for_Marina.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nice plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0ч</th>\n",
       "      <th>4ч</th>\n",
       "      <th>24ч</th>\n",
       "      <th>7д</th>\n",
       "      <th>14д</th>\n",
       "      <th>30д</th>\n",
       "      <th>180д</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>мРНК ИЛ-1β</th>\n",
       "      <td>1</td>\n",
       "      <td>2.60</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>мРНК ФНОα</th>\n",
       "      <td>1</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>мРНК IBA-1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ДА</th>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ДОФУК</th>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ГВК</th>\n",
       "      <td>1</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>НА</th>\n",
       "      <td>1</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.85</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>мРНК кальпастатин</th>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>мРНК µ-кальпаин</th>\n",
       "      <td>1</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.40</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>мРНК m-кальпаин</th>\n",
       "      <td>1</td>\n",
       "      <td>0.80</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>µ-кальпаин</th>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>m-кальпаин</th>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.30</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0ч    4ч   24ч    7д   14д   30д  180д\n",
       "мРНК ИЛ-1β          1  2.60  5.00  1.10  1.10  1.10  1.20\n",
       "мРНК ФНОα           1  3.50  5.10  1.80  1.50  1.20  1.90\n",
       "мРНК IBA-1          1  0.90  1.10  1.00  1.00  1.10  1.10\n",
       "ДА                  1  0.72  0.68  0.50  0.72  1.10  1.10\n",
       "ДОФУК               1  1.00  0.83  0.98  0.87  0.98  1.20\n",
       "ГВК                 1  0.98  0.98  0.90  0.95  0.98  0.98\n",
       "НА                  1  2.00  2.10  3.50  3.85  1.10  1.20\n",
       "мРНК кальпастатин   1  0.80  1.20  1.10  0.90  0.20  1.00\n",
       "мРНК µ-кальпаин     1  1.10  1.60  2.40  1.30  1.00  2.30\n",
       "мРНК m-кальпаин     1  0.80  1.00  1.50  1.10  1.10  1.30\n",
       "µ-кальпаин          1  1.00  1.80  2.50  2.80  1.10  1.70\n",
