data_create_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cpu\"\n",
    "# device = \"cuda\"\n",
    "import time,os,copy\n",
    "import idx2numpy \n",
    "import numpy as np \n",
    "from tqdm import tqdm\n",
    "\n",
    "seed = 42\n",
    "np.random.seed(seed)\n",
    "\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# OpenMP: number of parallel threads.\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"16\"\n",
    "\n",
    "create = False\n",
    "\n",
    "class_names = [\n",
    "    \"6\",\n",
    "    \"7\",\n",
    "    \"8\",\n",
    "    \"9\"\n",
    "]\n",
    "num_classes = len(class_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 1, ..., 2, 0, 2])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16788 7195\n",
      "16788 7195\n",
      "7195 7195 16788 16788\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr3 = []\n",
    "arr4 = []\n",
    "file1 = './MNIST/raw/train-images-idx3-ubyte' \n",
    "arr = idx2numpy.convert_from_file(file1)\n",
    "file2 = './MNIST/raw/train-labels-idx1-ubyte' \n",
    "arr2 = idx2numpy.convert_from_file(file2)\n",
    "for i in range(len(arr)):\n",
    "    if arr2[i] in [6,7,8,9]:\n",
    "        arr3.append(arr[i])\n",
    "        arr4.append(arr2[i])\n",
    "\n",
    "n_steps = 10\n",
    "train_images = np.asarray(arr3,dtype=np.float32) / 255.0\n",
    "train_labels = np.asarray(arr4,dtype=np.int64)\n",
    "train_labels -= 6\n",
    "display(train_labels)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5, 5, i + 1)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(class_names[train_labels[i]])\n",
    "\n",
    "# splitting the full dataset of 5 classes into test and train images to ensure the testing does not happen on images from train data.\n",
    "split = np.array_split(train_images,[int(len(train_images)*0.7)])\n",
    "train_sequences = split[0]\n",
    "test_sequences = split[1]\n",
    "print(len(split[0]),len(split[1]))\n",
    "split = np.array_split(train_labels,[int(len(train_labels)*0.7)])\n",
    "train_labels = split[0]\n",
    "test_labels = split[1]\n",
    "print(len(split[0]),len(split[1]))\n",
    "datalength = 5000\n",
    "# we restrict the training to first 5000 images of 21000\n",
    "train_sequences = np.tile(train_sequences[:, None], (1, n_steps, 1, 1))#[:datalength]\n",
    "test_sequences = np.tile(test_sequences[:, None], (1, n_steps, 1, 1))\n",
    "# train_labels = train_labels[:datalength]\n",
    "if create:\n",
    "    np.savez_compressed(\"./saved_data_MNIST_last4/test_labels.npz\",test_labels)\n",
    "    np.savez_compressed(f\"./saved_data_MNIST_last4/test_seqs_{seed}.npz\",test_sequences)\n",
    "    np.savez_compressed(\"./saved_data_MNIST_last4/train_labels.npz\",train_labels)\n",
    "    np.savez_compressed(f\"./saved_data_MNIST_last4/train_seqs_{seed}.npz\",train_sequences)\n",
    "\n",
    "print(len(test_labels),len(test_sequences),len(train_labels),len(train_sequences))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Salt and pepper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def salt_pepper(test_sequences,test_labels,prob,salt=0,pepper=1):\n",
    "  sampled_images = test_sequences\n",
    "  sampled_labels = test_labels\n",
    "  for j in range(len(test_labels)):\n",
    "    for i in range(np.shape(sampled_images[j])[0]):\n",
    "      for y in range(28):\n",
    "        for x in range(28):\n",
    "          r = np.random.random() \n",
    "          if r < prob:\n",
    "            sampled_images[j][i][y][x] = salt\n",
    "          elif r > 1-prob:\n",
    "            sampled_images[j][i][y][x] = pepper\n",
    "  return sampled_images,sampled_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if create:\n",
    "    for i in range(1,10,1):\n",
    "        d , l =salt_pepper(np.load(f\"./saved_data_MNIST_last4/test_seqs_{seed}.npz\")['arr_0'],np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0'],i/100.0)\n",
    "        np.savez_compressed(f\"./saved_data_MNIST_last4/snp_{i/100.0}_{seed}.npz\",d)\n",
    "    for i in range(1,6,1):\n",
    "        d , l =salt_pepper(np.load(f\"./saved_data_MNIST_last4/test_seqs_{seed}.npz\")['arr_0'],np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0'],i/10.0)\n",
    "        np.savez_compressed(f\"./saved_data_MNIST_last4/snp_{i/10.0}_{seed}.npz\",d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noise probability = 0.02\n",
      "noise probability = 0.04\n",
      "noise probability = 0.06\n",
      "noise probability = 0.08\n",
      "noise probability = 0.1\n",
      "noise probability = 0.12\n",
      "noise probability = 0.14\n",
      "noise probability = 0.16\n",
      "noise probability = 0.18\n",
      "noise probability = 0.2\n",
      "noise probability = 0.4\n",
      "noise probability = 0.6\n",
      "noise probability = 0.8\n",
