rerun of airfoils notebook

supervised-airfoils.ipynb

@@ -73,14 +73,29 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "JwZudtWauiGa",
-    "outputId": "802dc68e-ac0d-4a0e-ffb0-30153433b98b"
+        "outputId": "44bb304e-5839-41a1-9cb0-2f52a41f0d90"
       },
       "outputs": [
         {
-     "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Torch version 1.11.0+cu113\n",
+            "Downloading training data (300MB), this can take a few minutes the first time...\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/urllib3/connectionpool.py:847: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings\n",
+            "  InsecureRequestWarning)\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
           "text": [
-      "Torch version 1.7.1\n",
             "Loaded data, 320 training, 80 validation samples\n",
             "Size of the inputs array: (320, 3, 128, 128)\n"
           ]
@@ -118,7 +133,9 @@
     },
     {
       "cell_type": "markdown",
-   "metadata": {},
+      "metadata": {
+        "id": "u488C6ybnMCN"
+      },
       "source": [
         "If you run this notebook in colab, the `else` statement above (which is deactivated by default) might be interesting for you: instead of downloading the training data anew every time, you can manually download it once and store it in your google drive. We assume it's stored in the root directory as `data-airfoils.npz`. Afterwards, you can use the code above to load the file from your google drive, which is typically much faster. This is highly recommended if you want to experiment more extensively via colab."
       ]
@@ -145,20 +162,20 @@
           "height": 353
         },
         "id": "6y6YGxMeNECD",
-    "outputId": "91a5b3c7-4b87-4e31-d6e8-105d09d08f7e"
+        "outputId": "20e4ed46-2ec0-4144-d9da-e49a9f6aa6a2"
       },
       "outputs": [
         {
+          "output_type": "display_data",
           "data": {
-      "image/png": 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truncated
             "text/plain": [
               "<Figure size 1152x360 with 2 Axes>"
-      ]
+            ],
+            "image/png": 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truncated
           },
           "metadata": {
             "needs_background": "light"
-     },
+          }
-     "output_type": "display_data"
         }
       ],
       "source": [
@@ -205,12 +222,12 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "BGN4GqxkIueM",
-    "outputId": "b97ce299-8c12-402a-d90a-c583224c8971"
+        "outputId": "edafc7f9-9f88-4d6a-ebea-77500412ef1e"
       },
       "outputs": [
         {
-     "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Training & validation batches: 32 , 8\n"
           ]
@@ -258,7 +275,7 @@
         "\n",
         "Here's a overview of the architecure:\n",
         "\n",
-    "![An overview of the U-net we're using for this learning task](resources/supervised-airfoils-unet.jpg)\n",
+        "![An overview of the U-net we're using for this learning task](https://github.com/tum-pbs/pbdl-book/blob/master/resources/supervised-airfoils-unet.jpg?raw=1)\n",
         "\n",
         "First, we'll define a helper to set up a convolutional block in the network, `blockUNet`. Note, we don't use any pooling! Instead we use strides and transpose convolutions (these need to be symmetric for the decoder part, i.e. have an uneven kernel size), following [best practices](https://distill.pub/2016/deconv-checkerboard/). The full pytroch neural network is managed via the `DfpNet` class."
