From de224cc506aa0240d3a681f0b34b5007d476d048 Mon Sep 17 00:00:00 2001
From: Jonathan Taylor <jonathan.taylor@stanford.edu>
Date: Wed, 26 Jul 2023 15:52:39 -0400
Subject: [PATCH] update Ch2, Ch10

---
 Ch10-deeplearning-lab.ipynb | 903 +++++++++++++++++++++++++++++++++-
 Ch2-statlearn-lab.ipynb     | 952 ++++++++++++++++++------------------
 2 files changed, 1349 insertions(+), 506 deletions(-)

diff --git a/Ch10-deeplearning-lab.ipynb b/Ch10-deeplearning-lab.ipynb
index 18f79f3..80ee043 100644
--- a/Ch10-deeplearning-lab.ipynb
+++ b/Ch10-deeplearning-lab.ipynb
@@ -2140,7 +2140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "e2fe6de3",
    "metadata": {},
    "outputs": [
@@ -2555,6 +2555,69 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
+     ]
     }
    ],
    "source": [
@@ -2589,7 +2652,7 @@
    "id": "25528d72",
    "metadata": {},
    "source": [
-    "`SimpleModule.classification()` includes\n",
+    "`SimpleModule.multiclass()` includes\n",
     "an accuracy metric by default. Other\n",
     "classification metrics can be added from `torchmetrics`.\n",
     "We will use  our `summary_plot()` function to display \n",
@@ -2598,10 +2661,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "73755987",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "mnist_results = pd.read_csv(mnist_logger.experiment.metrics_file_path)\n",
     "fig, ax = subplots(1, 1, figsize=(6, 6))\n",
@@ -2625,10 +2699,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "f5269c40",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8b1fc2e19cda40d78484bfaade82ec6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Testing: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Runningstage.testing metric      DataLoader 0\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "      test_accuracy         0.9656999707221985\n",
+      "        test_loss           0.15191325545310974\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'test_loss': 0.15191325545310974, 'test_accuracy': 0.9656999707221985}]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mnist_trainer.test(mnist_module,\n",
     "                   datamodule=mnist_dm)"
@@ -2649,7 +2760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "97a0b304",
    "metadata": {},
    "outputs": [],
@@ -2670,10 +2781,488 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "ea685183",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (mps), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_311/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n",
+      "  warning_cache.warn(\n",
+      "\n",
+      "  | Name  | Type             | Params\n",
+      "-------------------------------------------\n",
+      "0 | model | MNIST_MLR        | 7.9 K \n",
+      "1 | loss  | CrossEntropyLoss | 0     \n",
+      "-------------------------------------------\n",
+      "7.9 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "7.9 K     Total params\n",
+      "0.031     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16fde280b03241409e1b3b889d4c5a74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+       "Validation: 0it [00:00, ?it/s]"
+      ]
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+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
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+       "Validation: 0it [00:00, ?it/s]"
+      ]
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+      ]
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+     "metadata": {},
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+    },
+    {
+     "data": {
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+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=30` reached.\n"
+     ]
+    }
+   ],
    "source": [
     "mlr_trainer = Trainer(deterministic=True,\n",
     "                      max_epochs=30,\n",
@@ -2691,10 +3280,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "id": "c0bd63e3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f8c6ccecd9364129871ae290735d57e4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Testing: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "Runningstage.testing metric      DataLoader 0\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+      "      test_accuracy         0.9235000014305115\n",
+      "        test_loss           0.3271547853946686\n",
+      "────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[{'test_loss': 0.3271547853946686, 'test_accuracy': 0.9235000014305115}]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "mlr_trainer.test(mlr_module,\n",
     "                 datamodule=mnist_dm)"
@@ -2713,7 +3339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "6b0d3daa",
    "metadata": {},
    "outputs": [],
@@ -2742,10 +3368,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "id": "67517b11",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n"
+     ]
+    }
+   ],
    "source": [
     "(cifar_train, \n",
     " cifar_test) = [CIFAR100(root=\"data\",\n",
@@ -2756,7 +3391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "id": "ee7b040e",
    "metadata": {},
    "outputs": [],
@@ -2787,7 +3422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "id": "bd9e74ad",
    "metadata": {},
    "outputs": [],
@@ -2809,10 +3444,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "id": "a553b926",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X:  torch.Size([128, 3, 32, 32])\n",
+      "Y:  torch.Size([128])\n",
+      "X:  torch.Size([128, 3, 32, 32])\n",
+      "Y:  torch.Size([128])\n"
+     ]
+    }
+   ],
    "source": [
     "for idx, (X_ ,Y_) in enumerate(cifar_dm.train_dataloader()):\n",
     "    print('X: ', X_.shape)\n",
@@ -2835,10 +3481,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "id": "94885e68",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, axes = subplots(5, 5, figsize=(10,10))\n",
     "rng = np.random.default_rng(4)\n",
@@ -2872,7 +3529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "id": "cdc2528a",
    "metadata": {},
    "outputs": [],
@@ -2917,7 +3574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "id": "845be1ae",
    "metadata": {},
    "outputs": [],
@@ -2953,10 +3610,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "id": "be768cbb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "===================================================================================================================\n",
+       "Layer (type:depth-idx)                   Input Shape               Output Shape              Param #\n",
