mean_var.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9786d796-3a08-44c7-b113-683ac8f7c904",
   "metadata": {},
   "source": [
    "## Some inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d3ee4b6d-c48e-4da2-8f73-7fe3911a4b2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/annac/mambaforge/lib/python3.8/site-packages/umap/distances.py:1063: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/home/annac/mambaforge/lib/python3.8/site-packages/umap/distances.py:1071: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/home/annac/mambaforge/lib/python3.8/site-packages/umap/distances.py:1086: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n",
      "/home/annac/mambaforge/lib/python3.8/site-packages/umap/umap_.py:660: NumbaDeprecationWarning: \u001b[1mThe 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\u001b[0m\n",
      "  @numba.jit()\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "import matplotlib.pyplot\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import nbinom\n",
    "from scipy.stats import entropy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import muon as mu\n",
    "from muon import atac as ac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b657f98b-8a43-43d3-b3d7-e195f93ded62",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import r_regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed757cdc-7e3e-463f-9248-dc27c2e6395c",
   "metadata": {},
   "source": [
    "## Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "38bd8af9-8247-47a6-a017-1f5240d64f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdata = mu.read(\"/home/annac/datasets/kidney_10x/kidney_filtered_withQC.h5mu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f8394d5-3564-4f2d-8f49-463466502d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 12721 × 67150\n",
       "    obs: 'n_features_per_cell', 'total_fragment_counts', 'NS', 'nucleosome_signal', 'nuc_signal_filter', 'tss_score'\n",
       "    var: 'gene_ids', 'feature_types', 'genome', 'interval', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'\n",
       "    uns: 'files'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atac = mdata['atac']\n",
    "atac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b44de6db-99c8-4d8d-8289-2411cbbe43bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "atac.write_h5ad('/home/annac/datasets/kidney_10x/kidney_filtered_atac.h5ad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d50f469d-2490-439b-a53a-c3eaf0ef54ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "just_matrix = atac.X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b367b96-8196-4333-9623-c6aea6fc8934",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Mean - var relations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0c0ec950-f757-4978-bc9c-0ad8f0f2a137",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_for_regions = StandardScaler(with_mean=False)\n",
    "scaler_for_cells = StandardScaler(with_mean=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "10a4bb78-8b25-41bf-a8ad-b070881be07f",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_for_regions.fit(just_matrix)\n",
    "scaler_for_cells.fit(just_matrix.T)\n",
    "\n",
    "mean_var_regions = pd.DataFrame({'vars': scaler_for_regions.var_, 'means': scaler_for_regions.mean_})\n",
    "mean_var_cells = pd.DataFrame({'vars': scaler_for_cells.var_, 'means': scaler_for_cells.mean_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a4afa728-ad4d-411f-92ed-6b4c01a5acc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67150, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_var_regions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c88ccaef-4dec-4e22-84c8-8e5bce75abfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12721, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_var_cells.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "9d34e951-0a71-463d-ae5d-4a7729a9e4bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='means', ylabel='vars'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=mean_var_regions, x=\"means\", y=\"vars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "df3e9f63-4ba3-4b1c-b84f-35c8541aa0ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='means', ylabel='vars'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YPBVR0tJpGeAQESWqqIe0HnvsMbzyyivuf//85z9HTk4O+vbti++++y6mG0dEFCtGkxUHTreipq4RB860sj8S0Xkm6gzPX//6V7z44osAgA8++AAffPAB3nvvPfzf//0ffv3rX+Nf//pXzDeSiKgr2AmbiKIOeE6ePInCwkIAwIYNG/Dzn/8cV111FQYMGIBRo0bFfAOJiLoiXCfsNXMqOBRJdB6IekgrOzsbR48eBQBs3LgREydOBAAIggCHwxHbrSMi6iJ2wiYioBMZnp/85Ce47rrrUFJSgoaGBkyZMgUAUFNTA4PBEPMNJCLqCnbCJiKgEwHPH/7wBxQXF6Ourg6PP/440tPTAQAnTpzA7bffHvMNJIoVo8mK+lYrms02ZGoU0KdxVtX5gJ2wiQiIMuCx2Wz45S9/iWXLlqG4uNjrsbvuuiumG0YUSyxaPX+JnbA/CzCsxU7YROePqGp4FAoFXnvttXhtC1FchCta5fTk1MZO2JTI2C6h+0Q9pHXNNddg/fr1zOhQ0oikaJUXvdTGTtiUiJh57l5RBzwlJSVYuXIltm7diosvvhhpaWlej99xxx0x2ziiWGDRKgHshE2Jhe0Sul/UAc+zzz6LrKwsbN++Hdu3b/d6TCKRMOChhMOiVSJKNMw8d7+oA55Dhw7FYzuI4oZFq0SUaJh57n5RNx4kSjYsWo0vFl0SRY+Z5+7XqdXSjx07hrfeegt1dXWwWr1Pbr///e9jsmFEscSi1fhg0SVR5zDz3P2iDng2bdqEGTNmYODAgfj+++9RXl6Ow4cPQxAEXHTRRfHYRqKYYNFqbLHokqjzxMzz0td2eAU9zDzHT9QBz3333Yd77rkHK1asQEZGBl577TXk5ubi+uuvx+TJk+OxjUSUgFh0SdQ1zDx3r6gDnr1792LdunWuP5bL0d7ejvT0dKxcuRIzZ87EbbfdFvONJKLEw6JLoq5j5rn7RF20nJaW5q7byc/Px4EDB9yP1dcHvtsjotTDoksiSiZRZ3guu+wybNmyBUOGDMHVV1+NX/3qV9i5cydef/11XHbZZfHYRiJKQCy6JKJkEnWG5/e//z1GjRoFAFixYgWuuOIKvPLKKxgwYACeffbZmG8gESUmTvcnomQiEQRBiOYPbrrpJvziF7/AuHHj4rRJsdXc3AydTgej0YjMzMye3hyilGM0WVl0SUQxF+vrd9RDWmfOnMHkyZPRu3dvXHvttfjFL36BESNGdHlDiCg5seiSKD7Em4lmsw2ZGgX0afyudUXUGR4AaGxsxD//+U+89NJL2Lx5M0pLS3H99dfjuuuuw4ABA+KwmZ3HDA8RESWSSAIZNvWM/fW7UwGPp2PHjmHdunVYu3Yt9u/fD7vd3uWNiiUGPERElCgiCWSMJisWrasJ2OdqbIn+vGnqGevrd5fW0rLZbPj666/x5Zdf4vDhw8jLy+vyBhEREaWicN3JxXXoImnqSdHrVMDz8ccf4+abb0ZeXh7mzZuHzMxMbNiwAceOHYv19hEREaWESAMZNvWMj6iLlvv27YuzZ89i8uTJ+Pvf/47p06dDpVLFY9uIiIiSQiR1OZEGMmzqGR9RBzwPPfQQfvaznyErKysOm0NERJRcIi0wjjSQYVPP+Ih6SOvmm29msENERITI63KAc4FMIJ6BDJt6xkfUGR4iIiJyiaQuRwxQxEBm6Ws7vLI3gQIZrqQeewx4iIjAJm/UOdEWGEcTyLCpZ2wx4CGi8x6bvFFndabAmIFMz+hSHx4iomQXTQ0Gka9I63Ko5zHgIaLzGpu8UVewwDh5cEiLKAKs70hdbPJGXcUC4+TAgIcoDNZ3pDY2eaNYYF1O4uOQFlEIrO9IfazBIDo/MOAhCoH1HamPNRhE5wcOaRGFwPqO8wNrMIhSHwMeohBY33H+YA0GUWrjkBZRCKzvCM1osuLA6VbU1DXiwJlW1jQRUcJihocohGjWvjnfcPYaESUTiSAIQk9vRDw1NzdDp9PBaDQiMzOzpzeHkpTYh4f1HS5GkxWL1tUELOgeW6LHmjkV5/X+IaKui/X1mxkeogiwvsNbNCtEExElAtbwEFHUOHuNiJJNjwY8n332GaZPn46CggJIJBKsX7/e63FBEPDggw8iPz8fGo0GEydOxP79+3tmY4nIjbPXiCjZ9GjA09bWhhEjRuDpp58O+Pjjjz+OP/3pT/jrX/+KL7/8EmlpaZg0aRLMZnM3bykReeLsNSJKNglTtCyRSPDGG2/gmmuuAeDK7hQUFOBXv/oV7rnnHgCA0WhEXl4enn/+eVx77bURPS+Lloni43hTe9DZa/mcpUVEXXTeFC0fOnQIJ0+exMSJE90/0+l0GDVqFLZt2xY04LFYLLBYLO5/Nzc3x31bic5H7E5MRMkkYQOekydPAgDy8vK8fp6Xl+d+LJDVq1djxYoVcd02InLh7DUiShYpN0vrvvvug9FodP939OjRnt4kIiIi6mEJG/D06dMHAHDq1Cmvn586dcr9WCAqlQqZmZle/xEREdH5LWEDnuLiYvTp0webNm1y/6y5uRlffvklRo8e3YNbRkRERMmmR2t4WltbUVtb6/73oUOH8O2336JXr14oKirCnXfeiYcffhglJSUoLi7GsmXLUFBQ4J7JRURERBSJHg14vv76a4wfP97977vvvhsAMHfuXDz//PO499570dbWhltuuQVNTU2oqqrCxo0boVare2qTiYiIKAklTB+eeGEfHiIiouQT6+t3wtbwEBEREcUKAx4iIiJKeQx4iIiIKOUx4CEiIqKUx4CHiIiIUl7CrqVFRESxYTRZUd9qRbPZhkyNAvo0roFG5x8GPEREKex4UzuWvLYDm/fXu382tkSPR2cPR0GWpge3jKh7cUiLiBKG0WTFgdOtqKlrxIEzrTCarD29SUnNaLL6BTsA8Nn+eix9bQf3L51XmOEhIgA9P+zBTETs1bda/YId0Wf761HfauXQFp03GPAQpbBIg5ieDjbCZSLWzKnghbkTms22kI+3hHmcKJUw4CFKUZEGMYkQbDATER+ZakXIxzPCPB5IT2cCiTqLAQ9RCoomiEmEYIOZiPjQpysxtkSPzwJ8vmNL9NCnR/e59nQmkKgrWLRMlIIiCWJEiRBsxCMTQYBOq8Sjs4djbIne6+djS/R4bPbwqAJZFkBTsmOGhygFRRPEJEKwEetMBJ1TkKXBmjkVqG+1osVsQ4ZaAX169MNQ8coEcoiMugsDHqIUFE0QkwjBhpiJWPraDq/t6EwmIpH11MVdp+3668QjE8ghMupODHiIUlA0QUyiBBuxykQkqmS/uMc6E5gIxfJ0fmHAQ5SCog1iEiXYiEUmIhGlwsU91pnARCiWp/MLAx6iFBVtEJOqwUYiSIWLe6wzgYlQLE/nFwY8RCmMQUxiSJWLeywzgYlQLE/nFwY8RERxlkoX91gF0YlQLE/nF/bhISKKM/HiHsj5enGPZY8gokhIBEEQenoj4qm5uRk6nQ5GoxGZmZk9vTnUjdjfI/Uk82d6vKk9aP1LfhLM0ooX8TNNxZl51DWxvn5zSItSUrJPASZ/yf6ZJspMuETDOjPqLszwUMoxmqxYtK4m4KyYsSX6pJgCnCpilZHhZ0p0/mGGhyiMVJgCnApimZEJ95mebrGkxGeazEN2RImOAQ+lnFSZApzMYt1oL9xnWnfWhDSVPCmGtoJJ9iE7okTHWVqUclJpCnCyima19kiE+0wBJPWK3VyJnCj+GPBQyuEU4J4X6yxbqM+00pCDmqNNnQqkEkWsA0Qi8schLUo5Oq0Sj80ejk9+OIPcDBUsdifUChlONZsxfnBv1kR0g1hn2cSeLb5ZkEpDDuZXFuOOdTUAkne4ksOwRPHHgIdSkgDg3R0nsLnWux7i8sG9e26jziPx6KJbkKXBwzPLUXumFRa7Eyq5FDVHm3DHuhqYrA4AyTtcGY9hWBZAE3ljwEMpx10PUZu8K1Mnu1gvNCnK0irwwueHU245glgHiCyAJvLHPjyUcg6cbsUVv/806OOb7r4cg3LTu3GLzl/x6KJb19CG37yxE1tqG9w/qzLk4JFZw1CUk9bVTe4xserEzJ5FlCrYh4coDNZDJI5Yd9E1mqxYuWEPLizKxvzKYq+hrVUb9uDJn41I2ot5rDoxsw8VUWAMeCjlcFp66qpvteLDvafx4d7TQR9P5ot5LAJEBvxEgXFaOqUcTktPXbyYh8eAnygwZngo5cSrYJZ6Xi+tEs/OHeluNfBNXSPWbjmU9LO0YikWBdCc4UWpiAEPpSSuTJ16jje144H1u7xm31UacvCnORW4Y10NRvbPZvYOXQ/4OcOLUhVnaRFRwgs186jSkINpwwswbnDvqGYzpbrOzJDjDC9KJJylRUTnnVAzj7bWNuCh6WUMdnx0pgCaM7wolbFomYgSXrhi5TaLvZu2JLWxKJxSGTM8RBQXsSx85cyj7sH9TKmMAQ8RxVysC1/jsTYX+eN+9scZa6mDRctEFFPxKnyN1dILFBr38zmcsdazYn39ZsCTZHi3QYkunmuZxWNtLvLH/cwZa4mAs7TOY7zbSC3xCl57OiiOZ+FrrNfmosC4nzljLRUx4EkSRpPVL9gBXF+85W/uwsOzhqHVbGfmJ0nEK3jt7PPGMkjSKmUhH2fhKyUDzlhLPQx4kkSwuw2tUob/uLQI9/zft9hc2+D+OTM/iStU8Lr0tR2dTpV39nljGXwZTVZ8U9eESkMOtnocj57Pez4WvlLy4Yy11MM+PEki2N3GgqpiPLf1kFewA5y7yBlN1u7YPIpCJKny7nrecEFStMdPfasVqzbswfzKYlQacrweqzTkYOXMcmYeKSlwEeLUwwxPkgh2t1FRmIWnPqoN+BjHmRNTvFLlnXneWNcpNJttMFkduGNdDX55+UAsmVwKADBZHFDIpbA5nBE/F6Wunq4ziwQXIU49DHiSRLD+GBZ76AsIx5kTT7xS5Z153miCpEguUp7bUFagw2Mbv/ca2qruuFhwqPX8lUyTL7gIcWrhkFaSEO82fFOsWRqOMyebeKXKO/O8kQZJx5vasWhdDa74/aeY9efPccXvPsXidTU43tQecBvEoVbfOp7NHGo9r8V6CLU76LRKDMpNx4VF2RiUm85gJ4kx4Eki4t3Gprsvx/rbx7j7mXCcObkEC167mirvzPNGEiRFc5ESt2HMwMBFy+LfdbZO6XxiNFlx4HQrauoaceBMa0IGA9GKV/0aUSQ4pJVkAvXH4Dhz8olXqjza542kTuHA6dao6nwKsjQ4aWwP+PuiZB1q7a7ak2Qa9okGp3pTT2LAkwI4zpyc4tXcLdrnDXf8dOYipdOEfv1kHGrtriAkXm0LEgGnelNP4pBWEvNMede3WaFPV3KcmTolVJ1CZy5SqTaltztrT1J52CfVjgtKLgkd8Dz00EOQSCRe/5WWlvb0ZiWESItIiboqXS1HlU9PHVGVIQfpav9EcbzqlHpKdwYhqTzsk2rHBSWXhB/SKisrw4cffuj+t1ye8Jscd6mc8qbE02axY15lMQTAqxC50pCDeZXFaLPYA/5dKg21dmcQkirDPoHqnQDAbHPggWlD4RQEmCwO6DTJe1xQckn46EEul6NPnz49vRkJhYvaUXcytttwx7oaLKgqxoLKYljsTqjkUtQcbcId62rw0k2jgv5tV+uUEqVBXXcGIcF6bgHewz6Jsm8CCVTvVF2ix8LxBix4/t8wWR0AztVAJcp2U2pL+IBn//79KCgogFqtxujRo7F69WoUFRUF/X2LxQKLxeL+d3Nzc3dsZrdKppR3Ip+Uu0MqvP9MtQImqyNoR+94ZRxiVSQci88g0iAkFiKZOZfIs7iCZaA376+HUxCwoKrYfSwxK03dKaEDnlGjRuH555/HBRdcgBMnTmDFihWorq7Grl27kJGREfBvVq9ejRUrVnTzlnavnk55R3oBSeSTcjyJ+6fRZIXN4cTWAw1Yu+UQTFZHUr7/7rzYi2I1bBurY7C7lxkINRyY6EPaoTLQW2sbsKCyGFqlDAuqilFRmAWL3YkTzWYAYNBDcSURBEHo6Y2IVFNTE/r374/f//73uPHGGwP+TqAMT2FhIYxGIzIzM7trU+PKaLJi8bqaoBegeJ7wIr2AGE1WLFpXE/DEF+9t7EmB9k+lIQfzK4txx7oad9DTXe8/Vhmm403tQS/2+XEI3g6cbsUVv/806ONi081Q4nEMivuzJ2uSYrFv4qmmrhGz/vx50Mf/8ouLoJBJ/TpxJ+PNAMVXc3MzdDpdzK7fCZ3h8ZWVlYXBgwejtjZwah0AVCoVVCpVN25V9+upRe2iubM8H+uMgu0f8aQupvKjef9dCVhimWHr7gLkWAzbxuMYjFfvpGgk+pB2uAx073QV/vDhD36duBMlQ0WpK6kCntbWVhw4cAA33HBDT29Kj+uJGTDRXEAS/aQcD5Gk8kWRvP+uBCzxGPbozot9qIumVilDdkcH6FCBYKoegz09pB1OqCHQSkMOlHJp2GVHEjngSYW6vPNVQgc899xzD6ZPn47+/fvj+PHjWL58OWQyGebMmdPTm5YQuvtuM5oLSKKflOMh3P7xXNk+3PvvasCS7Bm2YBdNrVKGtfMuwQPrd2FzbehAMFWPwZ6oqYpGsAy0OEvrhNEc8u8TORA9X+sSU0VCBzzHjh3DnDlz0NDQgN69e6OqqgpffPEFevfu3dOblnRicVcSzQUk0U/K8RBu/6jkrj6fkbz/rgYsyZ7dCHbRXDZtKJ7+qNYr2AECB4LdeQx2511/Tw1pRyNYBhpA2IAnUQPRRC8Wp/ASOuB5+eWXe3oTUkKs7kqiuYAkw0k51sKl8muONkX8/rsasKRCdiPQRdMpCLjv9Z0Bf983EOyuY7An7vq7MqTdXcFZqAx0Mt4MJXvWlBI84KHO8Tyh9UpT4oE3dkV0RxxOtBeQVOq0G4lQqfwVM8oAADdXFUf0/rsasKRKhs33ollT1xjy930DwXgfgz1519+ZIe2uBmexCJaS9WYo2bOmxIAn5fie0J6dO9Iv2BF15q4k2guI70lZXPA0VQv+YnWB7WrAkqwXlXA6EwjGs9Ytme76uxqcJfOsv1hIhazp+Y4BTwoJdELzLJQNpDN3JZ29gJwvBX+xuMBGErCEu9tOxouKp0DvL56Zq85kL5Lprr8rwVmsMll++zhd2aM9gyIhbrOx3Yp1N4/yaiQqSqas6fmMAU+MJMJUxUAnNLFQNpjuuithwV/0QgUskQaPidA3pjOCvb/HZg+PS+aqs8F4Mt31dyU4i0UmKxlveAJtc5UhB3+aU+HVSDSZs6bnEwY8MZAoX+RAJ7Sao02oNOQE7HvRnXclyZT6TySBApZIg8dECMI7I9T7W9Lx/mKZuepKMJ5MtVJdCc6iCZaCrZKebDc8wY6LLbUNkEgkeHNhJaQSSVJlTc93DHi6KJqTZbwvQIFOaGu3HMKf5lQAgF8b9+68K4lH6j9ZL+hdFS54PNFsRn2bFQ+9uQub49y632iyoslkQ5vVjjarA1kaBXIzVF36HCIJjgflpsfss+5KMJ5MtVJdCc4iDZaC3fytnFmO7UcCF5wn6g1PqONi8/56SCWSqIbjztfzVSJhwNNFkZ4suyMLFOiEZrI6cMe6GiybNhQPTS9Dm8XeI7UcsU79J0pWrSeECx5Pt1jwt08PxL11/4mmdhw5a8Kaj/Z7vVZ1x8W+s59Dd9fFdPX1kqVWqivBWSTBUqibv2Vv7vJaJd1XLD/TWAUWsTwOz+fzVSJhwNNFkXwpuqt+RadV4pFZw3DfGzu9Xmtk/2yMG9w7Los8Rqozd5fBTlznez1QuOAxW6uIe+t+o8mKT344gw07jvu91uYufg7dXRcTi9dLllopz+DM2G6DVimDVCpBu80Bo6lrmawDp1tDZkTunXwBhuZnQq2Q4Zu6Rq/CX3EfdzVYiWVgEavj8Hw/XyUSBjxdFMmXorvqV443teOht3djRGEW5o0ZAIvdiSyNAv1ztD0a7ADR312GOnG1Wx3ndT1QuAaHdocQ8u9jcTdd32pFboYqLoFVd9fFJFMdTizotEq0WR146O3dUQUG4TJZ4W7+jp5tx+0vfgPAdZyKhb8j+2dDn971LHisA4tYHResX0wcDHi6KJIvxcH6tpDPEYsLkOeX/cO9p/22IxHuIiJN/Yc7cd05sSTk6yTSVOCu8JwOq1XJIZVIIJdKkJMWOHisNORgfmUxjKbQ7z8WGZJmsy0uLQ+A7quLEfdvk8mKJVNKMbm8CQ+/s9eddagu0ePx2cMBIKV6R0UbGEQ6lTzSpVWAc/WEy6YNxbjBrqWCuhqsxCqwEN9vq8WGlTPL8eCbu7p0HCZT64JUx4CniyI5OWeqrSGfIxYXoGS5i4gk9R/uvfzm6iEBH9MqZVhQVQy1Qoaausakujh5XlR0GgWUMqnf0KQY0Kx+dy9Wziz3Ch7VChk27DyBO9bVYEFVcadm5kUznJCpVuBsW+yO60CvHc+6mEDZhGpDDtbfXolDDW2QSyU422aF3Slg0boa798r0eOhGWWQAMhJkuPLUzTnimiyLpEsreJpa20DHppehvwsTcjhsEjPX7EILHzfr1Ypw7JpQ3H/1CFotzo6dRwmU+uCVMeAJwbCZS66I2WeSncR4d6LTCrx259apQx/mlOB57Ye8iqM7MnCwGDTc31/ZrI6cK/HSXbRBANq6hr9Ahbx3xVF2e7p2eKdttFkxY6jTTBZHZ2amRftcII+XYmvDp8NGVilq+URZUZCvXagTEJX6zyCZTg21zZgxYbdqCjKxlMf1WLRBAPe+vZHr5lugKse5cE3d6GiKBs7jjYlXeFppOeKaDNBJqsDt483wCEIXsdEpSEHN1YVY+ePRjw7dyQsdqe7jqfdao9qm0LpamAR6P2arA7c9/rOLmXJz7ch00TGgCdGQmUuuiNF3xN3EfGaZhnuvcikEr/9uaCqGM9tPRT3mUmRCphBKNFj4XgDFjz/b/ewydgSPW4fb/CasltRmBV0NsvW2gYsqCzG2i2H0GSyuYe80tRyrJhZhgffdNVliJmeheMMUCmkyNIog96Zdqb2QadVYtzg3ijWp7m3y/N9PnxNOe5/Y6fX8GqgACra1+5snYfnsapRyjCiMAvbjzR6dcv13L9AZJ/DUx/Vxv34ivX3LNJzRTSZIKPJintf24HtRxqxoKoYCyqLYbE7UdhLg017T0MCCb46dBZ/+GC/+zkqDTn46UX9otqmULoaWMQrS55MrQtSHQOebhLvqavdfRcRyYWnsyfqcO9FHEbwHdIJdnHq7iG9oBmE/fVwCoLX9NzP9tfD4fOzcLUxVocTf5pTgQfW7/TKPkwo7Y1fT7oAt40bBKlEApPVgSNnTWFn6HX2RJ+fpYFWKcMj1wxDm9UOk9UBnUaBTI3CL9gRn8s3OIj2otqZOo9Ax2qlT7dcT+L+D/c5aJVyrJ13Cb6pa0RDW3yOr3hMZw72/RKHb5yCgJq6RqjkUiyaYPBbRkHkmXXx/Bw9v4eLJhhQoFPjH1sOBsxYPvjmLqyZUxGT81ewDFN1hIFFq8WGRRMMqCjM8spCie+/K1nyZGldkOoY8HSjeE5d7c67iEguPG1WR6dP1NG+FwGAUxAiPjnHW6iLuJgZEOuNxJNrUS8tAFejyHDLgfROV+EPH/7gdwH56PszsNidqCjKRk1do3toJlw6vivDCYGO6QOnW/2CHZFvEBPutRtNVnc9ltMpRB2YBTtWxX0XqDeMuP+zNKGzCiarHTe+8DUqDTmYdWHfkNO6OyNe05kDfb+0ShnWzrsET39Ui/te3+n+3WpDDt5eVIV2mx0/NpmhkEndQYBn1iXY57h2yyG88svL8Js3dgV83LORZFfOX8EyTCq5FKdbLNAqZeH3i0aJmrpGr+PBMzDuapY8WVoXpDIGPCmku+4iwt2VN5lseODNXV06UYd7L9HetXfnmmEWuwN/vv6igP1GgHMZGt96I3H7d/5oDFobU2nIgVIuDTod3HOoRRyaCZfhivVwqEMQ/Go1PPeBZwAV7rWN7Tbc+MLXAIC180aG/N1AgVkkwacnsbh2bIke/XO0ERXhbq1twENv7cbVw/Nx+eDeMavniedEBN/vV7ZWiQfW78LmWv+6pgff2oWpw/KRm6nGope+QUVRFtbOu8Qr6xLsczRZHTjW2B5yW8TPrSvnr2AZJtGlA3qFfB6jyYpl63cFrZtbNm0oa21SAAOeFNMddxHh7srbrPaYnKiDvZdo79q7qzAw0iAsUIZGq5ShoigbGoUMF/fPxsQhedh5rAmrPKZJi7O0zraGnh0VaEgmVJYmlsOhx5vasert3V5Dbb77wDOAimZmT7jgKE3lfzoLd6x67qPqEj1WzCgDANxcVRw00yh+Dnesq3H/bHNtPeZVDuhU5iXY0G9X168Ktw2e368Dp1v9gh3R1toGLJlcilaLHWvmVGDxuho8/XEtnuoojAdCf47RBNSdPX91tei5vtUa8v0/OG0oszMpgAFPDPlOLU5TydFqtqdM/w5RuBNYW4AhJU9dHV6K5q69uwoDIw3CAmVogs0wqy7R4507qmA0uWqUZFIJZFIJHM7QjQXFIRnPobFQWZpgF/bqEj2WzyhDQ8f080iGFZa8tsNvVpPnPvjuaJNXABVJUCEO/2VpFag25Pg9v/j7Spn/UGC4Y3WgPg3rbx8TNJvgmXVoaLOgxWxHzdGmoLU/0WZeQtXo6MIMqYVbvyqaOp9wAcOxRlfTwGqDHusXVuK9XSe86pZCfY4ZanmXFzAOF9B1NUsZLivZHuacRsmBAU+MeJ50PC9gvtOCk20KayDpajleumkUmtptfieHsSX6sLUPXR1eCndy1mkUIS9i8RBJECbO0jphNHs9HmyG2eb99Vi2fhcemz0cfbO17p8bTdawWRHP7EgkF5WCLA2e+NkINJqsaDXbYbU78fnBBkxfs8X9uYY7dsPtg9vHGVBl0Ad8bc+hDKVcind3nXRnUMTvkjjd3gn4TXueX1kMY7sVQJrXc4fKPFSX6CGVSpAR5mbEnXU4Dfz8b18Eff9igBlpQB8oSNYqZRhemIXD9W1QKaRYd/MobD3Q4DcsGsn6VdFkmyJtGri5th4r3t6NqcPy4Rt2eweHVpgsdjgEAWfbrFg8vgQLxxvwucd7ifRmJJKAritZymizkpS8GPDEgO9JJ9GmSMdSqGGbV76qw8qZ5dAqZZ0++USSmg+/lpSrE6z4XAfr2+KeYYskCBOHAHwDnlDTn7fUNuBIgwnpKnlEd9PzK4vx0pdH3NmRaC8qIwqzAvYAiuTYDbcPHIKAm5//Gm8vqgo4zV382cEzrQCANXMq0CtNid//a597e8Tp9mIWT6dR4JMfzuCOdTV4e1GV32sG21dVhhzMHTMA1zy9FYCrRuOioiyYrI6gx0qkw2+RXhx9A8Rgmb4qn4tvpOtXRZNtimZoUQzgH3prN57qOB58v7d9MlSotdiw1uc8WG3Q443bxwAA+mSqI88ahgnoOjtpI5Ks5A6frKT4d1z5PPkw4IkB3xNXqAtYInU9jlaoYRupRIInfzYCeZlqAOjUySfS1Hyok3OVIQdqhbTbVyeOJAjzfN+e2x9u+nNTu83vmAm0CKRMKoFEAiyfVgZjuxVvL6qKKMPl+bnOGzMg5LH7Y1M76tusnQpEbXZnRNN7lTKpe7bMs3NHel2MTFaH1/Y9O3ekeyZasEDad1+ZbQ58frDBPVz2j7mX4Hfvf+81OynQsRLJ8Fs0NU+ujNQ5wW6UttQ2QCKR4M2FlZBKJFGtXxVptimaeiXAdcxu7jiXmawOfPLDGeRmqGCxO9Foch2PL31xxD9rWVuPhzfsxapryiLarmgKt8MVPRtNrsyT3SnAKQgwWezQquQhs5ILxxlw/aVFMekF5YkBU89gwBMDviedeK0x1NNCnXw2769Hq9mOvEzXv6OdcRFNal6nda0Kv/T1Hdjik4aeV1mMzw804K1vj/sVIcYzwxZu6EQuk7inLfteXMJNQ1fJpQGPmdAFnmkBfxroROv5uYY7dpvNdpxtMOG0VoGiXlqvobZYZECMJivue2On+0IZbnssdmdEWSxxXx043Yqf/OVzAOemYj+28fuIMlpGkxXtVgfunFiC31w9BFaHE2dbrfi6rtG9CGak9WLHm9phtnm/t1A3Spv310Mqkfh1no7lDDvxO3uy2Ywmkw2tluD1Sp7Dd2daLdiw47hfJmdu5QB8fvCs399urq3HgTNteOHz3WGDhGgDumDfieNN7XjwzV249tIir6Dyz9dfFPL5fcViCLG7b8boHAY8MeB70gl3AdNE0BMiEcXq5BNItFNwrQ4nLizKxnyPfhviyXnNnIqgMy7ilWELdYc8d8wATPl/mzGyf7b7pOYZEDoFAdUl+oDvXwwUflLRt8t3hcFOtHdccW4x1nDHrtnqwJ4TzagozML3J1vQanGgT6Yq5LBCNBkQz+NAq5ShX7YmZDHpQH1aVAGs5zG8oKoYLWZ7yBXfT7dYoNMGX8l75cxy9EpTYNaFfd3vK9xyGuJFc0Rhllcxb2dulGLdcFT8HE81m3HPP78LeUwCriHFJ9/0n869ubYeTggB+xwB5wq8wwUJXQnoxO+LQxCw6u3dGFGU7ZdBC3e8t9sc7mVcdFpl1OepQNsUj95KFBkGPDHge9IRi0Z9Z+IsqCrG6IE5qG+1QnqqBbkZqqQ6uOO1fEUkvWt8T/bGdlvQu+GeyrCJQczpFgvqzpoAwOsOOVDdgfj5rw6SsZpfWYz13xyDUib1W8QymrvCUCfaWy8f5P53oGNXNKG0N7LS/JuzeW6HZ5ZA7L8i7oNIMiBiQCLWszy+8Xu/fSLWs4zsn418Xfg6EE+ex3BFYRaM7aGPhbqzJmiVMix9fWfAfSd2Cg4WFFWX6LGqo66t1WKHsf3c0hYvf1WHRztWY99a2xD24it+v3xngz4yaxh+88bOTjccNZqsaDLZ0Ga1o83qQJZGgdwMFR4LE7xOG9YHVofT6/PxFKjPkUh8r55BQqCAXp+u9LsZEM+lYwbmwNhuxYEzrX6BpednIQ6Lzqv0D75CHe9iYOe1je2hW0KEO56SZZHnVMWAJwZ872zF2SQSuMbfE3Fhy86Ix/IVkfau8Q2mQgVfkV44ohVJhkW8CxSb5fkKdlLr10uLx2YPx5EGE5rabe6M1Stf1eGhGWVBL7iR3hU2mWyYN2YA5lxa5BdUfnX4rPuiEmzh0UpDDu6cODii4R+d1rUg6umWJuRmqDA0P9MdCIYjfq7B6lk8G8GNG9w74ouD+NkZ263umU92pxD2WAGAIw2msBcpAEGXE7l//U5MG5aP3plq9zFdbcjB7342AruOG7FkcikAQCGTBs30id+vQKt5r5hRhhUzytBuc7iX9/C8mQp13J5oaseRsyas+Wg/auqa3N2/686aUJitxRM/GwFjuxVHz3oHr5WDcvDryaWoPd0act8FuvnwLYI2ttuCZtAemTUMiycY4OxYLiKSc6lvcB9qqZBQx7tn7VKLOxAPfckM19E5lRZ5TkYMeGJEvLN13ylZHFg6ZQgEAXAKTjz5/r6kn7XV2ZkQwUTauyZQMBUq+DrVbA7aryXQc0USyEQz7t7Zk1rfbC3SVXJ33dOsC/tCX1Uc9q7wdIsl5PYfb2r3W3fLM6iUSICF4wa5LyriTKjbxxkgl0qQoZbj3V0nUd9qdV90PJfFEAMosS+L2OY/2IU71PEufq7hFu58aHpZyPXBPAX67KoMOZgxvADv7joR9g5/aH5myOcXP89wbQnWbj2EX14+EDaHgIuKsqFWuJpN/mvPKazdcgiAqwjb97nE7xcAPPjmLowozMK8MQPgEAQM0qejxWzH96daoJK7Pod9J5qxYmY5dNrQx22aUoZPfjiDDTuOo6auKWQgMbK/GvWtVuSkKTHrwr5IV8txz/99i3lBMjgi315CgYqgpVIJlry6I2DN3X1v7MTF/bNRUZSNBZXFfrP2PI/FvSea0WaxQymTei3GG6gvlchkdbiP96VTSnH0bLvX8LjvDZdUgpDHi0wqCbgfxHOM3Sm4118LtAwOp7/HFwOeGGqzOvyWVKg05GD5tDJsr2sK+DfJlsaM5fIVkfSuCRZMhaoXyddpML+q2K9fS6DninQRVM8LjedFfvmbu/Dkz0bErAlaoLqng/VtIZ+v7qzJK6MU8G43QKZECuCWsQNRXqDDghe+9luDaNtBV8+Uf/5yNJ76qBZ/vv6ioHfYlYYczKroCyB82v6E0Ry0VYD4ue490RzyPTeabAGHMnwFC6q31DZg1Tt7MGNEAcoriyEF/AJC8cK8xqOjcCBKuRRnTeG7X9fUNWH5tDKs2LA76HpNN77wNZZNG4oHpw1Fm8Xu9f06eKbVXXQrZiYefGtXwMzE8jd34eFZw0LWi6yaWY7cDBW21jZg0QRD2FYangXTrs7MDRhRlB0yADDbHHj11tHuoZ6ao01Y+toOd5ACuAKREUVZ2F7nv3q9OHNQPL49Z+2FatjpmSH27EsVaFtNVge+q2tEgU4dcM2vKkMO0tVyHG9qx9k2K+Z3BHmB9rtE4l/DFWhdwUpDDp66zrWMTHmBDha7E9laRdLWdyYLBjwxEipbsWrD7qDFe0DypTE9LzDNZhsg8f95JCLpXRMqGyA2yzvbZsWh+jb3ndnCl74BAPcFXKdRIFur9AvMQtW1iBeMVrMdZ01W3DlxMHYca8Jij7u+CaW9cefEwTjR7H0Bj/XQX7gAypfnRSrkzLraBtw7pRRnWizu6d6ed8xD8zPx1HUXQS6T4IrS3lDJpSGHmsS+LOE+14P1bbj9RddnJBb+GtutSFe79l9BlgZtFnvI52gx2/Dzv20LOywcbmbhsmlD8N1RI+67eigWttuQppJBAPDxvtNeF8xgQ01Vhhw4BQFquSxkcbW471Zt2B10mE48R9z3+k5s/K9qV2M/j4SB3Sm4932wAEX8d0VRNhrbQgeebVa7e5gn2lYa4mccbEio2qDHwgkGLHj+3wDONY8MlkmKZPV63//veyx6HrsA8D83jsLH+067a6XE/lS+21ppyMH8qmJIIPELiKoNetwz6QJYbA7cv95107PnuNGdcfKcMPHyV3W4YkieV3uD1T8Zhnd3nPDLXrluOCSYMqyP181KlSEHj8wahqKcwLMsA+EU98gx4ImRcBeWUKnfZEtjdnVapfgFDVc/4du7Jth2+DbL8z3x6TSBs1DBPjOtUob/uLQI9/zft0GHgQDgulH9/WpaxP3w2OzhWBJg6G/lzHIcbmhDepBeNoGIAdTXHStBew4lnTS2Y9dxo9/fiBepcMHH6WYL+mVr3O872B3zw9eU46tDZzF6YE7IqdMnm83QKGQhC9C1ChkWTTC438dxYzvarQ7MeeZLd2FzpkYRduaa+D49h4V9T/4OQYBWKfO7iIrvVyaV4q1vj+PXr+44934Nevxq0gX4n21HYLI6sO9EM347axh+E6Co/MaqgWizOPDUR/uDHisVRVmoOdoUdphu6ZRSDM3PhFohQ7vNgev/8aVXl2uhY8gRCB2giNnRZnPooNFkdbi/g9EW+otBuOeQkGcAUKBTQ9mxLtzm/fXu37n/6iFY/e7esEGfp/69tO7jKVN97pLluQ9CZR7FTPC1lxYhQyXDg9OGwikALWY7tEoZTjWbce+rO2CyOtzvI00lR1vHtPz/t+kH3Du5FJv312P7kcag35GF4wxY8MK/vbY9N0MVdMaouP6apy21DfjNGzuxetYw1LdZwwYwnOIeHQY8MRLuwhJMdy1sGSunms04XN+GOZcWYX5lsfuCFmk9kucXdNEEQ6fX2PHMzognIQBhaxE8TwLBPrNwBbMLqlzBa7ghAM+hP41Shm+ONOLqP212X3wjPTHptEpXQXNHcanXidagx/yqAQEv6i1mW9jskE6jgADXUEGWVol9J5tR4zP8unl/PR5YvwuLJxggQeAaBVGTyYaGNqs72Nlz3Oh15z6htDd6pStRoFO7/8Zsc+J0swVPX3cRfv3qdzhy1oR/fHYQc8cMcNcViQLVgHy2vx4NbdaAQwe+wxueFlQV46E3d/kN922urYcAV63Fmk37sXJmOcw2R8A2CN8da8K/D58Neqw8MHUIcjuKlcMNjR092+7OfFUbzm23eEw9MHWI+3cDBSiegb5WKUeGWo5FEwwBa0UA12e//3QrKg05URX6G01WyKUSd0Dq2wyyypCD5TPKcKbFjPuvdm2zyeqAViVDq9kesLYOEIM+1++L21xlyMEHe0/B7hQwsigbWqUM7yyuQkObFUqPbQ73nb320iLsO9GMn1/cD/ev9y87eOKnI/DdsSYM6+saXuqTKceW2nr3dlw/qr/7fQQM8LI0uOHZL/1uSORBanpEWqXcr6ZnS20Dmsw2zPqzq19UsPMEp7hHjwFPjIS7sPTL1vgNc0RS7JtI6crjTe1Y8up3Qe9kw9Uj+X5Bg6XDffdLuGZ5JqsDS1/bgcdmD0f+1RqcMLbjxqqBqCjKdp9EAp0Egn1mkdw9Awg7BDAoNx06rRI/Nppw72s7OlW0Lr53pyDg6Y/2R9zvRKuUIVurhNXhxIs3jYJOo4DN4URzux3/PnIWa7ccwsVF2fjhVItX3UKwoQWxlkIiCX0Cb7XY3Rdtz6UuFlQVo6auEXdfORgtZjve2XnCL5BZNN6AJ386Ams63ufnBxvcFxatUg6TNXgjPIdTCDpLShAC94OpHBQ8W7WltgG3m+14eNYw5GWq/abii/v45Vsuw7C+Olw/qr9fRmtrbQPumjgY/7n2K1xUlIXcTFXIfecZdPh+rp/tr4fnmrG+AUq4ZSnEDId4Mc7WKpCpUWD84N4o1qfh4JnWiG4+xBsW8SbDNyCtNuixYmYZHn5nDz76/ozXczwyaxiON7WH3AdHz5pQU+d67nVfHsF/jh6AXmkqtFhskEKCpnabez0uzwAy3Hf2galDccNl/QP2Fqqpa4JaIcXXh8/ijx/ud//cN6Mr8g3wAODtRZV4/KfDsXaL9/5/6aZRId+vyWrHjS987fe9azWfO76DnSc4xT16DHhiJFzdRp9MddTFvomUroxkzZmnPqr1Sn37BipyicRr9oTv3VKgWptImuVplTI8Onu437o9vicR35NAsM8sku6+4Yj7wWiy4kiDKWRzu2AnpkC9RALx7XcidhB+YP0ur3S6GIDsOW7E2rkjIZFIMP/5f3v9XUXHXfTauZcgXS33CpJ0GjnSVZENNYnbBbjqSaYOywcACIIET31cG/ROfOWMcvf/97ywPDt3ZNCp/oAr4Al28t/SsXCp54Vo4pBcyAOsru6pqd3m7h7uGxyLAcYTG78PueikUxDw2m1jIHTU+US674Bzn6uYuXF2rOgtkUggBfDIrHLkZaphsTuRl6kOmJ3bUtsAtVyGV345Gg+9uStg1nNInwwUZKoxemAOHnxrd8AZYuJw4ac/nHG3N5BJJFhQWYzfXF2KIw3t7iDMN9gBgK+PNGJzbT0uLMwKOdypkkvdS9XcWFmMrDQlHt24N+D3etdxoztIC/edPN7UDrlUEnDfL6gqDnlMLqgqDlnHVWnIgVYpx3NbvvfrSfT5wQZUGXIC9iry/Mx9z6Ppau/i5c86hosD1VEFE21taCLdXMcLA54YiXTKdjR9Q7orXRnJgX66xRJyRtWNVQOxaIIBaoUMNXWNSFPKsb2uEas27HGf0AINL3he1NbfPsY9E0RshvbA+p3YXtfkVfOhVsigVcrcwziRDEGJr+F5Egj2mYVb7T2S3i1pKjmMJitOGM1oaj/XTC/QdO42i/+JKVgvkVCvJ15ItEoZnv6oNmChJOAKQJ7+5AAu7p/t/hxC1UCIQdKMEQW47pkv3PUkW7zu7HOwbHoZDtW3eaXoxSyHIAioKMyCVIKgwd/W2gaY7Q53LxPPfZWlVeKRWeV4+J29ftmdsSV6mKyh61XUChk23X05Wsw2pKnkkEklOOmziKsvzyU9fIPjSAtmnU4BU/7fZvfvrL+9Eit8CpeDrVcFuBZcffmWy9BituNwgwlqhQw7jjWhclAO3tt5Aps92gSMHpiDtXMvQbvNge0ewURpQWbAoTvPc0mR3lUk+1SIm7JGky3AEhI5WDGzHPtPt+Bvnx7EmjkVfsGO57HlWdDrGxx6BgCb99fj15MuwBMB+j6J/75kQC93EXK472RepjpoPVe47NCNVQOx6KVv8O4d1XjwzV1+s0JvrCqG3ekMeEPi7skmkfgNo/l+5mKAW2XIwelm/55VxxrbvRZcjWUj2M7eXCdbkMSAJ4aCTdkGgrebD3bAdGVqbzQCNTLzXTlaLZfiaKMp6HNolTLk61x3iAfr21wXl+9P+9VvbN5fD2eQ4QXg3BdU3KZ5YwZgu09NjmeX1efmXQKT1YEMtTyiISgAUCtl7jWtgMCfWbpaHtGaUKHu3L4+0ohifRqMHU0EQwUUsy/q6/cc4ucvvt+iXlq/3/Fksjpw+4vfQKuU4dVbR2Ne5QDMGeXfZFDcH099VIt5Ywa4/z5c0FhRlI1Vb+/BtZcWubNyt41zNYTrna7CN3WNuObpra5aDaUMD0wdgld+eRmONbZDo5TB4QR2HTciTRX6lNNqtrumyvfV+ReGGvRYO/cSLHjh3151UI/NHh6wRsWTTqNwB9OH69vwY1M7th1sCDqMU23Qo+ZoE2Zd6PpsxOBY/K5EUjBbZcjBxCF5XhdZq8PhbjQoNgl8Z+eJgMN0WqUMht7pWO4TrFQbcjCquBe21zWFPK7cRdNRzMAKthyM0WTFsvU7/fbV5toGLHvTVd81oTQXbWaHX01KJDckNXWNAYO+cFnNxR3HYlGOFtUGfcAC4UpDDjbuPonvjjYFHK4NdzOhVcpQOSgH2VqF+1zRaLJCpZDC7hAgAWBst3v9vmewrpBKsWKG62Yg3NAsADw4vQzXPfNFwG3x/KzS1fKwjSo9+V5r0jsKs+1O19IbgQJicbZqc7sNTe02pCllSFPJkaVRoNVidzdKFc/5Yg+oRC2YZsATY74njFCRswTwa9AmzuRpaAvd18N3am9nhrl8swjB7sSqDDlYMqU04HOIf/PIO3sC9jER6zfEE26wdvPiF9Rzm+ZcWuQ+WdbUNeHOiSW4YkguTjdbYLY73SfVcMWg4gmt0pCDDTtOYMfRJq/95fmZiSeFO64owW3jBuHLQ2ehkEkwqjgHDqeADLUcSrkMVrsdV5f3wep3vw84bCTOzlkyuRT/2nMKD0wdEvSk/+Cbu7FyRhnOtFqRqZEjW6uEyWqDPl3pXsm7ojAraIBVZciB0HH3+qc5FVj97t6QwyyBOs9GUrf01Ee1mFc5wJ2Ve+qjWry1qBK/9fjsPY8h39qgReMNfo3ofMmkEoy7IBdPvO9/Z7+5th6QAK/cMhqnW8zol61x3/EaTdagQWqVIQdqhSsDcKrZjPvX78T1o/qH7LK7fEYZauoa3cdkQ5sVUgnwqysvwMJxBmRozhUEh1rlXMD3WFBV7H6tR9/zHvZYPasc39U1BbzwLZs21C/Yce2HBjgRunjeM5gId0FvNFlRU9cYcu2vE0ZzyODj9nEGTF+z1f2z6hI9NiyuwsH6NuRlqsPOTgPgFwC0hwliLXan+1i8pH8vrJhZhgff3BVweRbxuQVBcDd/FAOSwjA3Ew6ngBUzXKu7i8GOzeHEJz+ccQd1by+uBBA8+BWHZMMNzfbJVOO6Z75wd+/2zN45nAKsdgeMJtcK9Q+/swe/uuoCQIDXOSjSfmPVBj2WTR8Kq90RsE+cVinDnFGBZ6uumFGOR9/dgw89snmePaB8e5MlCgY8cRRuWGrKsPyAj92/fifumjg45HN7pnA7O8zlm0UKdeK++pgxYPfiSDIDYprfk+8QQGHHqtsNHr1DVHIpKgqz3BeL57ceClhUmKUNPwTle+ILtL+CrYW0cNwg/Ofar9wnY/HibbMLeGhGGQ43tPktYCpmU6x2J74/bsSksryATc0AV/r+YH2b+0RYZcjBw9cMw/8suAQPv/u9O60+r7IYAvwvzvMqi131FBEO7YnHTmEvjftu3OqIrG4pTeU/88fzmAi3DXdNLAl6V1pt0GNLbT3GDMrxKzr33Ff3T5VgZP9sv4aFD19Tjq219cjTaSCXStArTQkIgFYlQ32rFaeazdAq5a46JYUs6IybmqNNOHbWhLe+O45Rxb3w2MbvMeuifnhh66GAgaRUIgkbLAbbL6ve2dvRXVnwy+IM76vzuvEQibVWk8ry0GZ24KKi7ID7SnxttSJ0Mztju8197Ilrf2VrFTDbnTjbZoXRZIM0zGwjqUTildnZvN+1zti9k0txLER2GHDNTvMrKDfkhA2OxeO4ypCDohwN2q12XFiUjSUhOiZ/U9eEh6aXYfnb55o/LppgCJmt/ffhs+ibpcGSdTXY7tEaoqIwC//3y9H4cO8pSDp+tyLAAqWewq3dJUDAvZMuwMDe6bA7BWRpFKipa8TN//21V2nA4vGDMPvifvjTph8woigL8ypdDVGzNAr0zdKgxWqDrcEJlUKG5nYb6s6aML+yGCM6zqcmqwOba+ux4u3dmDYsP2DmSwzUffeLq+fWLlxYlO0V8Hie8xO1YJoBTxyFG5aa6zGk4GlrbQOWTJaGbXnv+3zRHmS+RW+h7vIffmdvwPqDMT59WXzTuUW9tLA5nF5p/cJsLd5ZXIUHAxRRLp9R5v5dsXfJLWMH4vmtgb94APDQjLIQQxM5KNanYcygHOw+bsSaORXu+pkmk80rsxNslo/vMJz4OtOG5UOjkoW8Y7M7BfzycgNMltB3q77TU1dt2I0Hp5VhQWUxfnFZf/TOUOGDvadwaXEv98VZrZBBEATIJBKoFDJMKusT9sLr2XH2/d2n8NRHtag05GBSWV7I7RMvLiarwz2LZulrO6CQSb0a7unTle5lEgJtw91XDsby6UOx4q3dfhf4+VUDsOilGgzrqws4c0X0Y2M7NHKZ15AuAFgdTryz84RPbZEet48fhBtf8LhgdAwjThySiw/3ng54sQVcn/1v3tiJ+R3LQgQL4n45dhBCyVDLMSXEZ/PlobN+jQ+lEqAuQKAQyRCWb7aoQKcOOdzjeS7ZvL8ey9bvwqpryrHMYzkSccmLYALNNtpS24DbzHb0Tg89Oy1bq/Cq0cvWui7aXx8+G/IcCAAv3jQKMokEJ40W6DRyrN1yCMP66rDnRLNX80zxe7Wgqhgr3vY+h3lm+gJlh3YfN2JZRy1hwB48hhxcMSQXy6YOgcMJDM3PxI1VA72G9XYdN+KRWa6hnrElvfGbKTJYPSYEfH/ciOsvG4CzbTa8/d1xv+/G+oWV+GDPSdidAsoLdFAr5VjzUS021zb41UxVG/S4saoYGqUsZH8ozyHutVsP+ZUahLoebKltcNdPeRKfL1Gb6TLgiaNwVfRWh9OvGFf8kpwwmjG/shgquRTD+2WhyqCH3SEgXS2HRiHDxt0n/ArwQs2QCpSq7qVVel2s5FJJ0MLatVsOodFkxdLJQ2CxO3Cm1QKlTOrVCyNo4zqPniIX98/GWZPVPfXY02f76/HQW+e6Ur/8VR2mDstHbqbKK7PjaWttAyw2R9AOqnMri1HXYMJVQ/vgobd34w8fnHue6o7Ub0GWJqJlLgL9TBwqCabNYnenskPxvGA8fd1FECAEXP9qfkfdAnCue634nv98/UUhXwOAe5jRs15ia20Ddh1rDjuDqMqQA326EjV1TVDJj+C/F1zqN5RZHeLCC7iGB97ZeQJThuVjnk9W5d5XXb1mdBqFuwD7pLEdv7x8oNfnBgCtVlvHfnNgl7EZBTo1Vry12y8oDjRtf3NtA5atd2Uf2m2OkAXEW2obcP/UoSELrZcGGe4VtZjt7iEKT57fF/H4FuufKoqyka1VRlUPIy4V4vlcxfo0tFkdriHa8YPcU7rFIuHAdTP1OG5sx4iibPdnlJuhDro+ne9sIykkWDOnAovX1cDYbkO/bE3QwKXKkIOCLA2+85j2r1XKsGzqUIwozMLibC0Wjjd4bbeYhXpi414U52agojAL1o6bKlePHgumlPfBSaMZD765C/Wt1pDZOM9M3/1Th6LFbEe6Wg6JAAgQ0D9Hi//ZdiTovt9e1wRTgOaT1R3LR9z76g4M66vDc1v8M4TzK4vx/YlmLL5iML442IAt+88EHMJ0ZWIKMECvxY0vfI01cyqCDjFurq3HfVeX+g1ti58P4D2Rw2J3BjzHdXa2qsXuTNhmugx44ijcyrn9sjX459dHAbiiabPNgTGDcnDV0DwY22y465/f4tl5l+DJjd/7DeUsGm/A09ddhIUvfeO3wF0kFfeuBSW9py2vu3lUyLtHq82JzbUnMXpgDrI1/jMFgp0QxIvOsmlDMaKfDhKJgHuuugBLp0jR2GaFwym4Z5Vs3l+PX111AS7qGHZQyaRhe3eEGpoQG76dbrH4b5fHUGBjBGsh+UpTuoLPSC4E4VLZnheMqcPy8a5PpkJ8DAheuxFupkpBlganjGYMLdB5rWdksTuRppRj5cwyPLA+8NpML315BPMqi/HHD39wv36gE6pnfUmwwnSFTIKyAh2efH+f+/jTKl1LM/z541q/2p/l08rwt08PAnA18svSKmCyOGFzOvH596fddVyRTtsHXIHMglYrKoqysWRyKY41Bh4CAVzNFEORS6VBMyjVhhz0z0mD1e7EX35xkXuBz0C1P6HqnyIpQBaXCvn7Z6599ezckX41LdUlery1qAp2pxM/NrrOE4ECU6lE4tV7SKuU4bl5lwARzDbaXFuPW8cNwp/mVEAukeCE0YxfTyqFBN/7ZVB+PakUKzwKZr3qCN84N5wnbrfVbodWqcBJoxk3jC6GE4J3MNQxW/C9Xa5avRdvugzX/+ML9z6+fZwh4L4zWR1Yu+UQrrmwL/7fph+8Vo4HgP+9cRQcTiFg9vKWsQP9gh3x8wAkePynw/HsltDD/k++vw/3TxuC1e99H3D73HV0H9dGVJdldwoRfx+CnTciHVL0laVRJGwzXQY8cWI0WfFNXfCLXHWJHnuPN+O6Uf0Dzu54cFoZfnFZfzyx0b+3g/h8U4fl+60oHsl0dgAdPXW8f8dkdQS9e1TLpVgyeYhfA7bVPxnmzgyEK3x9cNpQ7P7RiPK+Wfj9v3YHTbU2tFrcw0Rr510SeAd7SFcpcHFR4NcOt+L1Z/vrcaLZDGuYE0igL7dcJsGMp7a6trGjx5DnSTIvU40P955y9bUJUSDre8HIy1QHrCcQ/zZY48NQQVV1iR4SuC5kFxdlYVJZHk43WyCRSLDnRDPWbjmEMYNy8NuZ5Wi3OeEQBJisDqSrZJAAuPvKwZj33L9R32rF9aP6QxJB3YrfNhj0sNgduHxwLvb8aMTI4mzMqxwArVKONJUMjwc51ldt2I2nr6tAv2wtVr69O2AwYPfsyhdAoAuE2eYqeK0ozHJPAAjEtyeKryaTFSuvCRwsLptehkfe9e5LI26z3GcfRlKDFe5Cd7rZggVVxSjQqfHnj2v9M17767H8rV2oKMpGTV2jX4dhMaMr+DQUBACLzYnFEwy4/+ohaGq3uZdeCJTNazbb0NRmRXk/HdosDqjkEtxYVYy7rhwMu8Oz+N/htW+C3jTtr8cj7+7BHVcMxuoAmU/x3CFmQ6YOy8ecUf3x+Ma9eGz2cNz4wtfY2pGtC2ZBVTFWvLUraLf2YF27qwz6oBnozbX1WHp1acgMoTghwBgmsA6WiQkk3BC650QO8WZLp1G4M/4quRRmmyPozVxVgLIK8ef9c7QJWb8DMOCJOXEoyWJ3YNWGPUEvcitnlOGtHcfx5nfH/b4MW2obsGrDHvzXxJKQQzkLKovRR6fxqsrff6olbPdNAAF/RyKRBP1iDinQBVz4cNWGPVg77xJIED4F2my2w2x3+tUBie8HOJe9EJltDuw50RzwQu5KfQ+BBMAdVwzGbeOdQVP2oWZytbTb8dXhsxHXOYg/y9IqsaCqGIte+gZLJpXioelleOitXUFrK8Qs1JLJpTBZHUEvGOH2Y4ZaAUHwv8CHWshxfuUA/OLZL/HET0fgpNEMpwD3Ba5Ap3Yv63CqxRIwNb9oQgmen38J6s62o0+mCm3W8M0XPYmLMN74gitoEteruumFf3dMK7cHDfI21zbgV1eV4qG3gx834lTvYAIFrOLPwmXf6lssQU/81QY9TFYHjp1tR0VRNu6aONi9MjgAPPreXr8aC/F1lk8v8xpSzgzTXmHJ5FLIwnS6BoCry/NhstqDFsm76za2HArYYbjKkIMrh+bhritL3Ct5F+jUyFQrcLrFgjOtFujTVV5LL/jql63B/35xBPd1bIOYwfvLx7W4dlR//KljSNt3GDbUTdPQAh1+9/73YYdpPOtSKoqyvTpcC4IQtEBZrEcMtjBrsLYadkfoYLs1zJpm4vc93ErpnuueBTsnAq5jVi4LfZz4TuSoNuRg/6kW9+cFuD6zp66rACDxm4n60IxyPPreXq/nrC7RY/WsYeibHXrWW09iwBNDPzaa3H0JCrM1WFBV7G7p7jvUcrrFgvICHf726cGgdTzhvkiusXWV+2K+72QzGttsfuP+Iq1SBqcgwOZwBux2arYFvysIdiIyWR1Y8Py/8d4d1WgP8fcAYLU70UenCXm3s3h8CbYcOPflUisCZ0c8h0BWvbPXa+bE67eNgd0hYNO+U+66oUB3IyKVQgqJBLh9/CA44d8q//bxBtzosSigeKI4cKYVNXWNeHT2cOz60Yi33vox7MlYzChIJJKgxc7hhqZsDid6p6v8arh8axFazTYoZTJ88sNpLHqpBr+8fCDUCik2BFjWYfF4A/7vl6ODToMGJFgyuRS3v/gNFk0wYPTAnJDbmK/T4KWbRqGpow+RuAjjo7OHd9yJn1uv6uN9p1FeoAv5fFaHI+RxI5dKog5Ya466+tjIpRIsnVLqzniJ34mKoiwsnVwKtVKGheMNcMI/kPz15Atgstghl0kxpawPJBIJfvrXbQBcQ0q+wY7nNte3WLyOgXB1XhabExlqedj3maFWoKHN1bhOq5ThlrED3TWAaSoZAAmcghO/vHxgwA7DW2obINm4z72Stz7dVUt0otkMY0fPlXd2nsBenz5b5/ZLDnYeM3o9r9jN2HcWk++xHirYj3TJF/F5xJ95LtMACEFnOyo6Om9H8zqAaxZgKJlRDA1FMjyukkvDZoy31NaH7DHVO0OFiqJs9/lx1cxyKKQSr9YOJqsD676sw2Ozh6HN6oCx3QatUoY0pRxZWgV+97MRUa0ckAgY8MTIsbMmLHl9h9/BJ57gfe+CJpXloandFrTqf/3CSjgD3MV7UsmlSFfLXYsmvrrDLwr3PBmJPV1Wvu07O0aPN24fg5NGC7LTFEFXlg51IjJZXUXM+0+1hKhl0GPbwQZcOqBXyPckl0mgVUrx/LxLkJ2mhEohxZo5FZBLpVhQWYybqwdCKpW4i1SDzpwo0eOBqUMwqrgX+mSq8cTGwGPj4myP8gLXzCDPOqDCXhps2nsaXx1ucM/u8q0NOjfdenDYbJz4ejt/NGJCaW7QO83TzeaQJ75tBxvwXV0jlk0d4nVHBrg+i+86empUFGbhxhfONTC7ojQPq98LvlL1yhnlAbtaiwGAtOO8XFGYFbppX4ke3x1t9Ns2kRigW+xOyCQSDOurQ26GOuDvisLd/Tab7fj15Aug/FCCTR5BRqBVrKsMOXhwehlufP7ffkXDwLnvn9XuwJ7jzXjzu+Pumg7PG5fTzRZ8f6IZD729xx1wp3nMtvMcZgs0GSA7TYm/3XAxFDIpvqlrDJu9aTbbMPe5r8J2a75ySJ47IH7qugo8t8W/ncPi8SW4ujzfXRvlS1zJW6uU4R9zL8FjPh2Pxddb59NnSxzGu+bprV7PJwYR4vCNyDe7FirYj6aI1jMbkpvpOnaqDXo4nHDfFCz1mb4uHmORLB3jSS6VhM22hKvfqzbo8fG+05hfVRyyTkr8fc+bm9vHGeAQBNjsTve5CXAF0FKf5xLXNLM6nJhYmotZF/b1ClSiXf4o0QMcXwx4YsBosuK+1/0XhwxUEQ+4vniCABRma/Dbd/0vQOI49OIJwXuWVBpycKrZjJLcdBxpaMPNYwdiyZRSr7WPXv6yDmvmVGDJazuwdp7/Scv1WvV4bOP3uH5Uf7Sa7e7uxWIRsbj9/cM051IppCjI0uKhjuZfvifHB6YNwaw/fx52+rNGIUOfDDWy05V40medogmlvbFk8hCs3LAbCyqLsbm2IWD6WauUYURhFupbXQXRx41mLJkyBAKAd3ed8vocxMZb6Sq536KAiyYYUFPXGFGh8eLxoYNThVyKqeV9cOu4QbA6nDjbasWD08uw6u09foFqH53GdeILkEr27CV0/7ShfkHTxNLeeHB6GYxmG9qtTrx22xh8cbAeEgkgiWBZh1BF62IgbLE7Qzftm16GGU9t8XsNrVKG60b1x/M+/WyqDXrcO/mC4MNGJXpowvSSabPYseaj/bh3UinuueoCnGg2I1OtgEouQ7PZitduHQOpFFDIpLDanTCabPjH3JEwtttwa/Ug3HPVBZDLXEX0EICzbRZoFHIMLchEXWO7O9CRSCT44VQLJBJXkKzTKAK2WKg05OAnFX3x/LxLYHE4UaxPw0qP3i/i+55bOQCLXvoGFUVZmDgkDxNKewfMCnle6N7bfcJVvxegQP/iomxIJRKo5a7ZXsH6qACutgrBissB1+e8oKoYvwvQBNKz4HZKWR8Mzc90b8eh+ja/m6ZAzS4B/2HYUMOLkRbR+talnG62dARiQ9FmsXl9zz2/32Jzz3AZ1pw0FV69dTROt1iQrVUgXSnHovEGr/0ibsfi8SXYebQp6CzS+ZXFePmrOjw8qxwPb9iDv316EAvHD8J9U4bghNE1WcP92fbPxu3jzmWbTVYHvq1rxM8u7oeHN+zBB3tPu5+7ukSPvlkaPPmzEWg126MKYJItiIkGA54YqG+1hqyIv7FqoPvfE0p7486Jg2GyOJChkYe8AN1cPRD3Xz0EZ9us7vbd39Q1Ys9xI26uHgR9hhIr396Nzw+e9SuWLc7RYnJZHiSQ4H9vGoWzrdaArxXqIrTulsvQ1GbFC9sOo6IwCy/eNMqd0vYcCvPs6TKxNBdLJpeivtUKs80BlVyKnT8aYXe6uqLaHQImlPbG0AKdXxZhz3EjIHGN1f/Y1I75VQMxwqOh2tACHVZ23NleP6o/AP/0c9Cp8SV6rJxZhv+6cjAsNlfA8XXHcggXFWUF7CQdvCYmBzdWD8R3x5rcNRi6MM0PFVIJ7pl0AZZ5zJpxLwNhNHtduBa+5CqgffGmUZhfVQy5TIJsrQJ2h4Dmdru7IdjxpnZcWJSN+ZXFsDlc02fVcinuf8O7qFN874HW5/FksgQvWgdcGaBFEwwo7KUJOjNu549GKCTA24uq0G5zwGS1Q6dRorXjQiOTSjCiKNs1ldfduLAeqg+lWDGzHMt9Fq8UpyA7BGfYafNbaxvwpGQf7r+6FP1z0vDQm7uw3We2TR+dGh/sOYW/f3bQPcV55cwy/HbDHnx+8CxuGTvQ3c3bYhPQr5fGr1Dfs7ePuCxCoH324Juu5mwA8N/bDgedvXgu6PgeSyaXwmYXgga6APC3Tw/iT3Mq/HoDVRv0mF81AH/4cB/uvnIwhvXTha3jyc0MnlkTG3+GG95psdjx4pdH3NsSaGhODCJ8gwnf4yhDLceM4QVYtcE/E52hlofNlHjuqypDDgqzNTjSYMLUYfl4b9cJ2ByC+zl8v99LXtuBF2+6DN8cCd7/p8qQA6kU+M9/fIWR/bPx2Ozh7tm404YX+GUA9RlKmG0OSCSuIWGlTIo2qx1pStd6bjKpBL/r6Eq8YmY5lr62A0+8/wOe/viAewmdcYN74+ryPkhXyaGQSfH6bWPQbLYjUy1HdpoSeZlqPBlieCkv8HyN85JECFT9mEKam5uh0+lgNBqRmRmfT/6bI2fxk79sC/r4q7eOhiAIaLM4kJWmdLfN/8svLsJt/xt8dsjLt1zm16+mukSP5dPL4BScaG634Zu6RowbnOd/gijRY/m0oTh81oQBOWkwWe041tjuF6yEymJUG3IwY0QBstNVfhdCz+nK143q7zVsV2XIwYVF2V6dTAt0aryz8wRurR6EXJ3aHbiIqgw5WDatDE0mC+Y//3XA2U6XFGXD7HAiN1MFk8WBNJUcSrnUqxV7qPdTZcjBksml+MOHP+CGywZgzwkjRhXnwO4QoNMq8O3RJq/FToFzhdEleRk43WKBSi7F2VYLLi3uhc8PNCC3Y7XqwmwtHt/oP01b3FdiEaXvdv35+ovca2D5DnnkZihxusXiN6VV3PdyiQTznv83tEoZXrr5Mvxwstmvadm5z1KP+6cOweSOhSwD2XhnNSb/MfTjaz78AT8dWYgXv6xDaX6m1/bu/LEJYzpa4K/5uNZrtovvMbxqZjmON5lgsbtaErz8VR3++cvREOBaUqDFYodCJkGr2e4uOlXIZK4pzEHS/eLn9t5/VePhd/YEf/2O/kzi31QZcnBJcS+UFei8GlyGy/CJXcRDNZ4UL/7hfsdzVmJRL9dF2mJ3ol+2Bv/acypgPd6CqmJcNTTPPaW+d4YK1/79C1cW6L+q0WK24+d/C35e+vP1F0EllwbcNvH9Dc3PDDmDzbMWcFJZHo6ebUe/bI3fKvLivhRnhwX7fl7YcYPjtXCsRgGpFFj0Ug0enT084PG0bNpQHG9sx9cd57aL+2djxYwy3Po/25GbqcI8j4Bx7bxL8PTHtV5r1Y0ZmAOVQopeWiXUChnMNoffyvHVJXo8fE05Wtqt0Cj9syXihBXPNfnaLHY0t0de4+L7HMlQFxNPsb5+M8MTA1pl6N0oFhdvPXACO441oaIoGzdWDUReRugOpE6ngJoANRVfHWrAmVYLvj7UiN9cXeo1ni9+gS8qysbpFgv6ZWvw70MNWOWxynQ0fT3uu3ooHnl3T8A7WKlEgvmVA7DzR+8Oxt/UNaJy0LkOzKP694JNcOL+qUMhBfBwgFlarplpu3FjVXHADM3U8j645sK+uN9nAcMqQw7+eetovLfrBJ766EDY7qC3m+24uH820lRyfHGgAU+8/4P78eoSPZ6bdwnmP39uccqLirLQO1PttbTEXVeW4LjR7FX8KxZR+w5DiX1BmtttuG/KEK8hx7VbDgVcWFT8DCeW5kEQ4LfMwtbaBkgAPDJrGD7+1eUw2RxoszgwvDAL977mvRSB5/EACYIv61Cih0omDVrDBbhmm9x51WAoJVI8OG0ojja2e2X8+mdrUHu61b1fQs12eWD9Ttw7uRQ3/f0LXDawF1686TI86LdIph7LZwzFI++6ZjqJxbNLJ5fiyFlT0L45rWZ76Nev9e4VtKW2AfdOLvUb8o0ku9HZ5mzBfkcQBLSY7e4g5Nm5I4NOFvCcUl9tyMGIomz3fjB2dG0ORSWXIjdT5ZfNqDbkYOH4Eix44d9h16nTaRTYdrABNXWNAOA+fl2Zk3PfhbVbDuHZuSPx7OZDQYd3Hpxehkff2+s15FRlyMGCqmLs/NGIx2YPh0wiwYoZZWhut6PNakcvrRIKmQRqmRR9szXI0iowc0QBlDIpjO1W/OUXF7sDj5duGuUOIkKtCi8K/jtpgfdFDIaDUn1Iqacx4IkBuUyC1T8ZhtwMlV+hZ0WRq8Bz9MAcjC3Re60CvWiCIWTh51eHzwatqRCbsTW121BRlI0FlcWwOlyZhh3HmrDIoyGhZ2GyeKGV4hBuGTvQaw2lQFkGqcS1/kwgm/fX4zdXD0F9iwU7fzRiWF+du3ligU6DV24ZhRaLA4U5Wneg8uzckUGH/7bUNmDplCGQSuCeuq2QSbF5/xmM6KfzC3bEv1m2fhdWzihHlaE3ThjNIT8rmVSCK0rz8Nt39gQs0D1Y34b3Fleh3e6EANfFUyaT4JaxA93DIOMvyPW7OJqsDixeV4OnrqvAb6YOcZ8k1XIprE4ndBoF7E4nVHI5VHIpxg3ujanD8iEB8PrtY6CSSbFs2hDY7QAkrkUuxWaIUrguvq/fPgZWuxMKqQRSqQQtFjtazXYo5FJoFTK0W+xYNMGAkUXZ0GkVUMqlUMqk7toR8UIkCIJfA7i5Ywbgt+/uDdklOU0lh8XmhE0i+NWeVRpyMGN4AY41tXvVRIQKPm8z291tCHwzfoBryOeht3bjwqJsfPT9GVx7aREe2/g9lk4pDZl1EFdkj3a2je/rRxLMhKv3CPe47+/kZqrQ0HKuCeapMAXsYsGruCyHm+DqyRNqGPB0sxk7fzS6zx/i+2lotUCnlePiouyQNTVVhhxkqOXYc9zoNeRmsjrw8ld1WDhhEOZVDoC1Y1ZhmkqGeydfACeAB6cNhVNw1V7ZHE5sPdCA6575AtdeWoSbqgZCIZdCrZBCrZCh3epA5SA9dBoFMjUKtFnskEqAAp0mTAYkcGAiChdYMPhIPQx4ushoskImleDdHd5DCZWGHKydOxLpajnqW6yQSiTQKuX43Qc/uE8eoWpEVs4ow5EGE175us7rhCQGU49v3ItfXj4QvdKUfjUGvjO0NtfWY+WGPe50svjYvVNKIYVrZojnytyRNNsStVnsGNQ7Hf1ztGi1ONxrVAFAvywN+gjAsaZ2XD+qP26sGgh5mEUI2yx2d4+Oc/tDj8llfYLWO22pbUCbzY7/9+EP+HWYfizpKjnsTsE9u+ulL48AgDtIyMtQ4UybFXan4NVnZOKQXLx7RzXMdtc++M3VQ9BudSA7TQmJAFe9is2BdKUcFrsD7VYnPtt/AodOt2LJlFK0WR1otThgd9iwNUCvoNe3H8W9k4dg2Qbv+psqQ447HS9ux9IppVj+1m6/fbTymjIcON2CCwuz8OS/9vkNH3jWS9zWMbNDLpHAbHdg0Us17tYEgQpZKw05sDmcMFntePoj/6nMWzsydDd7rCsVLmAwttvcw5aRrNkjBjB2jzoMX5WGHIiTnaLJvgRq1BZJMBNpB+1IfqfakIO9x5tR19je8W898nUaLJpQAr/MYcfQdnO7FXKpxP35Aa5jpm+WGo9u/B43V7vqB32HAReNL0GBTo1V7+zBhx7FruJjaQo5Vs0qwymjGddc2Ldj7TPv1185sww2hxNXDu3jdX6oLtHjvquHQAbArHGi2WxDmkoOnUbh7vQuDt3YZBL0Ttdg5ogCTCzN9RsKkiok6N8rcRvZUXJhwNMJRpOriLjd5oDV7vQbrwbODfmsnFGGY3YzMjQKv94P4gXolrED3X1AAFdV/tQ1WzBmUA7uv3oITjSb0WTyLlq+blR/DNKn+c2IgsdreE7/9V7Y0TVGfrrZgoIsNSaW5uKOiSWBZ3HtrwcEVwFto8mGnT82QRDg7syappJDI5dCJpXgaGM72qwOXJCXAZvDiZNG1/vum6XB7hNN+OMHtfhHmF4jdqd/h9fNtfX4MczyEmarEwvHG6CSSbF+YSVOGNu92viLBarpKjmcgoD3/6sap5rbsWRyKZ7Y+L07SPDNWoiLZF57aRFWbtjt7oxdU9eEp6+7CDaH06+XibtPz+kW3DnxAvzmjZ1+GRUxiHTP5KssDprBEnBu+KU0P9Mv2BH30bL1u7wWuvSdAgycGwp56qNaPDt3JP7zha/x6q2j3RerLbUNuG2cwS+Anl9ZjLOtVuRkKEOs4eNa2kAUScAQzZCP+L9Gky3krJd2qx2VEcy28VxtO1CjtkiCmWA3LZ6BKoCwXbYrO4aRcjJcWYUXbxoFs82BF788gmVTh2LWRQW4d8oF7nPEzh+NOHrWhGe3HPRbOuK3s4ahsJcW908diuVv7kKVQY/7pgxBm8UOrUoGjcLVPft4kwk3XNYfd13pmkShVcmgVcjcNSwt7Tb0ydTALjix6poytNucaPEplgWAvAw1Lh3QK6q6E2ZPqCcw4InSiaZ2HDlrwlMf7ceWEEM04tRos92JXh39baQSeNVIiENI1SV6v6ZnAHDtpUV4wCegEWd5tVlcCxL6zmQSba1twO3jDLj+H1+6f1Zt0GP97ZU41NCGvlkaCAKglkvx4HRXPUageqGdPzZBLpUgU62AUiZFxkA90tVynDS2Y/W7ezH74n64ckgeTjabIZe6agKWv+U9E2nZtKEYW5KLin69kKmR47Nfj4PJ5kBLux2ZGjnUchnOmqyw2p1IV8vx0a8uhxMCmtqs0GmUkEkBiz10bX2bxQ67IHhl0IBzwYVYXL1iw25XLx+J60R9/5u7/Jqhee5DAHhs9nB311bx9xZNMOCEsR3v+DTx8/y7BZXFAYdqfNsVbK11LUAZasbeAp8sRyBbahuwxON5Is1w+C7L4BAErxbzNUeb8NKXRzC0QIfKQfqQzykI57IZkQQMYoYnFN8ZPl93BP2+QzE1R5vw8pdHcFVZH8yvLMapZnME/U9ysGJmecCVucVgRgLvVbQ9m1GKNy3Lpg7F0imuNbmyOoL8lR4F8Hesq8EDU4dg6ZRS1LdYkZ+lhkomxVmTFa/88jIIAvDxvtP426cHcXFRNh6aUQa74MT9Vw9Bo8mCC/tlwy64ipidgquZXYZajtWzhrlruHQa72CjoGNasliH0itN6V7jqL7VCo1SjtwYFMYyeKFkwYAnCkaTFZ/8cAYbdhwPeVERiytbzHYcqm+DWiHDZ/vr8f2JZrxx+xjXXZoA9O2lwfIA/Tv+ev3F6JWudC2ZMKEED0wdCplUgjPNZqSpFX5rDlUaXKvy7vzRiIv6ZaNPlhoOQYAEwEe/uhztNgfarQ53dqOolwYquQxKqQRSAM02O/QZSryzuApWpxNKqRRmuxMtZhsmDe0DrVKGhlYL1Eo5ctKUsDqd0Crl+MfckdhxtAkf7D2FYX11kEoBk8WOC4uy3XU/7oUAXz9XTCve/S59bUfAWRfi4qj6DBWaTDb86cMfMKIoO+TFyyEIIVaRlmBe5QAs7ki7W+xOTB2Wjwy1ImgmxPPvxWDE8/fEC3WoICXSIAaATzdYf8H6mPjyfJ5IMxy+i9zKJRL8p8fMHc9MRLg+SgqpBIs7epKE6wYrDicCCNqE0XPNHjGAEp83UOuBReMNMFkd+J8vDmNEYRaWTysLOMV52fShcDicmDS0D+au/Qo/HdkPK6aXYcWGPe7hH7EW5ZGOoKLVbEeaSg51x377562jYbI4kKmRQymToqHNggE5aUhXygBBwK8nXYBfXXUBTB3TkCUSwCkIGJCjhUMQIAAw9E6H2e5EY5sVowfqMWloH6/sSVcFC0YYoND5iAFPFOpbrcjLVGNrbQP06Uo8Nns4CrI1eO+OakACtJltyElXQQLALgiQSSRQyKRQyCSYNqwPpg7PR3O7Df2yNbA6nbA5nLh/2lC0tNuRrna17HbdrdmRppTB6nBCKpFAEAQopK5ZCBaHA8unl8HmFNDS7hobP9Vsxmvbj+G/JpZAIZXC4nBCKnGt4uy7VIDnxebm6oEo0GmgkElR32LFf39+CHdMvMAvqyROGb/+H1+gtE+GV6r+2bkj8dZ3x/06uf5pTgV2/WgMGISIwzRi5iRYBmTxBAOe/qgWm2sb3DU3no97vp9Q64CJXWPFu20x2BAzG5EGEZ6/F8lQTKRBDBB+gcpgfUx8eT5PJBkWsdO0588LstR+GR5XU7sstFnsIYtYJRIJMtQKLJ08BAIEWGwOrJpZjmazHSeM7VDKzmWMxHYGlw3shWUdgYnv8MzyjsU3Ac+MyyGv3i0A0DdbA5kEUMlkMNkduHdSqavo3GLDihnlsDmdaDbboVXKoJbLIJMACrkcrXYH/vAfFyJNJYdc6hqCNtkcMFkdyFDLIZNIYHcKyM9UQ9cndJBQ3Ds95OPB6ICYBThEFBwDnig0m23QKKS4aUwR5lUOhM0pwOJwQiIFlDIpoFagvtWKNJUMEkjw4fen8LdPzzU4Wz59KMw2J1a9swe/uKw/ntviavbnOS1Z7B3y+wBDM3dMKEGfTDXuX+9dEyK2yldKJVj5zh5cN6o/TjebQw63VBRl46mPazFtWD5GDczBmo/2Y36QIRhxyri46rBYUwIg4Fo84r/DLbcQLgOydEqpO1gL1OhuQE4a3t11Anesq8GTPxsR8rPzDVAsdqc7AIo0iPD8vUhm30QaxLhmzFiCZjkqfbIcobIhYlfZQI3VPJ9PDHoXjS/Bx/tOu3++eHwJ2joaEPpmEedXFcNqEwJ2lRV763xT14gH1u/yKmBdNbMMWoUUxfo0tJjtmDosHz+p6IsmkxWv3TYGUgkAiYBVM8thtjvRZrEjXe3KmtidTjwwdSh+dZXD3azzt7OGoV28MejIuFjsTrSYHZBqpK7ghBkMIvLBgCcK2RoFFBoZ/rNyIH40mvHMZwdw/WWu9WZ8V5gWh2WG983Cwpe+web99Vjx1m48MG0ohvfL8mr5vqCq2Ks2JPjQDDBlWL7fxW5LbQNWvr0biyYYMLTANe19QWVx2OEUcW0bi93pV//hS3zc8++B4LNrttaGX24hXAbE93HfpR9evuUy97+jnR6skktdC06W6MNmQsRgxPP3ao42oUCnjujvQgUxnrO0Vl1TjgfW+2fXPDNq3x9vxkMzyrH8Le/fE4dp/vDBPq+CXjFIXDTeALlUCrVSBrvDtbTClUPy0CdThSH5mXh27kicbrEgN1MFm9N5Lqgw25GmlkMhlaLJZEW6Rg6tXIZVM8rRbj+XCUlTuDIrQ/Mz8dptY1wFskoZVHIZJAKQppCh1eYABLjXiMtNVyFDo2BwQkTdIikCnqeffhpPPPEETp48iREjRmDNmjW49NJLu3075HIpnE4JttTW492dJ3BhUTZOGNvx7s4TQdermeqxXs3m2gY0tFpRZdB7ZT48C1HDNQKc59M7RCQGJOLfi0svBONZE9Lc7ppGHk0AEsmQTrhVhMNlQMI97rloX6TTg8V/n2o2o6HVgmVTh+LRjXsDzvoRO7ieajLjnkml+NOmH9y/t3bLITx93UVB19BxBTHH8PA1w/CAT0au2qDHg9OHwupw4KqheVAppFgyZQj++OEPXoW42VoF+mVp0GZz4L8XXIo0ldw1VCkBHp5Z7hp66ch6pCtkaLM78F9XDAYk3n1OxIDECaDN6poyn6dTw9A7Da02O7I1SmRo5Cjvq+PQChGlrIQPeF555RXcfffd+Otf/4pRo0bhj3/8IyZNmoR9+/YhNze3W7elxWwH4Bpv9+wPEugOHjiXCfG8iDS129DL5442mtqQUI+3mh3uxyPNeKjkUmR2LMoXTQASyZAOELr/iOfwS6DH2yz2kI3TttTWewUgkUwPFvuMSCVAr3QlFjz3b8ys6It0pQzLpg51Nxu0O5348tBZ/NjYjn9sOeheLVsukWDp5FLIZVK0W+3I0irx8MzyjnWjXMtdiH1gfjXpAjS3W/DbWcNgsjrQYrYhU6Nwz87RKuWQy10dtZ1OAXdPHIyWjt8rUCuQppRBCkAqAaQSCSRw/a8TQE6aEsXMjBARRSzhA57f//73uPnmmzF//nwAwF//+le88847WLt2LZYuXer3+xaLBRbLuUUSm5ubY7YtYiYk0hkzgX5HJZf6ZT6iqQ0J9Xi6WgaT1fV4pAWrp5vNKNanoTrMEIxYH+L59+L/D9gp2qCHIAgBZ8mIQciSjllavtN+xaBEn6HEHVeUAPBunOY5LRhwDQkuHGeAWiHFwx11IK1mu1fm44UFl7oLVqUS12yiVpsdv//5hUhXu+pArA4nBEFA73Ql5FIJssv7oM1sx8oZ5bB7FL1qFK5ARCaTu57HaofJ6poWnJuhimiIJnCejoiI4iWhAx6r1Yrt27fjvvvuc/9MKpVi4sSJ2LYt8KJ4q1evxooVK+KyPWImpNXiyvRE2zZerBe5amieV7t439qQ4EGEK0AJpKojI+I7dRcIXbBaoFPDLjixaEIJ/vvzwwFny4hF0dc984VfxmTt3JGQSiQBV7hut9lds2RmlsPiEYRoFTKYbA785fqLkaFx9RJpsznQ0hFQaBUyCHC1AeidpsSqmWWuafLtdndzNAngrhXRdPyNQipBu8MBiQTIy1ShyWSFRSpFnww1LshjNoSI6HyW0KulHz9+HH379sXnn3+O0aNHu39+77334tNPP8WXX37p9zeBMjyFhYUxWW31WKMJQseSA+/uPIERRdko0Knx3s4TQde6mTosH8eNZnxb14hfTyrFmo/2Y9LQPORkqPDclsPYXFvvnqX1/NZD+CbICs/hZmk9NKMcAgSsfnevVzdgceFItVzqWgZB4lqg0LcPj0QigVIq9evDk9ExtHK2zQKVomNGjMMVvKR31IbYnYK7niRd7VorSgLA5nRlt5RyKeSQsECViIgiFuvV0lMu4PEV6x12pqEN7QJwotmMZz47iOsv698xS6vWa60ZcYpvr3QFzrZaka5W4C+f1OLXk0oBuKazQwAEuGqD0lUyaJVy93TbXlolrB0ra4vt4KUSQCoADsAvIHE6BTS1W5GpUboXBBU6ilbFqbsSuAp9ZQC0DD6IiCiBxfr6ndBDWnq9HjKZDKdOnfL6+alTp9CnT58e2abeOWk402hCP50a908d4urDIwFWdgy7tJrPBSg2pxOCAHdmZenkUjidAhRSCTRyVx2PWqNAaZiGZkRERNQ1CR3wKJVKXHzxxdi0aROuueYaAIDT6cSmTZuwaNGiHtuu3tnaHnttIiIiil5CBzwAcPfdd2Pu3LkYOXIkLr30Uvzxj39EW1ube9YWERERUTgJH/D8x3/8B86cOYMHH3wQJ0+exIUXXoiNGzciLy/0IoZEREREooQuWo6FWBc9ERERUfzF+vodWbtcIiIioiTGgIeIiIhSHgMeIiIiSnkMeIiIiCjlMeAhIiKilMeAh4iIiFIeAx4iIiJKeQnfeLCrxDZDzc3NPbwlREREFCnxuh2rdoEpH/C0tLQAAAoLC3t4S4iIiChaLS0t0Ol0XX6elO+07HQ6cfz4cWRkZEAikfT05sRNc3MzCgsLcfToUXaUjjHu2/jhvo0v7t/44b6NH3Hf1tXVQSKRoKCgAFJp1ytwUj7DI5VK0a9fv57ejG6TmZnJL1+ccN/GD/dtfHH/xg/3bfzodLqY7lsWLRMREVHKY8BDREREKY8BT4pQqVRYvnw5VCpVT29KyuG+jR/u2/ji/o0f7tv4ide+TfmiZSIiIiJmeIiIiCjlMeAhIiKilMeAh4iIiFIeAx4iIiJKeQx4ksjTTz+NAQMGQK1WY9SoUfjqq6+C/u7u3bsxe/ZsDBgwABKJBH/84x+7b0OTUDT79plnnkF1dTWys7ORnZ2NiRMnhvz98100+/b111/HyJEjkZWVhbS0NFx44YX4n//5n27c2uQSzb719PLLL0MikeCaa66J7wYmuWj27/PPPw+JROL1n1qt7satTS7RHrtNTU1YuHAh8vPzoVKpMHjwYLz77rtRvSYDniTxyiuv4O6778by5cvxzTffYMSIEZg0aRJOnz4d8PdNJhMGDhyIRx99FH369OnmrU0u0e7bTz75BHPmzMHHH3+Mbdu2obCwEFdddRV+/PHHbt7yxBftvu3Vqxfuv/9+bNu2DTt27MD8+fMxf/58vP/++9285Ykv2n0rOnz4MO655x5UV1d305Ymp87s38zMTJw4ccL935EjR7pxi5NHtPvWarXiyiuvxOHDh/Hqq69i3759eOaZZ9C3b9/oXligpHDppZcKCxcudP/b4XAIBQUFwurVq8P+bf/+/YU//OEPcdy65NaVfSsIgmC324WMjAzhhRdeiNcmJq2u7ltBEISKigrhgQceiMfmJbXO7Fu73S6MGTNG+Mc//iHMnTtXmDlzZjdsaXKKdv8+99xzgk6n66atS27R7tu//OUvwsCBAwWr1dql12WGJwlYrVZs374dEydOdP9MKpVi4sSJ2LZtWw9uWfKLxb41mUyw2Wzo1atXvDYzKXV13wqCgE2bNmHfvn0YO3ZsPDc16XR2365cuRK5ubm48cYbu2Mzk1Zn929rayv69++PwsJCzJw5E7t37+6OzU0qndm3b731FkaPHo2FCxciLy8P5eXleOSRR+BwOKJ6bQY8SaC+vh4OhwN5eXleP8/Ly8PJkyd7aKtSQyz27ZIlS1BQUOD1BabO71uj0Yj09HQolUpMnToVa9aswZVXXhnvzU0qndm3W7ZswbPPPotnnnmmOzYxqXVm/15wwQVYu3Yt3nzzTfzv//4vnE4nxowZg2PHjnXHJieNzuzbgwcP4tVXX4XD4cC7776LZcuW4Xe/+x0efvjhqF475VdLJ4qnRx99FC+//DI++eQTFijGSEZGBr799lu0trZi06ZNuPvuuzFw4ECMGzeupzctabW0tOCGG27AM888A71e39Obk5JGjx6N0aNHu/89ZswYDBkyBH/729+watWqHtyy5Od0OpGbm4u///3vkMlkuPjii/Hjjz/iiSeewPLlyyN+HgY8SUCv10Mmk+HUqVNePz916hQLkruoK/v2ySefxKOPPooPP/wQw4cPj+dmJqXO7lupVAqDwQAAuPDCC7F3716sXr2aAY+HaPftgQMHcPjwYUyfPt39M6fTCQCQy+XYt28fBg0aFN+NTiKxOOcqFApUVFSgtrY2HpuYtDqzb/Pz86FQKCCTydw/GzJkCE6ePAmr1QqlUhnRa3NIKwkolUpcfPHF2LRpk/tnTqcTmzZt8rqjoOh1dt8+/vjjWLVqFTZu3IiRI0d2x6YmnVgdt06nExaLJR6bmLSi3belpaXYuXMnvv32W/d/M2bMwPjx4/Htt9+isLCwOzc/4cXi2HU4HNi5cyfy8/PjtZlJqTP7trKyErW1te4gHQB++OEH5OfnRxzsAOAsrWTx8ssvCyqVSnj++eeFPXv2CLfccouQlZUlnDx5UhAEQbjhhhuEpUuXun/fYrEINTU1Qk1NjZCfny/cc889Qk1NjbB///6eegsJK9p9++ijjwpKpVJ49dVXhRMnTrj/a2lp6am3kLCi3bePPPKI8K9//Us4cOCAsGfPHuHJJ58U5HK58Mwzz/TUW0hY0e5bX5ylFVq0+3fFihXC+++/Lxw4cEDYvn27cO211wpqtVrYvXt3T72FhBXtvq2rqxMyMjKERYsWCfv27RM2bNgg5ObmCg8//HBUr8uAJ4msWbNGKCoqEpRKpXDppZcKX3zxhfuxyy+/XJg7d67734cOHRIA+P13+eWXd/+GJ4Fo9m3//v0D7tvly5d3/4YngWj27f333y8YDAZBrVYL2dnZwujRo4WXX365B7Y6OUSzb30x4Akvmv175513un83Ly9PuPrqq4VvvvmmB7Y6OUR77H7++efCqFGjBJVKJQwcOFD47W9/K9jt9qheUyIIghBlRoqIiIgoqbCGh4iIiFIeAx4iIiJKeQx4iIiIKOUx4CEiIqKUx4CHiIiIUh4DHiIiIkp5DHiIiIgo5THgISIiopTHgIeIiIhSHgMeIiIiSnkMeIiIiCjlMeAhom4xbtw4LF68GHfeeSeys7ORl5eHZ555Bm1tbZg/fz4yMjJgMBjw3nvvuf9m165dmDJlCtLT05GXl4cbbrgB9fX17sc3btyIqqoqZGVlIScnB9OmTcOBAwfcjx8+fBgSiQSvv/46xo8fD61WixEjRmDbtm3u3zly5AimT5+O7OxspKWloaysDO+++2737BQi6jYMeIio27zwwgvQ6/X46quvsHjxYtx222342c9+hjFjxuCbb77BVVddhRtuuAEmkwlNTU2YMGECKioq8PXXX2Pjxo04deoUfv7zn7ufr62tDXfffTe+/vprbNq0CVKpFLNmzYLT6fR63fvvvx/33HMPvv32WwwePBhz5syB3W4HACxcuBAWiwWfffYZdu7cicceewzp6endul+IKP64WjoRdYtx48bB4XBg8+bNAACHwwGdToef/OQn+O///m8AwMmTJ5Gfn49t27bhww8/xObNm/H++++7n+PYsWMoLCzEvn37MHjwYL/XqK+vR+/evbFz506Ul5fj8OHDKC4uxj/+8Q/ceOONAIA9e/agrKwMe/fuRWlpKYYPH47Zs2dj+fLl3bAXiKinMMNDRN1m+PDh7v8vk8mQk5ODYcOGuX+Wl5cHADh9+jS+++47fPzxx0hPT3f/V1paCgDuYav9+/djzpw5GDhwIDIzMzFgwAAAQF1dXdDXzc/Pd78GANxxxx14+OGHUVlZieXLl2PHjh0xftdElAgY8BBRt1EoFF7/lkgkXj+TSCQAAKfTidbWVkyfPh3ffvut13/79+/H2LFjAQDTp0/H2bNn8cwzz+DLL7/El19+CQCwWq1BX9fzNQDgpptuwsGDB3HDDTdg586dGDlyJNasWRPjd05EPU3e0xtARBTIRRddhNdeew0DBgyAXO5/qmpoaMC+ffvwzDPPoLq6GgCwZcuWTr1WYWEhbr31Vtx6662477778Mwzz2Dx4sVd2n4iSizM8BBRQlq4cCHOnj2LOXPm4N///jcOHDiA999/H/Pnz4fD4UB2djZycnLw97//HbW1tfjoo49w9913R/06d955J95//30cOnQI33zzDT7++GMMGTIkDu+IiHoSAx4iSkgFBQXYunUrHA4HrrrqKgwbNgx33nknsrKyIJVKIZVK8fLLL2P79u0oLy/HXXfdhSeeeCLq13E4HFi4cCGGDBmCyZMnY/Dgwfjzn/8ch3dERD2Js7SIiIgo5THDQ0RERCmPAQ8RERGlPAY8RERElPIY8BAREVHKY8BDREREKY8BDxEREaU8BjxERESU8hjwEBERUcpjwENEREQpjwEPERERpTwGPERERJTy/j92Gvin3uJBnwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=mean_var_cells, x=\"means\", y=\"vars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7139f5f-1273-40d6-8469-8c4c6d7eb723",
