Pancreas_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pancreas Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import random\n",
    "import scipy\n",
    "from scipy import stats\n",
    "import statsmodels.stats.multitest as ssm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings for editable pdfs\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # set size of tick labels\n",
    "# plt.rc('xtick', labelsize=15)\n",
    "# plt.rc('ytick', labelsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data, preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load MAF data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jamie/opt/anaconda3/envs/singlecell/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (35,36,53) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "# load genie version 8.1\n",
    "master_mut = pd.read_csv(\"R_script/cleaned_data/GENIE8.1_master_list_clinical_gen.tsv\", sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load panels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "DFCI_1 = pd.read_csv('R_script/Panels/DFCI_1.txt', sep='\\t')\n",
    "DFCI_2 = pd.read_csv('R_script/Panels/DFCI_2.txt', sep='\\t')\n",
    "DFCI_3 = pd.read_csv('R_script/Panels/DFCI_3.txt', sep='\\t')\n",
    "DFCI_3_1 = DFCI_3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DFCI_1['SEQ_ASSAY_ID'] = 'DFCI-ONCOPANEL-1'\n",
    "DFCI_2['SEQ_ASSAY_ID'] = 'DFCI-ONCOPANEL-2'\n",
    "DFCI_3['SEQ_ASSAY_ID'] = 'DFCI-ONCOPANEL-3'\n",
    "DFCI_3_1['SEQ_ASSAY_ID'] = 'DFCI-ONCOPANEL-3.1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSK_341 = pd.read_csv('R_script/Panels/IMPACT_341.txt', sep='\\t')\n",
    "MSK_410 = pd.read_csv('R_script/Panels/IMPACT_410.txt', sep='\\t')\n",
    "MSK_468 = pd.read_csv('R_script/Panels/IMPACT_468.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSK_341['SEQ_ASSAY_ID'] = 'MSK-IMPACT-341'\n",
    "MSK_410['SEQ_ASSAY_ID'] = 'MSK-IMPACT-410'\n",
    "MSK_468['SEQ_ASSAY_ID'] = 'MSK-IMPACT-468'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_panels = pd.concat([DFCI_1, DFCI_2, DFCI_3, DFCI_3_1, MSK_341, MSK_410, MSK_468]).groupby(\n",
    "    'Hugo_Symbol')['SEQ_ASSAY_ID'].apply(list).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancers = ['PAAD']\n",
    "\n",
    "centers = ['MSK', 'DFCI']\n",
    "\n",
    "panels = ['DFCI-ONCOPANEL-1', 'DFCI-ONCOPANEL-2', 'DFCI-ONCOPANEL-3', 'DFCI-ONCOPANEL-3.1', \n",
    "                        'MSK-IMPACT-341', 'MSK-IMPACT-410', 'MSK-IMPACT-468']\n",
    "\n",
    "races = ['Asian', 'Black', 'White']\n",
    "\n",
    "sample_types = ['Primary', 'Metastasis']\n",
    "\n",
    "variants = ['Missense_Mutation', 'Splice_Site', 'Nonsense_Mutation', 'Frame_Shift_Del', \n",
    "                         'Frame_Shift_Ins', 'Nonstop_Mutation', 'In_Frame_Del']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter MAF data by variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_mut['SEQ_ASSAY_ID'] = master_mut['SEQ_ASSAY_ID'].replace({'MSK-IMPACT341':'MSK-IMPACT-341', \n",
    "                                                                 'MSK-IMPACT410':'MSK-IMPACT-410', \n",
    "                                                                 'MSK-IMPACT468':'MSK-IMPACT-468'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (NOTE From Scanner's code: do not subset by Variant_Classification yet as that will skew our denominator and demographic numbers)\n",
    "\n",
    "filt_mut = master_mut[(master_mut['ONCOTREE_CODE'].isin(cancers)) & (master_mut['SEQ_ASSAY_ID'].isin(panels)) & (\n",
    "    master_mut['PRIMARY_RACE'].isin(races) & master_mut['SAMPLE_TYPE'].isin(sample_types))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dictionaries separated by primary/metstasis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-filtered by variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mut_dict['prim'] = filt_mut[filt_mut['SAMPLE_TYPE'] == 'Primary']\n",
    "mut_dict['met'] = filt_mut[filt_mut['SAMPLE_TYPE'] == 'Metastasis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by variants of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using this data for subsequent analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variants to include\n",
    "variants = ['Missense_Mutation', 'Splice_Site', 'Nonsense_Mutation', 'Frame_Shift_Del', \n",
    "                         'Frame_Shift_Ins', 'Nonstop_Mutation', 'In_Frame_Del']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_filt_mut = filt_mut[filt_mut['Variant_Classification'].isin(variants)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_mut_dict['prim'] =  var_filt_mut[var_filt_mut['SAMPLE_TYPE'] == 'Primary']\n",
    "var_mut_dict['met'] = var_filt_mut[var_filt_mut['SAMPLE_TYPE'] == 'Metastasis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demographic Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# looking at non-filtered by variant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_demo_dict = {}\n",
    "panel_demo_dict = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall (Primary + Metastasis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1955"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of unique patients\n",
    "len(filt_mut.groupby('PATIENT_ID').nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_demo_dict['overall'] = filt_mut.groupby('PRIMARY_RACE')['PATIENT_ID'].nunique()\n",
    "panel_demo_dict['overall'] = filt_mut.groupby('SEQ_ASSAY_ID')['PATIENT_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRIMARY_RACE\n",
       "Asian      98\n",
       "Black      85\n",
       "White    1772\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by race\n",
    "race_demo_dict['overall']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Pancreatic Cancer', size=15)\n",
    "plt.pie(race_demo_dict['overall'], \n",
    "        labels=['Asian', 'Black', 'White'],\n",
    "       autopct='%1.1f%%',\n",
    "       textprops = dict(rotation_mode = 'anchor', \n",
    "#                         va='center', \n",
    "#                         ha='center', \n",
    "                        size=15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEQ_ASSAY_ID\n",
       "DFCI-ONCOPANEL-1        22\n",
       "DFCI-ONCOPANEL-2       168\n",
       "DFCI-ONCOPANEL-3       212\n",
       "DFCI-ONCOPANEL-3.1      97\n",
       "MSK-IMPACT-341          17\n",
       "MSK-IMPACT-410         422\n",
       "MSK-IMPACT-468        1036\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by panels\n",
    "panel_demo_dict['overall']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1179"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total primary\n",
    "len(mut_dict['prim'].groupby('PATIENT_ID').nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_demo_dict['prim'] = mut_dict['prim'].groupby('PRIMARY_RACE')['PATIENT_ID'].nunique()\n",
    "panel_demo_dict['prim'] = mut_dict['prim'].groupby('SEQ_ASSAY_ID')['PATIENT_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRIMARY_RACE\n",
       "Asian      55\n",
       "Black      50\n",
       "White    1074\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by race\n",
    "race_demo_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEQ_ASSAY_ID\n",
       "DFCI-ONCOPANEL-1       17\n",