+       "===================================================================================================================\n",
+       "CIFARModel                               [128, 3, 32, 32]          [128, 100]                --\n",
+       "├─Sequential: 1-1                        [128, 3, 32, 32]          [128, 256, 2, 2]          --\n",
+       "│    └─BuildingBlock: 2-1                [128, 3, 32, 32]          [128, 32, 16, 16]         --\n",
+       "│    │    └─Conv2d: 3-1                  [128, 3, 32, 32]          [128, 32, 32, 32]         896\n",
+       "│    │    └─ReLU: 3-2                    [128, 32, 32, 32]         [128, 32, 32, 32]         --\n",
+       "│    │    └─MaxPool2d: 3-3               [128, 32, 32, 32]         [128, 32, 16, 16]         --\n",
+       "│    └─BuildingBlock: 2-2                [128, 32, 16, 16]         [128, 64, 8, 8]           --\n",
+       "│    │    └─Conv2d: 3-4                  [128, 32, 16, 16]         [128, 64, 16, 16]         18,496\n",
+       "│    │    └─ReLU: 3-5                    [128, 64, 16, 16]         [128, 64, 16, 16]         --\n",
+       "│    │    └─MaxPool2d: 3-6               [128, 64, 16, 16]         [128, 64, 8, 8]           --\n",
+       "│    └─BuildingBlock: 2-3                [128, 64, 8, 8]           [128, 128, 4, 4]          --\n",
+       "│    │    └─Conv2d: 3-7                  [128, 64, 8, 8]           [128, 128, 8, 8]          73,856\n",
+       "│    │    └─ReLU: 3-8                    [128, 128, 8, 8]          [128, 128, 8, 8]          --\n",
+       "│    │    └─MaxPool2d: 3-9               [128, 128, 8, 8]          [128, 128, 4, 4]          --\n",
+       "│    └─BuildingBlock: 2-4                [128, 128, 4, 4]          [128, 256, 2, 2]          --\n",
+       "│    │    └─Conv2d: 3-10                 [128, 128, 4, 4]          [128, 256, 4, 4]          295,168\n",
+       "│    │    └─ReLU: 3-11                   [128, 256, 4, 4]          [128, 256, 4, 4]          --\n",
+       "│    │    └─MaxPool2d: 3-12              [128, 256, 4, 4]          [128, 256, 2, 2]          --\n",
+       "├─Sequential: 1-2                        [128, 1024]               [128, 100]                --\n",
+       "│    └─Dropout: 2-5                      [128, 1024]               [128, 1024]               --\n",
+       "│    └─Linear: 2-6                       [128, 1024]               [128, 512]                524,800\n",
+       "│    └─ReLU: 2-7                         [128, 512]                [128, 512]                --\n",
+       "│    └─Linear: 2-8                       [128, 512]                [128, 100]                51,300\n",
+       "===================================================================================================================\n",
+       "Total params: 964,516\n",
+       "Trainable params: 964,516\n",
+       "Non-trainable params: 0\n",
+       "Total mult-adds (Units.GIGABYTES): 2.01\n",
+       "===================================================================================================================\n",
+       "Input size (MB): 1.57\n",
+       "Forward/backward pass size (MB): 63.54\n",
+       "Params size (MB): 3.86\n",
+       "Estimated Total Size (MB): 68.97\n",
+       "==================================================================================================================="
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cifar_model = CIFARModel()\n",
     "summary(cifar_model,\n",
@@ -3001,7 +3705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "id": "67e3e2d9",
    "metadata": {},
    "outputs": [],
@@ -3018,7 +3722,154 @@
    "execution_count": null,
    "id": "1ea339ff",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (mps), used: True\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "\n",
+      "  | Name  | Type             | Params\n",
+      "-------------------------------------------\n",
+      "0 | model | CIFARModel       | 964 K \n",
+      "1 | loss  | CrossEntropyLoss | 0     \n",
+      "-------------------------------------------\n",
+      "964 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "964 K     Total params\n",
+      "3.858     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b8c54fa3440349639c4b6abbeca76230",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Training: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: 0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "cifar_trainer = Trainer(deterministic=True,\n",
     "                        max_epochs=30,\n",
diff --git a/Ch2-statlearn-lab.ipynb b/Ch2-statlearn-lab.ipynb
index 19d50c4..4de57e8 100644
--- a/Ch2-statlearn-lab.ipynb
+++ b/Ch2-statlearn-lab.ipynb
@@ -97,10 +97,10 @@
    "id": "88d0bfff",
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-     "shell.execute_reply": "2023-07-26T05:29:13.659469Z"
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@@ -130,10 +130,10 @@
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-     "shell.execute_reply": "2023-07-26T05:29:13.664422Z"
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@@ -155,10 +155,10 @@
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-     "shell.execute_reply": "2023-07-26T05:29:13.670233Z"
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@@ -195,10 +195,10 @@
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-     "shell.execute_reply": "2023-07-26T05:29:13.675286Z"
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    "outputs": [
@@ -244,10 +244,10 @@
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-     "shell.execute_reply": "2023-07-26T05:29:13.680100Z"
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    "outputs": [
@@ -285,10 +285,10 @@
    "id": "425a714c",
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-     "shell.execute_reply": "2023-07-26T05:29:13.684966Z"
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@@ -358,10 +358,10 @@
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@@ -393,10 +393,10 @@
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@@ -431,10 +431,10 @@
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@@ -468,10 +468,10 @@
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@@ -509,10 +509,10 @@
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@@ -547,10 +547,10 @@
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@@ -586,10 +586,10 @@
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@@ -624,10 +624,10 @@
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@@ -649,10 +649,10 @@
