Data_visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "76df4bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "import pandas as pd\n",
    "from numpy import *\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot \n",
    "import plotly\n",
    "import chart_studio.plotly as py\n",
    "import plotly.offline as pyo\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "16b00c41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting wordcloud\n",
      "  Downloading wordcloud-1.9.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (460 kB)\n",
      "\u001b[K     |████████████████████████████████| 460 kB 30 kB/s  eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pillow in /home/ctg/miniconda3/lib/python3.9/site-packages (from wordcloud) (9.3.0)\n",
      "Requirement already satisfied: matplotlib in /home/ctg/miniconda3/lib/python3.9/site-packages (from wordcloud) (3.6.2)\n",
      "Requirement already satisfied: numpy>=1.6.1 in /home/ctg/miniconda3/lib/python3.9/site-packages (from wordcloud) (1.23.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/ctg/miniconda3/lib/python3.9/site-packages (from matplotlib->wordcloud) (21.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/ctg/miniconda3/lib/python3.9/site-packages (from matplotlib->wordcloud) (1.4.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/ctg/miniconda3/lib/python3.9/site-packages (from matplotlib->wordcloud) (1.0.6)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/ctg/miniconda3/lib/python3.9/site-packages (from matplotlib->wordcloud) (0.11.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/ctg/miniconda3/lib/python3.9/site-packages (from matplotlib->wordcloud) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/ctg/miniconda3/lib/python3.9/site-packages (from matplotlib->wordcloud) (4.38.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/ctg/miniconda3/lib/python3.9/site-packages (from matplotlib->wordcloud) (3.0.9)\n",
      "Requirement already satisfied: six>=1.5 in /home/ctg/miniconda3/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->wordcloud) (1.16.0)\n",
      "Installing collected packages: wordcloud\n",
      "Successfully installed wordcloud-1.9.2\n"
     ]
    }
   ],
   "source": [
    "!pip install wordcloud"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93102662",
   "metadata": {},
   "source": [
    "## import the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "393924a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ctg/Share/L_posts\r\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "219a8a48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     RP11-34P13.14\n",
       "3      LINC34P13.14\n",
       "12           SNHG17\n",
       "21         LINC0014\n",
       "Name: gene1, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutation_data=pd.read_csv(\"/home/ctg/Share/L_posts/Data/lnc_mutation.csv\")\n",
    "mutation_data.gene1.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b2e6dfb9",
   "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>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>TumorRef</th>\n",
       "      <th>TumorAlt</th>\n",
       "      <th>NormalRef</th>\n",
       "      <th>NormalAlt</th>\n",
       "      <th>Read_depth_tumor</th>\n",
       "      <th>...</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Pat</th>\n",
       "      <th>gene1</th>\n",
       "      <th>Sample1</th>\n",
       "      <th>Recurrence</th>\n",
       "      <th>VAF_tumor</th>\n",
       "      <th>recurrence</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>pathway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>137334</td>\n",
       "      <td>137334</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>...</td>\n",
       "      <td>AE04</td>\n",
       "      <td>A</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>AE04</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>Female</td>\n",
       "      <td>P53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>136062</td>\n",
       "      <td>136062</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>...</td>\n",
       "      <td>PE12</td>\n",
       "      <td>P</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>PE12</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.13</td>\n",
       "      <td>37</td>\n",
       "      <td>68</td>\n",
       "      <td>Male</td>\n",
       "      <td>JAK-STAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>136690</td>\n",
       "      <td>136690</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>...</td>\n",
       "      <td>AL12</td>\n",
       "      <td>A</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>AL12</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>Hippo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>137515</td>\n",
       "      <td>137515</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>...</td>\n",
       "      <td>AL12</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>AL12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>Female</td>\n",
       "      <td>JAK-STAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>137515</td>\n",
       "      <td>137515</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>...</td>\n",
       "      <td>PE08</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>PE08</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>7</td>\n",
       "      <td>62</td>\n",
       "      <td>Female</td>\n",
       "      <td>Hippo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   chrom   start     end ref alt  TumorRef  TumorAlt  NormalRef  NormalAlt  \\\n",
       "0      1  137334  137334   C   T         8         4         10          0   \n",
       "1      1  136062  136062   T   C        26         4         61          1   \n",
       "2      1  136690  136690   G   A        61         7         12          0   \n",
       "3      1  137515  137515   T   C        36         4         30          1   \n",
       "4      1  137515  137515   T   C        13         4         22          1   \n",
       "\n",
       "  Read_depth_tumor  ... Sample  Pat          gene1 Sample1 Recurrence  \\\n",
       "0    RP11-34P13.14  ...   AE04    A  RP11-34P13.14    AE04       10.0   \n",
       "1    RP11-34P13.14  ...   PE12    P  RP11-34P13.14    PE12       21.0   \n",
       "2    RP11-34P13.14  ...   AL12    A  RP11-34P13.14    AL12       10.0   \n",
       "3    RP11-34P13.14  ...   AL12    A   LINC34P13.14    AL12       20.0   \n",
       "4    RP11-34P13.14  ...   PE08    P   LINC34P13.14    PE08       20.0   \n",
       "\n",
       "  VAF_tumor recurrence  age     sex   pathway  \n",
       "0      0.20         34    5  Female       P53  \n",
       "1      0.13         37   68    Male  JAK-STAT  \n",
       "2      0.29         18    5    Male     Hippo  \n",
       "3      0.45          9   14  Female  JAK-STAT  \n",
       "4      0.46          7   62  Female     Hippo  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add two columns with random values\n",
    "# with random float values\n",
    "mutation_data['VAF_tumor'] = np.round(np.random.uniform(0.0, 0.6, size=(len(mutation_data), 1)), 2)\n",
    "# use random intergers\n",
    "mutation_data['recurrence'] = np.random.randint(5, 50, size=(len(mutation_data), 1))\n",
    "mutation_data['age'] = np.random.randint(0, 100, size=(len(mutation_data), 1))\n",
    "mutation_data['sex'] = np.random.choice(['Male', 'Female'], size=len(mutation_data))\n",
    "mutation_data['pathway'] = np.random.choice(['JAK-STAT','Hippo','P53'], size=len(mutation_data))\n",
    "\n",
    "mutation_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0baf04f5",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c003405a",
   "metadata": {},
   "source": [
    "#### The seaborn violin plot with numbers annotated as legends\n",
    "- groupby the column gene1 and get the count using the fucntion size()\n",
    "- use the those values for xticklabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "49d5d0db",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9881/1301327761.py:10: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  xticklabels = ['{0}\\nn={1}'.format(group, size) for group, size in sizes.iteritems()]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(rc={\"figure.figsize\": (17, 7)})\n",
    "color_ordered = [u'#4daf4a']\n",
    "col=[u'#e41a1c',u'#377eb8']\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "sns.violinplot(x=\"gene1\", y=\"VAF_tumor\",data=mutation_data, palette=color_ordered, split=False,width=1,scale='width',inner=None,linewidth=1,alpha=0.001\n",
    "              )\n",
    "#sns.stripplot(x='gene', y='VAF_tumor', data=chr_pos_repeated_without_duplicates_m_lincRNA_REL,hue='Pat',palette=col)\n",
    "sizes = mutation_data.groupby('gene1').size()\n",
    "xticklabels = ['{0}\\nn={1}'.format(group, size) for group, size in sizes.iteritems()]\n",
    "ax.set(xticklabels=xticklabels,\n",
    "       ylabel='Variant allele Frequency',xlabel='gene')\n",
    "\n",
    "for item in ax.get_xticklabels():\n",
    "    item.set_rotation(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ed5d898",
   "metadata": {},
   "source": [
    "#### Add striplot for adding values in the violin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1694d88b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_9881/992722305.py:10: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  xticklabels = ['{0}\\nn={1}'.format(group, size) for group, size in sizes.iteritems()]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(rc={\"figure.figsize\": (17, 7)})\n",
    "color_ordered = [u'#4daf4a']\n",
    "col=[u'#e41a1c',u'#377eb8']\n",
    "f, ax = plt.subplots()\n",
    "\n",
    "sns.violinplot(x=\"gene1\", y=\"VAF_tumor\",data=mutation_data, palette=color_ordered, split=False,width=1,scale='width',inner=None,linewidth=1,alpha=0.001\n",
    "              )\n",
    "sns.stripplot(x='gene1', y='VAF_tumor', data=mutation_data,hue='Pat',palette=col)\n",
    "sizes = mutation_data.groupby('gene1').size()\n",
    "xticklabels = ['{0}\\nn={1}'.format(group, size) for group, size in sizes.iteritems()]\n",
    "ax.set(xticklabels=xticklabels,\n",
    "       ylabel='Variant allele Frequency',xlabel='gene')\n",
    "\n",
    "for item in ax.get_xticklabels():\n",
    "    item.set_rotation(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "711b454f",
   "metadata": {},
   "source": [
    "#### Barplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "613c4dd7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1,font='bold')\n",
    "#sns.s\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context(rc={\"figure.figsize\": (5, 5)})\n",
    "#clrs = ['blue' if (x == A*) else 'red' for x in table_Sample.Sample ]\n",
    "#clrs = ['grey' if (x == A) else 'red' for x in table_Sample.Pat ]\n",
    "\n",
    "ax=sns.barplot(x=\"gene1\",y=\"recurrence\",data=mutation_data,color=\"red\",ci=None)# remove the error bars\n",
    "ax.set(xlabel='Samples',\n",
    "       ylabel='')\n",
    "for item in ax.get_xticklabels():\n",