       "m-кальпаин          1  1.00  2.30  3.00  3.50  2.00  0.85"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('hyp.csv',sep=';')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c302e48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IzMysBSJa2iNbDuwdEUsljQJukvTriLilqLIbMjMza1ore2RpQfSl6emotDUczHFDZmZmTetv8azFtBzg7cA7gHMi4tZGdb2yh5mZNS36VXrLrwmbtskrnC+iL6XK2hh4j6RtG13bPTIzM2vaUGYtRsR0YHrJus9Kmk2WAeSuojrukVVAUl/KLl3bjkvliyTNT2XzJR2SO2Zp7vE4SQslHVRF/GZm9SLKb4ORtJ6ktdPjMcC+wL2N6rtHVo1lqctcZK+IeFLSFsC1wC/zOyWtAVwFfD0iftXmOM3MSmnx98jGAT9K98lWAX4aEVc0quyGrHOtCTyTL0jTUC8HZqWuuZlZR2jl9PuI+APw7rL13ZB1nusliWwV/MPr9s0A9gD+adijMjMbQF+Fay36Hlnn2SsitgXeBUyTNDaVrw6sA0wCzml0sDNEm1kVIlR6azU3ZB0qIhYCfwG2TkXLgcMj4kLgFUlHNzjOGaLNbNgNZfp9q7kh61CS1ifLOfZAKuqNiBfS4xOAMyStVUlwZmZ1Wjlrcah8j6zzXC+pj2xJli9ExF/qK0TEAkk/BM4kyxZtZlYpr36/komIsQ3KJ5Q9JiJOa3FYZmZvWF9/dQN8bsjMzKxp7RgyLMsNmZmZNa2/DbMRy3JDZmZmTWvHtPqy3JCZmVnTPLRoK5UjVhlXdQiFxlT4hziQwz7weNUhFOqZ8NaqQ2jow999ouoQCo1WT9UhtI2HFs3MrKt51qKZmXW1Kgc03JCZmVnTPLRoZmZdrcpZi15rsUPkM0Cn55MkTasrmyfpouGNzMxscP1D2FrNPbIuIWkrsg8eu0taPbeAsJlZ5QL3yGxwRwH/CVwLHFxxLGZmr9MbKr21mntknWOMpLm55+sAs3LPjwD2A7YgS+PiIUYz6xjukRnAsojYobYBX6ntkLQT8EREPABcB+wo6S1FJ3GGaDOrQpX3yNyQdYcjgS0lLQIWAmsCHymq6AzRZlaFQKW3VnND1uEkrQIcBmwXERNSzrJDyBo3M7OO4FmLNpDdgcURkR8nvAHYWtK4iHi0orjMzF7VV+E9MjdkHaIgA/RMYGZ6unPdvj6gM1feNbOVUn917ZgbMjMza16/e2RmZtbNvGiwmZl1tXZM4ijLDZmZmTWtXx5atJXI9165v+oQCk1YdZ2qQyj006s7M6vwM30PVB1CQ3c8uaDqEArtst6WVYfQNn0VXtsNmZmZNc2zFs3MrKt51qKZmXU1z1o0M7Ou5qFFMzPralVOv29q0WBJUyQtljRX0l2SDs6Vn1xXd5GkddPjpbnycZIWSjqomVjMzKw6fSq/tVorVr+fmvJnHQbMSKu1lyJpDeAq4OsR8asWxGJmZhVo5er3ksZLul7SPZLulvSZgeoP2OhImiApJB2XnvekHtjM+roRcQ/QC6xbIk4kjQIuB2ZFxPQGdRZJWlfSWEk3S3p/Kv+KpNtSL3C69No38STNlvSn1EtcmsomSZpWcP58j3KupOck7Zn2nZcSVN4t6bTcMTtJ+h9J8yT9XtIa6QWfK2lp7toHS3pPqntn+v8W6RyN6r/ak5W0T3rtJ6bn+V7sREmzy7zOZmbDocVpXHqBz0XEVmSLph8vaetGlcv0nhYAH0yP9wceKqok6b0pxidS0Um5BmIusGHdITOAPYCLBrn+KOBS4LyIuDaVTYuInSJiW2AMcGCufg9wZOolljE1l5X5xlz5qRExEdgO2EPSdpJWBS4BPhMR2wP7kmV23isdPwc4Op1vFnAvsHtEvJss4/OZAAPUz/sq2Ws/JM4QbWZVCJXfBj1XxKMRcUd6vAS4B2iYKbjMZI/lwAJJ2wAfAy4AJub2nyTpGGAJcEREROogTY2Is2qVUnbjmtWBdYBJwDnAPgNc/3vAuIi4IFe2l6RTgDen89wN1IYmxwAvFZznCEm7Aa8Ap0XEFQP90MDhkiaTvUbjgK3JZpg+GhG3AUTE84OcYy3gR5I2T8eOGqQ+AJI+AtwG/FWueEz6QADZz1iYhyz1bqcD7L7RPlXOiDWzlUi7JntImgC8G7i1UZ2y97N+CJxC9qb+