      "noise probability = 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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IEpUcAAAQJCo5AAAgSG4n/WQFN4jB9k9OnDgx4XVnnnmm5pNPPtk7V7duXc12p9X3338/4XvFV03OMrfzS9JScPfT6tWrl3f80EMPabY7GK9YsSJnZcqQbNzPgr6XyWzYsEFzfOVbe6579+6ap0yZku1ipSroZ9OOPZwzZ07C66688krNN910k+bFixd71916662a7ZjJJUuWeNede+65mu309Bwo+Gdz9uzZmu2q/iIiCxcu/OlNY7/L42MSU2FfI/7z27dv12yXDbDT0+Pn7LT2eNmzoNQ/MC05AAAgSFRyAABAkIquu8pOXXvhhRe8c2effbZmO+08VfFVVEePHq35N7/5TZlfrxyCbhK3pk+frrl3797euV12+akObrsLbTdGkSj4JvFs+/rrrzV37txZs+3CiJs2bZrm008/PaX3ycGyAUE/mzNnztTcpUsXzfEp5Lvt9tPqI3YFa9ulIeJ/X9sNH+1GkCL+NGT73OdAUT2bgwYN8o7t76h0u6vsPbN/98l+fo899tDcrFkz75xdIf3yyy9PqQwZQncVAACoOKjkAACAIBVdd1UyduXGMWPGaLZdIiL+isfdunXTfOGFF3rX5XGF3KCbxNeuXav5lFNO0RxfzTOPs9syraiaxLPBzrKxm/bFNWnSRLOdVWJnROZZ0M+mZVe6njRpkncu/p36I9sVKeJ3edlVr+MzcvIop8+m7QJK5/dLfNPMpUuXarYbbcbfy36XHnjggd513333nebf/va3CV/PshshN2zYcGfFzhW6qwAAQMVBJQcAAASJSg4AAAhSUGNyAhJ0v78d+3T33Xdrtjvliviredpd5fMpzT71gh+TU96xAnHxqeEdOnTQbHe3jrM7HR988MHlLkcWBP1sJpKDqfn5UvDPJlLGmBwAAFBxUMkBAABB2m3nlwDlY1eiFhGZPHlyqdfFV5zOVRdVWZriA2qm92Tiz2VXxY2vXp2oiyr+vlu2bCl3OZB5oX7ucy3T3cLYOVpyAABAkKjkAACAIFHJAQAAQWJMDrJi1KhRmqdOneqds/3S7dq10zxu3LjsF6wU9I1nxkMPPaR5+fLlCa+zW3ls3rzZO1dAy/0DGVeI3zWhjxOiJQcAAASJSg4AAAgSKx4XpqJfVXXPPffU/O2333rnbBfVI488orlGjRrZL1h+VIhVVZcsWaK5bdu23rkqVapofu+99zRXr149+wXLrKJ/NuGpEM9mBcGKxwAAoOKgkgMAAIJEd1VhKvom8caNG2vu0qWLd27kyJG5KkahoEk8HEX/bMLDsxkOuqsAAEDFQSUHAAAEiUoOAAAIEmNyChP9/mGh3z8cPJth4dlMQ4GuksyYHAAAUHFQyQEAAEHaWXcVAABAUaIlBwAABIlKDgAACBKVHAAAECQqOQAAIEjBVXKccwc452Y459Y751Y658Y453bLd7lQds65b2L/bXfO3ZnvciE9PJthcc6d6Zxb5Jzb5Jz7xDl3bL7LhPQ45w5xzr3snPvKObfEOdc532XKlOAqOSIyVkRWi0hdETlMRNqIyIX5LBDSE0VR9R//E5H9RGSziEzPc7GQPp7NQDjn2onIcBE5V0RqiMjvReS/eS0U0rLjHxpPi8hzIrKniFwgIg8755rktWAZEmIlp5GITIui6LsoilaKyIsi8ts8lwnl11VKfkG+lu+CIG08m+G4XkT+FkXRW1EU/RBF0f+iKPpfvguFtBwsIvVE5LYoirZHUfSyiLwuIn/Kb7EyI8RKzu0icqZz7hfOufoicpKUfJmiuPUWkQcjFnYqZrcLz2bRc87tKiItRGSfHV0by3d0PVbNd9mQMU5Emua7EJkQYiXnX1Lyr8OvRWS5iLwjIk/ls0AoH+dcQynp2ngg32VBufBshmFfEakkIt1E5Fgp6XpsLiJX5bFMSN9HUtJKPsQ5V8k5115Kvm9/kd9iZUZQlRzn3C5S8i/DJ0SkmojsLSK1paTvGMXrTyIyJ4qiT/NdEKSHZzMom3f8751RFK2IomiNiIwSkZPzWCakKYqirSLSSUQ6ishKEblcRKZJyT9Eil5QlRwpGTTVQETGRFG0JYqitSIyUXj4il0voRWn2PFsBiKKovVS8gvQdh3TjVzEoiiaH0VRmyiK9oqiqIOIHCgib+e7XJkQVCVnx78oPhWRAc653Zxze0jJWI75eS0Y0uacayUi9YVZVUWNZzM4E0XkYudcHedcbREZKCWzc1CEnHP/55yrsmO83GApmQE5Kc/FyoigKjk7dBGRE0XkSxFZIiJbpeQBRHHqLSJPRFG0Md8FQbnxbIZjmIjME5HFIrJIRP4tIjfmtUQojz+JyAopGZtzgoi0i6JoS36LlBnsQg4AAIIUYksOAAAAlRwAABAmKjkAACBIVHIAAECQdrYDMKOS88Nl6XW5n/mRjfvJvcwPns2wFPWz6dxPxWcSUen3kpYcAAAQJCo5AAAgSDvrrgIKAs2ySIX9nIjwWckFns384e9752jJAQAAQaKSAwAAgkQlBwAABIkxOUhbLvvi6XuGleizx+ck9/g7RyGjJQcAAASJSg4AAAgS3VVIG83UyBc+e4Xjs88+09yqVSvvXP369TX//e9/19ymTZuslwvFK5NDIWjJAQAAQaKSAwAAgkR3FYCC8cUXX2i23RsrV670rpsyZYrmZcuWaf7lL3+ZxdLhR1u2bNE8YsQIzatWrfKus8fvvfee5lS7q1jBumLK5H2mJQcAAASJSg4AAAgSlRwAABCkghmT8/3332u++uqrvXPz5s3T/Morr2hu1qyZd12NGjU0H3DAAZrr1avnXdepUyfNRx99dFrlTQe79SJTivmz9Oqrr2oeO3asd27GjBmaN23apDk+NsMer1ixQjNjcnLj9ddf1zx+/PisvU+xfbZReGjJAQAAQaKSAwAAguR20hyYtbbCadOmecf9+/fXvGHDBr8QpozxZutUxP+MlStX1nzZZZdpHj58eMLXyHH3QNn/kKnJS9vv9u3bveMvv/xSc+3atTV/88033nVbt24t9fUefPBB7/iKK64o9bqWLVt6x/b+HnfccYkLnHnZuJ9F1Y4/Z84czV26dNG8du3ahD+T6nM/d+5czS1atEi3iKkK6tlM16JFizQ3bdo04XXjxo3T3KNHD812aEGeVbhn0z4vIiJPP/20Zvuc/upXv/Kua9CggeahQ4dqrlSpUqaLmK5S7yUtOQAAIEhUcgAAQJDyNrsqPqti3bp1mnfZJbt1L9sNMnr0aM12ozkRv0uNUf5ls3r1as2TJk3yzl155ZWaTzjhBM3vvvuud53ttkzWdZGoK+Odd97xjl988UXNybqrinnmUqGI38uOHTtqjndLpqNJkyaabbP6qFGjvOvsSsl2Zd4Q5HM1YDtDzr7vMccc411XoF1UFUKi77Fjjz3Wu27btm2lXme7ruJsN/Mdd9xRrnJmGy05AAAgSFRyAABAkKjkAACAIOVtTE6rVq2843TG4dgpbSIi3bt319y1a1fNdnVOEZGlS5dqnjp1quannnrKu86OHUHZ3HDDDZrHjBnjnbN9xbNmzcpaGX744Qfv2I6/ateunWY7LkiEcTjpev/99zW3bdvWO/f1119rTjYd3K5Ofu2112o+//zzE/6M/R74/PPPvXN2SrndOVtEZPfdd0/4msUgl5/T5557zju+8MILNdv7GR/3xDic3LHT+kVEatasqdn+fo1P+b/kkks016lTJ+Hr33zzzZofeOABzQMGDPCuO+SQQ1IscW7QkgMAAIJEJQcAAAQpp91VH374oeY777wzrdewKyOPHDnSO1etWrVSf+aII45I+Hq9e/fWfOKJJ3rnbrnlFs22ubxRo0apFbYCia8Wfc8995T5NeLdS7b70a6wmay7Y+HChZr79u3rnfv22281200dkb6NGzdqHjZsmOb4NHF7z379619rtt0eIv49q1q1asL3tVNY7ZIQ8c+GLUe8TMXeXZVLH3/8cb6LgJ2Irx5un83WrVtrfvbZZ73r9thjD832d/SBBx7oXTdx4kTNdtXk9evXp1fgHKElBwAABIlKDgAACBKVHAAAEKScjsn56quvNJdlafd99tlHsx37kWgMTlnY5eEPP/xw75zdBsBOQ2dMzs9NmTLFO060g3hcw4YNNV9zzTXeuV69emlOdYmBQw89VPPAgQO9c3ZMjt124Oyzz07ptfFzCxYs0BxfgsGyy/0/+eSTmvfaa6+U3ic+3sD+nN1CJM5el+p74efuvffehOd+97vfaY5/h6Yj2Zg7lndIz5AhQzTbMThxdvr3jTfe6J2zS68UE1pyAABAkKjkAACAIOW0u8o2a8anp3366acJf852UWViBU3bVTFo0CDNr732mnddsmZT+KpUqeIdJ9s13E7bt11UBxxwQLnLYe9tvEvUlmn27Nnlfq9Ckc/dqC27BEC8e9FOL0+n2+jRRx/1ji+99NJSr4t/P9hlIFA2EyZM0GynFov4n7EvvvhCc/wZtrvAV69eXfOMGTO862x3ZrLPb6KdtfFz9u9n1apVCa+L/9770YMPPugdf/TRR5oPPvhgzfHdCwoNLTkAACBIVHIAAECQctpdtdtuP71dshHe8WZIuzlc48aNNb/88ssJX8OOBI9v0Pnll19qtjO+4uwGZ6eddlrC6yAyefJk79iuaB3vyrIbvWWCXdnTzgiws6lE/Kbujh07ZrQM+VQozfaZ7kqwGw7aTQST6dmzp3dsu0FQNvfdd5/mZF33dvXw7du3e+d23XVXzbb7uEuXLt51c+bM0WxnvMYVyme9EFWqVMk7tn9XdtbpySef7F1nN1O2Fi9e7B3bz0AxDeWgJQcAAASJSg4AAAgSlRwAABCknI7JseJjOI4//njN8eluTzzxhGa7WmoyqU5DtOK7El9//fWaMzF1PWTxJQFuu+22nL33m2++qfnpp59OeJ29v/bzhsxI1k9/9dVXa0624rFdWffWW29N6bXr1aunedy4cakVNnD5mmrdvn1777hZs2aa33//fc3xFazXrVuX1XJVBEceeaR3PHLkSM0LFy7UvP/++5f7vfr06VPu18gVWnIAAECQqOQAAIAguZ00ZeasnXPSpEmabdO2iMjy5cs1pzN1Lf5nTOc17HTKHDTVZWt+XhDzL+10UxGRa6+9VnOylYyPO+44zbNmzcp0sZLJxv0siHtpp+/b58J2MYv4z6C9D23btvWus0sAbNmyRXOyZ3bu3LmaW7RokUKpyyXoZ9OuGG6n33///ffedXb1Yrsxa+vWrb3r7D2sVauW5vj9POecczTff//9ZSt0+QT7bNoVyG3X78SJE73rTjnlFM2vvvqq5nnz5nnXNW3aVLN95qpWrVr+wmZGqfeSlhwAABAkKjkAACBIeZtdFWebK6dNm+ads91VybrX7OZwdkXlunXretdt2LBBs52Zk4zdSDK++aAtO7LPzsARSX2zzZNOOikLpcm/fG7QaWcdnnHGGZrj3VWWbRK3uSzs89i8efO0XgM/99JLL2m2XU1xdkZbspmKlStX1pzsc2k3b0Zm2N9