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@@ -686,10 +686,10 @@
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@@ -728,10 +728,10 @@
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@@ -765,10 +765,10 @@
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@@ -810,10 +810,10 @@
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@@ -861,10 +861,10 @@
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@@ -898,10 +898,10 @@
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@@ -937,10 +937,10 @@
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@@ -992,10 +992,10 @@
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    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.042240Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.042111Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.226949Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.226329Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.093630Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.093506Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.215063Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.214794Z"
     }
    },
    "outputs": [
@@ -1031,10 +1031,10 @@
    "id": "56b5c382",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.229928Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.229597Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.233213Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.232504Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.216651Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.216527Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.218737Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.218479Z"
     }
    },
    "outputs": [
@@ -1075,10 +1075,10 @@
    "id": "2e624622",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.235702Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.235564Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.238916Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.238262Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.220135Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.220039Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.222171Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.221917Z"
     }
    },
    "outputs": [
@@ -1112,10 +1112,10 @@
    "id": "6f45bd25",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.241171Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.241044Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.244021Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.243599Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.223637Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.223538Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.225819Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.225592Z"
     }
    },
    "outputs": [
@@ -1148,10 +1148,10 @@
    "id": "6980f628",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.246394Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.246080Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.249174Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.248827Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.227226Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.227132Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.229135Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.228919Z"
     }
    },
    "outputs": [
@@ -1194,26 +1194,26 @@
    "id": "89e32100",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.251585Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.251451Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.254814Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.254443Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.230600Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.230510Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.233600Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.233339Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.85533175, -0.05576735, -0.51395487,  0.48888859, -1.48893364,\n",
-       "        0.26407493, -0.74669908,  1.01666253, -1.94048948, -1.78664297,\n",
-       "       -0.87797144,  0.62042405,  0.13462126,  0.09661092,  0.53420093,\n",
-       "       -0.36120885, -0.68012567, -2.26865778,  1.25520664,  0.51353373,\n",
-       "       -0.24985298, -0.41566987,  1.2875835 ,  0.38602031,  0.32079709,\n",
-       "       -0.26039978, -0.46997695, -0.34450981, -0.68854144,  0.90905649,\n",
-       "       -0.39245431, -0.22266431, -0.24441341,  0.47310767,  0.08102761,\n",
-       "        1.12966294,  0.50395953,  0.63130366,  0.50263731,  1.13393742,\n",
-       "        1.16885142, -0.89388268, -0.25282564,  0.32889275,  0.00619299,\n",
-       "        1.36017422,  0.10199394, -2.33500839,  1.12284979,  1.06710912])"
+       "array([ 0.55240094,  0.2879743 , -0.74444407, -0.67840831,  0.8641021 ,\n",
+       "        1.16201211, -1.38788924,  0.83871247,  0.53491648, -0.2840892 ,\n",
+       "       -1.97916855, -0.30707507,  1.08128056, -2.13895695, -0.22572911,\n",
+       "        0.38741037,  0.39217999, -1.02532918, -0.38393848, -0.10165739,\n",
+       "        0.36519395,  0.43557941,  0.47068719, -0.07333358,  0.91177478,\n",
+       "       -0.10860626,  0.94830924,  0.79217516,  1.74005911, -1.67759113,\n",
+       "       -0.24136372,  0.29637843, -0.70319857, -1.30004634,  1.2803208 ,\n",
+       "       -0.09100392,  0.77651661, -1.42868505, -2.27005102,  0.90133081,\n",
+       "        0.94080238, -0.79586233, -0.74620826,  0.53954272, -0.2585748 ,\n",
+       "       -1.16420401, -1.13683257, -0.72194704, -0.91694097, -0.55688956])"
       ]
      },
      "execution_count": 28,
@@ -1240,10 +1240,10 @@
    "id": "e3428155",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.257670Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.257502Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.259844Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.259473Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.234988Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.234900Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.236557Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.236327Z"
     }
    },
    "outputs": [],
@@ -1266,18 +1266,18 @@