       "DFCI-ONCOPANEL-2      105\n",
       "DFCI-ONCOPANEL-3      119\n",
       "DFCI-ONCOPANEL-3.1     37\n",
       "MSK-IMPACT-341         10\n",
       "MSK-IMPACT-410        254\n",
       "MSK-IMPACT-468        639\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by panel\n",
    "panel_demo_dict['prim']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "805"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total metastasis\n",
    "len(mut_dict['met'].groupby('PATIENT_ID').nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_demo_dict['met'] = mut_dict['met'].groupby('PRIMARY_RACE')['PATIENT_ID'].nunique()\n",
    "panel_demo_dict['met'] = mut_dict['met'].groupby('SEQ_ASSAY_ID')['PATIENT_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRIMARY_RACE\n",
       "Asian     46\n",
       "Black     36\n",
       "White    723\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by race\n",
    "race_demo_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEQ_ASSAY_ID\n",
       "DFCI-ONCOPANEL-1        5\n",
       "DFCI-ONCOPANEL-2       63\n",
       "DFCI-ONCOPANEL-3       93\n",
       "DFCI-ONCOPANEL-3.1     60\n",
       "MSK-IMPACT-341          7\n",
       "MSK-IMPACT-410        169\n",
       "MSK-IMPACT-468        411\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by panel\n",
    "panel_demo_dict['met']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate mutation frequency (for all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove patients with duplicate tumors sequenced by multiple panels\n",
    "# choose sample sequenced by larger panel\n",
    "\n",
    "# mut_tbl: mutation table in MAF format\n",
    "def remove_duplicates(mut_tbl):\n",
    "    # group by patient and panel to find the unique sample IDs\n",
    "    df = mut_tbl.groupby(['PATIENT_ID', 'SEQ_ASSAY_ID'])['SAMPLE_ID'].unique().to_frame()\n",
    "    # choose the last panel for each patient\n",
    "    samples = np.concatenate(df.groupby('PATIENT_ID').tail(1)['SAMPLE_ID'])\n",
    "    # filter out the duplicates\n",
    "    cleaned_mut = mut_tbl[mut_tbl['SAMPLE_ID'].isin(samples)]\n",
    "    \n",
    "    return cleaned_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count patients sequenced by each panel by race\n",
    "\n",
    "# mut_tbl: mutation table in MAF format\n",
    "# combined_panels: table with genes as rows, lists of panels that sequenced that gene as column\n",
    "# race_category: name of race column\n",
    "def calc_seq_counts(mut_tbl, combined_panels, race_category):\n",
    "    # first remove duplicates to get accurate sequenced count\n",
    "    mut_tbl = remove_duplicates(mut_tbl)\n",
    "    \n",
    "    # count number of patients for each panel\n",
    "    panel_race_counts = mut_tbl.groupby(['SEQ_ASSAY_ID', race_category])[\n",
    "        'PATIENT_ID'].nunique().reset_index().pivot_table(index='SEQ_ASSAY_ID', columns=race_category, \n",
    "                                                          values='PATIENT_ID', fill_value=0)\n",
    "    # count number of patients by race sequenced for one gene\n",
    "    def sum_gene_counts(row):\n",
    "        count = np.sum(panel_race_counts[panel_race_counts.index.isin(row)], axis=0)\n",
    "        return count\n",
    "    \n",
    "    # apply function to all genes\n",
    "    seq_cnt = combined_panels['SEQ_ASSAY_ID'].apply(sum_gene_counts)\n",
    "    \n",
    "    return seq_cnt\n",
    "#     return panel_race_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mutation frequency\n",
    "\n",
    "# mut_tbl: MAF mutation table\n",
    "# var_mut_tbl: mut_tbl filtered by variants we excluded\n",
    "# combined_panels: table with genes as rows, lists of panels that sequenced that gene as column\n",
    "# race_category: name of race column\n",
    "def calc_mut_freq(mut_tbl, var_mut_tbl, combined_panels, race_category):\n",
    "    \n",
    "    # calculate number of patients sequenced in each race category for all genes in panels\n",
    "    seq_cnt = calc_seq_counts(mut_tbl, combined_panels, race_category)\n",
    "    \n",
    "    # remove duplicates before counting mutations\n",
    "    var_mut_tbl = remove_duplicates(var_mut_tbl)\n",
    "    \n",
    "    # calculate number of patients w/ mutation in each race category for each gene in MAF table\n",
    "    mut_cnt = var_mut_tbl.groupby(['Hugo_Symbol', race_category])['PATIENT_ID'].nunique().reset_index().pivot_table(\n",
    "        index='Hugo_Symbol', columns=race_category, values='PATIENT_ID', fill_value=0)\n",
    "    \n",
    "    mut_seq = mut_cnt.join(seq_cnt, how='outer', lsuffix='_mut', rsuffix='_seq')\n",
    "    \n",
    "    # find any missing genes that weren't included in panel but still had mutations\n",
    "    # don't know why these are included in the panel in GENIE but not elsewhere\n",
    "    # total for DFCI is especially wrong (looking directly at the genomic_information.txt file)\n",
    "    missing = mut_seq[mut_seq['Asian_seq'].isna()]\n",
    "    \n",
    "    # remove missing genes\n",
    "    mut_seq = mut_seq[~mut_seq.index.isin(missing.index)]\n",
    "    \n",
    "#     # fill NaN mutation values with zero\n",
    "#     mut_seq = mut_seq.fillna(0)\n",
    "\n",
    "    # remove NaN mutation values (genes that were sequenced but have no mutation counts)\n",
    "    mut_seq = mut_seq.dropna()\n",
    "    \n",
    "    # compute mutation frequency\n",
    "    mut_columns = mut_seq.columns[mut_seq.columns.str.contains('_mut')]\n",
    "    seq_columns = mut_seq.columns[mut_seq.columns.str.contains('_seq')]\n",
    "    \n",
    "    mut_freq = mut_seq[mut_columns] / mut_seq[seq_columns].values * 100\n",
    "    mut_freq.columns = mut_freq.columns.str.replace('_mut', '')\n",
    "    \n",
    "    return mut_seq, mut_freq\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "mut_freq_dict = {}\n",
    "mut_seq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(mut_dict.items(), var_mut_dict.items()):\n",
    "    mut_seq_dict[k1], mut_freq_dict[k1] = calc_mut_freq(v1, v2, combined_panels, 'PRIMARY_RACE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Significance testing functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher's Test and FDR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performs Fisher's exact test for one gene between two races and calculates 95% confidence interval\n",
    "\n",
    "# row: gene row\n",
    "# group1: name of first race\n",
    "# group2: name of second race\n",
    "def fisher_one_gene(row, group1, group2):\n",
    "    seq_group1 = row[group1 + '_seq']\n",
    "    seq_group2 = row[group2 + '_seq']\n",
    "    \n",
    "    mut_group1 = row[group1 + '_mut']\n",
    "    mut_group2 = row[group2 + '_mut']\n",
    "    \n",
    "    a = mut_group1\n",
    "    b = mut_group2\n",
    "    c = seq_group1 - a\n",
    "    d = seq_group2 - b\n",
    "    \n",
    "    cont_tbl = pd.DataFrame(np.array([[a, c],\n",
    "                                     [b, d]]), index=[group1, group2], columns=['Mutation', 'No Mutation'])\n",
    "    \n",
    "    odds_ratio, p_val = scipy.stats.fisher_exact(cont_tbl)\n",
    "    \n",
    "    # calculate 95% confidence interval    \n",