       ]
@@ -378,12 +395,12 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "N1uV8k5VIoqT",
-    "outputId": "ceb8fb79-6154-423a-9d17-d6e4dd0c2718"
+        "outputId": "f91c9beb-5ca9-4516-ae74-7ba87b2a2b0e"
       },
       "outputs": [
         {
-     "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "Trainable params: 147363   -> crucial! always keep in view... \n"
           ]
@@ -440,14 +457,22 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "kkOYvwxqKa8n",
-    "outputId": "a973331c-9e8f-401f-dfd7-9b7177faca0f"
+        "outputId": "5daf4281-1151-4fc4-fa3a-74a9ff8435d9"
       },
       "outputs": [
         {
-     "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
-      "Found existing network, loading & skipping training\n"
+            "Training from scratch\n",
+            "Epoch: 0, L1 train: 0.29219, L1 vali: 0.23295\n",
+            "Epoch: 1, L1 train: 0.25406, L1 vali: 0.22507\n",
+            "Epoch: 2, L1 train: 0.22487, L1 vali: 0.21019\n",
+            "Epoch: 20, L1 train: 0.05228, L1 vali: 0.04134\n",
+            "Epoch: 40, L1 train: 0.03730, L1 vali: 0.03020\n",
+            "Epoch: 60, L1 train: 0.03236, L1 vali: 0.02523\n",
+            "Epoch: 80, L1 train: 0.03364, L1 vali: 0.02302\n",
+            "Training done, saved network\n"
           ]
         }
       ],
@@ -522,20 +547,20 @@
           "height": 286
         },
         "id": "Hlki3bU8JMTT",
-    "outputId": "2a36d013-5af2-4580-f782-86b65f6f8c11"
+        "outputId": "fa4b4911-85f2-478d-87e2-44b90bf5b83b"
       },
       "outputs": [
         {
+          "output_type": "display_data",
           "data": {
-      "image/png": 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truncated
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
-      ]
+            ],
+            "image/png": "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 truncated
           },
           "metadata": {
             "needs_background": "light"
-     },
+          }
-     "output_type": "display_data"
         }
       ],
       "source": [
@@ -576,29 +601,24 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-     "height": 386
+          "height": 353
         },
         "id": "N6OONK0bL4ev",
-    "outputId": "240ad666-e266-42d1-c13f-3573eddc7221",
+        "outputId": "99dcb709-c2c3-4407-f0d4-9e9a91cee3bb",
         "scrolled": true
       },
       "outputs": [
         {
-     "name": "stderr",
+          "output_type": "display_data",