TQ4YMKTXHnXfeeZrj3VUHHXSQ5kx0UeVqBiAtOQAAIEhUcgAAQJCo5AAAgCAVzJicVNmp3J07d/bO2d2u7e638ZWMt23bpvnrr7/WHJ8maVeMHDVqlOZ+/fp517Vp00Zzo0aNkv8BkBY77fGhhx5K6Wfiu+0OHjw4o2UqFIWyM/Ppp5+u+fLLL/fOff7552V+PTsFNr4jcrKdqpG6+Lgbu7RCsmn7dtmGZJ577rlSXy/+2ocffnhKr1fRZWP8nd1hYMKECQmvGzRoULnfy8rV9xYtOQAAIEhUcgAAQJCyvuJxqtPEvv32W83JpoTaTd5yudLilClTNPfq1cs7d8QRR2ieOXOmd852oZVB0KuqpuqBBx7QfO6552pOdcXqFStWeMd16tTJTMHKLthVVe2Kx3ba+J///Gfvuk2bNmlO9f4tWrRIcwF1TwX1bMa7Ee0yHFb879/em2Sv17FjR80LFizQ3K1bN+86u8pxjjdDDvbZTJVd8fj555/X3KBBA++6pUuX5qxMaWLFYwAAUHFQyQEAAEHK+uyqVEdQb968WfPHH3/snbMbu23dulVzLrurzjzzTM1jxozxzr3xxhua58+f752zXVlIzs6cExG5+OKLU/q5vffeW/NZZ52l2a56na58riZciGx3sYjIsGHDNNuNGtNlZ1TZzSILqLuqQrJdGnFffPGFZts9JSKycOFCzXaVZNsVLVJQmzxWOF999VWp/3/Pnj1zXJLsoCUHAAAEiUoOAAAIEpUcAAAQpIJZ8diuSly/fn3vnN2F3O6S27Vr17Teq7y7n8Z3zLVjclA2dhzOZZdd5p2Lr0CdiB2HY1emzoSKPgZHRGTAgAGaH3nkEe/cN998U+bXs+M77LgbEX/avx3v06NHjzK/D8ou0efdrgwvIvLwww9rvuGGGzTHV6a2Tj31VM2Mwcmf1atXe8eJpobXrFkzF8XJOlpyAABAkKjkAACAIBVMd1X16tU1H3rood45211lm7fT7a4qbxeEbarNxOuFzq52K+J3Q4wePVpzsu6patWqaX722We9c61atSpvESu8zz77TPMll1zinbObLMY/63ZFb7uitF1yQURk+PDhmteuXau5ffv23nV2OnK8WxjZl2g16vjyDvfee2+pPxP/+QsuuEDzuHHjMlFElFN85Xf7+9Xq0qVLLoqTdbTkAACAIFHJAQAAQcr6Bp3pSLaBX+XKlTXfcsst3rnzzz9fs+3+StW2bdu848cff1zziBEjNMdXfT3kkEM0z5o1yzu37777lrkcUsCbAKYzM23ixIne8XnnnVfm9507d67mli1blvnn86zgNwG0m2uefvrpid80ds/tatN333235qlTpyZ8jbfeektzoqZyEX/14wLqEi7YZzMdtutQxO8ijG+2adn7Yb8T7Ew8EX+2o/3uLiAF/2xmWrx7/80339TcunVrzXPmzMlZmTKEDToBAEDFQSUHAAAEiUoOAAAIUkGOyYnr0KGD5ldeeUVzfAzN73//e812BeXBgwd71x188MGaN2zYoLlv377edfPmzdNs/57i42xmzpypuWnTpqX/Icqm6Pv9Z8yYoTm+g3GyMVdW7969NU+YMCEzBcuPgu/37969u+bHHnss8ZvGvi8S3cu6det6x3aMnP2Z/fff37vOjg+wq+faHaxLK0cOZf3ZjP+d5vLPasfo2LF09nkWEWnevLlm+3zbMR0iBTsOx8rqs5nPe5lIfAr5mjVrNPfv31/z2LFjc1amDGFMDgAAqDio5AAAgCAVRXeV1a9fP83xqcnx7qsfxf+MduOxrVu3av7uu+8SXtewYUPN8RV3GzRosLNil1XRdVfZLjsRkYsvvljzhx9+6J3bZZef6tZ2CvLtt9/uXWdXtC6CZu9kCr67yq5IPHTo0ITXHX744d7xe++9p9luonnVVVd51+23336abRN+fKNG210S7/IqEEX3bCKpgn82My1Zd5Vd2bpPnz45K1O6Ykua0F0FAAAqDio5AAAgSFRyAABAkApmF/JUjR8/XnN8t/K3335bs51qHl86fuPGjZpPO+00zXZ8iIjITTfdpHmfffZJs8QVw4oVK7xjO/032ZTxs846S7Md01Hs0tn+Ip/++te/lppzrUDH4QBF7YUXXtC8fv36hNfVqlUrF8XJmFS+W2nJAQAAQaKSAwAAglR0U8itQlxNMkOKbprq5s2bveMzzjhD8+zZs71zdgq+3RG3yKeJJ1PhpqkGrOieTSRVIZ7NdevWab700ku9c3Z5h5EjR+asTFnAFHIAAFBxUMkBAABBKuruqkKUoS40msTDUiGaxCsIns2wFOSzWWyzMwsE3VUAAKDioJIDAACCRCUHAAAEqehWPC509J8CAMqD3yOZQ0sOAAAIEpUcAAAQpJ1NIQcAAChKtOQAAIAgUckBAABBopIDAACCRCUHAAAEiUoOAAAIEpUcAAAQpP8HlQy2kjrUVz0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1q8vV3xLU5AAAAICVUMgBAAAAK/lurgpjMTi/6eRwU5Nc5M2sJjvnnHMyzJ3u5ptvdt338ccfc9ysWTNtn61DiYlyu7id36pN02OPPcbx7373u1DzZBO/19IcFt6xY0eOgyyQS0TUvn17josXL86xnAWXiGjx4sUFfl42VxLps69CNOS1Nqdj2LVrF8fmAo1u5P3nNvUAkT5ruU2LawYl/86ZTYPFiv1aT2E+I/K3+/HHHzn+4osvtHRnnHEGxxUqVOB47969WrpvvvmG4/vuu49jswlbXiO5sK7ZDUUuyhol1OQAAACAlVDIAQAAACuFUl8UdnNNy5Ytte1x48ZxPHDgQI7N0VCymev444/neM6cOVo6OXpk69atHJszRK5Zs4bjzZs3+8h50eJ13b2al9zuETmKh4ioW7duHMsZdM3r3qpVq8IzC76fzfLly2vbstmibdu22r4NGzZwLEd3mIvxyW15b9x1111aOtlcJdOZzSUQjhYtWmjbs2fP5vjQoUMcm7PpBnnny3eoeR/J69u6deuMj20b+fs++eSTHJtNgzLdqaeequ077bTTOJZNzjt27NDSLV26lOPGjRtzbF7z5cuXcyxnLe/cubOWTs5SLf+Wx3UtUZMDAAAAVkIhBwAAAKyEQg4AAABYSRXSTpa4BtHevXtzPGXKFNd0V155JccdOnTQ9lWuXJljudLq559/7nouc9bkiLl3aMlO4q6ndO2112rbzz//PMdyBeONGzf6Ol6Chp9GcT0TfS297Ny5k2Nz5lu5r3v37hzLGXZjZvWz2bx5c47nz5/vmu6OO+7gWA4NXrlypZZu1KhRHMs+k6tWrdLS9erVi2OvFc8jkPhnc968eRzLPqlERF9++eWvJzXeb3JbDjX3Ij/j1a9SKlWqlLYt+w3JYe1m3qWQ3tUFZhg1OQAAAGAlFHIAAADASqlrrpJD19566y1t3zXXXMPxnj17OC5durSWTu6TzFlUx44dy/Hvf//7zDMbXCqqxMOYOmDGjBkc9+zZU9snq1hlc6FsxkiJxFeJR03OkNulSxeOvWY3T+jw4VQ8m0G9++67HHft2pXj3bt3a+nkbLVyBmtzRl75vpbXUw4zJtKHIfttWglJqp7NW2+9VduWf6P8TuNhXiPZvOS3uaps2bIcn3nmmdo+OVx98ODBrscIg/E3CM1VAAAAUHSgkAMAAABWSl1zlZdly5ZxPH78eI5lkwiRPuOxnFW3f//+0WUuM1ZXiW/bto3jTp06cbxw4UItXYyj21iUvf6zlLNrGUazpBxlIxft8/L9999zLEdEZiKCxXOtfjYl+dtdfvnl2j7znfoL2RRJRPTqq69yLBeJNEfkxChVz6a5aKacCd7sUiGboeS79JRTTtHS7d+/n+O6detyLGcwN8mFkKtXr15YtnMFzVUAAABQdKCQAwAAAFZCIQcAAACs5LtPToJmj41M1N8xg/4BVrf733TTTRw//vjjHFepUkVLJ2fzlKvKhyGCvhqep4vgmIl+Ns2h4XLVabm6tUmudFynTp3wMyYEvAdS92xmcn+4DRtOwj1FlJo+Vsn4sUKW43dmEOiTAwAAAEUHCjkAAABgpaMLT5IvodVTgbhVu0X9HW36DTMhZ6ImIpo6dWqB6cwZp8NuopKivha5rNpNyn0lv/PJJ5+s7XNropJTCBD9dsHOTM+byW+RlN8takn5Tbyuk9u+oE1tReXa5lJaf1PU5AAAAICVUMgBAAAAK6GQAwAAAFby3ScnDfy2yaa1bTFMUQ87Hj16NMdmHxx57tatW3M8ceLEUPMQBdxj7h577DGO5TQBRPrvJvvhTJ8+XUt3zDHHZHzeovhbp1GQ5yUp/YnAv6T1jUJNDgAAAFgJhRwAAACwkueMx0op3pmEaqdMJKXKrKjMqmo64YQTOJarDxPpTVQvvvgix2XKlIk+YyGKcwbrKJ9Nr6ZMr32rVq3iuGXLllq6kiVLcvzZZ59xXLp06ewym3upfzbTJAezeWPGY3tgxmMAAAAoOlDIAQAAACv5XqATcir1VeK1atXiuGvXrtq+kSNH5iobSYEqcXuk/tkETeKfTb8zRf8mEynrYhKE8duguQoAAACKDhRyAAAAwEoo5AAAAICVAvfJScoQbUtF3u6fg6GZiRb2/VvI75n4dv+iIKR7vsj0ycnlO96tb0kO3kuJfDaj/O3T9u7PdqoO1OQAAACAlVDIAQAAACsV1lwFAAAAkEqoyQEAAAAroZADAAAAVkIhBwAAAKyEQg4AAABYybpCjlKqhlJqjlJqh1Jqk1JqvFLq6LjzBZlTSu0x/juslBoXd74gGDybdlFKXamUWq6U2quUWq2Uah53niAYpdTpSqn3lVI/KKVWKaW6xJ2nsFhXyCGiCUT0PyKqTERnEVELIropzgxBMI7jlP7lPyI6iYj2EdGMmLMFweHZtIRSqjURDSeiXkRUhoguIKL/xpopCOTIPzReJ6LZRHQCEd1ARC8opWrHmrGQ2FjIqUlE0x3H2e84ziYiepuI6sacJ8jeZZT/B/LDuDMCgeHZtMe9RHSf4zgLHcf52XGcDY7jbIg7UxBIHSKqQkR/cxznsOM47xPRv4moR7zZCoeNhZwxRHSlUupYpVRVImpP+S9TSLeeRPScg4md0mwM4dlMPaXUUUTUiIgqHmnaWH+k6bFU3HmD0Cgiqhd3JsJgYyHnX5T/r8NdRLSeiD4hotfizBBkRylVnfKbNp6NOy+QFTybdjiRiIoTUTciak75TY8NiOjOGPMEwa2g/Fry25RSxZVSbSj/fXtsvNkKh1WFHKVUMcr/l+FMIvodEVUgonKU33YM6dWDiOY7jrMm7oxAMHg2rbLvyP+Ocxxno+M4W4loNBF1iDFPEJDjOAeJqDMRdSSiTUQ0mIimU/4/RFLPqkIO5XeaqkZE4x3HOeA4zjYimkJ4+NLuWkItTtrh2bSE4zg7KP8PoGw6RjNyijmOs9RxnBaO45R3HKctEZ1CRIvjzlcYrCrkHPkXxRoi6q+UOlopVZby+3IsjTVjEJhSqikRVSWMqko1PJvWmUJEA5RSlZRS5YhoEOWPzoEUUkqdoZQqeaS/3BDKHwH5TMzZCoVVhZwjuhJROyLaQkSriOgg5T+AkE49iWim4zi7484IZA3Ppj2GEdHHRLSSiJYT0RIieiDWHEE2ehDRRsrvm9OKiFo7jnMg3iyFA6uQAwAAgJVsrMkBAAAAQCEHAAAA7IRCDgAAAFgJhRwAAACwUmErAKe2V7JSiuOgnav9HiOMc5mHDOMgBcjZ9YzgN0mzKK5nkf9RY5KKZ1M+f785EZ5HCc9mlsx7Lcb7q8BriZocAAAAsBIKOQAAAGClwpqrUktWmUVdnZbjpqxUKErfFaIRV5NLgqrfA0tjnqFgSf8bksQ8SajJAQAAACuhkAMAAABWQiEHAAAArFTY2lXJbmyzVyTDVJVSfD2T3o5qGQxTjUlapnfAsxkbPJtCEvv/ZJAnDCEHAACAogOFHAAAALASmquSKbGzqoZdnZnE6tEwGN8LVeIpUsgQ8sQ+m7kkf6M1a9Zw3LRpUy1d1apVOX7kkUc4btGiRYS5y0hOn01b33dx8fOeRU0OAAAAWAmFHAAAALBSbDMee1UJ2zDjaBJ5VZX6rUbN5WzRaWbr94ra999/z7Fs3ti0aZOWbtq0aRx/++23HJ988slZ56EoXzu/74H9+/dzPGjQII43b96spZPbn332GcdRN1clpVko6r9lYbzT4xJG/vx8DjU5AAAAYCUUcgAAAMBKKOQAAACAlSLvk+PW7ma2pf3000+u+9yOcdZZZ2npypQpw3GNGjU4rlKlipauc+fOHDdp0iTjvEchF+fy29cm6W25kG4ffPABxxdeeKG2r3Tp0hzv3buXY7Nvg9zeuHEjx2H0yUmyqJ9Nv8f897//zfGECRM4fuKJJ7LOQ676auRClH1wCju+2z6/x4i6P1GurhFqcgAAAMBKKOQAAACAlXI647Gs/vr73/+u7evXrx/HO3fu1DPhMbzcD/M7lihRguNbbrmF4+HDh2d87IhYNavq4cOHte0tW7ZwXK5cOY737NmjpTt48GCBx3vuuee07T//+c8FpjvnnHO0bXl9zWaSbMUwS25k1zKKaur58+dz3LVrV463bdvm+hm/z/2iRYs4btSoUdAs+mXVsxnU8uXLOa5Xr55ruokTJ3J81VVXcSy7FsQsEc9mLqdNkc8LEdHrr7/OsXxOTz31VC1dtWrVOB46dCjH8u8pUW6bCjHjMQAAABRZKOQAAACAlQKPrgrSA94r3ZVXXhk0KxmTzSBjx47leO3atVo6s0kN/JP3h9kMeMcdd3DcqlUrjj/99FMtnWy29Gq6cGvK+OSTT7Ttt99+m+Owm6uSMpojKcxr2bFjR47NZkk3P//8M8fmNa5Tpw7HZrW6dPvtt3M8YsQIX+eNWxpmfJcj5GT+mjVrpqXLZROVraNCg36vuXPncpyXl8exbGok0kcYy+PLpiuTbGaO87fGjMcAAABQZKGQAwAAAFZCIQcAAACsFPoQcr/tyXIIGhHRww8/7J4Jl/4YckgbEdG6des4lsPk5OycZjrZ72b79u1aOq9ZmCOW+mGqAwcO5Hj8+PHaPrc+NOZQ86OOOorjbKcRMI//3nvvcSz7BUUkEcNUo/b5559zbK4yvWvXLo69rp+cnfzuu+/muG/fvq6fke+B7777Ttsnh5SbfQxKlizJcQbPd+qfzSC8+sHJ2HzXnnfeedFmLHvWPJtmXxv52+/evZtjc8i/fFdXqlTJ9fgPPfQQx1999RXHCxcu1NKdfvrpPnMcOgwhBwAAgKIDhRwAAACwUugLdHpV+3799dccjxs3Ttsnh4sWK+Ze9pLHN4d8u2ncuLHrvp49e3Lcrl07bZ+cmXfNmjUc16xZ09d5ixJzmPiTTz6Z8TFk8xQR0eWXX86xbN70au6oX78+x3KxRyL9vjJnTYZgZDX4sGHDODaHibu9F8z3QJ8+fTguVaqU63nlEFY5JYR5b8h8+M1TUeL1LMnfZ/To0dq+wYMHcyzf3RA+v0PIzdnD5bN5/vnnczxr1iwtXdmyZTmWf6NPOeUULd2UKVM4lt1BduzYESi/uYKaHAAAALASCjkAAABgJRRyAAAAwEqh98nx8sMPP3Bsto97tQ1XrFiR49WrV4eap4YNG3Js9smRywDIoZHok/Nb06ZN07bdVhA3Va9eneO77rpL23fttddy7NVPy405jfyPP/7IsVx24Jprrsn42GkRdfv4smXLOH7llVc4NvtXXXDBBRy/+uqrHA8YMMD12DLvW7du1fY9+OCDHP/vf/9zPUb58uULjL2kYVmFsPj9boMGDXLdln0e5fsU/PO658K4/2677TaOZR8ck9fw7wYNGnAs+2EtWLBAS+c23Yf5PXLVdwc1OQAAAGAlFHIAAADASjltrjr77LM5NoenySHaJjk8OYyVbGU1maxGf+edd1zTgTc5eyyR9wzFcti+bKKqUaNG1vmQzVBeQ4bnzZuX9bnSwG81cBjVymYTlSSHl/ttNpLnMmfNHjNmTIGfMd8PXjOp+zlvEsQ1JHfy5Mkcy6H9Zj6qVq3KcYkSJbR0splZTukgZ8D2ErTpMM5hzEHOHUUe5TE3b97smu7DDz8s8P8/7bTTtO0lS5ZwLJu1zJmW3fKQyb4woSYHAAAArIRCDgAAAFgpp81VRx/96+m8enibZs+ezXGtWrU4NhcBlAv6yUU4zUXj5Ln/9a9/cWxWjR533HEcX3rppb7yavvIDLeq2KlTp2rp5Ey2ZlOWXOgtDHJmzwceeIBjOZqKSM97x44dQ81D2oVRrRz2vS6rweUigl6uvvpqbbtZs2ah5ikOuXyHyGekSZMmBf7/Zp42btzIsdlkKT8nm4/N461YsYLj2rVrF3ieTMT53o3r3MWLF3fdfvTRRznu0KGDlu7+++8v8HgrV67UtuU182qiShrU5AAAAICVUMgBAAAAK6GQAwAAAFZShbQfRta4KFc7JSK66KKLODaHu3kNR/bDXCXXbfbcY445RtuWw0/z8vJ8nSukPjlRjV23q4PQEXPnzuW4ffv2runk8FbZz6tVq1ZaugiGn4Z+PZVSnLEgw2oz+ZyXjz76iGOv/i9yFWQ547E5nFzmUQ5h/eabb1yPXaVKFY6/++67QnKcNaueTa97omnTphzLVafNdDJu3bq1lk7OVP3555+7nlf2mzzvvPP8ZD0sUVzPRLxnR44cybGc8dhr9ni/f2tHjBjB8ZAhQ4JmMWwFZhg1OQAAAGAlFHIAAADASoGbq6KcUfL//u//tO3169cXeF6/vGZw9evpp5/muHfv3hl/PkORVIkHaeKIKB8ce+XDLd38+fO1dHLqgPfff59js1r2wgsv5Pi9997zn+HsWVslLofvy+di5syZWjp5/eR1aNmypZZOTgGwf/9+jr2q2GVTSqNGjXzkOiupa67KpJlSzhgumx9/+uknLZ2cvfi1117jWHY7INLvDzklh5mH66+/nuNJkyYFyntA1j6bspvGqFGjOJ4yZYqWrlOnThx/8MEHHH/88cdaunr16nEsn7lSpUpln9lwoLkKAAAAig4UcgAAAMBKgWc8jrK5o379+tq2bK7yOq9c4FHOaly5cmUt3c6dOzlesGABx15VoyeffDLHsmq1sDy5iWNm5KTMvpztDLpPPfWUti0X2/Rq1vAaeQXByAUxr7jiCo7N5ipJVonLmEivYvdqVpYLuzZo0MA1XZwLNcYp6Pf+xz/+wfGBAwdc08kRbWYTleS2oLL5nJoLsP4i7dcs7Pd8JseTs0/LdHKklUn+bTObq7744guOvZqokvbMoSYHAAAArIRCDgAAAFgJhRwAAACwUk5XIQ+DHMrdpUsXbZ9c7Vq2R5ozGR86dIhj2X4oV9MlIho8eDDHGzZs4FjOnEtEtGbNGo5r1qzp/QWOSEJbpZS0dlSTHPb4/PPP+/qMudpulDNz2r76vBu3+0Y+O0T+ZyKWx5P94N59910tnVyp2ovXDK62XaMgz7DZ70ZOreDVJ0pO2+BFziwuj2ceu2HDhr6OlzZe05cEuf8y+YzftHKFgcmTJ3Ns/p0zpxHI9ry5gpocAAAAsBIKOQAAAGCl2Bbo/E1GRDWeWRW9cuVKjn/88UeO45pp0azGa9y4McdmtbpsQsugqjJ1s6pG4dlnn+W4V69eHJsLrkqymdJsfqxUqVKIucuINbOqms0Mu3bt4lgOG//jH/+opdu7d6/rMdwsX76cY7/NUzlg1bNpNiPKaTikw4cPBzpex44dOV62bBnH3bp109LJWY7dhp1HJBHPZpxNqW7PY7Vq1bTtdevW5SI72cCMxwAAAFB0oJADAAAAVkrk6KoVK1Zo2+XKleP44MGDHOeyuUpW6Z1//vnaPjlr8tKlS7V9sikrab3Ok0aOnCMiGjBgQIHpZJMUEVGFChU4zsvL41jOek2U/BFkmYjyu3hVnS9ZskTbd91113H8yiuvcBx0EVvZFCkXi/Rqrgrjt/BqQrPpXiHSv4+5GLIbc5bckSNHcvz9999zLJuniIi+/PJLjuUsybIpmihRizzGIon32NVXX+0rXdJHLaImBwAAAKyEQg4AAABYCYUcAAAAsFLgIeRh9wnYs2cPx6effrq2T65C/vLLL3N82WWXZX3eIGS/DyKicePGcew1vDkDiR2m6tZ3Ieg9IPvh3HLLLdq+ffv2FXh8Mw/yeowePdo1XRj3acD7PhHDVL14fa/+/ftz/OKLL2r75HPrRR7z4osv5lj2uyHSh/2fdtppHH/11Ve+zhOGQu6byJ/NXPZxMId8V69evcB0ffv21babN2/O8f3338+xnO7DdOONN3I8ceLEjPIZocQ/m2Ez7y/ZL8trZnK3d3AY/eBCuscxhBwAAACKDhRyAAAAwEqJmfHYi6zWuuOOOzh+8MEHXdP5rf4K8pny5ctr29u3b+d40aJF2j45hDwDiW2uCkLOdktENGzYMI4ffvhhjs2