    "id": "df70172e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.262323Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.262139Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.266530Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.266141Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.237894Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.237800Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.240243Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.240012Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.        , 0.58428739],\n",
-       "       [0.58428739, 1.        ]])"
+       "array([[1.        , 0.73182592],\n",
+       "       [0.73182592, 1.        ]])"
       ]
      },
      "execution_count": 30,
@@ -1306,10 +1306,10 @@
    "id": "1d996956",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.269539Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.269386Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.272569Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.271780Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.241551Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.241465Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.243345Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.243110Z"
     }
    },
    "outputs": [
@@ -1317,8 +1317,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-3.0147683  -3.89425181]\n",
-      "[-0.22659461  1.61255642]\n"
+      "[-5.98279059  4.61432069]\n",
+      "[3.59629991 1.693651  ]\n"
      ]
     }
    ],
@@ -1348,10 +1348,10 @@
    "id": "37b3a30b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.275409Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.275284Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.279259Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.278925Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.244712Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.244621Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.247503Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.247229Z"
     }
    },
    "outputs": [
@@ -1394,10 +1394,10 @@
    "id": "b4f99e0e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.282826Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.282508Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.285616Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.285268Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.248952Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.248859Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.251464Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.251221Z"
     }
    },
    "outputs": [
@@ -1424,10 +1424,10 @@
    "id": "dbd70319",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.287760Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.287646Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.290801Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.290395Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.252836Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.252745Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.254923Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.254679Z"
     }
    },
    "outputs": [
@@ -1461,10 +1461,10 @@
    "id": "bf281844",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.292985Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.292843Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.295775Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.295412Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.256267Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.256175Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.258276Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.258036Z"
     }
    },
    "outputs": [
@@ -1498,10 +1498,10 @@
    "id": "f751b1dd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.297946Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.297809Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.300890Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.300481Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.259656Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.259554Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.261586Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.261349Z"
     }
    },
    "outputs": [
@@ -1544,10 +1544,10 @@
    "id": "cbf92de5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.303121Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.303004Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.306153Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.305697Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.262977Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.262888Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.264980Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.264727Z"
     }
    },
    "outputs": [
@@ -1580,10 +1580,10 @@
    "id": "efacc213",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.308317Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.308171Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.311023Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.310704Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.266382Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.266290Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.268317Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.268091Z"
     }
    },
    "outputs": [
@@ -1641,10 +1641,10 @@
    "id": "637fb67d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.313130Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.312981Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.818758Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.818230Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.269692Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.269601Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.584302Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.583988Z"
     }
    },
    "outputs": [
@@ -1683,10 +1683,10 @@
    "id": "109d681d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.821437Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.821210Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.906268Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.905878Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.586083Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.585936Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.657144Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.656846Z"
     }
    },
    "outputs": [
@@ -1721,10 +1721,10 @@
    "id": "e905982c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.908936Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.908699Z",
-     "iopub.status.idle": "2023-07-26T05:29:14.995469Z",
-     "shell.execute_reply": "2023-07-26T05:29:14.994686Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.658874Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.658762Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.733252Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.732954Z"
     }
    },
    "outputs": [
@@ -1768,10 +1768,10 @@
    "id": "27133a4e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:14.997995Z",
-     "iopub.status.busy": "2023-07-26T05:29:14.997801Z",
-     "iopub.status.idle": "2023-07-26T05:29:15.089963Z",
-     "shell.execute_reply": "2023-07-26T05:29:15.089604Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.734922Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.734810Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.815074Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.814777Z"
     }
    },
    "outputs": [
@@ -1808,17 +1808,17 @@
    "id": "ef41f90d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:15.092622Z",
-     "iopub.status.busy": "2023-07-26T05:29:15.092487Z",
-     "iopub.status.idle": "2023-07-26T05:29:15.212584Z",
-     "shell.execute_reply": "2023-07-26T05:29:15.211918Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.816776Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.816662Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.895207Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.894894Z"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1220c8b50>"
+       "<matplotlib.collections.PathCollection at 0x110227750>"
       ]
      },
      "execution_count": 43,
@@ -1866,10 +1866,10 @@
    "id": "16a9cb99",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:15.215419Z",
-     "iopub.status.busy": "2023-07-26T05:29:15.215027Z",
-     "iopub.status.idle": "2023-07-26T05:29:15.330192Z",
-     "shell.execute_reply": "2023-07-26T05:29:15.328644Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.896869Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.896757Z",
+     "iopub.status.idle": "2023-07-26T19:52:03.992506Z",
+     "shell.execute_reply": "2023-07-26T19:52:03.992215Z"
     }
    },
    "outputs": [
@@ -1907,10 +1907,10 @@
    "id": "6707420e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:15.334119Z",
-     "iopub.status.busy": "2023-07-26T05:29:15.333963Z",
-     "iopub.status.idle": "2023-07-26T05:29:15.405814Z",
-     "shell.execute_reply": "2023-07-26T05:29:15.405400Z"
+     "iopub.execute_input": "2023-07-26T19:52:03.994250Z",
+     "iopub.status.busy": "2023-07-26T19:52:03.994126Z",
+     "iopub.status.idle": "2023-07-26T19:52:04.056288Z",
+     "shell.execute_reply": "2023-07-26T19:52:04.055977Z"
     }
    },
    "outputs": [
@@ -1952,10 +1952,10 @@
    "id": "6c78ef8b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:15.408333Z",
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-     "shell.execute_reply": "2023-07-26T05:29:15.731605Z"
+     "iopub.execute_input": "2023-07-26T19:52:04.057973Z",
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+     "shell.execute_reply": "2023-07-26T19:52:04.337591Z"
     }
    },
    "outputs": [
@@ -1991,10 +1991,10 @@
    "id": "a18402fb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:15.734889Z",
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-     "shell.execute_reply": "2023-07-26T05:29:15.959066Z"
+     "iopub.execute_input": "2023-07-26T19:52:04.339647Z",
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+     "shell.execute_reply": "2023-07-26T19:52:04.520598Z"
     }
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    "outputs": [
@@ -2041,10 +2041,10 @@
    "id": "80e18950",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:15.962416Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.047676Z"
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+     "shell.execute_reply": "2023-07-26T19:52:05.957950Z"
     }
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    "outputs": [],
@@ -2067,10 +2067,10 @@
    "id": "fe8c404d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.051216Z",
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     }
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    "outputs": [
@@ -2117,10 +2117,10 @@
    "id": "a99034cb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.333539Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.451505Z"
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     }
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    "outputs": [
@@ -2157,10 +2157,10 @@
    "id": "c613a5df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.454682Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.604143Z"
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     }
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    "outputs": [
@@ -2202,10 +2202,10 @@
    "id": "bef21527",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.607805Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.607474Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.730768Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.542968Z"
     }
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    "outputs": [
@@ -2249,10 +2249,10 @@
    "id": "4435ed50",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.733591Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.736428Z"
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     }
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    "outputs": [
@@ -2288,10 +2288,10 @@
    "id": "7a2c664a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.739070Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.738902Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.741608Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.551055Z"
     }
    },
    "outputs": [
@@ -2329,10 +2329,10 @@
    "id": "810eafdf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.744281Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.744099Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.746807Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.554420Z"
     }
    },
    "outputs": [
@@ -2366,10 +2366,10 @@
    "id": "f7a9f652",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.749618Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.749428Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.751846Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.556103Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.557619Z"
