examples/chrov_viz_annot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c65b049b-2df9-4f84-abe4-2936eca9ae6d",
   "metadata": {},
   "source": [
    "---\n",
    "# 🏷️Annotations on chromosomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a735619-912a-4c34-9e67-b95a4ff27bbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "## requirements\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc2f5a4e-8b6c-4176-8c33-3e46df9ae937",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "## input parameters\n",
    "chromosome='1'\n",
    "arm='p'\n",
    "test=False\n",
    "# test=True\n",
    "input_dir_path='inputs'\n",
    "output_dir_path='outputs'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9391050-e675-451b-8fbd-bcd392fd0b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "## set the current directory to 'examples'\n",
    "import os\n",
    "if len(set(['chrov','examples','tests']) - set(os.listdir('.')))==0:\n",
    "    ## change the current directory from 'chrov' to 'examples'\n",
    "    os.chdir('./examples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f77f401e-14c9-413e-a462-3022edbb3ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "if test:\n",
    "    import logging\n",
    "    logging.basicConfig(level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112e508e-0f15-4840-be85-dcdb2023154c",
   "metadata": {},
   "source": [
    "**Demo data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3727676f-9b1a-4697-90a5-5e9732c93e06",
   "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>chromosome</th>\n",
       "      <th>p start</th>\n",
       "      <th>q end</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>cytoband</th>\n",
       "      <th>cytoband type</th>\n",
       "      <th>arm</th>\n",
       "      <th>chromosome arm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>121700001</td>\n",
       "      <td>125100000</td>\n",
       "      <td>1</td>\n",
       "      <td>2300000</td>\n",
       "      <td>p36.33</td>\n",
       "      <td>gneg</td>\n",
       "      <td>p</td>\n",
       "      <td>chr1p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  chromosome    p start      q end  start      end cytoband cytoband type arm  \\\n",
       "0          1  121700001  125100000      1  2300000   p36.33          gneg   p   \n",
       "\n",
       "  chromosome arm  \n",
       "0          chr1p  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cytobands to plot the chromosome\n",
    "cytobands=pd.read_table(f'{input_dir_path}/cytobands.tsv',index_col=[0])\n",
    "cytobands.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8353832-4447-4f35-bcec-b52a2a7a6555",
   "metadata": {},
   "source": [
    "## Segments/windows"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72e4fbc7-1a45-4dba-913e-f3ee59a34f44",
   "metadata": {},
   "source": [
    "### `arrows`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c91c82f6-a8f6-4a25-a209-a478be0bc4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.5, 2.0)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from chrov.viz.chrom import plot_arm\n",
    "from chrov.viz.annot import show_segments\n",
    "ax=plot_arm(\n",
    "    data=cytobands.query(expr=f\"`chromosome` == '{chromosome}' & `arm` == '{arm}'\"),\n",
    "    figsize=[8,1],\n",
    ")\n",
    "show_segments(\n",
    "    ax=ax,\n",
    "    y=1.5,\n",
    "    offy=0.5,\n",
    "    kind='arrows',\n",
    "    segments_kws=dict(\n",
    "        a=np.arange(1000000,40000000,1),\n",
    "        size=5000000,\n",
    "        overlap_fraction=0.5,\n",
    "        stretch_last=True,\n",
    "    )\n",
    "    )\n",
    "_=ax.axis('off')\n",
    "ax.set(ylim=[-0.5,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0790e9f7-b3ec-401e-88c3-9fb7f0f5fd4a",
   "metadata": {},
   "source": [
    "### `bands`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "227d1d88-2d5d-4af7-bc4d-0b4d812e09fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.5, 2.0)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=plot_arm(\n",
    "    data=cytobands.query(expr=f\"`chromosome` == '{chromosome}' & `arm` == '{arm}'\"),\n",
    "    figsize=[8,1],\n",
    ")\n",
    "show_segments(\n",
    "    ax=ax,\n",
    "    y=1.5,\n",
    "    offy=0.5,\n",
    "    kind='bands',\n",
    "    segments_kws=dict(\n",
    "        a=np.arange(1000000,40000000,1),\n",
    "        size=5000000,\n",
    "        overlap_fraction=0.5,\n",
    "        stretch_last=True,\n",
    "    )\n",
    "    )\n",
    "_=ax.axis('off')\n",
    "ax.set(ylim=[-0.5,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6dab7e9-cf71-4b2d-8f9f-d29636d9d9ad",
   "metadata": {},
   "source": [
    "## Loci e.g. gene names\n",
    "\n",