-     "output_type": "stream",
-     "text": []
-    },
-    {
           "data": {
-      "image/png": "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 truncated
             "text/plain": [
               "<Figure size 1152x360 with 2 Axes>"
-      ]
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAE8CAYAAABQAcL1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9ebQsy1Xe+e3Iqjrn3vsmvafBQgNC4omZxjYWk3HTDRgWxg1msZgbZNO4GUwbDLhxNxhauHvRg+02y0yCZQFuMa5ls2SgQdBgy8igCZtBMggJEO8J6U33vfvucM6pyozdf0RE5o7IiMjIrKozvdx31T1VmZGRkVlZuX/57R0RxMyYbbbZZpttttlmU2fdgNlmm2222Wab7XzYDAWzzTbbbLPNNhuAGQpmm2222WabbTZrMxTMNttss80222wAZiiYbbbZZpttttmszVAw22yzzTbbbLMBmKFgtktqRMRE9MH2/Q8Q0beXlJ2wny8lotdPbed5t23OzWyzzXbxbIaC2c6lEdEvEtGrIss/h4jeT0SL0rqY+auZ+bt20KaXWCfZ7puZX8vMf3Xbui+jEdGXENF7iOg2Ef0sEd1/1m2abbbZ8jZDwWzn1X4UwJcREQXL/1sAr2Xm+gzaNFuhEdFHAPhBmO/reQDuAPi+M23UbLPNNmgzFMx2Xu1nATwA4JPdAiJ6FoDPBvBjRPQKIvoNInqKiN5HRP+ciFaxiojoR4joH4nP32K3+TMi+ltB2b9GRP+RiJ4mooeI6DvF6jfYv08R0S0i+gQieiUR/brY/hOJ6C1EdMP+/USx7t8S0XcR0RuJ6CYRvZ6Inp1o87OJ6Ofs8V0non9PRMqu+1Yieret4x1E9DfEdq+09f9Tu+0f2Ta90h7Po0T0FcG5+QEi+mVb378jog9MtOmAiP4vIvpTInrEbnclVhbAlwL4N8z8Bma+BeDbAXweEd2dKD/bbLOdA5uhYLZzacx8BOCnAXy5WPwFAH6fmX8bQAPgGwE8G8AnAPhUAF87VC8RfSaAbwbw6QAeBPBpQZHbdp/3AfhrAL6GiD7Xrvsr9u99zHwXM/9GUPf9AH4ewPfAAM0/AfDzRPSAKPYlAP4mgOcCWNm2xOybADwM4DkwT9r/EwA3Jvm7YWDpXgD/C4D/h4ieL7b9OAC/Y9vw4wB+EsBfAvDBAL4MwD8nortE+S8F8F0w5/I/AXhtok3fDeDlAD7G1vUCAP8wUfYjAPy2+8DM7wawttvPNtts59RmKJjtPNuPAvh8Ijq0n7/cLgMzv42Zf5OZa2b+Exip+r8sqPMLALyGmX+PmW8D+E65kpn/LTP/LjNrZv4dAD9RWC9gIOIPmflf2nb9BIDfB/DXRZnXMPM7BfR8TKKuDYDnA/hAZt4w879nO1EJM/8MM/+ZbeNPAfhDAK8Q2/4xM7+GmRsAPwXgRQBexcwnzPx6GOcskwd/3j7RnwD4nwF8AhG9SDbGhnH+NoBvZObrzHwTwP8G4IsS7b8LwI1g2Q0As1Iw22zn2GYomO3cGjP/OoDHAXwuEb0MxvH9OAAQ0cutvP5+InoaxkFFpfjAPgDAQ+Lze+RKIvo4Ivo1InqMiG4A+OrCel3d7wmWvQfmidrZ+8X7OzDOM2b/J4B3AXi9DQF8q2jjlxPRf7LhgacAfGTQxkfE+yMAYOZwmdxvez6s1H/dHou05wC4CuBtYr+/aJfH7BaAe4Jl9wC4mSg/22yznQOboWC2824/BqMQfBmAXxLO7fthnsIfZOZ7YOT1MCkxZu+DeXJ29uJg/Y8DeB2AFzHzvQB+QNQ7NKXonwEI4/EvBvDegnZ5xsw3mfmbmPmlAP4bAH+PiD7Vxvt/CMDfAfAAM98H4PdQduwpa8+HDSvcD3Ms0h6HgYmPYOb77OteZk5BzdsB/Bei3pcCOADwzi3aOdtss+3ZZiiY7bzbj8HE/b8KNnRg7W4ATwO4RUQfCuBrCuv7aQCvJKIPJ6KrAL4jWH83gOvMfExEr4DJAXD2GAAN4KWJun8BwMttV7wFEX0hgA8H8HOFbWuNiD6biD7YyvY3YHIoNIBrMHDymC33N2GUgm3ss4joL9tEze8C8JvMLNUUMLOGgZF/SkTPtft+ARF9RqLO1wL460T0yUR0DcCrAPwrG3aYbbbZzqnNUDDbuTabL/AfYJzh68Sqb4Zx2DdhnNVPFdb3/wL4vwH8Kow8/6tBka8F8CoiugmTRPfTYts7AP5XAG+0EvrHB3U/AdM74