     }
    },
    "outputs": [
@@ -2413,10 +2413,10 @@
    "id": "24427538",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.754673Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.754528Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.757315Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.561317Z"
     }
    },
    "outputs": [
@@ -2454,10 +2454,10 @@
    "id": "18684ed5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.760251Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.760111Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.762782Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.563528Z",
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     }
    },
    "outputs": [
@@ -2495,10 +2495,10 @@
    "id": "34d27b73",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.765326Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.765144Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.767768Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.567363Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.569104Z"
     }
    },
    "outputs": [
@@ -2534,10 +2534,10 @@
    "id": "1bb799c5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.770122Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.769970Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.772652Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.573303Z"
     }
    },
    "outputs": [
@@ -2575,10 +2575,10 @@
    "id": "de853e7c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.775350Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.775182Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.777807Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.579142Z"
     }
    },
    "outputs": [
@@ -2611,10 +2611,10 @@
    "id": "e71bf672",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.780228Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.780099Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.782588Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.580765Z",
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     }
    },
    "outputs": [
@@ -2647,10 +2647,10 @@
    "id": "458fe5c9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.785646Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.785525Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.812287Z"
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     }
    },
    "outputs": [
@@ -2686,10 +2686,10 @@
    "id": "703832ac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.815878Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.815556Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.818526Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.608099Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.610009Z"
     }
    },
    "outputs": [
@@ -2726,10 +2726,10 @@
    "id": "dbf328b7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.821663Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.824596Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.611672Z",
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     }
    },
    "outputs": [
@@ -2768,10 +2768,10 @@
    "id": "65784eb0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.827692Z",
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-     "iopub.status.idle": "2023-07-26T05:29:17.830968Z",
-     "shell.execute_reply": "2023-07-26T05:29:17.830460Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.615285Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.617287Z"
     }
    },
    "outputs": [
@@ -2818,10 +2818,10 @@
    "id": "3ddb0d83",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.833837Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.836240Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.619022Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.620888Z"
     }
    },
    "outputs": [
@@ -2855,10 +2855,10 @@
    "id": "90e72009",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.839230Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.839048Z",
-     "iopub.status.idle": "2023-07-26T05:29:17.844524Z",
-     "shell.execute_reply": "2023-07-26T05:29:17.841903Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.622697Z",
+     "iopub.status.busy": "2023-07-26T19:52:06.622595Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.624509Z"
     }
    },
    "outputs": [
@@ -2894,10 +2894,10 @@
    "id": "fb3b6534",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.847591Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.847413Z",
-     "iopub.status.idle": "2023-07-26T05:29:17.851043Z",
-     "shell.execute_reply": "2023-07-26T05:29:17.850650Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.626305Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.628266Z"
     }
    },
    "outputs": [
@@ -2940,10 +2940,10 @@
    "id": "b146db22",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.854162Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.853950Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.860073Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.630146Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.632081Z"
     }
    },
    "outputs": [
@@ -2979,10 +2979,10 @@
    "id": "6bf7b4a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.864709Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.864481Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.868135Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.635652Z"
     }
    },
    "outputs": [
@@ -3026,10 +3026,10 @@
    "id": "288011d9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.875531Z",
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-     "shell.execute_reply": "2023-07-26T05:29:17.879831Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.637526Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.639703Z"
     }
    },
    "outputs": [
@@ -3066,10 +3066,10 @@
    "id": "3e3b1954",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.885549Z",
-     "iopub.status.busy": "2023-07-26T05:29:17.885336Z",
-     "iopub.status.idle": "2023-07-26T05:29:17.890989Z",
-     "shell.execute_reply": "2023-07-26T05:29:17.890528Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.641572Z",
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+     "iopub.status.idle": "2023-07-26T19:52:06.643852Z",
+     "shell.execute_reply": "2023-07-26T19:52:06.643605Z"
     }
    },
    "outputs": [
@@ -3149,10 +3149,10 @@
    "id": "d02bfb28",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:17.893962Z",