h4dKxxx7L8axZs7R9TZs25Vguxhp1NapXOmNf4qvE165dy/HAgQO1fW+88QbHZlW3nNFbNi/+6U9/0tINHz6c423btnHcpk0bLZ1svurRowfHfhdlzQGrnk2/Mx6bvJqPpRtuuIHjBDVRSYl4NnPZRGm+Py+99NICz/v1119r6WTzcRjQXAUAAACQBRRyAAAAwEqhzHgcdrWTrB43jRo1iuOKFStq+3bv3p3xuWR+Dx06pO2TM7iOGDGC4507d2rp6taty7Hb6ISibPr06dq2/C1l7NXk4/fahnH/+T2GV7qkzfpZmM8++4zjN998U9snmxHNanW5mKLcd8UVV2jpunfvzvHChQs5liMnzXM98sgjvvIuJX321VzxO4OzOeOx35E2buRIPCJ9tCO4y+V9eskll7juk7P5h908ZcrVd0ZNDgAAAFgJhRwAAACwEgo5AAAAYKVUDCFv27Ytx3PnzuXYbHe+4IILOJZDiYcMGaKlq1OnDseyf02fPn20dIsXLy4wPyeeeKK2/e6773Jcr169Aj+TodQPU50zZw7HnTp10vbJWaHlNSxWTC9z9+zZk+PJkyeHnUVXEczmmdNhqkHyL/vMmH2o5HXx6jclVa5cWdsuXbp0gZ8x+4R89NFHHMvZc83jxSj1z6YXObx/ypQpHMvnmYioQYMGHMt+kmH3icpBH6tEDCHPpUqVKmnbW7du5bhfv34cT5gwIWd5CgmGkAMAAEDRgUIOAAAAWCkVzVWSXORNVqcS/XYI+C/M73jcccdxfPDgQY7379/vmk4ODTdnjKxWrVph2c5UJFXiSin+IaIevicXWV2xYoW2T55bVp2OGTNGSycXYC1RokTGeUjQcOLEV4nLGYmHDh3qmq5hw4bathx6ftVVV3F85513aulOOukkjuV1KVWqlJZONpckqIlKSkxzVYLu7zRL/LMZ9hQtZnPVli1bOJ40aRLHvXv3zvpcOYbmKgAAACg6UMgBAAAAK6GQAwAAAFZKXZ8cafz48dq2HPLttWqxbOOUK7BWqFBBSydXOTeXkIhYYtr9TW7tw+bvfd111xWYzjzG4cOHs81SGoR+PXPZvwo0qesvl8u+OynsJ5T4PjlS0N/3rbfe4thc1kG+g2fMmMGx7BOZEuiTAwAAAEUHCjkAAABgpVQ0V4U9hC6X3GaELeR7xNpc5XcFY6/PyFmO582bp+2TQ/CbNm3Ksdcw8TTfA5SyKnHwlLqm5BQ2ITHMeByO7du3c5yXl6ftk9M7jBw5Mmd5iuCdjuYqAAAAKDpQyAEAAAArRd5clfRmhiD5C1qFmsG5ElslnnRJud/iXKDTr6T8VlHKVZV4CBI9S67FEvlsQiBorgIAAICiA4UcAAAAsBIKOQAAAGCl2IaQxzmsMQXt1Ylt90/Bb8cSdI+h3d8eiX02IRA8myHz+zcCQ8gBAAAAsoBCDgAAAFipsOYqAAAAgFRCTQ4AAABYCYUcAAAAsBIKOQAAAGAlFHIAAADASijkAAAAgJVQyAEAAAAr/T8gMVknz9INLAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SazXycxp2880323jGjBmR6bp27ZrofPfdd1/kMT3eMelvafj3WL/WcefOnROdL+787EIOAACwGJUcAADgpdy7q9I0NW299dbO6zZt2thYb5R5+eWXO+m23HLLNFm09FQ9EXc1yaybP4toQi2qCTfptcLNnrfddpuN47pJ3nzzTRvHrcyahUZv+k7i888/d17vtddeNo7rLo6zxx572FhvvKlXwRVxnzlt8ODBzmu9srXme/nU8/Ppsg4vx3DTTTfZeKONNsr0uuGpxlqWK+HWQxbDEm6//fZE7w8/I3ol8FatWtn4yiuvdNLppVNWXXVVG8+dO9dJ9/bbb9v4/PPPt3HcZ9SbJKfdwLfWsqQlBwAAeIlKDgAA8JJZQlNQ5MF6dn0MHz7cxnoGle7GEhGZM2eOjdu1a2fjM844w0mnV9SM06JFCxt//PHHNl5jjTUSvT/OEpoxa1+atjJ7kTLO7gpvPnfAAQfYWK+gG95AcuTIkflmrHaZl6cxxhZYFmUX9zzrZu+DDz7YOaZXPO7bt6+N999/fyedfq1XWB00aJCTTje56zxNnDjRSbf77rtX+BR1kfuzGXvxjH93hw0b5rzWKxHra62//vpOunfeeafqa+nzhWe4/ve//7Wx/h2vw+9SHuVZdabjfo/1prVxKx63bt3aed2hQwcb62EeX375pZPulVdeqZjuww8/jMyjfob33XdfJ51epVq/J7wxqO62zkjFsqQlBwAAeIlKDgAA8BKVHAAA4KXYMTlZ9/tnYcCAATYeNWpUZLqDDjrIxnvuuadzrH379jbWO62+/PLLkdcKr5qcs0L7/YvSr18/5/Utt9xiY11mM2fOrPlaWYxtqOIcpej3j1PU8gIrrbSS81qPF9DP8NixYxOdL25sQ0bj0Lx+NvU036effjoy3VlnnWXjiy66yMZvvfWWk04v86F3op8+fbqTrn///jbW09ProK7PZpoVmydNmmRjPSZVROS1116LPJ9+rcfQxPnxxx8j36N3m9fjVfX09PAxPcYunPccMCYHAAA0H1RyAACAl1JPIS+Knob24IMPOscOPfRQG3/77bc2Dk8118e08EZ/ehPAOnfX5dIkXlT3Y1w3wfjx4218+OGHO+l0c6nuLuzTp0/WWcxb6bur8qanBa+44oo2Dn8P9Xdl3LhxNj7wwANzzF1VvO6uitr4NG6TXd1dobs0RNzfa73ho94IUkSkV69eNk7atZKRUi7vkLRbS3cNxT1LScV172p6iZZNN93UOaZXSD/ttNOqzkMN6K4CAADNB5UcAADgpdJ0V2Uxu+PVV1+1sV4lWXeJiLirbepVdXV3l8j/bkpXR143ic+ePdvGe++9t42nTJnipKvX7LZqZt2k/J4W1l1V1KypMD3LRm/aF9alSxcbP/744zbWs+vyVsRq5GWcyapXmR49erRzLPyb+pP99tvPed27d28b61WvwzNy8lRAeeZWgOFNM/VK8F27dnWO6c+tf0v1CsrhdHfccUfk+TbeeGMbv/feezZed911k2S9HuiuAgAAzQeVHAAA4CUqOQAAwEulGZOTpzLuuL0EdR+TEzVdMI97ddxxx9n42muvtbHeAVfEXc1TTztuQKWcpponvbqtiMhuu+1m44ULF0a+b9q0aTbecMMNs89Y7bweL5enuCnJBX6HC1vx2HlDSZ7hsozhS4kxOQAAoPmgkgMAALy0dNEZqIesm90asPtrifL8DOGp+WPGjKmYLrzidNIuqjRNrA3eLFvKPH/zzTc2Dq9eHddFpXXq1Knq6zZ6WS6JL9/vsuSjSLXegyz+9sSdI+vlM8rwPaQlBwAAeIlKDgAA8BKVHAAA4KVmMSYna3n0Lda77zJt327SfF5xxRU21kuFh8/Rs2dPG48cOTJRHsKaY19/Gfq6w1ZYYYVE6fRWHhMmTKj5umX5/HlJ8/l8uSc+jn8sWtp7GLVDedz5ylBetOQAAAAvUckBAABeymTF47imq6TNWmVsfi9Qw6+quvLKK9v4u+++c47pLqrbbrvNxgXu+p63XFdVzWIl2Syev+nTp9t4gw02cI7p3cVfeuklG7dp06bq/Il4t0KuSAlXPPblN7kRdyGvdbp2Ne9Lo6jvRpqypCUHAAB4iUoOAADwUmk26Cxj02iBeWr4JvHOnTvbuHfv3s6xoUOH5nbdMn6PpKRN4lra+1bS+52nQp/NuK5J52QlKYusV8nN4ftW+mczC748p3RXAQAALEYlBwAAeIlKDgAA8FLqMTl57khaoumiRWn4MTl5asDvhzf9/g1477PGs5mDApca8ebZTMrjZ5gxOQAAoPmgkgMAALy0pO4qAACAhkRLDgAA8BKVHAAA4CUqOQAAwEtUcgAAgJe8q+QYY9Yzxkw0xnxpjJlljBlujFm66HyhesaYb0P/W2SM+WvR+UI6PJt+McYcZIyZZoyZa4x5xxjTveg8IR1jzEbGmMeMMV8bY6YbY/YrOk9Z8a6SIyIjRORTEWkvIpuJSA8ROa7IDCGdIAja/PQ/EVlTROaJyPiCs4X0eDY9YYzpKSJDRKS/iLQVkR1E5L+FZgqpLP6Hxr0icr+IrCwiR4vI/zPGdCk0YxnxsZLTSUTGBUHwfRAEs0TkIRHZuOA8oXb7S9MfyKeKzghS49n0x3kicn4QBFOCIPgxCIKPgiD4qOhMIZUNRWQtEbkyCIJFQRA8JiLPiMhhxWYrGz5WcoaJyEHGmNbGmA4isoc0/ZiisR0uIrcELOzUyIYJz2bDM8a0EJFuIrLa4q6NGYu7HlsVnTdkxohI16IzkQUfKzlPStO/DueIyAwReUFE7ikyQ6iNMWZdaerauLnovKAmPJt+WENEWorIASLSXZq6HjcXkT8XmCek96Y0tZKfboxpaYz5rTT93rYuNlvZ8KqSY4xZSpr+ZXi3iCwvIquKyErS1HeMxnWYiDwdBMG7RWcE6fBsemXe4v//axAEM4Mg+FxErhCRPQvME1IKgmCBiPQSkb1EZJaInCYi46TpHyINz6tKjjQNmuooIsODIJgfBMFsERklPHyNrp/QitPoeDY9EQTBl9L0B1B3HdON3MCCIHglCIIeQRCsEgTBbiKyvohMLTpfWfCqkrP4XxTvishAY8zSxph20jSW45VCM4bUjDHbikgHYVZVQ+PZ9M4oETnRGLO6MWYlETlFmmbnoAEZY35tjFlu8Xi5P0nTDMjRBWcrE15VchbrLSK7i8hnIjJdRBZI0wOIxnS4iNwdBME3RWcENePZ9MdgEXleRN4SkWki8i8RubDQHKEWh4nITGkam7OLiPQMgmB+sVnKBruQAwAAL/nYkgMAAEAlBwAA+IlKDgAA8BKVHAAA4KUl7QCcaFSyMebnN2QwkFmfL4tzZn2+OjBLTpJK5AcP3yP7hoT3Ku97nPV3LG+h/OZRnrndhAZ8Xuqp7s+mc/EGew6ipP2O5fD5G+rZRKyKZUlLDgAA8BKVHAAA4KUlrZOTabNbIze15p33OnRviDHGZrye978s3R9pyjCqG6+acwhN4qmU9Pei0O6qesri/pe0DLVSPJtl+Y1sNEn+btKSAwAAvEQlBwAAeIlKDgAA8FJdx+TgZ0vog202/f5a0vEvDdh/XVi/f9yYiDyXfsi7TAoc69Esn82keDZFpCRlWfbxUEnzV8XnYEwOAABoPqjkAAAALy1pxeOqlbG5sox5KkMetDJ0XWSRruxNtPUWdw+yvj/1vN/NfUXtMnnvvfds3L59e+fYlltuaePLLrvMxjvuuKOTLqo7mrJIr+z3Lm4IQlS6NGjJAQAAXqKSAwAAvJR5d1UZuxLYZPJ/Jf2sae9JUfeu6Pvqo3p+bz/++GMb6+6NWbNmOenGjh1r4w8++MDGa6+9duJrRX2uMnZv10vSsp4/f76NL730Uhvr8hMRadGihY1feumlROf2aWZec/4uhcXdizzvCy05AADAS1RyAACAl6jkAAAALyUek5NF32Lce3744QcbDxo0yDn2/PPP23jSpEk23nTTTZ10bdu2tfFTTz1l4zPPPNNJ16tXLxtvu+22ifKXZnXGcNoy9cfWc2xN3t+deirTuKp6yfNz9unTx3k9ceJEG8+dO9fG4e/Qjz/+aGP9+1DNmJwyTVvO8/pxz1/c1N04yy67rI1HjBhhYz0Gp6zKOB40rOjvY1ml+VtCSw4AAPASlRwAAOClwjboHDdunPP62GOPtfFXX33lZiJhs7I+ppuzw01cyyyzjI1PPvlkGw8ZMiRBzuvC600A9XTglVZaycbffvutk27BggUV3x9eVVXTZa1XWxVxy1evuJpFd1oBG66WoiyjhO+H7j7u3bu3jT/99FMnne7uWLRoUcX/Hvbcc8/ZuFu3btVntjq5P5uNMO142rRpNv7Vr35l46WWcv/dPHLkSBv37dvXxnpoQcHq+myWYRNb/byIiNx77702fvrpp238i1/8wknXsWNHG5999tk2btmyZab5qwEbdAIAgOaDSg4AAPBS5iseJ6VH5IuIfPnllzZOOuI/Ll242VTT3SBXXXWVjfVGcyIid9xxR6J8xGmuo+R1N8To0aOdY2eddZaNd9llFxu/+OKLTjrdbanvXVzZai+88ILz+qGHHrKx7q6qplyiyrNMZVuG71z43u+88842/vrrr22cdjZOly5dbKyb1a+44gon3amnnprq/PVWhjKrxhNPPGFjnfftttvOSae7qFZYYQUbl2UV+rwVmf+HH37YxhtttJGN33nnHSfdwoULbazzp7uuwmbPnm3jq6++OlX+6nUvaMkBAABeopIDAAC8RCUHAAB4qbAp5HoKmojIRRddZOPwmIuoKeR6SpuIO6Zm6tSpNn7mmWecdO+//76N9bibL774wkl32mmnVcxfHTT8FPKTTjrJxsOHD3eO6en9ScdkZLFKq1464P7777fxrrvumui6NSjFFPK8xwe8/PLLNu7Ro4dzbM6cOZH50PR34/rrr7fxUUcdFfke/Tvw4YcfOsf0lPLwOKGUn7/hn8009PMiIrLPPvvYWJdn+Ld2m222qZgui9Xlkyrr8g55j8NaccUVbfzNN9/YuGvXrk46/Vu9+uqr23jfffd10umynDx5so31cgIi7vifOmMKOQAAaD6o5AAAAC/VtbvqjTfesHF4NVq9Gd//ZELlceDAgTYeOnSok2755Ze3cdKmwH/961823n333Z1jn332WcVz1GHqW8M1iYdXiz733HNtrDdfFXHLJm5V2wMPPNDGunszrrvjtddes/GRRx7pHPvuu+9sfMstt9j40EMPjTxfnCqam0vRXZUH3Qx+xBFH2Piee+5x0un788tf/tLG4abuefPm2bh169YV3y/iTmHVze+ffPKJk05fS//+VDpnQqV5NpN2P+axonfU6vJTpkxx0qXprspCc3w248q5e/fuNp4wYYKTrl27djbWz8j666/vpNOb6UatkizibnqdVEZd6XRXAQCA5oNKDgAA8BKVHAAA4KVMtnVI2v+pl3MP7zgdR59TjwHQY3Di3hNHLw8f3hFZu/XWWxOdL6zRlmtPa+zYsc7rqB3Ew3S/74033ugc69evn431sgJx/bebbLKJjU855RQnnR6To7eQSDsmp9HLM4vvpl6qP267je23397Gf//73yPTtWrVqmKe9BgcEXdJB/3chj/HKqusEnmsUZ7NWrcSyeKzhacFv/766zbeaqutbBy3FUA973E9p6iXxVNPPeW81s+jHiOnx+CIJF+SY/PNN7exHof17LPPVpHLyvIsB1pyAACAl6jkAAAAL2XSXZW0qWmLLbawcXh62rvvvmtj3RQmIjJq1Cgb9+/fP00WHbp5bocddqj438Oy7tJo9N11w5ZbbjnnddwKxYcffriNzznnHBt36tTJSaenJEedO0x3Q4W7RPX7Hn/88chzwBXXvK+bqnWXVJhuStddSEndfvvtzuthw4ZVTKe7z0RELrnkkshzNsqzWdT1b7rpJhuHp9/re9ShQwcbr7feek66WbNm2Vh/jnDXStx3J0raciq6POtFf87w0grak08+aWP991AvvyDiLrfSSF1+tOQAAAAvUckBAABeyqS7KvHFlv75cuER3rrJKzxLY7/99rNx586dbfzYY49FXktvwhneNE5fO9xsqummbz2rq23btpHvSdqEWvYmvmqFVzo9+eSTbRzuyrr44osrniPtPdFlc+GFF9pYz6YScctmr732SnUtn2T9HYw7X5ruQb0a8oknnugci1pxV6+ILpKuG6Rsz2aeG1vG/V7dcMMNkem0jz76yMbh3+6o9/Xu3dt5HTezNUrZyqlacfc0zWdr2bJl5DG9wa0uLxGRCy64oOJ73nrrLee1zu+GG25Ydf7SqrVrjJYcAADgJSo5AADAS1RyAACAl+o6Jieub619+/Y21tMORUTuvvtuG8etlholbqVTbdlll3Ven3feeTbW43Cy7kv1QZGfW5eN/n6Exwfo8t1pp50izxf3PS3j1Mm8pzxnMTV30KBBNtZlFJ5OHnV/41ZTzrtMii7zPKdGZ/F5dNnssssuzrFHHnnExvo+hlew1mP6onYuF4lfmqIsz6MWl8e4z5LG1ltv7by+9NJLK6ZbZ511Up1f53fAgAGpzlHrddOgJQcAAHiJSg4AAPBSXbur4pqd9LRi3bQtIvLhhx/aOK7Zulbff/+981pv8Ki7RHxsEs9T1t174U0Azz33XBvHfT90+e66666p8lSWsinL96Vr164V89GiRQsnnS6zAw880MY777yzk04vNxC3Kav2/PPPV5Hj6pWlzOtBrxiuV7gNr0Kvl9fQmz9ut912Trr58+fbeMUVV4y87vXXX29j3V3VCM9inCJXYT7jjDMq/vfw9O+9997bxk888YSNp06d6qTTmx8ff/zxWWSxLmjJAQAAXqKSAwAAvFTX7qo4ejPGcePGOceimubjmrA322wzG7/88svOMd0cOnny5Mhz6GvpjSTDXSJRG0lWoxGaXtPKepVW3bQt4q58rctm0aJFTrrLLrvMxqeffnrV1y2Tsswy0d2448ePT/SeSZMm2Vg3j1dDP4+bb755qnM0ory7Kbt161bxWuHv2FprrWXjuJmK4RmrUfTmzXHK0k0bp6g8Jv0d0L994ffpWVPhbuANNtjAxq1atUqdz3qjJQcAAHiJSg4AAPASlRwAAOAls4Q+w7p1KOp+wT322MM59uCDD9pY74yrdycXcVdP1dOFw/3CCxcutPGcOXNs/MMPPzjphg4dauMrrrjCxssss4yT7o033rBxp06dJAO1L39ZWTk7sRMYNWqUjf/whz84x6LGp+y5555Ouvvvv7/mfKTsb8+jPEtdlh07dnRe62Ug4u6bHlO19tpr2/jRRx910nXp0qXWLDqSrjgbBIFXz6ae4i3iTifWY93CxowZY+ODDjooMp1+5n73u9/ZODyuUU9Rz2IcSxXPaa7PZiOsyvzJJ5/YeI011rBx+O+m/vtYxs8hEWVJSw4AAPASlRwAAOClwrqrws14c+fOtXHclFA9Hbye09jGjh1r4379+jnHFixYYON58+Y5x/QKrlVo+CbxLKZR3nzzzTbu379/xXPHmTlzpvN69dVXrzp/GTU3Z16exhibkbybjpPe79GjR9v4hBNOcI7p5zvpxpvTpk2zcdbdUzVo+GdT092IIiLrrbdexXTh+6/LJu58uttSf48OOOAAJ92NN95oY70sQR5Cz7633VVpll7Rwl3O77//fjYZyw/dVQAAoPmgkgMAALxU1xWP47oIZs+ebeO3337bOaY3ditqpUU9g2D48OHOsWeffdbGcd1TjbBaZ1bSfD49c05E5MQTT6x4vnBzq56ZoTdVbdeunZMuzf0vaznVM1/6WuHVwwcPHmxjvVpqeENHfe+TbrKrN4sssruq6Oc2zfWzzrOedRV3/nAXR9wK5Enk0d1T1mc6a1GfM2n3/MEHH5x5nopASw4AAPASlRwAAOAlKjkAAMBLdR2TE9cXqFdX7NChg3NsxowZNr7rrrtsvP/++0eeL89+9PCOuXpMztSpU51jW221VW75KELU9MO0n02PwznqqKOcY7o/P+m0R70ydZgP9/8nRY0T6dGjh/P622+/rZguPO5G51GP79DjbkTcaf96vE/fvn2rz+wSJL2H9bi/cXmp5zgcPZZKn0+vDC8icuutt9p4ww03rPiecD4GDhxo45EjRybKT6M/s2XM/6effuq81jvPayussEI9spM7WnIAAICXqOQAAAAvxa54nGZV1aRT/uLShTdW1Bt0nnXWWTa+6KKLEuUpa3ojUBGRL774wsbPPfecc0x3V1Wh0FVVs+4K0avdirjdEJdccomNW7RoEXmO1q1b23jChAnOsW233dbG4c1Ts+brBp3vvfeejU866STnmN5kMfyZ9ZIJekXpuNVR9XIRq666qnNM399DDjnExrp7JK2yrl69WClWPF5nnXVsHPc8Ri3pEF464Nhjj7Vx0i6qOiv9s5lW1G9V+Pdz3333rZguLOuutzSrzqfZbJWWHAAA4CUqOQAAwEuxs6vyHBkePrdukrr33nudY7q76vLLL7fxaqut5qTTs3PatGkTee2omTp6o00RdybXpZdeauOvvvrKSbfxxhvbeN111428btHiZijp8sii3PW1brrpJueYvpc6Doua6bHjjjvWnKe0nzGqmb7esyiyvvZLL71k4wceeCDyWnErGV955ZU27tOnT+S1pkyZYuNw3nUXyW9+85slZbsqcb85ZZwFUw+6G1jE3aBTl3XSVaqPP/5453XcbEckk/Z7GpX24osvjky33Xbb2fiZZ55JfK006rXqPC05AADAS1RyAACAl6jkAAAAL8VOIZeSTIXbbbfdbPyPf/zDxuExJjvssIONH3/8cRs/8sgjTjq9QqceX3PkkUc66Z5//nkb6/u0xhprOOlmzZoVmfcSTTkWqWN5Tpw40cZxOxjrnYnDU1b1mICipi9mdbkczplppvUYmjvvvDP6ojHjWrT27ds7r/UYOf0ePWVZRGTy5Mk2fuuttyLPV+B4mtI8m1lMiY8bpzd06FAb6+dZRGTzzTe3sR53M3/+fCedXsm+qB3Ul3S5HM5Zir+bUcJlrl/rKf8jRoyoW57Cor4DSxhXyhRyAADQfFDJAQAAXmqI7irdRHX00UfbeNSoUU66hQsXVnx/3IZ3egrlvHnznHQrrriijb/++msbh1dz7dixY2TeUypNk3haG220kY3ffPNN55guT73i7bBhw5x0egPWuJWMG2AqcOmbxIcMGWLjs88+2zmmuw3Dm/npqec63bRp05x0a665po11ebVq1cpJp1dDDndRRfGge0OkJL+1zVDpn804aVYNDi+98vnnn9tYb5g8YMCALLJYMQ8iuTyrdFcBAIDmg0oOAADwEpUcAADgpdhtHbKQ9VL62iabbOK8njp1qo0nTZpk4xkzZjjp9BLlegfW8I7IepfzcD9mGkm3VchLnmMXwrtE6zEZcUvC652m+/btm+ratU6dzXobi6zOWU9nnnlmxTgrSe930nE4WkY7GKOEqnmummNZJ/2cegmAffbZJzKdHocaFvX3K4vtGfL8/aQlBwAAeIlKDgAA8FIpp5AnbbrKesXPtLsU59BMmvs01aybB8PT7/XU4PC1dFfitttua+O4aeJJlbTJuqGnqcLR8FPIS/qMVFTUtOMaRWaynvdeX0svzfDHP/7RSaeXd9CrXDeC0P1kCjkAAGg+qOQAAAAvlbK7KgtpmgWLnv2kNHyTeCNrkO7Hhi3LRuouqaDhupLLoqTlnuuzmXVZ+vrdyAjdVQAAoPmgkgMAALxEJQcAAHjJ2zE5Da7QMTlF9Z3Xs7/Zg52rq850Gfvzy5inJWC8XAzKU0RKvvRKI0j5+8yYHAAA0HxQyQEAAF5aUncVAABAQ6IlBwAAeIlKDgAA8BKVHAAA4CUqOQAAwEtUcgAAgJeo5AAAAC/9fx1BauKqh+ceAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ii in range(1,10,1):\n",
    "    print(f\"noise probability = {2*ii/100.}\")\n",
    "    d = np.load(f\"./saved_data_MNIST_last4/snp_{ii/100.0}_{seed}.npz\")['arr_0']\n",
    "    l = np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(10):\n",
    "        plt.subplot(5, 5, i + 1)\n",
    "        plt.imshow(d[i+2][0], cmap=plt.cm.binary)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(class_names[l[i+2]])\n",
    "    #plt.show()\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/snp_{str(ii/100.0)}.png\",dpi=500,transparent=False)\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/snp_{str(ii/100.0)}.ps\")\n",
    "     \n",
    "     \n",
    "for ii in range(1,6,1):\n",
    "    print(f\"noise probability = {2*ii/10.}\")\n",
    "    d = np.load(f\"./saved_data_MNIST_last4/snp_{ii/10.0}_{seed}.npz\")['arr_0']\n",
    "    l = np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(10):\n",
    "        plt.subplot(5, 5, i + 1)\n",
    "        plt.imshow(d[i+2][0], cmap=plt.cm.binary)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(class_names[l[i+2]])\n",
    "    #plt.show()\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/snp_{str(ii/10.0)}.png\",dpi=500,transparent=False)\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/snp_{str(ii/10.0)}.ps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "if create:\n",
    "  for i in tqdm([i for i in range(1,10)]+[i/10 for i in range(1,10)]):\n",
    "    sampled_images = np.load(f\"./saved_data_MNIST_last4/test_seqs_{seed}.npz\")['arr_0']\n",
    "    sampled_labels = np.load(\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    sigma = (i*0.1)**0.5\n",
    "    mean = 0\n",
    "    for j in range(len(sampled_labels)):\n",
    "      for _ in range(len(sampled_images[j])):\n",
    "        row,col = sampled_images[j][_].shape\n",
    "        # gauss = np.array(np.random.normal(mean,sigma,size=(28,28))).reshape(row,col)\n",
    "        gauss = np.array(np.random.randn(row*col)) #* var ** 0.5 + mean\n",
    "        gauss = gauss*sigma + mean\n",
    "        gauss = gauss.reshape(row,col)\n",
    "        sampled_images[j][_] = sampled_images[j][_] + gauss\n",
    "    np.savez_compressed(f\"./saved_data_MNIST_last4/gauss_{i/10.}_{seed}.npz\",sampled_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gaussian noise sigma = 0.01\n",
      "gaussian noise sigma = 0.02\n",
      "gaussian noise sigma = 0.03\n",
      "gaussian noise sigma = 0.04\n",
      "gaussian noise sigma = 0.05\n",
      "gaussian noise sigma = 0.06\n",
      "gaussian noise sigma = 0.07\n",
      "gaussian noise sigma = 0.08\n",
      "gaussian noise sigma = 0.09\n",
      "gaussian noise sigma = 0.1\n",
      "gaussian noise sigma = 0.2\n",
      "gaussian noise sigma = 0.3\n",
      "gaussian noise sigma = 0.4\n",
      "gaussian noise sigma = 0.5\n",
      "gaussian noise sigma = 0.6\n",
      "gaussian noise sigma = 0.7\n",
      "gaussian noise sigma = 0.8\n",
      "gaussian noise sigma = 0.9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ii in [0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:\n",
    "    print(f\"gaussian noise sigma = {ii}\")\n",
    "    d = np.load(f\"./saved_data_MNIST_last4/gauss_{ii}_{seed}.npz\")['arr_0']\n",
    "    l = np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(10):\n",
    "        plt.subplot(5, 5, i + 1)\n",
    "        plt.imshow(d[i+2][0], cmap=plt.cm.binary)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(class_names[l[i+2]])\n",
    "    #plt.show()\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/gauss_{str(ii)}.png\",dpi=500,transparent=False)\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/gauss_{str(ii)}.ps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perlin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_perlin_noise_2d(shape, res):\n",
    "    def f(t):\n",
    "        return 6*t**5 - 15*t**4 + 10*t**3\n",
    "\n",
    "    delta = (res[0] / shape[0], res[1] / shape[1])\n",
    "    d = (shape[0] // res[0], shape[1] // res[1])\n",
    "    grid = np.mgrid[0:res[0]:delta[0],0:res[1]:delta[1]].transpose(1, 2, 0) % 1\n",
    "    # Gradients\n",