    "    item.set_rotation(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "499fd4c4",
   "metadata": {},
   "source": [
    "#### sort the column and plot the genes on y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5e67a43b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mutation_data.sort_values(\"recurrence\",ascending=False,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9da3e880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Count'), Text(0, 0.5, 'Genes')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(rc={\"figure.figsize\": (10, 10)})\n",
    "f=sns.barplot(x=\"recurrence\",y=\"gene1\", data=mutation_data,ci=None)\n",
    "f.set(xlabel='Count', ylabel='Genes')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dfe9a96",
   "metadata": {},
   "source": [
    "#### scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "818c69d3",
   "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>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>TumorRef</th>\n",
       "      <th>TumorAlt</th>\n",
       "      <th>NormalRef</th>\n",
       "      <th>NormalAlt</th>\n",
       "      <th>Read_depth_tumor</th>\n",
       "      <th>gene_type</th>\n",
       "      <th>Protein_coding</th>\n",
       "      <th>No_PC_overlap</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Pat</th>\n",
       "      <th>gene1</th>\n",
       "      <th>Sample1</th>\n",
       "      <th>Recurrence</th>\n",
       "      <th>VAF_tumor</th>\n",
       "      <th>recurrence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>137013</td>\n",
       "      <td>137013</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL18</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL18</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>137515</td>\n",
       "      <td>137515</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE04</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>PE04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>136048</td>\n",
       "      <td>136048</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL06</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>139213</td>\n",
       "      <td>139213</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>264</td>\n",
       "      <td>103</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE05</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>137588</td>\n",
       "      <td>137588</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE14</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>AE14</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    chrom   start     end ref alt  TumorRef  TumorAlt  NormalRef  NormalAlt  \\\n",
       "26      1  137013  137013   T   C        15         4         11          0   \n",
       "10      1  137515  137515   T   C        56         7         58          1   \n",
       "29      1  136048  136048   C   T         5         9         38          0   \n",
       "18      1  139213  139213   A   G       264       103         82          1   \n",
       "6       1  137588  137588   A   C        15         3         47          1   \n",
       "\n",
       "   Read_depth_tumor  gene_type  Protein_coding  No_PC_overlap Sample Pat  \\\n",
       "26    RP11-34P13.14  antisense            True          False   AL18   A   \n",
       "10    RP11-34P13.14  antisense            True          False   PE04   P   \n",
       "29    RP11-34P13.14  antisense            True          False   AL06   A   \n",
       "18    RP11-34P13.14  antisense            True          False   AE05   A   \n",
       "6     RP11-34P13.14  antisense            True          False   AE14   A   \n",
       "\n",
       "           gene1 Sample1  Recurrence  VAF_tumor  recurrence  \n",
       "26      LINC0014    AL18        20.0       0.15          49  \n",
       "10  LINC34P13.14    PE04        20.0       0.10          45  \n",
       "29      LINC0014    AL06        20.0       0.10          44  \n",
       "18        SNHG17    AE05        20.0       0.09          41  \n",
       "6   LINC34P13.14    AE14        20.0       0.37          41  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutation_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4c837933",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 565.375x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(mutation_data, hue=\"Pat\", palette=\"Set1\", size=5, hue_kws={\"marker\": [\"^\", \"v\"]})\n",
    "g.map(plt.scatter, \"TumorRef\", \"TumorAlt\", s=100, linewidth=.5, edgecolor=\"white\")\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cbb7da4",
   "metadata": {},
   "source": [
    "#### gridplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "50cf7b8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n",
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:703: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  plot_args = [v for k, v in plot_data.iteritems()]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(rc={\"figure.figsize\": (10, 10)})\n",
    "g =sns.FacetGrid(mutation_data, col=\"gene1\", hue=\"gene1\",size=2, aspect=1.8,col_wrap=4)\n",
    "#size = 100 * (mutation_data.TumorRef / mutation_data.TumorAlt.max())\n",
    "g = (g.map(plt.scatter, \"TumorRef\", \"TumorAlt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ee28742c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ctg/miniconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:1209: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(UserWarning(msg))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0wAAAI+CAYAAACPAIrJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB2iUlEQVR4nO3dd3xUVf7/8fedNEpCC52oAaUjslIERRaMAu5awJUmoEiV4qqgoKsS3a+6/uwKi0hRIDTBriigFBUEadJBZUmIAlKkJQSSzMz9/RFmyCRzUyaTSSa8no+HD5N7z517hknuyeeUzzFM0zQFAAAAAMjFVtIVAAAAAIDSioAJAAAAACwQMAEAAACABQImAAAAALBAwAQAAAAAFgiYAAAAAMACARMAAAAAWCBgAgAAAAALBEwlaMCAARowYEBJVwMAEIRoQwAgMEJLugKXssOHD5d0FQAAQYo2BAACgxEmAAAAALBAwAQAAAAAFgiYAAAAAMACARMAAAAAWCBgAgCUGNM0tfHgRpmmWdJVAQCUUiXdVhAwAQBKzNztc9VuRjvN2zGvpKsCACilSrqtIGACAJQIu9Ou+NXxkqT41fGyO+0lXCMAQGlTGtoKAiYAQIlYsGOBEk8lSpL2n9yvhTsXlnCNAAClTWloKwiYAAAB5+oxNGRIkmyyMcoEAPBQWtoKAiYAQMC5egxNZS3gdcrJKBMAwENpaSsImAAAAZWzx9CFUSYAgEtpaisImAAAAZWzx9CFUSYAgEtpaisImAAAAWPVY+jCKBMAoLS1FQRMAICAWZO8xmuPoYur53BN8poA1wwAUFqUtrYiNCB3AQCUaaZpatOhTWpTt40Mw3uPoCR1iOmgRXcvUroj3bJMREiEOsR0KI5qAgCCQEHbivb12mvjwY35tj1FRcAEACiyudvn6t5P7lVCzwQNaDnAslxEaIR6Ne8VwJoBAIJNQduKhG0JBWp7ioopeQCAIikNu7ADAC4tgWx7CJgAAEVSGnZhBwBcWgLZ9hAwAQB8Vlp2YQcAXDoC3fYQMAEAfFZadmEHAFw6At32EDABAHxSmnZhBwBcGkqi7SFgAgD4pDTtwg4AuDSURNtDwAQAKLTStgs7AKDsK6m2h4AJAFBopW0XdgBA2VdSbQ8b1wIACq2gu7B3iOkQwFoBAMqykmp7CJgAAIVW0F3YAQDwl5Jqe5iSBwAAAAAWCJgAAAAAwAIBEwAAAABYIGACAAAAAAsETAAAAABggYAJAAAAACwQMAEAAACABQImAAAAALBAwAQAAAAAFgiYAAAAAMACARMAAAAAWCBgAgAAAAALBEwAAAAAYIGACQAAAAAsEDABAAAAgAUCJgAAAACwQMAEAAAAABYImAAAAADAAgETAAAAAFggYAIAAAAACwRMAAAAAGCBgAkAAAAALBAwAQAAAIAFAiYAAAAAsEDABAAAAAAWCJgAAAAAwAIBEwAAAABYKFDANGfOHC1evLi46wIAAAAApUqBAqYXXnhBb731lsexuLg49e7du1gqBQAAAAClQWhBC5qm6fH9wYMHlZ6e7vcKAQAAAEBpUaARpooVK+rUqVNyOBzFXR8AAAAAKDUKNMLUsGFDbdu2TS+99JJ69eqlChUqSJKcTqcOHz6ca/QpL3Xr1vWtpgAAAAAQYAUKmHr16qWtW7dqzpw5mjNnjvv4yZMnddNNNxX4ZoZhaPfu3YWvJQAAAACUgAIFTP/4xz90+vRpvfvuuzp+/Lj7eGFGlnwpn/26n376SStXrtTmzZu1f/9+paamKioqSs2aNVOPHj10++23yzAMr9efPXtW06ZN07Jly3To0CFVqFBB11xzjQYPHqzrrrsuz3uvX79e7733nrZt26a0tDTVrVtX3bt31/Dhw90jbQAAAADKJsMsZBRz4sQJnTt3TnFxcapWrVqh043Xq1evUOUlad26dRo0aJD7+8suu0yVKlXSwYMHderUKUlS586dNWnSJIWHh+eq7z333KPExESFh4frqquu0okTJ/THH3/IMAw9/fTT6t+/v9f7JiQk6Pnnn5dpmqpdu7aqVaumffv2KSMjQ1deeaXmz5+vKlWqFPr9uMTFxUmSVqxY4fNrAAAuTbQhABAYBc6S51KtWjX31zabzacAqLBM01RMTIzuu+8+/f3vf1d0dLT73CeffKKnn35aq1ev1ptvvqnHHnvM49onn3xSiYmJat68ud5++23VqlVLpmlq0aJFmjhxop5//nlde+21atq0qcd1O3fu1AsvvCBJ+ve//63evXvLMAwdOXJEI0eO1K5du/T0009r0qRJxf7+AQAAAJSMAmXJ82bOnDkBCxZatmyppUuX6t577/UIliSpR48eGj16tCTpgw8+kNPpdJ/bvXu3Vq5cKZvNptdff121atWSlLWWqk+fPrrzzjvlcDg0ZcqUXPecMmWKnE6n7rzzTvXp08c93a9WrVp67bXXZLPZtHz5cu3du7e43jaAUsQ0TW08uNHnqcUAyj6eE0DZ5HPA1K5dO/3lL3/xZ10sRUZGKiwszPJ8p06dJEmnTp3SiRMn3MeXLVsmSWrfvr2uuOKKXNf16dNHkvTtt98qLS3Nffzs2bP6/vvvJcnr5ryxsbFq3769JGnp0qWFfTsAgtDc7XPVbkY7zdsxr6SrAqCU4jkBlE0+B0zZrVixQs8++6xGjBih++67z+NcWlqatmzZop9++skft/Lq/Pnz7q/LlSvn/nrr1q2SpDZt2ni9rmXLlgoPD1d6err27NnjPr5nzx5lZGQoPDxcLVu29Hpt69atJUnbtm0ravUBlHJ2p13xq+MlSfGr42V32ku4RgBKG54TQNlVpIDp8OHD+sc//qExY8Zo4cKF+vbbb7VhwwaPMmFhYRo3bpzuueeeYgsulixZIklq0qSJIiMj3ceTkpIkSZdffrnX68LCwlSnTh1JUmJiovu46+u6detajmy5XjP7dQDKpgU7FijxVNbv+v6T+7Vw58ISrhGA0obnBFB2+RwwpaWlafDgwdq1a5dq1aql/v37q3z58rnKhYWF6R//+IdM09TXX39dpMp6s3PnTi1cmPVQGj58uMe506dPS5IqV65seb3r3JkzZ3y6zlUWQNnk6jU2lLWO0SYbvccAPPCcAMo2nwOmefPmKTExUc2aNdOXX36pp556ShUrVvRa9uabb5YkbdmyxdfbeXX8+HE9+OCDstvtuuWWW/T3v//d43x6erok5bn+yZWGPPu0vsJc5yoLoGxy9RqbylrE7ZST3mMAHnhOAGWbzwHT8uXLZRiGnnjiiXw3cG3YsKFCQkLcU+T8ISUlRcOGDdOhQ4fUvHlzvfjii7nKRERESJIyMzMtXycjI0OS59qnwlznKgsguHnLbpWz19iF3mMALjwnUFhkUww+PgdMiYmJCgkJ0bXXXptv2ZCQEEVFRXlMeyuKs2fPaujQodq9e7caNmyomTNneqxdcqlUqZKkvKfNuc65ykoFm25XkGl7AIKHt+xWOXuNXeg9BuDCcwKFRTbF4ONzwJSRkaGIiAiFhIQUqPz58+f9Mhpz7tw5jRgxQlu3blVsbKzee+89Va1a1WvZ2NhYSdKBAwe8ns/MzNShQ4c8ymb/+tChQ5ajTMnJybmuAxCcvGW3suo1dqH3GADPCRQW2RSDk88BU/Xq1ZWWllagUaNff/1V58+fd2ek81V6erpGjhypjRs3ql69epo1a5Zq1KhhWb5Vq1aSpM2bN3s9v337dmVmZioiIkJNmzZ1H2/atKnCwsKUkZGh7du3e73W9ZquewAIXt6yW61JXuO119jF1Xu8JnlNIKsKoBThOYHCIpticAr19cJrr71WS5Ys0Zdffqm+ffvmWXbGjBkyDEPXXXedr7dTZmamHnzwQa1bt061atXS7Nmz8w3AunXrpnfeeUc//vijDhw4kGvz2vfff19S1sa32RNWREZGqmPHjlq1apUWLVrk3nPJJSkpSevXr5ckde/e3ef3BKDkZe8hNmW6e4S3P7Bdi+5epHSHdWKXiJAIdYjpEMDaAihNOsR04DmBArNqb/q26KtQm89/kiMAfP507rnnHn3xxReaPHmyrr32WjVq1ChXmYyMDE2aNEmffvqpbDab+vXr59O9HA6Hxo0bp2+//VY1atTQ7Nmzddlll+V7XfPmzdWlSxetWrVKjzzyiKZOnaqaNWvKNE0tWrTIXa+RI0fmunbUqFFavXq1Pv30U1177bXq3bu3DMPQ0aNHNXbsWDmdTt18881q0qSJT+8JQOmQvbdPutgj/PHejzWg5YASrBmA0i4iNEK9mvcq6WogSFi1Nwt3LqS9KeUMswgpOp577jnNnTtX5cuX14033qjvv/9e58+f17Bhw3Tw4EGtW7dOJ0+elGmaGj16tB588EGf7vPFF19o3LhxkqR69eqpVq1almWffvppNWvWzP39iRMn1K9fPyUlJSk8PFxXXXWVTp48qcOHD8swDD355JMaOHCg19eaNWuWXnzxRZmmqTp16qhq1arat2+fMjIyVL9+fc2fP1/VqlXz6T1JUlxcnCRpxYoVPr8GAN/ZnXY1mtRISaeSPKbU2GRTbNVY/TzmZ3r9UGrRhgDBg/YmuBXpk3nyyScVGRmp6dOna/ny5ZIkwzA0ffp0SVlpE0NDQzVy5EiNHj3a5/u4UnhL0sGDB3Xw4EHLsikpKR7fV6tWTR9++KGmT5+upUuXat++fapQoYI6deqkIUOGqH379pavNWjQIDVu3Fjvvvuutm/frj///FN169ZV9+7dNXz4cMt9pwAEh5y9fS70+gEA/In2JrgVaYTJ5eDBg/r444+1ZcsWHT16VA6HQ9WrV9e1116ru+++u0DT5y5F9A4CJceqt8+FXj+UdrQhQHCgvQl+fvlU6tWrpzFjxuRbLiMjQ+Hh4f64JQAUiSu7lZXs2a06x3YOXMUAAGUK7U3wC0gYa7fb9f7772vatGn69ttvA3FLAMgT2a0AAIFAexP8ijVgcjqd+vDDD/X222/r8OHDxXkrACgUslsBAAKB9ib4FTpgSkpK0scff6x9+/bJ4XDosssu01133eWx8askffnll3rjjTf022+/ybVMqnPnzn6pNAAAAAAEQqECpg8//FDx8fFyOBwex+fOnasJEyZo0KBBOnbsmB599FFt2LBBpmnKZrOpa9eueuCBB3IFVQAAAABQmhU4YPrf//6n+Ph42e12SVL58uUlSefOnZMk/b//9//UqlUrPf7440pKSlJoaKhuv/12DRs2TA0aNCiGqgMAAABA8SpwwDRv3jzZ7XbVrVtXL730ktq0aSNJ2rhxo8aPH6/Dhw9rxIgROn36tDp06KBnnnlGV1xxRbFVHAAAAACKm62gBTdt2iTDMPTUU0+5gyVJatu2rZ588klJ0pkzZ3TTTTfp3XffJVgCAAAAEPQKHDAdPHhQhmHoxhtvzHWuU6dOMgxDkjRq1Cj31wAAAAAQzAocMKWlpalKlSoKCwvLdS48PFxVqlSRJDVs2NBvlQMAAACAklTggMmV8c5KSEiIJCkiIqLotQIAAACAUqDAARMAAAAAXGoKtQ9TWlqaJk+e7PXc2bNnJcnyvMuYMWMKc0sAAAAAKDGFCpjOnTun//73v3mWye88ARMAAACAYFGogMk0zeKqBwAAAACUOgUOmPbu3Vuc9QAAAACAUoekDwAAAABgoVBT8rL75JNPJEkdO3ZU9erV/VUfAAAAACg1fA6YHn/8cYWGhmrjxo3+rA8AAAAAlBo+B0yVK1eWJJUvX95vlQEAAACA0sTnNUwNGjRQamqqe/8lAAAAAChrfA6Y7rrrLjkcDi1evNif9QEAAACAUsPnKXm9evXS999/r1deeUVhYWHq06ePQkN9fjkAAAAAKHV8jnCeeOIJVaxYUeHh4Xruuef01ltv6eqrr1Z0dLRsNu8DV4Zh6IUXXvC5sgAAAAAQSD4HTB9//LEMw5BpmpKk06dPa82aNV7LusoRMAEAAAAIJj4HTD169JBhGP6sCwAAAACUKj4HTC+++KI/6wEAAAAApY7PWfIAAAAAoKwjYAIAAAAAC37JA378+HEtW7ZMO3fu1J9//ilJio6OVosWLdStWzdVr17dH7cBAAAAgIAqUsDkcDj05ptv6r333pPdbpckd9Y8wzD0ySef6MUXX9TgwYP1z3/+UyEhIUWvMQAAAAAESJECpvHjx+vLL7+UaZoKDw9XixYtVLt2bUnSH3/8oZ07dyojI0PTpk3ToUOH9PLLL/ul0gAAAAAQCD4HTN98842WLFkiSbr//vs1cuRIVapUyaNMSkqK3n77bb377rv64osv1L17d8XFxRWtxgAAAAAQID4nffjggw9kGIYeeOABTZgwIVewJElRUVEaP368HnjgAZmmqcWLFxepsgAAAAAQSD4HTDt27JDNZtOQIUPyLTtkyBDZbDbt2LHD19sBAAAAQMD5HDCdPn1akZGRioqKyrdsVFSUoqKidPr0aV9vBwAAAAAB53PAVLlyZaWmpio1NTXfsikpKUpJSVHlypV9vR0AAAAABJzPAdPVV18tp9OpWbNm5Vt21qxZcjqdatGiha+3AwAAAICA8zlguuuuu2SapqZMmaI33nhDZ8+ezVUmNTVVr7/+uqZMmSLDMHT33XcXqbIAAAAAEEg+pxXv2rWrbr31Vn311Vd65513NGvWLF199dWqWbOmJOnIkSPauXOn0tPTZZqm/va3v+mWW27xW8UBAAAAoLgVaePal156SbVr11ZCQoLOnz+vjRs3yjAMSZJpmlk3CA3VwIEDNXbs2KLXFgAAAAACqEgBU1hYmCZMmKBBgwZp+fLl2rlzp/78809JUnR0tFq0aKGuXbuqVq1afqksAAAAAARSkQIml1q1amngwIH+eCkAAAAAKDV8TvoAAAAAAGUdARMAAAAAWCjylLwDBw7oq6++0s8//6wzZ84oMzPTsqxhGJo9e3ZRbwkAAAAAAVGkgOm1117TzJkz5XQ63Vnx8uLKoAcAAAAAwcDngGnevHmaNm2aJCkmJkYdOnRQdHS0QkP9kkcCAAAAAEqcz9HNwoULZRiGevbsqeeee042G8uhAAAAAJQtPkc5Bw4ckCQ98cQTBEsAAAAAyiSfR5gqVaqkjIwMRUVF+bM+AAAAAFBq+Dw01LJlS6WmpurEiRP+rA8AAAAAlBo+B0xDhgyRYRiaOnWqP+sDAAAAAKWGzwFT69at9cwzz2j+/PmaOHGifv/9d3/WCwAAAABKXJFygPfq1UsnT57Ua6+9psWLF6ty5cqqWLGiZXnDMPTNN98U5ZYAAAAAEDA+B0wOh0Pjx4/Xl19+KUkyTVOnTp3SqVOnLK9h41oAAAAAwcTngGn27NlasmSJJKlNmzbq2LFjsW5ce+zYMa1du1Y7d+7Ujh07tGfPHqWnp6tdu3ZKSEjI89rMzEzNnj1bn332mZKTkxUWFqYmTZpo4MCB6tq1a57X7t69W9OmTdPGjRt15swZ1axZU126dNGoUaNUrVo1f75FAAAAAKWMz9HNBx98IMMwNHz4cD3yyCP+rJNXS5Ys0X/+859CX5eenq77779fmzdvVkhIiK666iqdO3dOGzZs0IYNGzRs2DA9+uijXq9dvny5xo4dq8zMTEVHR6thw4ZKTExUQkKCli5dqgULFuiyyy4r6lsDAAAAUEr5nPTh4MGDMgxDI0aM8Gd9LEVGRur666/XiBEjNHnyZI0aNapA17388svavHmzYmJi9MUXX+izzz7T119/rSlTpig8PFzTp0/XypUrc1135MgRjR8/XpmZmRo1apS+++47ffTRR/ruu+9044036tixY3r44Ydlmqa/3yoAAACAUsLngKlSpUqKjIxUhQoV/FkfS3fffbfee+89jR07Vrfccouio6Pzveb48eNauHChJOn5559XgwYN3Ofi4uI0dOhQSdLkyZNzXTtjxgydO3dObdu21UMPPeSeahgVFaVXX31VUVFR2rlzp1atWuWPtwcAAACgFPI5YGrbtq1SUlJ05MgRf9bHr1auXKnMzEzFxsaqffv2uc737dtXkrRr1y4lJyd7nFu2bJkkqXfv3rmuq1y5srp37y5J+uqrr/xdbQAAAAClhM8B0/DhwxUeHq6XXnrJn/Xxq61bt0rK2jPKm1q1aikmJsajrCQdPnzYHQi2bdvW67Vt2rSRJG3bts1PtQUAAABQ2vgcMDVp0kSTJk3S999/r6FDh2r9+vVKS0vzZ92KLCkpSZJ0+eWXW5ZxnUtMTMx1XVhYmGrXru31Oleyh99++02ZmZl+qC0AAACA0sbnLHlNmzZ1f7127VqtXbs232sMw9Du3bt9vWWhnT59WlLWFDorrnNnzpxxH3PtJVW5cmXLvaOqVKkiSXI6nUpNTVXVqlX9UGMAAAAApYnPAVMwZIdLT0+XlDVSZCU8PFySdP78eZ+uy14eAAAAQNnic8A0Z84cf9ajWEREREhSnlPmMjIyJEnlypXz6brs5QEAAACULT4HTO3atfNnPYpFpUqVJF2cmueN65yrrHRxmt7p06dlmqbXaXmuaXs2m02RkZH+qjIAAACAUsTnpA/BIDY2VpJ04MAByzKudOKustm/zszM1OHDh71e99tvv0mSYmJi8py6BwAAACB4lemAqVWrVpKkLVu2eD1/5MgR/f777x5lJalu3bqqWbOmJGnTpk1er3Udz34dAAAAgLLF5yl5Gzdu9Ok6q32NikNcXJz+7//+T0lJSVq/fn2uzWsXLlwoSWrWrJmuuOIKj3PdunVTQkKCFi1apDvuuMPj3OnTp7V06VJJcm9gCwAAAKDs8TlgGjhwoGXKbSuBTitevXp19enTR3PnztWTTz6p6dOnq0GDBpKklStXasaMGZKk0aNH57p2yJAhWrx4sTZu3Kg333xTY8aMUUhIiFJSUjRu3DilpKSoWbNmuummmwL2fgAAAAAElmH6mB+8SZMmPt1w7969Pl13+PBh9ejRw/19RkaG0tLSFBoa6pF0YejQoRo2bJj7+/Pnz2vQoEH66aefFBISooYNGyotLc29dmnw4MGaMGGC13suXbpU48aNk91uV3R0tGrXrq3ExESlpaWpevXqmj9/fq6RqcKIi4uTJK1YscLn10DgmKapTYc2qU3dNoXuLAAAfyvtbQjPTABlhc8jTPkFPqmpqdq2bZumTp2qX3/9VZMnT1abNm18vZ0cDoc7M112drvd43j2/ZSkrHThc+bM0axZs/T5558rKSlJYWFhateunQYMGKBu3bpZ3rN79+667LLL9M4772jTpk365ZdfVLNmTd11110aNWqUoqOjfX4/CD5zt8/VvZ/cq4SeCRrQckBJVwcASjWemQDKCp9HmArKNE2NGjVKmzZt0kcffaTLLrusOG8XVEp77yAusjvtajSpkRJPJapB1Qb6eczPCrX53N8AAEVWmtsQnpkAypJiz5JnGIYee+wxpaSkaMqUKcV9O6BYLNixQImnEiVJ+0/u18KdC0u4RgBQevHMBFCWBCSteIMGDRQZGakffvghELcD/MrutCt+dbwMZc3Bt8mm+NXxsjvtJVwzACh9eGYCKGsCEjBlZmbq/PnzOnHiRCBuB/iVq6fUVNbsVaec9JgCgAWemQDKmoAETN9884070xwQTHL2lLrQYwoAufHMBFAWFVvAlJGRoeTkZE2fPl1PP/20DMNQp06diut2QLHI2VPqQo8pAOTGMxNAWVTglDWffPKJIiIidOutt0qSmjZtWuCbmKapWrVqed0gFgio9HTps8+y/m8lIkK64w7Zw0LcPaU5G3/pYo9p3xZ9yf4E4JKXfXQpzG7qjp+liGwDSjYZ2rJvrPrd4lCILcT9rFVERMlVGgAKoMB/5T3++OOqUaOGO2AqaDbycuXKqWvXrho3bpxq1arlWy0Bf1m3TurdO/9yq1ZpTazcWZ68cfWYrkleo86xnf1WRQAIRmuS17ifmR1+kxYvzlnClHRMmjvo4qFVq6TOnQNTQQDwUaG6xbMHSXPmzMmzbEhIiCpXrqzY2FiFhtL7jlKiY0epfn0pKUnyFvTbbFJsrNSxozrIoUV3L1K6w3o0KiIkQh1iOhRbdQEgWHSI6eB+Zhp2h1K+eUyRh47LyOdZCwClnc+RTLt27fxZDyAwQkOlZ5+V7r3X+3mnM+t8aKgiFKpezXsFtn4AEKQiQiM8n5n/sRXoWQsApV1AsuQBpUq/flmjTIZnFifZbFKDBlLfviVTLwAoS3jWAigjCJhw6XGNMuWcJkKPJwD4D89aAGUEARMuTTl7PunxBAD/41kLoAwoVPfOn3/+Wah04jkZhqHdu3f7fD3gNznXMtHjCQD+x7MWQBlQ6BEm0zSL9B9Qarh6PiV6PAGguPCsBRDkCtXFU758eQ0ePLi46gIUTI7NZ03TVOLJRNWvWl+Ga9pHQTZEzN7zSY8nAFgyTVObDm1Sm7ptLj5nCyr7s/app6SPPy7Q5uFsaAugtCjUX4gVKlTQmDFjiqsuQMHk2HzWkNTAW7mCbIg4YIDUpInUpo0fKwgAZcvc7XN17yf3KqFngga0HFD4F3A9a1NTpZtuyr88G9oCKEVI+oDg49p81qqX07WouCAbIhqG1Lat9WsBwCXO7rQrfnW8JCl+dbzsTnvhX8T1rL3xRv89vwEgQAiYEHysUtW6sKgYAPxmwY4FSjyVKEnaf3K/Fu5c6PuL8fwGEIR4IqF0y7Feyc3plFmjhsxjxzyifoch2erXl8GiYgDw3YVnr+NcmrZ8/ZgGnJVMSTYZ2rJvrPrd4lBI+Qq+rTXq10+Kj5eSkjwDJ5tNio0lKQSAUoeACaVbjvVK2RkX/ssuxJTWDummG+idBADfXXj2hkh63eOEKemYNHdQ1re+rDXKmWrchdElAKUUU/JQuuWzXsm88J8kOST9r6p0f/llvs2xBwBk6dhRZv1YOSxOOwzJbFDf97VGbGgLIIgUOGDau3ev1qxZU5x1AXLLZ7579lGmEEnxnaVfzyQWbY49AFzqQkO1dkg3hVicDjGlH4Z08300KOezndElAKUYI0wo/XL2RF7gkPS/KtL+Klnf/6+qtLCFZJPN90xOAADZnXbdX36Z9ldRrlEmv43ms6EtgCBBwITSz2KUKURSfJes/6Ss0SVHiOSUU/tP7teaZEZEAcAXa5LXaF9KkuK7KNcoU/bR/CI9Z13PdonRJQClmmGaVrk9Udzi4uIkSStWrCjhmpQOee4kb7dLjRq5syqZhqHUejX02acvyQyxKXpXov5sfnEUKiIkQnc0vkMRoewUD6BsKs42JN2erk9//lQ//7FLD9/3tiIPHZeR49kbHlGh6M9Z05Q2bcraPJz98ACUUnTnoNTIcyf5HFmVDNNU1H9eVf9rL5S7JsCVBYAyLCI0Qun2dE1c82/dNHKEbnjqHUlenr1F5drQFgBKMQImBJaXfZVM09S+k/u0ZcMU9U+T1ieNU98pPRVavqLnta69OxITme8OAP6W7fnscDq05evH1P+s9HH5j3R9jeoyjh2XWbOmjJ49S7qmABBQBEwILC/7KhmSGir7Xh9H9XXHF3TLkOc9r80+ysR8dwDwr2zPZ8/9l465vzKOHpU2biz83ksAEMRI+oDAymdfJVf2pdFnFnjPvjRggLRhg9S/f/HWEwAuNReez2Yez+fk6FDZr28f2HoBQAkjYEJg5bOvUr57Kbnmu7M4GAD868Lz2cjj+fyvTnYt3PtBYOsFACWMgAmBd2HvjZy9mK7RJfZSAoCSYe/TS8nVQi33XlrUwuDZDOCSQ8CEYmWapjYe3CiP7PUWvZiu0aXseyl5HWUCABSLBXsW68m/2i33XsoMMX16NnttCwAgSBAwoVjN3T5X7Wa007wd8zyO5+zFzD665MIoEwAEjt1pV/zqeC1sIe2vIsvnsy/PZqu2AACCAQETioVpmlr32zrFr46XpFyNa85ezOyjSy6uUaYi7SQPAMiTa/Tn+wPfK/FUouwhUnwXWT6fCzsDwBWISbnbAgAIBgRM8CtXw5uwLUHXv3u9Ek8lSpJH45qzF1Py7L00ZKhmhZqadecsLbp7kTrEdCiBdwIAlwbX6E/SqSQtunuREnom6JZ/zdSBall/IuQc/ZeyRpke/+ZxZToy8339BTsWeG0LACBYEDDBr1wN77jl4zyOZ5/CsSZ5jUcvpuTZe2nK1NG0o7qiyhXq1byXIkIjAvsmAOASkX3057nvn1PPpj01oOUAXV69gZ76q1NS7tF/KWuU6WDKQT2z+pkCvb6hrCQ/TLUGEIzY+RN+k73hPX7uuMe57FM4ejXrpUV3L1K6I13qYeqruxPVvXl9dc+WNS8iJIKRJQAoZt5Gfwa0HKAOMR107N/v66u79+Z6PjucDj329WM6lnZMC3Yu0LNdnlWozfufE9lfX/JsCwa0HFC8bw4A/ISACYWTni599lnW/3NYn7xG129KVOtQ6bPGUkaOny5Xz2LfFn3Vq3mviyeuKeY6AwAuuvAcd5xL05avH9OAs5IpySZDW/aNVb9bHIooX0G97rhTatE71+UJ2xJ0LO2YJCnxVKJl8JN9dMnUxex42dsCq0ALAEoTnlQonHXrpN65G1BJ6njhP0nqfJ/0bX3P8/QsAkApcOE5HiLpdY8TpqRj0txBWd+uWiV17uxRImcQlFfwk3N0yeVSbAtM09SmQ5vUpm4bGWy8DgQd1jChcDp2lFm/vpwWz3tX+tk1l3s/z/x1AChhHTvKrB+ba3NaF4chmQ3qSx075jrnCoJcI0au4OeF71/w2GMp59qlnC61toC06kBwY4QJ3rmm3qWmSlu2SPaLjdrh6hGqm7vTUJL39ODZZU8V3jm2s79rDQDIj8OhvdddpaaJSV5Ph5jSoZoVVTchQQoLu3iZ06H1q8YrIkZKz/bXgyFD8avjdUXlK3Rfq/skyZ3cx8ql1BbkTKvOVEQg+PAbi9zS06X//Ed69lmvp+taXOaQlOQl/ayU1aBWr1BdL9/ysiqEVSChAwAEUrb1p45dO9V04Td5Fq+7fqe0frDHsRBJ/5W0K8eUa9do0/ivx6t/y/4KtYWqQ0yHi8l9LFwqyX2sEmsACB4ETMht3TrLYCkveY0umTJ1LO2YrqhyRZnvTQSAUifb+lOLCQBupuR1Ip3DkJKqWE+5Ppp2VPO2z9N9re5TRGiEZ3KfS1Rh1nwBKL1Yw4Qs6enS4sXS3LnSgQNSjRqFuty1dsnb6JIk/avjv9iEFgBKSseOUv36UgESDliVCDHznnItSU+seOKSWZdUEFZrvti8FwgudG8g3yl4Vpy6GHGHSDo0brhm3X5jrnIRIRG6o/EdbEALACXF4ZD+/ndp8mSfLjdtNp2tW123PvWSuodmRUxrktfonc3veJQ7nHqYKWcXkFYdKDv4TYXPU/Bsko5WkGqmZY0uJd96PY0kAJRG69b5HCxJkuF0KvI/r6r/tVnPeLvTromrJuYZDNhkU8L2BA1sOVA226U3oYW06kDZcek9wZBbIaZquLim4D12S9b3z3SWJq75N1MxAKA0KuxzvkaNi2VtNqlBA6lvX/fpnFPNXLIHA6OWjNKgTwdp9Jej/fUuggZp1YGyhYAJUmho1giTaeZf9gJXgoc5raS2w6S5LeVOEQsAKGUK+pw3jKzg6KWXLpZ1OrOuDc2alFKQYGDiqoma8dMMSdKMn2bovP28395KMHClVc8ZULpkT6sOoPQzTLMQfyXDr+Li4iRJK1asKOGaSLLbdSymmqKPpOQZRdsNKdTMGl1qPMZz8W98p3g9ceMTrFUCgAAodBtit0uNGklJSXkHTgkJWaNJjRpJiYlZAdTPP7sDptVJq9VldpdC1fWB1g/o7dveLtQ1wSzdnq7Pfv4s37TqrO8FggMBUwkqyYDJ6XR6zC0/bz+vB/pU1KyPnLnKmjabDGfW8V/u6qxGH63W2ucfUOJtN7jL8OAHgMDyqQ1JSJDuvdfrKdMwZNSvfzE4cpVNSJAGXFxrk18wkOnI1NDPh8ppXmxPQm2hSnkiReVCyxW8rgBQSpD0oaw7c0Z65hnp3DmPw98nfau0Y3v0fY2X9Nem3fT4dac0t7lTE1dKsac852oaQ4ZI06dLzz6rRk89JW3erBvatNENhVjzBAAoQa6Na51OqXp1mcePy5DnnkuGaUpxcdLChVJEhNSrl9SkidSmjcdL5bfH0gOfP+ARLElZ0/geWfrIJTXKBKDsYISpBBX7CFN6ujRihDR7dr5Fx3Y39Hp7UwO2SQkfXzx+pIJU+fgZldu5N6vRJEgCgFKhUG3I6tVSl8JNo9OqVVLnzrmPu4Kv9NwjTBmOTA37fKjSbE591ljKyNYtyygTgGDFCFNZtm5dgYIluyH9t3VW3LyghfTsKqnBqaxzj90iVVwxnl5BAAhmrix5+a1fkrKy4sXGZl3jzbp1Uu/eXk+FS3K1Op3vk76tf/Eco0wAghVZ8sqyjh1l1q+v3KuSPM34i5QRlvW1I0SKv9AJObGzlNDq0sxwBABlSmGyoTqdMp95xp3kIZd8UpS7tp1Yc3nuc7QnAIIRAVMZZZqmNh75Sd/fH5dv1ruHbvU8NrdlVqrw//urJCOrV3DqpqnFWV0AQHHr108p9WrIkUcRV7Azv3keXW35BF+ubSeyZ1F1oT0BEIyYkldGzd0+V/d+cq+qhERqc5XciRxcXKNLhgwN/ctQhdgutnCuZb7lw8prcKvBAag1AKC42G1SfGdTr82zLuMKdn74/ln1adVfoTaLPxP69ZPi43NN8XMa0p+1olT5vn56ICR3q0N7AiAYETCVIaZpatOhTWpVu5XiV8dLkk45UhXfxTORg0v20SVTpkJsIcwtB4AyasGOBXqrwXGNqSJdcSorOHKFOoayRpeSqkoLW0iOU4lauHOhBrQc4P3FXKNMOVKU20ypxstT9N87La4DgCDElLwCWL9+vUaMGKH27durZcuW6t69u9544w2lpaWVdNU8zN0+V+1mtNODXz2oxFOJ7uMLWkj7qyjXWqbsa5ck5pYDQFlld9oVvzrevU7VNZfA0MW04jmn0k1cNVF2p936Rfv1k+rXl3lhLZNpGFmb3PbtWzxvAgBKCCNM+UhISNDzzz8v0zRVu3Zt1alTR/v27dPbb7+t5cuXa/78+apSpUpgK+UlpavD6dCWrx9T/7PSuR3TdbdN7pSurgYy+yiTt7VLrrnlD7d/ODDvAwAQEGuS17g70rJnQ/1fFckwpQans9YuLWyRVT7cLrVem6gfzz6oGy6/wfuLRkTI8eS/FDJ0mKSsfZwc8RMVYpUsAgCCFE+1POzcuVMvvPCCJOnf//63evfuLcMwdOTIEY0cOVK7du3S008/rUmTJgW2Yl5SuoZIet39XdZYUvaUrjnThc/4i9Sx4U1qFN3IfRVzywGgbOoQ00Fj2o7R5I2TPTrR4rtkjTAlfOw5utThN2nxYkmLp0qyTtKwcuoEXVnlQvBVVVrX3FTjgxvVpm4bGezbB6CMIGDKw5QpU+R0OtWjRw/16dPHfbxWrVp67bXXdOutt2r58uXau3evmjRpEriKXUjpaiYlZe3MnoNrHnr2lK7ZG8ip10ojb5Pqn0rUsoHLrBf1AgDKhBBbiJb8ukSGDJkyNbeltLe6tKlu1vnsX0tZ7cf+KhfXOuVis8mMvUKjzr6vDl2kOR9Lz3Y2tHTleB1LO6aEngnW658AIMiwhsnC2bNn9f3330uSenvZoC82Nlbt27eXJC1dujSgdVNoqBzxE70GS5J1SldXuvCRt0uySYmnErUmeU1x1xYAUMJcU/JMV5oHQ9pUT+5FTO6vL8i51ikXp1Nrh3TVvpQkJVxoWxJamjqWdkySFL86Pu/1TwAQRAiYLOzZs0cZGRkKDw9Xy5YtvZZp3bq1JGnbtm2BrJqki4kccu6n4dpDwzUP3UO2RrFnk56a13OeOsR0KPa6AgBKVoeYDlp09yIl9EzQjNtnqFJ4pXyvsWpnZLPJbFBf95dbJkOG14Br/8n9WrhzoR/fAQCUHOZiWUhMzFocW7duXYWFhXktc/nll3uUDRS7066Ja/6tjhemQWSX14aBkvSvjv9Sq9qtdEfjOxQRGlHcVQUAlAIRoRHq1byXJGl10mqdyTiT7zUT/vovHY74Uw2eesfzhHt06R3vFyprb7/41fHq26Iv074BBD2eYhZOnz4tSapcubJlGdc5V9lAWbBjgRJPJSq5hfTMqotzzLPvoSFlNVjVK1TXy7e8rBBbiCJCIgiUAOAS5xptSnekW5Zxtxd/DZFmLr+4Qe2FtUv3l1smI9O4OMUvB1Ome5SJtUwAgh0Bk4X0Cym7rUaXJCk8PNyjbCC49tIwZMgRYnqkC885umQqaz75FVWuUOfYzgGrIwCg9Mo+2lQg2TeodTq1d0w/7TvzQr6XMcoEoKzgCWYhIiJrFCYzM9OyTEZGhkfZQHCNLrm/z76fRlWp8n3DlVD/Rvf5iJAI1ikBAHzXr58UHy8lJkoNGqjByH9p0f9auUeo1iSv0Tubc0/PY5QJQFlBwGShINPtCjJtz5+yjy65pkFkTxf+bGdDaw98ozdv/y+9eQAA/wgNvTjK9OyziihX0T1CZXfaNXHVRI92KTubbIwyAQh6ZMmzEBsbK0k6dOiQ5ShTcnKyR9nilist7AVzs6V03X9yP6nCAQD+NWCAtGGD1L+/x2GrdsnFKSftEoCgR3ePhaZNmyosLEwZGRnavn27O4V4dps3b5YktWrVKiB1KuhCXabgAQD8yjCktm1zHaZdAnApIGCyEBkZqY4dO2rVqlVatGhRroApKSlJ69evlyR17949IHUq9EJdAACKEe0SgEsBU/LyMGrUKBmGoU8//VTvv/++TDNrysHRo0c1duxYOZ1O3XzzzWrSpEkJ1xQAAABAcSBgykPLli31+OOPS5ImTpyoLl26qGfPnoqLi9OuXbtUv359/d///V8J1xIAAABAcTFM17AJLK1bt07vvvuutm/frrS0NNWtW1fdu3fX8OHDVbFiRZ9f9+qrr5bD4VCdOnX8WFsAuHTUqVNHc+fOLelqlAjaEAAomoK2IaxhKoAOHTqoQwf/L1iNiIhw7+UEAEBh0IYAQGAwwgQAAAAAFljDBAAAAAAWCJgAAAAAwAIBEwAAAABYIGACAAAAAAsETAAAAABggYAJAAAAACwQMAEAAACABQImAAAAALBAwAQAAAAAFgiYAAAAAMACARMAAAAAWCBgAgAAAAALBEwAAAAAYIGACQAAAAAsEDABAAAAgAUCJgAAAACwQMAEAAAAABYImAAAAADAAgETAAAAAFggYAIAAAAACwRMAAAAAGCBgAkAAAAALBAwAQAAAIAFAiYAAAAAsEDABAAAAAAWCJgAAAAAwAIBEwAAAABYIGACAAAAAAsETAAAAABggYAJAAAAACwQMAEAAACABQImAAAAALBAwAQAAAAAFgiYStCAAQM0YMCAkq4GACAI0YYAQGCElnQFLmWHDx8u6SoAAIIUbQgABAYjTAAAAABggYAJAAAAACwQMAEAAACABQImAAAAALBAwAQAKDLTNLXx4EaZplnSVQEAXCIC1fYQMAEAimzu9rlqN6Od5u2YV9JVAQBcIgLV9hAwAQCKxO60K351vCQpfnW87E57CdcIAFDWBbLtCZp9mI4dO6a1a9dq586d2rFjh/bs2aP09HS1a9dOCQkJeV6bmZmp2bNn67PPPlNycrLCwsLUpEkTDRw4UF27ds3z2t27d2vatGnauHGjzpw5o5o1a6pLly4aNWqUqlWr5s+3CABBacGOBUo8lShJ2n9yvxbuXKgBLdlQFQBQfALZ9gTNCNOSJUs0YcIEJSQkaOvWrUpPTy/Qdenp6brvvvv08ssva9++fbr88stVpUoVbdiwQQ8++KBeeeUVy2uXL1+u3r1766uvvpJpmmrYsKFOnDihhIQE3XHHHfrtt9/89fYAICi5evgMGZIkm2yF6ulj7RMAID8524qitj2FFTQBU2RkpK6//nqNGDFCkydP1qhRowp03csvv6zNmzcrJiZGX3zxhT777DN9/fXXmjJlisLDwzV9+nStXLky13VHjhzR+PHjlZmZqVGjRum7777TRx99pO+++0433nijjh07pocffphGHsAlzdXDZyrrWeiU093TVxCsfQIA5CdnW1HUtqewgiZguvvuu/Xee+9p7NixuuWWWxQdHZ3vNcePH9fChVn/cM8//7waNGjgPhcXF6ehQ4dKkiZPnpzr2hkzZujcuXNq27atHnroIYWGZs1ejIqK0quvvqqoqCjt3LlTq1at8sfbA4Cgk7OHz6WgPX2sfQIA5CdnW3Hefr5IbY8vgiZg8sXKlSuVmZmp2NhYtW/fPtf5vn37SpJ27dql5ORkj3PLli2TJPXu3TvXdZUrV1b37t0lSV999ZW/qw0AQSFnD59LQXv6vM0/BwAgu5xtxcNLHy5S2+OLMh0wbd26VZLUunVrr+dr1aqlmJgYj7KSdPjwYR05ckSS1LZtW6/XtmnTRpK0bds2P9UWAIKH1eiSS349fYGefw4ACD7e2oqZP820LF9cbUmZDpiSkpIkSZdffrllGde5xMTEXNeFhYWpdu3aXq+77LLLJEm//fabMjMz/VBbAAgea5LXeO3hc3H19K1JXuP1fKDnnwMAgo+3tiKvYCi/tsdXQZNW3BenT5+WlDWFzorr3JkzZ9zHTp065T5nGN57T6tUqSJJcjqdSk1NVdWqVf1QYwAIDh1iOmjR3YuU7rDOWBoREqEOMR1yHc/eY5g94HL1DPZt0VehtjLdPAEA8mHVVhgyVKNCDb10y0sKsYXkus6q7SmKMt0iuVKPh4WFWZYJDw+XJJ0/f96n67KXB4BLRURohHo17+XTtdnno2eXfZSJfZwA4NJm1VaYMnU07ahCbCEBayvK9JS8iIgIScpzylxGRoYkqVy5cj5dl708ACBvRV37BAAo+0pbW1GmA6ZKlSpJujg1zxvXOVdZ6eI0vdOnT1vus+Satmez2RQZGemP6gJAmVfUtU8AgLKvtLUVZXpKXmxsrLZs2aIDBw5YlnGlE4+NjfW4TsoaYTp8+LDq1q2b67rffvtNkhQTE5Pn1D0AwEVFWfsEALg0lLa2okwHTK1atdJHH32kLVu2eD1/5MgR/f777+6yLnXr1lXNmjV19OhRbdq0SXfccUeuazdt2pTrOgBA3oqy9gkAcGkobW1FmZ6SFxcXp7CwMCUlJWn9+vW5zi9cmJW+tlmzZrriiis8znXr1k2StGjRolzXnT59WkuXLpUk9wa2AAAAAMqeMh0wVa9eXX369JEkPfnkk9q/f7/73MqVKzVjxgxJ0ujRo3NdO2TIEJUrV04bN27Um2++KYfDIUlKSUnRuHHjlJKSombNmummm24KwDsBAAAAUBIM0yqrQSlz+PBh9ejRw/19RkaG0tLSFBoa6pF0YejQoRo2bJj7+/Pnz2vQoEH66aefFBISooYNGyotLc29dmnw4MGaMGGC13suXbpU48aNk91uV3R0tGrXrq3ExESlpaWpevXqmj9/fq6RqcKIi4uTJK1YscLn1wAAXJpoQwAgMIJmDZPD4XBnpsvObrd7HM++n5KUlS58zpw5mjVrlj7//HMlJSUpLCxM7dq104ABA9xT77zp3r27LrvsMr3zzjvatGmTfvnlF9WsWVN33XWXRo0apejoaH+9PQAAAAClUNCMMJVF9A4CAHxFGwIAgVGm1zABAAAAQFEQMAEAAACABQImAAAAALBAwAQAAAAAFgiYAAAAAMACARMAAAAAWCBgAgAAAAALBEwAAAAAYIGACQAAAAAsEDABAAAAgAUCJgAAAACwQMAEAAAAABYImAAAAADAAgETAAAAAFggYAIAAAAACwRMAAAAAGCBgAkAAAAALBAwAQAAAIAFAiYAAAAAsEDABAAAAAAWCJgAAAAAwAIBEwAAAABYIGACAAAAAAsETAAAAABggYAJAAAAACwQMAEAAACABQImAAAAALBAwAQAAAAAFgiYAAAAAMACARMAAAAAWCBgAgAAAAALBEwAAAAAYIGACQAAAAAs+Bww3XvvvfrnP/9Z4PJjx47Vfffd5+vtAAAAACDgQn29cMOGDapevXqBy2/dulWHDx/29XYAAAAAEHABm5JnmqYMwwjU7QCgUEzT1MaDG2WaZklXBQBQhtHeBJ+ABEwOh0N//vmnypcvH4jbAUChzd0+V+1mtNO8HfNKuioAgDKM9ib4FHhKXmpqqs6cOeNxzOl06vDhw5YRsmmaSklJ0YcffqiMjAw1bty4aLUFgGJgd9oVvzpekhS/Ol59W/RVqM3nGcsAAHhFexOcCvwJzZo1S//97389jp08eVI33XRTga43DEN33nln4WoHAAGwYMcCJZ5KlCTtP7lfC3cu1ICWA0q4VgCAsob2JjgVakqeaZru/wzD8Pg+r/9q1qypf/7znxowgB8IAKWLq7fPUNYaS5tsil8dL7vTXsI1AwCUJbQ3wavAI0z33XefevbsKSkrcLr55ptVrVo1LV682PIam82myMhIRUVFFb2mAFAMsvf2SZJTTnr9AAB+R3sTvAocMEVFRXkEPm3btlXVqlVVr169YqkYABS37L19pi6uxXT1+jG3HADgD7Q3wc3nLHkJCQl66623/FkXAAgoV29f9sZL8uz1AwCgqGhvglvA9mECgNIk51zynJhbDgDwB9qb4Fegsb+NGzf67YZt27b122sBgK/WJK/xmEuek6vXb03yGnWO7Ry4igEAyhTam+BXoIBp4MCBMgzvUXFhGIah3bt3F/l1AKCoOsR00KK7FyndkW5ZJiIkQh1iOgSwVgCAsob2JvgVeHWZ1ea0heGP1wAAf4gIjVCv5r1KuhoAgDKO9ib4FShg2rt3b3HXAwAAAABKHZI+AAAAAICFgAZMe/bsCeTtAAAAAKBIin2HrDNnzujzzz/Xhx9+qL1795L0AQAAAEDQKLaAad26dfrggw/0zTffKCMjQ6Zp+iXTHgAAAAAEil8Dpj/++EMffvihPvroIx06dEhSVma8cuXKqVOnTurevbs/bwcAAAAAxarIAVNmZqa++eYbffDBB1q/fr2cTqd7NCkuLk5/+9vf1KVLF5UvX94f9QUAAACAgPE5YPr555/1wQcf6PPPP9fp06fdeyy1atVKW7dulSS9/PLLBEoAAAAAglahAqbU1FR9/vnn+uCDD9zJG0zTVJ06dXTHHXeoZ8+eio2NVZMmTYqlsgAAAAAQSAUOmB577DF9/fXXSk9Pl2maKl++vLp166YePXqoffv2xVlHAAAAACgRBQ6YPv/8cxmGofbt2+vOO+9U165dVaFCheKsGwAAAACUqEJvXHv8+HEdP35cqampxVEfAAAAACg1ChwwDRkyRNHR0fr111/16quvqkuXLho6dKi++OILpaenF2cdAQAAAKBEFDhgeuyxx/Ttt99q8uTJ+utf/ypJWrNmjR577DHdcMMNevLJJ7Vx48ZiqygAAAAABFqhsuSFhITo5ptv1s0336xjx47p448/1ocffqgDBw64N6ytU6eOu7xhGH6vMAAAAAAESqHXMLnUqFFDw4cP17JlyzR37lzdeeedKleunA4dOuQu079/f82cOVMHDx70S2UBAAAAIJAM07XjrB+kpqZqyZIl+vDDD7V9+/asG1wYZWrevLn+9re/afDgwf66XdCLi4uTJK1YsaKEawIACDa0IQAQGH4NmLL79ddftXjxYn3++ec6efKkJMlms7k3vAWNHQDAd7QhABAYPk/Jy0/Dhg31r3/9S999953eeOMN3XDDDcV1KwAAAAAoFoVK+pDdJ598Iknq2LGjqlevblkuLCxM3bt3V/fu3fXHH3/4ejsAAAAACDifA6bHH39coaGhhUolXrt2bV9vBwAAAAAB53PAVLlyZUlS+fLl/VYZAAAAAChNfF7D1KBBA6Wmpurs2bP+rA8AAAAAlBo+B0x33XWXHA6HFi9e7M/6AAAAAECp4fOUvF69eun777/XK6+8orCwMPXp00ehoT6/HAAAAACUOj5HOE888YQqVqyo8PBwPffcc3rrrbd09dVXKzo6Wjab94ErwzD0wgsv+FxZAAAAAAgknwOmjz/+WIZhyLXv7enTp7VmzRqvZV3lCJgAAAAABBOfA6YePXrIMAx/1gUAAAAAShWfA6YXX3zRn/UAAAAAgFLH5yx5AAAAAFDWETABAAAAgAW/5AE/fvy4li1bpp07d+rPP/+UJEVHR6tFixbq1q2bqlev7o/bAAAAAEBAFSlgcjgcevPNN/Xee+/JbrdLkjtrnmEY+uSTT/Tiiy9q8ODB+uc//6mQkJCi1xgAAAAAAqRIAdP48eP15ZdfyjRNhYeHq0WLFqpdu7Yk6Y8//tDOnTuVkZGhadOm6dChQ3r55Zf9UmkAAAAACASfA6ZvvvlGS5YskSTdf//9GjlypCpVquRRJiUlRW+//bbeffddffHFF+revbvi4uKKVmMAAAAACBCfkz588MEHMgxDDzzwgCZMmJArWJKkqKgojR8/Xg888IBM09TixYuLVFkAAAAACCSfA6YdO3bIZrNpyJAh+ZYdMmSIbDabduzY4evtAAAAACDgfA6YTp8+rcjISEVFReVbNioqSlFRUTp9+rSvtwMAAACAgPN5DVPlypV16tQppaamKjIyMs+yKSkpSklJUdWqVX29XZFMmjRJkydPzrPMM888o379+uU6npmZqdmzZ+uzzz5TcnKywsLC1KRJEw0cOFBdu3YtrioDAAAAKAV8Dpiuvvpqffvtt5o1a5bGjBmTZ9lZs2bJ6XSqRYsWvt7OL6Kjo3XFFVd4PVejRo1cx9LT03X//fdr8+bNCgkJ0VVXXaVz585pw4YN2rBhg4YNG6ZHH320uKsNAAAAoIT4HDDdddddWr16taZMmSK73a5hw4apYsWKHmVSU1M1ffp0TZs2TYZh6O677y5yhYuiU6dOevHFFwtc/uWXX9bmzZsVExOj6dOnq0GDBpKkFStW6OGHH9b06dN17bXX6qabbiquKgMAAAAoQT4HTF27dtWtt96qr776Su+8845mzZqlq6++WjVr1pQkHTlyRDt37lR6erpM09Tf/vY33XLLLX6reHE7fvy4Fi5cKEl6/vnn3cGSJMXFxWno0KGaMmWKJk+eTMAEAAAAlFFF2rj2pZdeUu3atZWQkKDz589r48aNMgxDkmSaZtYNQkM1cOBAjR07tui1DaCVK1cqMzNTsbGxat++fa7zffv21ZQpU7Rr1y4lJyfr8ssvL4FaAgAAAChORQqYwsLCNGHCBA0aNEjLly/Xzp079eeff0rKWi/UokULde3aVbVq1fJLZYtq7969GjdunI4dO6aKFSuqcePG+vvf/66GDRvmKrt161ZJUuvWrb2+Vq1atRQTE6Pff/9dW7duJWACAAAAyqAiBUwutWrV0sCBA/3xUsVqz5492rNnj/v7lStXaurUqbr33ns1YcIEhYSEuM8lJSVJUp6B0OWXX67ff/9diYmJxVZnAAAAACXHLwFTaVezZk3985//1I033qiYmBhFRkYqMTFR8+fP18KFCzV79myFhoZq/Pjx7mtce0ZVrlzZ8nVd586cOVO8bwAAAABAibgkAqY+ffrkOta4cWM9++yziomJ0SuvvKLZs2frnnvuUUxMjKSslOJS1rRDK+Hh4ZKk8+fPF0OtAQAAAJS0IgdMBw4c0FdffaWff/5ZZ86cUWZmpmVZwzA0e/bsot7SrwYPHqw5c+bo6NGjWrlype69915JUkREhCTl+X4yMjIkSeXKlSv+igIAAAAIuCIFTK+99ppmzpwpp9PpzoqXF1cGvdIkJCRE11xzjb7++msdOHDAfbxSpUqSLk7N88Z1zlUWAAAAQNnic8A0b948TZs2TZIUExOjDh06KDo6WqGhwTfLzzXtzm63u4/FxsZqy5YtHkFUTsnJye6yAAAAAMoen6ObhQsXyjAM9ezZU88995xsNps/6xVQv/76qySpdu3a7mOtWrXSRx99pC1btni95siRI/r999/dZQEAAACUPT5HOa6RlyeeeCKog6XVq1e7A6YbbrjBfTwuLk5hYWFKSkrS+vXrc123cOFCSVKzZs10xRVXBKayAAAAAALK50inUqVKioqKUlRUlD/r43e//vqrJk6cqL1793ocdzqd+uKLLzRu3DhJUpcuXdSyZUv3+erVq7uz6z355JPav3+/+9zKlSs1Y8YMSdLo0aOL+y0AAAAAKCE+T8lr2bKlVq9erRMnTqhatWr+rJNf2e12vf/++3r//fdVpUoV1a1bVyEhIUpOTnYnbWjTpo1eeumlXNc+9thj2rVrl3766SfddtttatiwodLS0txrlwYPHqybb745oO8HAAAAQOD4PMI0ZMgQGYahqVOn+rM+flevXj09/PDD6ty5s6KionTgwAHt3btXYWFh6tSpk1566SXNmTPHa6a7cuXKac6cORo3bpyuvPJKJSUl6eTJk2rXrp3eeustTZgwoQTeEQAAAIBAMcyC5AO3sHjxYj377LO66667NHz4cPemryiYuLg4SdKKFStKuCYAgGBDGwIAgVGkHOC9evXSyZMn9dprr2nx4sWqXLmyKlasaFneMAx98803RbklAAAAAASMzwGTw+HQ+PHj9eWXX0qSTNPUqVOndOrUKctrSuPGtQAAAABgxeeAafbs2VqyZImkrKQJHTt2DNqNawEAAADAG5+jmw8++ECGYWj48OF65JFH/FknAAAAACgVfM6Sd/DgQRmGoREjRvizPgAAAABQavg8wlSpUiWlp6erQoUK/qwPAAAAAJQaPo8wtW3bVikpKTpy5Ig/6wMAAAAApYbPAdPw4cMVHh6ul156yZ/1AQAAAIBSw+eAqUmTJpo0aZK+//57DR06VOvXr1daWpo/6wYAAAAAJcrnNUxNmzZ1f7127VqtXbs232sMw9Du3bt9vSUAAAAABJTPAZNpmv6sBwAAAACUOj4HTHPmzPFnPQAAAACg1PE5YGrXrp0/6wEAAAAApY7PSR8AAAAAoKwjYAIAAAAACz5Pydu4caNP17Vt29bXWwIAAABAQPkcMA0cOFCGYRTqGtKKAwAAAAgmPgdMUuFTi5OKHAAAAEAw8Tlg2rt3b57nU1NTtW3bNk2dOlW//vqrJk+erDZt2vh6OwAAAAAIuGJL+hAZGakbbrhBc+bM0V/+8heNHDlSv/32W3HdDgAAAAD8rtiz5BmGoccee0wpKSmaMmVKcd8OAWKapjYe3Mg0SwAA8kGbCQS3gKQVb9CggSIjI/XDDz8E4nYIgLnb56rdjHaat2NeSVcFAIBSjTYTCG4BCZgyMzN1/vx5nThxIhC3QzGzO+2KXx0vSYpfHS+7017CNQIAoHSizQSCX0ACpm+++UZ2u13R0dGBuB2K2YIdC5R4KlGStP/kfi3cubCEawQAQOlEmwkEv2ILmDIyMpScnKzp06fr6aeflmEY6tSpU3HdDgHi6ikzlLUHl002esxwSWANAoD85HxO0GYCZUOB04p/8sknioiI0K233ipJatq0aYFvYpqmatWqpdGjRxe+hihVsveUSZJTTneP2YCWA0qwZkDxmrt9ru795F4l9EzgZx2AVzmfE7SZQNlQ4BGmxx9/XC+88IL7e9M0C/RfRESE7rjjDi1atEi1atUqljeBwMjZU+ZCjxnKOtYgAMhPzufEeft52kygjCjUxrXZp6LMmTMnz7IhISGqXLmyYmNjFRrq8/64KEVy9pS50GOGss7bGgR+1gFkl/M58fDSh2kzgTLCMAs4Ib9JkyaqXr261qxZU9x1umTExcVJklasWFHCNcmf3WlXo0mNlHQqSaZy/8jYZFNs1Vj9POZnhdoIkFF25PzZ52cdpUUwtSFlXc7nhCFDIbYQOZwO2kygDAhIljwEvzXJa5R4KtHrg1+62GO2JpmAGmWLq9fY9bOfvXcYAKTczwlTpuxOO20mUEbQrYEC6RDTQYvuXqR0R7plmYiQCHWI6RDAWgHFK/u6vex/+LjWIPRt0ZfeYeASZ/WcMGSoeoXqevmWlxViC8l1HW0mEDxo6VEgEaER6tW8V0lXAwgo1u0ByI/Vc8KUqWNpxxRiC+E5AQS5QgVMf/75Z6HSiedkGIZ2797t8/UAEChWvcYujDIB4DkBXBoKvYapoOnErf4DgGDAuj0A+eE5AVwaCtXdUb58eQ0ePLi46gIApQbr9gDkh+cEcGkoVMBUoUIFjRkzprjqAgClBuv2AOSH5wRwaSCtOAAAAABYIGACAAAAAAsETAAAAABggYAJAAAAACwQMAEAAACAhQJnydu7d29x1gMAAAAASh1GmAAAAADAAgETAAAAAFggYAIAAAAACwRMAAAAAGCBgAkAAAAALBAwAQAAAIAFAiYAAAAAsEDABAAAAAAWCJgAAAAAwAIBEwAAAABYIGACAAAAAAsETAAAAABggYAJAAAAACwQMAEAAACABQImAAAAALBAwAQAAAAAFgiYAAAAAMBCaElXACi09HTps8+y/m8lIkK6446s/wMASgee3wCCEAETgs+6dVLv3vmXW7VK6ty52KsDACggnt8AghBT8hB8OnaU6teXDMP7eZtNatAgqxwAoPTg+Q0gCBEwIfiEhkrPPiuZpvfzTmfW+VAGUAGgVOH5DSAI8URCsTJNU5sObVKbum1kWPUo+qJfPyk+XkpK8mx4bTYpNlbq29f7dcyfB4Dil+NZa5qmEk8mqn7V+jKcTqlGDenYMc9r8nt+A0AJIWBCsZq7fa7u/eReJfRM0ICWA/z3wq5eynvv9TyeX+8k8+cBoPjleNYakhrkdw2jSwBKKabkwYPT6dTsrbPldDqL/Fp2p13xq+MlSfGr42V32ov8mh769fOcC++a+55X72QR5s+bpqmNBzfKtJpKAgDI0rGjzPr15bSaWGCzeQZGBXl+A0AJIWCCh1FLRmnQp4M0+svRRX6tBTsWKPFUoiRp/8n9WrhzYZFf00POufAF6Z0swvz5udvnqt2Mdpq3Y54fKg8AZVhoqF68uZxsVv1LTqc0ZIjn94wuASilCJjgdt5+XjO3zpQkzfhphs7bz/v8Wq7RJUNZ3Ys22Yp3lEkqeO9kzpEplzx6OIt9tAwAypDUjFQ9XXuP9leRHDnOOQzJbFBfeuONwj+/AaAEEDAFMX9PEXv4q4fdgYDdadcjSx/x+bVco0umsurmlLN4R5mkgvdOWo0y5dHDWeyjZQBQBrjapdvn3y5HiBTfRQrJUSbElH4Y0k0qV67wz28AKAEETEHMn1PEso8uufg6ypRzdMml2EaZBgyQNmyQ+vcv+DWFWP8UsNEyAAhyrnZp9YHVkqQFLeQxyuSQ9L+q0v3ll2U9Q315fgNAgNGdE6RyThHr26KvQowQzxTehUih/fDyh3MFAK5Rprdve9vjeH6pwrOPxmSXfZTJrxnzDENq27Zw1+TMslfA0SWpGN8HAASDHG2L0+nU2t/WqkNMB235Zrz6n5XSQ6XPGksZoVmjTAkfZ10aIim+s/TrmcSLz9DCPr8BIMAMk5RfJSYuLk6StGLFikJfm7AtQfd+cjGldkLPBJmm6ZnCe/VqqUuXfF8r45tlqvjD372OmITaQpXyRIrKhZbLdW9vqcLtTrsaTWqkpFNJ7ul42dlkU2zVWP085meF2ko4XrfbpUaNpMTErNGln3/OFTBZvZ9S9T4AXJKK0oYUSQHbls73Sd/Wl0Ic0i+TpAanskaXGo+RzBCeoQCCB1PygpC3KWITV03UxFUTJWVLSlDAFNoPp3xgOb0s51qm/JIffH/ge4+1Szm5RmfWJK8p1HsuFgVY/5RzLZZLsa3JAoDS7kLKcNOibXFNu1tz+YXvL6xlkrJGlxwhpawtAIB80K0ThLxNEcv+vcd0MW+bu7ovdCpj4lOavmN4nveb8dMMvd79dZULLec1+UH2USbXuQdaP6AbLr/B6+tFhESoQ0yHAr3XYjdggNSkidSmTa5T2QNTq9Ey13RIekgBXDJCQ7V2SFd1fOodr6dd0+4c2bI9zG0p7a0ubaorTfv7NJUPL1+62gIAyAN/5QUZu9Ou57+ZqF67pHCLnAM2GVqfNE59p/RUaL9+Uny8lJTkmRXOZpNiY/X2lSdkT8o7eYHdadfUTVM1pt0YjwAiZ8Bgd9r13HfPSZKW71+uSX+bVKBAIs81UQVYh2WGh2tzm3pqXf96r2uq8pTH+qc1yWu8rsVyyd5D2jm2c+HuCwCl2Zkz0jPPSOfOScpap/TLiV/VqFpDSdKBHfPVIkKKSvfMgueQlFRVWtgix+sZ0qZ6WV9uO7pNk/82ubjfAQD4DQFTkFmwY4Fq70jSosV5lTIlHdXXHV/QLUOe9z7KdCHJwf1t7tCBswd1LvOc5auVDyuvwa0G55v8IL/RJytzt8+1XBOldeuk3r3zvN6Q9Oh90tCxXq4vgg4xHbTo7kVKd1gHa/SQAiiT3n1Xev1197c2SU0kSaskSVY57byNLjWr3ky1I2tLympP4jvF+7++AFCMSPpQggq7YNeVgOC3PxP18yTpilO597eQLvbw3fp0fe1+6BeFOpWV3MA1ynRhdMlbkoNcLozwOM6l6dGvH9Pxs8c8JqfZZKhS5Rp6ZfIvajbjL+7kCJZJEXKMGDmcDj369WM6dvaYalasoZFtRuqqOs1l3HlnVgY/V2KGnCNkF5g2m5Kr2nTlKLuuqN6ABcQALhl+T/qQfVTJ4ZA5Y4ZkmvI2bm9KshvS75Wly09ltUWutqfxmIsBEwlyAJQFPL2CiHuKWIhnmtacvKZtLWAK7VwujPCESHrda4Gs0azXGg1UYkYBUm/nGDHyfN1j0tx/Z325apXUuXPu9N85GE6nnuzklCOkcKNaAIAccowq5TXB2ZA041rph8tzpwzPPrrE1GUAZQEjTCWosL2D6fZ0ffbzZ0p3pMuwO3THHY+pwsFjXuePu9K2XlHlCs37xzy1r9VGRuPGeabQ9spul9mooZyJSXmOZjX/Z4jSDYfHOa89i/mMGDkk/R4dqnqHUhQaXi7Pa1yjS1eNssseQk8mgEuL30eYzp+XGRUl2e15BkumJLtNinxCcthypwx3hEg2w6bpt01XeGi4IkIidEfjOxQRGuGfegJAgJFWPIhEhEaoV/NeGtBygPpfe59+Hzs0VxCTvYfPlT3v+pnXa96e9/NNoe1VaKj2junnNVjKfr+cwZJkkXrbNWJkEaeHSHqyk10L936Q7zVZo0tZwZLl/QAABVOunH6588Y8gyXpwujSX6SMMO8pwyXJaTp1JuOMBrQcoF7NexEsAQhqjDCVoKL2DqafP6vMqxqo4qFjMkxTpmEotV4NffbpS3KG2LTvxD69u/5ttf/pmC6LqKGRrUfqqtM2GQ0ayJSUeDJR9avWl1GunNStm7Rs2cVsdJmZ0pYtkt0uuyNTznnzFJZ23qMhNSWdrxKpx7pKpzJTL9Yr2w7vVqNM3katso+OXR5dX788+IvlyFTO0SUXRpkAXCp8bkN+/126807p/HlJktM0dTj1kOpUrKN9J37VVUcdMqRcz3tDnqNLGWEXT7Y5JP0SG6W+V/eTzbCpfFh5PfPXZ1SpXKWivUkAKAX4izKIRZSrqIj/vOJe32OYpqL+86r6XztACdsS9O/v/q2/JkqLF0se64OU1fA1yP5ir78uPfKIvLH6ITEklT+VqsmLcp9z7fDudf56aKjWDumWaw+P7KNjiacSPdcj5VjL5Fq7ZM8x9GW5dgoAkOXBB7M6xC6wScrK+H1ajSwuMbL93zW6lP3kpnqSMlPU7+p+rFUCUOYwJS/Y9esn1a+f9XWDBlLfvrI77Zq4aqIkaUNd6UgFyWlxuVOSWaOGVLWqVKNGkatjGoZS6tXQz02qS5JqVKihBf9Y4JF62+606/7yy7S/StaoknRxZ/jse3dMXDVRdme2PaKyvdcD0aF6P+c+Hxe49ofyuBYALmXHjkm33SbFxUknTsiUvGzHncXM9p9yfJ1pkx7unvW1zbBp5u0zldAzQQk9E7To7kVsswCgTCJgCnaukRfJvTZpwY4FSjqdJElqd0iqlWb9QdskGceOSYMGZTWoRWSYpraNukt/pB+XJB1LOya70+4xf31N8hrtS0lSfJeLadG9ZVdyjTJ5e6/Z1y7llH1UCwCgrGfnkiXSypXSd9/lmnKXnSHpRITnqNLurD4wj9Gl7OuUWKsEoCxjDVMJ8luGI9OUNm2S2rSR3XSo4VsN3QFTiCMrg1H9U94bR1PSb1VDdFnly2QcOGCZjKFAbDaZsVeo0WhT/0s5YLkfkyvb37lzKfprt2G64oTTI7uSiyFD9avW91yPZJrKWP+DPo06qHRnhmVVyMoEoKwrVBuSmipFReVbzNUCVB8n/fG6FObMGlWKfFzqu1v6uE1F9WvZn3VKAC4prGEqgPXr1+u9997Ttm3blJaWprp166p79+4aPny4KlSoUNLVkwxDattWkrRg+8XRJeliBiOrPZsMSf/q7NDI1t10Q441RXlq1kzavdvzmNOptUO6al/KxdfxtqbIle1vddJqPfVXpxI+zj26JEmmzNzrnwxD4R1uUK+C1xQAEBkpdemStcddHgxJO6tLJ6KkmX+RHth8YVQpXJrTSpL9LOuUAFxyGGHKR0JCgp5//nmZpqnatWurWrVq2rdvnzIyMnTllVdq/vz5qlKlik+v7e89NOxOu8fokovVKJMpaX8VqfGDUv0qsfplsmQkFWCUKSREOnlSuuaai3sjeRldcrHKXJduT9dnez9V5Pa9+rN5/azALwdGigDAu0K3IampWfssyToDniRVmiCllpfklO7dLi1tX10vdXtFIbYQnskALkmMMOVh586deuGFFyRJ//73v9W7d28ZhqEjR45o5MiR2rVrl55++mlNmjSphGuaJfvapeysRpkMZR13hEj7UpL0w5ARBRtlGjYsa2pHtqx13kaXXKwy10WERqhXi96SRfIGAIAfRUbq56vrqsmOQx6Hs69V2ln9QrAkSbYLo0rnj+uKKlcwqgTgkkXShzxMmTJFTqdTd955p/r06SPjwghIrVq19Nprr8lms2n58uXau3dvCdc0a3QpfnW85fkFLbJGk7JnPfpfFc+sdIPKL5NZP9brSI9baGhWCnLJI2ud2aC+7i+3TIbFMmIy1wFAyTpvP692dxy2zIBnSuow5GL5qbdNJfsdAIiAydLZs2f1/fffS5J69+6d63xsbKzat28vSVq6dGlA6+bNmuQ1SjyVaHneNcqUvSfRNbrksi8lSXvH9POckteli+cLDR0qlSuX9XW2rHV7x/TTvpQkj6l42ZG5DgBK1sNfPayUMFMrYz3bgrMX5prsyj66JGnpr0vJfgcAYkqepT179igjI0Ph4eFq2bKl1zKtW7fWDz/8oG3btgW4drl1iOmgRXcvUrojXQ6nQ49+/aiOpx33KLOghfTsKqnBqdx7HhkyVKNiDV3+wARp8kIpMVGqVy8rDW3z5lnf160rvfWW540HDJCaNFGDVldr0S+tlO5It6xjREgEvZQAUALO289r5taZkqTb+0ln/3Nx3VLNsdIbX0sj/i41qdZENptN5ULLaVL30jHdHABKGgGThcTErNGaunXrKiwszGuZyy+/3KNsSXJlnpOk1UmrcwVLkudappxZ6UyZOnr2qDYe/UmdXWuTXnxRKl/+4lql//f/pJz/Fhcy9EVI7vsDAEqXqZumuqdEn4uQVsZKcUnSiljpXAVpxJ1Z5Ua0HaGH2z9cQrUEgNKJgMnC6dOnJUmVK1e2LOM65ypbWmQfbXLJdGRqy+Etsl+bqRc7HlPVJnX0bp3WCgu5GAC5R4AG/FVq0kRq0ybrxIVRJPf3AICgMrjVYCWfTta5zHOSpA9aOZS2YLO+7NdaD4Rk9Z6VDyuvwa0Gl2Q1AaBUImCykJ6eFWxYjS5JUnh4uEfZ0iL7aFN29//l/oK/yIV9nSR57PMEAAg+lcpV0mvdXvM8eKd0e8lUBwCCCkkfLEREZC1wzczMtCyTkZHhURYAAABA2ULAZKEg0+0KMm0PAAAAQPBiSp6F2NhYSdKhQ4eUmZnpdWpecnKyR9nCOnr0qBwOh3u3dgBA4dSpU0dz584t6WqUCNoQACiagrYhjDBZaNq0qcLCwpSRkaHt27d7LbN582ZJUqtWrXy6R0REhEJDiVkBAIVHGwIAgcGT1kJkZKQ6duyoVatWadGiRWrdurXH+aSkJK1fv16S1L17d5/usWnTpiLXEwBwaaINAYDAYIQpD6NGjZJhGPr000/1/vvvyzRNSVnTIMaOHSun06mbb75ZTZo0KeGaAgAAACgOhumKAuDVrFmz9OKLL8o0TdWpU0dVq1bVvn37lJGRofr162v+/PmqVq1aSVcTAAAAQDEgYCqAdevW6d1339X27duVlpamunXrqnv37ho+fLgqVqxY0tUDAAAAUEwImAAAAADAAmuYAAAAAMACARMAAAAAWCBgAgAAAAALBEwAAAAAYIGACQAAAAAsEDABAAAAgAUCJgAAAACwQMAEAAAAABZCS7oCKJz169frvffe07Zt25SWlqa6deuqe/fuGj58uCpUqFDS1Suzjh07prVr12rnzp3asWOH9uzZo/T0dLVr104JCQl5XpuZmanZs2frs88+U3JyssLCwtSkSRMNHDhQXbt2zfPa3bt3a9q0adq4caPOnDmjmjVrqkuXLho1apSqVavmz7dYZpmmqZ9++kkrV67U5s2btX//fqWmpioqKkrNmjVTjx49dPvtt8swDK/Xnz17VtOmTdOyZct06NAhVahQQddcc40GDx6s6667Ls978/uKQOLnDUU1adIkTZ48Oc8yzzzzjPr165freFHaOpQdZfXvJcM0TbPIr4KASEhI0PPPPy/TNFW7dm1Vq1ZN+/btU0ZGhq688krNnz9fVapUKelqlkmzZs3Sf/7zn1zH83sApKen6/7779fmzZsVEhKiq666SufOnVNycrIkadiwYXr00Ue9Xrt8+XKNHTtWmZmZio6OVu3atZWYmKi0tDTVqFFDCxYs0GWXXeafN1iGrVu3ToMGDXJ/f9lll6lSpUo6ePCgTp06JUnq3LmzJk2apPDwcI9rT5w4oXvuuUeJiYkKDw/XVVddpRMnTuiPP/6QYRh6+umn1b9/f6/35fcVgcTPG/zBFTBFR0friiuu8FpmyJAhuvnmmz2OFaWtQ9lSZv9eMhEUduzYYTZp0sRs3LixuXDhQtPpdJqmaZp//PGH2bNnT7NRo0bmmDFjSriWZdfixYvNQYMGma+++qq5fPly84033jAbNWpkDhgwIM/r/u///s9s1KiRedNNN5n/+9//3Me/+eYbs0WLFmajRo3MFStW5Lrujz/+MK+55hqzUaNG5htvvGFmZmaapmmaZ86cMYcMGWI2atTIvOuuu9w/B7C2du1a86abbjJnz55tHj9+3OPcxx9/7P4cXnrppVzXPvDAA2ajRo3Mnj17mn/88YdpmqbpdDrNhQsXmo0aNTKbNm1q7t69O9d1/L4ikPh5g7+89dZbZqNGjcwJEyYU6jpf2zqUPWX17yUCpiAxcuRIs1GjRub48eNznUtMTDSbNGliNmrUyNyzZ08J1O7Sk5CQkO8D4NixY2bz5s3NRo0amevWrct13vUQ6dmzZ65zzz33nNmoUSOzf//+uc6dOnXKbN26NQ1QAaWkpJgZGRmW599++22zUaNGZrt27UyHw+E+vmvXLrNRo0ZmkyZNzKSkpFzXPfbYY5Z/iPL7ikDi5w3+4kvAVJS2DmVfWfl7iaQPQeDs2bP6/vvvJUm9e/fOdT42Nlbt27eXJC1dujSgdYO1lStXKjMz0+Pzya5v376SpF27drmHnF2WLVsmyfvnXblyZXXv3l2S9NVXX/m72mVOZGSkwsLCLM936tRJknTq1CmdOHHCfdz1GbRv397r1JQ+ffpIkr799lulpaW5j/P7ikDi5w0lrShtHSAFx99LBExBYM+ePcrIyFB4eLhatmzptUzr1q0lSdu2bQtk1ZCHrVu3Srr42eRUq1YtxcTEeJSVpMOHD+vIkSOSpLZt23q9tk2bNpL4vP3h/Pnz7q/LlSvn/tr1mbj+rXNq2bKlwsPDlZ6erj179riP8/uKQOLnDcVh7969GjdunO69916NHDlSb7zxhn799VevZX1t6wCXYPh7iYApCCQmJkqS6tata9lTfvnll3uURclLSkqSdPGz8cbb5+a6LiwsTLVr1/Z6nWvx4m+//abMzEw/1PbStWTJEklSkyZNFBkZ6T6e3+cXFhamOnXqSPL8/Ph9RSDx84bisGfPHn3xxRf68ccftXLlSr399tu6/fbb9cILL8jhcHiU9bWtA1yC4e8l0ooHgdOnT0vKGlq04jrnKouSV5jP7cyZM+5jrsxtlStXtkx17cp25XQ6lZqaqqpVq/qhxpeenTt3auHChZKk4cOHe5zz9fPj9xWBxM8b/KlmzZr65z//qRtvvFExMTGKjIxUYmKi5s+fr4ULF2r27NkKDQ3V+PHj3df4+qwEXILh7yUCpiCQnp4uSXmuw3ClQ3aVRckrzOeWfVpYYa7LXh6Fc/z4cT344IOy2+265ZZb9Pe//93jfCA+Pz47FBU/b/An19rM7Bo3bqxnn31WMTExeuWVVzR79mzdc8897ilSvj4rAZdg+HuJKXlBICIiQpLyHErMyMjwKIuSV5jPLfvamcJcl708Ci4lJUXDhg3ToUOH1Lx5c7344ou5ygTi8+OzQ1Hx84ZAGTx4sGrWrCm73a6VK1e6j/v6rARcguHvJQKmIFCQ6RQFGc5EYFWqVElSwT43V1nJ8/M2LfaVdg1D22w2j3U3yN/Zs2c1dOhQ7d69Ww0bNtTMmTO9/hv64/PL7zp+X1FU/LwhUEJCQnTNNddIkg4cOOA+7uuzEnAJhr+XCJiCQGxsrCTp0KFDllG0K82iqyxKnuuzyN6w5OTtc3N9nZmZqcOHD3u97rfffpMkxcTE5DkUDU/nzp3TiBEjtHXrVsXGxuq9996znM+c3+eXmZmpQ4cOeZTN/jW/rwgEft4QSK72xm63u4/52tYBLsHw9xIBUxBo2rSpwsLClJGRoe3bt3sts3nzZklSq1atAlgz5MX1WWzZssXr+SNHjuj333/3KCtlZbuqWbOmJGnTpk1er3Ud5/MuuPT0dI0cOVIbN25UvXr1NGvWLNWoUcOyvOvf1vW7ldP27duVmZmpiIgINW3a1H2c31cEEj9vCCRXavHsGcl8besAl2D4e4mAKQhERkaqY8eOkqRFixblOp+UlKT169dLknuDLpS8uLg4hYWFeXw+2bmyszVr1izXxqjdunWT5P3zPn36tHsDSj7vgsnMzNSDDz6odevWqVatWpo9e7Y7JbgV12fw448/eu31ev/99yVlbXxbsWJF93F+XxFI/LwhUFavXu0OmG644Qb38aK0dYAUHH8vETAFiVGjRskwDH366ad6//333XM1jx49qrFjx8rpdOrmm29WkyZNSrimcKlevbo749CTTz6p/fv3u8+tXLlSM2bMkCSNHj0617VDhgxRuXLltHHjRr355pvufS9SUlI0btw4paSkqFmzZrrpppsC8E6Cm8Ph0Lhx4/Ttt9+qRo0amj17tntfhrw0b95cXbp0kcPh0COPPKKjR49KkkzT1Pvvv69PP/1UNptNI0eOzHUtv68IJH7e4A+//vqrJk6cqL1793ocdzqd+uKLLzRu3DhJUpcuXTw2SS5KWwdIwfH3kmFarZJCqTNr1iy9+OKLMk1TderUUdWqVbVv3z5lZGSofv36mj9/vqpVq1bS1SyTDh8+rB49eri/z8jIUFpamkJDQz0WEQ4dOlTDhg1zf3/+/HkNGjRIP/30k0JCQtSwYUOlpaW55+IOHjxYEyZM8HrPpUuXaty4cbLb7YqOjlbt2rWVmJiotLQ0Va9eXfPnz6e3rgCyN/T16tVTrVq1LMs+/fTTatasmfv7EydOqF+/fkpKSlJ4eLiuuuoqnTx5UocPH5ZhGHryySc1cOBAr6/F7ysCiZ83FNWePXvc7VyVKlVUt25dhYSEKDk52b3gvk2bNnr77bdzJW8oSluHsqWs/r1EwBRk1q1bp3fffVfbt29XWlqa6tatq+7du2v48OEe04LgX7///rvi4uLyLTdmzBg9+OCDHscyMjI0a9Ysff7550pOTlZYWJiaNm2qAQMGuIeSrezatUvvvPOONm3apDNnzqhmzZrq0qWLRo0apejo6CK9p0vFRx99pCeeeKJAZefMmaPrrrvO41hqaqqmT5+upUuX6tChQ6pQoYJatmypIUOGqH379nm+Hr+vCCR+3lAUZ86c0bx587R161b973//04kTJ5SRkaHKlSurWbNmuu2223TbbbcpJCTE6/VFaetQdpTVv5cImAAAAADAAmuYAAAAAMACARMAAAAAWCBgAgAAAAALBEwAAAAAYIGACQAAAAAsEDABAAAAgAUCJgAAAACwQMAEAAAAABYImAAAAADAAgETAAAAAFggYAJwSfrwww/Vp08fXXvttWrcuLEaN26sWbNmlXS1AAAB5moDfvzxx5KuCkqp0JKuABCMGjdu7PO1//nPf3TXXXf5sTbBx9u/n2EYqlChgurVq6c2bdqof//+uuqqq4rl/u+++67+3//7f5Kk0NBQRUdHu+8PAIFEe+JfL7/8smbMmCFJuu222/Tqq6/6/FpnzpzR7NmzJUn33XefKlWq5Jc6IvgQMAE+qF69utfjaWlpSktLy7NMuXLliq1ewaZChQruIMXhcOjUqVP65Zdf9Msvv2jx4sV65plndPfdd/v9vjNnzpQkDRw4UBMmTFBYWJjf7wEABUF74j92u12ffvqp+/uvv/5aZ86c8TnQOXPmjCZPnixJ6tmzJwHTJYyACfDB2rVrvR6fNGmS++FqVQYXDR48WA8++KD7+4yMDH333Xd67rnndPjwYcXHx+vaa69VgwYN/HbPEydO6Pjx45Kk3r17EywBKFG0J/6zevVqHTt2TA0bNlR0dLTWr1+vzz//XP379y/pqiHIsYYJQKkRHh6um2++Wa+88oqkrN7CTz75xK/3OHfunPtrpuABQNnxwQcfSJLuuOMO9ejRQ1LWelWgqAiYgAD58ccf3QtL82K1+DTn9Xv37tXYsWPVsWNHtWzZUrfeeqtmzpwpu93uvmbz5s0aNWqUOnbsqKuvvlq33Xab5s2bJ9M086zD8uXLNWLECF1//fVq0aKFrr/+eo0YMUJff/215TWPP/64GjdurMcff1ymaWrx4sXq16+frrvuOjVu3FgfffRRfv9Ebq1bt3YHM7/++qtluc2bN+vRRx9Vly5ddPXVV6t169a6++67NW3aNJ09e9ajrOvf76abbnIfi4uLc/+bZj8OAKUZ7Unu9uTo0aP67rvvZLPZdOedd6pr166qUKGCdu3apb179+ZZR28GDhyouLg49/fZ24vGjRtr4MCBhX5NBC+m5AFB6Ntvv9WDDz6o9PR0RUVFKSMjQ/v379dLL72kXbt26bXXXtPixYsVHx8vp9OpyMhIZWRk6Ndff9W///1vHT58WI8++miu183IyNCECRP05ZdfSpJsNpuioqJ08uRJrV69WqtXr9Ztt92mF1980XIqm2maeuihh7Rs2TL39Tab730zTqfT67EXXnhBCQkJ7mMVKlTQuXPntGPHDu3YsUMfffSRZs6cqXr16kmSwsLCVL16dTkcDp08eVKSVLVqVYWEhLi/BoBLTVlpTz755BM5HA7dcMMNqlWrliSpa9eu+uSTT/TBBx/oqaeeKtS/S+XKlVW1alWv7YXrPC4djDABQejRRx9VXFycVq1apU2bNmnTpk0aMWKEJGnJkiWaNm2ann32Wd1zzz1au3atNm3apA0bNrizKc2cOVOJiYm5Xvf111/Xl19+KcMwNGrUKP3444/asGGD1q9frwceeECS9MUXX+jNN9+0rNvy5cu1YsUKTZgwQRs3btSGDRu0adMmdezYscDvb9OmTe7Fzpdddlmu82+99ZYSEhIUHR2tiRMn6scff9RPP/2kbdu2ac6cOWrWrJkSExP14IMPugOua6+9VmvXrnVP2ZCypm+sXbtWa9euZdoGgEtSWWlPXM/wO++8033MNS3v888/V0ZGRqH+XSZPnmzZXqxdu9a9vgyXBgImIAhdffXVeu2111S3bl1JUmRkpMaOHas2bdpIkl599VX16NFDTz31lKKjoyVl9YY999xziomJkdPp1FdffeXxmkeOHNGcOXMkScOHD9dDDz3kzghUuXJlPfLII7r//vslSbNmzdLRo0e91i0tLU2PP/64Bg8erMjISElSxYoVVbNmzXzfV0ZGhr755hs99thj7mM9e/b0KPP7779r2rRpKleunN599131799fVapUkZQ1inTdddcpISFBtWvX1q5du7Ry5cp87wsAl6qy0J5s3LhRSUlJqlixorp27eo+3r59e9WpU0enTp3SN998U6R/J1zaCJiAIDRs2DAZhpHrePZeN1cPYXYhISHq0KGDJOnnn3/2OLds2TLZ7XZFRERo+PDhXu87cuRIhYeHKzMzU8uWLfNapnLlyurTp0+B3se7776rG264QTfccIPat2+vli1bavTo0Tp8+LAkacKECWrevLnHNR9//LEcDoduvPFGNWnSxOvrRkZG6uabb5Ykff/99wWqCwBcispCe+IaCerWrZvKly/vPm4YhnvEKftoEVBYrGECgtDVV1/t9bhrr44qVap4ncomyd1DeObMGY/jO3fudL+2qycvp8qVK6tFixbasmWLu7y3uoWHh+f/JuS5z0jO+0ydOlXXXnttrnNbtmyRlJVm94YbbsjztSXp0KFDBaoLAFyKgr09SU1NdQdc2afjufTo0UNTp07VunXrdOjQIfdIGlAYjDABQciqAXItSK1YsaLltaGhWf0k2bMfSdKff/4pSe7FslZq167tUT6natWq5Xl9dmPGjNHPP/+sn3/+WVu3btXixYvVpUsXnT59Wk888YSOHDmS6xrX1I20tDQdP37c8j9XwHT+/PkC1wcALjXB3p4sWbJE586dU926dXXdddflOl+/fn21atVKTqezUNlagewYYQLgV9mzCBVG+fLl1bJlS02ZMkWDBw/WunXr9Oijj2rOnDke00UcDoekrGkk3jIzAQDKhoK0J66pdocOHbKcpu3y0UcfafTo0V6nIAJ5YYQJCJDsD/709HSvZVJSUgJVnVxcUyv++OOPPMu5zrvK+5vNZtMzzzyj0NBQbdiwQUuWLPE4X6NGDUlMtQNw6aI9yfLLL79o+/btBS5/8OBB/fDDDz7dC5c2AiYgQLLv2eBKapDTtm3bAlWdXFq0aCEpa+65VUN75swZj7npxSU2Nla33367JOmNN97wmO7hWtf0ww8/WP6hAABlGe1JFtfoUvPmzbVly5Y8/3MlAirMFhLZ93zKb4NelG0ETECAxMbGqly5cpKy9pbIyel06p133gl0tdy6deum0NBQpaena/r06V7LTJ06VRkZGQoLC/NI3Vochg0bJpvNpt9++81j3vk//vEPhYaG6uTJk3rrrbfyfI2MjAydPXu2WOsJAIFGe5L1fP/ss88kSd27d1fFihXz/O9vf/ubJOnrr7/WqVOnCnSP7Ou7SnLEDiWPgAkIkOyNwtSpU/Xll1+6N9Lbv3+/Ro8erV9++aXE6lerVi3de++9kqRp06bprbfecmc+OnPmjN544w3NnDlTkjRo0KAC7atUFFdeeaVuueUWSdLbb7/t/re6/PLLNXLkSEnSjBkzNH78eI9/N7vdrj179mjy5Mnq2rWr9uzZU6z1BIBAoz2RVqxYoZMnT0qSbr311nzLd+nSReXKlVNGRoY+//zzAt2jUqVK7sQVH330Ua7kFrh0kPQBCKBx48Zp/fr1Onr0qB555BGFhYUpIiJCqampqlixoqZOnaqBAweWWP0eeeQRHT58WF999ZX++9//6u2331ZUVJRSUlLkdDolSbfddpseeuihgNTngQce0LJly3To0CEtXrxY/fv3lySNHj1aDodDb7/9tj799FN9+umnKleunMqVK6eUlBR3YghJLO4FUCZd6u1J9ul4VmnPs6tQoYI6deqk5cuX68MPPyzwv03fvn315ptvKiEhQe+//76io6Nls9l0zTXX6PXXX/ep7gg+jDABAVS7dm0tXrxYvXr1cvdaVahQQT169NDHH3+sdu3alWj9wsPD9cYbb+itt95Sp06dVKVKFZ09e1ZVqlRRp06dNHnyZL366qsKCwsLSH2aNWumv/71r5KyelFda5YMw9BDDz2kzz77TPfcc4+uvPJK2Ww2paamqlKlSvrLX/6iIUOGaOHChWrdunVA6goAgXQptyeHDx92J28oyOiSi6vsnj17tGvXrgJd88ADD+jJJ59UixYtFBoaqj/++EMHDx7U8ePHC11vBC/DZBUbAAAAAHjFCBMAAAAAWCBgAgAAAAALBEwAAAAAYIGACQAAAAAsEDABAAAAgAUCJgAAAACwQMAEAAAAABYImAAAAADAAgETAAAAAFggYAIAAAAACwRMAAAAAGCBgAkAAAAALBAwAQAAAIAFAiYAAAAAsPD/AbVjKLKwfPF9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5,font='bold')\n",