psAPAHg7wP4bGZ+vKRtgT0I4FdgZPjfAPB9zPxrzPwOAP/YLnsEwEcBeOOE+qX9OAwcXQfwF2FUmZj9jzDn7DdtyOZXAHxIrCAzvx0m9PJaAI/CwNZgIuhss812tkY2d2m22WZ7BhoR/QiAh5n52866LbPNNtvZ26wUzDbbbLPNNttsAGYomG222WabbbYLZ0T0L+xgZL+XWE9E9D1E9C4i+h0i+gtF9c7hg9lmm2222Wa7WEZEfwUm5+jHmLmXbExEnwXg6wF8FsyAZv+MmT9uqN5ZKZhtttlmm222C2bM/AaY5OCUfQ4MMDAz/yaA+4KRT6M2Q8Fss80222yzXT57AfyB2h6GP5Ba1Iqnnx1jz773Kr/kefeZD6OiEyNDGRc58lE81ExBwW2GrSm13mSFW9id9cC+JtTZ26awkrH7Oo1zHdrY67y4PGc/7mJfU8KTDz0anddq57bN7WPMYZUWTZbjgjKRtXv4Os+FPd28/3FmTo2keS7tMz7jFfzEE+Go33l729ve+XYAx2LRq5n51TttWMT2AgUved59eMv3/HeAHnGJjSqrxzdq6r6GTG3hJVShUDO0j8H1A/spOQYqaGvhudC/9Sdim4I6wmUBoFDJNlPLlKxry2whvJVc06nrtnR5rJxYxuH6mOcbqjM4jF6dBe34hu97Sa7K0TbkwGPNCRfpoJLeqcrUyWKttzxz6iRMyW20qIsTy+N1h+3vH3TsNIXb+fuYfh/N1StNCQrXYCgQfunG/x4OJX7u7YknbuBNb/7BUdssqv/qmJk/dovdvhf+6K0vRMHoqhcvfDAVCDR3r522h6fXrfV2zqB4/ba31QtmQ85eUTkQxMp661X32sZkPam6Um0ZaqMsl7EeXO1SHcrZwPWrMq8SI8ofSuy0hItUUEHvVBW25TRsLBAwxgEBM48CAg3uvaZsm2vTuTdGd78vfW1vrwPw5bYXwscDuMHM7xvaaC9KwWgrdaZjT9SuAWDsPktVBK2HnYrm7VSJbWyHKoG/TUmZkSrB2M9jlrXrToGl3T5i17xrW3h9K+pff1PK7NN2vC/5TQzdHdylE/NnsdNA8J2lIvIc6ranjmicilGqEnjbFwDB0DbdPocP9sI67b0b7/zhjIh+AsCnAHg2ET0MMyrpEgCY+Qdghl3/LJhRSO/ATNk+aPuDgmJHvwcgOAsYiJlrR4nDdMeXczg5MDhLaDgvtm8gKIGBKU/XuZut3Gf4G9iD0ydFccn/tGyL+2b47aSqSjnjKWCQKivrIpAXQhiybWT53Kb7BIIZBgpsx1DAzF88sJ4BfN3Yes9WKdg1EJwXGAhtLBxMfRLNQsMW9Z5Ti+YSpGybnILceduFxB7WkboBKxUHAyAPAmM/h23j6ZAxxUqeoEvMfWuxu0dKNRh7ePsuv41l8wEKy+dgYAaBEcbYzUV9CrYfKNjlsZ8WEPAIiiuR02NWCgc5B36ZFYEhJz0mMDw2OTMKDYnvYN+xdll/z2slwgo7BIFTVQtSbSg4xaX32LFwEJ6aqWrBviwVOihVCcJiY9WBsTkBuzB1rrI2ptjuwwf7srNTCkpuOrtIwovZGAAo3X4MKJQ49qlgMAUaLiBoZFWCbfMMpsLANucw96QOxOFgl2Aw9RH2tHMShMW+jmwkxv5NwcFUMEidgl2emrHOVZZPAUyq3m1gYF/qwaVQJWYo2NJ2DQTbgsCY+ksAYVswOE9W6gz3dSw7BYRIG1MwsEuQCuvqBbdjj7QR1WAfiYQT9fye2nAK8JATWZyl4GAsGIxq18i8grFWmng41IIYEAw55EvhsPdtjBkKsrZtF7rzBAO5fQ7BQUk4IQUGkxSBCwIZMSuV7LcChODcbAEDQ/kOg/J8qodBzEGHqkEOBAoh4VRCCIkxDnLy/BgbAoQYHIwBg5RasKsQQnT8hELn39VRHjYYAwQzCIy1OXww3XYFBGcBA7k25ABhqnyf2u4ChgPGWHGC4ShAEN9PDAYydY1KeMxsE3XCMThIqQYpMCgNG5xhKCC0cFyAlJXCQ7Y7IsaBwRg7jVOaAoVtgGBbGNiFKkIXPo9A2KwUZGybX0jJtlNhYGo8tdRYTweDi/yE72xs+6c4/lII2BEQTIGBnMn6eoCQgoOpYHDaNnIkxFJLwUMyETCVooFyMChRC/JtHnf4MUc85kk9BwSl6kA+JLGf6ypW78UFBQadhwfVAjtfSkGOpHYJBDvL/kncuFM2FFbIhRPGhhFOSy04r4pECgLGAEHk2AZBYAq8Ra57t58oHORUg1gCYmzbXQGDP7rOuTEJCzFAiIotrrwoMxYM2vrRd8b7tpLkwhwQjIGBfeZH5Oys9rsTm5WCiOVuPNsAwWnDwFD9u4CDMWBw3u00EgzHvg/bRZlyyMDALo4tM0BRVD0YUg0kGJxRR/psPkLmMXkfeQwOEFJwkFMNxuZYZod82DLZMNaO+PQU8bDBGCDYBQxMmd/g0hrj3ITmhux8KQVTrAQITvvLCOO3KcuFFMaAwRhV4CKChXDYO5PsC4EgPtnSDiaYctZTAvKAwOG1NRYMRigEO0s2HFnHtt9wam8p9SClGsTAYIxa0O13+BSE4YuclT7RlwBBeV35A9gm8TC17eWCBZ6Vgp7tQyUYAoIpN7QhuXbKGAC57aaAQWnZ85BweNoAUqQYTASC3ERFUy3XFTECCL2wQqgajBnDd8z6XLine truncated