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-     "iopub.status.idle": "2023-07-26T05:29:18.126793Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.126353Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.645370Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.872051Z"
     }
    },
    "outputs": [
@@ -3381,10 +3381,10 @@
    "id": "ca6090af",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.129214Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.133327Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.876815Z"
     }
    },
    "outputs": [],
@@ -3416,10 +3416,10 @@
    "id": "7220aa87",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.136921Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.139936Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.880617Z"
     }
    },
    "outputs": [
@@ -3466,10 +3466,10 @@
    "id": "ca1939f0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.142925Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.142803Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.145934Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.882199Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.884152Z"
     }
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    "outputs": [
@@ -3525,10 +3525,10 @@
    "id": "cfb084e7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.149045Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.153625Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.885767Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.889075Z"
     }
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    "outputs": [
@@ -3565,10 +3565,10 @@
    "id": "2e02b6a5",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.156574Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.155920Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.158724Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.890791Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.892536Z"
     }
    },
    "outputs": [
@@ -3604,10 +3604,10 @@
    "id": "d391f8d0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.161446Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.161286Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.164876Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.896303Z"
     }
    },
    "outputs": [
@@ -3643,10 +3643,10 @@
    "id": "a222ca60",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.167288Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.167156Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.171112Z"
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+     "shell.execute_reply": "2023-07-26T19:52:06.899858Z"
     }
    },
    "outputs": [
@@ -3686,10 +3686,10 @@
    "id": "a75921ac",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.173985Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.173779Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.180116Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.179681Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.901454Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.905425Z"
     }
    },
    "outputs": [
@@ -3801,10 +3801,10 @@
    "id": "7469ff2b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.183321Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.183157Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.200939Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.907164Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.919470Z"
     }
    },
    "outputs": [
@@ -4687,10 +4687,10 @@
    "id": "ac455404",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.203476Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.203352Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.209504Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.209134Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.921228Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.925197Z"
     }
    },
    "outputs": [
@@ -4821,10 +4821,10 @@
    "id": "f41235d4",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.211995Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.211814Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.214581Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.926788Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.928634Z"
     }
    },
    "outputs": [
@@ -4861,10 +4861,10 @@
    "id": "31caba24",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.217421Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.217292Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.224781Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.930332Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.935928Z"
     }
    },
    "outputs": [
@@ -5085,10 +5085,10 @@
    "id": "594bedfa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.227488Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.227297Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.230280Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.229861Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.937585Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.939342Z"
     }
    },
    "outputs": [
@@ -5127,10 +5127,10 @@
    "id": "9a59b083",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.232510Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.232342Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.238242Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.237748Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.941016Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.944870Z"
     }
    },
    "outputs": [
@@ -5239,10 +5239,10 @@
    "id": "54457a11",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.240468Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.240293Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.245745Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.245311Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.946509Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.950214Z"
     }
    },
    "outputs": [
@@ -5350,10 +5350,10 @@
    "id": "8a4f3faf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.247984Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.247834Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.254533Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.254012Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.951907Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.956165Z"
     }
    },
    "outputs": [
@@ -5503,10 +5503,10 @@