    "    angles = 2*np.pi*np.random.rand(res[0]+1, res[1]+1)\n",
    "    gradients = np.dstack((np.cos(angles), np.sin(angles)))\n",
    "    g00 = gradients[0:-1,0:-1].repeat(d[0], 0).repeat(d[1], 1)\n",
    "    g10 = gradients[1:,0:-1].repeat(d[0], 0).repeat(d[1], 1)\n",
    "    g01 = gradients[0:-1,1:].repeat(d[0], 0).repeat(d[1], 1)\n",
    "    g11 = gradients[1:,1:].repeat(d[0], 0).repeat(d[1], 1)\n",
    "    # Ramps\n",
    "    n00 = np.sum(grid * g00, 2)\n",
    "    n10 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1])) * g10, 2)\n",
    "    n01 = np.sum(np.dstack((grid[:,:,0], grid[:,:,1]-1)) * g01, 2)\n",
    "    n11 = np.sum(np.dstack((grid[:,:,0]-1, grid[:,:,1]-1)) * g11, 2)\n",
    "    # Interpolation\n",
    "    t = f(grid)\n",
    "    n0 = n00*(1-t[:,:,0]) + t[:,:,0]*n10\n",
    "    n1 = n01*(1-t[:,:,0]) + t[:,:,0]*n11\n",
    "    return np.sqrt(2)*((1-t[:,:,1])*n0 + t[:,:,1]*n1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampled_images = np.load(f\"./saved_data_MNIST_last4/test_seqs_{seed}.npz\")['arr_0']\n",
    "sampled_labels = np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "for res in [1,14,7]:\n",
    "    if create:\n",
    "        for i in range(len(sampled_labels)):\n",
    "            texture = generate_perlin_noise_2d((28,28),(res,res))\n",
    "            for j in range(10):\n",
    "                sampled_images[i][j] += texture\n",
    "        np.savez_compressed(f\"./saved_data_MNIST_last4/perlin_{res}_x_{res}_{seed}.npz\",sampled_images)\n",
    "\n",
    "    d = np.load(f\"./saved_data_MNIST_last4/perlin_{res}_x_{res}_{seed}.npz\")['arr_0']\n",
    "    l = np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(10):\n",
    "        plt.subplot(5, 5, i + 1)\n",
    "        plt.imshow(d[i+2][0], cmap=plt.cm.binary)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(class_names[l[i+2]])\n",
    "    #plt.show()\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/perlin_{str(res)}.png\",dpi=500,transparent=False)\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/perlin_{str(res)}.ps\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rayleigh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if create:\n",
    "  for i in tqdm([i/100 for i in range(1,10)]+[i/10 for i in range(1,10)]):\n",
    "    sampled_images = np.load(f\"./saved_data_MNIST_last4/test_seqs_{seed}.npz\")['arr_0']\n",
    "    sampled_labels = np.load(\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    for j in range(len(sampled_labels)):\n",
    "      for _ in range(len(sampled_images[j])):\n",
    "        row,col = sampled_images[j][_].shape\n",
    "        rayl = np.array(np.random.rayleigh(i,row*col)).reshape(28,28) \n",
    "        sampled_images[j][_] += rayl\n",
    "    np.savez_compressed(f\"./saved_data_MNIST_last4/rayl_{i}_{seed}.npz\",sampled_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ii in [i/100 for i in range(1,10)]+[i/10 for i in range(1,10)]:\n",
    "    d = np.load(f\"./saved_data_MNIST_last4/rayl_{ii}_{seed}.npz\")['arr_0']\n",
    "    l = np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(10):\n",
    "        plt.subplot(5, 5, i + 1)\n",
    "        plt.imshow(d[i+2][0], cmap=plt.cm.binary)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(class_names[l[i+2]])\n",
    "    #plt.show()\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/rayl_{str(ii)}.png\",dpi=500,transparent=False)\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/rayl_{str(ii)}.ps\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Uniform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if create:\n",
    "  for i in tqdm([i/100 for i in range(1,10)]+[i/10 for i in range(1,10)]):\n",
    "    sampled_images = np.load(f\"./saved_data_MNIST_last4/test_seqs_{seed}.npz\")['arr_0']\n",
    "    sampled_labels = np.load(\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    for j in range(len(sampled_labels)):\n",
    "      for _ in range(len(sampled_images[j])):\n",
    "        row,col = sampled_images[j][_].shape\n",
    "        rayl = np.array(np.random.uniform(0,i,row*col)).reshape(28,28) \n",
    "        sampled_images[j][_] += rayl\n",
    "    np.savez_compressed(f\"./saved_data_MNIST_last4/uniform_0_{i}_{seed}.npz\",sampled_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ii in tqdm([i/100 for i in range(1,10)]+[i/10 for i in range(1,10)]):\n",
    "    d = np.load(f\"./saved_data_MNIST_last4/uniform_0_{ii}_{seed}.npz\")['arr_0']\n",
    "    l = np.load(f\"./saved_data_MNIST_last4/test_labels.npz\")['arr_0']\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    for i in range(10):\n",
    "        plt.subplot(5, 5, i + 1)\n",
    "        plt.imshow(d[i+2][0], cmap=plt.cm.binary)\n",
    "        plt.axis(\"off\")\n",
    "        plt.title(class_names[l[i+2]])\n",
    "    #plt.show()\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/uniform_{str(ii)}.png\",dpi=500,transparent=False)\n",
    "    plt.savefig(f\"./saved_data_MNIST_last4/uniform_{str(ii)}.ps\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "63fd5069d213b44bf678585dea6b12cceca9941eaf7f819626cde1f2670de90d"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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