           },
           "metadata": {
             "needs_background": "light"
-     },
+          }
-     "output_type": "display_data"
         }
       ],
       "source": [
@@ -646,54 +666,55 @@
           "height": 1000
         },
         "id": "dD5O58L9HLE-",
-    "outputId": "9ea61f54-2bc2-4881-f6bf-e37502e48383"
+        "outputId": "89c56574-3010-4069-9664-a59894f5dec1"
       },
       "outputs": [
         {
-     "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
+            "Downloading test data, this should be fast...\n",
             "Loaded 10/10 test samples\n",
             "\n",
             "\n",
-      "Average test error: 0.027735761646181346\n"
+            "Average test error: 0.028802116494625808\n"
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
-      "image/png": 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truncated
             "text/plain": [
               "<Figure size 1152x360 with 2 Axes>"
-      ]
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgUAAAE8CAYAAABQAcL1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9e7BtX1bf9R1zrb3Puff+nk033dBp6KYhYMSiSlKBhNIkJhqCRpCkigDREJ8pgSRqjJSRgFr8oWWipEKBxAqUCcpTIgIikbwslaQBK0gTiDwC/eLXv/49+nd/995z9l5rDv8Yc8w11lxzvfbjnL3PXePWuXvvtdea67HXmvMzv2PMMYmZsdhiiy222GKLLeZu+wAWW2yxxRZbbLHTsAUKFltsscUWW2wxAAsULLbYYosttthiwRYoWGyxxRZbbLHFACxQsNhiiy222GKLBVugYLHFFltsscUWA7BAwWKL3Tkjon9MRL/3to9jscUWOz9boGCxO2tE9Kb580T0xHz+yh3K+9tE9G8e41hPxYjo9xDRLxDRYyL6W0T0qbd9TIstttjN2QIFi91ZY+Zn9A/ArwP4A2bZd9328Z2aEdFbAfxPAL4ewFsA/BSA77nVg1psscVu1BYoWOypMyJyRPR1RPTLRPQKEX0vEb0lfHdJRH8tLH+diN5HRG8nom8C8M8A+EtBafhLmXKz24bv/hgR/UMiekhEv0JE/47Z7ncR0QeJ6M8Q0UeJ6CNE9CVE9EVE9I+I6FUi+o/N+t9IRN9PRN8TyvsZIvqcueeasS8F8H5m/j5mvgLwjQA+h4g+a8dLvdhii52ZLVCw2NNoXwvgSwD8TgCfDOA1AN8SvvujAJ4H8C4AnwDgjwN4wsx/FsD/AeBrgtLwNZlys9uG7z4K4F8C8ByAPwbgvyaif9ps+w4AlwDeCeDPAfjLAP4IgM+FwMjXE9F7zPpfDOD7ID36/wHAXyei1cxzTe2fBPAP9AMzPwLwy2H5Yost9hTYAgWLPY32xwH8WWb+IDNfQ3rEf4iISgBbSIP+6cxcM/NPM/MbE8vt3ZaZf4SZf5nF/g6AH4c09nbbb2LmLYDvBvBWAN/MzA+Z+f0Afh6AVQN+mpm/P6z/FyBA8fkzzzW1ZwB8PFn2cQDPTjz/xRZb7MwtVzEstthdt08F8INE5M2yGsDbAfxVSE//u4noBQB/DdKobieU27stEf1+AN8A4DdDYPw+gP/XbPsKM9fhvaoLL5nvn0AabbUP6Btm9kT0QYgSMOdcP5Ss+yZEybD2HICHmXIXW2yxO2iLUrDY02gfAPD