/VzS2K9AysrJ1+/tLtWRLvNNhAQC80HvvvafU1NQTzqelpdlrAAAA3qzI2zlXr16tbt262QSrWbNm9tzKlSsVHBys7777TmeffbZcGcv84arSM7N063tLNHv9frsabeqt7XV2lUinwwIAeFEO4efnp927d9sdBnnFxcXZc2znBAAAnqiwOUSRV6I1adJEf/31l8aOHavmzZvb46mnnrLnXL2ABriyAD9fvXZDK7WpVVaHUjI04O1F2nzgiNNhAQC8iLm3alZIH2/Hjh02sQQAAPBmReqJlp6eroYNG+qrr77SLbfcUnJRAV4qJNBPbw1oo75vLtQfu5N041u/6eMhHVQ5MsTp0AAAHqxFixa2eGaOCy+8UP7+/6SIZvXZ5s2b7U4EAAAAb1akIpoZfZ63FxqA4hcZEqB3B7XVtW8ssCvR+k1apOm3dVB0WKDToQEAPFSvXr3s1xUrVuiSSy5RmTJlcq8FBgaqVq1auuqqqxyMEAAAwA17oj355JP6888/9dZbb+W7S+ku6JUBd7HjYLKunrBAe5JS1LRapD68pb3KBLnf/+YAwFN4Qw7x7rvvqk+fPrbXrTvyhs8IAAA4l0MUuYjWu3dvzZo1y96hNP3RwsLC8l2fMWOGXBnJFdzJhn2HdM3rC3QwOV0dapfT5IFtFBzg53RYAOCVyCFcH58RAAAoyRyiyMtaoqKiWM4PlJK6MeF2a6fpkbZgU5zu/mi5JtzQUv5+RZ4JAgDAaZn+Zy+++KKmT5+ubdu2KS0tLd/1+Ph4x2IDAABwWpGLaJMnTy6ZSAAUqGm1KE0c0Fo3TV6sH/7YqxGfrNazVzeVr++J09MAAPg3xowZY1t23HfffXrkkUf08MMPa8uWLfrss880atQop8MDAABwVKGXs2RlZenpp59Wp06d1KZNG40cOVJHjx4t2egAWB3rlNf461vKz9dHnyzboce+/kNF3IkNAMBpffDBB5o4caItopnet3379rVFNVNAW7hwodPhAQAAuEcR7YknntB//vMf2wutatWqGjdunO68886SjQ5ArosaVbQr0IzJ87bo5VkbnA4JAOBh9uzZY3veGibnM31BjMsvv1xff/21w9EBAAC4SRHtvffe02uvvabvvvvOLun/8ssv7d1Ks0INQOm4smU1je7RyD5+8cc/9c68zU6HBADwINWqVdPu3bvt4zp16uj777+3jxcvXqygoCCHowMAAHCTIpppLtu9e/fc5127dpWPj4927dpVUrEBKMDATrG6t2s9+/j/vvxDny7f4XRIAAAPkTOF3bj77rv16KOPql69eurfv78GDRrkdHgAAADuMVggIyNDwcHB+c4FBAQoPT29JOICcAr3XFhPCcnpemf+Ft3/v1UKDwrQ+Q1jtGhzvPYdSlFMeLDaxkbbHmoAABTWU089lfu4T58+qlGjhhYsWGALaT169HA0NgAAALcpopkm5jfddFO+pfwpKSm6/fbbFRYWlntuxowZxR8lgHzMKtBRlzdS0tF0zVi+U7e/v1QRIQGKP5KW+5rKkcF262e3xpUdjRUA4L46dOhgDwAAABShiDZgwIATzt14443FHQ+AQvL19dHTVzfVhn2HtWpnYr4CmrEnMUVD3l+mCTe2pJAGADipL774otCv7dmzZ4nGAgAA4BFFtMmTJ5dsJACKzNfHx27fLEi2WbEmacyXf+iiRpXY2gkAKFCvXr0KvQo6MzOzxOMBAABw+8ECAFyP6YG2Jyn1pNdNIW13Yop9HQAABTGT1gtzUEADAADejiIa4MZOtgrtTF8HAAAAAAD+5XZOAK7HTOEsjGVbD+qiRhUVGsj/5AEAJ/ff//73lNdHjRpVarEAAAC4Gv4fNeDG2sZG2ymcZoiA2bp5Mu8u2KrPV+5Sv/Y1NaBjLZUv88+UXQAAcnz66af5nqenp2vz5s3y9/dXnTp1KKIBAACvRhENcGNmWMDoHo3sFE4zNiBvIS1njECfNtW1YFOctsYl65WfNuiNuZt0VctquuXcWNWuUMahyAEArmj58uUnnEtKStJNN92k3r17OxITAACAq/DJzs4+1QIWj2MSwcjISCUmJioiIsLpcIBiMXPNbjuF0wwRyGFWqJkCW7fGlZWZla3vft9jC2grtyfY6z4+0kVnVdRtXWqrVc1oB6MHAPfgzTnE6tWr1aNHD23ZskWuzJs/IwAAUPI5BCvRAA9gCmUXNapkp3CaIQKmV5rZ6mlWqhnma/cmlXVp42OvmfjLJv24dp++/2OvPVrVLKtbO9e2RTXfv98DAEAOk1CaAwAAwJtRRAM8hCmUdahT7pSv8fHxUbva5eyxYd8hTZy7WZ8u36mlWw/qtilLVbt8mG4+t7aubFlVwQF+pRY7AMA1vPzyy/memw0Lu3fv1pQpU3TppZc6FhcAAIArYDsn4OX2JaXonflb9P7CrUpKybDnypcJVP8OtewggrJhgU6HCAAuwRtyiNjY2HzPfX19VaFCBV1wwQV66KGHFB4eLlfmDZ8RAABwLoegiAbAOpyaoWmLt+vtXzdrZ8JRey4kwE/Xtq5mV6dVjw51OkQAcBQ5hOvjMwIAACWZQ/ie0XcH4HHKBPlr8Dmx+vmB8zTuuuZqVDlCR9Mz9e6Crery7Gzd+eEyrdpxbCgBAAAAAADehp5oAPIJ8PPVFc2rqmezKpq3IU5vzN2oX/46oK9X7bZH+9rRuq1zHZ3XoILtsQYA8BwpKSl65ZVXNHv2bO3bt09ZWVn5ri9btsyx2AAAAJxGEQ1AgUyB7Jx65e3xx64kvfXLJn2xcpcWboq3R/2KZXTLubVtwS3Qn0WtAOAJBg8erO+//15XX3212rZty80SAACAPOiJBqDQdiUc1eR5m/XRou22h5pRMSJIAzvF6vp2NRQRHOB0iABQYrwhhzC/3zfffKNOnTrJHXnDZwQAAIofPdEAFLsqUSF6+LJGmjfyAo28tKEtoO1NStVT365Tx7E/6fGv/rCFNgCAe6patarLT+AEAABwCkU0AEUWGRKg27vU0S8PXqBnr25qt3aalWlv/bpZnZ+ZrWHTVmjt7iSnwwQAFNHzzz+vESNGaOvWrU6HAgAA4HLoiQbgjJleaNe0rq6rW1XTz+v32yEEpl/ap8t32uPceuXtEIJOdcvRVwcA3EDr1q3tcIHatWsrNDRUAQH5t+nHx8c7FhsAAIDTKKIB+NdMgez8hjH2WLUjQW/M3aRvV++2Uz3NcXaVCN3auba6N6lsp38CAFxT3759tXPnTj355JOqWLEiN0AAAADyYLAAgBKxLS5Zb8/brGmLt+toeqY9VzUqRIPOidV1baorLIgaPgD34g05hFl9tmDBAjVr1kzuyBs+IwAAUPwYLADAUTXKher/ep6t+SMv0H0X1Vf5MoHamXBUj331hzqMnaVnZq7TvqQUp8MEAOTRsGFDHT3KgBgAAACXLaKNHz9etWrVUnBwsNq1a6dFixad9LUTJ07Uueeeq7Jly9qja9eup3w9AGeVDQvU3RfW068jLtCTvZuodvkwJaVk6LWfN+qcp2drxMertGHfIafDBABIeuqpp3Tffffp559/VlxcnL0rm/c4E+R5AADAUzheRJs2bZqGDx+u0aNHa9myZXb7wCWXXKJ9+/YV+HqT1Jl+HbNnz7bbDapXr66LL77Y9u8A4LqCA/x0fbsa+nF4F73Rr5Va1SyrtMwsTVuyXV1fmKub312s3zbFyct2mAOAS+nWrZvNry688ELFxMTkFrOioqLs16IizwMAAJ7E8Z5o5o5kmzZt9Oqrr9rnWVlZNmG6++67NXLkyNO+PzMz0yZ15v39+/c/4Xpqaqo9cpi7qOb70ysDcN7SrfF6Y84m/bB2r3L+S9SsepRu61xbl5xdSX6+NLQG4Dq8od/WnDlzTnm9S5cuRfp+5HkAAMCT8jxHO3unpaVp6dKleuihh3LP+fr62qX75u5jYSQnJys9PV3R0dEFXh87dqzGjBlTbDEDKD6takbrzf7R2rT/sCb+slmfLNuhldsTdMcHy1SzXKhuPidWV7eqrpBAP6dDBQCvUNQi2amQ5wEAAE/jaBHtwIED9g6jGaGel3m+bt26Qn2PESNGqEqVKjYhK4hJ3Mw2guPvUAJwHbUrlNHYK5to+EX1NWXBFr23cKu2xiXr0c9/14s//qV+7Wuqf4eaKlcmyOlQAcCjzZ0795TXO3fuXOjvRZ4HAAA8jaNFtOJofjt16lTbP8M0qy1IUFCQPQC4vgrhQRp+cQPdfl4d/W/JDr316yZtjz+qcbP+0utzNuqa1tV08zm1Vat8mNOhAoBHOu+880445+Pzz9Z6UxQrLeR5AADA1Tg6WKB8+fLy8/PT3r178503zytVqnTK9z733HM2ufr+++/VtGnTEo4UQGkKDfTXgI61NPu+8/Tq9S3UtFqkUjOy9P7CbTr/+Z815P2lWr7toNNhAoDHOXjwYL7DDACYOXOm7Wtmcq6iIM8DAACextEiWmBgoFq1aqVZs2blnjMNZ83zDh06nPR9zzzzjB577DGb1LVu3bqUogVQ2vz9fHV50yr6/M5O+uiW9jq/QQU7gODbNXvU+7X5uvb1Bfrhj73KymKiJwAUB9NQN+9hCmEXXXSRnn76aT344INF+l7keQAAwNM4vp3T9LEYMGCATZLatm2rl156SUeOHNHAgQPtdTOJqWrVqrZxrGGSuFGjRunDDz9UrVq1tGfPHnu+TJky9gDgecxWog51ytnjz72H9ObcTfp8xU4t2hJvjzoVwnTLubXVq0VVBQcwhAAAipvpY7Z+/foiv488DwAAeBLHi2h9+vTR/v37bcJkEqXmzZvbO485TWi3bdtmJznlmDBhgp32dPXVV+f7PqNHj9b//d//lXr8AEpX/Yrheu6aZrr/4gaaPH+zPly4TRv3H9HIGav13Pd/amCnWrqxXU1FhgY4HSoAuJ1Vq1ble56dna3du3fbrZUmRysq8jwAAOBJfLJNduRFzNQmsz0hMTFRERERTocD4F86lJKuaYu3a9Kvm7U7McWeCw30U5821TX4nFhVKxvqdIgAPIQ35BCmoGVW/x6fHrZv315vv/22GjZsKFfmDZ8RAABwLoegiAbAI6RnZumrVbv0xpxNWrfnkD3n5+ujy5pU1q2da6tx1UinQwTg5rwhh9i6desJRbUKFSqcdDqmq/GGzwgAABQ/imgnQXIFeDbzn7Rf/jpg+6b9uuFA7vlOdcvp1s511LleebvKAgCKihzC9fEZAQCAkswhHJ3OCQDFzRTIOtevoPdvbqev7j5HVzSvYlekzdsQpwFvL9Kl437RJ0t3KC0jy+lQAcBl/PTTT2rUqJFNII9nksmzzz5bv/zyiyOxAQAAuAqKaAA8ltnCOe66FprzwHka1CnW9kozWz3v+99KdX5mtt6cu9H2VAMAb2emZt5yyy0F3nk1d2Vvu+02vfDCC47EBgAA4CooogHweGa4wKgejbRg5IV64JIGqhAepD1JKXrym3XqOPYnjf1mrfb8PZQAALzRypUr1a1bt5Nev/jii7V06dJSjQkAAMDVUEQD4DUiQwN05/l19euI8/X0VU1Up0KYDqVm6I25m3TuMz/pvukrtf7voQQA4E327t2rgICAk1739/fX/v37SzUmAAAAV0MRDYDXCfL3U582NfTDsC56q39rtY2NVnpmtj5ZtkOXvDRXN01epPkbD9ghBQDgDapWrao1a9ac9PqqVatUuXLlUo0JAADA1VBEA+C1fH191LVRRU2/rYM+vaOjujepJF8f6ef1+3X9xN/U89V5+nLlLmVkMoQAgGfr3r27Hn30UaWknLi1/ejRoxo9erQuv/xyR2IDAABwFT7ZXrbUgtHnAE5la9wRvfXLZv1v6XalpB8rnlUrG6LB58SqT5vqCg30dzpEAA7x5BzCbOds2bKl/Pz8dNddd6lBgwb2/Lp16zR+/HhlZmZq2bJlqlixolyZJ39GAADA+RyCIhoAFCD+SJreW7BF7y3Yah8bkSEB6te+pgZ0rGWHEwDwLp6eQ2zdulVDhgzRd999l7ud3cfHR5dccoktpMXGxsrVefpnBAAASgZFtJMguQJQFEfTMvXxsh1665dN2hqXbM8F+vvqqpZVdfO5tVWnQhmnQwRQSrwlhzh48KA2bNhgC2n16tVT2bJl5S685TMCAADFiyLaSZBcATgTmVnZ+v73PXaS54rtCfacj4/U9ayKuq1zbbWuFe10iABKGDmE6+MzAgAAJZlD0NwHAArBz9dHlzaprG6NK2nxloN6c+4m/bh2r37449jRskaUbu1cRxc1qmhfCwAAAADwLBTRAKAITH+gtrHR9tiw77Dd5jlj2U4t25ag299fqtjyYbr53Fhd1bKaggP8nA4XAAAAAFBM2M4JAP/SvkMpenf+Fk1ZsFVJKRn2XLmwQDuAwAwiKBsW6HSIAIoBOYTr4zMCAABngp5oJ0FyBaCkHEnN0LTF2zXp183amXDUngsO8NW1ravr5nNqq0a5UKdDBPAvkEO4Pj4jAABQkjmE7xl9dwDACcKC/DXonFjNeeA8jbuuuc6uEqGU9Cy9t2Crzntutu78YJlW/j2UAAAAAADgXuiJBgDFzN/PV1c0r6qezapo/sY4O9Fz7p/79fXq3fZoFxut27rU1nn1Y+TLEAIAAAAAcAsU0QCgBIcQdKpb3h5rdydp4i+b9MWKXfptc7w96sWU0S2da+uK5lUU5M8QAgAAAABwZfREA4BStDvxqCbP26IPf9umw6nHhhDEhAdpYKdYXd+uhiJDApwOEcBJkEO4Pj4jAABwJhgscBIkVwBcQVJKuj76bZvenrdZe5NS7bmwQD/1bVtDA8+JVdWoEKdDBHAccgjXx2cEAADOBEW0kyC5AuBK0jKy9MXKXZo4d5PW7z1kz/n7+ujyppV1a+c6alSF/04BroIcwvXxGQEAgJLMIeiJBgAOCvT31dWtqumqllX185/79eacTVqwKU6frdhlj3PrldetnWvrnLrlbY81AAAAAIAzKKIBgAswBbLzG8TYY/WORL0xd6O+Wb1bv/x1wB6NKkfYYtplTSsrwM/X6XABAAAAwOuwnRMAXNT2+GRN+nWzpi3erqPpmfac6ZU2sFMtXde2hsoEcR8EKE3kEK6PzwgAAJwJeqKdBMkVAHeTkJym9xdu1Tvzt+jA4TR7LjzYXze2r6mBHWspJiLY6RABr0AO4fr4jAAAwJmgiHYSJFcA3FVKeqY+Xb7TDiHYdOCIPRfo56teLarolnNrq17FcKdDBDwaOYTr4zMCAABngsECAOBhggP81LdtDfVpXV0/rt2rN+du0pKtBzV9yQ57XNAwxvZNaxcbzRACAAAAAChmFNEAwM34+vro4rMr2WPp1oN6c+5Gff/HXv20bp89mlWL1K2d66hb40ry86WYBgAAAADFgSIaALixVjXL6o1+rbVp/2G99etmfbx0h1buSNSdHy5TjehQ3XxurK5pVV0hgX5OhwoAAAAAbo2eaADgQQ4cTtV787fovYVblZCcbs+VDQ1Qvw61NKBDTZUrE+R0iIDbIodwfXxGAADgTDBY4CRIrgB4g+S0DLsq7a1fNmtbfLI9F+Tvq6tbVdPN59ZWbPkwp0ME3A45hOvjMwIAACWZQ/ie0XcHALi00EB/9e9QS7PvP0/jr2+pptUilZqRpQ9+26YLnv9Zt09ZavupAQAAAAAKh55oAODBzGCBy5pWVvcmlfTb5ng70dMMH5j5+x57tK5Z1k707HpWRTuwAAAAAABQMIpoAOAFfHx81L52OXv8ufeQJs7dpM9W7NSSrQe1ZMpS1a4QplvOra3eLaoqOIAhBAAAAABwPHqiAYCX2puUosnztuiD37bqUEqGPVe+TJBu6lhTN7avqajQQKdDBFwKOYTr4zMCAABngsECJ0FyBQD5HU7N0NRF2/T2r5u1KzHFngsN9NO1ratr8Dmxqh4d6nSIgEsgh3B9fEYAAOBMUEQ7CZIrAChYemaWvlq1S2/O3ay1u5Nye6p1b1JZt3WurcZVI50OEXAUOYTr4zMCAAAlmUPQEw0AYAX4+ap3i2rq1byqft1wQG/M2WS/frlylz061ilnhxB0qV/B9lgDAAAAAG9CEQ0AkI8pkJ1br4I9ft+VaIcQfLlqt+ZvjLNHw0rhdghBj2ZVFOjvm/u+zKxsLdocr32HUhQTHqy2sdF2JRsAAAAAeAK2cwIATmtnwlHbM830TjuSlmnPVYoI1sBOtdS3XQ3N33BAY778Q7v/7qlmVI4M1ugejdStcWUHIweKDzmE6+MzAgAAZ4KeaCdBcgUAZy4xOV0fLNpqp3ruP5RqzwX7+yolI+uE1+asQZtwY0sKafAI5BCuj88IAACUZA7xzz4cAABOIzI0QHecV1e/jjhfz1zVVHUqhBVYQDNy7tCYFWpmqycAAAAAuDN6ogEAiizI30/XtqmuamVDdP1bv530daZ0ZrZ49hr/q5pUi1KN6FBVLxtqv5rDFOUAAAAAwB1QRAMAnLH9h49t6Tyd1TuT7HG8iGB/1Sj3T2Gt+t/FNXNUiQrJN7gAAAAAAJxEEQ0AcMbMFM7CuL1zbVsQ237wqLbFJ9vD9FRLSsnQmp1J9jieGexZOTJE1aNDcgtr1fMU2sqFBdpJogAAAABQGiiiAQDOWNvYaDuFc09iSm4PtLxMiatSZLAe6NZQfqYqlkdyWoZ2HDyq7X8X1cyR93FKepadCmqOhZviT/jeoYF++VavVS8bYle1mcfVyoYqOMCvBH9zAAAAAN6GIhoA4IyZwtjoHo005P1ltmCWt5CWUzIz148voBmhgf6qXzHcHsczg6MPHE47obC2/e9jd1KKktMytW7PIXsUpGJE0InbRP8uslUoEyTfAmICAAAAgJOhiAYA+Fe6Na6sCTe2tFM4zRCBHGYFmimgmetFZbZpVggPskermmVPuJ6akamdf28NtYU18zjun0LbodQM7U1KtceSrQdPeL/ZWmpXrhWwTdR8LRPEP48AAAAA8uP/JQAA/jVTKLuoUSUt2hyvfYdSbK80s9WzoBVoxTUdtHaFMvYoaBVbQnK6th8seJvoroQUpWVkaeP+I/YoiOm39k9h7VixLafIZvq0ldTvBQAAAMB1UUQDABQLU1jqUKec02H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"text/plain": [ "
" ] @@ -1621,10 +1621,10 @@ "id": "505827c8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.491458Z", - "iopub.status.busy": "2024-06-04T23:20:00.491361Z", - "iopub.status.idle": "2024-06-04T23:20:00.493334Z", - "shell.execute_reply": "2024-06-04T23:20:00.493123Z" + "iopub.execute_input": "2025-04-03T19:34:17.007832Z", + "iopub.status.busy": "2025-04-03T19:34:17.007758Z", + "iopub.status.idle": "2025-04-03T19:34:17.009760Z", + "shell.execute_reply": "2025-04-03T19:34:17.009526Z" }, "lines_to_next_cell": 0 }, @@ -1670,10 +1670,10 @@ "id": "9902f1be", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.494542Z", - "iopub.status.busy": "2024-06-04T23:20:00.494449Z", - "iopub.status.idle": "2024-06-04T23:20:00.496446Z", - "shell.execute_reply": "2024-06-04T23:20:00.496239Z" + "iopub.execute_input": "2025-04-03T19:34:17.011082Z", + "iopub.status.busy": "2025-04-03T19:34:17.010984Z", + "iopub.status.idle": "2025-04-03T19:34:17.013533Z", + "shell.execute_reply": "2025-04-03T19:34:17.013281Z" }, "lines_to_next_cell": 0 }, @@ -1711,10 +1711,10 @@ "id": "2f56e336", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.497684Z", - "iopub.status.busy": "2024-06-04T23:20:00.497595Z", - "iopub.status.idle": "2024-06-04T23:20:00.499525Z", - "shell.execute_reply": "2024-06-04T23:20:00.499269Z" + "iopub.execute_input": "2025-04-03T19:34:17.014952Z", + "iopub.status.busy": "2025-04-03T19:34:17.014857Z", + "iopub.status.idle": "2025-04-03T19:34:17.017092Z", + "shell.execute_reply": "2025-04-03T19:34:17.016838Z" } }, "outputs": [ @@ -1742,10 +1742,10 @@ "id": "720e92a9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.500875Z", - "iopub.status.busy": "2024-06-04T23:20:00.500774Z", - "iopub.status.idle": "2024-06-04T23:20:00.502897Z", - "shell.execute_reply": "2024-06-04T23:20:00.502626Z" + "iopub.execute_input": "2025-04-03T19:34:17.018363Z", + "iopub.status.busy": "2025-04-03T19:34:17.018264Z", + "iopub.status.idle": "2025-04-03T19:34:17.020419Z", + "shell.execute_reply": "2025-04-03T19:34:17.020143Z" }, "lines_to_next_cell": 0 }, @@ -1782,10 +1782,10 @@ "id": "08ad9e05", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.504280Z", - "iopub.status.busy": "2024-06-04T23:20:00.504187Z", - "iopub.status.idle": "2024-06-04T23:20:00.506272Z", - "shell.execute_reply": "2024-06-04T23:20:00.505977Z" + "iopub.execute_input": "2025-04-03T19:34:17.021778Z", + "iopub.status.busy": "2025-04-03T19:34:17.021689Z", + "iopub.status.idle": "2025-04-03T19:34:17.023927Z", + "shell.execute_reply": "2025-04-03T19:34:17.023719Z" } }, "outputs": [ @@ -1812,10 +1812,10 @@ "id": "7ec6643a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.507544Z", - "iopub.status.busy": "2024-06-04T23:20:00.507437Z", - "iopub.status.idle": "2024-06-04T23:20:00.509358Z", - "shell.execute_reply": "2024-06-04T23:20:00.509140Z" + "iopub.execute_input": "2025-04-03T19:34:17.025038Z", + "iopub.status.busy": "2025-04-03T19:34:17.024957Z", + "iopub.status.idle": "2025-04-03T19:34:17.026855Z", + "shell.execute_reply": "2025-04-03T19:34:17.026644Z" }, "lines_to_next_cell": 0 }, @@ -1857,10 +1857,10 @@ "id": "897e5bbb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.510666Z", - "iopub.status.busy": "2024-06-04T23:20:00.510571Z", - "iopub.status.idle": "2024-06-04T23:20:00.512527Z", - "shell.execute_reply": "2024-06-04T23:20:00.512309Z" + "iopub.execute_input": "2025-04-03T19:34:17.028041Z", + "iopub.status.busy": "2025-04-03T19:34:17.027956Z", + "iopub.status.idle": "2025-04-03T19:34:17.029967Z", + "shell.execute_reply": "2025-04-03T19:34:17.029770Z" } }, "outputs": [], @@ -1897,10 +1897,10 @@ "id": "2fd5410d", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.513710Z", - "iopub.status.busy": "2024-06-04T23:20:00.513635Z", - "iopub.status.idle": "2024-06-04T23:20:00.515194Z", - "shell.execute_reply": "2024-06-04T23:20:00.514994Z" + "iopub.execute_input": "2025-04-03T19:34:17.031103Z", + "iopub.status.busy": "2025-04-03T19:34:17.031005Z", + "iopub.status.idle": "2025-04-03T19:34:17.032784Z", + "shell.execute_reply": "2025-04-03T19:34:17.032581Z" }, "lines_to_next_cell": 0 }, @@ -1930,10 +1930,10 @@ "id": "46c135c3", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.516282Z", - "iopub.status.busy": "2024-06-04T23:20:00.516215Z", - "iopub.status.idle": "2024-06-04T23:20:00.517899Z", - "shell.execute_reply": "2024-06-04T23:20:00.517702Z" + "iopub.execute_input": "2025-04-03T19:34:17.033925Z", + "iopub.status.busy": "2025-04-03T19:34:17.033853Z", + "iopub.status.idle": "2025-04-03T19:34:17.035655Z", + "shell.execute_reply": "2025-04-03T19:34:17.035391Z" } }, "outputs": [], @@ -1958,10 +1958,10 @@ "id": "0fd401ba", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.519024Z", - "iopub.status.busy": "2024-06-04T23:20:00.518963Z", - "iopub.status.idle": "2024-06-04T23:20:00.520581Z", - "shell.execute_reply": "2024-06-04T23:20:00.520375Z" + "iopub.execute_input": "2025-04-03T19:34:17.036966Z", + "iopub.status.busy": "2025-04-03T19:34:17.036895Z", + "iopub.status.idle": "2025-04-03T19:34:17.038678Z", + "shell.execute_reply": "2025-04-03T19:34:17.038482Z" }, "lines_to_next_cell": 0 }, @@ -1998,10 +1998,10 @@ "id": "bcbe5405", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.521714Z", - "iopub.status.busy": "2024-06-04T23:20:00.521647Z", - "iopub.status.idle": "2024-06-04T23:20:00.524162Z", - "shell.execute_reply": "2024-06-04T23:20:00.523946Z" + "iopub.execute_input": "2025-04-03T19:34:17.039981Z", + "iopub.status.busy": "2025-04-03T19:34:17.039903Z", + "iopub.status.idle": "2025-04-03T19:34:17.042845Z", + "shell.execute_reply": "2025-04-03T19:34:17.042634Z" } }, "outputs": [ @@ -2053,10 +2053,10 @@ "id": "e85cd692", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.525418Z", - "iopub.status.busy": "2024-06-04T23:20:00.525349Z", - "iopub.status.idle": "2024-06-04T23:20:00.527505Z", - "shell.execute_reply": "2024-06-04T23:20:00.527286Z" + "iopub.execute_input": "2025-04-03T19:34:17.044098Z", + "iopub.status.busy": "2025-04-03T19:34:17.044000Z", + "iopub.status.idle": "2025-04-03T19:34:17.046353Z", + "shell.execute_reply": "2025-04-03T19:34:17.046077Z" }, "lines_to_next_cell": 2 }, @@ -2111,10 +2111,10 @@ "id": "f6e58256", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.528820Z", - "iopub.status.busy": "2024-06-04T23:20:00.528749Z", - "iopub.status.idle": "2024-06-04T23:20:00.530487Z", - "shell.execute_reply": "2024-06-04T23:20:00.530226Z" + "iopub.execute_input": "2025-04-03T19:34:17.047801Z", + "iopub.status.busy": "2025-04-03T19:34:17.047711Z", + "iopub.status.idle": "2025-04-03T19:34:17.049454Z", + "shell.execute_reply": "2025-04-03T19:34:17.049246Z" }, "lines_to_next_cell": 0 }, @@ -2140,10 +2140,10 @@ "id": "6d739b86", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.531627Z", - "iopub.status.busy": "2024-06-04T23:20:00.531561Z", - "iopub.status.idle": "2024-06-04T23:20:00.824303Z", - "shell.execute_reply": "2024-06-04T23:20:00.823941Z" + "iopub.execute_input": "2025-04-03T19:34:17.050664Z", + "iopub.status.busy": "2025-04-03T19:34:17.050574Z", + "iopub.status.idle": "2025-04-03T19:34:17.336665Z", + "shell.execute_reply": "2025-04-03T19:34:17.336230Z" }, "lines_to_next_cell": 0 }, @@ -2168,10 +2168,10 @@ "id": "701ef903", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.826191Z", - "iopub.status.busy": "2024-06-04T23:20:00.826070Z", - "iopub.status.idle": "2024-06-04T23:20:00.828818Z", - "shell.execute_reply": "2024-06-04T23:20:00.828538Z" + "iopub.execute_input": "2025-04-03T19:34:17.338641Z", + "iopub.status.busy": "2025-04-03T19:34:17.338513Z", + "iopub.status.idle": "2025-04-03T19:34:17.341236Z", + "shell.execute_reply": "2025-04-03T19:34:17.340919Z" }, "lines_to_next_cell": 0 }, @@ -2210,16 +2210,16 @@ "id": "b00fbcc9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.830418Z", - "iopub.status.busy": "2024-06-04T23:20:00.830277Z", - "iopub.status.idle": "2024-06-04T23:20:00.904250Z", - "shell.execute_reply": "2024-06-04T23:20:00.903956Z" + "iopub.execute_input": "2025-04-03T19:34:17.343066Z", + "iopub.status.busy": "2025-04-03T19:34:17.342971Z", + "iopub.status.idle": "2025-04-03T19:34:17.400006Z", + "shell.execute_reply": "2025-04-03T19:34:17.399649Z" } }, "outputs": [ { "data": { - "image/png": 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RNddco8cff1xffvmlEhISSnxsp06d9NJLL+m3337TokWL1LlzZxlj1KlTJy1atEj169eX4zgFM5RHqix/Z0pTr149zZ07V506dVLfvn21cOFCtWnTptCY3Nxc7dy5s0zPl5qaWiTT+vXr1bNnTyUnJ2vWrFmubHkAKgLFEziM0047TdOnT1ffvn11zjnnaNGiRapbt26ho9UlFfnBcsAFF1ygYcOG6csvv9Rbb71V4us0a9ZMH330kTp27Fhq8VmwYIF27NihqVOnFjqAYs2aNeX8ygqLj4/XxRdfrIsvvli5ubm68MILNXbsWI0ePbrE5Wj69eunN998U6+//rpGjx5d7tc8MJO0e/fuQrf/73//K3Z8s2bNdNttt+m2227TL7/8opNPPlmPP/64Xn/9dUklF6VmzZpp7969Rz3DdKQaNWqk+fPnKysrq9Cs5+rVq4/qecePH69p06Zp7Nixev7555WamqrExEQFg8Eyfa2H+8xr1qxZ5LORSv58Dna4Td2nn366Tj/9dI0dO1aTJk3S5ZdfrsmTJ+u6664r8TEHCuXcuXO1ePFijRo1SlL+gUTPPfec6tevr/j4eLVr1+6oslWEpk2bas6cOeratat69eqlRYsW6U9/+lPB/Z9//rm6d+9epudas2ZNodUFduzYoZ49eyonJ0fz5s1TvXr1Kjo+EDLs4wmUwdlnn60333xTq1evVu/evZWbm6sePXoUuhy8T9jBEhIS9Nxzz+nee+9Vv379SnyNwYMHKxgM6oEHHihyXyAQKCgAB2Y+Dp59yc3N1bPPPnvEX9+OHTsKXY+KilLLli1lrS11P7yLLrpIrVu31tixY/XFF18UuX/Pnj0FR5gXp1GjRvL7/UX2sTz0a8nKytK+ffsK3dasWTMlJiYqJyen4Lb4+Phii9LgwYP1xRdfaM6cOUXu2717twKBQIkZK0KvXr2Ul5enl156qeA2x3EKlvU5Us2aNdPAgQM1ceJEbd68WX6/XwMHDtS7776rFStWFBl/8O4hZfnMmzVrpvT09EK7ZWzatEnTpk07bLb4+HhJRX+p2LVrV5GZw5NPPlmSCn2WxWnSpImOPfZY/fOf/1ReXl7BJuzOnTvr119/1TvvvKPTTz+90Lq55clW0Vq3bq3//ve/2rt3r8455xxt2LCh4L4j3cczMzNT5557rjZs2KBZs2YVKrNAZcCMJ1BGF1xwQcGZR84//3zNnj27zAtTl2XZnK5du2rYsGEaN26cli1bpp49eyoyMlK//PKLpkyZon/961+66KKLdOaZZ6pmzZoaMmSIbrnlFhlj9Nprr5V5M2Bxevbsqbp166pjx46qU6eOfvzxRz399NPq27dvqZvwIiMjNXXqVPXo0UNdunTR4MGD1bFjR0VGRmrlypWaNGmSatasWeJansnJyRo0aJCeeuopGWPUrFkzzZw5s8g+lz///LPOPvtsDR48WC1btlRERISmTZumLVu26JJLLikY165dOz333HN68MEH1bx5c9WuXVtnnXWWbr/9dr333ns677zzNHToULVr106ZmZn6/vvv9c4772jt2rUhXYpmwIABOu2003Tbbbdp9erVOv744/Xee+8VbGo9mhm422+/XW+//bYmTJig8ePHa/z48Zo/f746dOig66+/Xi1bttTOnTu1dOlSffTRRwWvWZbP/JJLLtHIkSN1wQUX6JZbblFWVpaee+45HXfccQUHKZXk5JNPlt/v18MPP6z09HRFR0frrLPO0qRJk/Tss8/qggsuULNmzbRnzx699NJLSkpK0rnnnnvYr7dz586aPHmyWrduXfDL3imnnKL4+Hj9/PPPZdq/88CM6C233KJevXrJ7/cX+j6qSGeccYamTp2qfv36FWwxqVWr1hHv43n55Zfr66+/1jXXXKMff/yx0NqdCQkJGjBgQAWmB0LAs+PpgTB28JJIh3rsscesJHveeefZvLy8cj32YIcup3TAiy++aNu1a2djY2NtYmKibd26tb3jjjvsxo0bC8Z89tln9vTTT7exsbG2fv369o477rBz5swpsnxN165di10y59Blcl544QXbpUsXW6tWLRsdHW2bNWtmb7/9dpuenl7q13DArl277JgxY2zr1q1tXFycjYmJsa1atbKjR4+2mzZtKvF1rbV227ZtduDAgTYuLs7WrFnTDhs2zK5YsaLQEjjbt2+3w4cPt8cff7yNj4+3ycnJtkOHDvbtt98u9FybN2+2ffv2tYmJiVZSoaWV9uzZY0ePHm2bN29uo6Ki7DHHHGPPPPNM+9hjj9nc3Fxr7R/L6RS3bFNJyynFx8cXGVvcckPbtm2zl112mU1MTLTJycl26NCh9rPPPrOS7OTJk0t9fw8svTNlypRi7+/WrZtNSkqyu3fvttZau2XLFjt8+HCblpZmIyMjbd26de3ZZ59tX3zxxYLHlPUz//DDD22rVq1sVFSUbdGihX399dfLtJyStda+9NJLtmnTptbv9xd8by5dutReeumltmHDhjY6OtrWrl3bnnfeeXbJkiWlvgcHPPPMM1aSvfHGGwvd3qNHj2KXFCrucwsEAvbmm2+2qamp1hhT8LWU9vlLsvfcc0+p2Ur7nN566y3r8/ls+/btbUZGRpm+1uIcWKqpuEtxS18B4cZYexTTJACAIzZ9+nRdcMEF+vTTT0N+NDkAhAOKJwC4IDs7u9BBY8FgUD179tSSJUu0efPmch1JDwCVFft4AoALbr75ZmVnZ+uMM85QTk6Opk6dqs8//1wPPfQQpRNAtcGMJwC4YNKkSXr88ce1evVq7du3T82bN9eNN96om266yetoAOAaiicAAABcwTqeAAAAcAXFEwAAAK4I64OLHMfRxo0blZiY6MopzgAAAFA+1lrt2bNH9evXl89X+pxmWBfPjRs3Ki0tzesYAAAAOIz169erQYMGpY4J6+J54LRt69evV1JSksdpAAAAcKiMjAylpaWVeorlA8K6eB7YvJ6UlETxBAAACGNl2S2Sg4sAAADgCoonAAAAXEHxBAAAgCsongAAAHAFxRMAAACuoHgCAADAFRRPAAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK4IafEcN26c2rdvr8TERNWuXVsDBgzQTz/9FMqXBAAAQJgKafFcuHChhg8fri+//FJz585VXl6eevbsqczMzFC+LAAAAMKQsdZat15s27Ztql27thYuXKguXbocdnxGRoaSk5OVnp6upKQkFxICAACgPMrT11zdxzM9PV2SlJKS4ubLAgAAIAxEuPVCjuPo1ltvVceOHdWqVatix+Tk5CgnJ6fgekZGhlvxAAAAEGKuzXgOHz5cK1as0OTJk0scM27cOCUnJxdc0tLS3IoHAACAEHNlH8+bbrpJM2bM0CeffKImTZqUOK64Gc+0tDT28QQAAAhT5dnHM6Sb2q21uvnmmzVt2jQtWLCg1NIpSdHR0YqOjg5lJAAoYK2Vcr+WzV0o2TyZyBOlmD4yhn+HACAUQlo8hw8frkmTJmnGjBlKTEzU5s2bJUnJycmKjY0N5UsDQKlscKvsrmFSYKUO/FNoFZAyxko1npKJPt3bgABQBYV0U7sxptjbX3nlFQ0dOvSwj2c5JQChYG2e7Pb+UnCNpOAh9/okRcgcM10morkH6QCgcgmrTe0AEHZy5knB1SXc6UgKyu59WabGQ26mAoAqj3O1A6h27L7ZKv2fv6CUM8utOABQbVA8AVQ/TqbyZzZLYfe5EgUAqhOKJ4DqJ6KZJH8pA4zkb+xSGACoPiieAKodE3exih5UdOiYy90JAwDViGunzAQqG2sDUs4C2ewZkrNd8qfJxA6Uok4rccUGVA4moomU8DfZvf+UZCQdfCCkT4psL8Vd7FE6AKi6KJ5AMayzR3bXdVLet8rfJBuU8pbJ7psuRZ8r1XhUxkR6nBJHwyTcKPnTZDOflwI/77+xphR3uUzCDTImytuAAFAFUTyBYtj0O6W85fuvBQv/P+cD2b0NZRJHeBENFcjEnifF9M2f0Vae5KstY/hnEQBChX08gUPYwHop50OVfNSzlbJek7XZbsZCiBhjZPypMv76lE4ACDGKJ3Co3M9VeJ+/YthMKfc7V+IAAFBVUDyBIko/2vkPgZCmAACgqqF4AoeKbFOGQRFS5AkhjwIAQFVC8QQOYSJPlCJaq+QFxv1STF8ZX4qbsQAAqPQonkAxTI1/Sr4UFf4rYvIvEc1kku72KBkAAJUXxRMoholoKFPrPSn+RslXV1KM5G8skzhSJuUtGV+y1xEBAKh0WDsEKIHx15JJ/KuU+FevowAAUCUw4wkAAABXUDwBAADgCoonAAAAXEHxBAAAgCsongAAAHAFxRMAAACuoHgCAADAFRRPAAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK6geAIAAMAVFE8AAAC4guIJAAAAV1A8AQAA4AqKJwAAAFxB8QQAAIArKJ4AAABwBcUTAAAArqB4AgAAwBUUTwAAALiC4gkAAABXUDwBAADgCoonAAAAXEHxBAAAgCsongAAAHBFhNcBgOLYvF9ks6dIwfWSSZKJPU+K6ihj+F0JAIDKiuKJsGKtld3ziJT1b0l+SUFJftl906TIU6SaL8n4Ej1OCQAAjgTTRwgvWW/sL51Sfuk86P95y2XTb/MiFQAAqAAUT4QNa4OymS+UMiIo5SyQDax2LRMAAKg4FE+Ej8AvkrPlMIN8Us5CV+IAAICKRfFEGMkrwxgj2dyQJwEAABWP4onw4W8iKeowg4JS5IlupAEAABWM4omwYXwJUuyFyj+avTg+yX+sFNXJzVgAAKCCUDwRVkzi36WIZir6remXTKxMjSdZyxMAgEqKn+AIK8aXJJMyWSbhr5Kv7v4b46XYi2VqzZCJbO1tQAAAcMRYQB5hx/gSpIQbZRJulLUOM5wAAFQR/ERHWKN0AgBQdTDjWYlZJ13Kniqb84mkgBR5skzsxTIRDbyOBgAAUATFs5Kyuctld10j2b2SbP6NuYtlM1+SksfLxA7wMh4AAEARbMeshKyTvr90ZqqgdEqSHEmObPpI2bzvPEoHAABQPIpnZZQ9df9Mp1PCAJ9s5kQXAwEAABwexbMSyt+n05YyIsj5zAEAQNgJafH85JNP1K9fP9WvX1/GGE2fPj2UL1eNBCpoDNxgg5vz98kNrPM6CgAAngpp8czMzFSbNm30zDPPhPJlqp/Itir9o/NLkW3cSoMS2Lyf5ey8RnZbV9mdg2S395Cz/ULZnM+9jgYAgCdCelR7nz591KdPn1C+RLVk4i7OP3q9REGZuCGu5UFRNm+V7M6LJZurQrtFBH7IPzCsxnMyMd09ywcAgBfCah/PnJwcZWRkFLqgKOM/ViZ5vPI/Pv9B9+z/c9zVUvRZHiTDATbjAcnmSAoeco8jycpm3CVr2R0CAFC9hFXxHDdunJKTkwsuaWlpXkcKWya2v0ytKVLMuZJJlEycFNVBpsbzMomjZIzxOmK1ZQPrpLzFKnnVASs526WcRW7GAgDAc2G1gPzo0aM1YsSIgusZGRmUz1KYyNYyNR73OgYOFVxfhkG+Mo4DAKDqCKviGR0drejoaK9jhA3rZEn73pPNniE5OyV/Q5m4wVL0WTLGf/gngDd8yWUY5JRxHAAAVUdYFU/8wQa3yu68QgqulWQkWSm4TjZ3oRTdXarxlIyJ8jglihXRUvKnHWZGM4r9cAEA1U5I9/Hcu3evli1bpmXLlkmS1qxZo2XLlmndOtYzPBy7+9aDisuBo6L3H6iSs0B275MepEJZGOOTSfx76WMSbpDxJbqUCACA8BDS4rlkyRK1bdtWbdu2lSSNGDFCbdu21ZgxY0L5spWezftRyluiokdEF4yQst6QtdluxkI5mJg+Mknj8g/6kpS/4oCRFCHF/0WKH+5hOgAAvBHSTe3dunWTtaWd2hHFyv1K+b8TlHRUtCSbKeX9JEWd7FIolJeJGyjF9JFy5krBjZKvphTTU8aX4nU0AAA8wT6eYamsZZ1SH+6ML06K7e91DAAAwkJYreOJ/aJOVamznZJkYqWIFq7EAQAAqAgUzzBkIltLkSep8FmJDuaTYi/Jn00DAACoJCieYcrUeFLy1VH+ASkHzkK0/+OKOk0mcUQJjwQAAAhP7OMZpoy/vnTM+1L2O7LZ0yRnl+RPk4m7RIrpI2MivY4IAABQLhTPMGZ8iVL81TLxV3sdBQAA4KixqR0AAACuoHgCAADAFRRPIAxZG+DkCwCAKod9PIEwYZ0sKes12aw3JGezpGjZmL4yCdfJRDT3Oh4AAEeN4gmEAevsld15pRT4UX+cPCBH2jdDdt9/pZSXZaLaexkRAICjxqZ2IAzYvU8eUjoPCErKk919i6zN8yAZAAAVh+IJeMzafVL22yr5NKmO5OyQcua5GQsAgApH8QS8FvxdslmHGRQhm/eDK3EAAAgViifguagyjLEypizjAAAIXxRPwGv+NMnfWJIpZVBQiu7uUiAAAEKD4gl4zBgjk3CDpJLW7fRLkR1kIk90MxYAABWO4gmEARN7oUzCX/df8yt/9tOffzXiRJmaT3qUDACAisM6nkCYMAnDpZi+stlTpMD/JF+CTMy5UlQnGcPviACAyo/iCYQRE9FYJvF2r2MAABASTKMAAADAFRRPAAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAK1hOSZK1Vsr9Snbf+5KzS/I3kIkdKBPZwutoAAAAVUa1L57WyZTdPVzK/Vz5Z4pxJPlksybKxl4pk3S3jCntHNoAAAAoi2q/qd1m3CXlfrn/WlD558sO5l/Nfk3KetmjZPCKtY5s3o+yud/IBnd4HQcAgCqjWs942sDv0r4PlF82SxiT+ZIUd5WMiXQvGDxjs6fK7n1KCm7Yf4tPNvocmaQ7Zfz1PM0GAEBlV71nPHM/OfwYZ6eU90Pos8BzNvPfsumjDiqdkuRIOR/J7rhINril5McG1slmz5TNniUb3Bb6sAAAVELVesZTNleSUWkznvlyXQjjLmutFPg+v1j7jpWJ/JPXkTxlg9tl9zxWwr1Bydkpu/dpmeQHij4u/U4pd6H++D7yycacL5N0j4wvPpSxAQCoVKp38Yw8UfkHE5UmQopo5kYa19h9c2T3PCIF1/9xW8SJMkljZKLaepjMQ/tmqPRfQIJS9nTZpLtkTIwkyTp7ZXdetv99PPixjrTvPdng71LKf2RM9f5rBgDAAdV7U3vkqZK/ifKPZi+OX4o5V8aX4maqkLLZM2V33ywFfy98R+BH2Z1XyOZ+600wj9ng7zr8X4ec/BniA7LfloL/U8HBaIU4Ut4SKefjigsJAEAlV62LpzFGpsYEycSqaPn0Sf40maQ7PUgWGtbmymbcf+DaIfc6koKyGWNdThUmTA0dfpcLI5mkgms2a8phHuOTzZ569NkAAKgiqnXxlCQTeYJMrRlS3CWS2b8/nu8YKf5GmVpTqtRsp3IWSnZ3KQMcKfCdbOBXtxKFDRPbV8XPXB7gl6K7y/gS/rjJ2X6YZ3Wk4OYKSAcAQNXAzmeSTESaTNI9UtI9sjYoY0ra9F7JBTepTAdTBTdXuf1aD8dENJeNOV/a976Kvj8+SUYmYfghN6dKwYxixh/gl1iCCQCAAtV+xvNQVbZ0SpIvRYffnHxgXPVjkh+SYgfpQNEs2P3CV0um5r9lIltLkqyTIZvzmRTV/jDPGJSJHRjCxAAAVC7MeFYn0Wfl789qs0sYYPIPtoo43tVY4cKYKJnkB2UTbso/KMhmSf5mUnRnGRMha7NlMx6Wst9R4SW2iptF9klRp0nR3d37AgAACHMUz2rE+OKkhL/J7nmouHvz/5s4stqfm97460pxlxW6zdo82Z3X5x+pXmQJrkNLZ4QUe6FM0l1VewYdAIByonhWN3FDZGRk906QbOYft/tq5S94HsMMXbH2zZHyvi59TNwQmajTpKh2VeugNAAAKgjFs5oxxkjxQ6S4wflHuTs7JX99KaoTC52Xwma9pfx9P0s64YBPyvtRJukuF1MBAFC50DSqKWNipZjeXseoPJwNKv0sV84h53gHAACH4qh2oCx8x+jAfrDFM5K/lltpAAColCieQBmY2AtV+lJUlqWTAAA4DIonUBax/fOXVipyalXl3+ZvKsX0dzsVAACVCsUTKANjYmVSXpeizix6Z9QZMilv5C9XBQAASsTBRUAZGX8tmZR/ywZ+k3KX5N8YdapMRFNvgwEAUElQPIFyMhFNJcomAADlxqZ2AAAAuIIZTwCyeStlM1+RcuZLCkgRJ8nEXylFn1PtT6EKAKg4FE+gmrPZM2XT/678dUqD+TfmLZbd/ZUUe4WU9A/KJwCgQrCpHajGbHCzbPodyj8rU/Cge/afpSn7dSlnjgfJAABVEcUTqM6yp6j0U4H6ZDNfdSsNAKCKo3gC1ZjNXa7DnoM+7zu34gAAqjj28ayGrJMu7ftQcnZI/rr5B5D44r2OVWbW2SllvSubs0BSrhTZVibuUpmIJl5Hq3xMhPL37SztdKDFna0JAIDyo3hWI9ZaKfMF2b1PS8pVfqEISiZWShwtE3eJxwkPz+Yuk911jWQzVVCW8lbIZr0qJd1XKb6GcGKiO8vmzC9lhF+K7upaHgBA1cam9uok62XZvU8ov3RKBQeT2GzZjDGy2dM9ClY21smQ3XWtZLNUeIYuKMnKZtwje+CMQiibmP6SSVbJ/xQ4MvFXu5kIAFCFUTyrCetkye59qvQxe56QtcFSx3gqe5pk96rkfRJ9+WtRosyML0Em5WXJJCp/k/uBZZP8knwySWNlok7xLiAAoEphU3t1kbto/0xhKZzNUt63UtSp7mQqJ5vzqUrfFzEo5X7qVpwqw0S2klI/lrKnHbTfbBuZ2EtkItK8jgcAqEJcmfF85pln1LhxY8XExKhDhw76+uuv3XhZHMxJr9hxnijDbGw4z9iGMeNLlIm/Sr6Ul+VLeV2+xNspnQCAChfy4vnWW29pxIgRuueee7R06VK1adNGvXr10tatW0P90jiYv0EZx4Vv2TBRbVX6t6xPimrrVhwAAFBOIS+eTzzxhK6//npdffXVatmypZ5//nnFxcXp5ZdfDvVL42BRp0u+evpjH75D+aSIVjKRx7mZqnxiByt/38OSvgZHJm6oe3kAAEC5hLR45ubm6ptvvlGPHj3+eEGfTz169NAXX3xRZHxOTo4yMjIKXVAxjPHJJI9Vfmk79GP3SYqUSb7X9VzlYfx1ZGo8ofy8B68tuf/P8ddJ0Wd5kAwAAJRFSIvn9u3bFQwGVadOnUK316lTR5s3by4yfty4cUpOTi64pKWF72bfyshEd5JJ+Y8U2brwHVGnydSaLBN5kjfBysHE9JKpNV2KvVDy1ZJMkhR1pkzNl+RLvEPGlDQbCgAAvBZWR7WPHj1aI0aMKLiekZFB+axgJuo0mVpTZAPr9p+5qI6Mv77XscrFRLbYP3sLAAAqk5AWz2OOOUZ+v19btmwpdPuWLVtUt27dIuOjo6MVHR0dykjYz0Q0lNTQ6xgAAKAaCemm9qioKLVr107z5s0ruM1xHM2bN09nnHFGKF8aAAAAYSbkm9pHjBihIUOG6NRTT9Vpp52mCRMmKDMzU1dfzWn4AAAAqpOQF8+LL75Y27Zt05gxY7R582adfPLJmj17dpEDjgAAAFC1GWttaecg9FRGRoaSk5OVnp6upKQkr+MAAADgEOXpa66cMhMAAAAIq+WUAECSrHWkfe/LZv5HCvwgKUKK7ioTf+3+U6cCACojZjwBhBVrHdn00bLpt0uBlZKCknKknHmyOy+RzZ7hdUQAwBGieAIIL/vel/ZN23/FOeiOoCQrmz5KNlj0zGcAgPBH8QQQVmzmf1T6P01Wyp7iVhwAQAWieAIIL4EfVHim81CObO73bqUBAFQgiieAMHO4Yx6NZKJcSQIAqFgUTwDhJbqbJH8pA6xMdDd3sgAAKhTFE0BYMfHXquRN7X7JlyrF9nUzEgCgglA8AYQVE3WyTPIjyp/1PPBPlMn/ny9FpuZEGRPrUToAwNFgAXkAYcfE9peiOkjZU/IPJDJR+ZvXY/tSOgGgEqN4AghLxl9XSrj5wFwnAKAKYFM7AAAAXMGMJ46aDW6TghskX5LkbyJJMoZ5KgAAUBjFE0fMBtbI7hkv5SyQZPffGiUpV1axUkxvmfhrZCJbeBcSAACEDTa144jYwBrZHRdJOZ/oj9IpSbn7/58t7XtPdseFsjmLPEgIAADCDcUTR8TueViyWZKCpYwKSgrI7r5F1sl0KVl4sDZXNu8H2bzvZZ0sr+MAABAW2NSOcrPB7VLOfBWe6SxxtGQzpX0zpbiLQx3Nc9YGpcwXZTNfkezu/BtNnGzsxTKJf5MxMZ7mAwDASxRPlF9wg8pWOg+IkM1bWeWXxbHWyqaPlPa9d8gdWVLWq7J5K6SUV2Q4zzgAoJpiUzvKz1ej/I8xkRUeI+zkflG0dBZwpLzFUvYMVyMBABBOKJ4oNxPRSIo4QSrzHGYg/6wzVZzNelv5p3ksiZHNetOtOAAAhB2KJ46ISRxx4E+HGemXIv4kRXUMdSTvBdeq9IOtrBRc71IYAADCD8UTR8REd5VJ/qdkEvbfcui30v5C6k+Tqfl/MqYafKv5auqwf6V8ya5EAQAgHHFwEY6YiT1Xijlb2veRFFwvawOS3SEFNkq+WJnonlJMj2pzMI2J7Seb+1kpI3wysQPcigMAQNiheOKoGBMtxfbN/7PHWTwX01fa+1IJm9z9+TOisZd6EAwAgPBQDbZ/Au4wJlom5T9SZNv9t/hUcLCRv6lMyhsy/lpexQMAwHPMeAIVyPhTZWpNks1bmb+8kg1KUadIkafKmGo/JwwAqOYonkAImMgTpcgTvY4BAEBYYVM7AAAAXMGMZzVn836UzX5PcnZK/noysRfKRDT0OhYAAKiCKJ7VlLW5sumjpX3v6+Cz7djMZ2Xjr5NJuJ19EgEAQIViU3s1ZTMekvbN3H8teNBFUub/SVkve5QMAABUVRTPasgGd0jZb0myJY/Z+4KszXUvFAAAqPIontVR7icq/ZzikuxuKW+5G2kAAEA1QfGsjuy+ih0HAABQBhTP6ijiuDIMMlJE85BHAQAA1QfFszqKPEXyN9PBR7MX5peiusr467mZCiWwdp9scBv73AIAKj2KZzVkjJGp8bhkolW0fPolX4pM8r0eJMPBbGC1nF23ym5pK7uto+yWdnLS/yEb3Ox1NAAAjgjFs5oykS1lak2TYvpLitx/Y6wUd5lMrWky/vqe5qvubN53stsHSjlz9MeBYDlS9juyOy6QDaz3Mh4AAEfEWGtLXlPHYxkZGUpOTlZ6erqSkpK8jlNlWZsn2UzJJMgYzingNWut7PZeUnCdJKeYEX4pqqN8Kf/ndjQAAIooT19jxhMyJlLGV4PSGS7ylkjBtSq+dEpSUMpdJBvc4GIoAACOHsUTCDeB1WUYZKXAryGPAgBARaJ4AuHGxFbsOAAAwgTFEwg30V0kHWa3B1+KFHmyG2kAAKgwFE8gzBhfihR3mSRT8pj4G2VMpHuhAACoABRPIAyZxJFSzAX7r/mVPwPqk2Sk+BuluKu8CwcAwBHiMGYgDBkTKVNjvGzgOtns9yRnZ/7aqrEDWGMVAFBpUTyBMGYimsskjvA6BgAAFYJN7QAAAHAFxRMAAACuoHgCAADAFRRPAAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK6geAIAAMAVFE8AAAC4ImTFc+zYsTrzzDMVFxenGjVqhOplAAAAUEmErHjm5uZq0KBBuvHGG0P1EgAAAKhEIkL1xPfdd58kaeLEiaF6CQAAAFQiISueRyInJ0c5OTkF1zMyMjxMAwAAgIoUVgcXjRs3TsnJyQWXtLQ0ryMBAACggpSreI4aNUrGmFIvq1atOuIwo0ePVnp6esFl/fr1R/xcAAAACC/l2tR+2223aejQoaWOadq06RGHiY6OVnR09BE/HgAAAOGrXMUzNTVVqampocoCAACAKixkBxetW7dOO3fu1Lp16xQMBrVs2TJJUvPmzZWQkBCqlwUAAECYClnxHDNmjF599dWC623btpUkzZ8/X926dQvVywIAACBMGWut9TpESTIyMpScnKz09HQlJSV5HQcAAACHKE9fC6vllAAAAFB1hdUC8qgebO63slmvS3nLJRMtRZ8jE3eJjL+u19EAAEAIUTzhKrv3Kdm9T0nySwrm3xj4VTbrFanm/8lEtfcyHgAACCE2tcM1dt+8/aVTKiidkiRHsjmyu4bJOnu8iAYAAFxA8YRrbOYrKvlbzpFsppQ9zc1IAADARRRPuMJaK+UtkeSUPi53sTuBEHaskyW79yU5286Ss7mFnC3t5GTcJxtY53U0AEAFYR9PAJ6zzl7ZnVdIgR8l7V/hze6RsibLZk+XUl6TiWzlZUQAQAVgxhOuMMZIkafocN9yHFxUPdm9j0uBVSoonQWCks2W3XWzrC19thwAEP4onnCNib9GJW9q90kmTood4GIihAPrZEpZ76rk7w1HcjZIuYvcjAUACAGKJ1xjYnpI8cP3X/MfdI9PUrRMzRdkfJyhqtoJrpW07zCD/FLeShfCAABCiX084Spf4l9lozvKZr2Rv4C8oqSYnjJxl8r463kdD56ILMMYW8ZxAIBwRvGE60zUqTJRp3odA+EiopnkqyM5W0oZ5EjR3dxKBAAIETa1A/CUMX6Z+D+XMsIvRXWWifyTa5kAAKFB8QTgvbgrpLhr9l/xF/5/ZCuZGk94kQoAUMHY1A7Ac8YYmaRRsrEXyGa/LQXWSb5kmZi+UnQXGeM//JMAAMIexRNA2DCRLWQi/+F1DABAiLCpHQAAAK6geAIAAMAVFE8AAAC4guIJAAAAV1A8AQAA4AqKJwAAAFxB8QQAAIArKJ4AAABwBcUTAAAArqB4AgAAwBWcMhOoZKyzU8p+Xza4QcaXLMX0lYlo7HUsAAAOi+IJVCI281XZPQ9LCkryy8qR9v5LNvYimaT7ZEyk1xEBACgRm9qBSsJmT5fdM1ZSQJLd/38n/87sd2UzxnoXDgCAMqB4ApWAtY7s3n+VNkLKniwb3OZaJgAAyoviCVQGgZ+k4IbDDLJSzjxX4gAAcCQonkBlYDPLMMiUcRwAAN6geAKVgb+hJHOYQY7kb+pGGgAAjgjFE6gEjL+2FH2WJH9JIyRfqhTd2c1YAACUC8UTqCRM4l2SL1lFy6dPkl8m+REZwwppAIDwRfEEKgkT0UCm1lQppr+kA+t1Gimqo0zKmzLRHb2MBwDAYTE9AlQixl9fpsZ4WecfkrND8iXJ+Gp4HQsAgDKheAKVkPHFS754r2MAAFAubGoHAACAKyieKJa1ubI2W9Zar6MAAIAqgk3tKMTmfCab+aKU+6Ukm79+ZNxVUtxlHDENAACOCjOeKGCz3pTddbWU+5Wk/TOdwfWye8bK7r5Z1gY8zQcAACo3prAgSbLBDbIZ9+2/5hx8T/7/cj6Wst+V4i52OxoAwGPWWq36erXmvPyxtqzbpuTUJJ19WWe169lGPh9zWCg7iickSTZrShnGvCZD8QSAaiUYDOqff35Bc16ZL3+ET8GAI5/fp3mvL1KbbifqgfdGKjYh1uuYqCT4NQX58n5U4ZnOQ1kp8AsHGwFANfPmQ9M0Z+J8SVIwkP9zwgnm///7RT/q8eue9ywbKh+KJ/L5YnT4b4coGWPcSAMACAO5+3L1zhPvF+x1dSgn6OiTKV9oy/+2uRsMlRbFE5IkE322Sp/x9Esx57gVBwAQBn5e8qsy07NKHWOt1ZI5y9wJhEqP4ol8Mb0l/7GS/MXcmT/LaeKvcTUSAMBbebmHX83EGFOmcYBE8cR+xkTJ1Hx1f/mU8guoX/mlM0qmxr9kIlt5FxAA4LomrRvKH1F6VbDW6rhTm7mUCJUdR7WjgIloKB3zgZTzsWzOQsnm5pfN2AtkfMlexwMAuKxGarK6Dj5TC976vOCAooP5I3xqfGKaTujwJw/SoTKieKIQYyKlmF4yMb28jgIACAPD/3WNflm6Rr//vFHW+eMoI5/fp/jkeN355t848BRlxqZ2AABQoqRaiXrqy4c09P5LVLvhMfL5fUo6JlEDb+2rF5Y9qobHH3v4JwH2MzaMF2bMyMhQcnKy0tPTlZSU5HUcAAAAHKI8fY0ZTwAAALiC4gkAAABXUDwBAADgCoonAAAAXEHxBAAAgCsongAAAHAFxRMAAACuCFnxXLt2ra699lo1adJEsbGxatasme655x7l5uaG6iUBAAAQxkJ2ysxVq1bJcRy98MILat68uVasWKHrr79emZmZeuyxx0L1sgAAAAhTrp656NFHH9Vzzz2n3377rUzjOXMRgLKwzl7J5ki+GjLG73UcAKhWytPXQjbjWZz09HSlpKSUeH9OTo5ycnIKrmdkZLgRC0AlZXO+ks18Tsr9PP8GU0M27lKZ+D/L+OK9DQcAKMK1g4tWr16tp556SsOGDStxzLhx45ScnFxwSUtLcysegErGZs+U3XWVlPvVQTfuljJfkN15uayT6Vk2AEDxyl08R40aJWNMqZdVq1YVesyGDRvUu3dvDRo0SNdff32Jzz169Gilp6cXXNavX1/+rwhAlWedDNn00ZKspOAh9zpSYJVs5oseJAMAlKbc+3hu27ZNO3bsKHVM06ZNFRUVJUnauHGjunXrptNPP10TJ06Uz1f2rss+ngCKYzP/I7tnrPKLZwlMskztL2SMq3sUAUC1E9J9PFNTU5WamlqmsRs2bFD37t3Vrl07vfLKK+UqnQBQEhtYLckvKVDKoHTJ2S35j3EpFQDgcEI2FbBhwwZ169ZNjRo10mOPPaZt27YV3Fe3bt1QvSyA6sDElnFcTGhzAADKJWTFc+7cuVq9erVWr16tBg0aFLrPxRWcAFRBJuYc2axXShnhk6Lay/gSXMsEADi8kG37Hjp0qKy1xV4A4KhEtsu/qKQ1O61M/I1uJgIAlAE7XQKodIwxMjWfkyJP3n9LxP6LkRQlk/ywTPSZnuUDABSPwz0BVErGV0NKmSTlLZHdN1eyWTIRzaXYAfn3AQDCDsUTQKVljMnflzOqvddRAABlwKZ2AAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK6geAIAAMAVFE8AAAC4guIJAAAAV1A8AQAA4AqKJwAAAFxB8QQAAIArKJ4AAABwBcUTAAAArqB4AgAAwBUUTwAAALiC4gkAAABXUDwBAADgCoonAAAAXEHxBAAAgCsongAAAHAFxRMAAACuiPA6AAAAQCgsX7hS7z0zW6u+Xq2omEh1HHCa+t3YS3UapXodrdoy1lrrdYiSZGRkKDk5Wenp6UpKSvI6DgCgmsjNydPCtz/XvNc/0e5tGarfrK76XHe2Tu3ZRsYYr+OhDP595yRNHj9N/gifggFHkuTz+xQZFaGx/71Tbbqd6HHCqqM8fY3iCQDAQXZvS9ftZ9+ntSvWy+czchwrX4RPTsBR54EddOekWxURyQbDcPbptK9038DHir3P+Ixi4mP05rrnFJ8c73Kyqqk8fY19PAEAOMi4K57Uuh83SJIcJ39uxtk/Y/bp1K/1+gPveJYNZfPOE+/L5y++4ljHat/effrw1YUup4JE8QQAoMDaleu1dO53coJOsfdbazXtyQ+Uk53jcjKUleM4+uGLn0v8DCVJRvp+0Q/uhUIBiicAAPst+3jFYffhzMrI0q/L1roTCOVmjFGZ9sJlX11PUDwBANjPCToqS2spdTYNnjLG6MROx5e4qV3Kn7lu05WDi7xA8QQAYL+WZx4n65R+zG1UTKSanNTIpUQ4EoNuO7/EXw6Mzyg+KU49ruzicipIFE8AAAq0aN9cf2rXVP6I4n88+vw+9bn2bMUnxbmcDOVxRr9TddW9gyWp0Gfp8/sUExetB2eO5jP0CMspAQBwkE1rtuhvnf+hXZt3FxzVbnxG1rFqeWYLjZ9zt2LjYzxOibL44cuf9f5zc7Tqqz8WkD/3zz10TP0Ur6NVKazjCQDAUcjYsUczX5irD19doIwde1S3SW2d9+dz1OOqroqKjvQ6HhBWKJ4AAABwBQvIAwAAIOxQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK6geAIAAMAVEV4HAAAA1c/vv2zSe8/M1hfvL1FeTkAnnP4nDbipj9p0O9HraAghFpAHAACu+vqDb3XPBY/IcRw5AUdS/jnVgwFHl989UEPvv8TjhCgPFpAHAABhafe2dN130WMK5gUKSqckBff/+Y0H39WXM7/xKh5CjOIJAABcM/vfHysvJ08lbW/1+X16d8JMd0PBNRRPAADgmu8/XSXrlLyXnxN0tPKzVS4mgpsongAAwDXGSDJlGYSqiOIJAABc0/as1jKlNE9/hE9tz2rlYiK4ieIJAABc03NoN8UkxMjnK758BgOOBv6tn8up4BaKJwAAcE1izQQ9+P4oRcVGyef/o4b4I/L/POyxq3TK2a29iocQYwF5AADgqpO6tNTEn5/SrJc+0hfvLVZuTkAtzzhO5/+ll5qf3MTreAghFpAHAADAEWMBeQAAAIQdiicAAABcQfEEAACAKyieAAAAcEVIi+f555+vhg0bKiYmRvXq1dOVV16pjRs3hvIlAQAAEKZCWjy7d++ut99+Wz/99JPeffdd/frrr7roootC+ZIAAAAIU64up/Tee+9pwIABysnJUWRk5GHHs5wSAABAeCtPX3NtAfmdO3fqjTfe0Jlnnlli6czJyVFOTk7B9YyMDLfiAQAAIMRCfnDRyJEjFR8fr1q1amndunWaMWNGiWPHjRun5OTkgktaWlqo4wEAAMAl5S6eo0aNkjGm1MuqVasKxt9+++369ttv9eGHH8rv9+uqq65SSVv3R48erfT09ILL+vXrj/wrAwAAQFgp9z6e27Zt044dO0od07RpU0VFRRW5/ffff1daWpo+//xznXHGGYd9LfbxBAAACG8h3cczNTVVqampRxTMcRxJKrQfJwAAAKqHkB1c9NVXX2nx4sXq1KmTatasqV9//VX/+Mc/1KxZszLNdgIAAKBqCdnBRXFxcZo6darOPvtstWjRQtdee61OOukkLVy4UNHR0aF6WQAAAISpkM14tm7dWh9//HGonh4AAACVDOdqBwAAgCsongAAAHAFxRMAAACuoHgCAADAFa6dqx0AqoNgMKglc5Zr0TtfKmtvttKOq6/e156lek3qeB0NADxH8QSACrJ7W7ruPPch/fLNb/JH+OQErYzPaNK4qbpu3BW6+I7+XkcEAE+xqR0AKoC1Vvde+Jh+XbZWkhQMOLLWygk6kpX+b9TrWvDWZ96GBACPUTwBoAL8+NUvWvnZqvyiWQzjM5r00FRZa11OBgDhg+IJABXgy/eXyB/hL/F+61it+X6ddmza5WIqAAgvFE8AqAB5OQEZU4Zx+/JCHwYAwhTFEwAqQLOTGyuQFyx1THxynI5pkOJSIgAIPxRPAKgAXS46XYkpCTK+4qc9fX6fzht2jiKjIl1OBgDhg+IJABUgKiZKd0/+m/wRfvkjCv/TanxGfzqlqS7/x0UepQOA8MA6nkAl8fvPGzXj6dn6atZSBQNBte58gvrf1EcndPiT19Gw3yk9TtKzi8frrUdmaOGULxTIDSg1rZbO/0tvDbi5j2Lior2OCACeMjaM1/bIyMhQcnKy0tPTlZSU5HUcwDOfTf9aDwx+QlZWTiB/uR5/hE/BgKM/P3KlBv39fI8T4lDWWgUDQUVE8vs9gKqtPH2NTe1AmNv2+w49eMk/FQwGC0qnlL9AuSS9eMdrWr5wpVfxUAJjDKUTAA5B8QTC3H9fnFtw9pvi+CN8mvavWe6GAgDgCFA8gTD33cIfSjwbjpQ/87l8wQoXEwEAcGQonkC4K8Oi5GVauRwAAI9RPIEw1/as1vKVsDaklL+pve3ZrV1MBADAkaF4AmHu3Ot7KCIqosRJzWDA0YV/7etuKAAAjgDFEwhzterV1L1Tb1dEVIR8/j/+yvojfJKRbnrqWrXqeLyHCQEAKBvW+gAqgfa92+qVVU/q/efm6OsPvlUwL38B+X5/6aVmbRp7HQ8AgDJhAXkAAAAcMRaQBwAAQNiheAIAAMAVFE8AAAC4guIJAAAAV1A8AQAA4AqKJwAAAFzBOp4AgErPWqvvF/2oz2csVm52rpqd3FjdL+2kuMRYr6MBOAjFEwBQqe3amq4x/R/Wqq9+kT/CLxkpGAjq+dte1ejX/6oz+7f3OiIqOWutlny4XO8/N0drvl+nuKRYdRvcUedef7aSj2Gd8fJgAXkAQKXlOI5uOm20fv1urZyAU+g+YyTj9+lfnz6o40/70xE9fzAQ1KqvVytrT7YaHFdP9ZrUqYjYqEQcx9E///y8Zr88X/4In4L7v8+MzygpJUGPfnyvmrRq6HFKb7GAPACgWvjmw+X6ZelvRUqnJB2YVpk8fnq5n9daq/ef/1CXpg3TrZ3u1p19xuqqZjfpjnPu1+8/bzzK1KhMZj4/V7Nfni9JBaVTkqxjtWdXpu4+b5yCgaBX8SodiicAoNL6dOpX8keU/KPMCTj64r3F5S4Gbz08XU/+5SXt2pJe6PblC1bqljPu1KbfthxRXlQu1lpNefw9yRR/vxN0tHXddn058xt3g1ViFE8AQKW1LytH1il9jzHHscrNySvzc+7aslsTx0wu/rmCjrL2ZOvVe94qV86SBINBbft9h3Zu3qUw3vOt2tq5ebc2r9kqlfLR+CP9+m7hD+6FquQongCASqvh8Q0OO6ZW/ZqKiYsu83POe2ORnFLKbDDgaMFbnytrT3aZn/NQebl5emPsu7q0wTBd1vAGXVz/z/pzm9s0741FR/ycQGVA8QQAVFq9rumefxRRCYzP6Py/9JYpZcyhtq3fIb+/9B+PwUBQu7emlzqmJIG8gMb0f1ivjnmr0Kb8/638XeOvfLLCZlNx9FLq1lDdJrVL3NQuScG8oFp3OcG9UJUcxRMAUGkdUz9FNz11rSTJ5yvcDnw+o+NPa66Bf+tbrudMTk0qdcZTkowxSkxJKF/Y/T6cuEBLPlxeZNP6geuvP/CO1qxYd0TPjYpljNFFI/qVuKnd5/cpNa2Wzuh3qrvBKjGKJwCgUut3Q089+P4oHd/hjyWTkmol6tI7L9QjH92j6Niyb2aXpO6XdpTjFD1K/gCf36cOfU9RYs0jK57vPTdHppQpNH+ET7Ne/OiInhsVr9+NPdX7mu6SVOhANuMzSqgRr7H/vTN//ViUCQvIAwAqvQ5926lD33bas2uvcvflqUZq0hGXgXpN6qj/X3prxrOzi8x0GZ+RP8KnIfddfMRZ163aUOqBRMGAozUrmfEMFz6fTyNeulFdLjpD7z//Yf4C8omx6nZxR/W57izVSE32OmKlQvEEAFQZRzoLeagbJwxVdFyUpv5rlgK5gfx9/KxUt3Ft3TFxuJq3bXLEzx0bH628fSUfZW98hlN9hhljjNr3bqv2vdt6HaXSo3gCAHAIv9+v6x++UhePHKCvZ32r7D3ZSjv+WJ3UtaV8vqPbS63r4I6a9dLcQouRH8w6Vl0HnXlUrwGEK4onAAAlSEpJVI8rulToc154a199OHG+rJNX5CAmf4RPdRrXVueLTq/Q1wTCBQcXAQDgogZ/qqdxs+9Wwv7dAiIi/fJH5u+Pmnb8sXr0ozGKio70MiIQMsx4AgDgstadT9Cbv7+gRe98qVVf/6KIyAi179NWbc9qVa41R4HKxtgwPkdXRkaGkpOTlZ6erqSkJK/jAAAA4BDl6WtsagcAAIAr2NQOAPDE2pXr9cV7S5STnaOmJzXSGeefqsgo9m0EqjKKJwDAVVl7sjX+iif1xftL5PP7ZHxGwbygklOTdNebt6rtWa29jgggRNjUDgBwjbVW9174qL6atVSS5AQdBfOCkqSMHXt0V9+H9OvytR4mrJqstcrO3KdAXsDrKKjmKJ4AANes/GyVvp33vZxg0cXTrWPlBB29OX6aB8mqprzcPE15/H1d0fQvOj/xSp0be5nu7PuQvvvkB6+joZpiUzsAwDUL3vpc/gi/goFgsfcHA44+ffdLBfICiojkR9TRyMvN0119x2nZ/BWy+xeqt47VNx8u15LZyzTqtZt11mWdPU6J6oYZTwCAa/amZ+pwq/gFA45ysnNdSlR1TX/yg0Kl8wAn6Mhaq0evfka7t6V7lA7VFcUTAOCaY5vVO+yYxJQExSbEuJCm6rLWavrTHxQpnQcLBh19OHGBe6EAUTwBAC7qdXW3Umc8fX6fzht2jnw+fjwdjX2Z+7R13fZSxxhj9Nv3/3MpEZCPv9kAANfUbpiqax+6XJJ06JkhfX6fGhxXTxff0d+DZFVLRFREkff3UMYYRUVHuRMI2I/iCQBw1cV39Neo127RsX/6Y7N7VEyk+l7fQxM+fVDxyfEepqsaIqMi1a7XyfL5S/4xHwwEdWb/9i6mAjiqHQDggbMv76yzLuukjb9uVm52ruo2qa3YhFivY1Upl466QN/MWV7sff4Inxq0OFbt+5zsbihUe8x4AgA8YYzRsc3rqUnrRpTOEDipS0vd8epNioj0y/iMfH6f/BH5P/YbHFdf42ffJb/f73FKVDeuzHjm5OSoQ4cOWr58ub799ludfPLJbrwsAADVWo8ruujUXm0055UFWvP9/xQZHakz+7fXaee2pXTCE64UzzvuuEP169fX8uXFT/kDAIDQqJGazAFbCBsh39T+wQcf6MMPP9Rjjz0W6pcCAABAGAvpjOeWLVt0/fXXa/r06YqLizvs+JycHOXk5BRcz8jICGU8AAAAuChkM57WWg0dOlQ33HCDTj311DI9Zty4cUpOTi64pKWlhSoeAAAAXFbu4jlq1CgZY0q9rFq1Sk899ZT27Nmj0aNHl/m5R48erfT09ILL+vXryxsPAAAAYcrY0s5dVoxt27Zpx44dpY5p2rSpBg8erPfff1/moFMnBINB+f1+XX755Xr11VcP+1oZGRlKTk5Wenq6kpKSyhMTAAAALihPXyt38SyrdevWFdpHc+PGjerVq5feeecddejQQQ0aNDjsc1A8AQAAwlt5+lrIDi5q2LBhoesJCQmSpGbNmpWpdAIAAKBq4cxFAAAAcIVr52pv3LixQrRVHwAAhIDjOFr60ff6bNpX2peVo0Yt09Tr6u6qWTvZ62iopFwrngAAoPLYtTVdd537kH5Z+pv8EX7JWjnWauKYybr1+WHqfXV3ryOiEmJTOwCg2tqXlaNdW3YrkBfwOkpYsdbq7vPG6bfv1kqSgoGggkFH1rEK5gX1+HXP6pu5nAYb5ceMJwCg2ln97Rq9/uA7+mLGYjmOVWxCjHpfc5YuvfNCNiNLWjZ/hX5e8muJ9/t8Pr05bprandPGxVSoCiieAKq07Rt3at0PvysqNkot2jdTZFSk15HgsWXzV2h0n7Fygo4cJ//Yg+y9+zTjmdn6fMZiPfnFWKXUrelxSm99PmOx/BF+BQPBYu93go6WL1iprD3ZikuMdTldyay1+nbe95r5wlytW7VBiTXj1f2STupxZZewylmdUTwBVEnbN+zQ0ze/rM/fWyy7v1wkHZOoS0ZeoItGnFfo5BaoPoKBoMZd/i8FA8GC74sDnKCj7Rt26MU7XtOo/9ziUcLwkJudW7Zx+3LDptA5jqNHr35GH732ifwRPgUDjoyRVny2Sm89Ml2PL7hPdRvX9jpmtcc+ngCqnF1b03XLmXfpi5lLCpWLjO179OLt/9GLt7/mYTp46atZS7Vz8+4ipfOAYMDRgsmfK2PnHpeThZcmrRvJCTqljklOTVJiSoJLiQ7v3X/+Vx+9/omk/M9RkqyVZPO3fIzp/zCr64QBiieAKueth6drx8ZdcgLF/+B854n39fvPG11OhXCwdsV6+SNK/9EXDAS16dctLiUKTz2u7KLImEiphA0DxmfU/y+95ff73Q1WgmAwqHf/+b5UQq90Ao7WfL9O333yg7vBUATFE0CV4jiOPvj3vFJna/wRPs15Zb6LqRAuYuKjC/brLE10XLQLacJXQo143THxJhlj5PMXrgrGZ3R8hz9p0O3ne5SuqE2/btGOjbtKHeOP8GvZxytcSoSSUDwBVCn7MnOUlZFd6hhrpa3rt7uUCOHkzP7tD7u5tV7TOmrUklM7dx10hp5YeL/a9z5Zxpc/9ZlSr6aG3n+JHv1ojGLCqJyX5ZcJSSXuYgH3cHARgColOi5KkdERysspeV1GY4ySj0lyMRXCRd3GtXX2ZZ318ZufllhCrvjHRRx8tl+rjsfrwfdHKy83T3k5AcUmxITle1O/WR0lHZOojO0l75sbDATVqvMJLqZCcZjxBFCl+P1+nXVZ51L34wsGgjr7ii4upoIk/fDlz3rosgkaXO86Da5/vcZf+aR+Wrza9Rx/e3GYOg5oLyl/86s/wi+f3yefz+i68Veo55BurmcKd5FRkYpLjA3L0ilJEZERGnBTn4KZ2UP5/D7Vb15Xp/Ro7XIyHMrYMD7EKyMjQ8nJyUpPT1dSErMTAMrm91826S+n3qGcrNwi+3oan1HHAafpnnf+7lG66mnGM7P19M3/LljmRsrf1zYYdPTXZ/+s84ad43qm1d+u0fzJn2nPzr2q17SOzhnSVcfUT3E9BypGIC+g+wY+pi9nfiOf31fwd9/nM0qslajH59+rRi3TPE5ZNZWnr1E8AVRJq79do4cun6D1qzbKGCNrrXx+n3oN7aabnrpWUTFRXkesNn5Z+pv+0n5kiUccy0gvfPuYmp7UyNVcqHqCwaAWvfOlZr7wodat2qiE5DidfXkXnfvnHpyRKoQongCg/LOYrPh0lX777n+Kjo3Saee2rfZnpPHCY9c8o49e/6RgpvNQ/gifel99lm59YZjLyQBUhPL0NQ4uAlBlGWPUuvMJas0BBZ5avvCHEkunlL/Y93LWVwSqBQ4uAgCE1OEWbJfy98MDUPVRPAEAIdW+V9sii5AfzOf3qX3vti4mAuAViicAIKTOH94rfxme4iY1Tf5s5/l/6eV6LgDuo3gCAEIqrcWxuuvNWwvWyzzA5/cpIsKvu98aofrN6nqYEIBbOKodAHDUsvdm69fl/5MkNWvTSLEJsUXGbF67Vf99Ya6Wzvtexkhtzz5J/W44R7UbprodF0AFYjklAIArcvfl6uU7J2nmix8pJytHkhQdF63zhp2ja8ZeynqpQDXAckoAgJALBoL6x/nj9e3HKwqd9zwnK0dT//Vfrfn+f3po1l3yR/g9TAkgnLCPJwDgiCx690st/ej7QqXzAOtYLf3oe3069SsPkgEIVxRPAMAR+e9LH5W6/qbP79N/X/rIxUQAwh3FEwBwRDb/tlVOMbOdBzhBR5t+3eJiIgDhjuIJADgiyamJxa/NuZ8xUo3aHBgK4A8UTwDAETnnqm6l3m/LMAZA9ULxBAAckZ5Duqp+s7ryFXMudn+ET/Wb1dU5V3XxIBmAcEXxBAAckdiEWD2+4D61PKOFJMkYk39qTEktz2ihxxfcV+xC8gCqLxaQBwActV+Xr9X3n/woSWrd5QQ1a9PY20AAXMMC8gAAVzVr05iyCeCw2NQOAAAAV1A8AQAA4AqKJwAAAFxB8QQAAIArKJ4AAABwBcUTAAAArqB4AgAAwBUUTwAAALiC4gkAAABXUDwBAADgCk6ZCQBANeY4jr6d970+m/a19mXlqFHLNPUc2k01ayd7HQ1VEMUTAIBqave2dN3Vd5x+XvKr/BF+WVlZx+qVf7ypW58fpt5Xd/c6IqoYNrUDAFANWWt193njtXrZGklSMBCUE3BkHatgXlCPX/esvpm73OOUqGoongAAVEPfLfxBPy1eLSfgFHu/z+fTm+OmuZwKVR3FEwCAaujzGYvlj/CXeL8TdLR8wUplZmS5mApVHcUTAIBqKCcrp0zjcvflhTgJqhOKJwAA1VDj1g3lBIvfzH5AcmqSkmoluJQI1QHFEwCAaqjHFV0UGRMpmeLv9/mMzr+xl/z+kjfHA+XFckoAAFSQQF5An89YrC/eX6Kc7Fw1bd1Iva89S8fUT/E6WhEJNeJ1xyvDNfayCTI+U2j20/iMWrRvrsF39PcwIaoiY621XocoSUZGhpKTk5Wenq6kpCSv4wAAUKKt67bpjnMe0IZfNsnn98k6VsZnZIzy18S85iyvIxZrxWer9Ob4aVr8wbeyjlXNujXU/y+9NXDEeYqJi/Y6HiqB8vQ1iicAAEcpGAjqutYjtOnXzQoWtzyRkR6ZO0Ztz2rtfrgyys3JU15OnuISY2VMCdvfgWKUp6+xjycAAEfpq/8u1e8/bSy+dCp/Tcy3Hp7ubqhyioqOVHxSHKUTIUXxBADgKH3x/hL5I0r+keoEHS396Dvl5bI0Eao3iicAAEcpLzdPh9txzVopkBd0JxAQpiieAAAcpeYnN1Gph0wYqW6T2hysg2qP4gkAwFHqOaSbIiIjSlwT08howE192H8S1R7FEwCAo5RUK1EjX71JxphC+3oak7+c0qm92qj/Tb09TAiEBxaQBwCgAnQdfKZSGx6jKY+9py/eW6xgwFH95nU04KZzdd4N5+TPiKLK+v3njZr+1AdaNPUr5e3LVfNTmqr/8N46s397ZroPwjqeAABUMMdx5AQdymY18c3c5frH+ePlBJ2CJbV8fp+coKNzrz9btz4/rEqXz7BZx7Nx48b7NzP8cRk/fnwoXxIAAM/5fD5KZzWRmZ6pey98VIG8YKF1XA+cgnTWS/M09z8LvYoXdkK+j+f999+vTZs2FVxuvvnmUL8kAACAK+b+5xPlZOXKOsVvQDY+o6kTZrqcKnyF/NexxMRE1a1bN9QvAwAA4LofvvxJxqjEdVytY/Xr8v8pLzdPkVGR7oYLQyGf8Rw/frxq1aqltm3b6tFHH1UgEAj1SwIAALjC5/dJZdh/0+djISEpxDOet9xyi0455RSlpKTo888/1+jRo7Vp0yY98cQTxY7PyclRTk5OwfWMjIxQxgMAADgqp5x9kua9vqjE+31+n07s2EL+CL+LqcJXuev3qFGjihwwdOhl1apVkqQRI0aoW7duOumkk3TDDTfo8ccf11NPPVWoXB5s3LhxSk5OLrikpaUd3VcHAAAQQl0Hn6GadZLzZz6L4QQdDf57f5dTha9yL6e0bds27dixo9QxTZs2VVRUVJHbV65cqVatWmnVqlVq0aJFkfuLm/FMS0tjOSUAABC2fl2+Vnf0uF97du4tOHWqP8KnYMDRteMu1yUjB3gbMMTKs5xSuTe1p6amKjU19YiCLVu2TD6fT7Vr1y72/ujoaEVHcx5bAABQeTRr01iv/vKUPnx1gT6b9rVysnN0XLtmOu+Gnmp6UiOv44WVkC0g/8UXX+irr75S9+7dlZiYqC+++EJ/+9vf1KdPH7366qtleg4WkAcAAAhvIZ3xLKvo6GhNnjxZ9957r3JyctSkSRP97W9/04gRI0L1kgAAAAhjISuep5xyir788stQPT0AAAAqGRaVAgAAgCsongAAAHAFxRMAAACuoHgCAADAFRRPAAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK6geAIAAMAVFE8AAAC4guIJAAAAV1A8AQAA4AqKJwAAAFxB8QQAAIArKJ4AAABwRYTXAQAARf26fK2+X/SjjDFq0+1ENT4xzetIAHDUKJ4AEEa2/b5DYy/5p1Z+/pOMMZIka61O7t5Kd076q2rWqeFtQAA4CmxqB4AwkZmRpRFdx2jV179Iyi+c1lpJ0neLftBt3e/VvqwcLyMCqAQO/rcj3FA8ASBMzHl5vras3aZgwClynxNwtH7VBn086VMPkgGoDBbPWaaRPe9Xn5hL1TvqEv2tyz+0aOpXYVVCKZ4AECY+/M8CWZX8A8L4jOa+ttDFRAAqi7cfnaE7+4zVsvkrFcwLygk6+uGLn3X/RY/p/0a94XW8AhRPAAgTu7dlqJTeKetYpW9Ndy8QgEph9bdr9NLI1yVJTvCPLSYH/vz2ozO09KPvPMl2KIonAISJek1qy+czJd7v8/tUr2kdFxMBqAzee26O/BElVzp/hE8znpntYqKSUTwBIEyce30POU7JU55O0FGf6852MRGAymDVV78Uu2/4AcGAU3DQotcongAQJrpf0lGtu5xQ7Kyn8Rm1732yzjj/VA+SAQhnkdGRFTLGDRRPAAgTEZERemjWXTp/eG9Fx0YV3B4TH62L/nae7p12h/x+v4cJAYSjM89vL1PabjoRPnXsf5qLiUpmbDgdY3+IjIwMJScnKz09XUlJSV7HAQDXZO3J1upv18gYo+anNFFsfIzXkQCEqV1bdmvIcTcrJzOnyO46xhhFRPn10vdP6Njm9ULy+uXpa8x4AkAYikuM1UldWqp15xMonQBKVbNODY2ffbdik2Ilk182jcnfRScqNlL3TR8ZstJZXpwyEwAAoJJreUYLvbH2OX302idaNn+FrOPoxI4nqNfQbkqqleh1vAJsagcAAMARY1M7AAAAwg7FEwAAAK6geAIAAMAVFE8AAAC4guIJAAAAV1A8AQAA4AqKJwAAAFxB8QQAAIArKJ4AAABwBcUTAAAArqB4AgAAwBUUTwAAALiC4gkAAABXUDwBAADgCoonAAAAXEHxBAAAgCsongAAAHAFxRMAAACuoHgCAADAFRRPAAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK6I8DoAAODoWWv17ccrtGT2twoGHLVo30ydBp6uqOhIr6MBQAGKJwBUclvXb9c/+o3Xb9/9T/4Iv2SkYF5QybdO1L1T/65WnU7wOiIASArxpvb//ve/6tChg2JjY1WzZk0NGDAglC8HANVObk6ebj/7Pq39Yb0kKRgIKpgXlCRl7NyjUb3HasPqTV5GBIACISue7777rq688kpdffXVWr58uT777DNddtlloXo5AKiWPpnyhTau3iwn4BS5zzpWgdw8TZ3wXw+SAUBRIdnUHggE9Ne//lWPPvqorr322oLbW7ZsGYqXA4Bq65N3vpDxGVnHFnt/MOBowVuf6eanr3M5GQAUFZIZz6VLl2rDhg3y+Xxq27at6tWrpz59+mjFihWheDkAqLayMrJLLJ0H7MvMcSkNAJQuJMXzt99+kyTde++9uvvuuzVz5kzVrFlT3bp1086dO0t8XE5OjjIyMgpdAAAla9SygfwRJf9TbnxGaccf62IiAChZuYrnqFGjZIwp9bJq1So5Tv6+RnfddZcGDhyodu3a6ZVXXpExRlOmTCnx+ceNG6fk5OSCS1pa2tF9dQBQxZ037BwFi9m/8wDrWJ3/l94uJgKAkpVrH8/bbrtNQ4cOLXVM06ZNtWlT/hGUB+/TGR0draZNm2rdunUlPnb06NEaMWJEwfWMjAzKJwCUoknrRrr87oF648F3ZYyRtX9sdjc+o1N6nKSeQ7p6mBAA/lCu4pmamqrU1NTDjmvXrp2io6P1008/qVOnTpKkvLw8rV27Vo0aNSrxcdHR0YqOji5PJACo9obef4kaHFdfk8dP0/9++F2SVKN2sgbc1EeD7zhfEZEs2QwgPITkX6OkpCTdcMMNuueee5SWlqZGjRrp0UcflSQNGjQoFC8JANVajyu66OzLO2vXlt0K5AVVq35N+f1+r2MBQCEh+zX40UcfVUREhK688kplZ2erQ4cO+vjjj1WzZs1QvSQAVGvGGKXU5d9YAOHL2IN3CAozGRkZSk5OVnp6upKSkryOAwAAgEOUp6+F9JSZAAAAwAEUTwAAALiC4gkAAABXUDwBAADgCoonAAAAXEHxBAAAgCsongAAAHAFxRMAAACuoHgCAADAFRRPAAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK6geAIAAMAVFE8AAAC4guIJAAAAV0R4HaA01lpJUkZGhsdJAAAAUJwDPe1AbytNWBfPPXv2SJLS0tI8TgIAAIDS7NmzR8nJyaWOMbYs9dQjjuNo48aNSkxMlDHG6ziVTkZGhtLS0rR+/XolJSV5HafS4n08eryHFYP3sWLwPh493sOKUVXeR2ut9uzZo/r168vnK30vzrCe8fT5fGrQoIHXMSq9pKSkSv0NHS54H48e72HF4H2sGLyPR4/3sGJUhffxcDOdB3BwEQAAAFxB8QQAAIArKJ5VWHR0tO655x5FR0d7HaVS4308eryHFYP3sWLwPh493sOKUR3fx7A+uAgAAABVBzOeAAAAcAXFEwAAAK6geAIAAMAVFE8AAAC4guJZTYwdO1Znnnmm4uLiVKNGDa/jVBrPPPOMGjdurJiYGHXo0EFff/2115EqlU8++UT9+vVT/fr1ZYzR9OnTvY5UKY0bN07t27dXYmKiateurQEDBuinn37yOlal8txzz+mkk04qWKj7jDPO0AcffOB1rEpv/PjxMsbo1ltv9TpKpXLvvffKGFPocvzxx3sdyxUUz2oiNzdXgwYN0o033uh1lErjrbfe0ogRI3TPPfdo6dKlatOmjXr16qWtW7d6Ha3SyMzMVJs2bfTMM894HaVSW7hwoYYPH64vv/xSc+fOVV5ennr27KnMzEyvo1UaDRo00Pjx4/XNN99oyZIlOuuss9S/f3+tXLnS62iV1uLFi/XCCy/opJNO8jpKpXTiiSdq06ZNBZdPP/3U60iuYDmlambixIm69dZbtXv3bq+jhL0OHTqoffv2evrppyVJjuMoLS1NN998s0aNGuVxusrHGKNp06ZpwIABXkep9LZt26batWtr4cKF6tKli9dxKq2UlBQ9+uijuvbaa72OUuns3btXp5xyip599lk9+OCDOvnkkzVhwgSvY1Ua9957r6ZPn65ly5Z5HcV1zHgCxcjNzdU333yjHj16FNzm8/nUo0cPffHFFx4mA6T09HRJ+cUJ5RcMBjV58mRlZmbqjDPO8DpOpTR8+HD17du30L+RKJ9ffvlF9evXV9OmTXX55Zdr3bp1XkdyRYTXAYBwtH37dgWDQdWpU6fQ7XXq1NGqVas8SgXkz7zfeuut6tixo1q1auV1nErl+++/1xlnnKF9+/YpISFB06ZNU8uWLb2OVelMnjxZS5cu1eLFi72OUml16NBBEydOVIsWLbRp0ybdd9996ty5s1asWKHExESv44UUM56V2KhRo4rsnHzohZIEVC3Dhw/XihUrNHnyZK+jVDotWrTQsmXL9NVXX+nGG2/UkCFD9MMPP3gdq1JZv369/vrXv+qNN95QTEyM13EqrT59+mjQoEE66aST1KtXL82aNUu7d+/W22+/7XW0kGPGsxK77bbbNHTo0FLHNG3a1J0wVcwxxxwjv9+vLVu2FLp9y5Ytqlu3rkepUN3ddNNNmjlzpj755BM1aNDA6ziVTlRUlJo3by5JateunRYvXqx//etfeuGFFzxOVnl888032rp1q0455ZSC24LBoD755BM9/fTTysnJkd/v9zBh5VSjRg0dd9xxWr16tddRQo7iWYmlpqYqNTXV6xhVUlRUlNq1a6d58+YVHAzjOI7mzZunm266ydtwqHastbr55ps1bdo0LViwQE2aNPE6UpXgOI5ycnK8jlGpnH322fr+++8L3Xb11Vfr+OOP18iRIymdR2jv3r369ddfdeWVV3odJeQontXEunXrtHPnTq1bt07BYLDgSLrmzZsrISHB23BhasSIERoyZIhOPfVUnXbaaZowYYIyMzN19dVXex2t0ti7d2+h3+DXrFmjZcuWKSUlRQ0bNvQwWeUyfPhwTZo0STNmzFBiYqI2b94sSUpOTlZsbKzH6SqH0aNHq0+fPmrYsKH27NmjSZMmacGCBZozZ47X0SqVxMTEIvsWx8fHq1atWuxzXA5///vf1a9fPzVq1EgbN27UPffcI7/fr0svvdTraCFH8awmxowZo1dffbXgetu2bSVJ8+fPV7du3TxKFd4uvvhibdu2TWPGjNHmzZt18skna/bs2UUOOELJlixZou7duxdcHzFihCRpyJAhmjhxokepKp/nnntOkor8XX3llVcOu7sN8m3dulVXXXWVNm3apOTkZJ100kmaM2eOzjnnHK+joRr6/fffdemll2rHjh1KTU1Vp06d9OWXX1aLrZis4wkAAABXcFQ7AAAAXEHxBAAAgCsongAAAHAFxRMAAACuoHgCAADAFRRPAAAAuILiCQAAAFdQPAEAAOAKiicAAABcQfEEAACAKyieAAAAcAXFEwAAAK74f1UK6YsuRibzAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -2257,17 +2257,17 @@ "id": "f2cad047", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.905738Z", - "iopub.status.busy": "2024-06-04T23:20:00.905623Z", - "iopub.status.idle": "2024-06-04T23:20:00.982351Z", - "shell.execute_reply": "2024-06-04T23:20:00.982085Z" + "iopub.execute_input": "2025-04-03T19:34:17.401540Z", + "iopub.status.busy": "2025-04-03T19:34:17.401434Z", + "iopub.status.idle": "2025-04-03T19:34:17.460475Z", + "shell.execute_reply": "2025-04-03T19:34:17.460195Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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UVME6l19++aXeeecdffjhh7rpppv0xBNP6Msvv1RsbGypn9urVy+98MIL2rRpkxYvXqzevXvLGKNevXpp8eLFatSokQKBQOEM5cmqyL+ZsjRs2FDz5s1Tr169NGTIEC1cuFAdO3YsMiYnJ0cHDx6s0PMlJyeX+fcsIiJCl156qSZPnqzMzMxK/f0FnEbxBMrRvXt3zZ49W0OGDNFFF12kxYsXq0GDBkXOVpdU7AdLgcsvv1xjx47Vl19+qddff73U7aSmpurjjz/W+eefX+YPjgULFujAgQOaOXNmkRMoNm/eXMmvrKhatWrpqquu0lVXXaWcnBxdccUVeuSRRzRx4sRSl6MZNmyYXnvtNb3yyiuaOHFipbdZMJN04tm4P/30U4njU1NTdffdd+vuu+/WDz/8oE6dOumJJ57QK6+8Iqn0opSamqqjR4+e0gzTqWjatKnmz5+vjIyMIrOeGzduPKXnnTx5smbNmqVHHnlE//73v5WcnKy4uDj5/f4Kfa3lveeJiYklnild2vtzvPJ2dZ977rk699xz9cgjj2jatGm67rrrNH369GJLBR2voFDOmzdPS5cu1YQJEyTln0j07LPPqlGjRqpVq5a6du16StlOhxYtWujDDz9Unz59NHDgQC1evFhnnnlm4eNffPGF+vXrV6Hn2rx5c7lroWZmZspaqyNHjlA8EdI4xhOogP79++u1117Txo0bNWjQIOXk5GjAgAFFbscfE3a82NhYPfvss3rooYc0bNiwUrcxatQo+f1+/fnPfy72WF5eXmEBKJj5OH72JScnR88888xJf30HDhwo8nFERITatm0ra22Zx+FdeeWVat++vR555BEtWbKk2ONHjhwpPMO8JE2bNpXX6y12jOWJX0tGRoaysrKK3Jeamqq4uDhlZ2cX3lerVq0Si9KoUaO0ZMkSffjhh8UeO3z4cNCPjRs4cKByc3P1wgsvFN4XCAQKl/U5WampqRoxYoSmTp2q3bt3y+v1asSIEZoxY4bWrFlTbPzxh4dU5D1PTU1VWlpakcMydu3apVmzZpWbrVatWpKK/1Jx6NChYjOHBbuNj38vS9K8eXOdccYZ+sc//qHc3NzCXdi9e/fWjz/+qLfeekvnnntukXVzK5PtdGvfvr3ee+89HT16VBdddJF27NhR+NjJHuN54mEhBV/HjBkzlJKSUuqhOkCoYMYTqKDLL7+88Mojl156qebOnVvhhakrsmxOnz59NHbsWE2aNEkrVqzQxRdfrPDwcP3www9688039c9//lNXXnmlzjvvPCUmJmr06NG68847ZYzRyy+/XOHdgCW5+OKL1aBBA51//vmqX7++1q9fr6eeekpDhgwp8+Sh8PBwzZw5UwMGDNAFF1ygUaNG6fzzz1d4eLjWrl2radOmKTExsdS1PBMSEjRy5Eg9+eSTMsYoNTVV7777brEfrt9//7369++vUaNGqW3btgoLC9OsWbO0Z88eXX311YXjunbtqmeffVZ/+ctf1LJlS9WrV08XXnih7rnnHr399tsaOnSoxowZo65duyo9PV2rV6/WW2+9pS1btqhu3bon/fqVZ/jw4erevbvuvvtubdy4Ua1bt9bbb79duKv1VGbg7rnnHr3xxhuaMmWKJk+erMmTJ2v+/Pnq0aOHbr31VrVt21YHDx7U8uXL9fHHHxdusyLv+dVXX6377rtPl19+ue68805lZGTo2Wef1VlnnVV4klJpOnXqJK/Xq0cffVRpaWmKjIzUhRdeqGnTpumZZ57R5ZdfrtTUVB05ckQvvPCC4uPjdckll5T79fbu3VvTp09X+/btC3/Z69Kli2rVqqXvv/++Qsd3FsyI3nnnnRo4cKC8Xm+Rv0enU8+ePTVz5kwNGzascI9JnTp1TvoYz8GDB6tx48bq0aOH6tWrp61bt+qll17Szp07y9yjAoQMt06nB0LZ8Usinejxxx+3kuzQoUNtbm5upT73eCcup1Tg+eeft127drXR0dE2Li7Otm/f3t577712586dhWM+//xze+6559ro6GjbqFEje++999oPP/yw2PI1ffr0KXHJnBOXyXnuuefsBRdcYOvUqWMjIyNtamqqveeee2xaWlqZX0OBQ4cO2QceeMC2b9/exsTE2KioKNuuXTs7ceJEu2vXrlK3a621+/btsyNGjLAxMTE2MTHRjh071q5Zs6bIEjj79++348aNs61bt7a1atWyCQkJtkePHvaNN94o8ly7d++2Q4YMsXFxcVZSkaWVjhw5YidOnGhbtmxpIyIibN26de15551nH3/8cZuTk2Ot/Xk5nZKWbSptOaVatWoVG1vSckP79u2z1157rY2Li7MJCQl2zJgx9vPPP7eS7PTp08t8fQuW3nnzzTdLfLxv3742Pj7eHj582Fpr7Z49e+y4ceNsSkqKDQ8Ptw0aNLD9+/e3zz//fOHnVPQ9/+ijj2y7du1sRESEbdWqlX3llVcqtJyStda+8MILtkWLFtbr9Rb+3Vy+fLm95pprbJMmTWxkZKStV6+eHTp0qF22bFmZr0GBp59+2kqyt912W5H7BwwYYCXZTz75pMj9Jb1veXl59o477rDJycnWGFP4tZT1/kuyDz74YJnZynqfXn/9devxeGy3bt2sz+er0Ndakqeeesr26tXL1q1b14aFhdnk5GQ7bNgwu2jRopN+TsBJxtpTmCYBAJy02bNn6/LLL9dnn30W9LPJASAUUDwBwAEnnm3s9/t18cUXa9myZdq9ezcnhACoETjGEwAccMcddygzM1M9e/ZUdna2Zs6cqS+++EJ//etfKZ0AagxmPAHAAdOmTdMTTzyhjRs3KisrSy1bttRtt92m22+/3e1oAOAYiicAAAAcwTqeAAAAcATFEwAAAI4I6ZOLAoGAdu7cqbi4OEcucQYAAIDKsccu19qoUSN5PGXPaYZ08dy5c6dSUlLcjgEAAIBybNu2TY0bNy5zTEgXz4LLtm3btk3x8fEupwEAAMCJfD6fUlJSyrzEcoGQLp4Fu9fj4+MpngAAACGsIodFcnIRAAAAHEHxBAAAgCMongAAAHAExRMAAACOoHgCAADAERRPAAAAOILiCQAAAEdQPAEAAOAIiicAAAAcQfEEAACAIyieAAAAcATFEwAAAI6geAIAAMARQS2ekyZNUrdu3RQXF6d69epp+PDh+u6774K5SQAAAISooBbPhQsXaty4cfryyy81b9485ebm6uKLL1Z6enowNwsAAIAQZKy11qmN7du3T/Xq1dPChQt1wQUXlDve5/MpISFBaWlpio+PdyAhAAAAKqMyfc3RYzzT0tIkSUlJSU5uFgAAACEgzKkNBQIB3XXXXTr//PPVrl27EsdkZ2crOzu78GOfz+dUPAAAAASZYzOe48aN05o1azR9+vRSx0yaNEkJCQmFt5SUFKfiAQAAIMgcOcbz9ttv15w5c7Ro0SI1b9681HElzXimpKRwjCcAAECIqswxnkHd1W6t1R133KFZs2ZpwYIFZZZOSYqMjFRkZGQwIwFAIWutvtqxXQu2bFJOIKD2yfV1yZlnKTLMsaOQAKBGCep313HjxmnatGmaM2eO4uLitHv3bklSQkKCoqOjg7lpACjT3vSjuuXtWVqzb6/CPPlHHeUFAvrTovl65pJh6pnSxOWEAFD9BHVXuzGmxPtfeukljRkzptzPZzklAMGQ6/dryGsva/Ohg/Kf8C3QY4zCPR69c80NaplUx6WEAFB1hNSudgAINfM2/aiNBw+U+FjAWuUFAvq/5cs0ecBAh5MBQPXGtdoB1DhzN34vTyl7ZCTJb63e/YHL+wLA6UbxBFDjpOfmKFDOHpmsvDyH0gBAzUHxBFDjpCYmyVvGjKeR1Kx2onOBAKCGoHgCqHGubteh2ElFJ/pFh07OhAGAGoTF6oBS5AUCmr95k2Z9t0770zOUkpCgkW3bqccZjUtdsQFVQ4vEJN3ds5eeWPKZjKTjK6jHGHVv1FhXt+vgVjwAqLYcuXLRyWI5JbjFl52tm96eqeW7dsprjPzWFv455MxW+vvFgxXu9bodE6fone836JmlX+m7A/slSYlR0bqhQyfddk53FpEHgAoKmeWUgKpqwscfasXuXZJUuEu24M/3f/hOTRNq63fn9XItH06PYWe11tAzW2l/RoZyAn7VrxVbuJg8AOD04zsscIJtaWn68McfSj3r2Ur678rlyszNdTYYgsIYo+RatXRGXDylEwCCjO+ywAk+3/aTyjv+JD03Vyv37HYkDwAA1QXFEzhBXiBwWscBAIB8FE/gBJ0aNCx3TJjHo7bJyQ6kAQCg+qB4AidoV6++OtRvUOoC415jNOys1kqKjnE4GQAAVRvFEyjBPwcOUVJ0TJHreZtjt9SkOnrggn6uZQMAoKpiOSWgBE1r19Z71/5CL6/6Vm+tW6NDWVlqGBuna9p10DXtOqhWRITbEQEAqHJYQB4AAAAnrTJ9jV3tAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guIJAAAAR1A8AQAA4AiKJwAAABxB8QQAAIAjKJ4AAABwBMUTAAAAjqB4AgAAwBEUTwAAADiC4gkAAABHUDwBAADgCIonAAAAHEHxBAAAgCMongAAAHAExRMAAACOoHgCAADAERRPAAAAOILiCQAAAEdQPAEAAOAIiicAAAAcQfEEAACAIyieAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guIJAAAAR4S5HQAoyfcH9uv1tau1LS1NCVFRGnZWa/Vq0lQeY9yOBgAAThLFEyHFWqvJny/SC8uXyWuM/NbKa4xmrF+rrg0b6T+XXqH4yEi3YwIAgJPArnaElJdXrdALy5dJkvzWFvlzxe5d+u2H77mWDQAAnBqKJ0KGPxDQs8u+Lv1xazV/y2ZtPHjAwVQAAOB0oXgiZHx/8ID2pB8tc4zHGC3YstmhRAAA4HSieCJk5Pr95Y4xkrIrMA4AAIQeiidCRvPaiYrwessc47dW7ZLrOZQIAACcThRPhIy4yEiNaHO2vKUsmeQxRo3j4tW7aTNngwEAgNOC4omQct/5vdUyqU6x9Tq9xig6LExPXTKMtTwBAKiiKJ4IKfGRUXpz5DX67bnnqUFsrCSpVni4rm7XQe9e8wt1qN/A5YQAAOBkGWuPLZIYgnw+nxISEpSWlqb4+Hi348AFAWuZ4QQAIIRVpq8x44mQRukEAKD64JKZVVhaVpbeWr9WC3/arLxAQJ0bNNQ17TqocXyC29EAAACKoXhWUSt279KY2TN0JCdbBcdKLN2xXc9/s1R/GzBIl7dp62o+AACAE7GrvQpKy8rSmNkzdDQ3R8cfoOu3Vn5r9bt5H2jlnt2u5QMAACgJxbMKemv9Wh3JyVaglPPCPMbopW+/cTgVAABA2SieVdDCnzarrKUI/NZqPtczBwAAISaoxXPRokUaNmyYGjVqJGOMZs+eHczN1Rh5gcBpGQNn7D56RCt279JPhw+7HQUAAFcF9eSi9PR0dezYUTfddJOuuOKKYG6qRunSoJG+3rG91F3tXmPUuQELrbvtuwP79dfFC/TZ1p8KZ6jb16uve8/vrfNTmroZDQAAVwS1eA4ePFiDBw8O5iZqpGvaddBz33xd6uN+azWmUxcHE+FE6/fv08g3X1N2Xl6RwyLW7t2j0bNn6Lmhl6l/81TX8gEA4IaQOsYzOztbPp+vyA3FnREfr8cuGiSPMfIet8B6wf/f3KkrpcZlDy/4VNl5efKfMCsdkGSt1cRPPuJwCABAjRNSxXPSpElKSEgovKWkpLgdKWQNb91WM0ddqyFntVJcRKRiwsN1buMUvTB0uH7fu48MV/xxzU+HD+vrnduLlc4CVtL+jAwt/IkTwAAANUtILSA/ceJEjR8/vvBjn89H+SxDh/oNNGXgELdj4ATbfGnljvEYo21p5Y8DAKA6CaniGRkZqcjISLdjhIyM3FzN3rBOszes04HMTDVNqK2r27VX/+ap8npCarIax0mIiip3TMBa1a7AOAAAqpOQKp742d70o7pmxhvafPiQjPJ3z25NO6wFP23Whc1a6JkhlyrC63U7JkpwdnI9pcQnlDnzGeH16kKOwwUA1DBBnTY7evSoVqxYoRUrVkiSNm/erBUrVmjr1q3B3Gy1cPsH72pr2mFJKjwruuCYwflbNmnKl1+4Ewzl8hij+87vXeaYX5/TQ/HM7gMAapigFs9ly5apc+fO6ty5syRp/Pjx6ty5sx544IFgbrbKW79vr5bt3FHmySmvrF6hzNxcZ4Ohwi45s5UeHTBQMeHhkiSv8chICvN4dHu3c3VH93PdDQgAgAuCuqu9b9++sqWUJ5RuyfZt8hhT6gLxknQ0J0cb9u9T54aNHEyGyhjZtp2GnNlKH/34g3Yc8SkxKlqDWp6ppOgYt6MBAOAKjvEMQRWt6lT60BcTHq7hrdu6HQMAgJDAqdEhqFujM8qc7ZSk6LBwta6b7FAiAACAU0fxDEEd6jdQx/oNilyV6HgeY3Rt+w6Fxw8CAABUBRTPEPXUJcPUIDZORlJB/fQcK6I9zmis3/Xs5Vo2AACAk8ExniHqjLh4vXftL/TmujWauX6tDmZmqklCbV3TroOGnHmWwlnDEwAAVDHGhvBp5z6fTwkJCUpLS1N8fLzbcQAAAHCCyvQ1drUDAADAERRPAAAAOILiCYSgvECAiy8AAKodTi4CQkRGbq7+u3K5Xlm1UruOHlGk16uhZ7XWL7t005l16rgdDwCAU0bxBELA0ZwcXTvjda3bv6/w4gHZfr9mb1ind7//Tv8dPkLdz2jsckoAAE4Nu9qBEDDlyy+KlM4CfmuVG/Br3PvvKNfvdykdAACnB8UTcFlWXq6mr11V6mVSA9bqQGaG5m360eFkAACcXhRPwGXbfT5l5OaWOSbM49G6fXsdSgQAQHBQPAGXRVTgKlTW2gqNAwAglFE8AZelxCeoWe3aMmWM8VurC5u3cCwTAADBQPEEXGaM0bhu56q0VTu9xujcM1LUrl59R3MBAHC6UTyBEDCizdn67bnnScovmubYn5J0dr36evqSYS6mAwDg9GAdTyBE3NG9p4ae1VpvrF2tLYcPKzYiQkPPbKXeTZvJY8raEQ8AQNVA8QRCSPPaibrv/AvcjgEAQFCwqx0AAACOoHgCAADAERRPAAAAOILiCQAAAEdQPAEAAOAIiicAAAAcwXJKyr8O9pfbt+nt7zfoUGamzoiP18i27dS6brLb0QAAAKqNGl8803NydNt7c/TZtq3yGqOAtfIYo5dWLNfojp31wAX9ZFi8GwAA4JTV+F3tEz75SF9s3yZJ8lsre+xPSfrvym/1f98uczEd3BCwVuv37dWynTu0PyPD7TgAAFQbNXrGc7svTe//8J1sGWOe+2apxnTsonCv17FccM+M9Wv1zy+/0PYjPkmSxxgNTG2p+3v3VaO4eJfTAQBQtdXoGc8FWzaXO+ZgZqbW7tvrQBq47f+WL9M98+YWlk4pf/bzox836orXp2nP0aOlfu7WtMN6+7v1eu/777QvPd2JuAAAVDk1esYzx++XMUbWljXnmT+uurHWatXePTqYmaEz4uJ1Vp26bkdy1b6MdD36+aISH/NbqwOZGfrX10v0yIUXFfu8CR9/qAVbNhfOnHuM0WWt2uhPffurVkREkJMDAFB11Oji2a5efQXKKZ1hHo9aJiU5lMgZczf+oEmfLdQ2X1rhfe2S6+mhvv3VpWEjF5O5Z86G9WUecuG3VjPXr9MfL+irqLBwSdKR7Gxd/dbr2pp2uMjnBqzVnO/Wa7svTa9eMUphnhq9YwEAgEI1+idit0ZnqEXtRHlLOWvda4yGnNlKSdExDicLnre/W69fv/+2th9XOiVp3f59umbG61q+a6dLydy1zZcmTzmrF2T783QgM7Pw49fXrtaWw4cKT0Y7XsBaLd25Q59s/vG0ZwUAoKqq0cXTGKMnBw9VTHh4sfLpMUYpCQn6Q+++7oQLguy8PD208FNJKja7F7BWfmv1p0XznQ8WAmpHRZV7yIWRFB8RWfjxG+tWlzlL6jFGb61bc3oCAgBQDdTo4ilJbZLr6d1rfqFr23dUrfD8XajJMTEa162HZo26TnViqs9s58KfNutwVlapjwes1ao9u/XjwQMOpgoNw85qXeLMZQGvMbqweQvFRf5cPPell73UUsBa7S7jhCQAAGqaGn2MZ4GUhAQ93Le/Hu7bX/5AQN5qekzerqNHZFR8tvNEO48eUWpSHScihYyWSXV0Was2evu74sd6eoyRMUZ3dO9Z5P56tWrJl51V6uvpNUYNY+OCkhcAgKqoejasU1BdS6ckJUXHlFs6JaluNTqmtTIeHTBQo85un180pcLDL+pGx2jqZSPUoX4DSZIvO0ufbf1J3RqdUebz+a3VyLbtgh0bAIAqgxnPGqR/81TFhIUrIy+3xMeNpBaJSTX2GvURXq8m9b9Yv+nRUx9v+lEZublKTUpSn6bNFebxKDM3V5M+W6g31q0pssRWSbPIHmPU44zGurB5C0e/BgAAQhnFswaJCQ/X+J7n6y+LFxR7rODUqom9+tT4a9M3iI3T9R06Fbkv1+/XTW/P1NKdO4otwXVi6QzzeHRlm7P1xwv6VesZdAAAKoviWcPc2KmLjDH6+5LPlJ7788xnnZgY/anvAGboSvHBxu/11Y7tZY4Z07Gzzm2conManVGtluACAOB0oXjWMMYY3dipi64+u73mb9mcf+Wi+Hj1btKMhc7LMH3tanmMKfWCAx5jtH7/Pj3Q50KHkwEAUHVQPGuo6PBwXXLmWW7HqDJ2+HxlXuUqYK22+3ylPg4AADirHaiQujExKuvIV3NsDAAAKB3FE6iAEW3OLnMpKivpSpZOAgCgTBRPoAIub91WLROTil1aVcpf7zM1MUmXt27rQjIAAKoOiidQAdHh4Zo24iqdn9K02GPnpTTRayOuUsyxS64CAICScXIRUEF1Y2I0dfgIbTp0UEuPLa3U7YzGapGY5HIyAACqBoonUEktEpMomwAAnAR2tQMAAMARzHgC0Jq9e/Sfb7/R/C2blOsPqGP9BhrdqbMubtGyxl9CFQBw+lA8gRrune836Lcfvi8jyX9skfyvd27Xlzu26YYOnfRQnwspnwCA04Jd7UANtvvoEd390QcKWFtYOiUVXqXp5VUr9MHGH9yKBwCoZiieQA32+trVZV4K1GOMpq5Y7mAiAEB1RvEEarAVu3eXew36lXt2OZgIAFCdcYxnDZSWlaW5P/6gAxkZahgbp4tTW6pWRITbsSrsYGaG3ly3RvM3b1aO36/ODRvquvYdWeLoJIR5PDJSmZcD9Xr4/RQAcHpQPGsQa62eXfa1/vX1EuX4/fIaI7+1ipkfrt/37qNr23d0O2K5vt21U6PnzFB6Tq7ssbq0eu9uTV2xXH+58CJd066DywmrlguaNtOnm38s9XGvMerXrLmDiQAA1RlTGTXIf779Ro8v+Uw5fr+kn89gzsjL1R/mf6xZ69e5Ga9cvuws3ThnpjJyfy6dUv7XYSX94dN5Wrpzu3sBq6DhrdooISpKnlLOWg9Yq5s6d3U4FQCguqJ41hAZubma8tUXZY55bMli+QMBhxJV3oz163QkJ7vUYxI9xug/y79xOFXVFhcZqf8Ov1JxEZEykgrqp9cYeYzRpP4Xq2vDM9yMCACoRtjVXkMs+mmLMnJzyxyz++hRLd+9U90aNXYoVeUs/mlLmcci+q3V4q0/OZanumhfr74WjrlFszas1aebNynH71enBg11bbuOSklIcDseAKAacaR4Pv3003rssce0e/dudezYUU8++aS6d+/uxKZxTFpWZgXHZQU5ycnz2/JnYwMVGIPi4iMjNbpjF43u2MXtKACAaizou9pff/11jR8/Xg8++KCWL1+ujh07auDAgdq7d2+wN43jNK7gzFVKQu3gBjkFXRo2KvVYRCl/V3vnBo0cTAQAACoj6MXz73//u2699VbdeOONatu2rf79738rJiZGL774YrA3jeP0bNxEjeLiVFpt8xij9vXqq1Wduo7mqoyrz+4grzGlfg35J8IwYwcAQKgKavHMycnRN998owEDBvy8QY9HAwYM0JIlS4qNz87Ols/nK3LD6eExRpMuvFjm2EkjJz4W7vHoT/0GlPLZoaF+bKymDBoijzHyHvc1FPz/L7uco/7NU92KBwAAyhHU4rl//375/X7Vr1+/yP3169fX7t27i42fNGmSEhISCm8pKSnBjFfj9G7aTK9ePlLt6xV9P3qc0VhvjrxGHes3cClZxQ1ueZbeueYGjWhztupExyg+MlLnpzTVi5deoQm9+siUsSseAAC4K6TOap84caLGjx9f+LHP56N8nmY9Gqdo1lXX6afDh3UgM0P1Y2N1Rly827EqpXXdZE0eMNDtGAAAoJKCWjzr1q0rr9erPXv2FLl/z549atCg+OxaZGSkIiMjgxkJxzStXVtNa9d2OwYAAKhBgrqrPSIiQl27dtUnn3xSeF8gENAnn3yinj17BnPTAAAACDFB39U+fvx4jR49Wuecc466d++uKVOmKD09XTfeeGOwNw0AAIAQEvTiedVVV2nfvn164IEHtHv3bnXq1Elz584tdsIRAAAAqjdjbSkXvg4BPp9PCQkJSktLU3x81ToBBgAAoCaoTF8L+gLyAAAAgBRiyykBgJR/Faq3v1uvl1Ys17p9exXm8ahfsxa6pcs56tKQy6ICQFVF8QQQUgLW6t55czVzwzp5jFHAWvn9fs3btFEf/viDnrh4sIa3but2TADASWBXO4CQ8vZ36zVzwzpJ+SW0gN9aWUn3zJur3UePuJQOAHAqKJ4AQspLK5bLU8alT62k19eudi4QAOC0oXgCCCnr9u0tMtN5ooC1WnXC1dAAAFUDxRNASAnzlP1tyUiK8HqdCQMAOK0ongBCyoXNW8hbzq72fs2aOxcIAHDaUDwBhJRbOp9T6q52rzFKjqmlYWe1djgVAOB0oHgCCCmdGzbSExcPlteYwpOMCuY/k6Jj9PLlVyo6PNy9gACAk8Y6ngBCzvDWbXVu4xS9vna1Vu3ZowivV/2aNdews1pTOgGgCqN4AghJDWLj9Jse57kdAwBwGrGrHQAAAI5gxhOnbF96urb70pQQFaXmtRMlSaaMs5IBAEDNRPHESdt06KD+unih5m/ZpIJzkCM8XuUE/IoOC9Pglmfpli7nqHXdZFdzAgCA0MCudpyUTYcO6orXp2nhT5t1/MI3OQG/JCkzL09zvluvy6a/okU/bXElIwAACC0UT5yUyZ8tUnpujvxlXNrQb63yAgGNe/8dpefkOJjOfTl+v9bu3aNVe3YrIzfX7TgAAIQEdrWj0vZlpOuTzT+q9Mr5MyspPTdH73y/QVe36xDsaK7zBwL69zdf68Vvv9GhrCxJUkx4uK5p10F39zxfUWEsBQQAqLmY8USl7fT5KlQ6C4R5PFq9d0/Q8oQKa63umTdXTyz5vLB0SlJGbq5eWrFcY2bPVI7f72JCAADcRfFEpSVERVX6cyK83iAkCS1fbN+q2d+tL/GxgLX6eud2zdqwzuFUAACEDoonKq1Z7US1rZsso4otmZQXCKhfsxZBTuW+6WtWy1vGMlJG0rTVK50LBABAiKF44qT87rzekmy51dNrjM5MqqNeTZo6EctVWw4fKvNkKytpa1qac4EAAAgxFE+clL7Nmutfg4YqLiJSkuQ5Yaav4KMmCbX10mVXFHu8OkqMii7366x9EocpAABQXXBWO07akLNaaUCLVM3btFFb09KUFwjoQGaGdvh8igkP18DUM3VRassacXynJF3Wuo0+2/ZTqY97jNHlrds6mAgAgNBC8cQpiQwL09CzWrsdIyQMPbOVnlv2tTaXsMvda4wSo6N1bfuOLqUDAMB97GoHTpPIsDC9esUodWnYSFL+DGfByUYtEpM0fcRVqhsT42ZEAABcxYwncBol16ql16+8Wmv27tEX27bKbwPq2vAMdWt0hkwNOM4VAICyUDyBIGhXr77a1avvdgwAAEIKu9oBAADgCGY8a7j1+/Zq9nfrdTAzUw1j4zSizdlqWru227EAAEA1RPGsoXL8ft378Vy9/d0GeY1HOnb19aeWfqlfdjlH951/AcckAgCA04pd7TXUXxbN1zvfbZAk+W1AfmsLlwB6fvky/efbb9yMBwAAqiGKZw20PyNDr61ZpdIv7ig9u+wr5fj9jmUCAADVH8WzBlr00+YyrykuSYeysrRi9y6HEgEAgJqA4lkDZeblVWhcVgXHAQAAVATFswZqVaduuWOMpJZJScEPAwAAagyKZw3UtWEjtUxMKryc44m8xqhvs+ZqFBfvcDKUJCsvV/vS05XNDDQAoIpjOaUayBijfwy8RFfNeF3ZeXlFjvf0GqM60TH6U78BLiaEJP1w4ID+9fUSzd34vfzWKtLr1RVtztbt3c5Vw7g4t+MBAFBpxtpyzjJxkc/nU0JCgtLS0hQfz+zb6bbp0EE9u+xrvf3deuUGAooJC9fIs9vptnO6q16tWLfj1Wgr9+zWtTNeV47fX+wXg9pR0Zo56lqlJCS4mBAAgHyV6WsUTyjX71d6bo5iIyIV5uHoC7dZazXg5Zf0U9phBUr45+k1Rr2aNNVLl41wIR0AAEVVpq/RMqBwr1e1o6IpnSFi6c4d2nz4UImlU5L81mrRT1u0w+dzOBkAAKeGpgGEmB8OHih3jJW0sQLjAAAIJRRPIMTEhIVXaFx0eMXGAQAQKiieQIjp06xZuYc9JEVHq3ODhg4lAgDg9KB4AiEmKTpG17fvqJJXWc03rtu5Cvd6HcsEAMDpQPEEQtDEXn10RZuzJeWfxR7m8chjjIykcd16aEzHzu4GBADgJLCcEhDCfjhwQHO+W6+DmRlqFBevy9u01RlcUQoAEEIq09e4chEQws6sU0e/O6+X2zEAADgt2NUOAAAAR1A8AQAA4AiKJwAAABxB8QQAAIAjKJ4AAABwBMUTAAAAjqB4AgAAwBEUTwAAADiC4gkAAABHUDwBAADgCIonAAAAHEHxBAAAgCOCVjwfeeQRnXfeeYqJiVHt2rWDtRkAAABUEUErnjk5ORo5cqRuu+22YG0CAAAAVUhYsJ744YcfliRNnTo1WJsAAABAFRK04nkysrOzlZ2dXfixz+dzMQ0AAABOp5A6uWjSpElKSEgovKWkpLgdCQAAAKdJpYrnhAkTZIwp87Zhw4aTDjNx4kSlpaUV3rZt23bSzwUAAIDQUqld7XfffbfGjBlT5pgWLVqcdJjIyEhFRkae9OcDAAAgdFWqeCYnJys5OTlYWQAAAFCNBe3koq1bt+rgwYPaunWr/H6/VqxYIUlq2bKlYmNjg7VZAAAAhKigFc8HHnhA//3vfws/7ty5syRp/vz56tu3b7A2CwAAgBBlrLXW7RCl8fl8SkhIUFpamuLj492OAwAAgBNUpq+F1HJKAAAAqL5CagF51AzLd+3U/1Z9q5W7dyvS69XFqWfqmnYd1DAuzu1oAAAgiCiecNQ/v/pC//xqibzGyH/sKI+Nhw7qxW+/0YuXXaHuZzR2OSEAAAgWdrXDMR9v2qh/frVEkgpLpyQFrFWWP0+3vDNLvuMumQoAAKoXiicc859vv5HHmBIfC1ir9Jwczdqw1uFUAADAKexqhyOstVq6c4cC5Syi8NX27RrdsYtDqRBKMnJz9fKqb/Xq6pXa4fMpNiJSw1u30c2du6pJQm234wEATgOKJwDXHcnO1rUz39C6fXtV8KvJkZxsTVu9UjPXr9O0EaPUvl59VzMCAE4du9rhCGOMujZsVOqu9gKcXFQzPb7kM23Yv08nzof7rVVmXq5+/d7b5c6WAwBCH8UTjrml8zmllgePMYoJj9AVbdo6nApuS8/J0Zvr1hQ54ex4AWu144hPi37a4mwwAMBpR/GEYy5Kbak7up8rSfIeN/PpMUaRXq/+b9hwxUdGuRUPLtl8+JCy8vLKHOM1Rmv27nEoEQAgWDjGE4767bnnq1eTpnp55Qqt3LNbEV6vBqaeqWvbd1CjOC6LWhOFe73ljrGSwr38ngwAVR3FE47r1qixujXiWE7ka5mYpPq1YrUn/WipYwLWql+zFg6mAgAEA1MIAFzl9Xh02zndS3/cGF3QpJnOqlPXwVQAgGCgeAJw3Q0dOumWzl0l/Xz8b8Gf7evV1z8HDXEtGwDg9GFXOwDXGWP0+959dUWbs/X62tXamnZY8ZFRGnZWa/Vp2kxeD78jA0B1QPEEEDJa103Wg30udDsGACBImEYAAACAIyieAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guIJAAAAR1A8AQAA4AiKJwAAABxB8QQAAIAjKJ4AAABwBJfMBKqYg5kZmvPdBm33pal2VJSGntVazWsnuh0LAIByUTyBKuSlFcs1+bOFygsEFObxKGCt/vHlFxrVtp3+3G+Awr1etyMCAFAqdrUDVcSs9ev050XzlRsIyErKDQTkt1aS9Oa6NfrzovnuBgQAoBwUT6AKyJ/Z/LzUx62kaWtWaV96unOhAACoJIonUAV8t3+fth/xlTnGWqt5mzY6lAgAgMqjeAJVwNHcnHLHGGOUXoFxAAC4heIJVAFNE2rLlDMmYK1aJCY5kgcAgJNB8QSqgHq1YtW/eaq8puT6aWRUL6aW+jRt7nAyAAAqjuIJVBEP9Omn2lFRxcqn1xh5PUaPXzxYYR7+SQMAQhc/pYAqonF8gmZffb2Gt26r8GMF00g6P6Wp3rjyavVq0tTdgAAAlMNYe2whwBDk8/mUkJCgtLQ0xcfHux0HCBnpOTnan5GhhKhI1Y6KdjsOAKAGq0xf48pFQBVUKyJCtSIi3I4BAEClsKsdAAAAjqB4okQ5fr8yc3MVwkdiAACAKoZd7Shi8dYtem7Z11qyfZus8tePHNOps65r34kzpgEAwCmhSaDQq6tXavTsGfpyx3YVzHNuTTusPy2cr3Hvv628QMDVfAAAoGqjeEKStMPn04MLPpGUfwWcAvbY7eNNP+qtdWvcCQcAcJW1VjZnpQJpf1Tg4E0KHP6dbPYiWcuEBCqHXe2QJL2+dnW5Y/678ltd3a6DA2kAAKHCWr9s2h+krBmSvJL8kryyWW9LET2k2v+W8dRyOSWqCmY8IUlav39vkZnOE1lJ3x/Yz8lGAFDTpP/7WOmU8kvncX/mLJX13e9GKlRRFE9IkqLCwuQp5TrgBSK8YTLljAEAVB/WZsumv1jGiICU9YGsf4djmVC1UTwhSerfvGWZM55eYzQwtaWDiQAArstdLdkj5QyyUvZiR+Kg6qN4QpI0uOWZahwXL28JM5oF99zc5RxnQwEA3GVzKzDIVHAcQPHEMZFhYXrlipE649g1Vr3GI68xMpIivF49dckwta9X392QAABnhbdS/glFZbFSeHsn0qAa4Kx2FGqSUFvzrr9Rn2zepPlbNinH71f7evU1os3ZSoiKcjseAMBhxpMkG3WJlPW+fj6x6HheKexMKbyj09FQRVE8UUS416tBLc/UoJZnuh0FABACTPwfZHPXSv4tko5ft9MrmXiZ2v/gxFNUGLvaAQBAqYwnUabOWzKxv5E8jZRfOBOlmDEydefIhKW6HRFVCDOeAACgTMYTK8XeJhN7m9tRUMUx4wkAAABHUDwBAADgCIonAAAAHEHxBAAAgCMongAAAHAExRMAAACOoHgCAADAEUErnlu2bNHNN9+s5s2bKzo6WqmpqXrwwQeVk5MTrE0CAAAghAVtAfkNGzYoEAjoueeeU8uWLbVmzRrdeuutSk9P1+OPPx6szQIAACBEGWutdWpjjz32mJ599llt2rSpQuN9Pp8SEhKUlpam+Pj4IKcDUFVlHMlUdmaO4uvEyuv1uh0HAGqUyvQ1Ry+ZmZaWpqSkpFIfz87OVnZ2duHHPp/PiVgAqqiVC9bq1Udm6NtPVkuS4pJiNexXF+vqCcMVHRvtcjoAwIkcO7lo48aNevLJJzV27NhSx0yaNEkJCQmFt5SUFKfiAahiPn3tM93T/2GtXLC28L4jB49q+qOzNb7Pg8o8muliOgBASSpdPCdMmCBjTJm3DRs2FPmcHTt2aNCgQRo5cqRuvfXWUp974sSJSktLK7xt27at8l8RgGrv6OF0PXHzM7LWKuAPFHks4A9o08qfNH3ybHfCAQBKVeljPPft26cDBw6UOaZFixaKiIiQJO3cuVN9+/bVueeeq6lTp8rjqXjX5RhPACWZ/eQHeuaul1TWt6+4pFi9ufv/5A3jmE8ACKagHuOZnJys5OTkCo3dsWOH+vXrp65du+qll16qVOkEgNJsWbtNnjCP/Ln+UsccOXhUvgNHlFi/tnPBAABlCtrJRTt27FDfvn3VtGlTPf7449q3b1/hYw0aNAjWZgHUAFG1IqUK7KuJiI4IfhgAQIUFrXjOmzdPGzdu1MaNG9W4ceMijzm4ghOAaqjX5d014x/vlvq4x+tR+95tVCs+xsFUAIDyBG3f95gxY2StLfEGAKfi7PNbq12v1vKElfwtLBAI6Nr7RzicCgBQHg66BFDlGGP08Ox71abHWZIkb5hX3jCvjDEKjwzTvVNvV5f+7V1OCQA4kaMLyAPA6RKfFKd/LPqT1ny2QZ/N/EpZ6Vlq2jZFA35xgeKT4tyOBwAoAcUTQJVljFH73m3Uvncbt6MAACqAXe0AAABwBMUTAAAAjqB4AgAAwBEUTwAAADiC4gkAAABHUDwBAADgCIonAAAAHEHxBAAAgCMongAAAHAExRMAAACOoHgCAADAERRPAAAAOILiCQAAAEdQPAEAAOAIiicAAAAcQfEEAACAIyieAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guIJAAAAR1A8AQAA4AiKJwAAABwR5nYAAACAYLA5X8umvyLlrpJMhBR1kUzMtTLeM9yOVmNRPAEAOIG1OVLW+7KZc6TAQcnbRCZmlBTRS8YYt+OhAgJHnpDSn5PkleTPvzP9Rdn0l6XEF2Qie7gZr8aieAIAcBwbOCh78BdS3vfKPyItIOV9L5v9oRQ5UKr9dxkT7nZMlMFmfXSsdEqFpbPw/63s4V9JyYtkPHEupKvZOMYTAIDj2MN3S3k/HvsocOzPY+Ul+yPZo8+4EQuVYNNfVOkVJyDZDClzlpORcAzFEwCAY2zuD1LO5yo6S1ZkhJTxX1mb5WQsVIK1ASn3W/38S0Mp43KWOhMIRVA8AQAokLNEUjnHcNqjUu56R+LgZBiV+x4WjoPTKJ4AABQqe5bsZ6XNiMJtxhgpvKvKqzgmorszgVAExRMAgALhnSXZcgZFSmGtnUiDk2Rq3azSf4nwSCZWih7uYCIUoHgCAFAgvIMU1k75S/CUxCPFjJTxxDqZCpVkoi6Uib3z2EfHv5ceyUTJJD7Pe+gSiicAAMcYY2Rq/1Py1FHRH5HHjgcM7ygT+zs3oqGSTOztMklvSFFDJW/z/FnqWr+WqfuhTERXt+PVWKzjCQDAcUxYilT3HSljumzmLClwWPI2lom5Soq+XMZEuB0RFWQiOslEdHI7Bo5D8QQA4ATGkyjF3iYTe5vbUYBqhV3tAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guIJAAAAR1A8AQAA4AiKJwAAABzBOp4AAMBxNm+LbMarUvYnks3NvypUzA0ykT3cjoYgongCAABH2eyFsod+LSkgyZ9/Z/YnstkfydYaJ0/cb9yMhyBiVzsAAHCMDRyUPXS7pDwVlk7p5/9Pf1o2a74LyeAEiicAAHBOxpuSciXZUgZ4ZTNecjAQnETxBAAAjrE5y5S/i700filnuVNx4DCKJwAAcJA5dkNNRPEEAACOMZE9yxnhlSLKG4OqiuIJAACcE32FZGJUegXxy9S60clEcBDFEwAAOMZ4EmQSn5dMpIrWEG/+43ETZCLPcyUbgo91PAEAgKNMRDep7jwp8w3ZrE8k5UjhnWVirpUJb+t2PAQRxRMAADjOeOtJsbfLxN7udhQ4iF3tAAAAcATFEwAAAI6geAIAAMARFE8AAAA4IqjF89JLL1WTJk0UFRWlhg0b6oYbbtDOnTuDuUkAAACEqKAWz379+umNN97Qd999pxkzZujHH3/UlVdeGcxNAgAAIEQZa611amNvv/22hg8fruzsbIWHh5c73ufzKSEhQWlpaYqPj3cgIQAAACqjMn3NsXU8Dx48qFdffVXnnXdeqaUzOztb2dnZhR/7fD6n4gEAACDIgn5y0X333adatWqpTp062rp1q+bMmVPq2EmTJikhIaHwlpKSEux4AAAAcEili+eECRNkjCnztmHDhsLx99xzj7799lt99NFH8nq9+sUvfqHS9u5PnDhRaWlphbdt27ad/FcGAACAkFLpYzz37dunAwcOlDmmRYsWioiIKHb/9u3blZKSoi+++EI9e/Ysd1sc4wkAABDagnqMZ3JyspKTk08qWCAQkKQix3ECAACgZgjayUVfffWVli5dql69eikxMVE//vij/vjHPyo1NbVCs50AAACoXoJ2clFMTIxmzpyp/v37q1WrVrr55pvVoUMHLVy4UJGRkcHaLAAAAEJU0GY827dvr08//TRYTw8AAIAqhmu1AwAAwBEUTwAAADiC4gkAAABHUDwBAADgCMeu1Q4ANYHf79eyD1dq8VtfKuNoplLOaqRBN1+ohs3rux0NAFxH8QSA0+TwvjT9/pK/6odvNskb5lHAb2U8RtMmzdQtk67XVfde5nZEAHAVu9oB4DSw1uqhKx7Xjyu2SJL8eQFZaxXwByQr/d+EV7Tg9c/dDQkALqN4AsBpsP6rH7T28w35RbMExmM07a8zZa11OBkAhA6KJwCcBl++s0zeMG+pj9uA1ebVW3Vg1yEHUwFAaKF4AsBpkJudJ2MqMC4rN/hhACBEUTwB4DRI7dRMebn+MsfUSohR3cZJDiUCgNBD8QSA0+CCK89VXFKsjKfkaU+P16OhYy9SeES4w8kAIHRQPAHgNIiIitAfpv9W3jCvvGFFv7Uaj9GZXVrouj9e6VI6AAgNrOMJVBHbv9+pOU/N1VfvL5c/z6/2vdvostsHq02PM92OhmO6DOigZ5ZO1ut/m6OFby5RXk6eklPq6NJfD9LwOwYrKibS7YgA4CpjQ3htD5/Pp4SEBKWlpSk+Pt7tOIBrPp/9tf486u+ysgrk5S/X4w3zyJ8X0C//doNG/u5SlxPiRNZa+fP8Cgvn93sA1Vtl+hq72oEQt2/7Af3l6n/I7/cXlk4pf4FySXr+3pe1cuFat+KhFMYYSicAnIDiCYS4956fV3j1m5J4wzya9c/3nQ0FAMBJoHgCIW7VwnWlXg1Hyp/5XLlgjYOJAAA4ORRPINRVYFHyCq1cDgCAyyieQIjrfGF7eUpZG1LK39XeuX97BxMBAHByKJ5AiLvk1gEKiwgrdVLTnxfQFb8Z4mwoAABOAsUTCHF1GibqoZn3KCwiTB7vz/9kvWEeyUi3P3mz2p3f2sWEAABUDGt9AFVAt0Gd9dKGf+mdZz/U1x98K39u/gLyw349UKkdm7kdDwCACmEBeQAAAJw0FpAHAABAyKF4AgAAwBEUTwAAADiC4gkAAABHUDwBAADgCIonAAAAHME6ngCAKs9aq9WL1+uLOUuVk5mj1E7N1O+aXoqJi3Y7GoDjUDwBAFXaob1peuCyR7Xhqx/kDfNKRvLn+fXvu/+ria/8Rudd1s3tiKjirLVSzmeyGa9Ked9LJlYm6hIpZpSMJ8nteFUKC8gDAKqsQCCg27tP1I+rtiiQFyjymDGS8Xr0z8/+otbdzzyp57c2T8pdJdl0ydtMJizldMRGFWJtQDbtD1LWW5K8kvzHHvFIJkEm6WWZ8LNcTOg+FpAHANQI33y0Uj8s31SsdEpSwbTK9MmzK/281lrZjNdk910ge/Bq2UM3y+7vr8DBMbJ5m08xNaqUzNeOlU7p59IpSQHJ+mQP/TL/FxRUCMUTAFBlfTbzK3nDSv9RFsgLaMnbS+XP85c6pkTpz8v6HpQC+4ven/OV7IFRsnlbTyItqhprrWz6fySZUkb4pcBOKXu+k7GqNIonAKDKysrIlg2UfcRYIGCVk51b4ee0/v2yR6eU8qhfskdlj/6r4iHL2pb1y/p3y/r3KYSPfKu5Avsk/3ZJZb03YbI5S51KVOVRPAEAVVaT1o3LHVOnUaKiYiIr/qRZb6vsouGXst6XDRyt+HOewNoc2aPPyu7rnb87f9/5sgeGyWa+fdLPCVQFFE8AQJU18KZ++WcRlcJ4jC799SCZMsacyPp3q/wfj3lS4GCFn7PI89tc2UO35c+qHr8rP+8H2bTfKXDknyf1vAgCT7LkbazSd7VLUp5MxDlOJaryKJ4AgCqrbqMk3f7kzZIkj6doOfB4jFp3b6kRvx1Sqec0nkRJxU9WOmGU5Emo1PMWypwp5SxW8VnVYx+nPy2b+/3JPTdOK2OMTMxNKn0G3CN5GkqRFzoZq0qjeAIAqrRhv7pYf3lnglr3+HnJpPg6cbrm91fobx8/qMjoSuxml6SooSp7V7tXiuwrc5LF02ZMU9kzaF7ZzNdP6rkRBDHXSlFXHvvAe9wDx5ZTSnxBxrAsekXxSgEAqrweQ7qqx5CuOnLoqHKyclU7OT5/MfmTYMJSZKOvkzJfKeFRjySvTOxvTj5s3o8q9xjSPGY8Q4UxHinhESl6UP4vDSwgf0oongCAaiMuMfa0PI+Jv1/WREkZ/5V03Bnx3jNkEh6VCW97Ck8eI9mcMgZ4JHN6vg6cHsYYKfICmcgL3I5S5VE8AQA4gTFemfh7ZWN/KWUvPHblohZSRPf8GbBTEX2JlPG6ii5GfryATNTgU9sGEKIongAAlMJ4akvRl53e54wZI5