Plot_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Val1  Val2  Val3\n",
      "Genome position                  \n",
      "0                 0.0   0.0   0.0\n",
      "10                0.4   0.4   0.4\n",
      "20                0.2   0.2   0.2\n",
      "30                0.3   0.3   0.3\n"
     ]
    }
   ],
   "source": [
    "df = {'Genome position': [0, 10, 20, 30], 'Val1': [0, 0.4, 0.2, 0.3], 'Val2': [0, 0.4, 0.2, 0.3],  'Val3': [0, 0.4, 0.2, 0.3]}\n",
    "\n",
    "df = pd.DataFrame(df)\n",
    "df = df.set_index('Genome position')\n",
    "\n",
    "columns=list(df.columns)\n",
    "\n",
    "# Rename column names\n",
    "# data.rename(columns={'gdp':'log(gdp)'}, inplace=True)\n",
    "\n",
    "# Merge dataframes\n",
    "# big_df = pd.merge(big_df, df, on='Genome position', how='outer')\n",
    "\n",
    "# Filter NaN\n",
    "# data[\"Val1\"].fillna(0, inplace = True) \n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualise(df, length, labels, style=\"o\"):\n",
    "\n",
    "    \"\"\"\n",
    "    df: dataframe\n",
    "    length: length of genomes\n",
    "    lables: lables of stats\n",
    "    \"\"\"\n",
    "\n",
    "    axes = df.plot.line(subplots=True, figsize=(16, 8), legend=False, style=style)\n",
    "\n",
    "    for i in range(len(axes)):\n",
    "        axes[i].set_xlim(0, length)\n",
    "        axes[i].yaxis.grid()\n",
    "        axes[i].set_axisbelow(True)\n",
    "        axes[i].set(ylabel=labels[i])\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DA9YCHItPvPerofagJ/fPtgMAwAox5MOj1ia5b87xTJKXze1QVWuTvCHJq5O89EgDVdXGJBuTZPXq1dm+ffvxrhWYxysfmUnN096PzOT3XYcAAKwQQwbbef8ePuz4F5O8q7ufqpqv++RN3ZuTbE6S6enp3rBhw3EqEVjQXetmtx8fpp67Lq5DAABWiiG3Is8kOXvO8bokDxzWZzrJlqr6iyRvSvIrVXXlgDUBS/E970lOXXVo26mrZtsBAGCFGHLFdkeSc6vqnCT3J7kqyQ/M7dDd5xx8XVW/meR3u/vjA9YELMXBB0R94r3JIzPJc9fNhloPjgIAYAUZLNh294GquiazTzueSnJzd99TVVdPzt801PcGjqML3izIAgCwog25Ypvuvj3J7Ye1zRtou/tHhqwFAACAk9OQ99gCAADA4ARbAAAARk2wBQAAYNQEWwAAAEZNsAUAAGDUBFsAAABGTbAFAABg1ARbAAAARk2wBQAAYNQEWwAAAEZNsAUAAGDUBFsAAABGTbAFAABg1ARbAAAARk2wBQAAYNQEWwAAAEZNsAUAAGDUBFsAAABGTbAFAABg1ARbAAAARm3QYFtVl1bVnqraW1XXzXP+iqraXVWfrqqdVfXyIesBAICj2n1L8v4XJtefOfvv7luWuyLgKE4ZauCqmkpyY5KLk8wk2VFVW7v73jndPpFka3d3VV2Q5JYkLxiqJgAAWNDuW5LfeUfy5P7Z40fumz1OkgvevHx1AQsacsX2oiR7u3tfdz+RZEuSK+Z26O5Hu7snh2ck6QAAwHL5xHu/GmoPenL/bDuwYg22YptkbZL75hzPJHnZ4Z2q6g1J/mWSb0ly+XwDVdXGJBuTZPXq1dm+ffvxrhUAAPLKR2ZS87T3IzP5fX+Dwoo1ZLCd9/8Tvqah+2NJPlZVr0jyM0m+d54+m5NsTpLp6enesGHD8a0UAACS5K51s9uPD1PPXRd/g8LKNeRW5JkkZ885XpfkgSN17u4/SPJtVXXWgDUBAMCRfc97klNXHdp26qrZdmDFGjLY7khyblWdU1WnJbkqyda5Harq26uqJq8vTHJaki8OWBMAABzZBW9OXveB5LlnJ6nZf1/3AQ+OghVusK3I3X2gqq5Jsi3JVJKbu/ueqrp6cv6mJG9M8taqejLJ/iTfP+dhUgAAcOJd8GZBFkamxpYjp6ene+fOnctdBgAAAAOoql3dPb2U9wy5FRkAAAAGJ9gCAAAwaoItAAAAoybYAgAAMGqCLQAAAKMm2AIAADBqgi0AAACjJtgCAAAwaoItAAAAoybYAgAAMGqCLQAAAKMm2AIAADBqgi0AAACjJtgCAAAwaoItAAAAo7ZgsK2q11TV26tq/WHtPzpoVQAAALBIRwy2VfUvkvyvSf5ukk9U1Y/POX3N0IUBAADAYiy0Yvu6JK/u7p9I8veSXFZV75+cq6ELAwAAgMVYKNie0t0HkqS7H85s0H1OVf3bJKedgNoAAADgqBYKtn9WVa88eNDdT3X325PsSfJ3Bq8MAAAAFmGhYPsPknzq8Mbu/udJzl7M4FV1aVXtqaq9VXXdPOf/YVXtnnzdUVUvWnTlAHCy231L8v4XJtefOfvv7luWuyIAWJFOWeDc30mSqiPeTnv/QgNX1VSSG5NcnGQmyY6q2trd987p9udJXtnd/7WqLkuyOcnLFlk7AJy8dt+S/M47kif3zx4/ct/scZJc8OblqwsAVqCFgu0vLHCuk7z6KGNflGRvd+9LkqrakuSKJE8H2+6+Y07/TyZZd5QxAeDZ4RPv/WqoPejJ/bPtgi0AHOKIwba7X/UMx16b5L45xzNZeDX27Un+3Xwnqmpjko1Jsnr16mzfvv0ZlgYAK9srH5mZ9yMI+pGZ/L7fgwBwiIVWbJ9WVS9Mcn6S0w+2dfeHjva2edr6COO/KrPB9uXzne/uzZndppzp6enesGHD0YsGgDG7a93s9uPD1HPXxe9BADjUQg+PSpJU1U8l+aXJ16uS/FyS1y9i7Jkc+pCpdUkemGf8C5L8epIruvuLixgXAE5+3/Oe5NRVh7adumq2HQA4xFGDbZI3JfmeJJ/v7rcleVGSr1/E+3YkObeqzqmq05JclWTr3A5V9beSfDTJD3X3Z5dUOQCczC54c/K6DyTPPTtJzf77ug+4vxYA5rGYrciPd/dXqupAVT0nyUNJvvVob+ruA1V1TZJtSaaS3Nzd91TV1ZPzNyV5T5JvSvIrk6cvH+ju6WP8WQDg5HLBmwVZAFiEIwbbqvrlJB9J8qmqOjPJryXZleTRzPP5tvPp7tuT3H5Y201zXv+jJP9oyVUDAADAxEIrtn+a5OeTPD+zYfYjmf1M2ud09+4TUBsAAAAc1RHvse3uG7r7u5K8IsmXkvxGZj+O58qqOvcE1QcAAAALOurDo7r7c939s939kiQ/kOQNST4zeGUAAACwCIv5uJ9Tq+p1VfVbmV2x/WySNw5eGQAAACzCQg+PujjJW5JcntmHRW1JsrG7HztBtQEAAMBRLfTwqJ9M8uEk7+zuL52gegAAAGBJjhhsu/tVJ7IQAAAAOBZHvccWAAAAVjLBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGLVBg21VXVpVe6pqb1VdN8/5F1TVH1bVX1fVO4esBQAAgJPTKUMNXFVTSW5McnGSmSQ7qmprd987p9uXkrwjyZVD1QEAAMDJbcgV24uS7O3ufd39RJItSa6Y26G7H+ruHUmeHLAOAAAATmKDrdgmWZvkvjnHM0lediwDVdXGJBuTZPXq1dm+ffszLg4AAICTw5DBtuZp62MZqLs3J9mcJNPT071hw4ZnUBYAAAAnkyG3Is8kOXvO8bokDwz4/QAAAHgWGjLY7khyblWdU1WnJbkqydYBvx8AAADPQoNtRe7uA1V1TZJtSaaS3Nzd91TV1ZPzN1XV85LsTPKcJF+pqp9Icn53/9VQdQEAAHByGfIe23T37UluP6ztpjmvP5/ZLcoAAABwTIbcigwAAACDE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYtUGDbVVdWlV7qmpvVV03z/mqqg9Mzu+uqguPOugDn07e/8Jk9y1DlAwAAMDIDBZsq2oqyY1JLktyfpK3VNX5h3W7LMm5k6+NSX716CN38sh9ye+8Q7gFAABg0BXbi5Ls7e593f1Eki1JrjiszxVJPtSzPpnkzKpas6jRn9yffOK9x7VgAAAAxueUAcdem+S+OcczSV62iD5rkzw4t1NVbczsim7+3pqvZvF+ZCa/v337cSsYAACA8Rky2NY8bX0MfdLdm5NsTpLp5089fb6euy4bNmx4BiUCAAAwdkNuRZ5Jcvac43VJHjiGPvM7dVXyPe95JvUBAABwEhgy2O5Icm5VnVNVpyW5KsnWw/psTfLWydORvzPJI9394OEDHaqS556dvO4DyQVvHqJuAAAARmSwrcjdfaCqrkmyLclUkpu7+56qunpy/qYktyd5bZK9Sf5bkrcddeDnvzj5n3cOVTYAAAAjM+Q9tunu2zMbXue23TTndSf5sSFrAAAA4ORWs9lyPKrqy0n2LHcdHLOzknxhuYvgmJi7cTN/42b+xsvcjZv5Gy9zN27ndfc3LuUNg67YDmRPd08vdxEcm6raaf7GydyNm/kbN/M3XuZu3MzfeJm7cauqJd97OuTDowAAAGBwgi0AAACjNsZgu3m5C+AZMX/jZe7GzfyNm/kbL3M3buZvvMzduC15/kb38CgAAACYa4wrtgAAAPA0wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRO2W5C1iqs846q9evX7/cZQAAADCAXbt2faG7v3kp7xldsF2/fn127ty53GUAAAAwgKr63FLfM+hW5Kq6tKr2VNXeqrpugX4vraqnqupNQ9YDAADAyWewYFtVU0luTHJZkvOTvKWqzj9Cv59Nsm2oWoBjd9u+23LJrZfkgg9ekEtuvSS37bttuUsCAIBDDLlie1GSvd29r7ufSLIlyRXz9PvxJL+d5KEBawGOwW37bsv1d1yfBx97MJ3Og489mOvvuF64BQBgRRky2K5Nct+c45lJ29Oqam2SNyS5acA6gGN0w5035PGnHj+k7fGnHs8Nd96wTBUBAMDXGvLhUTVPWx92/ItJ3tXdT1XN130yUNXGJBuTZPXq1dm+fftxKhFYyIOPPXjEdtchAAArxZDBdibJ2XOO1yV54LA+00m2TELtWUleW1UHuvvjczt19+Ykm5Nkenq6N2zYMFDJwFxrbl0zb7hdc8aauA4BAFgphtyKvCPJuVV1TlWdluSqJFvndujuc7p7fXevT3Jrkn96eKgFls+mCzfl9KnTD2k7fer0bLpw0zJVBAAAX2uwFdvuPlBV12T2acdTSW7u7nuq6urJeffVwgp3+bdenmT2XtvPP/b5PO+M52XThZuebgcAgJWgug+/7XVlm56e7p07dy53GQAAAAygqnZ19/RS3jPkVmQAAAAYnGALAADAqAm2AAAAjJpgCwAAwKgJtgAAAIyaYAsAAMCoCbYAAACMmmALAADAqAm2AAAAjJpgCwAAwKgJtgAAAIyaYAsAAMCoCbYAAACMmmALAADAqAm2AAAAjJpgCwAAwKgJtgAAAIyaYAsAAMCoCbYAAACMmmALAADAqA0abKvq0qraU1V7q+q6ec5fUVW7q+rTVbWzql4+ZD0AAACcfAYLtlU1leTGJJclOT/JW6rq/MO6fSLJi7r7xUl+NMmvD1UPAAAsxm37bsslt16SCz54QS659ZLctu+25S4JOIpTBhz7oiR7u3tfklTVliRXJLn3YIfufnRO/zOS9ID1AADAgm7bd1uuv+P6PP7U40mSBx97MNffcX2S5PJvvXwZKwMWMuRW5LVJ7ptzPDNpO0RVvaGqPpPktsyu2gIAwLK44c4bng61Bz3+1OO54c4blqkiYDGGXLGtedq+ZkW2uz+W5GNV9YokP5Pke79moKqNSTYmyerVq7N9+/bjWykAAGR2hfZI7f4GhZVryGA7k+TsOcfrkjxwpM7d/QdV9W1VdVZ3f+Gwc5uTbE6S6enp3rBhwwDlAgDwbLfm1jXzhts1Z6yJv0Fh5RpyK/KOJOdW1TlVdVqSq5Jsnduhqr69qmry+sIkpyX54oA1AQDAEW26cFNOnzr9kLbTp07Ppgs3LVNFwGIMtmLb3Qeq6pok25JMJbm5u++pqqsn529K8sYkb62qJ5PsT/L93e0BUgAALIuDD4i64c4b8vnHPp/nnfG8bLpwkwdHwQpXY8uR09PTvXPnzuUuAwAAgAFU1a7unl7Ke4bcigwAAACDE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGDXBFgAAgFETbAEAABg1wRYAAIBRE2wBAAAYNcEWAACAURNsAQAAGLVTjnSiqirJP0jSSW5N8uokVyT5TJKbuvsrJ6RCAAAAWMARg22SG5N8S5LTMhtovz7J7yR5bZLzkmwavDoAAAA4ioWC7d/v7r9bVacm+XySNd39RFV9OMldJ6Y8AAAAWNhC99geSJLufjLJju5+YnJ8IMlTJ6A2AAAAOKqFgu3nq+obkqS7Lz3YWFXPS/LEYgavqkurak9V7a2q6+Y5/w+ravfk646qetFSfwAAAACe3Y4YbLv7su5+dJ5TX07yfUcbuKqmMnuf7mVJzk/ylqo6/7Buf57kld19QZKfSbJ5sYUDwMnutn235ZJbL8kFH7wgl9x6SW7bd9tylwQAK9JCT0W+8Cjvfego5y9Ksre7903G25LZh1Dde7BDd98xp/8nk6w7ypgA8Kxw277bcv0d1+fxpx5Pkjz42IO5/o7rkySXf+vly1gZAKw8Cz086hcWONeZ/fifhaxNct+c45kkL1ug/9uT/LujjAkAzwo33HnD06H2oMefejw33HmDYAsAhzlisO3uVz3DsWu+YeftWPWqzAbblx/h/MYkG5Nk9erV2b59+zMsDQBWtgcfe/CI7X4PAsChFlqxfVpVvTCz98mefrCtuz90lLfNJDl7zvG6JA/MM/YFSX49yWXd/cX5BuruzZncfzs9Pd0bNmxYTNkAMFprbl0zb7hdc8aa+D0IAIda6KnISZKq+qkkvzT5elWSn0vy+kWMvSPJuVV1TlWdluSqJFsPG/tvJflokh/q7s8usXYAOGltunBTTp86/ZC206dOz6YLNy1TRQCwci1mxfZNSV6U5K7ufltVrc7sCuuCuvtAVV2TZFuSqSQ3d/c9VXX15PxNSd6T5JuS/EpVJcmB7p4+th8FAE4eB++jveHOG/L5xz6f553xvGy6cJP7awFgHtU9722vX+1QtaO7X1pVuzK7YvvlJH/c3d9xIgo83PT0dO/cuXM5vjUAAAADq6pdS13wXOjjfn45yUeSfKqqzkzya0l2JXk0yaeeQZ0AAABw3Cy0FflPk/x8kudnNsx+JMnFSZ7T3btPQG0AAABwVEd8eFR339Dd35XkFUm+lOQ3Mvs5s1dW1bknqD4AAABY0FGfitzdn+vun+3ulyT5gSRvSPKZwSsDAACARVjMx/2cWlWvq6rfyuyK7WeTvHHwygAAAGARFnp41MVJ3pLk8sw+LGpLko3d/dgJqg0AAACOaqGHR/1kkg8neWd3f+kE1QMAAABLcsRg292vOpGFAAAAwLE46j22AAAAsJIJtgAAAIyaYAsAAMCoCbYAAACMmmALAADAqAm2AAAAjJpgCwAAwKgJtgAAAIyaYAsAAMCoCbYAAACMmmALAADAqAm2AAAAjNqgwbaqLq2qPVW1t6qum+f8C6rqD6vqr6vqnUPWAgAAwMnplKEGrqqpJDcmuTjJTJIdVbW1u++d0+1LSd6R5Mqh6gAAAODkNuSK7UVJ9nb3vu5+IsmWJFfM7dDdD3X3jiRPDlgHAAAAJ7Ehg+3aJPfNOZ6ZtAEAAMBxM9hW5CQ1T1sf00BVG5NsTJLVq1dn+/btz6AsAAAATiZDBtuZJGfPOV6X5IFjGai7NyfZnCTT09O9YcOGZ1wcAAAAJ4chtyLvSHJuVZ1TVacluSrJ1gG/HwAAAM9Cg63YdveBqromybYkU0lu7u57qurqyfmbqup5SXYmeU6Sr1TVTyQ5v7v/aqi6AAAAOLkMuRU53X17ktsPa7tpzuvPZ3aLMgAAAByTIbciAwAAwOAEWwAAAEZNsAUAAGDUBFsAAABGTbAFAABg1ARbAAAARk2wBQAAYNQEWwAAAEZNsAUAAGDUBFsAAABGTbAFAABg1ARbAAAARk2wBQAAYNQEWwAAAEZNsAUAAGDUBFsAAABGTbAFAABg1ARbAAAARk2wBQAAYNQEWwAAAEZt0GBbVZdW1Z6q2ltV181zvqrqA5Pzu6vqwiHrAQAA4OQzWLCtqqkkNya5LMn5Sd5SVecf1u2yJOdOvjYm+dWjjXvvF+/NJbdektv23XacKwYAAGCMhlyxvSjJ3u7e191PJNmS5IrD+lyR5EM965NJzqyqNQsN2uk8+NiDuf6O64VbAAAABg22a5PcN+d4ZtK21D7zevypx3PDnTc8owIBAAAYv1MGHLvmaetj6JOq2pjZrco5ff3pT7c/+NiD2b59+7FXCAAAwOgNGWxnkpw953hdkgeOoU+6e3OSzUmy6pxVTwffNWesyYYNG45TuQAAAIzRkFuRdyQ5t6rOqarTklyVZOthfbYmeevk6cjfmeSR7n5wMYOfPnV6Nl246fhWDAAAwOgMtmLb3Qeq6pok25JMJbm5u++pqqsn529KcnuS1ybZm+S/JXnb0catVNacsSabLtyUy7/18qHKBwAAYCSq+2tuaV3Rpqene+fOnctdBgAAAAOoql3dPb2U9wy5FRkAAAAGN7oV26r6cpI9y10Hx+ysJF9Y7iI4JuZu3MzfuJm/8TJ342b+xsvcjdt53f2NS3nDkE9FHsqepS5Ls3JU1U7zN07mbtzM37iZv/Eyd+Nm/sbL3I1bVS353lNbkQEAABg1wRYAAIBRG2Ow3bzcBfCMmL/xMnfjZv7GzfyNl7kbN/M3XuZu3JY8f6N7eBQAAADMNcYVWwAAAHjaqIJtVV1aVXuqam9VXbfc9bB4VfUXVXV3VX36WJ5yxolVVTdX1UNV9cdz2v67qvoPVfWnk3//5nLWyJEdYf6ur6r7J9fgp6vqtctZI/OrqrOr6j9W1Z9U1T1VtWnS7vobgQXmz/W3wlXV6VX1qar6z5O5++lJu2tvBBaYP9feSFTVVFXdVVW/Ozle8rU3mq3IVTWV5LNJLk4yk2RHkrd0973LWhiLUlV/kWS6u32e2AhU1SuSPJrkQ939wknbzyX5Unf/q8l/WPqb3f2u5ayT+R1h/q5P8mh3//xy1sbCqmpNkjXdfWdVfWOSXUmuTPIjcf2teAvM35vj+lvRqqqSnNHdj1bVqUn+U5JNSf7HuPZWvAXm79K49kahqv6XJNNJntPd33csf3eOacX2oiR7u3tfdz+RZEuSK5a5JjgpdfcfJPnSYc1XJPng5PUHM/vHGivQEeaPEejuB7v7zsnrLyf5kyRr4/obhQXmjxWuZz06OTx18tVx7Y3CAvPHCFTVuiSXJ/n1Oc1LvvbGFGzXJrlvzvFM/LIYk07y76tqV1VtXO5iOCaru/vBZPaPtyTfssz1sHTXVNXuyVZl2+lWuKpan+QlSf4orr/ROWz+EtffijfZCvnpJA8l+Q/d7dobkSPMX+LaG4NfTPLPknxlTtuSr70xBduap81/iRmP7+7uC5NcluTHJlslgRPnV5N8W5IXJ3kwyS8sazUsqKq+IclvJ/mJ7v6r5a6