    "    SE = np.sqrt(1/a + 1/c + 1/b + 1/d) \n",
    "    \n",
    "    c1 = round(odds_ratio * np.exp(-1.96 * SE), 3)\n",
    "    c2 = round(odds_ratio * np.exp(1.96 * SE), 3)\n",
    "    \n",
    "    CI = (c1, c2)\n",
    "    \n",
    "    return cont_tbl, odds_ratio, p_val, CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performs Fisher's exact test for all genes between two races and computes FDR\n",
    "\n",
    "# mut_tbl: table with genes as rows and *_mut/*_seq counts as columns (* is name of race group)\n",
    "# group1: name of first race\n",
    "# group2: name of second race\n",
    "\n",
    "def fisher_all_genes(mut_tbl, group1, group2):\n",
    "    fisher = mut_tbl.apply(lambda x: fisher_one_gene(x, group1, group2), axis=1)\n",
    "    \n",
    "    fisher_tbl = pd.DataFrame(fisher.to_list(), index=fisher.index).iloc[:,1:].rename(\n",
    "        columns={1:group1 + '_' + group2 + '_odds_ratio', \n",
    "                 2:group1 + '_' + group2 + '_pval', \n",
    "                 3:group1 + '_' + group2 + '_CI'})\n",
    "    fdr = ssm.multipletests(fisher_tbl[group1 + '_' + group2 + '_pval'], method='fdr_bh')[1]\n",
    "    fisher_tbl.insert(2, group1 + '_' + group2 + '_fdr', fdr)\n",
    "    \n",
    "    return fisher_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performs Fisher's exact test for all race comparisons, and computes FDR\n",
    "\n",
    "# mut_tbl: table with genes as rows and *_mut/*_seq counts as columns (* is name of race group)\n",
    "# groups: list of tuples with race comparisons\n",
    "def fisher_fdr_all_groups(mut_tbl, groups):\n",
    "    fisher_tbl = pd.DataFrame()\n",
    "    for group in groups:\n",
    "        fisher_tbl = pd.concat([fisher_tbl, fisher_all_genes(mut_tbl, group[0], group[1])], axis=1)\n",
    "\n",
    "    return fisher_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change if doing different comparisons\n",
    "groups = [('Black', 'White'), \n",
    "          ('Black', 'Asian'), \n",
    "          ('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Significant genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finds significant genes based on FDR cutoff specified\n",
    "\n",
    "# pvals: table of p-values, genes as rows, race comparisons as columns\n",
    "# mut_freq: table of mutation frequencies, genes as rows, races as columns\n",
    "# fdr_cutoff: float\n",
    "def find_sig_genes(pvals, mut_freq, fdr_cutoff):\n",
    "    cols = pvals.columns[pvals.columns.str.contains('fdr')]\n",
    "    pval_cols = pvals[cols]    \n",
    "    \n",
    "    sig_pval = pval_cols[(pval_cols <= fdr_cutoff).any(1)]\n",
    "    sig_genes = sig_pval.index\n",
    "    sig_pval = pvals.loc[sig_genes]\n",
    "    \n",
    "    sig_pct = mut_freq.loc[sig_genes]\n",
    "    \n",
    "    return sig_genes.to_list(), sig_pval, sig_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finds significant genes for all groups\n",
    "\n",
    "# race_pairs: list of tuples with race comparisons\n",
    "# pvals: table of p-values, genes as rows, race comparisons as columns\n",
    "# mut_freq: table of mutation frequencies, genes as rows, races as columns\n",
    "# fdr_cutoff: float\n",
    "def find_sig_genes_groups(race_pairs, pvals, mut_freq, fdr_cutoff):\n",
    "    sig_pval_dict = {}\n",
    "    sig_freq_dict = {}\n",
    "    sig_gene_list = []\n",
    "    for pair in race_pairs:\n",
    "        pair_cols = ['_'.join(pair) + '_pval', '_'.join(pair) + '_fdr', \n",
    "                     '_'.join(pair) + '_odds_ratio', '_'.join(pair) + '_CI']\n",
    "        pair_pval = pvals[pair_cols]\n",
    "        pair_mut_freq = mut_freq[[pair[0], pair[1]]]\n",
    "        sig_genes, sig_pval_dict[pair], sig_freq_dict[pair] = find_sig_genes(pair_pval, pair_mut_freq, fdr_cutoff)\n",
    "        sig_gene_list = sig_gene_list + sig_genes\n",
    "    \n",
    "    # remove duplicates\n",
    "    sig_gene_list = set(sig_gene_list)\n",
    "    \n",
    "    return sig_gene_list, sig_pval_dict, sig_freq_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Composition plot functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variant Composition Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate variant composition for genes of interest\n",
    "\n",
    "# mut_tbl: MAF table\n",
    "# genes: list of genes of interest\n",
    "def variant_comp(mut_tbl, genes):\n",
    "    # filter mut_tbl by genes of interest\n",
    "    filt_mut = mut_tbl[mut_tbl['Hugo_Symbol'].isin(genes)]\n",
    "    comp_mut = filt_mut.groupby(['Hugo_Symbol', 'Variant_Classification'])['PATIENT_ID'].nunique().reset_index().pivot_table(\n",
    "        index='Hugo_Symbol', columns='Variant_Classification', values='PATIENT_ID', fill_value=0)\n",
    "    pct_mut = comp_mut.div(comp_mut.sum(axis=1), axis=0) * 100\n",
    "    \n",
    "    return comp_mut, pct_mut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel Composition Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate panel composition for genes of interest\n",
    "\n",
    "# mut_tbl: MAF table\n",
    "# genes: list of genes of interest\n",
    "def panel_comp(mut_tbl, genes):\n",
    "    # filter mut_tbl by genes of interest\n",
    "    filt_mut = mut_tbl[mut_tbl['Hugo_Symbol'].isin(genes)]\n",
    "    comp_mut = filt_mut.groupby(['Hugo_Symbol', 'SEQ_ASSAY_ID'])['PATIENT_ID'].nunique().reset_index().pivot_table(\n",
    "        index='Hugo_Symbol', columns='SEQ_ASSAY_ID', values='PATIENT_ID', fill_value=0)\n",
    "    pct_mut = comp_mut.div(comp_mut.sum(axis=1), axis=0) * 100\n",
    "    \n",
    "    return comp_mut, pct_mut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top Mutations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find genes most frequently altered overall and by race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mutations most common in all patients, count number of patients with that mutation\n",
    "\n",
    "# mut_tbl: MAF table\n",
    "def common_mut_overall(mut_tbl):\n",
    "    common_mut = mut_tbl.groupby('Hugo_Symbol')['PATIENT_ID'].nunique().sort_values(ascending=False)\n",
    "    \n",
    "    return common_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# most common mutations for each race\n",
    "\n",
    "# mut_tbl: MAF table\n",
    "# race_category: race column name\n",
    "# races: list of races of interest\n",
    "def common_mut_race(mut_tbl, race_category, races):    \n",
    "    common_dict = {}\n",
    "    \n",
    "    for race in races:\n",
    "        race_tbl = mut_tbl[mut_tbl[race_category] == race]\n",
    "        common_tbl = common_mut_overall(race_tbl)\n",
    "        common_dict[race] = common_tbl\n",
    "        \n",
    "    return common_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in var_mut_dict.items():\n",
    "    common_mut_dict[key] = common_mut_overall(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_mut_race_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in var_mut_dict.items():\n",
    "    common_mut_race_dict[key] = common_mut_race(value, 'PRIMARY_RACE', ['Asian', 'Black', 'White'])   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 20 mutations overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hugo_Symbol\n",