    "#sns.s\n",
    "sns.set_style(\"white\")\n",
    "sns.set_context(rc={\"figure.figsize\": (13, 8)})\n",
    "g = sns.PairGrid(mutation_data, vars=[\"TumorRef\", \"TumorAlt\"],hue=\"Pat\",\n",
    "                 palette=dict(P=\"red\", A=\"green\"),hue_kws={\"marker\": [\"^\", \"v\"]},aspect=1.5,size=3)\n",
    "g.map(plt.scatter);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb16ac71",
   "metadata": {},
   "source": [
    "## Word cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5c936650",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'JAK-STAT Hippo P53'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate pathways names into a single string\n",
    "pathways =\" \".join(list(set(mutation_data['pathway'])))\n",
    "pathways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "39eb9223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the word object\n",
    "wc = WordCloud()\n",
    "# then call generate_from_text:\n",
    "wordcloud = WordCloud(max_font_size=60).generate(pathways)\n",
    "# Set stopwords\n",
    "stopwords = set(STOPWORDS)\n",
    "#stopwords.update([\"NaN\"])\n",
    "# Generate a word cloud object\n",
    "wordcloud = WordCloud(width=800, height=600, background_color='white',stopwords=None,contour_width=2, contour_color='blue', colormap='RdBu', random_state=None).generate(pathways)\n",
    "# Plot the word cloud\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.tight_layout(pad=0)\n",
    "# Save the word cloud image\n",
    "plt.savefig(\"mutations.png\", dpi=300)\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3014f59",
   "metadata": {},
   "source": [
    "## sankey plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5a8d2cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateSankey(df,cat_cols=[],value_cols='',title='Sankey Diagram'):\n",
    "    \"\"\"\n",
    "    The function to convert the format of the input datfarme for the sankey plot\n",
    "    \"\"\"\n",
    "    # maximum of 6 value cols -> 6 colors\n",
    "    colorPalette = ['#EFF9DA','#CBE4F9','#F9D8D6','#D6CDEA','#f0e442','#fffbf2']\n",
    "    labelList = []\n",
    "    colorNumList = []\n",
    "    for catCol in cat_cols:\n",
    "        labelListTemp =  list(set(df[catCol].values))\n",
    "        colorNumList.append(len(labelListTemp))\n",
    "        labelList = labelList + labelListTemp\n",
    "        \n",
    "    # remove duplicates from labelList\n",
    "    labelList = list(dict.fromkeys(labelList))\n",
    "    \n",
    "    # define colors based on number of levels\n",
    "    colorList = []\n",
    "    for idx, colorNum in enumerate(colorNumList):\n",
    "        colorList = colorList + [colorPalette[idx]]*colorNum\n",
    "        \n",
    "    # transform df into a source-target pair\n",
    "    for i in range(len(cat_cols)-1):\n",
    "        if i==0:\n",
    "            sourceTargetDf = df[[cat_cols[i],cat_cols[i+1],value_cols]]\n",
    "            sourceTargetDf.columns = ['source','target','count']\n",
    "        else:\n",
    "            tempDf = df[[cat_cols[i],cat_cols[i+1],value_cols]]\n",
    "            tempDf.columns = ['source','target','count']\n",
    "            sourceTargetDf = pd.concat([sourceTargetDf,tempDf])\n",
    "        sourceTargetDf = sourceTargetDf.groupby(['source','target']).agg({'count':'sum'}).reset_index()\n",
    "        \n",
    "    # add index for source-target pair\n",
    "    sourceTargetDf['sourceID'] = sourceTargetDf['source'].apply(lambda x: labelList.index(x))\n",
    "    sourceTargetDf['targetID'] = sourceTargetDf['target'].apply(lambda x: labelList.index(x))\n",
    "    \n",
    "    # creating the sankey diagram\n",
    "    data = dict(\n",
    "        type='sankey',\n",
    "        node = dict(\n",
    "          pad = 15,\n",
    "          thickness = 20,\n",
    "          line = dict(\n",
    "            color = \"black\",\n",
    "            width = 0.5\n",
    "          ),\n",
    "          label = labelList,\n",
    "          color = colorList\n",
    "        ),\n",
    "        link = dict(\n",
    "          source = sourceTargetDf['sourceID'],\n",
    "          target = sourceTargetDf['targetID'],\n",
    "          value = sourceTargetDf['count']\n",
    "        )\n",
    "      )\n",
    "    \n",
    "    layout =  dict(\n",
    "        title = title,\n",
    "        font = dict(\n",
    "          size = 10\n",
    "        )\n",
    "    )\n",
    "       \n",
    "    fig = dict(data=[data], layout=layout)\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5dc2e589",
   "metadata": {
    "collapsed": true
   },
   "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>chrom</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>ref</th>\n",
       "      <th>alt</th>\n",
       "      <th>TumorRef</th>\n",
       "      <th>TumorAlt</th>\n",
       "      <th>NormalRef</th>\n",
       "      <th>NormalAlt</th>\n",
       "      <th>Read_depth_tumor</th>\n",
       "      <th>gene_type</th>\n",
       "      <th>Protein_coding</th>\n",
       "      <th>No_PC_overlap</th>\n",
       "      <th>Sample</th>\n",
       "      <th>Pat</th>\n",
       "      <th>gene1</th>\n",
       "      <th>Sample1</th>\n",
       "      <th>Recurrence</th>\n",
       "      <th>VAF_tumor</th>\n",
       "      <th>recurrence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>137013</td>\n",
       "      <td>137013</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL18</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL18</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>137515</td>\n",
       "      <td>137515</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>56</td>\n",
       "      <td>7</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE04</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>PE04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>136048</td>\n",
       "      <td>136048</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL06</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>139213</td>\n",
       "      <td>139213</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>264</td>\n",
       "      <td>103</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE05</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>137588</td>\n",
       "      <td>137588</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE14</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>AE14</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.37</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>137515</td>\n",
       "      <td>137515</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL12</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>AL12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>137334</td>\n",
       "      <td>137334</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE04</td>\n",
       "      <td>A</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>AE04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>137825</td>\n",
       "      <td>137825</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL06</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>137302</td>\n",
       "      <td>137302</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>38</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE05</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>138156</td>\n",
       "      <td>138156</td>\n",
       "      <td>G</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE09</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>PE09</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>137269</td>\n",
       "      <td>137269</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE05</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>136579</td>\n",
       "      <td>136579</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>84</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL09</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL09</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.53</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>138041</td>\n",
       "      <td>138041</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE09</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>PE09</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>137563</td>\n",
       "      <td>137563</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>199</td>\n",
       "      <td>31</td>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE05</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.15</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>136586</td>\n",
       "      <td>136586</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL18</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL18</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>136586</td>\n",
       "      <td>136586</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE11</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>PE11</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>139213</td>\n",
       "      <td>139213</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL03</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL03</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>138266</td>\n",
       "      <td>138266</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>48</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL32</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>AL32</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>136652</td>\n",
       "      <td>136652</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PL04</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>PL04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>137515</td>\n",