HpZln/lx/I9DdT3X3i5OsS3JRVb1wmUtiCY4wf669Fa6qvi/JQ92965mONaZgO5Pk7DnH65I8sEy1sETd/cDk34eSfCyzW8sZl7+c3D928D6yh5a5Hpagu/9y8kv/K0l+La7BFWtyf9hvJ/mt7v7opNn1NxLzzZ/rb1y6++Ek2zN7f6Zrb2Tmzp9rbxS+O8nrJ8/j2ZLk1VX1b3IM196Ygu2OJOdW1TlVdVqSq5JsXeaaWISqOmPyEI1U1RlJLknyxwu/ixVoa5Ifnrz+4ST/9zLWwhId/OUw8Ya4BlekyQNQ/o8kf9Ld/3rOKdffCBxp/lx/K19VfXNVnTl5vSrJ9yb5TFx7o3Ck+XPtrXzd/e7uXtfd6zOb736vu38wx3DtnTJYlcdZdx+oqmuSbEsyleTm7r5nmcticVYn+djs7/uckuTD3f3/LG9JLKSqPpJkQ5KzqmomyU8l+VdJbqmqtyf5/5L8g+WrkIUcYf42VNWLM3sLx18k+cfLVR8L+u4kP5Tk7sm9Yknyk3H9jcWR5u8trr8Vb02SD04+hePrktzS3b9bVX8Y194YHGn+/k/X3mgt+ffeaD7uBwAAAOYzpq3IAAAA8DUEWwAAAEZNsAUAAGDUBFsAAABGTbAFAABg1ARbAJ61qmp1VX24qvZV1a6q+sOqesNy1zW0qnp9VV03eX1lVZ0/59x7q+p7l686AFg6H/cDwLNSzX649h1JPtjdN03a/naS13f3Ly1rcSdQVf1mkt/t7luXuxYAOFZWbAF4tnp1kicOhtok6e7PHQy1VTVVVe+rqh1Vtbuq/vGkfUNVba+qW6vqM1X1W5OQnKr6nqq6q6rurqqbq+rrJ+1/UVX/YrIivLOqLqyqbVX1Z1V19cHvX1XXzvl+Pz1f0VX1aFX9QlXdWVWfqKpvnrS/uKo+OXnvx6rqb07a31FV907at0zafqSqfrmq/ockr0/yvqr6dFV9W1X9ZlW9aRE/z09Pari7ql5wnOcGAJZEsAXg2eo7kty5wPm3J3mku1+a5KVJ/qeqOmdy7iVJfiLJ+Um+Ncl3V9XpSX4zyfd3999NckqSfzJnvPu6+7uS/L+Tfm9K8p1J3pskVXVJknOTXJTkxUn+XlW9Yp66zkhyZ3dfmOT3k/zUpP1DSd7V3RckuXtO+3VJXjJpv3ruQN19R5KtSa7t7hd3958dPLeIn+cLkxp+Nck75/1fEABOEMEWAJJU1Y1V9Z+rasek6ZIkb62qTyf5oyTflNngmSSf6u6Z7v5Kkk8nWZ/kvCR/3t2fnfT5YJK5wXTr5N+7k/xRd3+5u/9Lkser6szJ97skyV2ZDdwvmPP95vpKkv9r8vrfJHl5VT03yZnd/fvzfO/dSX6rqn4wyYHF/y9y1J/no5N/d2X25weAZXPKchcAAMvkniRvPHjQ3T9WVWcl2TlpqiQ/3t3b5r6pqjYk+es5TU9l9vdpHeX7HXzPVw57/1fmvP9fdvf/vqSfIjnawzIuz2wgfX2S/62qvmOR4y725zn48wPAsrFiC8Cz1e8lOb2q5m6v/RtzXm9L8k+q6tQkqar/vqrOWGC8zyRZX1XfPjn+ocxuFV6sbUl+tKq+YfL91lbVt8zT7+syu405SX4gyX/q7keS/Neq+vtzv3dVfV2Ss7v7Pyb5Z0nOTPINh4335STfOMDPAwAnjP/CCsCzUnd3VV2Z5P1V9c+S/JckjyV516TLr2d2i+2dk4dD/ZckVy4w3uNV9bYk/7aqTkmyI8lNR+o/z/v/fVX9nSR/OHkW1aNJfjDJQ4d1fSzJd1TVriSPJPn+SfsPJ7mpqv5Gkn1J3pZkKsm/mWxVriTv7+6HJ+MftCXJr1XVO/LVwPyMfx4AOJF83A8AjEhVPdrdh6+6AsCzmq3IAAAAjJoVWwAAAEbNii0AAACjJtgCAAAwaoItAAAAoybYAgAAMGqCLQAAAKMm2AIAADBq/z8wgD6FKlyY5wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
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