sxU/m72088icmbfxZ11KDTuk0gVHByEQAADjJhzWSS/iOZgpOTwlQ4DxSWKpP0XxkT4VY8IKiY8QQAwGEmoptUb7GUNVc2d5WksPzjByN6VmrNUaCqoXgCAOACYyKk6Etloi91OwrgGHa1AwAAwBHMeAIAXLFl7TYteXuZsjOz1aJDU/W89ByFR4S7HQtAEFE8AQCOyjiSqcnX/0tL3lkmj9cj4zHy5/qVkByv+1+7S50vbO92RABBwq52AIBjrLV66IrH9NX7yyVJAX9A/tz89Sx9B47o/iF/1Y8rt7iYsHqy1soGMmRtbvmDgSCieAIAHLP28w369pPVCvhPXL9SsgGrgD+g1ybPciFZ9WRtjmz6f2T39ZPd20l2TzsFDt4im7PU7WiooSieAADHLHj9izKvoe7PC+izGV8qLzfPwVTVk7U5sodulT3yNymws+BeKedz2YPXy2a+42o+1EwUTwCAY46mpctaW+YYf15A2ZllXcscFZLxspTzpfKvkHQ8vyQrmzZBNnDQhWCoySieAADHnJHasNwxcUmxio6NciBN9WWtlU3/n4qXzuPlSZkznYoESKJ4AgAcNPDGvmXOeHq8Hg0de5E8Hn48nRKbIQV2lTPII5u7wZE4QAH+ZQMAHFOvSbJu/ut1kqQTrwzp8XrU+KyGuurey1xIVs2YcEkVuPSmiQx6FOB4FE8AgKOuuvcyTXj5Tp1x5s+73SOiwjXk1gGa8tlfVCuhlovpqgdjIqSIXpJKP5FL8stEDnAqEiBJMra8o7xd5PP5lJCQoLS0NMXHx7sdBwBwGllrtfPH3crJzFGD5vUUHRvtdqRqxeYslT14vUo+ztMreVvI1H1bxpRVToHyVaavceUiAIArjDE6o2X5Jxvh5JiIblLC32TTfi8pT/m73o0kv+RtLpP0H0onHOfIrvbs7Gx16tRJxhitWLHCiU0CAFDjmejLZOotlon9nRQ1VIq+XKb2v2XqviPjbeB2PNRAjsx43nvvvWrUqJFWrlzpxOYAAMAxxpMkxd5akVONgKAL+oznBx98oI8++kiPP/54sDcFAACAEBbUGc89e/bo1ltv1ezZsxUTE1Pu+OzsbGVnZxd+7PP5ghkPAAAADgrajKe1VmPGjNGvfvUrnXPOORX6nEmTJikhIaHwlpKSEqx4AAAAcFili+eECRNkjCnztmHDBj355JM6cuSIJk6cWOHnnjhxotLS0gpv27Ztq2w8AAAAhKhKr+O5b98+HThwoMwxLVq00KhRo/TOO+/IHHdpCr/fL6/Xq+uuu07//e9/y90W63gCAACEtsr0taAtIL9169Yix2ju3LlTAwcO1FtvvaUePXqocePG5T4HxRMAACC0hcQC8k2aNCnycWxsrCQpNTW1QqUTAAAA1QvXagcAAIAjHLtkZrNmzRTCl4UHAAAnsDYg5XwhmzVPshkyYWdK0SNkvHXcjoYqimu1AwCAYqz/gOyhW6S8tcqvC1ZWVjo6RYr/s0zMCJcToipiVzsAoMbKysjWoT2HlZeb53aUkGKtlT30Sylvw7F78iT5JQUk5cn6fi+b/bl7AVFlMeMJAKhxNn67Wa/85S0tmbNUgYBVdGyUBt10oa75/RVKrJfgdjz35Xwp5a0uY4CRTf+3TOT5jkVC9UDxBFCt7d95UFvXbVdEdIRadUtVeES425HgshXz12ji4EcU8AcUCOSfe5B5NEtznp6rL+Ys1b+WPKKkBokup3SXzf5E+RWhtJnggJTzlWzgqIwn1sFkZbPW5h+TmjFd8m+STLxM9FAp6rKQylmTUTwBVEv7dxzQU3e8qC/eXip7rFzE143T1fddrivHDy1ycQvUHP48vyZd90/58/yFfy8KBPwB7d9xQM/f+7Im/O9OlxKGCJtVwYE5QY1RGdYGZNMmSFmzJXmVf2iAkc1dLh19QUp6RSaM5RzdxjGeAKqdQ3vTdOd592vJu8uKlAvf/iN6/p7/6fl7XnYxHdz01fvLdXD34WKls4A/L6AF07+Q7+ARh5OFFhN2lvKLWxk8SZIJocMSMl46Vjqln7Pb/Ftgj+zhX7G6TgigeAKodl5/dLYO7DykQF6gxMff+vs72v79TodTIRRsWbNN3rCyf/T58/za9eMehxKFqOjhkiIklbZnwCMTc52M8TqXqQzW+mXTXypjhF/K+17KXepYJpSM4gmgWgkEAvrgP58o4C+5dEqSN8yjD1+a72AqhIqoWpGFx3WWJTIm0oE0oct44mVq/035xfPEcumRwjtItW5xIVkp/FulwN5yBnlls5c4Egelo3gCqFay0rOV4cssc4y10t5t+x1KhFBy3mXdyt3d2rBFfTVty7GAJmqwTNKrUkRvFc58epJlYn8jk/Q/GRPtar6iSv9F82dG+bve4SZOLgJQrUTGRCg8Mky52aWvy2iMUULdeAdTIVQ0aFZP/a/trU9f+6zU4zyv/+OVnHx2jInoKpP0vKzNkWyOZGqF5mvjbSKZRMkeKmNQnkzEOY5FQsmY8QRQrXi9Xl14be8yj+Pz5/nV//oLHEwFSVr35ff667VTNKrhLRrV6FZNvuFf+m7pRsdz/Pb5sTp/eDdJkjfMK2+YVx6vRx6P0S2Tr9fFo/s6ninUGRMh44kNzdIpyZhwmVo3qPRjUr2St6kUcZ6TsVACY0P4FC+fz6eEhASlpaUpPp7ZCQAVs/2HXfr1OfcqOyOn2LGexmN0/vDuevCt37mUrmaa8/RcPXXHf+QN88h/7KQvb5hHfn9Av3nmlxo69iLHM238drPmT/9cRw4eVcMW9XXR6D6q2yjJ8Rw4PazNlT18h5T9qfLn1Qr+7XskT22ZpFdkwlq6mLD6qkxfo3gCqJY2frtZf71uirZt2CljjKy18ng9Gjimr25/8mZFREW4HbHG+GH5Jv26232lH15npOe+fVwtOjR1NBeqH2v9UtZc2YzXji0gHycTfZkUfZWMt47b8aqtyvQ1jvEEUC217Nxc/1k7RWs+26BNq35SZHSEul/SucZfkcYNc576QF7vzzOdJ/J6PXr76bm667mxDidDdWOMV4oeIhM9xO0oKAXFE0C1ZYxR+95t1L53G7ej1GgrF64rtXRK+Yu2r1y0zsFEANzCyUUAgKAqb8F2SfJ4QvOkFQCnF8UTABBU3QZ2lsdb+o8bj9ejboM6O5gIgFsongCAoLp03MD8ZXhKmtQ0+bOdl/56oOO5ADiP4gkACKqUVmfo/tfuKlwvs4DH61FYmFd/eH28GqU2cDEhAKewnBIA4JRlHs3Ujyt/kiSldmyq6Njil1PcvWWv3ntunpZ/slrGSJ37d9CwX12kek2SnY4L4DRiHU8AgCNysnL04u+n6d3nP1Z2RrYkKTImUkPHXqSbHrmG9VKBGoB1PAEAQefP8+uPl07Wt5+uKXLd8+yMbM3853vavPon/fX9++UN87qYEkAo4RhPAMBJWTzjSy3/eHWR0lnABqyWf7xan838yoVkAEIVxRMAcFLee+HjMtff9Hg9eu+Fjx1MBCDUUTwBACdl96a9CpQw21kg4A9o1497HEwEINRRPAEAJyUhOa7ktTmPMUaqXY8TQwH8jOIJADgpF/2ib5mP2wqMAVCzUDwBACfl4tF91Ci1gTwlXIvdG+ZRo9QGuugXF7iQDECoongCAE5KdGy0nljwsNr2bCVJMsbkXxpTUtuerfTEgodLXEgeQM3FAvIAgFP248otWr1ovSSp/QVtlNqxmbuBADiGBeQBAI5K7diMsgmgXOxqBwAAgCMongAAAHAExRMAAACOoHgCAADAERRPAAAAOILiCQAAAEdQPAEAAOAIiicAAAAcQfEEAACAIyieAAAAcASXzAQAoAazNiDlLJHNmifZDJmwM6XoK2S8ddyOhmqI4gkAQA1lAwdlD94q5a1WfiWwsrLS0X9I8X+WiRnhdkRUM+xqBwCgBrLWyh78pZS37tg9eZL8kgKS8mR9v5fN/ty9gKiWKJ4AANREOV9LeauUXzZLYmSP/tvJRKgBKJ4AANRANvsTlX3EXUDK/Uo2cNSpSKgBKJ4AANRENrOC47KDmwM1CsUTAIAayISdpdJ3sx/jSZI8tZ2IgxqC4gkAQE0UfZmkCEmmlAEeKfpaGeN1MBSqO5ZTAgDgNLE2V8r+RDbrE8lmy4S3kqKvlPHWdztaMcYTLyU8Kps2XvnzUMfPfnqk8PYysbe6lA7VFcUTAIDTwPp3yh4cI/m3SPJKCshmfyQdferYmphXuhuwBCb6EslbX/boc1LOQklW8tSVibleqnWjjIl2OyKqGYonAACnyNo82YM3Sv5tx+4pmD20+f/13S95z5CJ7OlKvrKYiK4ySc/L2hzJ5kimlowpbfc7cGo4xhMAgFOVvUDyb1bpJ+t4ZNOfdzBQ5RkTIeOJpXQiqCieAACcIpv9qfJ3r5fGL+V8kT+rCNRgFE8AAE5VhQqllWxe0KMAoYziCQDAKTLhbZV/jfNSR0jexhIn66CGo3gCAHCqoi+XFK7S18SUTMwNHD+JGo/iCQDAKTKeRJmEvym/eB5/rKfJv0X0lmKudyccEEJYTgkAgNMgf03MRrLp/ydlfyLJL3mbyMTcIMVcI2PC3Y6IILJ5m2UzXpayPsw/5je8bf56qJEDmOk+DsUTAIDTxER0kol4StYGJPkpmzWEzf5c9tBY5S+ndWxJrZyvZXOWSNGj8i8gQPmUFORd7c2aNZMxpsht8uTJwdwkAACuM8ZD6awhbOCI7OFxknJVdB3XY/+f+YaUNcuFZKEp6DOef/rTn3TrrT9f6zUuLi7YmwQAAHBG5izJZqrgKlXFeWTTp8pEX+FkqpAV9OIZFxenBg0aBHszAAAAjrO5K5R/EllpxTMg5W2QtTkyJsK5YCEq6Ge1T548WXXq1FHnzp312GOPKS+PxXMBAEB14VFZy2gVHYegznjeeeed6tKli5KSkvTFF19o4sSJ2rVrl/7+97+XOD47O1vZ2dmFH/t8vmDGAwAAOCUm8jzZrLfLGOGRwrvKGM7nliRjrS1tbrhEEyZM0KOPPlrmmPXr16t169bF7n/xxRc1duxYHT16VJGRkcUef+ihh/Twww8Xuz8tLU3x8fGViQkAABB01mbJ7rtQChxUaVevMrWfk4nq52wwB/l8PiUkJFSor1W6eO7bt08HDhwoc0yLFi0UEVH8OIa1a9eqXbt22rBhg1q1alXs8ZJmPFNSUiieAAAgZNnc9bIHx0j2sH4+1tMryS8T+zuZ2F+6ls0JlSmelZ73TU5OVnJy8kkFW7FihTwej+rVq1fi45GRkSXOhAIAAIQqE95GSp4nZc6SzfpIsllSeHuZmKtlwovvAa7JgnbAwZIlS/TVV1+pX79+iouL05IlS/Tb3/5W119/vRITE4O1WQAAAMcZT7xUa7RMrdFuRwlpQSuekZGRmj59uh566CFlZ2erefPm+u1vf6vx48cHa5MAAAAIYUErnl26dNGXX34ZrKcHAABAFcOiUgAAAHAExRMAAACOoHgCAADAERRPAAAAOILiCQAAAEdQPAEAAOAIiicAAAAcQfEEAACAIyieAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guIJAAAAR1A8AQAA4AiKJwAAABxB8QQAAIAjKJ4AAABwBMUTAAAAjghzOwAAoLgfV27R6sXrZYxRx75nq9nZKW5HAoBTRvEEgBCyb/sBPXL1P7T2i+9kjJEkWWvVqV87/X7ab5RYv7a7AQHgFLCrHQBCRLovQ+P7PKANX/8gKb9wWmslSasWr9Pd/R5SVka2mxEBVAHHf+8INRRPAAgRH744X3u27JM/L1DssUBeQNs27NCn0z5zIRmAqsBmL1bg4BjZPWfL7mmrwIFrZLM+DKkSSvEEgBDx0f8WyKr0HxDGYzTv5YUOJgJQVdj0/5M9dLOU85WkPEl+Kfdb2cN3yB593O14hSieABAiDu/zqYzeKRuwStub5lwgAFWCzV0ne+Rvxz7yH/fIsb0n6S/IZn/udKwSUTwBIEQ0bF5PHo8p9XGP16OGLeo7mAhAVWAzpknyljHCK5vxilNxykTxBIAQccmtAxQIlD7lGfAHNPiW/g4mAlAl5K5Q0ZnOE/ml3FUOhSkbxRMAQkS/q89X+wvalDjraTxG3QZ1Us9Lz3EhGYDQFlmBMRFBT1ERFE8ACBFh4WH66/v369JxgxQZ/fMPiahakbryt0P10Kx75fWWtTsNQE1kovqr7ErnlaIucipOmYwNpXPsT+Dz+ZSQkKC0tDTFx8e7HQcAHJNxJFMbv90sY4xadmmu6FpRbkcCEKKsf7/s/oskm6nCE4oKGUnhMnXfkwlrGpTtV6avceUiAAhBMXHR6nBBW7djAKgCjLeulPii7KFbJHv0hEcjZRKfDlrprCyKJwAAQBVnIjpLyQulzNmyOV9KCshEdJWir5DxJLodrxDFEwAAoBownlip1vUyta53O0qpOLkIAAAAjqB4AgAAwBEUTwAAADiC4gkAAABHUDwBAADgCIonAAAAHEHxBAAAgCMongAAAHAExRMAAACOoHgCAADAERRPAAAAOILiCQAAAEdQPAEAAOAIiicAAAAcQfEEAACAIyieAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guIJAAAAR1A8AQAA4AiKJwAAABxB8QQAAIAjKJ4AAABwBMUTAAAAjqB4AgAAwBFhbgcAAJw6a62+/XSNls39Vv68gFp1S1WvEecqIjLc7WgAUIjiCQBV3N5t+/XHYZO1adVP8oZ5JSP5c/1KuGuqHpr5O7Xr1cbtiAAgKci72t977z316NFD0dHRSkxM1PDhw4O5OQCocXKyc3VP/4e1Zd02SZI/zy9/rl+S5Dt4RBMGPaIdG3e5GREACgWteM6YMUM33HCDbrzxRq1cuVKff/65rr322mBtDgBqpEVvLtHOjbsVyAsUe8wGrPJycjVzynsuJAOA4oKyqz0vL0+/+c1v9Nhjj+nmm28uvL9t27bB2BwA1FiL3loi4zGyAVvi4/68gBa8/rnueOoWh5MBQHFBmfFcvny5duzYIY/Ho86dO6thw4YaPHiw1qxZE4zNAUCNleHLLLV0FshKz3YoDQCULSjFc9OmTZKkhx56SH/4wx/07rvvKjExUX379tXBgwdL/bzs7Gz5fL4iNwBA6Zq2bSxvWOnfyo3HKKX1GQ4mAoDSVap4TpgwQcaYMm8bNmxQIJB/rNH999+vESNGqGvXrnrppZdkjNGbb75Z6vNPmjRJCQkJhbeUlJRT++oAoJobOvYi+Us4vrOADVhd+utBDiYCgNJV6hjPu+++W2PGjClzTIsWLbRrV/4ZlMcf0xkZGakWLVpo69atpX7uxIkTNX78+MKPfT4f5RMAytC8fVNd94cRevUvM2SMkbU/73Y3HqMuAzro4tF9XEwIAD+rVPFMTk5WcnJyueO6du2qyMhIfffdd+rVq5ckKTc3V1u2bFHTpk1L/bzIyEhFRkZWJhIA1Hhj/nS1Gp/VSNMnz9JP67ZLkmrXS9Dw2wdr1L2XKiycJZsBhIagfDeKj4/Xr371Kz344INKSUlR06ZN9dhjj0mSRo4cGYxNAkCNNuD6C9T/ut46tOew8nL9qtMoUV6v1+1YAFBE0H4NfuyxxxQWFqYbbrhBmZmZ6tGjhz799FMlJiYGa5MAUKMZY5TUgO+xAEKXsccfEBRifD6fEhISlJaWpvj4eLfjAAAA4ASV6WtBvWQmAAAAUIDiCQAAAEdQPAEAAOAIiicAAAAcQfEEAACAIyieAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guIJAAAAR1A8AQAA4AiKJwAAABxB8QQAAIAjKJ4AAABwBMUTAAAAjqB4AgAAwBEUTwAAADiC4gkAAABHUDwBAADgiDC3A5TFWitJ8vl8LicBAABASQp6WkFvK0tIF88jR45IklJSUlxOAgAAgLIcOXJECQkJZY4xtiL11CWBQEA7d+5UXFycjDFux6lyfD6fUlJStG3bNsXHx7sdp8ridTx1vIanB6/j6cHreOp4DU+P6vI6Wmt15MgRNWrUSB5P2UdxhvSMp8fjUePGjd2OUeXFx8dX6b/QoYLX8dTxGp4evI6nB6/jqeM1PD2qw+tY3kxnAU4uAgAAgCMongAAAHAExbMai4yM1IMPPqjIyEi3o1RpvI6njtfw9OB1PD14HU8dr+HpURNfx5A+uQgAAADVBzOeAAAAcATFEwAAAI6geAIAAMARFE8AAAA4guJZQzzyyCM677zzFBMTo9q1a7sdp8p4+umn1axZM0VFRalHjx76+uuv3Y5UpSxatEjDhg1To0aNZIzR7Nmz3Y5UJU2aNEndunVTXFyc6tWrp+HDh+u7775zO1aV8uyzz6pDhw6FC3X37NlTH3zwgduxqrzJkyfLGKO77rrL7ShVykMPPSRjTJFb69at3Y7lCIpnDZGTk6ORI0fqtttucztKlfH6669r/PjxevDBB7V8+XJ17NhRAwcO1N69e92OVmWkp6erY8eOevrpp92OUqUtXLhQ48aN05dffql58+YpNzdXF198sdLT092OVmU0btxYkydP1jfffKNly5bpwgsv1GWXXaa1a9e6Ha3KWrp0qZ577jl16NDB7ShV0tlnn61du3YV3j777DO3IzmC5ZRqmKlTp+quu+7S4cOH3Y4S8nr06KFu3brpqaeekiQFAgGlpKTojjvu0IQJE1xOV/UYYzRr1iwNHz7c7ShV3r59+1SvXj0tXLhQF1xwgdtxqqykpCQ99thjuvnmm92OUuUcPXpUXbp00TPPPKO//OUv6tSpk6ZMmeJ2rCrjoYce0uzZs7VixQq3oziOGU+gBDk5Ofrmm280YMCAwvs8Ho8GDBigJUuWuJgMkNLS0iTlFydUnt/v1/Tp05Wenq6ePXu6HadKGjdunIYMGVLkeyQq54cfflCjRo3UokULXXfdddq6davbkRwR5nYAIBTt379ffr9f9evXL3J//fr1tWHDBpdSAfkz73fddZfOP/98tWvXzu04Vcrq1avVs2dPZWVlKTY2VrNmzVLbtm3djlXlTJ8+XcuXL9fSpUvdjlJl9ejRQ1OnTlWrVq20a9cuPfzww+rdu7fWrFmjuLg4t+MFFTOeVdiECROKHZx84o2SBFQv48aN05o1azR9+nS3o1Q5rVq10ooVK/TVV1/ptttu0+jRo7Vu3Tq3Y1Up27Zt029+8xu9+uqrioqKcjtOlTV48GCNHDlSHTp00MCBA/X+++/r8OHDeuONN9yOFnTMeFZhd999t8aMGVPmmBYtWjgTppqpW7euvF6v9uzZU+T+PXv2qEGDBi6lQk13++23691339WiRYvUuHFjt+NUOREREWrZsqUkqWvXrlq6dKn++c9/6rnnnnM5WdXxzTffaO/everSpUvhfX6/X4sWLdJTTz2l7Oxseb1eFxNWTbVr19ZZZ52ljRs3uh0l6CieVVhycrKSk5PdjlEtRUREqGvXrvrkk08KT4YJBAL65JNPdPvtt7sbDjWOtVZ33HGHZs2apQULFqh58+ZuR6oWAoGAsrOz3Y5RpfTv31+rV68uct+NN96o1q1b67777qN0nqSjR4/qxx9/1A033OB2lKCjeNYQW7du1cGDB7V161b5/f7CM+latmyp2NhYd8OFqPHjx2v06NE655xz1L17d02ZMkXp6em68cYb3Y5WZRw9erTIb/CbN2/WihUrlJSUpCZNmriYrGoZN26cpk2bpjlz5iguLk67d++WJCUkJCg6OtrldFXDxIkTNXjwYDVp0kRHjhzRtGnTtGDBAn344YduR6tS4uLiih1bXKtWLdWpU4djjivhd7/7nYYNG6amTZtq586devDBB+X1enXNNde4HS3oKJ41xAMPPKD//ve/hR937txZkjR//nz17dvXpVSh7aqrrtK+ffv0wAMPaPfu3erUqZPmzp1b7IQjlG7ZsmXq169f4cfjx4+XJI0ePVpTp051KVXV8+yzz0pSsX+rL730UrmH2yDf3r179Ytf/EK7du1SQkKCOnTooA8//FAXXXSR29FQA23fvl3XXHONDhw4oOTkZPXq1UtffvlljdiLyTqeAAAAcARntQMAAMARFE8AAAA4guIJAAAAR1A8AQAA4AiKJwAAABxB8QQAAIAjKJ4AAABwBMUTAAAAjqB4AgAAwBEUTwAAADiC4gkAAABHUDwBAADgiP8Ht953WX0x69wAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -2306,10 +2306,10 @@ "id": "f1436671", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.984089Z", - "iopub.status.busy": "2024-06-04T23:20:00.983822Z", - "iopub.status.idle": "2024-06-04T23:20:00.992694Z", - "shell.execute_reply": "2024-06-04T23:20:00.992450Z" + "iopub.execute_input": "2025-04-03T19:34:17.461904Z", + "iopub.status.busy": "2025-04-03T19:34:17.461808Z", + "iopub.status.idle": "2025-04-03T19:34:17.473479Z", + "shell.execute_reply": "2025-04-03T19:34:17.473231Z" }, "lines_to_next_cell": 0 }, @@ -2379,10 +2379,10 @@ "id": "3a2d2b24", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.994226Z", - "iopub.status.busy": "2024-06-04T23:20:00.994147Z", - "iopub.status.idle": "2024-06-04T23:20:00.997621Z", - "shell.execute_reply": "2024-06-04T23:20:00.997244Z" + "iopub.execute_input": "2025-04-03T19:34:17.474981Z", + "iopub.status.busy": "2025-04-03T19:34:17.474910Z", + "iopub.status.idle": "2025-04-03T19:34:17.478619Z", + "shell.execute_reply": "2025-04-03T19:34:17.478294Z" } }, "outputs": [ @@ -2830,10 +2830,10 @@ "id": "dc8c9fa7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:00.999073Z", - "iopub.status.busy": "2024-06-04T23:20:00.998978Z", - "iopub.status.idle": "2024-06-04T23:20:01.001611Z", - "shell.execute_reply": "2024-06-04T23:20:01.001348Z" + "iopub.execute_input": "2025-04-03T19:34:17.480167Z", + "iopub.status.busy": "2025-04-03T19:34:17.480027Z", + "iopub.status.idle": "2025-04-03T19:34:17.483291Z", + "shell.execute_reply": "2025-04-03T19:34:17.482911Z" } }, "outputs": [], @@ -2863,10 +2863,10 @@ "id": "17af050a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.003143Z", - "iopub.status.busy": "2024-06-04T23:20:01.003055Z", - "iopub.status.idle": "2024-06-04T23:20:01.007137Z", - "shell.execute_reply": "2024-06-04T23:20:01.006871Z" + "iopub.execute_input": "2025-04-03T19:34:17.484706Z", + "iopub.status.busy": "2025-04-03T19:34:17.484593Z", + "iopub.status.idle": "2025-04-03T19:34:17.489391Z", + "shell.execute_reply": "2025-04-03T19:34:17.489071Z" } }, "outputs": [ @@ -3327,16 +3327,16 @@ "id": "b2902747", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.008552Z", - "iopub.status.busy": "2024-06-04T23:20:01.008452Z", - "iopub.status.idle": "2024-06-04T23:20:01.157711Z", - "shell.execute_reply": "2024-06-04T23:20:01.157301Z" + "iopub.execute_input": "2025-04-03T19:34:17.490835Z", + "iopub.status.busy": "2025-04-03T19:34:17.490732Z", + "iopub.status.idle": "2025-04-03T19:34:17.570177Z", + "shell.execute_reply": "2025-04-03T19:34:17.569797Z" } }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -3372,16 +3372,16 @@ "id": "81bc1753", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.159274Z", - "iopub.status.busy": "2024-06-04T23:20:01.159152Z", - "iopub.status.idle": "2024-06-04T23:20:01.340885Z", - "shell.execute_reply": "2024-06-04T23:20:01.340610Z" + "iopub.execute_input": "2025-04-03T19:34:17.571743Z", + "iopub.status.busy": "2025-04-03T19:34:17.571623Z", + "iopub.status.idle": "2025-04-03T19:34:17.650916Z", + "shell.execute_reply": "2025-04-03T19:34:17.650458Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3414,10 +3414,10 @@ "id": "12fa137c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.342467Z", - "iopub.status.busy": "2024-06-04T23:20:01.342352Z", - "iopub.status.idle": "2024-06-04T23:20:01.345327Z", - "shell.execute_reply": "2024-06-04T23:20:01.345090Z" + "iopub.execute_input": "2025-04-03T19:34:17.652404Z", + "iopub.status.busy": "2025-04-03T19:34:17.652286Z", + "iopub.status.idle": "2025-04-03T19:34:17.655868Z", + "shell.execute_reply": "2025-04-03T19:34:17.655546Z" } }, "outputs": [ @@ -3456,10 +3456,10 @@ "id": "bfbbb0bc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.346645Z", - "iopub.status.busy": "2024-06-04T23:20:01.346551Z", - "iopub.status.idle": "2024-06-04T23:20:01.349500Z", - "shell.execute_reply": "2024-06-04T23:20:01.349288Z" + "iopub.execute_input": "2025-04-03T19:34:17.657269Z", + "iopub.status.busy": "2025-04-03T19:34:17.657183Z", + "iopub.status.idle": "2025-04-03T19:34:17.660557Z", + "shell.execute_reply": "2025-04-03T19:34:17.660246Z" } }, "outputs": [ @@ -3542,16 +3542,16 @@ "id": "4a183e53", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.350672Z", - "iopub.status.busy": "2024-06-04T23:20:01.350588Z", - "iopub.status.idle": "2024-06-04T23:20:01.504539Z", - "shell.execute_reply": "2024-06-04T23:20:01.504262Z" + "iopub.execute_input": "2025-04-03T19:34:17.661954Z", + "iopub.status.busy": "2025-04-03T19:34:17.661850Z", + "iopub.status.idle": "2025-04-03T19:34:17.770259Z", + "shell.execute_reply": "2025-04-03T19:34:17.769959Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -3597,17 +3597,17 @@ "id": "fca5af66", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.505992Z", - "iopub.status.busy": "2024-06-04T23:20:01.505912Z", - "iopub.status.idle": "2024-06-04T23:20:01.626714Z", - "shell.execute_reply": "2024-06-04T23:20:01.626470Z" + "iopub.execute_input": "2025-04-03T19:34:17.771606Z", + "iopub.status.busy": "2025-04-03T19:34:17.771512Z", + "iopub.status.idle": "2025-04-03T19:34:17.854077Z", + "shell.execute_reply": "2025-04-03T19:34:17.853788Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -3649,10 +3649,10 @@ "id": "64352331", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.628099Z", - "iopub.status.busy": "2024-06-04T23:20:01.628029Z", - "iopub.status.idle": "2024-06-04T23:20:01.633228Z", - "shell.execute_reply": "2024-06-04T23:20:01.633002Z" + "iopub.execute_input": "2025-04-03T19:34:17.855537Z", + "iopub.status.busy": "2025-04-03T19:34:17.855440Z", + "iopub.status.idle": "2025-04-03T19:34:17.859928Z", + "shell.execute_reply": "2025-04-03T19:34:17.859696Z" } }, "outputs": [], @@ -3682,10 +3682,10 @@ "id": "224f03e9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.634598Z", - "iopub.status.busy": "2024-06-04T23:20:01.634524Z", - "iopub.status.idle": "2024-06-04T23:20:01.636446Z", - "shell.execute_reply": "2024-06-04T23:20:01.636188Z" + "iopub.execute_input": "2025-04-03T19:34:17.861374Z", + "iopub.status.busy": "2025-04-03T19:34:17.861290Z", + "iopub.status.idle": "2025-04-03T19:34:17.863635Z", + "shell.execute_reply": "2025-04-03T19:34:17.863360Z" }, "lines_to_next_cell": 2 }, @@ -3719,10 +3719,10 @@ "id": "7b5e3fe5", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.637751Z", - "iopub.status.busy": "2024-06-04T23:20:01.637686Z", - "iopub.status.idle": "2024-06-04T23:20:01.640873Z", - "shell.execute_reply": "2024-06-04T23:20:01.640636Z" + "iopub.execute_input": "2025-04-03T19:34:17.864990Z", + "iopub.status.busy": "2025-04-03T19:34:17.864891Z", + "iopub.status.idle": "2025-04-03T19:34:17.868728Z", + "shell.execute_reply": "2025-04-03T19:34:17.868443Z" }, "lines_to_next_cell": 2 }, @@ -3775,10 +3775,10 @@ "id": "8e1d2d85", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.642205Z", - "iopub.status.busy": "2024-06-04T23:20:01.642130Z", - "iopub.status.idle": "2024-06-04T23:20:01.835589Z", - "shell.execute_reply": "2024-06-04T23:20:01.834884Z" + "iopub.execute_input": "2025-04-03T19:34:17.870023Z", + "iopub.status.busy": "2025-04-03T19:34:17.869951Z", + "iopub.status.idle": "2025-04-03T19:34:18.396294Z", + "shell.execute_reply": "2025-04-03T19:34:18.393209Z" } }, "outputs": [], @@ -3807,17 +3807,17 @@ "id": "5b76ee22", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:01.838692Z", - "iopub.status.busy": "2024-06-04T23:20:01.838473Z", - "iopub.status.idle": "2024-06-04T23:20:02.097401Z", - "shell.execute_reply": "2024-06-04T23:20:02.097122Z" + "iopub.execute_input": "2025-04-03T19:34:18.401043Z", + "iopub.status.busy": "2025-04-03T19:34:18.399099Z", + "iopub.status.idle": "2025-04-03T19:34:18.517689Z", + "shell.execute_reply": "2025-04-03T19:34:18.516696Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -3877,17 +3877,17 @@ "id": "9b1b74c2", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:02.098796Z", - "iopub.status.busy": "2024-06-04T23:20:02.098709Z", - "iopub.status.idle": "2024-06-04T23:20:02.211186Z", - "shell.execute_reply": "2024-06-04T23:20:02.210897Z" + "iopub.execute_input": "2025-04-03T19:34:18.521528Z", + "iopub.status.busy": "2025-04-03T19:34:18.520466Z", + "iopub.status.idle": "2025-04-03T19:34:18.643973Z", + "shell.execute_reply": "2025-04-03T19:34:18.642828Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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aJMMKSipbVLIFyqLmyjV3Uo2toegpTO3RFioWLFigrKwsz+Pq6moNGTKk078vFW0AAAAAALP56sGWbLfp++mOdj2frx5sQE9hWqJtwIABioiIUHl5udfx8vJyORzt+4+5vc8ZExPjt+dbZ+p9qEcbFW0AAAAAADO4e7AdvT20tKpey9/dHtBzJPSOVmVto+exw25T9uQ0erChRzIt0RYdHa3Ro0dr3bp1mjJliiTJ5XJp3bp1mjt3bsg8Z2fqRUUbAAAAAMAkHe3B5t4euv7OC7Vxx7f0YANk8tbRrKwszZw5U2PGjFFGRoYWLVqk2tpazZo1S5I0Y8YMDRo0SLm5uZKahx0UFxd7/n337t3avHmz+vTpoxNOOCGg5wwlvWPcFW0k2gAAAAAAnevoPmwuwwi4B5tF8krIHbk9NDrSSg824BBTE21Tp07VN998o7vvvltlZWUaOXKk8vLyPMMMdu7cKavV6rn+66+/1plnnul5/NBDD+mhhx7SBRdcoDfffDOg5wwlvaIYhgAAAAAA6Hy++rDZe0UFdO9156ToP0VlXveyPRTwzWIYRkcm9XZL1dXVstvtqqqqUnx8fKd9n8ff2qb7XtmiK0YN0iM/Htlp3wcAAHS+rlo/oGP4OQHoifz1YQvUM7PHeaaQsj0UPVWgawimjpqol3sYQgNbRwEAAAAAwdeRPmzuHmzupBrbQ4FjI9Fmolj31tEmEm0AAAAAgI45ugebuwot0D5sRzqyBxuVa0DgSLSZqHfMoURbAz3aAAAAAADt56sHW7LdptHD+gV0f99eUdp3oMnzmB5sQPuQaDORZ+soU0cBAAAAAO3krwdbaVW9Xvq4NKDnWHz1KFmtFnqwAR1Eos1EvaOZOgoAAAAAaL+O9GCTDvdhGze8P4k1IAhItJmolyfRRkUbAAAAAKB1He3BZpG8EnL0YQOCj0SbiXqzdRQAAAAAEAB/PdjGn5oU0P3XnZOi/xSVed1PHzYg+Ei0mSj2iK2jhmHIYuETBAAAAACAt9Z6sD313o6AnuPiNId+PSmtRUUclWxAcJFoM1FsTPPb7zKkhoMu2aIiTI4IAAAAABBKgtWDzZ1UyxzeP5jhATgKiTYT9ToisVbX6CTRBgAAAAA93NF92FyGQQ82IIyQaDNRhNUiW5RV9U0u1TYcVELvaLNDAgAAAACYxFcftr69ogK6lx5sQGgg0Way2OhI1Tc16kATAxEAAAAAoKfy14dt34GmgO6nBxsQGki0mSw2OkKVtVJtw0GzQwEAAAAAmKAjfdjowQaEFqvZAfR07smjBxqpaAMAAACAnqigpDLgPmxHogcbEHqoaDNZbHTzj6CORBsAAAAAdHtHDzvISE1QRU1gSba+vaK8tpLSgw0IPSTaTOauaKttZOsoAAAAAHRnvoYdJMbFaECfmIDuX3z1KFmtFnqwASGMRJvJ2DoKAAAAAN2fv2EHFTUNqqhpaPVedx+2ccP7k1gDQhw92kzm3jpaS6INAAAAALqlQIYd9LFFyqLDfdfc6MMGhBcSbSY7XNHG1lEAAAAACHdOl6H8bXv14ubdyt+219OT7VjDDvbXH9St40+Sw27zOu6w27Rk+ij6sAFhgq2jJqOiDQAAAAC6B1892JLtNn3n5OMCuj9lQKzemXdRi2EJVLIB4YNEm8no0QYAAAAA4c9fD7bSqno9U7AroOdIjLMpwmpR5vD+wQ8QQJcg0Way2JhDU0cb2DoKAAAAAOEokB5srXEPO8hITQhmWABMQI82k8VGNSfa6pqoaAMAAACAcBRIDzY3hh0A3RsVbSaLjWn+EdRR0QYAAAAAYcE94MDdR62sOrAk23XnpOg/RWVeSTmH3absyWkMOwC6CRJtJnP3aKujRxsAAAAAhDxfAw/6HGoJdCwXpzn060lpDDsAujESbSbrfWjqKIk2AAAAAAht/gYe7G9o/e9zR/ZgY9gB0L3Ro81kvTwVbWwdBQAAAIBQFejAA3qwAT0bFW0mo6INAAAAAELL0T3YMlITAh540K93tCprGz2P6cEG9Cwk2kzWix5tAAAAABAyfPVgS7bbdOHJxwV0/8JJp8ph70UPNqCHItFmsli2jgIAAABASPDXg620ql5PF+wK6Dkc9l70YAN6MHq0mcy9dbTJaajJ6TI5GgAAAADomQLtweaPRc2VbxmpCcEMC0CYIdFmMvfWUYntowAAAABglkB7sEkMPADgH1tHTRYdaVVUhEVNTkN1jQdl7xVldkgAAAAA0O0dPfCgrDqwJNt156ToP0VlXkk5Bh4AcCPRFgJ6RUWoyXmQijYAANDjLF68WA8++KDKyso0YsQI/elPf1JGRobf6xctWqQlS5Zo586dGjBggH70ox8pNzdXNputC6MGEO58DTzoG2DRw8VpDv16UlqLqaRUsgGQSLSFhN4xkaquP6i6BhJtAACg51i1apWysrK0dOlSjR07VosWLdKECRO0detWJSYmtrj+6aef1vz587V8+XKdffbZ+vzzz3XttdfKYrHokUceMeEVAAhH/gYe7DvQ1Op9FjVXrrmTagw8AOALPdpCQC8mjwIAgB7okUce0ezZszVr1iylpaVp6dKlio2N1fLly31ev2HDBp1zzjm6+uqrlZKSou9973uaNm2aCgoKujhyAOEq0IEH9GAD0F4k2kKAe/IoW0cBAEBP0djYqI0bN2r8+PGeY1arVePHj1d+fr7Pe84++2xt3LjRk1j73//+p1deeUWXXHKJ3+/T0NCg6upqry8APVegAw/69Y72euyw27Rk+ih6sAE4JraOhoDDFW0k2gAAQM+wZ88eOZ1OJSUleR1PSkrSli1bfN5z9dVXa8+ePTr33HNlGIYOHjyoG264Qb/61a/8fp/c3Fzl5OQENXYA4eHoYQcZqQmqqAls4MHCSafKYe9FDzYAbUaiLQT0PpRoq2XrKAAAgF9vvvmm7rvvPv35z3/W2LFj9eWXX+qWW27Rvffeq4ULF/q8Z8GCBcrKyvI8rq6u1pAhQ7oqZAAm8TXswBFvU8qA2IDud9h70YMNQLuQaAsBsYe2jh6gog0AAPQQAwYMUEREhMrLy72Ol5eXy+Fw+Lxn4cKFuuaaa/TTn/5UknT66aertrZWP/vZz/TrX/9aVmvLrigxMTGKiYkJ/gsAELL8DTsoq65XWXXrFW1HDjwAgPagR1sIiKWiDQAA9DDR0dEaPXq01q1b5znmcrm0bt06ZWZm+rynrq6uRTItIqJ5HWUYx2ptDqAnCGTYQWx0hCxi4AGAzkGiLQS4E21UtAEAgJ4kKytLy5Yt05NPPqnPPvtMc+bMUW1trWbNmiVJmjFjhhYsWOC5fvLkyVqyZIlWrlypkpISrV27VgsXLtTkyZM9CTcAPVsgww7qGp26dfxJcthtXscZeAAgGNg6GgJiY5p/DLUNJNoAAEDPMXXqVH3zzTe6++67VVZWppEjRyovL88zIGHnzp1eFWx33XWXLBaL7rrrLu3evVvHHXecJk+erN/97ndmvQQAJurIsIOUAbF6Z95FLe6nkg1AR5FoCwGxUYcq2prYOgoAAHqWuXPnau7cuT7Pvfnmm16PIyMjlZ2drezs7C6IDEAo8zXsINlu0/knHRfQ/YlxNkVYLQw8ABB0JNpCQC93jzYq2gAAAACgVf6GHZRW1WvVB7tavZdhBwA6Gz3aQkDvQ1tH6+jRBgAAAAB+BTLsIDrCyrADAKYh0RYC3MMQ6pg6CgAAAAB+BTLsoNHpYtgBANOwdTQExEZT0QYAAAAAx8KwAwChjkRbCHBXtB0g0QYAAAAAHkdPFu0fGx3QfQw7AGAWEm0hwJ1oq2XrKAAAAABI8j1ZNCqi9Yo0hh0AMBuJthDg3jpKRRsAAAAA+J8s2uQ8fMQieZ1n2AGAUMAwhBBARRsAAAAANAtksmjf2CglxTPsAEDooaItBLgTbfVNLjldBp++AAAAAOixApksuq+uSf+8fpSsVgvDDgCEFBJtIaB3zOEfw4Emp/rE8GMBAAAA0P0dPewgIzVB5dWBTRbdU9ugy0YO6uQIAaBtyOiEgJhIqywWyTCkusaDJNoAAAAAdHu+hh0kxsWoV1REQPcnxtmOfREAdDEyOiHAYrGod3Sk9jccVF2DU4ozOyIAAAAA6Dz+hh1U1DQc814miwIIZQxDCBG9DvVpq2PyKAAAAIBuLJBhB/G2SFl0eJKoG5NFAYQ6Em0hItaTaGPyKAAAAIDuK5BhB9X1B3Xr+JPksDNZFEB4YetoiIiNbv5RUNEGAAAAoDurqAls2EHKgFi9M++iFsMSqGQDEMpItIUIKtoAAAAAdDe+por27x0d0L2JcTZFWC3KHN6/k6MEgOAh0RYiYunRBgAAAKAb8TdVtE9M61NFGXYAIJyRaAsR7kRbLYk2AAAAAGGutamiFTVSlNWiJpchi+R1DcMOAIQ7hiGEiN6HerQdYOsoAAAAgDAWyFTRfr2j9eerRzHsAEC3Y3qibfHixUpJSZHNZtPYsWNVUFDQ6vXPPvusTjnlFNlsNp1++ul65ZVXvM7v379fc+fO1eDBg9WrVy+lpaVp6dKlnfkSgqKXu6KtgYo2AAAAAOErkKmiFTUN6tc7Wu/Mu0jPzB6nR68aqWdmj9M78y4iyQYgrJmaaFu1apWysrKUnZ2twsJCjRgxQhMmTFBFRYXP6zds2KBp06bp+uuv16ZNmzRlyhRNmTJFRUVFnmuysrKUl5enFStW6LPPPtOtt96quXPnavXq1V31stqld8yhirYmEm0AAAAAwlegU0Urauo9ww4uGzlImcP7s10UQNgzNdH2yCOPaPbs2Zo1a5an8iw2NlbLly/3ef2jjz6qiRMn6s4779Spp56qe++9V6NGjdJjjz3muWbDhg2aOXOmvvOd7yglJUU/+9nPNGLEiGNWypmtVxRTRwEAAACEH6fLUP62vXpx827lb9vbpqmiANDdmDYMobGxURs3btSCBQs8x6xWq8aPH6/8/Hyf9+Tn5ysrK8vr2IQJE/TCCy94Hp999tlavXq1rrvuOg0cOFBvvvmmPv/8c/3hD3/wG0tDQ4MaGho8j6urq9v5qtqv96HJO3VsHQUAAAAQJnxNFo2JbL0qjamiALoz0yra9uzZI6fTqaSkJK/jSUlJKisr83lPWVnZMa//05/+pLS0NA0ePFjR0dGaOHGiFi9erPPPP99vLLm5ubLb7Z6vIUOGdOCVtU+vQ8MQ6pg6CgAAACAMuCeLHt2PreHg4TEIR6fcmCoKoLszfRhCsP3pT3/Se++9p9WrV2vjxo16+OGHdeONN+q1117ze8+CBQtUVVXl+dq1a1cXRtyst3sYAltHAQAAAIS4QCaL9o2NUlI8U0UB9CymbR0dMGCAIiIiVF5e7nW8vLxcDofD5z0Oh6PV6w8cOKBf/epXev755zVp0iRJ0hlnnKHNmzfroYce0vjx430+b0xMjGJiYjr6kjok9lCi7QAVbQAAAABCXCCTRffVNemf14+S1WpRRU29EuOat4tSyQagOzOtoi06OlqjR4/WunXrPMdcLpfWrVunzMxMn/dkZmZ6XS9Ja9eu9Vzf1NSkpqYmWa3eLysiIkIulyvIryC4Yg9tHa0l0QYAAAAgxAU6WXRPbQNTRQH0KKZVtElSVlaWZs6cqTFjxigjI0OLFi1SbW2tZs2aJUmaMWOGBg0apNzcXEnSLbfcogsuuEAPP/ywJk2apJUrV+rDDz/U448/LkmKj4/XBRdcoDvvvFO9evXSsGHDtH79ev3jH//QI488YtrrDMThija2jgIAAAAIHU6XoYKSSq+qtIamwAoZmCwKoKcxNdE2depUffPNN7r77rtVVlamkSNHKi8vzzPwYOfOnV7VaWeffbaefvpp3XXXXfrVr36lE088US+88ILS09M916xcuVILFizQT37yE1VWVmrYsGH63e9+pxtuuKHLX19b9PL0aKOiDQAAAEBo8DVVtG9slGrrWy8QYLIogJ7KYhhGa/0re6Tq6mrZ7XZVVVUpPj6+S77n9j21+s5Db6pPTKSKciZ0yfcEAADBY8b6AW3HzwkInHuqqL+/MA7qa9PX+5oTcEde494cytADAN1JoGuIbjd1NFzFHjF1lNwnAAAAADMFMlXUZUiLrx4lh53JogDgZurWURwWG9P8ozAMqeGgS7aoCJMjAgAAANBTBTJVtLSqXv16R+udeRe16OHG0AMAPRWJthDR64jEWm3DQRJtAAAAAEwT6FTRipp6RVgtyhzev5MjAoDwQKItRERYLbJFWVXf5FJdo1P8bwoAAABAVzl6smj/3tEB3cdUUQDwRqIthMRGR6q+qVF1TB4FAAAA0EV8TRaNiWx96ydTRQHANxJtISQ2OkKVtVJdY+ujsgEAAAAgGPxNFm04ePiIRb6nimZPTqMXGwAchamjIcQ9eZSKNgAAAACdLZDJon1jo5QUz1RRAAgUFW0hJDa6+cdBog0AAABAZwtksui+uib98/pRslotTBUFgACQaAshhyva2DoKAAAAoHMFOll0T22DLhs5qJOjAYDuga2jIYSKNgAAAABdpW+vqICuY7IoAASOirYQQo82AAAAAJ3B6TJUUFLp2f6Z0j9WD/93a6v3MFkUANqORFsI8STaGtg6CgAAACA48opKlbOm2Ksfm9UiuQypd3SEahudTBYFgCAh0RZCPFtHm6hoAwAAANBxeUWlmrOisMVkUdehA/MmnqLE+JgWiTiH3absyWlMFgWANiLRFkKoaAMAAAAQLE6XoZw1xS2SbG4WSUvWb9M78y7SxWkOr62lTBYFgPYh0RZCYmPo0QYAAAAgOApKKr2q1I5mSCqtqldBSaUyh/dX5vD+XRccAHRTTB0NIbFRJNoAAAAABEdFjf8kW3uuAwAcGxVtISQ25lCPtka2jgIAAAAI3NFTRTNSE9Q7JrC/7iXG2To5OgDoOUi0hRB3j7ZaKtoAAAAABMjXVNHEuBhFHKPFmkXNQw8yUhM6N0AA6EFItIWQ3oemjh4g0QYAAAAgAP6milbUNEiSekVZdaDJJYvkdY07B5c9OY2hBwAQRPRoCyG9PBVtbB0FAAAA0LpjTRWVpDhblP589Sg57N7bQx12m5ZMH6WJ6cmdGyQA9DBUtIUQKtoAAAAABOpYU0Wl5sq2fr2j9c68i1r0cKOSDQCCj0RbCPFUtDVQ0QYAAACgdW2ZKhphtShzeP9OjggAwNbRENI7pjnRdqCJijYAAAAArQt0WihTRQGg61DRFkJio5p/HE1OQ40HXYqOJA8KAAAAoJnTZXht/zxzSF/1jo5QrZ/WM0wVBYCuR6IthLi3jkrNfdpItAEAAACQmqeL5qwp9urJ5p4o6gtTRQHAHGRyQkh0pFVREc3/E6xrok8bAAAIHf/617/U2NjoefzVV1/J5Tr8F/y6ujr9/ve/NyM0oNvLKyrVnBWFLQYfuJNsF558nJKZKgoAIYGKthDTKypCTc6Dqm2gTxsAAAgd06ZNU2lpqRITEyVJaWlp2rx5s44//nhJUk1NjRYsWKBf/vKXZoYJdDtOl6GcNcUyWrlmS1mN1t95oTbu+JapogBgMhJtIaZ3TKSq6w/qgJ8+CwAAAGYwDKPVxwA6R0FJZYtKtqOVVtVr445vmSoKACGAraMhxt2nrbaRraMAAABAT1dR03qSra3XAQA6F4m2ENM7urnIkIo2AAAAAIlxtmNf1IbrAACdi62jIYaKNgAAEKpeffVV2e12SZLL5dK6detUVFQkSdq3b5+JkQHdV6+oCFkskr/d2hY1Dz7ISE3o0rgAAL6RaAsxvQ8l2uqoaAMAACFm5syZXo9//vOfez22WGi8DnSE02WooKTSM9BAhqHZT230JNksktdQBPd/cdmT0xh8AAAhgkRbiIk9tHW0roGKNgAAEDpcLpfZIQDdWl5RqXLWFPscfDA2NUFXnTVEv391q9d5h92m7Mlpmpie3JWhAgBaQaItxMS6K9qaqGgDAACh46WXXtKkSZOoWgM6QV5RqeasKJS/Wb4/GTtUl44cpEtHDvKqeMtITaCSDQBCDMMQQowtqvlHsnnnPuVv2yuny9//bgEAALrOlClTNHjwYP3617/Wl19+aXY4QLfhdBnKWVPsN8lmkZT7ny1yugxFWC3KHN5fl40cpMzh/UmyAUAIItEWQvKKSvXcpt2SpP8Wl2vasvd07gOvK6+o1OTIAABAT1dSUqKf//znWrlypU4++WRdcMEFeuqpp3TgwAGzQwPCWkFJpc/tom6GpNKqehWUVHZdUACAdiPRFiLc5eK1Dd5bRsuq6jVnRSHJNgAAYKohQ4bo7rvv1rZt2/Taa68pJSVFc+bMUXJysm644QZ98MEHZocIhKWKGv9JtvZcBwAwF4m2ENBaubj7WM6aYraRAgCAkHDhhRfqySefVGlpqR588EF98sknGjdunEaMGGF2aEDYSYyLCfA6WydHAgAIBoYhhIC2lItnDu/fdYEBAAC0Ii4uTt/97ne1Y8cObdmyRcXFxWaHBIQ0p8vwGmZwVko/rf/8m1bvsah5umhGakLXBAkA6BASbSGAcnEAABBODhw4oGeffVbLly/X22+/rdTUVGVlZenaa681OzQgZOUVlSpnTbHXB+y9oyNU23i4dYxF8trl4h51kD05jcEHABAmSLSFgEDLwCkXBwAAZnrvvfe0fPly/etf/1JjY6OuuOIKvfbaa7rwwgvNDg0Iae5+zEc3gnEn2a46a4i+c/JxLRJxDrtN2ZPTNDE9uQujBQB0BIm2EJCRmqBku01lVfU++7RRLg4AAMyWlpamrVu36swzz1Rubq6uvvpq2e12s8MCQl5r/Zjd1n/+jX53+em6OM3htbU0IzWBSjYACDMk2kJAhNWi7MlpmrOikHJxAAAQksaPH69nnnmGgQdAGx2rH7Pk3Y+ZnswAEN6YOhoiJqYna8n0UXLYvbeHOuw2LZk+inJxAABgqj/+8Y86cOCAfv3rX+vOO+9UXl6e2SEBYYF+zADQs1DRFkImpifr4jSHnthQontf+kwDekfrnXkXUckGAABM9+9//1tTp05Vr169FBUVpUceeUQPPPCA7rjjDrNDA0Ia/ZgBoGehoi3ERFgtmnrWUFks0p7aRn1b12h2SAAAAMrNzdXs2bNVVVWlb7/9Vr/97W913333mR0WEPJGDe0rW5T/v3ZZJCXTjxkAug0SbSGoT0ykhh/XR5L08Vf7zA0GAABA0tatW3XHHXcoIiJCknT77berpqZGFRUVJkcGhBany1D+tr16cfNuvfPFN7pt1WbVN7l8Xks/ZgDoftg6GqLOGGzXlxX79fFXVbrolCSzwwEAAD1cXV2d4uPjPY+jo6Nls9m0f/9+JSYmmhgZEDryikqVs6a4xfCDCKv0s/OG64XNu73OOew2ZU9Oox8zAHQjJNpC1BmD7HqucLc+/qrK7FAAAAAkSX/961/Vp08fz+ODBw/qiSee0IABAzzHbr75ZjNCA0yXV1SqOSsKZfg453RJI4bYdceEk1VQUqmKmnolxjVvF6WSDQC6FxJtIeqMIX0lSR9/VSXDMGSx8D9gAABgnqFDh2rZsmVexxwOh5566inPY4vFQqINPZLTZShnTbHPJJvUvEU0Z02xLk5zKHN4/64MDQDQxUi0hai05HhFWi3as79BpVX1Gti3l9khAQCAHmz79u1mhwCErIKSyhbbRY9kSCqtqldBSSWJNgDo5hiGEKJsURE6KSlOktg+CgAAAISwihr/Sbb2XAcACF8k2kLYGYPtkpg8CgAAAISyhNjogK5LjLN1ciQAALORaAthZwzuK4mKNgAAACBUOF2G8rft1Yubdyt/214daHTqyfztrd5jkZRsbx5+AADo3ki0hbAjK9oMw19rVQAAgPC1ePFipaSkyGazaezYsSooKGj1+n379unGG29UcnKyYmJidNJJJ+mVV17pomjR0+UVlercB17XtGXv6ZaVmzVt2Xsa+Zv/6rXPKhR5aHro0SPM3I+zJ6cxYRQAegASbSHsZEecoiOtqq4/qB1768wOBwAAIKhWrVqlrKwsZWdnq7CwUCNGjNCECRNUUVHh8/rGxkZdfPHF2r59u/79739r69atWrZsmQYNGtTFkaMnyisq1ZwVhS2GHjQcdEmS5nxnuJZOHyWH3Xt7qMNu05LpozQxPbnLYgUAmIepoyEsKsKqtOR4bd61Tx/vrlLKgN5mhwQAAKBt27bp73//u7Zt26ZHH31UiYmJ+s9//qOhQ4fqtNNOC/h5HnnkEc2ePVuzZs2SJC1dulQvv/yyli9frvnz57e4fvny5aqsrNSGDRsUFRUlSUpJSQnKawJa43QZyllTrNb2mPx741e6dfxFujjNoYKSSlXU1Csxrnm7KJVsANBzUNEW4jzbR3ftMzcQAAAASevXr9fpp5+u999/X88995z2798vSfroo4+UnZ0d8PM0NjZq48aNGj9+vOeY1WrV+PHjlZ+f7/Oe1atXKzMzUzfeeKOSkpKUnp6u++67T06n0+/3aWhoUHV1tdcX0FYFJZUtKtmOVlpVr4KSSkVYLcoc3l+XjRykzOH9SbIBQA9Doi3EMRABAACEkvnz5+u3v/2t1q5dq+jow5MWL7roIr333nsBP8+ePXvkdDqVlJTkdTwpKUllZWU+7/nf//6nf//733I6nXrllVe0cOFCPfzww/rtb3/r9/vk5ubKbrd7voYMGRJwjIBbRU3rSba2XgcA6L5ItIU4d0Vb0ddVcroYiAAAAMz1ySef6PLLL29xPDExUXv27OnU7+1yuZSYmKjHH39co0eP1tSpU/XrX/9aS5cu9XvPggULVFVV5fnatWtXp8aI7ikxznbsi9pwHQCg+6JHW4gbflwfxUZHqK7RqW3f7NdJSXFmhwQAAHqwvn37qrS0VKmpqV7HN23a1KahBAMGDFBERITKy8u9jpeXl8vhcPi8Jzk5WVFRUYqIiPAcO/XUU1VWVqbGxkavCju3mJgYxcTEBBwX4MtZKf3UKzpCBxp9b1O2qHnoQUZqQtcGBgAIOVS0hbgIq0XpAw/1aWP7KAAAMNlVV12lefPmqaysTBaLRS6XS++++67uuOMOzZgxI+DniY6O1ujRo7Vu3TrPMZfLpXXr1ikzM9PnPeecc46+/PJLuVwuz7HPP/9cycnJPpNsQHs5XYbyt+3Vi5t3K3/bHv3mpeJWk2ySlD05jX5sAAAq2sLBGYPtKtheqY+/2qcfjR5sdjgAAKAHu++++3TjjTdqyJAhcjqdSktLk9Pp1NVXX6277rqrTc+VlZWlmTNnasyYMcrIyNCiRYtUW1vrmUI6Y8YMDRo0SLm5uZKkOXPm6LHHHtMtt9yim266SV988YXuu+8+3XzzzUF/nei58opKlbOm2Ofwg5mZw/Tf4nKvcw67TdmT0zQxPbkrwwQAhCjTK9oWL16slJQU2Ww2jR07VgUFBa1e/+yzz+qUU06RzWbT6aefrldeeaXFNZ999pkuvfRS2e129e7dW2eddZZ27tzZWS+h050xpK8kKtoAAID5oqOjtWzZMm3btk0vvfSSVqxYoS1btuipp57y2tIZiKlTp+qhhx7S3XffrZEjR2rz5s3Ky8vzDEjYuXOnSktLPdcPGTJEr776qj744AOdccYZuvnmm3XLLbdo/vz5QX2N6Lnyiko1Z0Wh3wmjmcP76515F+mZ2eP06FUj9czscXpn3kUk2QAAHhbDMEzrsL9q1SrNmDFDS5cu1dixY7Vo0SI9++yz2rp1qxITE1tcv2HDBp1//vnKzc3VD37wAz399NN64IEHVFhYqPT0dEnStm3blJGRoeuvv17Tpk1TfHy8Pv30U40bN87nc/pSXV0tu92uqqoqxcfHB/U1t8f2PbX6zkNvKjrSqqJ7Jig60vT8KAAAOEqorR86yzvvvKNzzz3X7DDaraf8nNB2Tpehcx943W+Szd2H7Z15F7FFFAB6oEDXEKYm2saOHauzzjpLjz32mKTmvhxDhgzRTTfd5POTyalTp6q2tlYvvfSS59i4ceM0cuRIz7Spq666SlFRUXrqqafaHVeoLcAMw9CInP+quv6gXrrpXKUPspsdEgAAOEqorR86S3R0tAYNGqRp06Zp+vTpSktLMzukNukpPye0Xf62vZq27L1jXvfM7HHKHN6/CyICAISSQNcQppVGNTY2auPGjRo/fvzhYKxWjR8/Xvn5+T7vyc/P97pekiZMmOC53uVy6eWXX9ZJJ52kCRMmKDExUWPHjtULL7zQaiwNDQ2qrq72+golFotFZwzuK4ntowAAwFxff/21br/9dq1fv17p6ekaOXKkHnzwQX311VdmhwZ0SEWN70q29l4HAOiZTEu07dmzR06n09ODwy0pKUllZWU+7ykrK2v1+oqKCu3fv1/333+/Jk6cqP/+97+6/PLLdcUVV2j9+vV+Y8nNzZXdbvd8DRkypIOvLvjOGOyePLrP3EAAAECPNmDAAM2dO1fvvvuutm3bpiuvvFJPPvmkUlJSdNFFF5kdHtBuiXG2oF4HAOiZulWzL/eo98suu0y33XabRo4cqfnz5+sHP/iBZ2upLwsWLFBVVZXna9euXV0VcsAOJ9qoaAMAAKEhNTVV8+fP1/3336/TTz+91Q82gVAXHWlVa53XLJKS7TZlpCZ0VUgAgDDUpkTbL37xC+3fv9/z+JlnnlFtba3n8b59+3TJJZcE9FwDBgxQRESEysvLvY6Xl5fL4XD4vMfhcLR6/YABAxQZGdmiV8ipp57a6tTRmJgYxcfHe32FGvfW0a3lNapvcpobDAAA6PHeffdd/eIXv1BycrKuvvpqpaen6+WXXzY7LCAgTpeh/G179eLm3crftlefl9do9j8+lLt59dEJN/fj7MlpDEIAALSqTYm2v/zlL6qrq/M8/vnPf+6V+GpoaNCrr74a0HNFR0dr9OjRWrduneeYy+XSunXrlJmZ6fOezMxMr+slae3atZ7ro6OjddZZZ2nr1q1e13z++ecaNmxYQHGFqmS7TQP6RMvpMlRcGlo95AAAQM+xYMECpaam6qKLLtLOnTv16KOPqqysTE899ZQmTpxodnjAMeUVlercB17XtGXv6ZaVmzVt2XuauOgtVdY26vRBdv1h6kg57N7bQx12m5ZMH6WJ6ckmRQ0ACBeRbbn46AGlHR1YmpWVpZkzZ2rMmDHKyMjQokWLVFtbq1mzZkmSZsyYoUGDBik3N1eSdMstt+iCCy7Qww8/rEmTJmnlypX68MMP9fjjj3ue884779TUqVN1/vnn68ILL1ReXp7WrFmjN998s0Oxms1isej0QXa9sfUb/fO9HWpocikjNYFP1AAAQJd66623dOedd+rHP/6xBgwYYHY4QJvkFZVqzopCHf23GNehA9dkDtPlZw7SpSMGqqCkUhU19UqMs7HuBgAErE2JtmCbOnWqvvnmG919990qKyvTyJEjlZeX5xl4sHPnTlmth4vuzj77bD399NO666679Ktf/UonnniiXnjhBaWnp3uuufzyy7V06VLl5ubq5ptv1sknn6z/+7//07nnntvlry+Y8opK9cH2byVJ/1e4W/9XuFvJdpuyJ6fxyRoAAOgy7777rtkhAO3idBnKWVPcIsl2pD+s/Vw/HDVYEVaLMof377LYAADdh8VoQ1ma1WpVWVmZEhMTJUlxcXH66KOPdPzxx0tq7pc2cOBAOZ3h3UOsurpadrtdVVVVIdGvzd8nb+7P1ChjBwDAfKG2fgim1atX6/vf/76ioqK0evXqVq+99NJLuyiq9unOPye0Ln/bXk1b9t4xr3tm9jiSbACAFgJdQ7S5ou3uu+9WbGysJKmxsVG/+93vZLc3T8Q8sn8bgqO1T94MNSfbctYU6+I0B+XsAACgU0yZMsXzYeuUKVP8XmexWML+A1d0XxU19UG9DgAAX9qUaDv//PO9Bg2cffbZ+t///tfiGgRPQUmlSqv8/8/ekFRaVa+Ckko+eQMAAJ3C5XL5/HcgnCTG2Y59URuuAwDAlzYl2sJ9oEA44pM3AAAQSv7xj39o6tSpiomJ8Tre2NiolStXasaMGSZFBrQuIzVBCb2jVVnb6PO8Rc3TRTNSE7o2MABAt2I99iWH3XHHHdqyZUtnxQIf+OQNAACEklmzZqmqqqrF8ZqaGs/keCAU7dhbq/om31ub3Q1Ysien0Y4FANAhbUq0vfjiizrttNN09tlna/ny5aqtre2suHBIRmqCku02+fvfvUVSMp+8AQCALmIYhiyWliuTr776ytO3FzCb02Uof9tevbh5t/K37dXe/Q26/skPVdfoVEr/WDnivSsyHXYbA8YAAEHRpq2jX3zxhd566y0tX75ct9xyi2655RZdeeWV+ulPf6qzzz67s2Ls0SKsFmVPTtOcFYWySF5DEfjkDQAAdJUzzzxTFotFFotF3/3udxUZeXgZ6XQ6VVJSookTJ5oYIdAsr6hUOWuKvfocR0dY1eh0aaDdpn/dkKn+vWNUUFKpipp6JcY1f2jNehoAEAxtnjp6/vnn6/zzz9fixYu1atUq/f3vf9e5556rk08+Wddff72uueYaJSUldUasPdbE9GQtmT6qxYLBYbcpe3Ian7wBAIBO5542unnzZk2YMEF9+vTxnIuOjlZKSop++MMfmhQd0CyvqFRzVhR6fTgtSY3O5iEe152b6mm5wiAxAEBnsBiGcfT/h9rsyy+/1N///nctXbpU+/fvV0NDQzBiM011dbXsdruqqqoUHx9vdjgeTpehDV/u0YzlBTIk5c+/SMl9e5kdFgAAUOiuH4LtySef1NSpU2WzhWd/2J7yc+qJnC5D5z7wutcH00dLttv0zryLqF4DALRZoGuINvVo86W2tlZvv/221q9fr2+//VbHH398R58SfkRYLTrvpOM08FBy7at9B0yOCAAA9DQzZ84M2yQbureCkspWk2ySVFpVr4KSyi6KCADQE7U70fbOO+/ouuuuU3Jysm6++WaddNJJevvtt/XZZ58FMz74MKx/rCRpx946kyMBAAA9jdPp1EMPPaSMjAw5HA4lJCR4fQFmqahpPcnW1usAAGiPNiXaSktLdf/99+uUU07R+eefry1btuiRRx5RaWmpli9frnPOOaez4sQRhvXvLUnauZeprwAAoGvl5OTokUce0dSpU1VVVaWsrCxdccUVslqtuueee8wODz2Yu/dasK4DAKA92jQMYciQIRowYICuueYaXX/99TrllFM6Ky60wlPRVklFGwAA6Fr//Oc/tWzZMk2aNEn33HOPpk2bpuHDh+uMM87Qe++9p5tvvtnsENFDZaQmyBFvU1m174o1i5qHiWWkUnkJAOg8bapoe/rppzV37lxt2LBB11xzjebPn68DB+gT1tWGJbB1FAAAmKOsrEynn366JKlPnz6qqqqSJP3gBz/Qyy+/bGZo6OEirBadPtju85x79EH25DQGIQAAOlWbEm1bt27VPffcoz59+mjQoEF69NFHdeONN3ZWbPBj6KGKtp1UtAEAgC42ePBglZaWSpKGDx+u//73v5KkDz74QDExMWaGhh7uxc27tba4XJLUt1eU1zmH3aYl00dpYnqyGaEBAHqQNm0d/cc//qHHHntMN9xwgyTptdde06RJk/TXv/5VVmuHB5giQO4ebZW1jaqpb1KcLeoYdwAAAATH5ZdfrnXr1mns2LG66aabNH36dP3tb3/Tzp07ddttt5kdHnoQp8tQQUmlKmrqVVt/UPes+VSS9IvvDNft3zvZcy4xrnm7KJVsAICu0KZE286dOzVp0iTP4/Hjx8tisejrr7/W4MGDgx4cfOsTE6n+vaO1t7ZRO/bWKX2Q7xJ5AACAYLv//vs9/z516lQNHTpU+fn5OvHEEzV58mQTI0NPkldUqpw1xSqt8u7Hlj4oXrd/72RFWC3KHN7fpOgAAD1ZmxJtBw8elM3mPaUnKipKTU1NQQ0Kxza0f6z21jZqZyWJNgAAYJ7MzExlZmaaHQZ6kLyiUs1ZUSjDx7lPd1drbXEZW0QBAKZpU6LNMAxde+21Xv036uvrdcMNN