    "Interactive ideogram for debugging: https://bioinformatics.mdanderson.org/ideogramviewer/Ideogram.html?genelist1=PPP1CC"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1474f300-b47b-4cf4-a0fa-5e9b6ba2d42c",
   "metadata": {},
   "source": [
    "**Demo data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5cdd0cf-d331-46dd-9b74-e5d627930bbf",
   "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>gene symbol</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>gene start</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ATAD3B</td>\n",
       "      <td>1</td>\n",
       "      <td>1471765</td>\n",
       "      <td>0.005913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gene symbol chromosome  gene start     value\n",
       "0      ATAD3B          1     1471765  0.005913"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genes=pd.read_table(f'{input_dir_path}/genes.tsv',index_col=[0])\n",
    "genes=genes.assign(\n",
    "    value=lambda df: df['gene start']/df.groupby('chromosome')['gene start'].transform(max)\n",
    "    )\n",
    "genes.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9a21522-ff4c-44eb-a0cc-11ced4062e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "## input parameters\n",
    "test=False\n",
    "# test=True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f051d40c-d53d-4673-8d48-0603aa7b7996",
   "metadata": {},
   "source": [
    "## Chromosomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e1d9804d-6486-43e0-8ae8-952c1d04a6a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import roux.lib.df as rd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2be95d7f-ce68-4b90-a504-411e36ce79e0",
   "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>gene symbol</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>gene start</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19816</th>\n",
       "      <td>P2RY4</td>\n",
       "      <td>X</td>\n",
       "      <td>70258166</td>\n",
       "      <td>0.450313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8086</th>\n",
       "      <td>ACTG1</td>\n",
       "      <td>17</td>\n",
       "      <td>81509413</td>\n",
       "      <td>0.981100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10523</th>\n",
       "      <td>ZNF638</td>\n",
       "      <td>2</td>\n",
       "      <td>71276561</td>\n",
       "      <td>0.294660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10550</th>\n",
       "      <td>BCL11A</td>\n",
       "      <td>2</td>\n",
       "      <td>60450520</td>\n",
       "      <td>0.249905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9957</th>\n",
       "      <td>NRTN</td>\n",
       "      <td>19</td>\n",
       "      <td>5805067</td>\n",
       "      <td>0.099127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>CCDC24</td>\n",
       "      <td>1</td>\n",
       "      <td>43991359</td>\n",
       "      <td>0.176739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1850</th>\n",
       "      <td>CD160</td>\n",
       "      <td>1</td>\n",
       "      <td>145719471</td>\n",
       "      <td>0.585439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16736</th>\n",
       "      <td>AHR</td>\n",
       "      <td>7</td>\n",
       "      <td>16916359</td>\n",
       "      <td>0.106373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15270</th>\n",
       "      <td>ISOC1</td>\n",
       "      <td>5</td>\n",
       "      <td>129094749</td>\n",
       "      <td>0.711786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17913</th>\n",
       "      <td>PPDPFL</td>\n",
       "      <td>8</td>\n",
       "      <td>49054311</td>\n",
       "      <td>0.338183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      gene symbol chromosome  gene start     value\n",
       "19816       P2RY4          X    70258166  0.450313\n",
       "8086        ACTG1         17    81509413  0.981100\n",
       "10523      ZNF638          2    71276561  0.294660\n",
       "10550      BCL11A          2    60450520  0.249905\n",
       "9957         NRTN         19     5805067  0.099127\n",
       "757        CCDC24          1    43991359  0.176739\n",
       "1850        CD160          1   145719471  0.585439\n",
       "16736         AHR          7    16916359  0.106373\n",
       "15270       ISOC1          5   129094749  0.711786\n",
       "17913      PPDPFL          8    49054311  0.338183"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=(genes\n",