   "metadata": {},
   "source": [
    "## Pearson correlation between cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b393aa04-f3a7-44f4-af9f-50461ef24d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_over_regions = pd.DataFrame.sparse.from_spmatrix(just_matrix)\n",
    "df_over_cells = pd.DataFrame.sparse.from_spmatrix(just_matrix.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae18613c-facb-46b3-a7d2-7b759c354a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "just_matrix = just_matrix.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "89de7638-becd-4f96-aff4-85b707e3c105",
   "metadata": {},
   "outputs": [],
   "source": [
    "just_matrix = just_matrix.astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "af7f8c83-3b53-4dae-9ad2-171925541b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "just_matrix.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0096a88e-404c-4b93-bd7f-41988ad0b9d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67150,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "just_matrix[1, :].shape"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f9d9fb0a-4484-4248-86e9-1aaa4235fe30",
   "metadata": {},
   "source": [
    "| 0\n",
    "| 1\n",
    "| 2\n",
    "|\n",
    "|\n",
    "|\n",
    "| 12721\n",
    "__________________________________\n",
    "0 1 2         ...              67150\n",
    "\n",
    "\n",
    "\n",
    "when we're selecting one row its _0_1_2______________67150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "43df2c07-4a6a-455f-bcb7-4a79642b4a16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/annac/datasets/kidney_10x\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "74940431-dc5f-4b8a-83bf-dcd2c8038669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12721, 67150)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "just_matrix.shape # 12k rows and 67k cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "855871b9-f8e8-4f7d-8e8a-f27d1145d05b",
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix_for_genes = just_matrix\n",
    "matrix_for_cells = just_matrix.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6be4d125-26c0-49f6-8d4b-82edb00e27fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('matrix_for_genes', matrix_for_genes)\n",
    "np.save('matrix_for_cells', matrix_for_cells)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f858194d-e8b0-4642-bd2c-3c7417ea3f26",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_for_genes_first = matrix_for_cells[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3f00081-b246-4b49-a1f4-6909db602806",
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiprocessing import Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e604b1a1-0a81-4f26-9191-3c43b8deabfb",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[14], line 7\u001b[0m\n\u001b[1;32m      5\u001b[0m corr_matrix_genes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(first)\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m gene_a \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m67150\u001b[39m):\n\u001b[0;32m----> 7\u001b[0m     \u001b[38;5;28mnext\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mr_regression\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmatrix_for_genes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmatrix_for_cells\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgene_a\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m   \u001b[38;5;66;03m# X = (n_samples, n_features), Y = (n_samples,)\u001b[39;00m\n\u001b[1;32m      8\u001b[0m     arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mvstack((corr_matrix_genes, \u001b[38;5;28mnext\u001b[39m))\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# plot the heatmap\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m#sns.heatmap(corr)\u001b[39;00m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.8/site-packages/sklearn/utils/_param_validation.py:211\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    206\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m    207\u001b[0m         skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m    208\u001b[0m             prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m    209\u001b[0m         )\n\u001b[1;32m    210\u001b[0m     ):\n\u001b[0;32m--> 211\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    213\u001b[0m     \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[1;32m    214\u001b[0m     \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[1;32m    215\u001b[0m     \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[1;32m    217\u001b[0m     msg \u001b[38;5;241m=\u001b[39m re\u001b[38;5;241m.\u001b[39msub(\n\u001b[1;32m    218\u001b[0m         \u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;124m\\\u001b[39m\u001b[38;5;124mw+ must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    219\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    220\u001b[0m         \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[1;32m    221\u001b[0m     )\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:318\u001b[0m, in \u001b[0;36mr_regression\u001b[0;34m(X, y, center, force_finite)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[38;5;129m@validate_params\u001b[39m(\n\u001b[1;32m    257\u001b[0m     {\n\u001b[1;32m    258\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray-like\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    264\u001b[0m )\n\u001b[1;32m    265\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mr_regression\u001b[39m(X, y, \u001b[38;5;241m*\u001b[39m, center\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, force_finite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m    266\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute Pearson's r for each features and the target.\u001b[39;00m\n\u001b[1;32m    267\u001b[0m \n\u001b[1;32m    268\u001b[0m \u001b[38;5;124;03m    Pearson's r is also known as the Pearson correlation coefficient.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;124;03m    chi2: Chi-squared stats of non-negative features for classification tasks.\u001b[39;00m\n\u001b[1;32m    317\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 318\u001b[0m     X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcoo\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat64\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    319\u001b[0m     n_samples \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    321\u001b[0m     \u001b[38;5;66;03m# Compute centered values\u001b[39;00m\n\u001b[1;32m    322\u001b[0m     \u001b[38;5;66;03m# Note that E[(x - mean(x))*(y - mean(y))] = E[x*(y - mean(y))], so we\u001b[39;00m\n\u001b[1;32m    323\u001b[0m     \u001b[38;5;66;03m# need not center X\u001b[39;00m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.8/site-packages/sklearn/utils/validation.py:1147\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1142\u001b[0m         estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[1;32m   1143\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1144\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1145\u001b[0m     )\n\u001b[0;32m-> 