       "KRAS       1090\n",
       "TP53        845\n",
       "SMAD4       266\n",
       "CDKN2A      244\n",
       "ARID1A       97\n",
       "RNF43        69\n",
       "KMT2D        53\n",
       "BRCA2        48\n",
       "GNAS         48\n",
       "KDM6A        46\n",
       "TGFBR2       40\n",
       "ATM          36\n",
       "PIK3CA       31\n",
       "KMT2C        30\n",
       "RBM10        30\n",
       "APC          29\n",
       "BCOR         27\n",
       "ARID1B       26\n",
       "ERBB4        26\n",
       "SMARCA4      25\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 20 primary\n",
    "common_mut_dict['prim'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hugo_Symbol\n",
       "KRAS       743\n",
       "TP53       606\n",
       "SMAD4      191\n",
       "CDKN2A     190\n",
       "ARID1A      80\n",
       "KMT2D       53\n",
       "BRCA2       41\n",
       "ATM         38\n",
       "RNF43       37\n",
       "KDM6A       35\n",
       "PIK3CA      29\n",
       "RECQL4      24\n",
       "TGFBR2      23\n",
       "SMARCA4     23\n",
       "GNAS        22\n",
       "RB1         21\n",
       "KMT2C       21\n",
       "BRCA1       20\n",
       "BCOR        19\n",
       "MAP2K4      19\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 20 metastatic\n",
    "common_mut_dict['met'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_mut_seq_dict = {}\n",
    "top_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in common_mut_dict.items():\n",
    "    top_20 = value.head(20).index\n",
    "    top_mut_seq_dict[key] = mut_seq_dict[key].loc[top_20]\n",
    "    top_mut_freq_dict[key] = mut_freq_dict[key].loc[top_20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TumorPortal genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subset mutation data by tumor portal genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load tumor portal genes\n",
    "genes = pd.read_csv('R_script/cleaned_data/sig_mut_genes_pancreas.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove TumorPortal genes that are not in data\n",
    "prim_genes = genes[genes['Hugo_Symbol'].isin(mut_seq_dict['prim'].index)]  \n",
    "met_genes = genes[genes['Hugo_Symbol'].isin(mut_seq_dict['met'].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 27 26\n"
     ]
    }
   ],
   "source": [
    "# check genes are same among prim and met data\n",
    "# if numbers are different, need to change downstream analysis\n",
    "print(len(genes), len(prim_genes), len(met_genes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Hugo_Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>GATA6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hugo_Symbol\n",
       "14       GATA6"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prim_genes[~prim_genes.iloc[:,0].isin(met_genes.iloc[:,0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prim_genes == met_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from here assuming that Tumor Portal genes are the same in prim and met datasets\n",
    "# both prim and met have the same tumor portal genes of interest\n",
    "# if not all True in printouts above, need to modify code\n",
    "\n",
    "# GATA6 not in metastasis data set, using met_genes for downstream analysis\n",
    "prim_genes = met_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp_mut_seq_dict = {}\n",
    "tp_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(mut_seq_dict.items(), mut_freq_dict.items()):\n",
    "    tp_mut_seq_dict[k1] = v1.loc[prim_genes.iloc[:,0]]\n",
    "    tp_mut_freq_dict[k1] = v2.loc[prim_genes.iloc[:,0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 20 + Tumor Portal Combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Top 20 + TP data together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use these for prevalence bar charts\n",
    "comb_mut_seq_dict = {}\n",
    "comb_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(top_mut_seq_dict.items(), tp_mut_seq_dict.items()):\n",
    "    comb_mut_seq_dict[k1] = pd.concat([v1, v2]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(top_mut_freq_dict.items(), tp_mut_freq_dict.items()):\n",
    "    comb_mut_freq_dict[k1] = pd.concat([v1, v2]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher's Test and FDR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outer join Top 20 primary and metastatic genes together for FDR calculation\n",
    "# so we can compare genes from both in FDR scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RBM10', 'APC', 'ARID1B'], dtype='object', name='Hugo_Symbol')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for visualization, these are the genes from Top 20 primary not in Top 20 metastasis\n",
    "comb_mut_seq_dict['prim'][~comb_mut_seq_dict['prim'].index.isin(comb_mut_seq_dict['met'].index)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RECQL4', 'RB1', 'MAP2K4', 'MDM2', 'ERBB2'], dtype='object', name='Hugo_Symbol')"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_mut_seq_dict['met'][~comb_mut_seq_dict['met'].index.isin(comb_mut_seq_dict['prim'].index)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_genes = comb_mut_seq_dict['prim'].index | comb_mut_seq_dict['met'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_pm_mut_seq_dict = {}\n",
    "comb_pm_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(mut_seq_dict.items(), mut_freq_dict.items()):\n",
    "    comb_pm_mut_seq_dict[k1] = v1.loc[comb_genes]\n",
    "    comb_pm_mut_freq_dict[k1] = v2.loc[comb_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_pval_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-ce1fee6a180c>:24: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  SE = np.sqrt(1/a + 1/c + 1/b + 1/d)\n",
      "<ipython-input-40-ce1fee6a180c>:27: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  c2 = round(odds_ratio * np.exp(1.96 * SE), 3)\n",
      "<ipython-input-40-ce1fee6a180c>:26: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  c1 = round(odds_ratio * np.exp(-1.96 * SE), 3)\n"
     ]
    }
   ],
   "source": [
    "for key, value in comb_pm_mut_seq_dict.items():\n",
    "    comb_pval_dict[key] = fisher_fdr_all_groups(value, groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Significant genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_sig_pval_dict = {}\n",
    "comb_sig_freq_dict = {}\n",
    "comb_sig_genes_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(comb_pval_dict.items(), comb_pm_mut_freq_dict.items()):\n",
    "    comb_sig_genes_dict[k1], comb_sig_pval_dict[k1], comb_sig_freq_dict[k1] = find_sig_genes_groups(\n",
    "        groups, v1, v2, 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): Empty DataFrame\n",
       " Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       " Index: [],\n",
       " ('Asian',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       " Index: []}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black, White]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): Empty DataFrame\n",
       " Columns: [Black, Asian]\n",
       " Index: [],\n",
       " ('Asian',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Asian, White]\n",