7mfkF83fJzB9i5i0z/6fM/FsA/A6I5P+vhe0GpxTt25aILgD8AID/CsDbmfkFAD8KgPY4h3fpGyJyAH4TgA/POdfMuu+HUSOI6AGA94bliy222FNgCxQs9jTatwH4Jh1uR0RvI6IvDu9/NxH9U0RUAHgDIutrL/slAJ/WV+jAtmsAFwBeBlAF1eBf2PMcPpeIvjS4Af4UgGsAPznnXDP2gwA+m4j+IBFdQmIbfpaZf2HPY11sscXOxBYoWOxptG8G8EMAfpyIHkIa088L370DwPdDGvV/CODvQNwCut0fIqLXiOgvZsrNbsvMDwH8CQDfCwn0+4qw/33sfwbwZaG8fxXAl/a4OIbOtWXM/DKAPwjgm0K5nwfgD+95nIstttgZGTEPKqKLLbbYiRkRfSMkmPGP3PaxLLbYYnfLFqVgscUWW2yxxRYDsEDBYosttthii52dEdFfCcnOfq7neyKiv0hEv0REP5vkRekvd3EfLLbYYosttth5GRH9s5BhxP89M3925vsvgiQv+yJIfNA3M3M2nsjaohQstthiiy222JkZM/9dAK8OrPLFEGBgZv5JAC8Q0SeNlbtAwWKLLbbYYovdPXsnTJIzAB8MywbtKBkN3/rsPX73254DaJ/cLHvYLe12lp2y1+bILiV+XB2gkP2LOLrdxH14Svf6XsfSbPzoYb5aOsSp0h43zqGqs9nHMLDfU/r5b8p+7o2XP8bMb7vt45hjv+/3/TZ+5ZU0g/iw/fRP/6P3A7gyi76dmb/9oAeWsaNAwbvf9hze901fCbgRIcJNuKWnrLPLuqdifodKau42s9f34+sY45nlb//Bx8J2szYT2+V63bbtcF/SHA1vxrrzyp153HvojmT29b6f+MT+9QYaVEfd73KnkCsj3TYHAN11MuVgfjl9ZfWtO7ZNs+3g14M2VvZtGLOc0Kf/2Lf+2i0fymx75ZWP4+/9/f921jZl8buvmPm37rHbD8FkPoVkPc1lMm3ZabsPbgsIyOX/jmGOdqh8j73+8W+L2UDg+TyBANjp2HcCpjM1mnF/8kDf2HP3u5u8ZW4KCIj4KECg5Z4iEADTzvtkjSGdrTl/+9sPQdKsExF9PoCPM/NHxjY6/wmR9sLhmY3f0Pr71uKO5tVgc9c/IdsJCA617x2KOpgXzPOs+5X9xFvU49Txfpbpz913qRQMdnUFMGgvN8JN2BAQDG83bz9n28ienfGhGvpoRPQ/AvhdAN4aJkT7BgArAGDmb4PMr/JFAH4JwGPIlO2jdjwo2Nd1sO/3fXasHr8td1dAOCYYnCtE7HHMhwqNyJWzMyiMtXhHtsmgcQI2k6HCNtRpUKeUk27HfPiQqKkqwbGBYAGBW7IDQwEzf/nI9wzgq+eWeztKwW0AwU3WhPsAgp7bGTX25Gh2XMEk26HMm0q7ofvZCw4m3Md3XS0Ycx0MXaZ9evynqhbsAgQLDJyBMW6uctrTzs99MFsfu+WaktxuysExGvtZyoI7ONlOthOGgaH9zgaEQ4PBHbW5YLCrWjDH0gZ2LJ5gTizBnPWmnNOhYWDX8jgT8/H02OHdB8eym4eCfVSAOU/1KdWieiy7qAZTGsgTUAsOZnMD8k7otHdSDw7ZWp2pWjDFDt2o37ZNdRv0ByHuto+pdgxVoa/MpwYWFijYwQ7x1J8SDKS2CxwcGgxOGSBmHNcpwUBqs/3RE1q8Q6oF56o89F2mqWrB2HZTttnFppR5SCDYpUG/TddCuu87CQk6+uAM7DhQcIykRZPQeIeablcQ2adh3dWlcJftNoFAf4oDNpR7xxzkyjzlxnyH4+qLJ2Cm3kaqL05zSozATakNY7/5Pg3woYDglOML7LHdHUBY3Ad52/WJHH0SZtZIh6gZbBm7AMIc1eAuqABDdlNAMHapx77foeGbDAeHarHuiAtBG4MhOBgDg2P1/PexfdwGQ7fHXYCBnN0ZQHjqlYJdrO+OPwQQHLt7sA8gTFUNzrXBH7NjAsGhn0Fb3syGd5JL4YbdCOdgY6rBXDA4hlqQBhm2vhsdRrgfEExp5M8NBPrsvM+DQWeiDp8OFOxipwAEQ/ub2uAd0p0wBR4mKw+3OALB2CwYuKnD3QEQDgUGd8FmZTEcUA32vVw3OTRx1wZ8VyA4VCO6i9qSyy751NsJ1KVT7DSgYBeVYAwITqFindO7nwIGd00tmHAuk4HgNp+3GTEJk9wJIy3dqFpwR1wIqfWpBunlug21QM3+rnNVgqlAcCwYOKSrpa+spxYWGGdTd98cFMxt+E8BCA7RU56TjOhQYHAO8HAoIDgl+J4JB/uAwUnZ7GzhIwmLBooccie01ttRAbAAccishp28BkcAgjkwcFuxFt38EWdyj+9tS6Dh8WwICGZNoDSxJutbb+4PPLWhXkYmADguEMy5vDv57yfCwT6Nzr6xBacemzB0CXNgMMZQQ2rBTWc3PDQQTIWBUwu6BJD8JncYEJZAw