WN2+Wo/mfRFx44qHFloOHB4RFwKvSDp6gLr3AfMkHQsgaTRwLnBoRLyLrKEbnau/IcVv8pekXtBRwPkDBSdpU+BkYJ+I2A64Ml1DDO3rEv8GXJ96jgfVxdlID9lr/bW68mW5nuNAr5eZ2bDrG8KWHzlK2+Sic0oaC/wMOHGgjkOp6fcRcbuk9YFpZL2BZvVGxAvp8QnAbyVdERHPFdQ9g6zxuFnSlWSNIMCT6Yc8FLgMIPW4nomIZwa49tMM/nOvCbwAPCdpA+AAYDbZUOGGknaKiNvSZJVlEdHb4DxrAbXxvUmDXLPmGODSiHhSFS7CaWY2FEP5Hll+5KiRNI/iZ8BPIuLygeqWnmEYEQdExJVl6w/hvAvIenxnDlDnKeBfge9ExLNkvbD5wC/IhuCQtBNwNnBsg9N8WNJNwHXA5weJaR5wJ9mQ5Qzg5lT+MnAE8B1J84DfMHAv6xvA1yTdTNbTKmMDYGrJumZmHaHFsxYF/AC4JyK+NWj9CN9GGak69R7Zc33Lqg6hUKeufv9SVLmueGPP9L1YdQgNefX7obth8XVNDQH9+9uOKf1+87kHLxjwWml07UayDkut7ftSRFxVVN8re5iZWdNa+ak5Im6C8qsQuyEzM7Omea1FMzPrak6saSuVt45aq+oQCm22ytiqQyi0X2feUuRHo1erOoSGRnfovagT+zaoOoS26a8wkYsbMjMza1qVq9+7ITMzs6Y5saaZmXU198jMzKyr9cr3yMzMrItVObTYisSaQyZnljYzG1FanI9sSKrskU2NiLMkbQXcmBYlLkXOLG1m1lGqnH7flh6ZOiSzdHrcMJuyOjMDdT72CyTdVXQNSdMkTSrzmpmZtVsMYWu1dg4tVp1ZuqyOyUCdP0jSu4Bth/rDOEO0mVWhyqHFdjZkRZml805KDdVZpMzSqfzVN/705v9I7pj6zNKt0CgD9a2S5gN7A9vk9g2UgXpu6skdWLC/3uGS7iBLF7MNWQbqvNOBrza4xlyydDIriIjpETExIiaOW71hZnAzs5bqI0pvrdbuyR5VZpauDQeOerVAuiY1BN/P1eukDNQ1uwBLgXlF10jXuWSga5iZDacRO9mj4szSO5FNCNmTlNQyIv6moF4nZaCumQIcNsg5zMw6Roy0yR55FWaWPkbSHLKMy+MlfXiAc3VEBuqcWyNi4UDnMDPrJFX2yEZkhmhJi4CJEfFk1bFUqVMzRI9ZZdWqQyi0dU9nrsrfuavfF90q7gyP9i6pOoRCnbz6/Ycfu7CpjGL/OOHw0u835y76aUuzl3llDzMza5oXDW6xiJhQdQxmZiuTXucjMzOzblblZA83ZDbsHnulaJJp9Zb1v1x1CIVeHLNO1SEUerZv+eCVKtKp92EvedOLVYfQUMPZcCU5jYuZmXU198jMzKyruUdmZmZdra/Cr3K5ITMzs6ZVmcbFDZmZmTVtRC9RBc4IbWY20o3YRYPrOCO0mdkI1fUZojskI/SZkn6XkkrumFK2LKzFVHDMTEmHpsfnSZqSHh+UcpHdKem3aXX62jH5nuXSlH16Qi2Lc93590wZoWtJQhfnrvEPKW/ZPEk/k/Tmgpj+Pr2m69ZfQ9KhRa+tmVlVYgj/tVorhxarzgj9UET8NVkW5plk6Vd2JlvVviFJXwF6ImJKKroJ2Dki3g1cTJZPraYH+PeUD2zOIPEA3JjLHzY1V355ykC9PXAP8Mm6mEYDxwGPl7iGmVnl+iJKb63WyqHFoozQE3P7T5J0DLCElBFaEqQhx1qltHJ9TX1G6H0GuP6s9P/5wNiIWAIskfSSpLVTepZ6k4D9gPG5so2BSySNA1YF/je3bwzFSTXfnhphgEsj4owB4gTYVtLpwNrAWOCauv3HAz8CPtfgGmsB/110YkmTgckA71hrC5wl2syGQ9cPLeZUmRG6tl5Of+5x7fmbJJ2R6/XVrAOcBJyVK/sOMC1lh/4UK2aHfqTg2gtTr2sX4BOSthjk55oJnJCucVrdNdYEjmTFLNMLc727hvnOImJ6REyMiIluxMxsuFQ52aOlDVlE3A6sT9agtUJ9RugzJL2hpFERcWquIaj5VkScC2wo6f2pbC1gcXr8iVrFNJPyfcCtA1xmGfAiMGqQcNYAHk33/+ob55OAsyOiMxf+MzMrMFLukQGVZoRuxqeAqWnSxRTgUkk3AvnEnDcBUyKiaGhx05Qdeg5wQ0SsMPmjzr+QNYi/Ae6t2yeyYVkzs67RT5TeWm1EZoi2TKdmiH6urzNTHq/VM6bqEAptNaozV79f1Ld08Er2OmuvslrVITR0yQO/aCpr8wHjDyj9fvPrh37tDNFmZtZZ+kbQZA8zM1sJtXJoUdIMSY8XfUe3iBsyMzNrWkSU3kqYSfZ95FI8tGjDrlPvRe37prdWHUKh3Zd15gTW9cd27ufgk16uMjtWY5tli/iMSK2cxBERN0iaULZ+5/5LNDOzrjGU6feSJqflBGvb5Gau7R6ZmZk1bShLT6V1cwvXzn0j3JCZmVnTnFjTzMy62khaa9HMzFZCrZy1KOki4HfAFpIelvTJgeq7R2ZmZk1r8azFI4dS3w2ZmZk1rR2LAZc1IocWCzIqn1zLzlxUR9JWKVvz+PT8F5Jul3R3/bRQSX0pHcwCSVekslczO9fVnS3pT7nEoX2pfKyk6yTdIWm+pEMKYhol6X5J04quIemuoXzPwsysnfqiv/TWait9j0zSRmSZoI+KiFpW62Mj4mlJY4DbJP0sIp6S1AO8EBE7SNoTOLnEJY6OiDnpWrVVVl8CPhQRz6f0MLdImlV33GTAq7KaWVeocgH6EdkjG4KxwNXA7Ii4O1f+z5LmAbeQZY/ePJWPIWuEinwz9bquk/TOQa4r4ExJfwB+C2wEbPDqziydzN8B5zW4xlzg7YUnzn3R8NEXFhdVMTNruSrTuIzkhiyfJmAUMD43xHdcKh8PfA3YS9JWAKmntS/w1xGxPXAnr2VwbpQhGuDzKWnnRWQ5zQZyNLAe8FfpmL/w+izRJ5J9WbA+38nnc8lBFxad2BmizawKVSbWHMlDi5tIWg94CtgdmBMRr07hTPeX7omICyUtBs6XtAdZhuhnIuJFSVsCO+fOeThw8yDXfQpYdZA6awGPR8QrkvYCNqnb90FgF+CYQc5jZtYR+iscWhzJDdlTwI+Bt5IN331c0syIWKEnExH/Lele4NPAD4Dj0rDfn8iGF5H0z8CuwCcaXO/fJJ0IrEaWcXogPwF+JWkOMJfXZ4neGDg5InqlluaeMzNrmypnLY7IDNGpt3VFRGxbcSiV6tQM0Z2qY1e/f6lTV79/seoQGurU1e937HlL1SE09LVFFzb1yXnL9Xcq/X5z7+O3OUO0mZl1Fg8ttlhELAJW6t6YmdlwqnJocUQ2ZGZmNrzcIzPrANsu78xbipuNf7rqEAqt/e6eqkNo6KCr1686hEIzli+qOoSGvtbk8e6RmZlZV+uLvsqu7YbMzMyaVuUMeDdkZmbWNGeINjOzruYemZmZdTXPWjQzs67mWYtmZtbV2pEws6wRkcYlZVa+V9L3U+bkn0jaV9LNku6T9J6CYxZJWjdla75Z0vtT+Vck3ZbOM125lXvrMj4vTWWTalmc684/RdLiXOqY51KKGCSdl3KG3S3ptPqY0uMLctmiX3cNSdMkTWrV62dm1qyIKL212ohoyJJ3AN8GtgO2BI4CdiPL4vylBseMAi4FzouIa1PZtIjYKS04PAY4MFe/Bzgy5QMrY2ouf9iNufJTI2JiinUPSdvlD5L0LrzElpl1kf6I0lurjaSG7H8jYn5E9AN3A9dF1vTPByY0OOZ7wLiIuCBXtpekWyXNB/YGtsnta5Qh+ojU67pN0oEF++sdLukOsqSd2wBb1+0/Hfhqg2vMBY5odGJniDazKrhH1hrLc4/7c8/7gTdJuiY1BN/P1bsPmCfpWABJo4FzgUMj4l1kDV0+c/OGwKMF174k9bqOAs4fKEhJm5L1EveJiO2AK+uusQuwFJhXdI10nUsand8Zos2sCv1E6a3VVprJHhHxNwXFZwAB3CzpSl5r/J5oZHJdAAAGHklEQVSUNBY4FLgMQNJuZJmjnxngMk8z+Gu6JvAC8JykDYADgNm5/VOAwwY5h5lZR/H3yCoUEU9J+lfgOxFxuKTvkQ1HLgJuA5C0E3A2cGyD03xY0g7AWODzg1xvnqQ7yYY/7wdurqtya0QsTMlBzcy6QpWzFkdkhmjLOEP00JzYt0HVIRSauPFfqg6hUCevfv/jTl39vndR1SE0dMejNzWVtXnMmE1Kv98sW/aAM0SbmVln8dCimZl1Na/sYWZmXc09MjMz62pVLhrsyR5WiqTJETG96jjqOa6h69TYHNfQdXJsw2kkfSHa2mty1QE04LiGrlNjc1xD18mxDRs3ZGZm1tXckJmZWVdzQ2Zldeo4vOMauk6NzXENXSfHNmw82cPMzLqae2RmZtbV3JDZCiTtnzJhL5D0harjqSepR9Kdkq6oMIbxkq6XdE/K9P2Zuv0nS4paxu+KYtwil6F8rqTnJZ1YUSwzJD1ey3pet6+y10rSaEm/lzQvn7Fd0qYpL+F9ki6RtOowxbPC6yRpB0m3pN/hnFrGe2XOTn+nf5C043DE2InckNnrSOoBziFLL7M1cKSk+sSfVfsMcE/FMfQCn4uIrYCdgeNrr5Ok8cB+wIMVxkdE/CmXw