    "     ).sample(10,random_state=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7972a840-1ee1-4105-bf3b-5b3e90d92858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fhsFgQJ06deQO7b5Za72I37CnpKQgIiJCjpBIwGAw4PXXX8ft27dttUrKA51Oh9jYWLRq1QpLly6Fj48Pli5dKndYNkeOHEFqaioee+wx2z3KrVu38Omnn5Z50+X7UadOHVSqVEmWe4Xw8HD4+PgUumbHxsbKMmrlH3/8gQsXLrjt+pmXl4cvvvgCEydORMeOHVGvXj0MGjQI3bt3xzfffFOqy2rWrBlOnDiB9PR0JCQk4LfffkNqaqpL1xhHz3kREREwGAzIzMy0+/uUlBSntR/HjBmDLVu2YPfu3SXu0LuoZ9CsrCwMGzYMOp0OixYtkq3P4gMHDmDOnDnYsmULWrRogeHDhyumqxUhjUZjazLpDqxhRWXO+mZux44d+O6771C9enW5Q/IoZrPZ1v6cgNatW+PXX3+1m/bOO++gTp06eOWVV9xepV2psrKycPnyZQwcOFDuUNyqWbNmtv6NrK5eveqRtY2kLF++HJGRkXadd3uSvLw8qNX27+LUarVH9GEE3HsY1+l0SEtLw7Zt2zBz5ky5Q7pv1atXR0REBA4cOGCrIazX63Hy5Em8+OKLMkfn3azJqmvXrmH16tXlehRVpd0j9e7du1DNmKFDh6JXr1549tlnZYrKsZs3byI1NVWWF81+fn548MEHceXKFbvpV69eRdWqVd0ez9KlS9GsWTO39SlkMBhgMBgKXdM0Gk2ZXdNCQ0MB3Ovz9O+//y5W/0RFPec1atQIvr6+OHDgALp27QoAuHLlCm7fvl2ow3Xr/MaPH49NmzbZ+jC7X8V5BtXr9Rg6dCj8/PywZMkS2Vpn5OTkYMiQIXj11VfRoUMH1K5dGw8++CAWL16MV199VZaYlMIrE1ZZWVl2bwquXr2KEydOoEKFCqhRo4aMkXmmKVOmYPPmzVi8eDF0Op2tb4rg4GC3Van1FO+99x66deuGGjVqQK/XY82aNdizZw+2bdsmd2iKERQUVKhtekBAAMLDw72637QJEyagZ8+eqFmzJm7fvo0pU6ZAo9Ggf//+cofmVkOHDkW/fv3w1VdfoXv37jh16hR++OGHUh9pTYnMZjOWL1+OwYMHu/XNmDs98cQT+OqrrxAdHW1rErhs2TI899xzcodWItu2bYPFYkFcXBwuXbqE//znP6hXr959jSrmTtnZ2fj3339tv9+4cQPnzp1DWFgYoqOjMWTIEHz11VeoVasWqlevjjlz5iAqKsptzQxcUdS94927d3H9+nVbzY8LFy4AACpXrozKlSvLErMjzvZLREQExowZg7Nnz+Lrr7+G2Wy23beFhobCz89PrrCd7oOKFSti+vTpePrpp1GlShWkpKRg4cKFuHXrltvP/6KOe3EC0MfHBxEREW6pMelsG1aoUAHTpk1D3759UblyZVy+fBlvv/02YmNj0aVLlzKJp6htNXLkSIwbNw7NmzdHq1atsG/fPuzatQvfffddqcVQnOfCzMxMrFu3rkSjFUopav1btGiBzz77DFqt1tYkcMOGDZg4caJLyylqHdetW4eIiAjUqFEDp0+fxrhx49C7d2907ty5yHkX9ZwXHByM5557Dp988glCQ0MRHByMadOmoWnTppIJqylTpuCXX37Bxo0bERwcjMTERAD3yp+AgAAA90bZTExMtK3T6dOnERwcjBo1akCn0xU7Nr1ejyFDhthqs2VlZSErKwsAbM0j3eW9996DxWKxjXJcq1YtfP7555gwYQK6deumuKaB7qSyKLGeWRmzDoEpNnjwYKxYscL9Ad2H/Px83Lx5U+4wisVRp7AzZsxA37593RzN/atWrZrsfSINHz4cv//+OxISEhAaGorGjRvjnXfeUczNvVKPywEDBqBBgwb44IMP5A5FtuPohRdewL59+5CamoqIiAi0bdsW06dPL/FQs/dLzmNl165d+Pzzz3Ht2jVUr14dQ4cOla2DTcB9x8T27dvRpUsXXLhwQZbkrTv2eVZWFr788kts374dqampiIyMRM+ePTFmzJgyf9Auy/34448/4r333sPNmzdRoUIF9O3bF9OnT7e9DS9NpbmfDh48iJdeeqnQ9D59+mDmzJmwWCyYO3cuvv/+e2RmZuKRRx7BtGnTSvRGXUpp7Jui7h1XrFghmUCcMmUKpk6dWqJlA+7bL6+//joef/xxyf9bvXo1WrVqVSox3M8+cbYPFi9ejAEDBuDQoUNISUlBxYoV0bx5c3zwwQdo3rz5fcV4v9u8qONerH379hgyZMh9J6Bd2ZbOtuGiRYvQu3dvHD9+HOnp6YiOjkbnzp3x0UcfISoqyqWYirvtirOt1q1bh8WLFyMxMRF16tTB66+/7vJ9r7NtVJznwv/+978YP3687f67OIqzDYpa/+TkZHz++efYv38/0tPTUbVqVTz//PPFGuVXuM5FreO8efMwa9YsJCUloUqVKhg0aBAmTZpkd910tD7Fec7Lz8/HJ598gi1btqCgoADt2rXDtGnTJJt/O5rf8uXLbYPFTJ06VbJfveXLl6N///62OIuKzdH2B+5tM2tTxLK+T9u7dy86duyIPXv2FBoBskuXLjAajdi5c2eZ9nvnSnk3d+5c7Ny5E5s3by7W35f0WcMrE1aeQKmJAU+mhISV0vG4LBqPo3t4rPyPtxwTnr7PPWU/euJ+8oR942n7pTzsk/KyzZW4LZW27eTYRnJvg9JeZ3euT0liL4s4lXiOlbay3L8lTVix03UiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUpVTGtV64cCG++uqr0piVW7322msAUC5jt1gsqFWrFuLj4+UOBS+++CIGDhyIVatWleows0pSt25dXLt2rUxHZyjKa6+9htGjRyv6fCvN4/LFF18EAI86pkrjOCoPx0FxlGUZVp7KpLIqW5R4fSuNfa7UfVvS/aik/VXS/aS0srs0zzE5y9/72S/l7XxR0nkAON/mSjnOnR3fcm7P+y1HymK7FlUGlNV5XdTxU5bnZnHKPVfX25V9WpL1E8fesGFDnD17ttj/7yjO+42pLO/TlHQ/L7Xd6tatWyr36ePHj8fo0aNLFFyJ5eXlWbZu3WrJyMiwfU2ePNn2s/Uz8Xfhz507d7Z07ty50P9PnjzZ9hUcHCz5Zf1/8XyEn4vnmZGRYZkyZYolLy/PLm6pL/H/iadbl2eN0/q5cPniWKzrbl1/4foIt49wGeJtlJuba7c84efCn4XbWGo9HG1TZ38nnD527FjLpUuXLLm5uXb/Z12ucLsI96fwb8TbSjh/R+tV1DZydExJrUPnzp0dHi8ZGRmWqVOnOo1bfBwK/7Zz586FzgfxsSL+XWo9pkyZYjvfxLFLHY/i7SR1/kntb/G55ige8XElPC6dHcPOjifhNhg7dqztmJLab8L5CfeLOFap407qeHFUzgjPU0fbTmpbiePdunWr3fqI97vUOSguJyZPnuz0OJA6/hwtw9l6ODpXpGJztj+klilcH+GxIrUPHc1TOA+pbWctkxwdY1LHg6NyULx+ws+lYpM6/8Tnuvh8kdpOUuemo/JNapnC65uzdRX/v/h8lipXpf7WWRkm3Fe5ubmFluGoHBAu2zoPqeuNeH8Kj19x/I6uxeKYxeeCo3V0VO45Ku/EP1v/pzj3I46uqeLzR/jd0bknjE3qnkxYVonXxVHZa/1cWHY7ulY6K/uF55fw+uno/srZfYF1XRwdI1LLc3RcCq/DXbt2LbS9nZW1xfnZ0bEmXhfxPnRWhjsqC4VxS+3Poo5fR8eCo/tk8c9ZWVmSx7j1PJC65jorc6TKM/G2lSrfxWWB1HRHsQqP8+JeW4XrJN7mUvfcwv+VOv+dxSfcnuL/c3ZOC8tc4X4Q7xOpewyp41XqeHB0fIu3q/h/He3HyZMnOy2XnD0vWbeT8L5KWF6I19vZ/Ymja554XsJz09kxVJyyV+rYEm976/4TP6uL19vR87rwu3W+RT1zCtdPfMyLz+uiyofc3FzJ+xZn55DUeSEVk6NjUviz+PnW2X2keJ9IbR/hdd+6/aXOf0dliLMy29lzlnj/S90Pic894bkjVcYX98u6rverVGpYabVa+Pv7IyQkxDZNpVLZfrd+Jv4u/KygoAAAbNOt/y/MZur1esnlW/9fPB/r30vNMyQkBFH/P/aihqkU/594unXZ1litfyNcvjgW67qLPxduE/EyhOsrtZ2F6y78WbiNpdZDarsW9XfC6VU1GvikpMA/Jgb+/v62/7MuV7hdhPtT+DfibSWcv6P1Em8D8TZydExJ7YuCggKHxwtwL+ssXA9x3MJlite5oKCg0PkgPlbEv0utcxQA461b0FatanfMiver8FwQH2/i80+4zsLjXGq9xD+LjyshZ8eweNmOtnlVjQb+/v6280S8f4TzEx5z4liljjvh/wvnIY7Lui7Wv3G07YQ/W+crjte6Ltb5WddXPA9xnMLlq1Qqp8eBeF3EP0ttc6n1cHSuSK2Ts/0h3p7WbSg1D6l9KPw/8TkkFZeVtUxydIxJbWdH5aCVeL9ap4ljkzr/hJ+Ly1Xr+ohjlTo3HZWFUssUXt8cXZuk/t/Rehf1t87KMPH9gbjMcFT2CpdtnYfU9UZIuL30en2h+B1di8XrJD4XHK2j1HzE6y9eR+HP1v8pzv2Io2uq+PxxdG0VnjfC2KXuyaw/S62Lo7JXqux2dK10dr21fmZdjtQ9mFSZK9wXUvdrwuU7W56j8q6goMBW/ubl5RXa3s7KWkf7X+o8F8Yrni7cR9Z96KwMd1QWCuOW2p9FHb+OjgVn+0Gq7BOzngdS9yvOyhzx8SC1baWuseL7Tevv4ulShMd5ca+twnUSXz+k7rmF/yt1/hfF0XXA2TktvMcTxiyOX/yc5mg/i+95HJVhUttVyNE9mnUeRZVLjp6XrNtJeF8lLC/Ex6Kz+xPrPpV6JhbOy7qewmuZ1DFUnLJX6tgSb3vhthR+Ll5vR8/rwu/Wr6KeOcXr52j7OHo2FXP2XOLoHBKTisnRMSn82brOUrkJ4TYVn7NS+014Lgm3f1HP3I6uec7yBI7KffEyxNtM6txztewpbV7dh9UAGZt3eZJRYWEI++03ucPweANUKmR/843cYbjNqLAwuUNQJG87Du4HyyTPvb556r71pP3lyWV3eSt/y9v5Up7Og/JwnJen7Wklx3aV47xWwrlZluuthPUTU1pM5e16IievTlgREREREREREZHyMGFFRERERERERESKwoQVEREREREREREpiuwJK4vFgoSEBGg0GrlDKeTu3bvIycmROwy30mg0aNKkCcxmMwDAbDYjISEBJpNJ5siISG7169dHUFCQ3GF4vOzsbNSuXVvuMBQr38cHqampcodBREQkKT8/H8nJybLGYDAYkJiYCIvFImscVDxJSUmFOvWnexSRsEpNTUVgYKDcoRSSmpqKzMxMucNwq7CwMNStW9d2whgMBqSmpiIvL0/myIhIbvXr10dUVJTcYXi8zMxMxMbGyh2GYmX7+SElJUXuMIiIiCRlZWXJnrDKycnhtbIcSU5O9rqKMsUle8KKiIiIiIiIiIhIiAkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRfGROwA56UaMkDsEj8Dt6B7etp29bX2Li9ulaLZtNHWqrHHIyVOPE92IEcjJyICnjaPjSfvLk9ZFzLZue/bIGkdxlbeysDwdO+Uh1vIQo5gcMXvLMt0ZgxLWT0xpMSktHiUrtYSVRqOx+12r1Rb6TPxd+HNAQABMJlOh/xfOJzg42OHyraMMCkcbFP+9eJ4+Vas6XSdnsQh/ty5T/Llw+VKxC7eD8HPhdPEyHMUm/hvhz9b5FSdOnU7ncH0dbX/xdrT+n3W5wvmI52n9G2fbytF6iecp/kz8u/XvpNYjMDDQ4fFS3LiFyxTvF/H54OhYkord+rOj41W8TKlzQRizo+NOuN+Lsw/Ex5XUsqTiklq2+G+BwuvrLCbhMScVq3j7SsXj6PiQKq/Efyf82TpfZ/MRL1Pq76X+T6vVFnkciOdb1DKk1sPRttBqtZJli7PlF7UdxOWEeB9KzVM8D/EyrNuoqGPM2TzEpParo/Nd+F38eXGX7+/vX2h/OCoLpZYpPE6KKssdlRlS0xz9rbMyTLy/iioLpNbXOg+fqlWh8fUF0tIklyucFhwcXKxyR6osE297qWU5KmOlPnNUXgjXqyjOrqni667UNcpRfI7KOEfr4qjstRKui6NrpbPrrfUz8fnlqFwpqrwo6nfx8hzFGBgYaFs38XVQfF13tr6OfnZ0rDm6/5Aqk8THraOyUBi31P4s7vHrqJwrznpIscYrdb/irMwp6hosdV8j/hvh70VdD4SxOvr7os55Z8e3VCxS578rMTp6XhD/Lj62Hf1c1LORFKn/Ef+9o7LQ2T28+N5Eqlxytv6OnmOE83B2zys+bxw9EzsqJx2tV3HKXkdlvJBGo4FKpSo0XRyDo+f1os5HR9dq8TSp7ePsPl2oOM8ARc1DKqaijkln11tHMTqL1dExUNR9WlHlrtRxWdT9d3HKOeEyXCl7SpvKYrFYZFs6gPz8fMTHx6NWrVoICgqSM5RC4uPjERwcjMqVK8sditvk5OTgypUriI2Nhb+/v23/1K5dGzqdTu7wiEhGZ8+eRZUqVVChQgW5Q/FoCQkJyM7ORmxsrNyhKNKdO3eQlpaGuLg4uUMhIiIqJDU1FUlJSWjQoIFsMWRkZODGjRuoV68efHy8ulGV4pnNZpw7dw7VqlVDWFiY3OGUuqlTp2JqCWr6sg8rIiIiIiIiIiIqVRERESX6f0UkrMxms9whSDKbzYqNrSyZTCa75pkmkwkGgwEmkwkyV8gjIhmZzWaWA25gsVhgNBrtymH6H+v2MRqNPBaJiEhxlPAMabFYZI+Bis9sNnvsPc3o0aNL9P+y1w/Mzs5GamoqatasKXcohWRmZiIjIwP5+fkICgpCUFAQ/P39JdsBewqz2Yy7d+/i8uXL8PPzg1qtRkJCAnJycuDn5wdfX1/4+fnBx8cHPj4+0Gg0hb6Lf1arFZEXJaISysjIQEFBAVJSUqDVauHn5wetVmv72VpmUMlkZ2cjJSUFRqMRPj4+8PX1LfRlLY8d9U3hyXJzc5Gamop//vkHKpXKds1x9CX83Nu2FRERuYfJZEJWVhYyMzORnJyM3Nxc2eNJTU1FYmIiqlSp4rCPK1KG9PR0WCwWFBQUICwsTNY+o5RG1j6s8vLycOXKFeh0OtSoUUNxN5L5+fnQ6/XIyclBVlYWzGYzfHx8EBQUhODgYOh0Oo9sE5yVlQWj0YiCggIUFBRAr9cjPz8fRqPRlqm3PiSo1WqoVCrbvpN6WLUmrqSSW46SXEo7FojoXqLAWi7k5+fbvltrAqlUKvj6+tolsazfmSwoPpPJhOzsbJjNZhgMBhQUFMBgMNi+hG9M1Wq1ZCLL+uXj4+NxSUSTyYScnBy7mlbiL3FNYQBOk1tS03m8EhGRM8IklV6vh9lshr+/P0JCQhAUFORw0Cx3MJvNSElJQUpKCjQaDaKjo4vd0Ta5X3Z2NjIyMpCRkQGTyQSdTofw8HCEhIR43H2cq2RLWJlMJly5cgUqlQq1a9dWfNbXbDYjNzcXer0eWVlZyMvLg0qlQkBAgK32VUBAgMfe4FofDKQeVgsKCmCxWGzVGMUPACqVynaimUwmu4cJqeZF1ocKR0kuqWnWxBkRuZewbBCWC/n5+TAYDLbzW6PRFKqRZf3u7RdiV1gsFlszbalklsFggNFotP29SqWy1dISJ7OsX0q//t4vs9nsMJklNU1IKrnlLNnFY5iIyDuYTCbo9XpkZmbaKjQEBAQgJCQEISEhiqsZU1BQgNu3byMrKwvh4eGIioryyAoXnsJsNiMzMxPp6enIysqCRqNBaGgowsPDERAQIHd4spAlYWWxWHDjxg1kZ2ejTp06ijuxi8NgMCArK8v2ZTKZbLWvgoKCoNPp4OvrK3eYbmGxWGAwGGwPq8IHV0cPrNbmQ35+fraHJeGDhDWZJTVNql8X60OZs1pbUkkuIio7ZrNZMsntqFaWsHzQarWs5XKfrGWmVDLL+iW89Gs0GqcJLW/YD8LkllRCy1lyC0CRzRKF1x9ee4iIyhej0WiXpLJYLAgMDLQlqfz8/OQO0SmLxYL09HQkJiZCrVajSpUqCAkJkTssKkJ+fj7S09ORnp4Og8GAgIAAhIeHIzQ01GNfNkqRJWGVnJyMpKQk1KxZ0yOqJlosFluzwaysLFubZWHtq8DAQI+/4ZcifGAVP7QKawH4+PgUemB11CeOtePnopJbws+kOh1Uq9XFTnIJa4sRUclYawmJa2oKa2wChZPcwtpZfOi/f9aXDFJf1iSXsMy0JhUdJbR8fX29an9Yr0HOElrWgUqE1zkrV5oletN2JSJSEqPRiMzMTGRmZiI7OxsA7JJU5bFigsFgwO3bt6HX6xEWFobKlSuztlU5YLFYoNfrkZ6eDr1eD5VKhZCQEISHh3tFjsHtCSu9Xo/r168jIiICkZGR7ly02xiNRrvaV0ajERqNxpa8CgoKKpeFXGkzmUx2D6nCn8W1L8SJLK1WC19f32KdoNZRMlxNckmdGsXpf0v4M/vjInKNtd8ma00s1spyP3GzQ6mEljgR4+Pj4zChZU0weuN+cZTccjRNfN0RXlOszTcd1eBicouIqGQMBoOtJpU1SaXT6RASEoLg4GCPeH6zWCzIyMhAQkICVCqVrbaVN16jyyODwYD09HSkpaWhoKAAWq3WVuvKE45PKW5NWBUUFODy5csIDAxUZCfrZcFisSA3N9eu9pXFYoG/v79d7SveaP5PcfrLAu49tIofVq0JrZI+tFof2JwluYQPHM6aKrqa5PLWBzuiohiNxiJrZanVasl+srRaLcvZUmRNLDpLakk1O3Q02iETjbDrRL44zRLFt2/WWsPFaZboTU0JiIicMRgMtppUOTk5AP6XpAoJCfHYGkgGgwEJCQnIzMxESEgIqlSp4rEJD09kbeGVlpaGzMxMWCwWBAcHIzw8HEFBQR51T+W2hJXZbMaVK1dgNptRp04djz35i2I0GpGdnY2srCzo9XoYjUao1Wq72ldKbwctJ2tTFqmaWcXpL0ur1ZbZjbqwFldxk1yOmioKE1nFTXIReSthrSxhjayCggK7mkCORjAsbm1NKj5r8sVRMsvaZM7KWmvO2YiHLOf+x/pSpbjNEl1JbolrcfElChF5moKCArsklUqlQlBQkK0mlTc9p1prW1ksFlSpUgWhoaEs88sZo9GIjIwMpKWlIS8vD76+vggLC0N4eLhH5BXckrCyWCy4desWMjMzUadOHfj7+5f1IssFi8WCvLw8W+0r6zDdWq3WrvN23qQXjyv9ZTmqmeXubW3t6Lc4SS5HDx4ACo2qWJwkFy9G5A0cjWAoVStLqlxgTZSyU5zRDoXlXXFGO2S5Vpij5JajhJej5FZxmyVyHxCREuXn59uSVLm5ubYKA9YklTdf741GIxITE5Geno7g4GBER0eztlU5ZM0tpKWlISMjAyaTCUFBQQgPD0dwcHC5zSm4JWGVmpqKhIQEVKtWDWFhYWW9uHLLZDLZ1b4yGAxQq9XQ6XR2ta94M+g6d/WXVdas/XEVJ7ElrO0lPs2tTRUdjaAoleTigwh5EovF4nAEQ9bKUgZrWedstENhLVW1Wl1k5/DcZ86Jk1tFNU2USm4VNWKi8LrC/UFEZcmapMrIyEBeXh7UajWCg4MREhKCoKAgr05SScnMzERCQgLMZjOioqIQHh7OcrqcMplMyMzMRFpaGnJycqDRaGy1rspb5aEyT1hlZ2fj2rVrqFChAqpUqVKWi/IoFosF+fn5ttpX2dnZsFgs8PPzs6t9xYK2ZMSjlYmTWtaHIXF/WcKfld73ivABxJUkl5hKpXKa0HKU5CIqb6RqZVm/C2tliUcuZK0s93LU7FBYY0vc7LA4tbSoeIQvUIrTLFHcBN66P4rTLJHJLSIqDuvzk7UmFZNUrjOZTEhMTERaWhqCgoIQHR3tEc3KvFl+fj7S0tKQnp4Oo9GIwMBAhIeHIyQkpFycD2WasDIYDLh8+TK0Wi1q1qzJh9cSMJlMyMnJsdW+KigogEqlsqt9pdVqeUNXiorbX5ZUc6Ky7i+rrEmNqljU6IpSRYmwKUlRzRT5UEJK52qtLHETQ9bwcb/ijHYo7hzeWQ0tpb+gUCpHyS1HNbgcJbeK0yyR1xEi72JtBmVNUuXn50Oj0dglqfgM6rqsrCzcunULJpMJUVFRqFChAsvWcs5sNkOv1yM9PR1ZWVlQqVQIDQ1FeHg4AgICFLt/yyxhZTabce3aNRgMBtSpU4ftYEuZuPaV2WyGr6+vXeft5TVZUh446i/L+hBkpaT+ssqS9WHElSSXVFNFAEXW2hJ/zqaKpATCmpri79aHb2GtLPF3ltfysCZRnCW1pJodOhvt0JPKdrm40ixRKrlV3GaJTEASlU/CJFVGRgYKCgqg0WhsI/uxD+DSYTKZkJSUhLt37yIwMBBVq1aFVquVOywqBQUFBUhPT0daWhoMBgO0Wi3Cw8MRFhamuEEHyixhlZCQgLt376J27doIDAwsi0XQ/2c2m+1qX+Xn50OlUiEwMNCWvPL39+dNmZtY+8uSenAV9pfl4+Mjmcjyln7KrE0Vi9NMUfiZmPDhpDhJLusXkTtYa2pKJbOEyW1h/3mslaUc1nLK2WiHwtp1wmaHjkY7ZPlTusQj8Dprlii+hjhLbkk1S+QDMJF8LBYLcnNzbTWpCgoK4OPjY0tSBQYG8hwtI9nZ2bh16xaMRiMiIyNRsWJF3pt4CIvFguzsbKSlpSEzMxMAEBISgvDwcOh0OkXs5zJJWKWlpeHWrVuIjo5GhQoVSnv2VISCggJb7ausrCyYzWb4+PjY1b5SWubUG3hDf1llTViLq7hJLvHbd8C+Y+DiJrl4E0SlTTgYBGtllV9ms9lhMsv6JW526CiZxWaHZcuVZolSL0iEzRKlklpMbhGVLovFgpycHFuSymAw2CWplPJA7Q1MJhPu3LmDu3fvwt/fH1WrVi13nXeTc0aj0VbrKj8/H35+fggLC0NYWJis/ZiVesIqNzcXV69eRWhoKKKjo1mIyMxsNiM3N9dW+yovLw8qlQoBAQG25JWS26x6i5L2l2VNZlFhxRlVUdz8xFFTRVeTXDyv6H4Ut1aWtZamVJnAY085rJ3DOxrtUJigBOxHrHXUlxaTIWVPmNwqqlmiVHLLlWaJ3J9E/2Ot8WFNUhmNRvj6+trVpOI1Tj45OTm4desWCgoKbLWtWIZ5FmttxrS0NGRkZMBisSAoKAjh4eGy9AlXqgkro9GIK1euQKPRoHbt2jx4FchgMNjVvjKZTNBoNAgKCkJwcDB0Oh37G1MY69t7qZpZzvrLEv7Mc7H4hJ0DuzKqorgotTY1ESayikpysT8uKoqwVpa4dpZULU1xMouJbeVx1OxQmNASNjsEUKzRDlmWuI+19q+jZJZ4mlhxmyUyuUWeytq9iThJFRoaipCQEL5cVxiz2Yzk5GSkpKRAq9WiatWqCAgIkDssKgMmkwkZGRlIS0tDbm4ufHx8EBYWhvDwcLf1Z1YqCasnnngCy5YtAwDk5eWhTp06HP6yHLBmT/V6PbKyspCbmwsAhWpf8eZIuYrTXxYgPWoZa2KUHusDp6tJLjHhSFjORlIUJ7mIpGppWpNZUrWypJoZsixQLnGzQ6kmiMLbOWtTUkc1tNg3mnykklvOkl3i23ThNcDZiInsr5GUzmw229WkMplM8PPzs9WkYpJK+XJzc3Hr1i3k5+cjIiIClSpV4n2pB8vLy0NaWhrS09NhMpmg0+kQFhaG0NDQMt3vLiWsNm3aJDm9T58++Oqrr2wHae/evUsrPnIjo9FoV/vKaDTaal9Zv1j7qnyQ6i9Lqn8cqf6yrL+zH5WyJXxoKW6fXFLFtVqtdjnJVRr71WQy4d9//0WtWrWgVquRn5+PjRs3wmw2o0OHDoiKiirxMqh0iBPbwoQWa2V5BmuzQ2cJLWGS3Nrs0NmIh3zokJ91vxa3WaL4GmG9PhS3WSKv+VTWzGYzsrKykJmZCb1eD5PJBK1Wa0tScZCo8sdsNiMlJQXJycnQarWIjo7mgGsezmw2Q6/XIy0tDVlZWdBoNAgNDUV4eHiZnMMuJaysFzPxv0RHR6NNmzY4deoU4uPjJWsOUPlirX1lTV7l5ubCYrHA39/flrziaBzlk7AmhlT/OML+sqRqYRT1AJuZmYnvvvsOS5cuxd9//+2u1fJ41qaKzmptSU1z1B9XcfviknqQOXXqFLp27YqkpCQ0aNAAv/76K7p3746rV6/aHoS3bduG5s2bu3MTkYusD8PiJJZUrSxHIxjyGlB+FGe0Q2F5UZzRDvlgqRzCmr6OmiUKR0wsbnLLUbNE7nsqLpPJZJekMpvN8Pf3tyWptFotjycPkJeXh1u3biEvLw8VK1ZEZGQk7xG8QEFBga3WlcFggL+/P8LDwxEaGlpqLzxdSlh169YNGo0Gy5YtQ2RkJIB7VQEHDhyIt956C61atWKB46GMRiOys7NtnbcbjUao1WoEBQVBp9NBp9NxpAgPcL/9Zfn6+uLvv//GypUrsX79eoSGhuKZZ57BwoULZVwbsj7AuJrkEhMO/a7RaDBz5kz4+vrixRdfxObNm/Hnn3+iVq1a+PLLL6HT6TB06FAkJiZix44dMqw1lQaz2SzZ6bujWlnCmpk6nY43qeWQte8+Z6MdCjuHV6vVksks670BjwHlkkpuOavBJZXcctYsUa1WIyAgAD4+Prhz5w6++eYbTJw4Uaa1JTlY+73Jzs62S1JZ+6RyV9835F4WiwUpKSm4c+cOfH19UblyZYSEhMgdFrmBxWJBVlYW0tLSoNfroVKpbEnpkh4DLvdhNWfOHMyZMwdfffUVevTogdOnT2PhwoUYM2YMGjVqVKJgqHywWCzIy8uz1b5KSUmBxWJBixYt5A6NypCwWZH14TUpKQmHDh3CxYsXERISgk8++QTfffcd+vXrx+R1OSWsxeUouTVy5Ei8//77iIiIQF5eHj755BMMHToUWq0WLVu2xNWrV9G+fXukpKTIvTpUyoS1sqT6zktJSUGDBg1sL7XIcwg7h3c04qHRaERaWhqqVq2KOnXqyB0ylQJxH43OmiVak1uZmZkIDg7Ggw8+iJMnT+Lhhx9m6wsvc/XqVdy8eRPR0dF2NanIO+Tl5eHKlStISUlB27Zt+QLDyxgMBqSnpyM5ORlJSUlo2bJliZqJulxP64033kCHDh1sb9bfeustnD9/ngeiF1GpVAgICEBAQABCQkKgVquRl5cnd1hUxjQajW2//9///R+WLl2Kffv2oVu3bujfvz9q166NGTNm4MEHH2SyqhxTq9W2mhOOHDx4EFFRUahbty4MBgN+/vlnjB8/3jbUtEajsauJQZ5D2PeRmNFoxNmzZ9nXoYeyDgrh4+PjcDQok8mECxcusP8SDyLc70WxNl2/du0aO3z3cgEBAYiIiEBMTIzcoZAM/P39UaVKFclRUcnz+fr6IiIiAjqdDkajscTPhfeVZXrooYfw999/Q6VSYeLEiXww8TI7duxA9+7dER4ejsDAQDz99NN477330L17d+zcuVPu8MgNnn/+eTRt2hQJCQlYt24dnnjiCT6kepFmzZphxowZuHXrFj799FPUqFED3377re0BZf78+axx64WszYSYsPZe1qZhPAa8k/WFBfu3I5VKBbVajfPnz7O2pZeyHgPkvUrrGLjvnrACAgKwePFibNiwAWfPnkWlSpVKHAwp38qVKzFixAg8++yzmDNnDqKiomA0GpGRkWHreHnp0qUYOHCg3KFSGRo+fDgWLlyIPXv2YODAgejevbvcIZEbffrpp+jWrRuWL1+OihUrYvfu3Xj99dfxxRdf4ODBg/j333+xefNmucMkIiIiGRUUFODff/+VOwwiKsdc7sNK7Pr16wCAGjVqlEpApGwPPPAAxo0bh9GjR9umJSUlISMjAw888AC++uorzJkzB/Hx8TJGSe6Qm5uLH3/8EcuWLcPt27fx/PPPY+bMmTh27Bhr13iB7Oxs/PPPP4iLi0NQUBDS09OxefNm6PV6dOzYEXFxcXKHSG5mNptx7tw5VKtWDWFhYXKHQzK5ePEiQkNDERUVJXco5AZvvvlmoWkVK1YEAKSmpiI5ORlr1qxhH1YeSmr/A0BISAgCAwNx+vRp7n8vlZ6ejps3b6JBgwasaeWlcnNzcfnyZSQlJaFNmzYOuxMoiss1rHJzc3H06FFUqFABDRo0sPssLy8PP/74IwYNGnRfwZDyXb9+HZ06dXL4eceOHfHWW2+5MSKSS0BAAAYPHozBgwfj1KlT2LNnD6KiovDoo4/iqaeewrPPPos+ffrIHSaVEZ1Oh2bNmtl+12q1aNq0KWJiYu77gkSehZ0tE3m+48ePF5oWGxsLALh06RIA4LHHHnNrTOQ+c+fOxUMPPVRoFLCqVauiQoUK+Oeff2SKjIiUon///ti7dy/q169/X//vUsLq4sWL6Ny5M65fvw6VSoW2bdti3rx5CA8PBwBkZGRg6NChTFh5sIYNG2Lp0qWYOXOm5OfLli0rlMgkz1ejRg088cQTeO2117B161YsXboU/fv3R35+vtyhURmxWCy4du0aqlevDh8fHxgMBpw9exbHjh1D9+7d2UycANw7TojIc+3evbvQNLa+8B6xsbF444038NJLL9lNt7a+GDZsmN3LLSLyXA8//LDd7zqdDk2bNoXRaETfvn3h7+8PADh27JhL83