       "      <td>137515</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE08</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>PE08</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>137302</td>\n",
       "      <td>137302</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>35</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE10</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>AE10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>136915</td>\n",
       "      <td>136915</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PL12</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>PL12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.39</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>136964</td>\n",
       "      <td>136964</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>61</td>\n",
       "      <td>14</td>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PL04</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>PL04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>137263</td>\n",
       "      <td>137263</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE05</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.36</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>139213</td>\n",
       "      <td>139213</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE02</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>PE02</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>137369</td>\n",
       "      <td>137369</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE04</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>PE04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>137622</td>\n",
       "      <td>137622</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "      <td>101</td>\n",
       "      <td>2</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PL14</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>PL14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.17</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>136473</td>\n",
       "      <td>136473</td>\n",
       "      <td>C</td>\n",
       "      <td>T</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PL04</td>\n",
       "      <td>P</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>PL04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>136049</td>\n",
       "      <td>136049</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL06</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>137013</td>\n",
       "      <td>137013</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>46</td>\n",
       "      <td>7</td>\n",
       "      <td>69</td>\n",
       "      <td>3</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE29</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE29</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>137622</td>\n",
       "      <td>137622</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL06</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>139460</td>\n",
       "      <td>139460</td>\n",
       "      <td>A</td>\n",
       "      <td>G</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL06</td>\n",
       "      <td>A</td>\n",
       "      <td>LINC0014</td>\n",
       "      <td>AL06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.19</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>139233</td>\n",
       "      <td>139233</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>277</td>\n",
       "      <td>101</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE05</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>136690</td>\n",
       "      <td>136690</td>\n",
       "      <td>G</td>\n",
       "      <td>A</td>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AL12</td>\n",
       "      <td>A</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>AL12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>136062</td>\n",
       "      <td>136062</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE12</td>\n",
       "      <td>P</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>PE12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>135947</td>\n",
       "      <td>135947</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PE04</td>\n",
       "      <td>P</td>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>PE04</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.49</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>136866</td>\n",
       "      <td>136866</td>\n",
       "      <td>T</td>\n",
       "      <td>C</td>\n",
       "      <td>192</td>\n",
       "      <td>76</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>AE05</td>\n",
       "      <td>A</td>\n",
       "      <td>SNHG17</td>\n",
       "      <td>AE05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    chrom   start     end ref alt  TumorRef  TumorAlt  NormalRef  NormalAlt  \\\n",
       "26      1  137013  137013   T   C        15         4         11          0   \n",
       "10      1  137515  137515   T   C        56         7         58          1   \n",
       "29      1  136048  136048   C   T         5         9         38          0   \n",
       "18      1  139213  139213   A   G       264       103         82          1   \n",
       "6       1  137588  137588   A   C        15         3         47          1   \n",
       "3       1  137515  137515   T   C        36         4         30          1   \n",
       "0       1  137334  137334   C   T         8         4         10          0   \n",
       "32      1  137825  137825   G   A         1        10         47          0   \n",
       "16      1  137302  137302   G   A        38        12         11          0   \n",
       "28      1  138156  138156   G   T         3         7         27          0   \n",
       "15      1  137269  137269   T   C        20        11         11          0   \n",
       "35      1  136579  136579   G   A        84        11         15          0   \n",
       "27      1  138041  138041   G   A        12         9         19          0   \n",
       "17      1  137563  137563   T   C       199        31         62          2   \n",
       "25      1  136586  136586   T   C        11         4         16          0   \n",
       "23      1  136586  136586   T   C        27         6         13          0   \n",
       "34      1  139213  139213   A   G        29        33         51          2   \n",
       "11      1  138266  138266   C   T        48         6         25          1   \n",
       "21      1  136652  136652   A   G        22         4         20          0   \n",
       "4       1  137515  137515   T   C        13         4         22          1   \n",
       "7       1  137302  137302   G   A        35         8         10          0   \n",
       "24      1  136915  136915   T   C        25         3         20          1   \n",
       "22      1  136964  136964   T   C        61        14         57          3   \n",
       "14      1  137263  137263   A   G        20        11         10          0   \n",
       "5       1  139213  139213   A   G        31         4         45          2   \n",
       "9       1  137369  137369   G   A        13         3         10          0   \n",
       "36      1  137622  137622   G   A        74        12        101          2   \n",
       "20      1  136473  136473   C   T        21         3         22          1   \n",
       "30      1  136049  136049   G   A         9         5         38          0   \n",
       "12      1  137013  137013   T   C        46         7         69          3   \n",
       "31      1  137622  137622   G   A        28         9         89          0   \n",
       "33      1  139460  139460   A   G        10         3         53          1   \n",
       "19      1  139233  139233   C   A       277       101         87          2   \n",
       "2       1  136690  136690   G   A        61         7         12          0   \n",
       "1       1  136062  136062   T   C        26         4         61          1   \n",
       "8       1  135947  135947   T   C        45         6         22          1   \n",
       "13      1  136866  136866   T   C       192        76         23          1   \n",
       "\n",
       "   Read_depth_tumor  gene_type  Protein_coding  No_PC_overlap Sample Pat  \\\n",
       "26    RP11-34P13.14  antisense            True          False   AL18   A   \n",
       "10    RP11-34P13.14  antisense            True          False   PE04   P   \n",
       "29    RP11-34P13.14  antisense            True          False   AL06   A   \n",
       "18    RP11-34P13.14  antisense            True          False   AE05   A   \n",
       "6     RP11-34P13.14  antisense            True          False   AE14   A   \n",
       "3     RP11-34P13.14  antisense            True          False   AL12   A   \n",
       "0     RP11-34P13.14  antisense            True          False   AE04   A   \n",
       "32    RP11-34P13.14  antisense            True          False   AL06   A   \n",
       "16    RP11-34P13.14  antisense            True          False   AE05   A   \n",
       "28    RP11-34P13.14  antisense            True          False   PE09   P   \n",
       "15    RP11-34P13.14  antisense            True          False   AE05   A   \n",
       "35    RP11-34P13.14  antisense            True          False   AL09   A   \n",
       "27    RP11-34P13.14  antisense            True          False   PE09   P   \n",
       "17    RP11-34P13.14  antisense            True          False   AE05   A   \n",
       "25    RP11-34P13.14  antisense            True          False   AL18   A   \n",
       "23    RP11-34P13.14  antisense            True          False   PE11   P   \n",
       "34    RP11-34P13.14  antisense            True          False   AL03   A   \n",
       "11    RP11-34P13.14  antisense            True          False   AL32   A   \n",
       "21    RP11-34P13.14  antisense            True          False   PL04   P   \n",
       "4     RP11-34P13.14  antisense            True          False   PE08   P   \n",
       "7     RP11-34P13.14  antisense            True          False   AE10   A   \n",
       "24    RP11-34P13.14  antisense            True          False   PL12   P   \n",
       "22    RP11-34P13.14  antisense            True          False   PL04   P   \n",
       "14    RP11-34P13.14  antisense            True          False   AE05   A   \n",
       "5     RP11-34P13.14  antisense            True          False   PE02   P   \n",
       "9     RP11-34P13.14  antisense            True          False   PE04   P   \n",
       "36    RP11-34P13.14  antisense            True          False   PL14   P   \n",
       "20    RP11-34P13.14  antisense            True          False   PL04   P   \n",
       "30    RP11-34P13.14  antisense            True          False   AL06   A   \n",
       "12    RP11-34P13.14  antisense            True          False   AE29   A   \n",
       "31    RP11-34P13.14  antisense            True          False   AL06   A   \n",
       "33    RP11-34P13.14  antisense            True          False   AL06   A   \n",