8QONavfvkBwyNn/0rKm/OBTVYMxMDiEEnCbasLIfkeBYOaztQ9j5bad3JhOkPEHwWAfteBEXQhzYgmA/tOYAgbHGo1g99sXZGj3M1fOnwoEU8o9RRDoszsPCGfS2btdpeBgMv9QFsQbqhl1P1Ph4NhgcION/qx5D24ICI75/NmyRwFh3xwIA2Bw6r39Q1nfJcwFII6BwTFtiuozphKkP/Uu6sBhwGf6usdIJHb3AGFxH+xuc1SC0ZTJt1BjToWDQ4DBHbNDAMFNXy7d3yQ46FnnGDPy3ZjNeMTmqgSpDcHBEBi0yjBqwU24EObEAEwBgkPCwOHiJfLLDwULze91rg/JednpQUHO5gLBXBiYUlnN7XVPgYN9e/PnEFA40fYFgttmp0m99l3B4JyCDg9gtvLPNXS5yzgEBnMb/H3dDH3bDrkU9gGCqcd60+CZ298+oHBOrpCOLaMPJtjk+T4PDASHzKp4iLH+Yw37PmrBXYGGgdOfdWn2AYcpIwqmqAYH9vXfdRdCXwM9BQymlHko3hprcPcBgl1h4BTVJz2mU5675Di2uA92s87TMQMIjgUDU8ocHSI4ohrs03jfgYZ/sII4BBAc4llMyxi43UbhoAcMztqNMGL7uA5UOegMZwuv9lJaMJirFhzahTAtEHB4m6lpkdvbjB/bbZs9xqcGEBYouEEbAoKbkF3n5CIYKqNv+2PFFpwAUBwNCI79/E0IIBzsxc8Fg126tPuoEpNG8+xY9o42BAe9l7kHDA4xEiE38iBXZp9KcEgg2AUEDpHx8BDzETwVgMAALVAw0yZVQpl1+oBgVs6C3qik6WXYcvq2O6ZicA42a5RC/1eDQHDTz90IHNx1eX+KzVUJGM36uZ57rkG3YDDVjdCUd/i+w9wGdwwI9oWBY80b0FfurrBwd90LfDYndTpQkFpakx4SCKbWADvHEUyAg7lgsKtacMKgscsz0nsJjjhUcVKjPhRE2AcGN6EW3LLtO+JAASGFgzlgcEy1ABhumKeoBLsAwTGnZz6E7Tu74Z2Eg0UpGLBjVmy7zra4S/mnkm74Fhv+WTkKplrPs7MrEOzreZmcl+CAYDDHdlEjBtc/QddBDg5y7oQpYDC2HwJHYJgT5zEEGIcCglOHgZztAwh3Bg4YCxTsZbuqBNn1bgBAdkkidJNqwLine truncated
           },
           "metadata": {
             "needs_background": "light"
-     },
+          }
-     "output_type": "display_data"
         },
         {
+          "output_type": "display_data",
           "data": {
-      "image/png": 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truncated
             "text/plain": [
               "<Figure size 1152x360 with 2 Axes>"
-      ]
+            ],
+            "image/png": "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 truncated
           },
           "metadata": {
             "needs_background": "light"
-     },
+          }
-     "output_type": "display_data"
         },
         {
+          "output_type": "display_data",
           "data": {
-      "image/png": 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truncated
             "text/plain": [
               "<Figure size 1152x360 with 2 Axes>"
-      ]
+            ],
+            "image/png": "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 truncated
           },
           "metadata": {
             "needs_background": "light"
-     },
+          }
-     "output_type": "display_data"
         }
       ],
       "source": [
@@ -786,5 +807,5 @@
     }
   },
   "nbformat": 4,
- "nbformat_minor": 1
+  "nbformat_minor": 0
 }