1147\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1148\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1149\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1150\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1151\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1152\u001b[0m \u001b[43m    \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1153\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1154\u001b[0m \u001b[43m    \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1155\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1156\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1157\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1158\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1159\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1160\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1161\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1163\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[1;32m   1165\u001b[0m check_consistent_length(X, y)\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.8/site-packages/sklearn/utils/validation.py:959\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    953\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    954\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    955\u001b[0m             \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[1;32m    956\u001b[0m         )\n\u001b[1;32m    958\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 959\u001b[0m         \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[43m            \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    961\u001b[0m \u001b[43m            \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    962\u001b[0m \u001b[43m            \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    963\u001b[0m \u001b[43m            \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    964\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    967\u001b[0m     n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.8/site-packages/sklearn/utils/validation.py:120\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;66;03m# First try an O(n) time, O(1) space solution for the common case that\u001b[39;00m\n\u001b[1;32m    116\u001b[0m \u001b[38;5;66;03m# everything is finite; fall back to O(n) space `np.isinf/isnan` or custom\u001b[39;00m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;66;03m# Cython implementation to prevent false positives and provide a detailed\u001b[39;00m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;66;03m# error message.\u001b[39;00m\n\u001b[1;32m    119\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m np\u001b[38;5;241m.\u001b[39merrstate(over\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 120\u001b[0m     first_pass_isfinite \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39misfinite(\u001b[43mxp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_pass_isfinite:\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36msum\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.8/site-packages/numpy/core/fromnumeric.py:2324\u001b[0m, in \u001b[0;36msum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m   2321\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m out\n\u001b[1;32m   2322\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\n\u001b[0;32m-> 2324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrapreduction\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msum\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeepdims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2325\u001b[0m \u001b[43m                      \u001b[49m\u001b[43minitial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhere\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/mambaforge/lib/python3.8/site-packages/numpy/core/fromnumeric.py:86\u001b[0m, in \u001b[0;36m_wrapreduction\u001b[0;34m(obj, ufunc, method, axis, dtype, out, **kwargs)\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     84\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m reduction(axis\u001b[38;5;241m=\u001b[39maxis, out\u001b[38;5;241m=\u001b[39mout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mpasskwargs)\n\u001b[0;32m---> 86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mufunc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpasskwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# calculate the correlation matrix on the numeric columns\n",
    "# corr = df_over_cells.corr() - using Pandas - slow\n",
    "\n",
    "first = r_regression(matrix_for_genes, matrix_for_cells[0])\n",
    "corr_matrix_genes = np.array(first)\n",
    "for gene_a in range(1, 67150):\n",
    "    next = r_regression(matrix_for_genes, matrix_for_cells[gene_a])   # X = (n_samples, n_features), Y = (n_samples,)\n",
    "    corr_matrix_genes = np.vstack((corr_matrix_genes, next))\n",
    "\n",
    "# plot the heatmap\n",
    "#sns.heatmap(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "159ef978-579f-4a56-bc78-4684cea5f5cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the matrix\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "37288504-352a-45cb-b6f7-bc4ceac2c603",
   "metadata": {},
   "outputs": [],
   "source": [
    "first = r_regression(matrix_for_cells, matrix_for_cells[0])\n",
    "corr_matrix_genes = np.array(first)\n",
    "\n",
    "for cell_a in range(1, 12721):\n",
    "    next = r_regression(matrix_for_genes, matrix_for_cells[gene_a])   # X = (n_samples, n_features), Y = (n_samples,)\n",
    "    arr = np.vstack((corr_matrix_genes, next))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d2d2a107-1171-484a-8193-6cc55158603f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12721"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69acde4b-f0fa-433c-9ba6-c1ecceec80aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2db670cd-4b74-49d3-b200-b210b4ee4f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the correlation matrix on the numeric columns\n",
    "corr_regions = df_over_regions.corr()\n",
    "\n",
    "# plot the heatmap\n",
    "sns.heatmap(corr_regions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba2031c-7f41-469c-b991-6e1e68995fed",
   "metadata": {},
   "source": [
    "## Shanon entropy between cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "043382b6-f91f-4fda-aa00-959cf7568560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1x67150 sparse matrix of type '<class 'numpy.float32'>'\n",
       "\twith 5028 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "just_matrix[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0eb5924d-d0da-4718-a66c-c9b50be46ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<12721x1 sparse matrix of type '<class 'numpy.float32'>'\n",
       "\twith 521 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "just_matrix[:,  1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1949a3bf-76d5-4235-ab7b-ec5f52502cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<1x67150 sparse matrix of type '<class 'numpy.float32'>'\n",
       "\twith 5028 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "just_matrix[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4fb9ea2-e156-4ef2-8126-529c9c232748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[nan]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy(just_matrix[:,  1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d43bfb98-9832-4047-9545-5ae1f813bef1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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