    "id": "8f1e6e56",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.256972Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.256754Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.261662Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.261232Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.957764Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.960933Z"
     }
    },
    "outputs": [
@@ -5589,10 +5589,10 @@
    "id": "f1d0e795",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.263825Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.263676Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.268126Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.267724Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.962511Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.965541Z"
     }
    },
    "outputs": [
@@ -5681,10 +5681,10 @@
    "id": "392f10a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.270321Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.270149Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.277370Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.276917Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.967213Z",
+     "iopub.status.busy": "2023-07-26T19:52:06.967130Z",
+     "iopub.status.idle": "2023-07-26T19:52:06.972621Z",
+     "shell.execute_reply": "2023-07-26T19:52:06.972377Z"
     }
    },
    "outputs": [
@@ -6100,10 +6100,10 @@
    "id": "5f5b7906",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.279435Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.279296Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.287423Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.286943Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.974018Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.979118Z"
     }
    },
    "outputs": [
@@ -6523,10 +6523,10 @@
    "id": "b13c5728",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.289828Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.289661Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.296360Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.295846Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.980745Z",
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+     "iopub.status.idle": "2023-07-26T19:52:06.985408Z",
+     "shell.execute_reply": "2023-07-26T19:52:06.985181Z"
     }
    },
    "outputs": [
@@ -6802,10 +6802,10 @@
    "id": "d5a2c3be",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.298956Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.298779Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.306530Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.305978Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.986815Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.992322Z"
     }
    },
    "outputs": [
@@ -7192,10 +7192,10 @@
    "id": "a6278441",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.308697Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.308549Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.311058Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.310729Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.994021Z",
+     "iopub.status.busy": "2023-07-26T19:52:06.993917Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.995520Z"
     }
    },
    "outputs": [
@@ -7235,10 +7235,10 @@
    "id": "2aa065a8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.313814Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.313627Z",
-     "iopub.status.idle": "2023-07-26T05:29:18.316245Z",
-     "shell.execute_reply": "2023-07-26T05:29:18.315821Z"
+     "iopub.execute_input": "2023-07-26T19:52:06.997234Z",
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+     "shell.execute_reply": "2023-07-26T19:52:06.998866Z"
     }
    },
    "outputs": [
@@ -7286,10 +7286,10 @@
    "id": "832c1b7d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.318251Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.321594Z"
+     "iopub.execute_input": "2023-07-26T19:52:07.000500Z",
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    "outputs": [
@@ -7341,10 +7341,10 @@
    "id": "2ca2e7cd",
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@@ -7436,10 +7436,10 @@
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@@ -7492,10 +7492,10 @@
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@@ -7540,10 +7540,10 @@
    "id": "81fdedb1",
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    "outputs": [
@@ -7581,21 +7581,13 @@
    "id": "61850515",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-07-26T05:29:18.559255Z",
-     "iopub.status.busy": "2023-07-26T05:29:18.559034Z",
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-     "shell.execute_reply": "2023-07-26T05:29:18.679833Z"
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    "outputs": [
-    {
-     "name": "stderr",
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-     "text": [
-      "/Users/jonathantaylor/anaconda3/envs/islp_freeze_39/lib/python3.9/site-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
-      "  scatter = ax.scatter(\n"
-     ]
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     {
      "data": {
       "text/plain": [
@@ -7638,10 +7630,10 @@
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@@ -7669,10 +7661,10 @@
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@@ -7717,10 +7709,10 @@
    "id": "2b6fb1ad",
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-     "shell.execute_reply": "2023-07-26T05:29:18.941344Z"
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@@ -7755,10 +7747,10 @@
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    "outputs": [
@@ -7792,10 +7784,10 @@
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@@ -7829,10 +7821,10 @@
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-     "shell.execute_reply": "2023-07-26T05:29:19.318622Z"
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    "outputs": [
@@ -7870,10 +7862,10 @@
    "id": "400690e6",
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    "outputs": [
@@ -7907,10 +7899,10 @@
    "id": "6bbeca8b",
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@@ -7945,10 +7937,10 @@
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@@ -8057,10 +8049,10 @@
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     }
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    "outputs": [
@@ -8113,7 +8105,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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