       " Index: []}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>PRIMARY_RACE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian, Black, White]\n",
       "Index: []"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_mut_freq_dict['prim'].loc[comb_sig_genes_dict['prim']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_CI</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_odds_ratio, Black_White_pval, Black_White_fdr, Black_White_CI, Black_Asian_odds_ratio, Black_Asian_pval, Black_Asian_fdr, Black_Asian_CI, Asian_White_odds_ratio, Asian_White_pval, Asian_White_fdr, Asian_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_pval_dict['prim'].loc[comb_sig_genes_dict['prim']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Asian vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['prim'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>PRIMARY_RACE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian, White]\n",
       "Index: []"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['prim'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['prim'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>PRIMARY_RACE</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black, White]\n",
       "Index: []"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['prim'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs Asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['prim'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>PRIMARY_RACE</th>\n",
       "      <th>Black</th>\n",
       "      <th>Asian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black, Asian]\n",
       "Index: []"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['prim'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): Empty DataFrame\n",
       " Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       " Index: [],\n",
       " ('Asian',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       " Index: []}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black, White]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): Empty DataFrame\n",
       " Columns: [Black, Asian]\n",
       " Index: [],\n",
       " ('Asian',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Asian, White]\n",
       " Index: []}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>PRIMARY_RACE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian, Black, White]\n",
       "Index: []"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_mut_freq_dict['met'].loc[comb_sig_genes_dict['met']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_CI</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_odds_ratio, Black_White_pval, Black_White_fdr, Black_White_CI, Black_Asian_odds_ratio, Black_Asian_pval, Black_Asian_fdr, Black_Asian_CI, Asian_White_odds_ratio, Asian_White_pval, Asian_White_fdr, Asian_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_pval_dict['met'].loc[comb_sig_genes_dict['met']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Asian vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['met'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>PRIMARY_RACE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian, White]\n",
       "Index: []"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['met'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['met'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black, White]\n",
       "Index: []"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['met'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs Asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['met'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Black</th>\n",
       "      <th>Asian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black, Asian]\n",
       "Index: []"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['met'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutation Prevalence Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Primary Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = comb_mut_freq_dict['prim'].sort_values(['Asian'], ascending=False)\n",
    "ax = df.plot(kind='barh', figsize=(10, 15), width=0.8)\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('Primary Tumor Mutations in Pancreas Cancer', fontsize=20, **hfont)\n",
    "plt.xlabel('Patients with Mutation (%)', fontsize=15, **hfont)\n",
    "plt.ylabel('Gene', fontsize=15, **hfont)\n",
    "plt.xticks(size=15, **hfont)\n",
    "plt.yticks(size=15, **hfont)\n",
    "plt.xlim((0, 100))\n",
    "ax.invert_yaxis()\n",
    "plt.legend(prop={'family':'Helvetica', 'size': 15}, loc='lower right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/pancreas/Top20+Tumor_Portal/pancreas_top20+TP_prim.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metastatic Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = comb_mut_freq_dict['met'].sort_values(['Asian'], ascending=False)\n",
    "ax = df.plot(kind='barh', figsize=(10, 15), width=0.8)\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('Metastatic Tumor Mutations in Pancreas Cancer', fontsize=20, **hfont)\n",
    "plt.xlabel('Patients with Mutation (%)', fontsize=15, **hfont)\n",
    "plt.ylabel('Gene', fontsize=15, **hfont)\n",
    "plt.xticks(size=15, **hfont)\n",
    "plt.yticks(size=15, **hfont)\n",
    "plt.xlim((0, 100))\n",
    "ax.invert_yaxis()\n",
    "plt.legend(prop={'family':'Helvetica', 'size': 15}, loc='lower right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/pancreas/Top20+Tumor_Portal/pancreas_top20+TP_met.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variant Composition Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_var_comp_cnt_dict = {}\n",
    "comb_var_comp_pct_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(var_mut_dict.items(), comb_mut_freq_dict.items()):\n",
    "    comb_var_comp_cnt_dict[k1], comb_var_comp_pct_dict[k1] = variant_comp(v1, v2.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = comb_var_comp_pct_dict['prim'].plot(kind='bar', stacked=True, figsize=(30, 15))\n",
    "chartBox = ax.get_position()\n",
    "ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height])\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=2, prop={'size':20})\n",
    "plt.title(\"Variant Composition for Primary Pancreas Tumor Mutations\", size=35)\n",
    "plt.xlabel('Gene', size=25)\n",
    "plt.ylabel('Percent', size=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = comb_var_comp_pct_dict['met'].plot(kind='bar', stacked=True, figsize=(30, 15))\n",
    "chartBox = ax.get_position()\n",
    "ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height])\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=2, prop={'size':20})\n",
    "plt.title(\"Variant Composition for Metastatic Pancreas Tumor Mutations\", size=35)\n",
    "plt.xlabel('Gene', size=25)\n",
    "plt.ylabel('Percent', size=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel Composition Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_panel_comp_cnt_dict = {}\n",
    "comb_panel_comp_pct_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(var_mut_dict.items(), comb_mut_freq_dict.items()):\n",