UpYfXOO++gUaNG+Pvvv5Geno7x48dj6tSpmDRpEi9KXuKLL75Ajx498Ntvv6FTp06IioqCv78/8vPz8d133+HKlSv45Zdf5A6TZKJWq9GzZ0/07NkTd+7ckTscKiMXLlxAly5dcOPGDdSpUwfbt2/HkCFDUK1aNezevRtvvfUWDhw4gLp168odKpUhcQ1KlUqFhg0b4saNG8jIyEBGRgY73ybycGazmc19vNgjjzyCo0ePFkpYWalUKr648BIffvih3e9arRbBwcFYv3693fTJkye7Myxyo9OnT6NTp05o1aoVAMDHxwdhYWFQqVTo0KEDIiMj72u+LvVhFRUVhZ07d+LBBx8EcO/N6dSpU3HixAnMmTMHOp0O0dHRrP7v4a5du4ZFixbh4MGDSExMRJ06dRAbGwudTodRo0ahVq1acodIbiZup56WlobNmzeztqWH6t27NywWCz7++GMsW7YM27ZtQ+PGjTFx4kTUrFkTAwcORGhoKFatWiV3qFSGfH198eSTT9r6KlKpVIiJiUFSUhL0ej3u3r2LLVu28J7Ay7APK++i0WiQkJBgexD5z3/+g6FDhyIoKIgvs71AYmIi8vPzUbNmTbvpwv5tyTs0bdrU7veIiAjExcXhzz//BHDvZWdeXh7vCTzYn3/+icGDB+PFF1/ElClTkJ+fj8uXL6NDhw7Yu3fvfbfCcilhFRISgkOHDtm1P7x+/TqWL1+Ob775BmvWrMHjjz/OA9HL8KJE4oQV+67xbJGRkdi+fTseeughZGdnIzg4GLt370bFihURExOD48ePo3///hwZyMM1btwY48aNw/DhwwEU7nT9xIkTaNasGcsBL8OElXdRq9VITEy0JaxCQkLw+++/IyoqigkrL8ZnA7I+GxgMBrz33nvYtWsXhg0bhsWLF8sdGpWhjIwMjBo1ClevXsWKFStgNBpLnLByqQ5vvXr18PfffxeaPnToUPTq1QtPP/30fQVB5dvNmzdx8eJFW+ea5PkyMzPtvrKzs5Gfn2/7Xa/Xyx0ilaGsrCxUqFABwL326Tqdzu7htHr16khKSpIrPHKTZs2aOe2HQKvV8oHVi61fvx6NGzeWOwxyMzb/8m56vd52X5iXl4esrCy5QyKZ3Lp1Cxs2bECrVq0QGhqKs2fPMlnlBUJDQ7F27Vq88sor6NWrF44dO1bi7iFcSlg988wzWLt2reRnCxYsQP/+/Xmh8nCffvopfv/9dwBAWloaOnbsiKFDh+Krr75CXFwcunXrhvT0dHmDpDIXFhaG8PBw21fbtm0xY8YMREREIDw8nCMCebjo6Ghbp7oAMHPmTNsw5gCQnJxsG4yDPNfixYsxa9Ysh5/Xr18fV69edWNE5G5LlizBs88+iwEDBuDQoUMAgPj4eIwaNQoDBw7Eo48+KnOERFSWTpw4ge7du9t+j46ORnh4OJ555hm8++67CAsLw5EjR2SMkNwtJSUFY8eORa9evaDX6/HHH3/ghx9+YL+mXmbo0KHYsGEDjh07BqPRWKJ5udQkUApHAvEu1atXx6ZNm9C0aVOMHDkSR48exfz58xEeHg6DwYBRo0ahYcOG+Oabb+QOlcpQaGgo3n//fbRs2RIAYDAYkJ+fD51OB5VKhfj4eLzyyitsCuShRo0ahUceeQQjRoywTcvNzcXly5cRExODuXPn4o8//uAADF5G3CSQPNtnn32GyZMno3Hjxvjnn39gsVjw/vvv4+TJk2jTpg0GDRrExLUXUKvVePnllxEYGAgAWLhwISZMmIDAwECkpqba/m727NlyhUhlaPjw4YiJicHEiRMBAMHBwViyZAkqVaoEo9GI77//HhaLhX1aeoHs7Gx8/vnnmD17NmJjY/Hxxx+jevXqtu5CyPNlZ2dDp9PZfs/NzcWlS5cQFRWFiIiI+65p5dIogSaTCWfPnkXdunUREBBg91lOTg4uXbqERo0a8aD0YMnJybamQDt37sTKlSsRGxuLjIwMNGjQAAsWLEDPnj1ljpLKmnXY0vbt2wMo3IdVWFgYa1t6sKKqdD///PMYPHiwm6Ihudy+fRuzZ8/G5MmTERISYvdZRkYGPv74Y0yYMIF9GXmo5cuX4+uvv8bgwYPxxx9/oH379jhw4AA+++wzW21b8nyPPfYYLly4YPu9TZs2tlGCrV1FcLRQz3XgwAGMGTPGblqrVq2g0+mQkZGB0aNHo1+/fjJFR+4UExMDvV6PsWPHon///sjPz0dSUhJMJpNdGcCm4p6rcePGWLlyJdq2bWubplKpEBwcXKLrgEsJq1WrVmHBggW2at9Cfn5+GDZsGMaPH+9waFMq/2rWrIkzZ86gZs2aUKlU8PGxP4Q0Gg2ys7Nlio7cZcCAAcjNzXX4eeXKlTFlyhQ3RkRKUrt2bblDIDeYPXs2MjMzCyWrgHu1MPV6PWbPno0ZM2bIEB2VtevXr+OJJ54AALRr1w6+vr6YNm0a/Pz8ZI6M3GnPnj2FprH1hff4999/ERERYfv9ww8/RKVKlWz3iFWqVGGfll7CmqieOXMmZs2aherVq6NFixZYv349LBYLLBYLVCoVW194sL59++KJJ57AuHHjMH369FKbr0tNAtu1a4fRo0fjhRdesE0TXpR+/PFHLFiwAPv27Su1AElZPv/8cyxbtgybNm3Cpk2b8NNPP2HhwoXQ6XTw9fXFsGHDUKlSJaxbt07uUMmNxDWsyLPdvHkT/v7+qFSpEgDgjz/+wPLlyxEeHo709HSMGDECrVu3ljlKKmuNGjXC4sWLbW/SxE0CDxw4gJEjR+Ls2bMyR0plQa1WIykpyfawGhwcjFOnTsFgMHCUQC+TmZmJQ4cOoaCgAC1atLAlK5iw8nwVKlTA5s2bC/VXZx0lMDk5GT179sTdu3dlipDcRTwydE5ODu7evYuqVava1a6pWbOmu0MjNzp48CCGDRsGtVqN5cuXIyAgADExMYVa57nCpRpWFy5cQKtWrRx+3rx5c5w/f/6+gyHlmzBhAq5fv44GDRogJiYG165dw5AhQ1CjRg38+uuvePjhhx12zE9EnqFv376YNGkSevTogY0bN6JPnz549tln0aRJEyQkJKB9+/ZYv349evToIXeoVIauXr3q9IG0WrVquHbtmvsCIrebNGmSre+igoICfPzxx4iLi0NOTg4yMzMBsO8iT2ftdDsxMRHAvcTl6tWr0aRJE5kjI3do2rQpfv75Z4cDLKxfvx5NmzZ1c1Qkh5UrV9r6rwPuvcw2mUyoUaMGX2Z7kVatWuH48eP44IMP0LNnT4wYMQJXrlxBXl6e7W/Wr1/v0jxdOnqys7NtNyBS9Ho9cnJyXAqAyp958+bh5MmTGDZsGIYMGYJu3brhySefxG+//YbDhw+jcuXKcodIMktKSsKHH34odxhURs6ePYuGDRsCuDdy6CeffIIVK1agY8eOWLp0qa1fI/JsAQEBThNS165dK9EbNVI2a99Fx48fx/Hjx9GmTRtcuXIFN2/exKVLl3D8+HGcOHFC7jCpjL3zzjuoXbs2/vzzTxw9ehQdO3bEihUr5A6L3OS1117Dl19+iYULF8JsNtumm0wm7Nu3D/Pnz8err74qY4TkLtOmTUNWVpbcYZAC5Ofn486dO1CpVNBqtQgJCUFoaKjty1Uu1bCqW7cuDhw44LCztP3793PISi9Rv3591K9fH8D/qv0+8MADMkdFSpGYmIhp06YxaeGhfHx8oNfrAdyrZdOtWze7z7t164Z33nlHjtDIjVq2bIlVq1bhsccek/z822+/RYsWLdwcFbmLVN9FAHDx4kU2CfQiR48exfbt222DsSxbtgw9evTgC2wv0bdvX7z55psYO3YsJk6ciDp16gAAtFotKlSogHHjxuHZZ5+VOUpyBw62RACwY8cODBs2DFWqVMG2bdugVqvd2yRwwIAB+OCDD9CmTZtCSauTJ09i8uTJePvtt+87GCr/DAYDEhIS2G+Bhzt16pTd73l5edDr9baRQIQjBpHnad++PdauXYvGjRujadOm2LNnj93Lit27d6Nq1aoyRkjuMGHCBDz55JMIDQ3Ff/7zH1tfRqmpqfjoo4+wYsUKbN++XeYoiags3b17F9WqVbP9HhYWBq1Wy5oWXmTGjBl45plnsHbtWsTHxwMAHnnkEbRr1w5PPvmkzNGRO3FEUO/2yiuvYOXKlZg4cSLef/99FBQU4PLlyyWer0sJqzfeeANbt25Fs2bN0KlTJ9SrVw8VK1bE7du38fXXX+PRRx/FG2+8UeKgqPw6d+4cHn74YY4A4eEeeughqFQq29uUGjVqSI4EQp7ps88+Q7t27XD79m20bdsW77//Pk6dOoVHHnkE8+bNw7fffovFixfLHSaVsQ4dOmDhwoUYN24c5syZg7CwMDz11FP4448/kJCQgPnz59tGkSPP8+abb0pOr1y5Mvuw8jLnzp2z9WFldevWLbsRIzmUvWf68MMPMWHCBLRq1cqun2Nr6wvyLg888IDt/r9q1apo2rQpfv31V7vmouyA33P9+eefOHDggK3GrZRbt265/FLbpVECgXs1aObMmYM1a9YgPj4eLVq0QJUqVfDQQw9h/PjxHM7YCwmbBJ48eZIJKy9QqVIlzJw5Ex07dgRQeCSQs2fPomfPnjwOPNjly5fxwQcf4JdffkFWVhbCwsLQuXNnpKamYvTo0ejdu7fcIZKb3Lp1Cz/++CMuXbqEmjVrIjAwEL1797ardUGep0OHDna/79+/H82aNUOLFi1w9+5d3Lp1CyqVCrt27ZIpQnIHtVpt9wILANq0aQMA+OuvvziUvYfTaDRISEhAZGSk3XR2F+J91Go1vvzyS1sfRSqVChqNBkaj0e7vBg8eLEd45AYFBQV2uaDc3FxcvnwZMTExyMjIwPTp07F06VKXm4y7nLASu379OgAOXestpDKmNWvWRKVKlXD06FHk5ubi4sWLvDHxcF26dEG7du3wwQcfALg3EsjNmzfRoEEDqNVqnDx5Ek2bNrV7o0KeyWKx4M6dO8jLy0NGRgbq1q3Ljra9lNlsxrlz51CtWjWEhYXJHQ65WXBwME6ePAmj0cg+rLyIeCh7AEhJSQFw7+WWFYey90xqtRqJiYlMWFGhY0H8bECeLz09Ha+++ip27NgBPz8/TJw4Ee3bt8eGDRvw2WefoXHjxnjjjTfw/PPPuzRfl5oEWlksFhw9ehTXrl2DxWJBVFQUqlevziZAXuDcuXN44YUXULt2bdu0wMBAaLVaVKtWDQkJCbh48aKMEZI7jBo1CtnZ2Q4/r1GjBpYvX+7GiMidzGaz7eZDpVIhKioKubm5to7YybusW7fO1nfJY489hpycHDz11FPsaJfIC0gloqzPA3yZ7R34/EcAjwO6N2rsgQMHMGTIEGzbtg1TpkzBK6+8gvj4eOzatcuu2bArXE5Y7d69G8OHD8e///4Li8WCNm3aQKVSISEhAcuWLXM4WhB5hkaNGqFly5Z2Q9QK36KcOHECX3/9tYwRkjs888wzTj8PDw9nlV8P5uvra9cE4D//+Y/D/mzIc5nNZvTv3x/r1q3DAw88gPr166NChQo4ffo0nn/+eTz33HNYu3Ytb2KJvEB8fDw2btyIa9euITIyEhEREejSpYtt1DjyXMJ+i6zq1auHKlWqYPfu3QDYb5E3KE6jrZ9++okvszzY1q1bsWLFCjzxxBMYM2YMHn74YVSuXBmTJk1y3yiBly5dQo8ePdCyZUvMmTMH9erVs/VTsGDBAnTv3h2nTp3ixcmDPfroo05HgAsODmbSknDlyhWMGjWKI4R5KPFNyZIlSzB8+HCZoiG5zJ07Fzt37sSmTZvQo0cPW5PAt956C/v27cPQoUMxd+5cjB8/Xu5QiagMffrpp5g8eTLMZjMiIyPRtGlT6PV6jBs3Dp988gkmTJggd4hUhqZNm2brt8jK2rfZ008/LVNU5G5msxlGoxFnzpyBn5+fXTPRjRs3YvLkyfjnn3+YsPJgt2/fRv369QEAtWrVglarxYMPPlji+bqUsPryyy/RqlUr/P7777Zp169fR9WqVbF792506tQJc+bMwfz580scGCnT3LlznX4eExNje5tC3kuv19uVE+TZStgVIpVTy5cvx6xZs9CjR49Cnz399NOYOXMmE1Ye7NSpU3a/WywW/PPPPwgMDERqaiqSkpIAcHQ4T7d792588MEHmDRpEsaNG4fw8HBcv34dWVlZ+P777/Huu++iRYsWfJnpwV544QX2YUU4e/YsnnrqKdy4cQMAMGjQIIwaNQpPPPEETp8+jZEjR+KXX36ROUoqSxaLBT4+/0svaTQa+Pr6lni+LiWs9uzZg08//VTyM5VKhfHjx+O9994rcVBERESkbPHx8ejUqZPDzzt16oQxY8a4MSJyp4ceeqjQ6HA9evRA165dcfPmTZw5c4ajw3mBxYsXY8SIEZg6dard9KCgIHz44YdITEzEokWLmLDyUGzyTVZvv/02YmNjsWDBAqxduxa7d++Gv78/evToga1bt3JAHi9gsVjQsWNHW9IqLy8Pa9euxXvvvWfX9/GxY8dcmq9LCavr1687rdbVqFEjydFCyPMI+yoIDw9H9erV4ePjw+agRF5i8uTJCAwMBHBvGNu5c+eibt26SEpKgsFgAADMnj1bzhCpjAUEBCA9Pd1hx8qZmZnw9/d3c1TkLlevXpWcnpiYiICAAISGhnIgBi9w+PBhrFq1yuHnAwcOxKBBg9wYEbkTa1iT1ZEjR7B9+3Y89NBDaNeuHZo0aYJ27drhhRde4CiBXmLKlCl2v/v4+CAsLAzR0dEwGo33PV+XElZZWVm2BxQpgYGByMnJue9gqHwQ91VQt25dBAUFYcyYMeyrgMgLPPbYY3Z92bVp0wbXr19HUFAQzp07h5ycHL519QKtW7fGokWLsGjRIsnPFy5ciNatW7s5KnIXqdHhgHuJyiNHjmDJkiX4+++/WcPKwyUlJaFWrVoOP69duzYSExPdFxC5VUFBAc6dO4fg4OBCNWgMBgNOnTqFRo0aMWHhBVJSUhAdHQ0ACA0NRUBAAKpVqyZzVORO4oRVbm4uLl++jJiYGPd1ug4A586ds7vwZGZmAgDS09ORkpJy34FQ+SDVV0FSUhISEhLwyCOPsK8CL9G0aVO7hERERATi4uLw559/wmKxMHHt4fbs2VNoWmldlKj8eP/99/H4448jNTUVEyZMwAMPPID8/HycOnUK8+fPx8aNG9mnoRfZt28fli5dioyMDOTn56NDhw5YsGCB3GFRGcvLy4Ofn5/Dz319fVFQUODGiMidVq9ejQULFuDQoUOFPtNoNBg8eDDGjx+Pl156SYboyJ1UKhX0ej38/f1hsVigUqlgMBiQmZlpl7AMCQmRMUoqj1xOWHXs2NGu+mebNm0AAH/99Zft4CTP5aivgsDAQPZV4EV69+5t97tWq0VwcDAqVqwoT0BE5HZt2rTBDz/8gJdffhn/93//B7VajWeeeQZHjhyBXq/H2rVr8eijj8odJpWhxMRErFixAkuXLkVmZib69euH1NRUTJs2DS1atJA7PHKTb775BkFBQbbfrU2B8/Ly2CzUw33zzTeYMGECNBpNoc/UajXefvttLFiwgAkrL2CxWOw62a9evTqWLFmCDRs2wGw22/IErHXr2X799VesX78eFSpUwEsvvWSXrExLS0Pfvn2xa9cul+apsrjQ+FiqfyprrapKlSrZpjmqJk7lX+3atbFq1Sq0bdvWNk04Esgff/yBQYMGOezbgjxTeno6bt68iQYNGrDatxf48MMPC03TaDQIDw9HWlqa7WZk8uTJ7g6NZJCTk4Nt27YhPj4eYWFhqFSpErp27eq0CwEq/3r27Il9+/bhqaeewosvvoiuXbtCo9GgR48emDx5MhNWXqJWrVqFXlY3adIEAHDy5EnbNN4XeqbIyEgcPny4ULNQ67OBr68vWrRogeTkZHkCJLfZu3ev3e8GgwH5+fnQ6XR2ZUT79u3dHRq5yZo1azBo0CB07doVGRkZiI+Px5w5c9C7d28EBAQgKSkJ0dHRLictXaphJZWIsh6AjjpdJc/CvgoIuPfWdPv27ejQoQOCg4PtPsvMzMSePXvQpUsXaLVamSKksrRhw4ZC03Q6HZo2bYoTJ07g2LFjyMvLY8LKSwQGBuKZZ56B2WzGuXPnUK1aNSarvMDWrVvx+uuv49VXX0XdunXlDodkcu3atULTrl+/DoDPBt4gOzvb1j2MFL1ez24ivIQ4EcWX2d5n1qxZmD17Nl5//XUAwI8//oiNGzciNzcXw4YNu+/5unT0xMfHo3///pIFU0ZGBgYMGIArV67cdzCkfOyrgABgyZIlmDt3bqFkFXCvbfq8efPw9ddfyxAZucPx48cLfe3YsQO9evVCpUqVYDAYMHLkSLnDpDL2119/YcuWLXbTTp06hW7duiEyMhIvv/wy8vPzZYqOytr+/fuh1+vRrFkztGzZEgsWLGBfpl7KYrEgPj4eZ8+eLdFIUFT+1K1bFwcOHHD4+f79+5nQ9hJqtRoajcb21bRpU3z44YfQarW2aT4+LvdGROVIfHw8evbsafu9Z8+e6N+/PyZPnozFixff93xdOmpmzZqF6tWrS3aWFhoaiurVq2PWrFkORwwizyDuq8DPzw8+Pj747bff2FeBl/juu+8wadIkh5+PHz8eH374IcaMGePGqEguV69exccff4ysrCyEhITg7NmzvEH1Ah9++CEef/xx9OjRAwBw5swZbNy4Ea1atcJzzz2HWbNmITo6ulCfh+QZWrVqhVatWuHLL7/EDz/8gGXLluHNN99Ep06dcPToUdSvX1/ypQZ5lqtXr+Lpp5/GuXPnAABVq1bFypUrERMTI3Nk5A4DBgzABx98gDZt2qBx48Z2n92+fRuTJ0/G22+/LVN05E7i2vdmsxlmsxkDBgzAwYMHMW/ePJjNZpmiI3cICQlBUlISateubZtWq1YtfPvtt+jVqxdu3rx5X/N1qQ+ruLg4rF69Gs2bN7dNE1b7PXr0KAYMGGA33Dl5Fqm+CmJjYxEZGWn3hoV9FXi28PBwnDx50lbdX1zt9/r162jSpAnS0tJkjpTKUkpKCqZNm4b//ve/6Ny5M8aOHYt27dpxlEAvUaVKFWzevBmPPPIIgHujBubl5WHSpEkICwvDunXrMGXKFNuDLHm+CxcuYPPmzdi8eTMOHz6MJ598Eps2bZI7LCpDzz77LM6ePYvJkyfD398fn3/+OWrXro1PPvmETQK9gMFgQOfOnbF//3506tQJ9erVA3Cv5c2dO3eg1+uxc+dO+Pr6yhwpuVt6ejpOnjyJuXPnYtOmTXjxxRfx4Ycfsq9rD9a7d280adIE06ZNA2A/gvihQ4fQo0cP5Obmlm0fVtevX0dkZKTDzytVqoQbN264FACVL1J9FQg7XSfvYDQakZyc7PBmNDk5mc0CPFh2djY+//xzzJ49G7Gxsdi8eTPatWuHy5cvyx0auVFaWhqioqJsv+/btw8DBgyw/d68eXPeE3iZuLg4qFQqDBgwAIcPH8ayZcvkDonK2P79+/HTTz/ZBuNp1aoV+vXrh7y8PJkjI3fw9fXF9u3bMWfOHKxZswb79u2DxWJB586d8dRTT2HEiBG4cOECGjVqJHeo5Ea3b9/GrFmzkJCQAKPRiBMnTvAY8AJvvPEG/vrrL8nPHn/8cWzevBnffvuty/N1qQ+r0NBQpw8kly5dkmwuSJ5j165daNCggcN+zBo2bIg//vhDhsjInRo2bIidO3c6/Hz79u1o2LChGyMid4qJicHMmTPx6quvYtmyZahcuTLOnz+PpKQknD9/HqdOncKpU6fkDpPKWFRUlK02bUFBAY4fP45q1arZPtfr9Xyr7qU0Gg169+7N2lVe4M6dO3ZNwKtUqQI/Pz+nHXGTZ/H19cXbb7+NEydOIDs7Gzk5OZg9ezYCAgLQtm1b26iR5PkyMjLwzjvvIDY2FpcvX8agQYPw888/M1nlJawvLh599FE0b94c06dPt6vA0KFDByxfvtzl+bpUw+qxxx7D/Pnz8cQTT0h+Pm/ePLRr187lIKj8+PLLLzFy5EiH/Zi98sormD17No8DDzds2DC8+eabaNiwoa3/GqvNmzdj+vTpmD17tkzRUVm7c+cOAGDmzJmYNWsWLBYLwsLC0KlTJ/z+++9IS0uDSqVyucovlS/du3fHu+++ixkzZuDnn39GYGCgXa3LU6dOsR8bIg+nUqmQlZVl1xRcpVIhLy/PLmnFF9reYd++fVi6dCnOnz+PevXqoUOHDliwYIHcYZEbzJw5EzNmzEDlypWxdu1atG/f/r77LKLy6ZNPPsHUqVPRqVMnBAQE4JtvvkFgYCDi4uJKNF+X+rA6fvw4WrdujR49euDtt99GXFwcEhIScPv2bSxcuBC//PILDhw4gIcffrhEQZFy1axZE7/99hvq169vmyZsEvjPP/+gc+fOtr7NyHO99NJLWLNmDerVq4eWLVuiWrVqWL9+Pc6fP4/nn38ea9eulTtEKiP//vtvoWkFBQW4c+cOIiMjbSOJsp8Cz5aSkoI+ffpg//79CAoKwvLlyxEXF4dq1aohLCwMHTt2RKtWrTB9+nS5QyU3unjxIkJDQ+2ai5LnUqvVhfo2bd26NVQqFQ4cOACLxcIXGB4uMTERK1aswNKlS5GZmYl+/frhjz/+wPz58/kC24uo1WoEBASgU6dO0Gg0tgHZzp49C2G6Yf369TJGSWWpbt26mDBhAl555RUA91rcLFu2DEuXLoVOp7vv+bpUw6pp06b46aefMGzYMNtIAG3atAFwr6PNH3/8kckqD5eUlOS0iYePjw+Sk5PdGBHJZfXq1Xj66afx3Xff4dq1awgICMADDzyAKVOmoF+/fnKHR2Vo2LBhGD16NPr06WOblpubi/z8fFSrVg3Z2dlo0aIFrly5ImOUVNYqVaqEffv2ISMjA0FBQVCpVHYdrK9bt46jxBF5uN27dxealpubCwAcgMML9OzZE/v27cNTTz2FL7/8El27doVGo8FDDz0kd2jkZoMGDbJLXgcHB0Or1SI0NBQu1I+hcuz69evo3r277fd27dph+fLlSEpKQp06de57vi4lrACgR48e+Pfff/Hbb7/h0qVLCAgIQHR0NLp27YrAwMD7DoTKh6pVq+LMmTOIjY21TdNoNNBoNADuNQGpUqWKXOGRm3Xs2BH9+vWDXq/HmTNnsHXrVhw5cgRVqlThWzUPtnv3buzduxfvv/++bSQQjUYDlUoFjUYDk8kkWQuLPIswYWlVv3593LhxA1lZWbZpfJvqXYT3BOT52rZti88//xybNm1CQUEBOnbsiJdffhlarRZVq1aVOzwqY1u3bsXrr7+OV1991a4vM4PBUKjmHXm2FStW2P2u1+tx8+ZN1KtXj8eClzAajfD397f9rtFooFarS1zD1qWE1a5duzBmzBgcPHgQzzzzDADAbDYDuHdQNmzYEIsXL+aDqgfr3r07Jk2ahK5du9oOyAoVKiA8PBy5ubmYMmVKoT6NyPOcPn0aPXv2xI0bN1C3bl2sXbsW/fr1Q3p6OtRqNebMmYOffvoJvXv3ljtUKiOLFi3ChAkTcOrUKaxevRo6nQ5xcXHw8XH5PQiVU6GhoYWmJSYm2poCkHeqVasWH068iLjPkrlz5yI5ORlff/213KGRG+zfvx9Lly5Fs2bNUL9+fQwcOBAvvPAC4uPjERYWJnd4JKOgoCDUrVuX1wMvYrFYMGTIEGi1Wtu0bdu2ISUlxa5JoKsvMl3qw+rpp59Ghw4d8MYbb0h+Pm/ePOzevdvWXJA8T1JSEh5++GFoNBqMGTPG1onaP//8g4ULF8JkMuHYsWPsu8LDdevWDT4+Pnj33XexatUqbNmyBZ07d8Y333wDABg7diyOHj2KgwcPyhwplQW1Wo3ExESkpqaiV69e0Gq12Lhxo626b1JSEqKjo9lnCRGRhxP3WbJz50489dRTyM3NhVrt0mDkVI5lZ2fjhx9+wLJly3D48GGYTCbMnj0bw4YNY9NwIi8xdOjQYv2dqyMFupSwkupwW4gdbnuHf//9F6+++iq2bdtma5OsUqnQpUsXLFy4ELVr15Y5QiprlSpVwq5du9C4cWNkZWUhJCQER44cQbNmzQDcKwtatWqF9PR0eQOlMmFNWEVGRiIjIwP9+/fHoUOH8MMPP6BTp05MWBEReQmtVotLly6hevXqtmn+/v64dOkSqlWrJmNkJJcLFy5g6dKlWLVqFdLT0/Hkk09i06ZNcodFROWUS68+2OE2AfcSl7/++itSUlJw6NAhHDx4ECkpKfj111+ZrPISd+/eReXKlQHcq/Kr0+kQHh5u+zw8PBx6vV6u8MiNQkND8csvv2DkyJHo3r075syZI3dIRETkJuI+SwDA19cXBoNBpohIbnFxcZg5cyZu3rzJEaOJqMRc6mxEqsNtIXa47V3Cw8PRvHlzucMgmYjbpLONuveQ2vefffYZHnroIYwYMQK7du2SKTIiInInqT5L8vLyMGrUqBL1WULln0ajQe/evdmfKRGViEsJK6kOt63Y4TaRdxHeoIpvTvPz8+UMjcqYo5bkL7zwAurVq8ebUyIiLzF48OBC01566SUZIiEiIk/kUh9W7HCbiICy61SPyoe9e/fi0UcfdTgiYGpqKn755RcMGjTIzZEREREREZGncClhBbDDbSIiIiIiIiIiKlsuJ6ys0tLScOnSJVgsFtStW9euw2UiIiIiIiIiIqL7dd8JKyIiIiIiIiIiorKgljsAIiIiIiIiIiIiISasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFIUJKyIiIiIiIiIiUhQmrIiIiIiIiIiISFGYsCIiIiIiIiIiIkVhwoqIiIiIiIiIiBSFCSsiIiIiIiIiIlIUJqyIiIiIiIiIiEhRmLAiIiIiIiIiIiJFYcKKiIiIiIiIiIgUhQkrIiIiIiIiIiJSFCasiIiIiIiIiIhIUZiwIiIiIiIiIiIiRWHCioiIiIiIiIiIFOX/Af6bAuB9U9+MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from chrov.viz.chrom import annot_chroms,_concat_chroms\n",
    "from chrov.viz.annot import annot_labels\n",
    "for loc in ['out','in']:\n",
    "    fig,ax=plt.subplots(figsize=[15,3])\n",
    "    ax_chrom,df1=annot_chroms(\n",
    "        # ax=ax,\n",
    "        data=cytobands,\n",
    "        chromosomes=data['chromosome'].unique(),\n",
    "        ax_chrom=ax,\n",
    "        chrom_y=0,\n",
    "        out_data=True,\n",
    "        )\n",
    "    # ax_chrom.set(ylim=[-0.5,0.1])\n",
    "    ## rescale the genome coordinates by the joined chromosomes\n",
    "    colx='gene start'\n",
    "    df2=_concat_chroms(\n",
    "        data,\n",
    "        col_start=colx,\n",
    "        col_end=colx,\n",
    "        col_chroms_start=colx+' chroms',\n",
    "        col_chroms_end=colx+' chroms',\n",
    "        genome_ends=df1.set_index('chromosome')['genome end'],\n",
    "    )\n",
    "    _=annot_labels(\n",
    "        ax_chrom=ax_chrom, # A\n",
    "        loc=loc,\n",
    "        colx=colx+' chroms',\n",
    "        data=df2,\n",
    "        chrom_y=0,\n",
    "        col_label='gene symbol',\n",
    "        # yoff_scales=[0.7,0.6,0.01],\n",
    "        fig=fig,\n",
    "        test=test,\n",
    "        )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "chrov",
   "language": "python",
   "name": "chrov"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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