6h3796eY88991zwIoRPwxJitWnnPm3fW2t2KAAAoJtbvXp1wNdeeumlnRgJejqny1DOmmKfSTa3nDXFujjNwVZRAIAp2pRomzlzZotj06dPD1owCNzQQ33adjJ5FAAAdLIpU6YEdJ3FYpHT6ezcYNCjFZRUttgueiRDUmlVvQpKKtk6CgAwRZsSbX//+987Kw600bCE5smjO0i0AQCATuZyucwOAZAkVdT4T7K15zoAAIKNUaFhKmVAc6JtZyWJNgAAAPQMiXG2Y1/UhusAAAi2NlW0IXQMTWjeOvp11QE1HHQqJjLC5IgAAEBP8Jvf/KbV83fffXcXRYKeaNTQvoqOsKjR6btLm0WSw25TRmpC1wYGAMAhJNrC1IA+0YqNjlBdo1NffXtAw4/rY3ZIAACgB3j++ee9Hjc1NamkpESRkZEaPnw4iTZ0qof+u7XVJJskZU9OYxACAMA0JNrClMVi0dCEWG0pq9HOvXUk2gAAQJfYtGlTi2PV1dW69tprdfnll5sQEXqKNR99rWVvl0iSfn5+qlZ/VOo1GMFhtyl7cpompiebFSIAACTawtmw/s2Jth17a80OBQAA9GDx8fHKycnR5MmTdc0115gdDroJp8tQQUmlKmrq1XjQpbtfLJIk3XDBcM3//in65cRTPecT45q3i1LJBgAwG4m2MDasf3Ofth0MRAAAACarqqpSVVWV2WGgm8grKlXOmmKvijVJOsXRR3d87yRJUoTVoszh/c0IDwAAv0i0hbGhCYcmj+4l0QYAALrGH//4R6/HhmGotLRUTz31lL7//e+bFBW6k7yiUs1ZUShfndi2lO3Xa5+Vsz0UABCySLSFsWH9mxNt29k6CgAAusgf/vAHr8dWq1XHHXecZs6cqQULFpgUFboLp8tQzppin0k2qXngQc6aYl2c5mCbKAAgJJFoC2PDEpq3ju769oBcLkNWFhsAAKCTlZSUmB0CurGCksoW20WPZEgqrapXQUkl20YBACHJanYAaL+BfW2KtFrUeNClsmr/CxIAAAAgHFTUBLamDfQ6AAC6GhVtYSwywqrB/Xpp+9467dhbp4F9e5kdEgAA6Obq6+v1pz/9SW+88YYqKirkcrm8zhcWFpoUGbqDxDhbUK8DAKCrkWgLc0P799b2vXXaWVlL+TwAAOh0119/vf773//qRz/6kTIyMmSx0LoCwZORmqC4mEjVNBz0ed4iyWG3KSM1oWsDAwAgQCTawtywQ5NHdzB5FAAAdIGXXnpJr7zyis455xyzQ0E3tGnnt6pt9J9kk6TsyWkMQgAAhCx6tIU59+TRHZUk2gAAQOcbNGiQ4uLizA4D3dC3tY26+ZlNchnSWSn95LB7bw912G1aMn2UJqYnmxQhAADHRkVbmBt6qKJtJxVtAACgCzz88MOaN2+eli5dqmHDhpkdDsKY02WooKRSFTX1SoyL0V/f/p++rqpX6oDe+vusDPWKijjifPN2USrZAAChjkRbmBvWv7ckacfeWpMjAQAAPcGYMWNUX1+v448/XrGxsYqKivI6X1lZaVJkCCd5RaXKWVOs0irv6aGRVoseu/pM9Ylp/msKPYgBAOGGRFuYc1e0Vdcf1L66RvWNjTY5IgAA0J1NmzZNu3fv1n333aekpCSGIaDN8opKNWdFoQwf5w66DO2qrNNpA+1dHhcAAMFAoi3M9YqOUGJcjCpqGrR9b51GkmgDAACdaMOGDcrPz9eIESPMDgVhyOkylLOm2GeSTWoeeJCzplgXpznYJgoACEsMQ+gGPAMR2D4KAAA62SmnnKIDBw6YHQbCVEFJZYvtokcyJJVW1aughC3IAIDwRKKtGxia0NynjYEIAACgs91///26/fbb9eabb2rv3r2qrq72+gJaU1HjP8nWnusAAAg1bB3tBlLcFW2VJNoAAEDnmjhxoiTpu9/9rtdxwzBksVjkdDrNCAthIjHOFtTrAAAINSTauoGhhxJtVLQBAIDO9sYbb5gdAsJYRmqCHHabyvxsH7VIcthtykhN6NrAAAAIEhJt3cCw/s1bR3dU0qMNAAB0rgsuuMDsEBDGIqwWXXxqop56b2eLc+7RB9mT0xiEAAAIWyTauoFhCc0VbeXVDapvcsoWFWFyRAAAoLt66623Wj1//vnnd1EkCEe7Kuv0XOFuSVKcLVI19Qc95xx2m7Inp2lierJZ4QEA0GEk2rqBvrFRnoXKzso6nZQUZ3ZIAACgm/rOd77T4pjFcrj6iB5t8MflMnTHsx+pttGps1L66Z8/HaeNO75VRU29EuOat4tSyQYACHck2roBi8WiYf1jVbS7Wjv2kmgDAACd59tvv/V63NTUpE2bNmnhwoX63e9+Z1JUCFVOl6GCkkpV1NRr4/Zv9X5JpWKjI/TQlSMUHWlV5vD+ZocIAEBQkWjrJoYl9D6UaKNPGwAA6Dx2u73FsYsvvljR0dHKysrSxo0bTYgKoSivqFQ5a4pVetTgg8tGDvT0GAYAoLuxmh0AgsM9eXQHk0cBAIAJkpKStHXrVrPDQIjIKyrVnBWFLZJskrSyYJfyikpNiAoAgM5HRVs34R6IsKOSRBsAAOg8H3/8sddjwzBUWlqq+++/XyNHjjQnKIQUp8tQzppiGa1ck7OmWBenOejJBgDodki0dRPuiradbB0FAACdaOTIkbJYLDIM7zTKuHHjtHz5cpOiQigpKKn0WcnmZkgqrapXQUklPdoAAN0OibZuIuVQn4uvvj2gg06XIiPYFQwAAIKvpKTE67HVatVxxx0nm81mUkQINRU1/pNs7bkOAIBwQqKtm3DE2xQdaVXjQZdKq+o15NBWUgAAgGAaNmyY2SEgxCXGBZZ0DfQ6AADCCWVP3YTVatGQfr0kMRABAAAE3+uvv660tDRVV1e3OFdVVaXTTjtNb7/9tgmRIdRkpCaof+9ov+ctkpLtNmWkJnRdUAAAdBESbd3I0ENVbKs/2q38bXvldLXWghYAACBwixYt0uzZsxUfH9/inN1u189//nM98sgjJkSGUNPkdCkywveQA/fR7MlpDEIAAHRLJNq6ibyiUr1fUilJ+teHX2nasvd07gOvMzodAAAExUcffaSJEyf6Pf+9731PGzdu7MKIEKoeenWryqsbFG+LVFJ8jNc5h92mJdNHaWJ6sknRAQDQuejR1g3kFZVqzorCFiPUy6rqNWdFIYsZAADQYeXl5YqKivJ7PjIyUt98800XRoRQVFBSqb+92zww49GrztT5Jx2ngpJKVdTUKzGuebsolWwAgO6MRFuYc7oM5awpbpFkk5pHp1sk5awp1sVpDhY1AACg3QYNGqSioiKdcMIJPs9//PHHSk7mg72exukyPIm0eFuU7n6xSIYhTR0zRBeekihJyhze3+QoAQDoOiTawlxBSaVKq/yPRjcklVbVq6CkkkUOAABot0suuUQLFy7UxIkTZbN5T4s8cOCAsrOz9YMf/MCk6GCGvKJS5awpbrEWTYiN1l0/ONWkqAAAMBeJtjBXUeM/ydae6wAAAHy566679Nxzz+mkk07S3LlzdfLJJ0uStmzZosWLF8vpdOrXv/61yVGiq/hrXSJJlXWNevfLPbQuAQD0SCTawlxinO3YF7XhOgAAAF+SkpK0YcMGzZkzRwsWLJBhNKdYLBaLJkyYoMWLFyspKcnkKNEVWmtdItG6BADQs5FoC3MZqQlKtttUVlXvc7FjUfN0p4zUhK4ODQAAdDPDhg3TK6+8om+//VZffvmlDMPQiSeeqH79+pkdGroQrUsAAPDPanYA6JgIq0XZk9MkNSfVfMmenManiQAAIGj69euns846SxkZGSTZeiBalwAA4B+Jtm5gYnqylkwfJYfde3uoRdIfpo6kPwYAAACChtYlAAD4FxKJtsWLFyslJUU2m01jx45VQUFBq9c/++yzOuWUU2Sz2XT66afrlVde8XvtDTfcIIvFokWLFgU56tAyMT1Z78y7SM/MHqdFU0cqKT5GhqT6JqfZoQEAAKAbcbcu8cciKZnWJQCAHsr0RNuqVauUlZWl7OxsFRYWasSIEZowYYIqKip8Xr9hwwZNmzZN119/vTZt2qQpU6ZoypQpKioqanHt888/r/fee08DBw7s7JcREiKsFmUO768pZw7SdeekSpKeLthpclQAAADoTiKsFs06O8XnOXezElqXAAB6KtMTbY888ohmz56tWbNmKS0tTUuXLlVsbKyWL1/u8/pHH31UEydO1J133qlTTz1V9957r0aNGqXHHnvM67rdu3frpptu0j//+U9FRUV1xUsJKT8aPVjREVZ9/FWVinZXmR0OAAAAuomDTpfWfFwqSbJFef91wmG3acn0UbQuAQD0WKZOHW1sbNTGjRu1YMECzzGr1arx48crPz/f5z35+fnKysryOjZhwgS98MILnscul0vXXHON7rzzTp122mnHjKOhoUENDQ2ex9XV1W18JaGnf58YTUh3aM1HX+uf7+9U7hWnmx0SAAAAuoEnNmzXJ7urFGeL1H9vPV/b99apoqZeiXHN20WpZAMA9GSmVrTt2bNHTqdTSUlJXseTkpJUVlbm856ysrJjXv/AAw8oMjJSN998c0Bx5Obmym63e76GDBnSxlcSmq7OGCpJWr15t/Y3HDQ5GgAAAIS7XZV1evi/n0uSfnXJqUru20uZw/vrspGDlDm8P0k2AECPZ/rW0WDbuHGjHn30UT3xxBOyWAL7H/2CBQtUVVXl+dq1a1cnR9k1xh2foOOP663aRqde3Lzb7HAAAAAQhpwuQ/nb9urFTbs19+lCHWhyKiM1QVPHdI8PpwEACCZTt44OGDBAERERKi8v9zpeXl4uh8Ph8x6Hw9Hq9W+//bYqKio0dOhQz3mn06nbb79dixYt0vbt21s8Z0xMjGJiYjr4akKPxWLR1RlD9duXP9PT7+/U1RlDA04+AgAAAHlFpcpZU6zSqnqv45ec7pCV6jUAAFowtaItOjpao0eP1rp16zzHXC6X1q1bp8zMTJ/3ZGZmel0vSWvXrvVcf8011+jjjz/W5s2bPV8DBw7UnXfeqVdffbXzXkyI+uGowYqOtOrTr6v18VcMRQAAAEBg8opKNWdFYYskmyTlrC5WXlGpCVEBABDaTK1ok6SsrCzNnDlTY8aMUUZGhhYtWqTa2lrNmjVLkjRjxgwNGjRIubm5kqRbbrlFF1xwgR5++GFNmjRJK1eu1IcffqjHH39cktS/f3/179/f63tERUXJ4XDo5JNP7toXFwL69Y7WJekOvbD5az39/k6NGNLX7JAAAAAQ4pwuQzlrimW0ck3OmmJdnOagLxsAAEcwPdE2depUffPNN7r77rtVVlamkSNHKi8vzzPwYOfOnbJaDxfenX322Xr66ad111136Ve/+pVOPPFEvfDCC0pPTzfrJYS8q8cO0wubv9aLm3fre6claX/DQaZCAQAAwK+CkkqflWxuhqTSqnoVlFQqc3h/v9cBANDTWAzDaO2Dqh6purpadrtdVVVVio+PNzucDjMMQ5m5r6us2nuxlGy3KXtymiamJ5sUGQAA3Ud3Wz90V/ycAvPi5t26ZeXmY1736FUjddnIQZ0fEAAAJgt0DdHtpo6ipVc/LWuRZJOksqp6zVlRSH8NAAAAeEmMswX1OgAAegoSbd2cu7+GL+5Sxpw1xXK6KGwEAABdb/HixUpJSZHNZtPYsWNVUFAQ0H0rV66UxWLRlClTOjfAHiojNUGJcTF+z1vUvDsiIzWh64ICACAMkGjr5gLtr/Hetr3K37ZXL27erfxte0m8AQCATrdq1SplZWUpOztbhYWFGjFihCZMmKCKiopW79u+fbvuuOMOnXfeeV0Uac8TYbVocL9ePs+5O/xmT06j3y8AAEcxfRgCOldFjf8k25FufLpQ+w40eR7Tvw0AAHS2Rx55RLNnz/ZMm1+6dKlefvllLV++XPPnz/d5j9Pp1E9+8hPl5OTo7bff1r59+7ow4p7jnS/2qHDnPlkk9e8TrT37Gz3nHKwTAQDwi0RbNxdo34wjk2zS4f5tS6aPYhEFAACCrrGxURs3btSCBQs8x6xWq8aPH6/8/Hy/9/3mN79RYmKirr/+er399tvH/D4NDQ1qaGjwPK6uru5Y4D1A40GX7l5dJEmaeXaKFv4gTQUllaqoqWdyPQAAx0CirZvLSE1Qst2msqp6tWUzqKHmbQE5a4p1cZqDxRQAAAiqPXv2yOl0Kikpyet4UlKStmzZ4vOed955R3/729+0efPmgL9Pbm6ucnJyOhJqj7P83RL975taDegTrdsuPkkRVosyh/c3OywAAMICPdq6uQirRdmT0yQd7qcRKHf/toKSyqDHBQAA0BY1NTW65pprtGzZMg0YMCDg+xYsWKCqqirP165duzoxyvBXWnVAf1z3hSRp/vdPlb1XlMkRAQAQXqho6wEmpidryfRRyllT7DUYoW+vqBZbRn0JtM8bAABAoAYMGKCIiAiVl5d7HS8vL5fD4Whx/bZt27R9+3ZNnjzZc8zlckmSIiMjtXXrVg0fPrzFfTExMYqJ8T89E81T6t1bQ58p2Km6RqdGD+unK84cZHZoAACEHRJtPcTE9GRdnObw6q/hMgz95K/vH/PeQPu8AQAABCo6OlqjR4/WunXrNGXKFEnNibN169Zp7ty5La4/5ZRT9Mknn3gdu+uuu1RTU6NHH31UQ4YM6Yqwu528otIWH8ZK0oTTkmSldQgAAG1Goq0HObq/htNltNq/zaLmqVIZqQldFiMAAOg5srKyNHPmTI0ZM0YZGRlatGiRamtrPVNIZ8yYoUGDBik3N1c2m03p6ele9/ft21eSWhxHYPKKSjVnRaHPdWDuK1s0NCGWoVgAALQRibYezN2/bc6KQlkkn4us7MlpDEIAAACdYurUqfrmm2909913q6ysTCNHjlReXp5nQMLOnTtltdJSuDM4XYZy1hS3OiyLoVgAALSdxTCMtgyj7BGqq6tlt9tVVVWl+Ph4s8PpdL62DFgkLbpqpC4bSW8OAAAC0dPWD+GKn1Oz/G17NW3Ze8e87pnZ45g4CgCAAl9DUNEG7/5t1fW67z+fqby6QU4XOVgAAIDuKNBhVwzFAgCgbajFh6TD/dsuO3OQfjJ2mCTp3xu/MjkqAAAAdIZAh10xFAsAgLYh0YYWrhjVvF10w7a9+urbOpOjAQAAQLBlpCYo2e4/iWaRlMxQLAAA2oxEG1oY3C9Wmcc39+J4vnC3ydEAAAAg2CKsFs0+73if59yjDxiKBQBA25Fog08/Gj1YkvR/hV+JeRkAAADdT0FJpSQpJtL7rwQOu01Lpo/SxPRkM8ICACCsMQwBPk1Md2jhi0XavrdOG3d8qzEpbBsAAADoLj7atU95n5bJYpFeuPEc7atrUkVNvRLjmreLUskGAED7kGiDT71jInXJ6cn698av9O+NX5FoAwAA6EYe+u9WSdLlZw7SqcnxJkcDAED3wdZR+PXDUc3bR1/+uFQHGp0mRwMAAIBg2LBtj97+Yo+iIiy6bfxJZocDAEC3QqINfo1NTdDgfr1U03BQ/y0uMzscAAAAdJBhGHro1eZqtmkZQzUkIdbkiAAA6F5ItMEvq9XiqWr798avTI4GAAAA7eF0Gcrftlcvbt6txW98qcKd+2SLsmruhSeYHRoAAN0OPdrQqh+OGqxH132ht7/Yo5c//loHXQZNcgEAAMJEXlGpctYUq7Sq3uv4+Scep8R4m0lRAQDQfZFoQ6uG9o/VCYl99GXFft349CbP8WS7TdmT0xj7DgAAEKLyiko1Z0WhDB/n1haXK6+olLUcAABBxtZRtCqvqFRfVuxvcbysql5zVhQqr6jUhKgAAADQGqfLUM6aYp9JNrecNcVyulq7AgAAtBWJNvjlXqD54l6SsUADAAAIPQUllS22ix7JkFRaVa+CksquCwoAgB6ARBv8YoEGAAAQnipq/K/h2nMdAAAIDIk2+MUCDQAAIDwlxgU26CDQ6wAAQGBItMEvFmgAAADhKSM1Qcl2m/zNiLeoebhVRmpCV4YFAEC3R6INfrFAAwAACE8RVouyJ6f5HIbgXttlT05ThNXfSg8AALQHiTb45V6gSfKbbGOBBgAAEJompidr1NC+LY477DYtmT5KE9OTuz4oAAC6uUizA0Bom5ierCXTRylnTXGLwQi3f+8kFmgAAAAhaldlnTbv2idJeuCHp8sWFaHEuObdCHxQCgBA5yDRhmOamJ6si9McKiipVEVNvV7cvFuvb/lGr2+p0I0XniCLhYUaAABAqHliw3a5DOm8Ewdo6llDzQ4HAIAega2jCEiE1aLM4f112chByr3iDNmirCrcuU+vflpudmgAAAA4Sk19k1Z9sEuSdP25qSZHAwBAz0GiDW2WFG/TT889XpL0+1e36KDTZXJEAAAAONKqD3Zpf8NBnZjYRxecdJzZ4QAA0GOQaEO7/PyC45XQO1r/+6ZWqz7cZXY4AAAAOOSg06W/v7tdUnM1G20+AADoOiTa0C5xtijddNEJkqQ/rP1cb26t0Iubdyt/2145Xb4GyQMAAKArvPppuXbvO6D+vaM15cxBZocDAECPwjAEtNvVY4fqsTe+1J79jbr27x94jifbbcqenMZEUgAAABP89Z3/SZKmjxsmW1SEydEAANCzUNGGdntjS4X27m9scbysql5zVhQqr6jUhKgAAAB6ro07vtWmnfsUHWHV9HHDzA4HAIAeh0Qb2sXpMpSzptjnOffG0Zw1xWwjBQAA6AJOl6H8bXt170ufSpIuHZms4+JiTI4KAICeh62jaJeCkkqVVtX7PW9IKq2qV0FJpTKH9++6wAAAAHqYvKJS5awp9lqbvbn1G+UVldLKAwCALkZFG9qlosZ/kq091wEAAKDt8opKNWdFYYsPQPfub6SVBwAAJiDRhnZJjLMFfJ17KwNTSQEAAILH3crD18qKVh4AAJiDraNol4zUBCXbbSqrqve5uJOkqAiLvqyoUda/Nnt9yspUUgAAgI6jlQcAAKGHija0S4TVouzJaZIki59rmpyGFr74aYsFIFNJAQAAOo5WHgAAhB4SbWi3ienJWjJ9lBx2722kyXabfnPZaYqK8J2CYysDAABAx7WllQcAAOgabB1Fh0xMT9bFaQ4VlFSqoqZeiXE2ZaQmqKCkUk1O/0k0tjIAAAB0jLuVh7/toxZJDnvz2gwAAHQNEm3osAirpUWyjK0MAAAAnSvCatHs847Xb14qbnHOva8ge3KaIqz+Gn0AAIBgI9GGTtHWqaRHV8SxIAQAADi2Lyr2S5JskVbVH3R5jjsYPgUAgClItKFTBDyV9Jv9rU4lJQkHAADg2766Rj2/6StJ0t+vPUuyWFgzAQBgMhJt6BTuqaRzVhTKIvlMtjU5DS18oajFcfdU0p+dn6rVH5X6TcIBAAD0ZKs+2KX6JpfSkuM1bnh/WSwk1gAAMBtTR9FpWptKmnNp61NJDUl/eaukRXNfdxIur6i0k6IGAAAIfQedLv0jf4ck6dpzUkiyAQAQIqhoQ6dq71RSfww1N/fNWVOsi9McbIkAAAA90muflWv3vgNK6B2tS0cMNDscAABwCIk2dLqOTCX1xZBUWlWvgpLKFs8LAADQE/z93e2SpGkZQ2SLijA3GAAA4MHWUZgi0KmkrelIsg4AACBcFX9drfdLKhVhteiacSlmhwMAAI5Aog2mcE8l7cjGz2Ak6wAAAMLNExtKJEnfT3e06IULAADMRaINpnBPJZXUrmRbpNWiIQm95HQZyt+2Vy9u3q38bXvldLW97xsAAECoc695nn5/h54r3C1JmnVOirlBAQCAFujRBtO4p5LmrCn2mi6abLfp0hHJevyt5k9rfaXODroMXfqndxVhteib/Q1e92ZPTtPE9OTODh8AAKBL5BWVtlgvRUVYVFHd0MpdAADADBbDMCgBOkp1dbXsdruqqqoUHx9vdjjdntNltJhKGmG1+FxUJtttmnvhCXp03ReqqGm5uHRXxy2ZPopkGwCgS7F+CA/h9nPKKyrVnBWFPj94tIg1DwAAXSXQNQQVbTCdr6mkUnPF28VpjhZJOEl6dN0XPp/LUPOiM2dNsS5OcyjC2pEucAAAAOZxugzlrCn2mWRzY80DAEBoIdGGkOYrCZe/ba/PajY3Q1JpVb0KSiqVkZrgs1oOAAAg1BWUVHpV9h/tyDWPrw8tAQBA1yPRhrBTUeN/wXmktcVlyvrX5hZbT+nhBgAAwkGga55ArwMAAJ2PqaMIO4lxgY2xX/7u9hafApdV1WvOikLlFZV2RmgAAABBE+iaJ9DrAABA5wuJRNvixYuVkpIim82msWPHqqCgoNXrn332WZ1yyimy2Ww6/fTT9corr3jONTU1ad68eTr99NPVu3dvDRw4UDNmzNDXX3/d2S8DXSQjNUHJdpvaswHU3eMkZ02xGg+6lL9tr17cvFv52/bK6WIuCAAACB3HWvNY1Fyt7+5hCwAAzGd6om3VqlXKyspSdna2CgsLNWLECE2YMEEVFRU+r9+wYYOmTZum66+/Xps2bdKUKVM0ZcoUFRUVSZLq6upUWFiohQsXqrCwUM8995y2bt2qSy+9tCtfFjpRhNWi7MlpktRi4RlI8s3dz2Rc7jpNW/aeblm5WdOWvadzH3idSjcAABAy3GsefxNHJSl7chr9ZwEACCEWwzBMLeMZO3aszjrrLD322GOSJJfLpSFDhuimm27S/PnzW1w/depU1dbW6qWXXvIcGzdunEaOHKmlS5f6/B4ffPCBMjIytGPHDg0dOvSYMYXb2PeeKq+oVDlrilv0YLsk3aG/vbu9zc/nXqIumT5KE9OT5XQZDFIAAASM9UN4CMef02WPvaOPvqryOkbfWQAAulagawhThyE0NjZq48aNWrBggeeY1WrV+PHjlZ+f7/Oe/Px8ZWVleR2bMGGCXnjhBb/fp6qqShaLRX379vV5vqGhQQ0Nh6dYVldXB/4iYJqJ6cm6OM3RIhlWUFLZrkSboeZkW86aYrlc0r0vt0zisaAFAABdac/+Bn36dfPa9MEfnaHoSCsfAAIAEMJMTbTt2bNHTqdTSUlJXseTkpK0ZcsWn/eUlZX5vL6srMzn9fX19Zo3b56mTZvmN+OYm5urnJycdrwCmC3Camkxzt7dz6Ssqt7nVovWuLeV/uLpwhbn3IMU3BVvAAAAne2FTbt10GVoxGC7rhwzxOxwAADAMZjeo60zNTU16cc//rEMw9CSJUv8XrdgwQJVVVV5vnbt2tWFUSLYWuvh1hFHDlJgcAIAAOhshmHo3xu/kiT9iCQbAABhwdRE24ABAxQREaHy8nKv4+Xl5XI4HD7vcTgcAV3vTrLt2LFDa9eubXX/bExMjOLj472+EN4mpidryfRRcti9x90n9I7q0PO6K94KSio79DwAAADHUrS7WlvKahQdadWlZww0OxwAABAAUxNt0dHRGj16tNatW+c55nK5tG7dOmVmZvq8JzMz0+t6SVq7dq3X9e4k2xdffKHXXntN/fv3P/pp0ANMTE/WO/Mu0jOzx+nRq0bqmdnj9N6C8Uq22zpc6VZWdUD52/bqxc27lb9tLxVuAAAg6J7d2LzLYsJpDtljO/ZhIQAA6Bqm9miTpKysLM2cOVNjxoxRRkaGFi1apNraWs2aNUuSNGPGDA0aNEi5ubmSpFtuuUUXXHCBHn74YU2aNEkrV67Uhx9+qMcff1xSc5LtRz/6kQoLC/XSSy/J6XR6+rclJCQoOjranBcKU/jq4ZY9OU1zVhTKInn1cDv6cWvuffkzVdY2eh4zKAEAAARTfZNTL27+WpJ05ejBJkcDAAACZXqPtqlTp+qhhx7S3XffrZEjR2rz5s3Ky8vzDDzYuXOnSktLPdefffbZevrpp/X4449rxIgR+ve//60XXnhB6enpkqTdu3dr9erV+uqrrzRy5EglJyd7vjZs2GDKa0Ro8bet1GG36c9XnxlQxduRSTbp8KCEvKJSP3cAAAAE7rXPylV1oEnJdpvOOWGA2eEAAIAAWQzDYM/bUaqrq2W321VVVUW/tm7M6TJUUFKpipp6JcbZlJGaoAirRXlFpZqzonnqaFv+47CoOVn3zryLFGEN5hgGAEA4YP0QHsLl5zRzeYHWf/6N5l54gu6YcLLZ4QAA0OMFuoYwvaINMIt7W+llIwcpc3h/T3KsvYMUjhyU4HQZ9HADAADtUlZVr7e/+EaS9CO2jQIAEFZM79EGhKKJ6cm6OM3hVfFWVl2v21ZtPua9a4vLlPWvzSqtqvcco4cbAAAI1P8VfiWXIWWkJChlQG+zwwEAAG1Aog3w4+hBCvnb9gZ03/J3t7c45u7htmT6KE1MT/a7bRUAAPRMnrVBdb2eyt8hSfrRGKrZAAAINyTagABlpCYo2W5TWVV9m3q3Sc3bSi2SctYUy+WS7n252G/FG0k4AAB6lryiUuWs8V4bWCRFR9DlBQCAcMMwBB/CpUkuup6/QQkWtW1wwtHcabSfnZ+q1R+VtrrtlEQcAIQm1g/hIdR+Tu61ha91hEXyVMMDAABzBbqGINHmQ6gtwBBafH3qnGy36ZJ0h/7mY9toR7lTaEumj5Ikn9+b/m8AYD7WD+EhlH5OTpehcx943ev/60diojkAAKEj0DUEW0eBNvI1KCEjNUEFJZWdkmhzbzud/9wnqqpravGJN/3fAAAITwUllX6TbJL3RPMj+8YCAIDQRaINaIejByVIHevhdiyGpH11TX7P0f8NAIDwU1HjP8nWnusAAID5SLQBQRJhtSh7cprmrChs0bOtoz3cjsX9ifcvni5scc5d8RZI/zcAANB1EuNsQb0OAACYj1FGQBBNTE/Wkumj5LB7L4gddpv+fPWZSrbb1NX1Y8ahr7+8VdJie4o7CZdXVCqpuVdM/ra9enHzbuVv2yunixaOAAB0Fnc1vL+1gUXNH4plpCZ0ZVgAAKADqGgDgsxfD7cIq0VWq8VnxZtZ2rLtFAAABNeR1fBHcyffsien0eYBAIAwQkUb0AncPdwuGzlImcP7exbI/ireku02/fz8VFmkFp9qux/3jY3qlGq4I7edtlbxRrUbAADB514bRB6VTHPYbZ5BRwAAIHxQ0QZ0sdYq3s4c2k85a7yryhyHqsokdXn/tyMnnt6zulhl1VS7AQAQbGcM7quDLkMWSfddka6U/n0YWAQAQJgi0QaYwNfUUqn1JJwkLZk+ymcibuGkU3Xvy5918sRT76mn7mo396ftTDQFAKB93vr8G0nSyKF9NS1jmMnRAACAjiDRBoQYf0k4KbT6v9HfDQCA4Hjri+ZE2wUnHWdyJAAAoKPo0QaEmc7o/9ZegfZ3k1qfaEr/NwBAT3XQ6dLbX+yRJJ1Pog0AgLBHRRvQjbSn/1tnbTsNtOJNh66hGg4A0BN99NU+1dQflL1XlEYM7mt2OAAAoINItAHdTHv6v3XWttMjK96OVlZVrxtWtDzuPkf/NwBAT7B+a/O20fNOHMD/2wAA6AZItAE9SGtJOJ+DFuJjVH/Qpaq6pk4ZstDaubb0fyMRBwAIV+sPDUJg2ygAAN0DiTYAkvxXvK0tLvNZ7dbZQxeOVQ3nrniT2HoKAAhPlbWN+nh3lSQGIQAA0F0wDAGAh69BC/6GLDjsNv356jOVbLd1eKhCW7kTfPOf+0RzVrQ+iIFBCwCAUPX2F9/IMKRTHHFKircd+wYAABDyqGgDcEzt6e/WFRVv++qa/J6zqDkRd8/qYpVVU+0GAAg9b33ePG2UajYAALoPKtoABMRXtZukkKx4kw4n4o5Mskne1W6SqHgDAJMtXrxYKSkpstlsGjt2rAoKCvxeu2zZMp133nnq16+f+vXrp/Hjx7d6fShzuQxPfzYSbQAAdB9UtAHosI5WvHVlNVxbBi0wZAEAOteqVauUlZWlpUuXauzYsVq0aJEmTJigrVu3KjExscX1b775pqZNm6azzz5bNptNDzzwgL73ve/p008/1aBBg0x4Be33WVm19uxvUK+oCI1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" ] @@ -3950,10 +3950,10 @@ "id": "9b95fed9", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:02.213627Z", - "iopub.status.busy": "2024-06-04T23:20:02.213362Z", - "iopub.status.idle": "2024-06-04T23:20:02.216618Z", - "shell.execute_reply": "2024-06-04T23:20:02.216339Z" + "iopub.execute_input": "2025-04-03T19:34:18.648478Z", + "iopub.status.busy": "2025-04-03T19:34:18.648052Z", + "iopub.status.idle": "2025-04-03T19:34:18.650900Z", + "shell.execute_reply": "2025-04-03T19:34:18.650616Z" } }, "outputs": [], @@ -3988,17 +3988,17 @@ "id": "6c58d281", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:02.218730Z", - "iopub.status.busy": "2024-06-04T23:20:02.218629Z", - "iopub.status.idle": "2024-06-04T23:20:03.019903Z", - "shell.execute_reply": "2024-06-04T23:20:03.019670Z" + "iopub.execute_input": "2025-04-03T19:34:18.652318Z", + "iopub.status.busy": "2025-04-03T19:34:18.652223Z", + "iopub.status.idle": "2025-04-03T19:34:19.196366Z", + "shell.execute_reply": "2025-04-03T19:34:19.196078Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -4041,10 +4041,10 @@ "id": "ed965079", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:03.021337Z", - "iopub.status.busy": "2024-06-04T23:20:03.021228Z", - "iopub.status.idle": "2024-06-04T23:20:03.030000Z", - "shell.execute_reply": "2024-06-04T23:20:03.029729Z" + "iopub.execute_input": "2025-04-03T19:34:19.197720Z", + "iopub.status.busy": "2025-04-03T19:34:19.197629Z", + "iopub.status.idle": "2025-04-03T19:34:19.204934Z", + "shell.execute_reply": "2025-04-03T19:34:19.204693Z" }, "lines_to_next_cell": 2 }, @@ -4235,16 +4235,16 @@ "id": "c487bbc7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:03.031335Z", - "iopub.status.busy": "2024-06-04T23:20:03.031249Z", - "iopub.status.idle": "2024-06-04T23:20:03.418631Z", - "shell.execute_reply": "2024-06-04T23:20:03.417729Z" + "iopub.execute_input": "2025-04-03T19:34:19.206154Z", + "iopub.status.busy": "2025-04-03T19:34:19.206083Z", + "iopub.status.idle": "2025-04-03T19:34:19.372965Z", + "shell.execute_reply": "2025-04-03T19:34:19.372705Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -4264,7 +4264,7 @@ "id": "7976c47b", "metadata": {}, "source": [ - "The `axhline()` function draws a horizontal line line on top of any\n", + "The `axhline()` function draws a horizontal line on top of any\n", "existing set of axes. The argument `140` plots a horizontal\n", "line at height 140 on the dendrogram; this is a height that\n", "results in four distinct clusters. It is easy to verify that the\n", @@ -4284,10 +4284,10 @@ "id": "345edc5f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:03.421007Z", - "iopub.status.busy": "2024-06-04T23:20:03.420875Z", - "iopub.status.idle": "2024-06-04T23:20:04.678569Z", - "shell.execute_reply": "2024-06-04T23:20:04.678043Z" + "iopub.execute_input": "2025-04-03T19:34:19.374297Z", + "iopub.status.busy": "2025-04-03T19:34:19.374201Z", + "iopub.status.idle": "2025-04-03T19:34:21.414046Z", + "shell.execute_reply": "2025-04-03T19:34:21.413192Z" } }, "outputs": [ @@ -4409,10 +4409,10 @@ "id": "90d617e0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:04.684581Z", - "iopub.status.busy": "2024-06-04T23:20:04.684302Z", - "iopub.status.idle": "2024-06-04T23:20:05.125639Z", - "shell.execute_reply": "2024-06-04T23:20:05.125298Z" + "iopub.execute_input": "2025-04-03T19:34:21.418594Z", + "iopub.status.busy": "2025-04-03T19:34:21.418042Z", + "iopub.status.idle": "2025-04-03T19:34:21.608174Z", + "shell.execute_reply": "2025-04-03T19:34:21.606156Z" }, "lines_to_next_cell": 