+6vgBeBn1cUzkxg//rCDnitlgN7R8T2wA7A/pJ2Br5OluF9c+AZ4JPDFM9MVnydvgGcln6PX0nPIfsb3Txtk4HzhinGjuOGzOq9B1gQEfdHxMvAxcAhkpbWKkiaKenQKoKTtDHwt8D3c2XDHltEPBoRd6THS8ga1lom06nAKfDa4nP5uCTtWUFvch9gYUQ8kGK4In2Snyvp5Xb3hiLiBrJ8ffVWeK1SfCdLeizF93S7fqeRqf37GZW2IMsOf1kq/xHwwVxs0yQ9mGJbKmliC+Mpep2CLI8hwFrAI+nxIcCP089wC7C2pHEpxokptrkp1mmtirETuSGzehsBD+WeP8xrb9Cd4D/I3viqS35UJ+WOezdwq6SDgcURUZ/hux9oaeqKIfoocFHueQ9wbPqU/0jxIe01wGsFWXznpvhmtTmOHklzgceB3wALgWcjojdVqf8b6AG+nGKb087YkhOBb0p6CDgL+GIqH+hvtQf4fa4XN6K5IbN6RW+2ATwt6Z3DHUyepAOBxyPi9rpdlcWWMon/jOzNphc4leI3jofJGrthl+7vHAxcmiseS3EPaVhIejONXysYxvgioi+94W9MNiKxVVG1KmJLPg2cFBHjgZOAH6TyRn+rUPHvd7i5IbN6DwPjc883JvvEfiJwefrkenAVgQG7AgdLWkQ25Lm3pAuqik3SKLJG7CcRcTnwdmBTYF6KcWPgDklvBc4F3ivpD+SGRYfJAcAdEZHP0LkJFfXEkoFeK9K+h4czoIh4FphNds9zbUm1RdVrfwM1wx3bJ4DL0+NLyRpbaPy3ChW8flVyQ2b1bgM2T7O2ViUbkpoVEZdHxLbDMdTTSER8MSI2jogJKa7/iohjqohNksg+Gd8TEd9K8c2PiPUjYkKK8WFgx4h4LG37RMR2wN8PR4w5R5IbVkyTGR6MiMo+sQ/0WklaG9gNuK7dcUhaL10PSWOAfcnud14P1O7LfQL4ZaqzCTAOKBoObZdHgD3S472B+9LjWcDH0+zFnYHnIuLR9G/zI0Bls3qHm9O42OtERK+kE4BryMbZZ0TE3RWH1Yl2BT4GzE89QYAvRcRVFca0gjSEtx/wqfR8Q+Bq4OVc3BsC3wT+ro1xXATsCawr6WHgqxHxgwbVrwXWB27M3pN5G9kb+WUN6jdjHPCjNFt3FeCnEXGFpD8CF0s6HbiT14bzbgNWBe5Msb2D7LXbqxXBFL1OwD8A3049xJd4baHgq4APAAvIZqTWfn9fJ5v5uJGkfmAdYIykmRExHPf0hp1X9jBbiaSJKVMiYlJd+WURUclM1HqSZkfEnnVlHRFfJ8dWI2km2e94Ua7sBOCuiJhdUVht5R6Z2crlCYq/bzR1uAMZwL8WlHVKfJ0cW815ZL/nvGuA5yqIZVi4R2ZmZl3Nkz3MzKyruSEzM7Ou5obMzMy6mhsyMzPram7IzMysq7khMzOzrvb/AdAiW0e7EAKlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(\n",
    "    df, \n",
    "    vmin=df.values.min(), vmax=df.values.max(), center=1,\n",
    "    cmap=sns.diverging_palette(220, 20, n=len(np.unique(df.values)), s=95)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.heatmap(\n",
    "    df, \n",
    "    vmin=0, vmax=df.values.max(),\n",
    "    cmap=sns.cubehelix_palette(dark=0, light=1,n_colors=len(np.unique(df.values))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Not simple Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "def value_to_color(val):\n",
    "    palette = sns.diverging_palette(220, 20, n=256) # Create the palette\n",
    "    color_min, color_max = [0, 5.1]\n",
    "    val_position = float((val - color_min)) / (color_max - color_min) # position of value in the input range, relative to the length of the input range\n",