       "19    RP11-34P13.14  antisense            True          False   AE05   A   \n",
       "2     RP11-34P13.14  antisense            True          False   AL12   A   \n",
       "1     RP11-34P13.14  antisense            True          False   PE12   P   \n",
       "8     RP11-34P13.14  antisense            True          False   PE04   P   \n",
       "13    RP11-34P13.14  antisense            True          False   AE05   A   \n",
       "\n",
       "            gene1 Sample1  Recurrence  VAF_tumor  recurrence  \n",
       "26       LINC0014    AL18        20.0       0.15          49  \n",
       "10   LINC34P13.14    PE04        20.0       0.10          45  \n",
       "29       LINC0014    AL06        20.0       0.10          44  \n",
       "18         SNHG17    AE05        20.0       0.09          41  \n",
       "6    LINC34P13.14    AE14        20.0       0.37          41  \n",
       "3    LINC34P13.14    AL12        20.0       0.27          40  \n",
       "0   RP11-34P13.14    AE04        20.0       0.02          39  \n",
       "32       LINC0014    AL06        20.0       0.02          38  \n",
       "16         SNHG17    AE05        20.0       0.31          38  \n",
       "28       LINC0014    PE09        20.0       0.11          36  \n",
       "15         SNHG17    AE05        20.0       0.18          36  \n",
       "35       LINC0014    AL09        20.0       0.53          35  \n",
       "27       LINC0014    PE09        20.0       0.57          35  \n",
       "17         SNHG17    AE05        20.0       0.15          33  \n",
       "25       LINC0014    AL18        20.0       0.50          33  \n",
       "23       LINC0014    PE11        20.0       0.22          32  \n",
       "34       LINC0014    AL03        20.0       0.22          32  \n",
       "11   LINC34P13.14    AL32        20.0       0.05          32  \n",
       "21       LINC0014    PL04        20.0       0.36          32  \n",
       "4    LINC34P13.14    PE08        20.0       0.00          31  \n",
       "7    LINC34P13.14    AE10        20.0       0.31          29  \n",
       "24       LINC0014    PL12        20.0       0.39          29  \n",
       "22       LINC0014    PL04        20.0       0.50          29  \n",
       "14         SNHG17    AE05        20.0       0.36          28  \n",
       "5    LINC34P13.14    PE02        20.0       0.24          26  \n",
       "9    LINC34P13.14    PE04        20.0       0.24          26  \n",
       "36       LINC0014    PL14         NaN       0.17          26  \n",
       "20         SNHG17    PL04        20.0       0.29          24  \n",
       "30       LINC0014    AL06        20.0       0.06          22  \n",
       "12         SNHG17    AE29        20.0       0.19          21  \n",
       "31       LINC0014    AL06        20.0       0.04          20  \n",
       "33       LINC0014    AL06        20.0       0.19          20  \n",
       "19         SNHG17    AE05        20.0       0.00          18  \n",
       "2   RP11-34P13.14    AL12        20.0       0.46          18  \n",
       "1   RP11-34P13.14    PE12        20.0       0.55          13  \n",
       "8    LINC34P13.14    PE04        20.0       0.49          12  \n",
       "13         SNHG17    AE05        20.0       0.38          10  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mutation_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd2ba11b",
   "metadata": {},
   "source": [
    "### create the count column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f6bb7b74",
   "metadata": {
    "scrolled": true
   },
   "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>gene1</th>\n",
       "      <th>gene_type</th>\n",
       "      <th>sex</th>\n",
       "      <th>Pat</th>\n",
       "      <th>pathway</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Female</td>\n",
       "      <td>A</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Female</td>\n",
       "      <td>A</td>\n",
       "      <td>P53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Female</td>\n",
       "      <td>P</td>\n",
       "      <td>Hippo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Female</td>\n",
       "      <td>P</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Female</td>\n",
       "      <td>P</td>\n",
       "      <td>P53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Male</td>\n",
       "      <td>A</td>\n",
       "      <td>Hippo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Male</td>\n",
       "      <td>A</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Male</td>\n",
       "      <td>A</td>\n",
       "      <td>P53</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Male</td>\n",
       "      <td>P</td>\n",
       "      <td>Hippo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LINC0014</td>\n",
       "      <td>intergenic</td>\n",
       "      <td>Male</td>\n",
       "      <td>P</td>\n",
       "      <td>P53</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>Female</td>\n",
       "      <td>A</td>\n",
       "      <td>Hippo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>Female</td>\n",
       "      <td>A</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>Female</td>\n",
       "      <td>A</td>\n",
       "      <td>P53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>Female</td>\n",
       "      <td>P</td>\n",
       "      <td>Hippo</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>Female</td>\n",
       "      <td>P</td>\n",
       "      <td>P53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>Male</td>\n",
       "      <td>A</td>\n",
       "      <td>P53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LINC34P13.14</td>\n",
       "      <td>lincRNA</td>\n",
       "      <td>Male</td>\n",
       "      <td>P</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>Female</td>\n",
       "      <td>A</td>\n",
       "      <td>P53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>Male</td>\n",
       "      <td>A</td>\n",
       "      <td>Hippo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RP11-34P13.14</td>\n",
       "      <td>antisense</td>\n",
       "      <td>Male</td>\n",
       "      <td>P</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SNHG17</td>\n",
       "      <td>intonic</td>\n",
       "      <td>Female</td>\n",
       "      <td>A</td>\n",
       "      <td>Hippo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SNHG17</td>\n",
       "      <td>intonic</td>\n",
       "      <td>Female</td>\n",
       "      <td>A</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SNHG17</td>\n",
       "      <td>intonic</td>\n",
       "      <td>Female</td>\n",
       "      <td>P</td>\n",
       "      <td>Hippo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SNHG17</td>\n",
       "      <td>intonic</td>\n",
       "      <td>Male</td>\n",
       "      <td>A</td>\n",
       "      <td>JAK-STAT</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>SNHG17</td>\n",
       "      <td>intonic</td>\n",
       "      <td>Male</td>\n",
       "      <td>A</td>\n",
       "      <td>P53</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gene1   gene_type     sex Pat   pathway  count\n",
       "0        LINC0014  intergenic  Female   A  JAK-STAT      2\n",
       "1        LINC0014  intergenic  Female   A       P53      1\n",
       "2        LINC0014  intergenic  Female   P     Hippo      1\n",
       "3        LINC0014  intergenic  Female   P  JAK-STAT      2\n",
       "4        LINC0014  intergenic  Female   P       P53      1\n",
       "5        LINC0014  intergenic    Male   A     Hippo      1\n",
       "6        LINC0014  intergenic    Male   A  JAK-STAT      3\n",
       "7        LINC0014  intergenic    Male   A       P53      2\n",
       "8        LINC0014  intergenic    Male   P     Hippo      1\n",
       "9        LINC0014  intergenic    Male   P       P53      2\n",
       "10   LINC34P13.14     lincRNA  Female   A     Hippo      1\n",
       "11   LINC34P13.14     lincRNA  Female   A  JAK-STAT      1\n",
       "12   LINC34P13.14     lincRNA  Female   A       P53      1\n",
       "13   LINC34P13.14     lincRNA  Female   P     Hippo      3\n",
       "14   LINC34P13.14     lincRNA  Female   P       P53      1\n",
       "15   LINC34P13.14     lincRNA    Male   A       P53      1\n",
       "16   LINC34P13.14     lincRNA    Male   P  JAK-STAT      1\n",
       "17  RP11-34P13.14   antisense  Female   A       P53      1\n",
       "18  RP11-34P13.14   antisense    Male   A     Hippo      1\n",
       "19  RP11-34P13.14   antisense    Male   P  JAK-STAT      1\n",
       "20         SNHG17     intonic  Female   A     Hippo      2\n",
       "21         SNHG17     intonic  Female   A  JAK-STAT      1\n",
       "22         SNHG17     intonic  Female   P     Hippo      1\n",
       "23         SNHG17     intonic    Male   A  JAK-STAT      2\n",
       "24         SNHG17     intonic    Male   A       P53      3"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_mu = mutation_data.drop_duplicates().groupby(['gene1','gene_type','sex','Pat','pathway'],dropna=False)['gene1'].count().reset_index(name=\"count\")\n",
    "counts_mu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "39c689e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=generateSankey(counts_mu,cat_cols=['gene1','gene_type','sex','Pat','pathway'],value_cols='count',title='Word Etymology')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3d254e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# override gray link colors with 'source' colors\n",
    "opacity = 1.2\n",
    "# change 'magenta' to its 'rgba' value to add opacity\n",
    "data['data'][0]['node']['color'] = ['rgba(128, 128, 0, 0.8)' if color == \"magenta\" else color for color in data['data'][0]['node']['color']]\n",
    "data['data'][0]['link']['color'] = [data['data'][0]['node']['color'][src].replace(\"0.8\", str(opacity))\n",
    "                                    for src in data['data'][0]['link']['source']]\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    valueformat = None,\n",
    "    valuesuffix = None,\n",
    "    # Define nodes\n",
    "    node = dict(\n",
    "      pad = 100,\n",
    "      thickness = 2,\n",
    "      line = dict(color = \"black\", width = 0.2),\n",
    "      label =  data['data'][0]['node']['label'],\n",
    "      color =  data['data'][0]['node']['color']\n",
    "    ),\n",
    "    # Add links\n",
    "    link = dict(\n",
    "      source =  data['data'][0]['link']['source'],\n",
    "      target =  data['data'][0]['link']['target'],\n",
    "      value =  data['data'][0]['link']['value'],\n",
    "      \n",
    "      #label =  data['data'][0]['link']['label'],\n",
    "      color =  data['data'][0]['link']['color']\n",
    "))])\n",
    "\n",
    "fig.update_layout(title_text=\"Mutation data of lncRNAs in ALL\",\n",
    "                  font_size=12)\n",
    "\n",
    "# save this file as a standalong html file:\n",
    "fig.write_html(\"sankey_mutations.html\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}