0 }, @@ -4579,7 +4579,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -4622,8 +4622,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -4635,7 +4635,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4, diff --git a/Ch13-multiple-lab.Rmd b/Ch13-multiple-lab.Rmd index 8e2e780..31c215f 100644 --- a/Ch13-multiple-lab.Rmd +++ b/Ch13-multiple-lab.Rmd @@ -1,3 +1,16 @@ +--- +jupyter: + jupytext: + cell_metadata_filter: -all + formats: ipynb,Rmd + main_language: python + text_representation: + extension: .Rmd + format_name: rmarkdown + format_version: '1.2' + jupytext_version: 1.16.7 +--- + # Multiple Testing diff --git a/Ch13-multiple-lab.ipynb b/Ch13-multiple-lab.ipynb index fc7b29d..e07abb2 100644 --- a/Ch13-multiple-lab.ipynb +++ b/Ch13-multiple-lab.ipynb @@ -31,10 +31,10 @@ "id": "bd52a066", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:06.119783Z", - "iopub.status.busy": "2024-06-04T23:20:06.119425Z", - "iopub.status.idle": "2024-06-04T23:20:06.998312Z", - "shell.execute_reply": "2024-06-04T23:20:06.998023Z" + "iopub.execute_input": "2025-04-03T19:32:52.301476Z", + "iopub.status.busy": "2025-04-03T19:32:52.301112Z", + "iopub.status.idle": "2025-04-03T19:32:53.091465Z", + "shell.execute_reply": "2025-04-03T19:32:53.091001Z" } }, "outputs": [], @@ -61,10 +61,10 @@ "id": "90117cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:06.999889Z", - "iopub.status.busy": "2024-06-04T23:20:06.999773Z", - "iopub.status.idle": "2024-06-04T23:20:07.001596Z", - "shell.execute_reply": "2024-06-04T23:20:07.001373Z" + "iopub.execute_input": "2025-04-03T19:32:53.093032Z", + "iopub.status.busy": "2025-04-03T19:32:53.092903Z", + "iopub.status.idle": "2025-04-03T19:32:53.094748Z", + "shell.execute_reply": "2025-04-03T19:32:53.094563Z" }, "lines_to_next_cell": 2 }, @@ -100,10 +100,10 @@ "id": "db60f055", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.002832Z", - "iopub.status.busy": "2024-06-04T23:20:07.002760Z", - "iopub.status.idle": "2024-06-04T23:20:07.004664Z", - "shell.execute_reply": "2024-06-04T23:20:07.004449Z" + "iopub.execute_input": "2025-04-03T19:32:53.095939Z", + "iopub.status.busy": "2025-04-03T19:32:53.095871Z", + "iopub.status.idle": "2025-04-03T19:32:53.097801Z", + "shell.execute_reply": "2025-04-03T19:32:53.097556Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "2f769e32", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.005916Z", - "iopub.status.busy": "2024-06-04T23:20:07.005847Z", - "iopub.status.idle": "2024-06-04T23:20:07.009111Z", - "shell.execute_reply": "2024-06-04T23:20:07.008893Z" + "iopub.execute_input": "2025-04-03T19:32:53.098889Z", + "iopub.status.busy": "2025-04-03T19:32:53.098816Z", + "iopub.status.idle": "2025-04-03T19:32:53.101990Z", + "shell.execute_reply": "2025-04-03T19:32:53.101797Z" } }, "outputs": [ @@ -177,10 +177,10 @@ "id": "662e8a87", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.010301Z", - "iopub.status.busy": "2024-06-04T23:20:07.010222Z", - "iopub.status.idle": "2024-06-04T23:20:07.026475Z", - "shell.execute_reply": "2024-06-04T23:20:07.026253Z" + "iopub.execute_input": "2025-04-03T19:32:53.103079Z", + "iopub.status.busy": "2025-04-03T19:32:53.103010Z", + "iopub.status.idle": "2025-04-03T19:32:53.121558Z", + "shell.execute_reply": "2025-04-03T19:32:53.121347Z" }, "lines_to_next_cell": 0 }, @@ -213,10 +213,10 @@ "id": "b25f9969", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.027636Z", - "iopub.status.busy": "2024-06-04T23:20:07.027567Z", - "iopub.status.idle": "2024-06-04T23:20:07.034757Z", - "shell.execute_reply": "2024-06-04T23:20:07.034523Z" + "iopub.execute_input": "2025-04-03T19:32:53.122771Z", + "iopub.status.busy": "2025-04-03T19:32:53.122702Z", + "iopub.status.idle": "2025-04-03T19:32:53.129771Z", + "shell.execute_reply": "2025-04-03T19:32:53.129510Z" }, "lines_to_next_cell": 0 }, @@ -314,10 +314,10 @@ "id": "4c158b0b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.036106Z", - "iopub.status.busy": "2024-06-04T23:20:07.036002Z", - "iopub.status.idle": "2024-06-04T23:20:07.060212Z", - "shell.execute_reply": "2024-06-04T23:20:07.059960Z" + "iopub.execute_input": "2025-04-03T19:32:53.130993Z", + "iopub.status.busy": "2025-04-03T19:32:53.130922Z", + "iopub.status.idle": "2025-04-03T19:32:53.151941Z", + "shell.execute_reply": "2025-04-03T19:32:53.151731Z" }, "lines_to_next_cell": 0 }, @@ -426,16 +426,16 @@ "id": "093264ed", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.061515Z", - "iopub.status.busy": "2024-06-04T23:20:07.061433Z", - "iopub.status.idle": "2024-06-04T23:20:07.311200Z", - "shell.execute_reply": "2024-06-04T23:20:07.310542Z" + "iopub.execute_input": "2025-04-03T19:32:53.153230Z", + "iopub.status.busy": "2025-04-03T19:32:53.153148Z", + "iopub.status.idle": "2025-04-03T19:32:53.319180Z", + "shell.execute_reply": "2025-04-03T19:32:53.318528Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -482,10 +482,10 @@ "id": "1a3ac106", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.314351Z", - "iopub.status.busy": "2024-06-04T23:20:07.313832Z", - "iopub.status.idle": "2024-06-04T23:20:07.344704Z", - "shell.execute_reply": "2024-06-04T23:20:07.344468Z" + "iopub.execute_input": "2025-04-03T19:32:53.323056Z", + "iopub.status.busy": "2025-04-03T19:32:53.322678Z", + "iopub.status.idle": "2025-04-03T19:32:53.356260Z", + "shell.execute_reply": "2025-04-03T19:32:53.355323Z" } }, "outputs": [ @@ -550,10 +550,10 @@ "id": "b9f1eac7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.346038Z", - "iopub.status.busy": "2024-06-04T23:20:07.345960Z", - "iopub.status.idle": "2024-06-04T23:20:07.347883Z", - "shell.execute_reply": "2024-06-04T23:20:07.347675Z" + "iopub.execute_input": "2025-04-03T19:32:53.359943Z", + "iopub.status.busy": "2025-04-03T19:32:53.359613Z", + "iopub.status.idle": "2025-04-03T19:32:53.367392Z", + "shell.execute_reply": "2025-04-03T19:32:53.366785Z" }, "lines_to_next_cell": 2 }, @@ -589,10 +589,10 @@ "id": "0cee9c30", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.349050Z", - "iopub.status.busy": "2024-06-04T23:20:07.348979Z", - "iopub.status.idle": "2024-06-04T23:20:07.351013Z", - "shell.execute_reply": "2024-06-04T23:20:07.350820Z" + "iopub.execute_input": "2025-04-03T19:32:53.370653Z", + "iopub.status.busy": "2025-04-03T19:32:53.370309Z", + "iopub.status.idle": "2025-04-03T19:32:53.377086Z", + "shell.execute_reply": "2025-04-03T19:32:53.376491Z" } }, "outputs": [ @@ -631,10 +631,10 @@ "id": "1f7cee90", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.352174Z", - "iopub.status.busy": "2024-06-04T23:20:07.352109Z", - "iopub.status.idle": "2024-06-04T23:20:07.381192Z", - "shell.execute_reply": "2024-06-04T23:20:07.380965Z" + "iopub.execute_input": "2025-04-03T19:32:53.381069Z", + "iopub.status.busy": "2025-04-03T19:32:53.380804Z", + "iopub.status.idle": "2025-04-03T19:32:53.429473Z", + "shell.execute_reply": "2025-04-03T19:32:53.428127Z" }, "lines_to_next_cell": 2 }, @@ -671,10 +671,10 @@ "id": "59cdfe23", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.382469Z", - "iopub.status.busy": "2024-06-04T23:20:07.382398Z", - "iopub.status.idle": "2024-06-04T23:20:07.384782Z", - "shell.execute_reply": "2024-06-04T23:20:07.384577Z" + "iopub.execute_input": "2025-04-03T19:32:53.432686Z", + "iopub.status.busy": "2025-04-03T19:32:53.432364Z", + "iopub.status.idle": "2025-04-03T19:32:53.435417Z", + "shell.execute_reply": "2025-04-03T19:32:53.435162Z" }, "lines_to_next_cell": 2 }, @@ -715,10 +715,10 @@ "id": "bf01100e", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.385897Z", - "iopub.status.busy": "2024-06-04T23:20:07.385834Z", - "iopub.status.idle": "2024-06-04T23:20:07.388040Z", - "shell.execute_reply": "2024-06-04T23:20:07.387835Z" + "iopub.execute_input": "2025-04-03T19:32:53.436763Z", + "iopub.status.busy": "2025-04-03T19:32:53.436655Z", + "iopub.status.idle": "2025-04-03T19:32:53.439076Z", + "shell.execute_reply": "2025-04-03T19:32:53.438865Z" } }, "outputs": [ @@ -767,10 +767,10 @@ "id": "60146e08", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.389205Z", - "iopub.status.busy": "2024-06-04T23:20:07.389140Z", - "iopub.status.idle": "2024-06-04T23:20:07.776782Z", - "shell.execute_reply": "2024-06-04T23:20:07.776519Z" + "iopub.execute_input": "2025-04-03T19:32:53.440229Z", + "iopub.status.busy": "2025-04-03T19:32:53.440124Z", + "iopub.status.idle": "2025-04-03T19:32:53.833595Z", + "shell.execute_reply": "2025-04-03T19:32:53.833313Z" }, "lines_to_next_cell": 2 }, @@ -827,16 +827,16 @@ "id": "2ec0aae4", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.778173Z", - "iopub.status.busy": "2024-06-04T23:20:07.778094Z", - "iopub.status.idle": "2024-06-04T23:20:07.836461Z", - "shell.execute_reply": "2024-06-04T23:20:07.836186Z" + "iopub.execute_input": "2025-04-03T19:32:53.834950Z", + "iopub.status.busy": "2025-04-03T19:32:53.834875Z", + "iopub.status.idle": "2025-04-03T19:32:53.877086Z", + "shell.execute_reply": "2025-04-03T19:32:53.876823Z" } }, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -868,10 +868,10 @@ "id": "1de94dbc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:07.837958Z", - "iopub.status.busy": "2024-06-04T23:20:07.837840Z", - "iopub.status.idle": "2024-06-04T23:20:08.163274Z", - "shell.execute_reply": "2024-06-04T23:20:08.162972Z" + "iopub.execute_input": "2025-04-03T19:32:53.878355Z", + "iopub.status.busy": "2025-04-03T19:32:53.878280Z", + "iopub.status.idle": "2025-04-03T19:32:54.189626Z", + "shell.execute_reply": "2025-04-03T19:32:54.189218Z" } }, "outputs": [], @@ -897,10 +897,10 @@ "id": "b0aa032c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:08.164892Z", - "iopub.status.busy": "2024-06-04T23:20:08.164804Z", - "iopub.status.idle": "2024-06-04T23:20:08.167176Z", - "shell.execute_reply": "2024-06-04T23:20:08.166950Z" + "iopub.execute_input": "2025-04-03T19:32:54.191140Z", + "iopub.status.busy": "2025-04-03T19:32:54.191057Z", + "iopub.status.idle": "2025-04-03T19:32:54.193550Z", + "shell.execute_reply": "2025-04-03T19:32:54.193343Z" } }, "outputs": [ @@ -942,10 +942,10 @@ "id": "ba6dcb50", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:08.168349Z", - "iopub.status.busy": "2024-06-04T23:20:08.168282Z", - "iopub.status.idle": "2024-06-04T23:20:08.170285Z", - "shell.execute_reply": "2024-06-04T23:20:08.170079Z" + "iopub.execute_input": "2025-04-03T19:32:54.194685Z", + "iopub.status.busy": "2025-04-03T19:32:54.194612Z", + "iopub.status.idle": "2025-04-03T19:32:54.196704Z", + "shell.execute_reply": "2025-04-03T19:32:54.196500Z" }, "lines_to_next_cell": 0 }, @@ -986,10 +986,10 @@ "id": "44c1b26a", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:08.171469Z", - "iopub.status.busy": "2024-06-04T23:20:08.171399Z", - "iopub.status.idle": "2024-06-04T23:20:08.173322Z", - "shell.execute_reply": "2024-06-04T23:20:08.173118Z" + "iopub.execute_input": "2025-04-03T19:32:54.197806Z", + "iopub.status.busy": "2025-04-03T19:32:54.197720Z", + "iopub.status.idle": "2025-04-03T19:32:54.199706Z", + "shell.execute_reply": "2025-04-03T19:32:54.199496Z" }, "lines_to_next_cell": 2 }, @@ -1036,10 +1036,10 @@ "id": "f23a1f9f", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:08.174454Z", - "iopub.status.busy": "2024-06-04T23:20:08.174389Z", - "iopub.status.idle": "2024-06-04T23:20:08.176567Z", - "shell.execute_reply": "2024-06-04T23:20:08.176360Z" + "iopub.execute_input": "2025-04-03T19:32:54.200867Z", + "iopub.status.busy": "2025-04-03T19:32:54.200798Z", + "iopub.status.idle": "2025-04-03T19:32:54.203144Z", + "shell.execute_reply": "2025-04-03T19:32:54.202843Z" } }, "outputs": [], @@ -1070,17 +1070,17 @@ "id": "3c3018cc", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:08.177790Z", - "iopub.status.busy": "2024-06-04T23:20:08.177725Z", - "iopub.status.idle": "2024-06-04T23:20:08.323352Z", - "shell.execute_reply": "2024-06-04T23:20:08.323097Z" + "iopub.execute_input": "2025-04-03T19:32:54.204649Z", + "iopub.status.busy": "2025-04-03T19:32:54.204557Z", + "iopub.status.idle": "2025-04-03T19:32:54.353161Z", + "shell.execute_reply": "2025-04-03T19:32:54.352863Z" }, "lines_to_next_cell": 2 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1120,10 +1120,10 @@ "id": "d49b3de7", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:08.324883Z", - "iopub.status.busy": "2024-06-04T23:20:08.324753Z", - "iopub.status.idle": "2024-06-04T23:20:08.369825Z", - "shell.execute_reply": "2024-06-04T23:20:08.369575Z" + "iopub.execute_input": "2025-04-03T19:32:54.354663Z", + "iopub.status.busy": "2025-04-03T19:32:54.354563Z", + "iopub.status.idle": "2025-04-03T19:32:54.405469Z", + "shell.execute_reply": "2025-04-03T19:32:54.405244Z" }, "lines_to_next_cell": 2 }, @@ -1172,10 +1172,10 @@ "id": "18eaeb65", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:08.371125Z", - "iopub.status.busy": "2024-06-04T23:20:08.371048Z", - "iopub.status.idle": "2024-06-04T23:20:08.374282Z", - "shell.execute_reply": "2024-06-04T23:20:08.374049Z" + "iopub.execute_input": "2025-04-03T19:32:54.406764Z", + "iopub.status.busy": "2025-04-03T19:32:54.406668Z", + "iopub.status.idle": "2025-04-03T19:32:54.410015Z", + "shell.execute_reply": "2025-04-03T19:32:54.409790Z" }, "lines_to_next_cell": 2 }, @@ -1225,10 +1225,10 @@ "id": "8e2f821c", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:08.375701Z", - "iopub.status.busy": "2024-06-04T23:20:08.375620Z", - "iopub.status.idle": "2024-06-04T23:20:10.082601Z", - "shell.execute_reply": "2024-06-04T23:20:10.082342Z" + "iopub.execute_input": "2025-04-03T19:32:54.411194Z", + "iopub.status.busy": "2025-04-03T19:32:54.411115Z", + "iopub.status.idle": "2025-04-03T19:32:56.102203Z", + "shell.execute_reply": "2025-04-03T19:32:56.101891Z" }, "lines_to_next_cell": 2 }, @@ -1276,17 +1276,17 @@ "id": "f10d3bf0", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:10.084069Z", - "iopub.status.busy": "2024-06-04T23:20:10.083976Z", - "iopub.status.idle": "2024-06-04T23:20:10.194167Z", - "shell.execute_reply": "2024-06-04T23:20:10.193945Z" + "iopub.execute_input": "2025-04-03T19:32:56.103547Z", + "iopub.status.busy": "2025-04-03T19:32:56.103471Z", + "iopub.status.idle": "2025-04-03T19:32:56.242320Z", + "shell.execute_reply": "2025-04-03T19:32:56.242062Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -1336,10 +1336,10 @@ "id": "d37287ae", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:20:10.195499Z", - "iopub.status.busy": "2024-06-04T23:20:10.195421Z", - "iopub.status.idle": "2024-06-04T23:22:59.020789Z", - "shell.execute_reply": "2024-06-04T23:22:59.020469Z" + "iopub.execute_input": "2025-04-03T19:32:56.243677Z", + "iopub.status.busy": "2025-04-03T19:32:56.243581Z", + "iopub.status.idle": "2025-04-03T19:35:57.109614Z", + "shell.execute_reply": "2025-04-03T19:35:57.109250Z" } }, "outputs": [], @@ -1382,10 +1382,10 @@ "id": "ecaf03eb", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:22:59.022409Z", - "iopub.status.busy": "2024-06-04T23:22:59.022324Z", - "iopub.status.idle": "2024-06-04T23:22:59.099406Z", - "shell.execute_reply": "2024-06-04T23:22:59.099145Z" + "iopub.execute_input": "2025-04-03T19:35:57.111340Z", + "iopub.status.busy": "2025-04-03T19:35:57.111258Z", + "iopub.status.idle": "2025-04-03T19:35:57.218314Z", + "shell.execute_reply": "2025-04-03T19:35:57.218027Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "ceacd8b8", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:22:59.100800Z", - "iopub.status.busy": "2024-06-04T23:22:59.100726Z", - "iopub.status.idle": "2024-06-04T23:22:59.102948Z", - "shell.execute_reply": "2024-06-04T23:22:59.102723Z" + "iopub.execute_input": "2025-04-03T19:35:57.219806Z", + "iopub.status.busy": "2025-04-03T19:35:57.219728Z", + "iopub.status.idle": "2025-04-03T19:35:57.222043Z", + "shell.execute_reply": "2025-04-03T19:35:57.221848Z" } }, "outputs": [ @@ -1478,10 +1478,10 @@ "id": "2434f370", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:22:59.104184Z", - "iopub.status.busy": "2024-06-04T23:22:59.104114Z", - "iopub.status.idle": "2024-06-04T23:22:59.106202Z", - "shell.execute_reply": "2024-06-04T23:22:59.105984Z" + "iopub.execute_input": "2025-04-03T19:35:57.223240Z", + "iopub.status.busy": "2025-04-03T19:35:57.223171Z", + "iopub.status.idle": "2025-04-03T19:35:57.225253Z", + "shell.execute_reply": "2025-04-03T19:35:57.225057Z" } }, "outputs": [ @@ -1546,17 +1546,17 @@ "id": "34d6a02b", "metadata": { "execution": { - "iopub.execute_input": "2024-06-04T23:22:59.107343Z", - "iopub.status.busy": "2024-06-04T23:22:59.107277Z", - "iopub.status.idle": "2024-06-04T23:22:59.196949Z", - "shell.execute_reply": "2024-06-04T23:22:59.196706Z" + "iopub.execute_input": "2025-04-03T19:35:57.226335Z", + "iopub.status.busy": "2025-04-03T19:35:57.226270Z", + "iopub.status.idle": "2025-04-03T19:35:57.263024Z", + "shell.execute_reply": "2025-04-03T19:35:57.262794Z" }, "lines_to_next_cell": 0 }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1584,8 +1584,8 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "main_language": "python", - "notebook_metadata_filter": "-all" + "formats": "ipynb,Rmd", + "main_language": "python" }, "language_info": { "codemirror_mode": { @@ -1597,7 +1597,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.11" } }, "nbformat": 4,