    "    ind = int(val_position * (n_colors - 1)) # target index in the color palette\n",
    "    return palette[ind]\n",
    "\n",
    "def heatmap(x, y,v, size=None, grid=True):\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    # Mapping from column names to integer coordinates\n",
    "    x_labels = [v for v in sorted(x.unique())]\n",
    "    y_labels = [v for v in sorted(y.unique())]\n",
    "    x_to_num = {p[1]:p[0] for p in enumerate(x_labels)} \n",
    "    y_to_num = {p[1]:p[0] for p in enumerate(y_labels)} \n",
    "    \n",
    "    if size is not None:\n",
    "        size_scale = 80\n",
    "        ax.scatter(\n",
    "        x=x.map(x_to_num),\n",
    "        y=y.map(y_to_num),\n",
    "        s=size * size_scale,\n",
    "        c=v.apply(value_to_color), # Vector of square color values, mapped to color palette\n",
    "        marker='s')\n",
    "    else:\n",
    "        ax.scatter(\n",
    "        x=x.map(x_to_num),\n",
    "        y=y.map(y_to_num),\n",
    "        s=100,\n",
    "        c=v.apply(value_to_color), # Vector of square color values, mapped to color palette\n",
    "        marker='s')\n",
    "    \n",
    "    # Show column labels on the axes\n",
    "    ax.set_xticks([x_to_num[v] for v in x_labels])\n",
    "    ax.set_xticklabels(x_labels, rotation=45, horizontalalignment='right')\n",
    "    ax.set_yticks([y_to_num[v] for v in y_labels])\n",
    "    ax.set_yticklabels(y_labels)\n",
    "    if grid:\n",
    "        ax.grid(False, 'major')\n",
    "        ax.grid(True, 'minor')\n",
    "        ax.set_xticks([t + 0.5 for t in ax.get_xticks()], minor=True)\n",
    "        ax.set_yticks([t + 0.5 for t in ax.get_yticks()], minor=True)\n",
    "        ax.set_xlim([-0.5, max([v for v in x_to_num.values()]) + 0.5]) \n",
    "        ax.set_ylim([-0.5, max([v for v in y_to_num.values()]) + 0.5])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_melt = pd.melt(df.reset_index(), id_vars='index') # Unpivot the dataframe, so we can get pair of arrays for x and y\n",
    "df_melt.columns = ['x', 'y', 'value']\n",
    "heatmap(x=df_melt['y'], y=df_melt['x'], v=df_melt['value'], size=df_melt['value'].abs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap(x=df_melt['y'], y=df_melt['x'], v=df_melt['value'], grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}