    "    comb_panel_comp_cnt_dict[k1], comb_panel_comp_pct_dict[k1] = panel_comp(v1, v2.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PKf/nauk8q/0fAXlf0kecuJHyLsYYY4yxwvF2jVR5XeBmDyHIh9c0ZZv1hveTAUyCbMVYoBUy5A+MFyALC/Q4PnPxmWprqmTt71wAVQ3hagD4SQl7DcBNTuKNh33L1oOQBWU2Q7g6kD969HCpAKo5iPMud46nheP9sGH/u5fQeTDZ8DlLTI5vExTs4fO4kzg3mXxvG2BoYacd3z+VsPsA9NVen9eOgU0JHwjgY0M6OlhMh/59HwbQ0RAuBgVb9v3HSbw1YN/S8ryWbjWtQZCt99VWjXsABJrE56ccJwHgGwC1HXx2AoBhkBWXcQ7CJClx3eUknuv768b5MlDZbpOTcPcbjufvAFobwsSiYEvTmU7iHGtyXp2CHFpRPfY+kAXaas+JWcV4zahpHmsh/HDDPm40uRZqomBPltctpkE/Fw9CFuSSEs4GF71ZXKRdPZdmWwjfwrAPo4sj3VbPV0P8+jmy0+Tcqwz7/OECZI+QXMjKm2ehtJrX0vOaYd+edJKOdpB5zSjIVv1kEqYqZGVNnhLnQ07ivEsJdwWysDQHsjAz1BC2lvJa7W251sJ3GA37fKtLMVwz6rF2mG8Y0qAelxmG43YGsidJSxju21qYKACvG/bDYe9W4/mlnTt5kC3OowxhG8G+h8ElACFO4u4A+963pyF76gQbwhHk9fMBgD8tXI/69bMeQENDuFAoPV0BDDLs30IAdQ3b+ADoA1mAqYf7ysl+PQZZ+f1/cHy/qgNZoKvHlwegjYOwsbDPs2cAqOwgbH3Ia2t/Uc9Ni9dbsd5LUbgeQnp+tggFn9FaQha662E3AHgF+c88XWCfv4ZDFoTr4XPhvKdMSd/P9XP5LOSzlK8SjuDked7Csd6kfI7eQ6gy8q/JJJjnz9HK+XgSWj5jFp+Dz1WPQ4KFdFo6j+BG7xrDdv8E8AuAIcbzRwnTDPa/ea7AyfOD1fO4sPuAkn920q+pvQA6Ga4RPwCjDfG+4SDO5oZwb8NkBAPIPLa59v7PhT2neeGFF1544YUXXm6UxesJKK8L3K8QusfwwPuW4X2nw0Mo4VpBFmIJyBZsUU7CCsPicHgWyFaxp5WwY52EXa6E+x+Aik7C2iB7EunhTSsKCvvDyMnnvmfYd9PC/yJ+RhPYF7rNdhI2GMA2JewFR9857H8wCwBrAfg5CJuonA8CsgAvA4YCLsM2P1lMszEdp+GgQk8LP1UJmwegqYNwakHKJQBNnMT5oCENr5mEuV15/zAsDOsErWeBg/eSlPjuchAmQU2XG+fMQGW7TQ7C+MF+uKc/XFxjnxiO0a0Owo01hDsPJwUtyC8QE5CF/MFW99PFMZitxDvWRdgYyHxOD7/OybVgA7BCCZsNoLqFNAjIngElkUeo55LDa00J/64hXZ2KI91Wz1eT+P+AobJECRsJmY+peU8unFcyf+Hq/NfCBTm6Pk3CvqjEuc1JuLsM+yYAPGoh/vpK+Dxn14wWXi2ES7K6Hy7i3GTluCnhnzPs5yDlPT9YH+LqPiWOk3AwnJDx/NKW553EWwP2w3sOdBDOH/ZDBx5ydE0btjOtYELBYaNWOdonZZt4yPxP3+YNF+HrwL7hTlsH4Sw982lhJynxLXYQpr8SZrPFeAtUBhbXYnK93WXl3ClE3ElOwhm/b2cVLMb0XoW8D8c7OTcPKuHHOgjnqfv5JbgYlquQ3+Mm5TPUYUjVio8C3y1kr039/Ymu4jPZXt23BAvptHQeofAVQlZ/o/kAWKZ8xrtFPY8Lsw/w3LPTPjgZHhNyxAKn+wjZKEMPM8/iMSixvIsXXnjhhRdeeOGlvCzcpboUIKJYyJ4ZqmXqP0KIy1biEkJsg2ydCsgeH50tJuMA7IcSMcZ7CcB/lFVtzcJpE4/qE4lfA/CIcDI0mBAiF7JFnT5kzWNEFOYofDGKUV7nCCHOlMBnPIf8IdvOQP4ANiWEuALZqjlXWxUB4FELn3ENwNNCiByzN4UQByDnlNL5Q1Y2mg6tppmqvHZnYu8RQojjTt5/CbLgA5DHZYgxAMnJsdXJuscLIf4whtMJOQSeOkTTs0RkMwRTh5baJoRwNsyWHq8QQuS5CuclfSALIXVPObvGIHtSHVX+d3geGrcTQiQ5eX8K8odNCYZs/eppTyF/aJ5MyGPh6FrIBTAYcuhKQBbEPWMW1sTwEsojLCOitpC913TnAPzsYrOSTvcQ4WDIIiGHwlInCfeH7Inyg1l4zRTldRsiMh3WVgiR6cb1+RFkxRgAtNSGC7XiOyHEl64CCSH+Rv73QJD5uDNPKa8/93Q+Q0R1APxLWXUVslEGAEAIkSMsDp8qhFgOOU8gIHsFtHQSXLVJCPGpk3iPQjYM0Jk+b0D2otHzdwFZgXfMQVg1fkfDEqmyISvKXA2NNBT5wxT9IoQY5+Kz90PO2aEzHUrM6jOf5p+QQ0oBwL0m90DA/j642eR9szTkug5VblyAff5qRwixCXJIXp0/5NyMpx2Ez4bsGa1z9Czlqfv5eCHEXothi8Mc5fXjJu8PUF57e7i4InPjN1oeZGMaXS9HYUuYp56dBgvHw2ICssemrgYRVTEJw3kXY4wxxlgJ4AohLyEiPyKqSUTPQQ65c5Py9lIhxI4iRL9VeW21gGamhYIP9UG8voMwjyG/EmSNVljmlJBzj/yk/RsI4DZX2xSDKOW1sx/fRaFWbMx0VHCqE0L8CTk8je4+C5+x1kIB2DbD/5+7CP+r8vomIvJ3GDJfMoAFzgJoP5jVAhKz/euB/HkgsgBMt/DZHyqvqyF/zhRdlvK6kTZuelmmHrctQojfnAUWQmTBvpKvh4MCQ1U6XH+fGZBD2+gc5QklST0Wy11dC0KIswDmO9jekfOQc+V4HBEFEVETInobcpiiAOXt8dp360hJp/tvIcR/XYQx5j0zXITfjvxK8QDI4WqKRAghDOmwek+0kveYhX3C0fWlza3XQPs3F8AsNz6j0IgogIjqEtGrkPl7pPL2x44KtS0qzPOG2bwZRlaeN9S5L74XQmx1EK4wvhPW5kXrr7z+0GEoe2pFY0frSTKn5cV/av8GQ87JZaTmFTcX9TPLoYUWKgrVfOQi7Cu8zajPUo7OYU/cz6/BQ3mNYjlkryQAeICI9EpTEFFD5M/hucNFA6VyRwhxCPKZGQASicjKnGbFzRPPTvuEEI7mPdLjPQQ5uoDO7DrhvIsxxhhjrASYtr5lxa4GEQmLYXfCvgWxHa3FdHvIQu86kEO5BSO/EgaQk+Pq1JZVzmyxEOaE8jrCQZjbldcbLH42IAsz7tZeNwewxo1tC0MtWLXUGtodRJQAOea77juLm36L/AlYWxERaQWajlgpAFN/bB0WricwV8MT5MSv511ss8FChSIArIYcNxwAYomouuGHaGvl9c9azzSnhBBbiSgF+ZV8rWFfEKNWrjYEMIuIXi1iIag3qcfInfPqHe11CORxcNjzCsB2Kz2pYC1PKBFaRWVTZZU7x0LvnVaXiMKFEGlOwm+1eG4X1QAiGuA6GAA5tKbDHhaakk63q8ogwD4vyYYcQtQhIUQ2EV1Afg9OlwVlWo/SLpDnQnXIe2KAIVhj5bXVe6KllsiapZDDdkVq8XeF+fn4tPJ6jRDihEmYorrTjeeN1XDSM5iIAiHnfmgO2WglDLLRhvq8UVt57bHnDe1Z6FZl1VcWP9sql9+/dp9X99lZ77frhBCniCgN8vyOJaLKQohTjsITUQzkOX4zZE+sUMgeKqpayuuqKJi/b1dedyWidwG87SLvu5G4+yy1w0L+qoZ3lJd54n7+txAixWLcxUIIkUlESyB/z4RCViDoFaHqfW6OcduyjoiqQ/6eaQKgEmS+afy9HaIHh7ymPXYdevDZyUo+D8i8Xu8lZ/YcqeZdg4noIIAp2sgKjDHGGGOskLhCqPRIhxwu5w2zVt9aC8AXICf6reRGvBUthrMyrJD68B3sIIxa8DaQiLpa/Hy1UCnGYajio/6IKYkh6mob/t9tcTs1XCTkjyNnwy2ctRCn+r25DC+EuEKklvc5/K5Vf7oOYhouEfnDOQH2x83qMdPD3mUSB4QQx4joK8ihWQA5fMmjRLQZstJyM2TheSZKOa0QtIayyuox2gvZSljP82vDeQGS1WHGrOQJJaU67AtFC3ONEWRB904n4Q+5ma6SdAzAOCHETJchSz7d7uY9KRYrqCydU0RUEcBbkEO0BVmIV2flnpjmYpgbO0KILCKaAzlfESArfuwK2YgoFMBDyip1CFZPS4acR+99syHrtIqgUZBDaFl9hoAbYYvjeaMagArK/0XpVW3GyvWjPu/kAVhguHc6E6i8jkH+cKrXacMb/htyrjx3ntfNvofNkAWreg/aVwAMI6KNkBVZmyEbApgOG3UDKPZnKbg4hz14P/fWPWwO8hu4DQDwpfZb5jFtXQ5c9EQuS7ThOD+CbBBgOSOAe3lscfDUs1NxPUcuBfA2ZEW3D4D3AYwlonWQc05tBvB7KR7mmTHGGGOsVOIKIc/IAmDsNp8DOeTECchhKNY5GlJM+9G4EPkF2u4wtpR2xEpvAKe0objUVpA3o3Dd+z3x40gt7AshIr9iLghRj0OOq+HiFMmG/11VCLn7vRXme7byw9ZS61MhxCUiyoEcgxwo2GpW/d+dFq3qcTNrYTgIstDtDu1/G4A7tQUArhLRz5BDYsy3OoeGFxiPl9Xjfk1rkR6trXLVm6ekzpPiVKhjAfNrzBmr125RnULBgpksyMrrg5CtbX90Y2z8kk631/IeIoqDLAiqW4g4rdwTC3PspiG/Qqg7EcVqw+zo+iK/AuMMrLfKdlcqCg7VdxXyeSMJsifEBkd5HBGFQPbQdTRvjzOWnjcs9j5UmZ0HkYb/XfV8dZeVc0AdetYH+b173VXgmYeI6kGe47EFQrtW4HsQQggi6gN53jVSwnXVFgC4TEQbAHwBYNkNVsDqifzMyFP3c0/dw4w2AzgMWXHQUZsfpjHye4OsEkIY78dlEhG1g8w3K7gKa8Lq77Ti4qlnp2K552u9zboDWAnZEACQPazuQ/7QdalE9D2A2UKI7wvxuYwxxhhjNxyuEPKMs0IIqz1lzAyHfWXQNsiWd9sgW4ynA8jShxYjooHw/HjhgGylXRzzs3hijpfDymuCLCBxOpyRm9QfeO78KDKG9fQPxcJydx/1CiHj/hXHcTMrDEslovaQhbLPQBZ2+hi2uVtbxhLRQCHERjc+31OM+1Zsx6gMKuyxcPca81Sh6DohxMBijK88F+bORH5lUB6AZZDzJe0CcBLAFbXCg4hmw36YIlfcPnZCiH1E9CNkJbMvgIEAJipB1OHiZpXgcH5/FPF5413YVwatBbAYsgfOCQAZai9mIhoL4I0ifF5hGa/b4q7Et3IOFKbw14zdM4/Wi2Ix8iuDsiGH2/oOwF+QlceZasUaEW1CfgMHU1pv2RaQvTaehhwKUFUBQE9t2UNEjwghnPU8YUXjqfu5V+4FWiXkXABjIc/xx2A/VFm5GC5O6/25FPn5QQbkb7C1APZBNgDIVPN8IkqCfe8wT/LUs1OxEULsIqIGAJ6HvLfWMwSJgHy+76v1/n9MCHHUU+ljjDHGGCuLuEKolNMKBkYoqz4D8IKLeWVCSzZV5oQQl4lIHcaiayluqfWz4f/WKN4KoYvK62Ai8rHY2tb43ZWV8f0tnXNaLzJ1SAjjHEHqcXPnPFbDmh4z7fjPBzBfm8T3Dm25C7JgTG+ZWB3AaiK6QwhhbGnvbRcN/xfrMSpjzI6FldbGZfUaYwCI6GYA3ZRVjwghFrnYzFP3xGnIL5R/ClqFEBE1AtBKWy8AfO6h9LiFiKIADFZWjRRCvOtiM688b6DgdRsOa9d/cVLzoDNCiHiHId3THfnD0eUA6CiEcDWnkaXvQatEmgJgChFVgrz/3QE5N2UDJWhDAJuIqLkQIsl60ksFm7cTYNGNcD+fC1lhTJA9tfU5Ti+g5HpJminJc+JJ5FfepgFoI4TY52Ibb+WbQBl9dhJCZEDeUydq8zTpvfw7AKipBG0HYKOWd5Xma4MxxhhjzKs80RODFU1z5A9LcgXAKy4qgwDrEzuXBHXYlkSvpcK17ZCt+HR9izn+88prgv2PFWfUiaHz4Hy4uNLE6v7VhMImhaoAACAASURBVP2QEMZhftTjdpMbn68et/MOQ2mEEGlCiG+EEC8LIW6BrASaAEAfjisAcszyUkX7QazOdWTpGBFRNOx/zLs8RmWAcR+sni+1DP+Xh2NxI+msvP7RQmUQ4Ll74lfIL1hLJCK9cmiQEmajEKI0zUul6oD8gtMkyHmGXPHW84ZxfgpvPG+oQwLGaT0FioN6js+3UBkE5Be0WyaEOCeEWCyEeEEI0RCy1910JUgEgNHuxlvM7HolEJGfo4AK45BYpdKNcD8XQhxBfgOsWsifO2tBIYaOVKlDPHv7nFCv10muKoOIqEIJp8eVMv/sJIQ4JoT4QgjxtBDiJsjfysuUIDUh591ljDHGGGMOcIVQ6Vddef2XEOKKw5D5biupxFiwVXnd0WupcEGbL0gdruJOImpSjB/xB+Skv7o2Frdrrbz+Sx2ap5Rr5TpIgXDXUHDOFHWCWkvHTOvto84l4mySW1NCiBNCiNcAvKmsvpOICjskhl1vMHJjpnEL1J5sVs8rY7ji7A3nFdr8LCeVVYW5xi7BexNus8JR74m/uQpMRMEo3Fx2btMKONX7ytNaHvKYsu4/nkhLIanHdruFxicAcGtJJcYZIcQFAAeUVXc4CluCtiG/EQEgK9SKg7vneC0Ubq4hO0KI/UKIIbA/hws7L1JxMc5/Y5w7ykxj10FKjRvhfm42NFxRh4tTz4viPCfUZzerz21uXa+Q35+V39+FSYtL5fHZSQjxPwAPAFCHevZ23sUYY4wxVqpxhVDpp7Z8c1k4Q0SJKNxk0MVljfL6Xq1bf2n1b9gX5nxORIUaRlE77tcJITIheyHpHrEQhy+Ah5VVPxUmLV7SmogSLITrp7zerh0nlbrPtYmopYU4+yK/VXsegF8sbOPI18prX9hPGu6Oy4b/gwoZjxn1GN1HRIEOQ+ZTC6T/Ki8TOcN+6Md+Five1GOx+QabOL08cOueCJn3enLOLLWHxQOQwwnpBZYpAJZ7MC3ucvd5427kT/LtDWuV1wOKUIFfKEKIiwB+VVY9V0xRu3uOP1FMn6tT74NFrmgqCiFEOoBUZZWVhju9Sig5JeFGuJ8vgRzhQLdXCGGl4sQZdX4YK+dEb4vxqs9uVp/bSup6LUxarCp3z05aA4ZvlFVezbsYY4wxxko7rhAq/U4rrxsTUUVHAbUH+skoxpZkhfAl8ocB8wMwTZs3ptTRhrKYoKy6BcBsdyuFiOgfkHPTGM1UXt9DRHe5iOofsB9+Z4Y76fAygn3vmoIBiG6FnBtBN9sk2HoAx5T/Jzj7oaoN0TNGWfWtEOKMIYw710OY4f9U01CupQFQe3fVLmQ8ZmYiv9AhCsBIZ4G1SrUHlVVl6bxyRb3GGgJ43FlgIuoD+1au5elY3CjUe+LtzgISUQSA8SWbHHtCiP3Ib6UcCOAD5e0vhBBXPZkeN6nH9lZn90Ii8odsVOFNnyA/L4yHi3tQCflQed2ZiFw2/rDAnXO8FoAXXUVYhPtgaRi2Vu0B87DDUACI6CEATUs2OcWq3N/PhRDpQogKQgjSlvrFEK0750QbAD0sxqs+P1p9bnPner0N9g2jrKYl2tnvv0IoM89OZTzvYowxxhgrtUplQT2zsw35Y4wHAphkVsGiFc5MA9DVg2krQBvS7p/Kqq4AvtIK5hwiomAiepyINpRoAgt6A8CPyv+PAviBiJq72pCI7iSiLQA+gvkY5l8COKL8v9jRsHRE1BvAO8qqVdoQCGXJo0T0qtkbRFQHwFLkV1YeBzDPGE4IkQv7SroOAD42K5gkojDI1vaVtVV5sD+GuhFENIWInBZCaOO6q4XH20x6MFmi7ccfyqphxVUxKoQ4ANniVvc6ET1qFpaI6kIeI/2zT8G+IKCsWw/7YSo/IyLToSq1ghh13/+AfWtSVjZsUl63IaInzQIRUSxkj9V4TyTKYJryWm3xX9oLb9V7YVUAr5sF0iriv4K1lvklRgixF/bX9MtENN7ZPDNEFENE/1eMyfgK9q3tZxHRYFeFmESUQETvEtEok7c3Ka8fJKLOJmFARLUBfA8g2EI6PyKiCa56bRNRDAD1Pv6jo7AepM4NMkCZm8sOEbVH6R6SsQC+nxeaek50dHLMGkNeo1afv9Qhh5tq55Qrm5TXLxCR6RClRNQKwAqraRFCnER+AzuCbDRWXMrSs9MCInqNiCo5C6RVjqu9NEtD3sUYY4wxVmoVangs5jlCiEwimgZAL8AYAKAeEc0AsB9yGIGmAJ6GbM12DcAXKP4hRCwTQszQfvjoE2n3hvzBtgByeIxTkIX3EZBzv7SGnJS1AuwnafZEWnOJqCfkD3K90OV2ANuJ6BfIwpa9kBOF+wOIg5yPojuAOi7ivkJET0D+8PIFEAPgNyKapcWbAjkR9AMA7lc2TYH9JORlwWLIVqvvEFFXAHMhxxcPg6zUGYz8QisB4FkhhHFYNd1UyHNG/z6eB3C7ds7/BXksbwHwDOx7VE0UQqg/cHVBWthniGgXgA2QrUvPQg5jEgmgBeTQTurwR0VtbT4f+XMmPQU5hOKfsB8GZKEQYmEh4n4e8jyNhyxcmEdE/QAsguxhVRHA3ZD5gj7USB6Ap7RhjsoFIYQgooGQwzOGQJ5ja4loIWSBxWnI6+5eyBaw+tCCWQAe1yruWNnyE+T120z7/3OtwPwryO87EnI+machr4OTAHYBuMeDaVwOOeF2jLJuixBijwfT4DYhRBIRfY384ZXGaPfyeZBDNIVCzjfxNGTekw7gO8hhO71lGGQ+q88RMgZyCKQvAeyA7OUZCqA+gPaQ80qcgmzIUWRaHvQQZOOdapDPCdMgGwEshjxXUyHz4UqQzw93Iv/eMNEk2sWQjRviIfOs77TnhlWQzwcxkPfHAZAVjrsAXIXzufwqQh6rEUS0FbKwdBdkgXO2FmcbyHuVPlTqNdg30PCWeQBGQz5/+ULm8VMghwzMgDzuvSCfpQjyGbi/d5JaKHw/d98aAH8CaKT9P1cbwnIF5DUSB9kgrT9kg625cNELBgCEEH8T0e+Qv6sIsoHYH5CNmNR5QQcLIfTKmumQPbuCIJ9DthDRVAA/QM61UwXyN4M+vPH3ABrA2nCb85H/+28cET0F4G/Y90CfLIT4wUJc6n6WpWenOMheYP8iok2Qw0Lvhvxdlgt53dwBYCDk70hAHvePPZhGxhhjjLEyhyuEyobRkA+7eq+V1rDvuq/LhWxBdgVerBDSDIEscP8n5I+qUMhKgcHeTJQZIcQlIroXwFuQxy8AMs3ttMWVfbDvFaXG/aM21MIiyIIbf8hjM8RBXKcBdBZCnHJrJ7xvNeSP1DcgC7tMW/BCVgY9L4T4zlFE2g/V+yELVTtpq5tADofoyGQ4+A4Mboa1CeZfFUKstBDOmc8gC6n0FqZx2qL6vTARCyGStVbS6wDU0Fbfqy1mciB/xK9x8H6ZJYTYR0SdIAtLIyAL1B6B43m70gH0FELs8lASWTHS8odHIQuF9J6nD8N82KBUyELiZzyUPACAECJbK8Afoawu7b2DdM9CVrbp+UoXmE/OnQVZ2NrM5D2P0Rpe3AU5740+XFNtyHuRp9JwRhuWajnyK2UaAhhXyPgytaHn1kA+j/hCNhIxayhyHPIct3p+EYBbtcWZawAGloaeykKIi1pPwK8hn6H8IZ/VzHpMTIAscC8zFUJ8P3ef1phrAOTwnGGQ9/2B2mI0H8BYWKgQ0gyC/C7Ctf+boGBvyOu9DIUQp4noaciKSB/IypXh2mL0J+RIBDsspmUsZGWgXvFVXVtUX6MQyuCzkw1AR21xJgPA/UKIEyWfJMYYY4yxsouHjCsDtJ4UdwL4HPYt1FS/A7hbCPGZxxLmhJDGQPbk+BqyBaozf0P+kHdUkVCihBDXhBAjASRCzktwzMUm2ZCVIA8BaOSiguMbyB+Ty+D4+7uifW5jIcSfbia/VBBCjIX8Ieno2O2FPEenWIjrMoBukIWTzn7U/QGguxDiH9qEsmaWQLYUPOjqYyELF24XQpi12naLECIHskJrIIBvIQvuCjUEnYP4D0BWbr0P+UPdTB5kC/6mheyJVCZoPcMaQg5rkuUgWDbkMI6NhBCbPJQ0VgKEEH9DNorY5CBILvLPe7Neg56gDhl5CbLXR6mnzcHWGrJyw1GeuhlAKyHECo8lzAkhxAXIivenIXumOgwK2SJ+bAmk4RSAtpA9bP52EfwqZM/hQZANUczi2wTZEMhRo4GrkPlZUyGEq3sbIHstfQ7n91NAPqN8A6CZEOJLC/F6hBBiNWTF5F4HQY4C6CeEeM1zqSo+fD93nxBiJ+Q18quDIOcA/J8Q4lE4zsvM4t0OWQHzFuSwahfg+Nld32Y+ZC8gR/lPBuRzaCshRIobabkIWck8FLKS6jQcP+O4rYw8O00EsACy160zmZCVco2EEJ4efpwxxhhjrMwhx2WorDTS5kVoD9lCLA/yx8H/hBB/eTVhLmjzs7QFkAA5HEkegIsADgPYLYQ47Xhr79DGa28EOWxCJOSPpQuQPYJ2FmZycCIKB3AX5FBnYVp8hwH8JIQoth95nqAN3aBX4D0hhJitrfeB7FlVB/LYJUOeo9uL8FnNIIfwiIEs7D0L4L9CCGeFf2bxxEJWztWE/E5tkIW1SQB+0wpDyxxtDrF2kBWaUZCFDycBbHKn8KE8IKJgyGssAbKF70XISspNQghHBW2sjCKiepD3lkqQwzGeArDZ29cyEa1G/px+U4UQz3ozPYVBRDUg8/jKkBUQpwFsFUIkeTNdrmjnxC2Q50QQZB5/GMB2IYRHhqXV5uppAyAWcsivK5D3wn0A/nBnfjrt/tca8p51EbJS50chRFoR0tYIMo+sCNlr6CKAA5D3wdTCxOsJ2txMLSF7zEdCHtO9kNd8njfTVlz4fu4+ImoE2estGkAaZMXMRq1hjifTYYO8VptBXlsXkP/8ccWTaXFXWXh20uZNawD5GzgMsqIvFTIP2C6EyPBi8hhjjDHGyhSuEGKMFYqjCiHGGLuRaRUph5HfC7uF1pqdMcYYY4wxxhhjzKt4yDjGGGOMseLzCvKfr7ZyZRBjjDHGGGOMMcZKC64QYowxxhgrBkTUHcAQZVWR5yNjjDHGGGOMMcYYKy6+3k4AY4wxxlhZpM1d8SZkA5sEAI2VtzcLIb72RroYY4wxxhhjjDHGzHCFEGOMMcZY4UQD6GWy/gyAAR5OC2OMMcYYY4wxxphTJITwdhrKtOjoaJGQkODtZDDmcfv27UNGRgYAoEaNGoiOjvZyihhjzLPS09Oxf//+6//7+/sjLCwMlStXhp+fnxdTxhhjjDHdjh07koUQMd5OB2OMMcZYacA9hIooISEB27dv93YyGGOMMcYYY4wxZkBER72dBsYYY4yx0sLH2wlgjDHGGGOMMcYYY4wxxhhjJYsrhBhjjDHGGGOMMcYYY4wxxso5rhBijDHGGGOMMcYYY4wxxhgr57hCiDHGGGOMMcYYY4wxxhhjrJzjCiHGGGOMMcYYY4wxxhhjjLFyjiuEGGOMMcYYY4wxxhhjjDHGyjmuEGKMMcYYY4wxxhhjjDHGGCvnuEKIMcYYY4wxxhhjjDHGGGOsnPP1dgIYY4wxxhhjjDHGyrodO3Yk2Gy2wT4+Pt2EEBHeTg9jjDHGbhxElJqXl7c6Nzd3eosWLZIcheMKIcYYY4wxxhhjjLEi2LFjR4Kfn9+y2NjY8PDw8HR/f/9kIvJ2shhjjDF2AxBCIDs72y8tLa3v2bNnu+7YseN+R5VCPGQcY4wxxhhjjDHGWBHYbLbBsbGx4bGxsRcCAgJyuDKIMcYYY55CRAgICMiJjY29EBsbG26z2QY7CssVQowxxhhjjDHGGGNF4OPj0y08PDzd2+lgjDHG2I0tPDw83cfHp5uj97lCiDHGGGOMMcYYY6wIhBAR/v7+Od5OB2OMMcZubP7+/jnO5jLkCiHGGGOMMcYYY4yxIuJh4hhjjDHmba6eR7hCiDHGGGOMMcYYY4wxxhhjrJzjCiHGGGOMMcYYY4wxxhhjjLFyjiuEGGOMMcYYY4wxxhhjjDHGyjlfbyeAMcYYY4wxxhhjrDxLePW7Ft5OgzNJE+7d4e00MMbKrk+f+aFU53HPT+3AeRxjGu4hxBhjjDHGGGOMMcaKjIhaqIu/v3/ziIiImxs0aFD/4YcfrrF48eKwa9eumW7bp0+fBOP26tKqVau6Zttt3Lgx+KGHHqqRkJDQqEKFCs38/f2bV65cuXHXrl1vmjFjRoT6efv27fMnohZVqlRpXJj9y8nJwYcffhjdtm3bxMjIyJv9/PyaR0ZG3nzbbbclfvDBB9E5OTku923RokUVzcIMHz68MhG1+OCDD6LN3r906ZLP+PHjK7Vp06aO/tmhoaFNGzduXH/o0KFV/vrrL3+z7c6fP297+eWX4xs3blw/LCysqb+/f/O4uLgm99xzz03Lly8Pc7SvVapUaawefx8fnxahoaFNmzVrVu+dd96JcbSvffv2rUFELYKCgpolJyfbHMWv7y8RtZgwYUKMWZjJkydHEVGLYcOGVXa0rdXzRd+fffv2mR4nq5YvXx42aNCgqrfeemudihUrNiWiFi1atDA9N1n5o14Pe/bsCXAUrnXr1nX0sJMnT44yvv/jjz8G9+zZs2blypUb+/v7Nw8JCWlWrVq1Rh06dKg9evTo2EuXLtmV2Ts7f0+dOuXbqFGj+kTU4v77709wdG0C+XmRMU1qHvXiiy9WdrT9xx9/HOXoGvv2229Djdehn59f89jY2CbdunW7af369RXM4kxPT/cJDQ1tSkQtevToUdNh4hXLly8P69mzZ80qVao0DgoKahYYGNi8evXqjXr37l1z8eLFYY7S42pxlj+sXr06pHfv3jUTExMbhoeHNw0ICGhepUqVxh06dKi9YsWKUCvp/uOPPwKCgoKaEVGLXr16me7rrFmzIgYMGFCtRYsWdUNCQpyGLSzuIcQYY4wxxhhjjDHGis2LL754GgByc3ORlpZm27dvX9Dy5cujFi9eHN2wYcMr8+fPP9ykSZOrZtt27NgxrUmTJpnG9QkJCXbhr169Sk8++WS1+fPnx9hsNrRs2TL97rvvTgsICBAnT57037JlS+j3338fsXTp0tQ1a9YcLuo+HTp0yK9Hjx61//777+CoqKhrHTp0uBgXF5dz9uxZv02bNoW99NJLYTNmzIhZuXLlwVq1ajkskR09enTVPn36XPT1tV4kt2HDhgqPPPJIrXPnzvnFxsbmtG/f/mJ8fHzO5cuXfXbv3h08ZcqUuKlTp8Zu3Lhxb7t27a7o261evTrkkUceqZWWluZ70003ZfXu3ftCaGho7qFDhwI2btxYcfXq1RG9evW6sGDBgqSgoCBh9tlPPPHEufDw8Nzc3FwkJSUFrF27NnzUqFHVN27cGLZ27dpDatjU1FSflStXRhIRsrKyfKZPnx45atSo867279133608ePDglMjIyDzLBwVAy5YtM9q1a5du9p7xfCkun376acyGDRvCAwICRPXq1bMuXboUVBKfw0ovm80mcnNzacqUKdGffPLJSeP7u3fvDvjtt99C9XDG9z/77LPIoUOH1hRCoE2bNuldu3ZNs9ls4ujRowG7d+8O3rhxY8W+ffumNWrUyOU5vHfvXv+uXbvWOXr0aMCQIUPOfvbZZyd8fArf/8Nms4kFCxZEv/fee6fM8qjZs2dHO9ovXeXKlbMffvjhFAC4fPmyz/bt2yusWbMmYu3atRGzZs069Pjjj6ep4WfOnBmRkZFhIyKsXbs24syZM8fi4uJyzeJOTU31eeihh2quX78+PCAgQLRp0+ZSYmJilp+fnzh69GjApk2bKq5YsSJy/fr1Z1966aVz+r1Il5aWZps1a1alkJCQ3EGDBp0zxh8VFWX6uQCwbt26sC1btoQ2bdr0ctu2bS9VqFAh7/jx4/4bNmwI7927d8Vhw4adnjRp0ilH2+fk5OCxxx6r6er7mThxYvy+ffuCgoOD82JjY7OPHDkS6HSDQuAKIcYYY4wxxhhjjDFWbD744IMChWLHjx/3HTJkSPXVq1dHdOnSpc727dv/rlKlSoHuQj179kwbNmxYiqvPGDhwYPWFCxdGJyYmZi5ZsuTQzTffbFd4eu3aNUyfPj1y5cqV4UXbG9mCvVu3bokHDhwIuv/++1Nmz559LDQ0NE99f+DAgdWXLVsW1a1bt8QdO3bsVd/XVa9e/erBgwcDJ02aFP3SSy8lW/ns//3vf4G9e/dOvHLlim3UqFEnx44de8bPz88uzN69e/2HDx9eNS0t7XqPnB07dgT26dMn8erVqz5vvvnm8ddee+2cWhB58OBBv169etVesWJF5IABA/IWL1581OzzR44cebZu3brZ+v/bt28PvP322+uvW7cu/Lvvvgu59957M/T3ZsyYEXnlyhWfp5566uzcuXMrzZkzJ8ZVhVD16tWvHjt2LOCNN96I//jjjwsUrjvTrl27dLNzrSS9+uqrZyZMmHCyadOmWYcOHfKvV69eoXqbsbIrKirqWkxMTM6iRYuiPvzww5PG6/Gzzz6LFkKgffv2F9evX2+X/6Snp/uMHDmyOhFh2bJl+3v16lWgQnPdunUV4uLizLtSKrZs2RLUq1evxJSUFL9x48Ydf/311wtUcLhLT/PSpUsr9u3b96L63s6dOwN37twZ0qlTp7R169Y5zFerVKmSbbwuX3zxxcofffRR/KhRo6oZK4RmzZoV4+Pjg0GDBp2dNm1a7NSpU6PHjh171hhvbm4uevbsWWvz5s1hrVu3Tl+4cOGRhIQEu8r3zMxMev/992P2798fWLdu3QLp2Ldvn/+sWbMqhYWF5bqbd7z55punzbY5cuSI3y233NLgk08+iR8+fPj5GjVqmDYIeO211+L37t0b/Prrr58YM2ZMNUef89577x1PSEjIbtiw4dVVq1aF9ujRo4476bSCh4xjjDHGGGOMMcYYYyWqWrVq11auXHm4VatW6WfOnPEfM2ZMfGHjWrduXYWFCxdGV6xYMXfdunUHjJVBAODr64vnnnvuwrJly44ULeXAv/71r9gDBw4ENWvW7PLixYuTjJU9oaGheYsXL05q1qzZ5QMHDgS9+eablczieeWVV04HBgbmTZgwobJxSChHnn/++WoZGRm2Z5999sxbb71VoDIIAOrVq5e9atWqwx07drxeOTN06NDqmZmZPs8888yZf/7zn+eMrdJr166ds2rVqoNhYWG5S5YsiV67dq3pcE5Gt9xyS1arVq3SAWDr1q1228yZMyfGx8cHr7766tn27dtf3L9/f9APP/zgNN5Bgwadi4mJyZkxY0alQ4cOFdy5Uubuu+++fMstt2S508OLlT8DBw48n5yc7Ldw4UK7ipGrV6/SkiVLops1a3a5fv36BXo6bt++PTAjI8NWu3btTLPKIADo1KnT5ejoaIc9VQBg5cqVoV26dKmblpbmO2XKlCPFURkEAI888siFwMDAvBkzZhQYuvKzzz6LBoCnnnrKUmW26uWXXz4HACdPnvQ/derU9Yvnt99+C9y1a1eFNm3aXBo7duxpPz8/MXfuXNNhM6dPnx65efPmsOrVq19du3btQWNlEAAEBQWJMWPGnJs+ffpxd9PoSnBwsGkvypo1a+Y0b948Iy8vD46GnPvpp5+CP/roo/j/+7//O920adMC54WqR48e6Y0bN75alJ5ernCFEGOMMcYYY4wxxhgrcTabDaNGjToNACtWrIjMy3NrhLDrpkyZEgMAjz76qMPW2DpHQ6G5Y968edEA8Oqrr56y2cynxbHZbBg5cuRpAPjiiy9M58SpWrVq9pAhQ84mJyf7vfHGG3GuPnfv3r3+//3vf8MCAgLE2LFjz7gKr++rtl2ov7+/0+1q1KiR069fv2QAmDp1qmmazQghCACI8keN+uWXX4L27NkT3KZNm0u1a9fOGTBggB6vaeGuLjg4OG/UqFEns7KyfF555ZUqVtPAmDc99dRTF4KCgvJmzpxpd34vWLCgYkpKiu/AgQNNe8ZVqlQpFwDOnTvnZ7VS2GjmzJkRDzzwQGJeXh4tWbLkwJAhQy4UJh4zERER17p165a6adOmikePHr1eQZuZmUlfffVVVMuWLTMaNmyY5W68al6v5huffvppDAD0798/JS4uLrd9+/YXjxw5ErhmzZoQYxwzZ86MAYAXXnjhTFhYmNObR3Hk+1adPHnSd9euXRX8/f1F48aNCzROyMjIoCeeeKJmvXr1Mt96663TZnF4GlcIMcYYY4wxxhhjjDGP6Ny5c4bNZhMXLlzw3b9/f4HW1N9880348OHDKxuX5OTk6zUxv/32WwgAdOrU6VJJp/fgwYN+p0+f9rfZbOKee+4xbdGvu/feey/ZbDZx+vRpf0e9XcaNG3cmKirq2rRp02LVAlczGzZsCAGAhg0buuwx4GC7KzExMU6369KlyyUA2L59e4ECWDPbt28P1I//bbfddllfrxfsPv744ykA8OCDD16Mioq69u2330ZeuHDBafnj0KFDU+rUqZO5YsWKqC1btliek2fz5s2hZufK8OHDK2/YsMFSjyfGCiMiIiKvR48eF37++eeK6rX++eefx4SEhOQ+8cQTqWbb1a9f/2qjRo2upKam+rZq1areO++8E/PLL78EZWVlOZyTR/XRRx/FDBo06KaQkJDcNWvW7Ovdu7fTPKkwhgwZkpybm0tTp06N0td98cUX4Wlpab5PPPGEyznBzPz73/+uBABVq1a9Gh8ffw0Arly5QsuXL48KCQnJfeyxx1IBQKlItqugzsnJwa5duyoAQLdu3Yp9n93x008/BQ8fPrzysGHDKvfp0yehQYMGjS5cuOA3fvz44/q+qYYOHVr1xIkTAXPmzDli1sPTG7h/I2OMMcYYY4wxxhjziKCgIBEeHp6bkpLie/r0ad969eplq+9v2LAhfMOGDQXmyc+69wAAIABJREFUpxgyZEiyXimSnJzsB8geLiWd3uPHj/sBQHh4eG5ISIjTVuchISHX9+3YsWN+tWrVKpC+ihUr5o0cOfLkiBEjaowYMaLyokWLTOfuAYDTp0/7AUB8fLxb+6lvV7ly5WxXYRMSErIB4Pz586YllRMnTowNDw/Pzc3NRVJSUsDatWvDs7KyfDp16pTWtWvXDAC4dOmSz8qVKyNDQkJy+/fvnwoAfn5+uO+++1JmzJgRO2PGjKgRI0Y4LEi22Wx4++23TzzwwAOJL7/8crUtW7bst7Kfv/32W4heOWUUHh5+rWPHjpfN3mOsOAwZMiR58eLF0VOmTIl+//33T+/fv99/y5YtYf369TtvNocYAPj4+GDZsmWHHnvssYRt27aFjho1qjoA+Pr6ivr161/p0aNH2ksvvXQuMjLSdPupU6fGAcCXX3556Pbbb79SEvvVpUuXjJtuuilr3rx50W+99dYZHx8fzJo1KyYsLCx3wIABqYcPHzYdFk138uRJ/+HDh1cGgMuXL/vs2LGjwo4dO0J8fHzw1ltvndDDzZw5M/LSpUu2fv36Jet564MPPnhx2LBh19asWRNx/vz5Y3qF9rlz53xzcnIIAG666SaX+VpJ2rp1a4UPP/zw+pCnFSpUyJs0adKR559/vkBPrRUrVoTOmTOn0qhRo060aNHC7Z5VJYUrhBhjjDHGGGOMMcaYxwgh61XM5kiYNGlS0rBhw1KsxOPj41PkYYGSk5Ntb7/9dqxx/ahRo85GR0fnKsOjWfosZ/ume/HFF5OnTZsWu3Tp0uht27ada9WqlemcEu5+dmG209PryKxZsyppcSE4ODivTp06mQ8//HDKK6+8cr2C5/PPP4/MyMiw9evX77w6z8bgwYOTZ8yYETt79uxoZxVCANCnT59Lbdu2vfTLL7+ELVq0qOLDDz980Vl4AHjxxRdNJ3kvLFfnQnF9DisfOnTocDkxMTFzwYIF0RMnTjz96aefRufl5eHZZ591eq4nJiZm//rrr/t37twZuGrVqrAdO3YE//777xV2795dYffu3RVmzZoV88MPP+wzVpYDQLt27S5t3rw5bMiQIQkbNmzYV7t2bbvK4vHjx1dKS0uzK+9/4IEHUm+77Tan89YY9e/fP3ncuHFVV65cGVqrVq3sX3/9NXTAgAHnHM2jozp16pS/XmFis9lEZGTktc6dO6e9/PLLZzp16nS9knbWrFkF5iRSK5KnTZsWNXr06HOA/ZBzJWXy5MlRSUlJAeq6Dh06pHfv3t2uR9KIESPOjxgx4vyVK1do7969AR9//HHMCy+8UPOXX34JmT9//jE9XHJysu2ZZ55JaNKkyeWxY8eeLfEdcANXCDHGGGOMMcYYY4wxj7hy5QpdvHjRBgBxcXEFhtexIjo6OufEiRMBSUlJ/jfffHOBORvckZKSYlNbe+v0HknVqlXLAYDU1FTfjIwMctZLKCMj4/q+Va1a1WGvHl9fX7z55psn+vXrV/vll1+u+tNPPx0wC6f38Dl16pTTFvlGeo+ikydPBrgKe/ToUX8AiImJMU3v3r17d9etW9dpi3ylYNeuIq9ly5ZZDRs2vLJnz57gn376KfiOO+5w2qPhgw8+OHHrrbc2GD16dNU+ffq4rBAqbq7OBU+nh5V+AwYMSB49enS1pUuXVly4cGF0w4YNr7Rt29ZS5Uvz5s2zmjdvfr3XyP/+97/AJ598MuH333+v8MILL1Rbv379IeM2//nPf46++eabcV9++WXMnXfeWW/dunX7GjRocP36nDZtWqwxv0hISLjqboXQkCFDUt55550qM2bMiK5WrVq2EALPPfecpeHiWrZsmbFt27Z9zsLs3LkzcOfOnSE1a9bMMvbk0yuS586dG61XCMXFxV3z8/MTOTk5dOTIEf+GDRsWKd83M2/evGizHofGCiFdcHCwaN68edasWbOOX716lRYsWBDTqVOndH24wGeffbZaWlqa75o1a/b7+pauKhieQ4gxxhhjjDHGGGOMecTatWtDcnNzKSoq6pqrigZHWrZsmQEA69atCytqeurWrZsthNhhXPS0JSYmZsfFxWXn5ubSqlWrQp3FtWrVqtDc3FyKj4/PNhsuTtW3b9+Lbdq0Sf/555/Dli9fbrofHTt2zACAPXv2VEhJSbGZhTHToUOHdG27YHXuJTNr164NBYBbbrklw2r8ql9//TVo9+7dFQDg7rvvrkdELdRlz549wQAwZcqUGOcxAa1atcq8//77Uw4ePBg4adKk6MKkpyhcnQuMGQ0ZMiQlMDAw7x//+EeNc+fO+Q0cOLBQc+wAQLNmzbLmzZt3BAC2bt1qmicQEebNm3fs6aefPnvq1Cn/9u3b19u1a9f1it+TJ0/uNp6/VntcquLj46917tw5de3atRELFy6Mbtq06eWWLVsW25Bn+pxjR44cCTTmGa1atWoIAAcOHAhat25dBUD2HLr55psvA8Dq1aud5sOFtW3btn3GY2e1B+I999xzCQA2btx4PW27d+8OzsrK8mnatGkjdf969OhRBwC++eabSCJqUa9evQYlsT/OcIUQY4wxxhhjjDHGGCtxubm5eOedd+IBoHfv3m4XUuqeeeaZ8wDw5ZdfRh8/ftxp0+vMzExLk7U78+ijjyYDwMSJE+MdDV2Um5uLiRMnxgNA//79LRUKv//++8eJCK+99lpVs3jr1auXfeutt166evUqjRs3rsBQZkb6vjZo0CC7devW6dnZ2TRu3Lg4R+GPHz/uu2DBghgg/5i6Sy/YbdWqVfpDDz2UbLYEBgbmffPNN5EXL150WQ753nvvnQwMDMybMGFC5cuXL3O5JSvVoqOjc7t27Zp69uxZv6CgoLynnnqqwDwy7ggPD88FXA/l+J///OfE0KFDT587d86vY8eO9bZt2xZUlM81M2jQoOTs7GxKTU31LUpFl1FmZiYtW7Ys0sfHBw8++KBpntGuXbtLADBt2rTrFclPPvnkeQD45JNP4tLT053mDcWR77vjxIkTfoCcC0pf171791SzfbvzzjsvAkC1atWuPvTQQ8ndu3dP9WRaAR4yjjHGGGOMMcYYY4yVsJMnT/oOGjSo+rZt20Lj4+Oz//Wvf50pbFydO3e+3Ldv3+SFCxdGd+7cuc7ixYsPNW7c2G4IodzcXMyYMSNyxYoV4atWrTpclLSPGTPm7PLlyyN37twZ0rdv3xozZ848pg4dl5GRQU8++WT1nTt3hiQmJmbqwxy50rZt28xevXqlfP3111GZmZmmBZyffvrp8TvuuKPep59+Gh8REZE7evTos35+fnZhDhw44P/iiy9WfeaZZ87rwxtNnjz5+J133llvypQpcVWrVs0eOXKkXYHukSNH/Hr06FH74sWLtgceeCClc+fOdsM2WZGRkUFff/11pM1mw6JFi44kJCSY9orq3bu3z4oVKyJnzJgR+dJLLyWbhdElJCTkDBky5OykSZPip06d6rISjDFve/fdd0/df//9abGxsTkRERFOJ7vZu3ev/9KlS8OfffbZlKioKLthCPPy8jB69Oh4AGjZsqXpMGWqyZMnnwoODs6bOHFilS5dutRZuXLlgXbt2jkdltEd3bt3T583b97BvLw8uu+++4ptCMc5c+ZEpKWl+d5xxx0XFy9efNQsTGpqqk+VKlVu/u677yJSUlKOR0VF5Q4ePPjC/PnzozZv3hzWpUuXWgsWLEiqUaOGXZ6TlZVFH374YfTff/8dNHfu3GNmcRfWd999F9K1a9cMm82+0+WePXsCPvjgg3gA6NGjx/Xj9P777582i+fbb78N/fHHHys2a9bs8qJFi0z3v6RxhRBjjDHGGGOMMcYYKzbDhw+vDMgCzrS0NNu+ffuCduzYEZKTk0ONGze+PH/+/CPx8fGFmj9IN3v27GM+Pj5i/vz5Mc2aNWvUqlWr9IYNG14JCAgQp06d8tuyZUvY2bNn/bp27Vrk1tcVK1bMW7NmzYEePXrUXrJkSfTGjRsrtm/f/mJsbOy1c+fO+W7cuLHi+fPn/erVq5f57bffHggNDbU8A/p77713cvXq1ZHHjh0zne+nWbNmWV9//fWBfv361Ro/fnzV6dOnV2rbtm16fHx8zuXLl3327NkTtHPnzhCtp9H1AshWrVplLl68+GD//v1rvfrqq9VnzJhR6bbbbrsUGhqad/jw4YCNGzdWzMrK8unZs+eFuXPnFqpQcubMmZHp6em29u3bX3RUGQQAgwYNOr9ixYrIWbNmxbiqEAKAcePGnZk3b16Mo2Oi27x5c6h+rhmFh4dfe/311wtUzP3jH/+oWqFCBdPvZ8KECacSExOdDg/3/fffh0yfPj0aAPQeTElJSYF9+vRJ0MN89dVXSc7iYOVLYmJitqvzRnfhwgXbmDFjqo0fP75q8+bNM+rVq5cZGhqad/78ed9ffvkl9MSJ/2fvvsOavNoGgN9PQhaQEDLYICoBFFHRQlGsFq0gVdxWqLWoFKmt2DpRxFlbsVpprcooglpURAUVi8qQDkdFQH15aUFEUGTvsEfyfH9o+GII2/3ev+vKlZLnzPjkpNe5c855zODxeG3+/v6Pe1Ken59fsaqqqnTLli2GTk5OptHR0dkffPBBr4O7ylAoFFiwYMFzP8srNDRUCACwZMmSTscCTU1N6Ycfflh15swZfnBwMG/Dhg1lVCoVzp8/nzNv3ryBiYmJXDMzM8sxY8aITU1Nm6hUKpmfn0+/du0ap6qqSmXp0qUlz7vdLi4uJhwORzJy5Mh6fX39lra2NiIvL4/x559/ciQSCeHm5lY6a9YscX/r+fXXX7lnz57lAgCUlpbSAADS0tLUZGMMn89vCw4O7tH90RkMCCGEEEIIIYQQQgi9QHl+U1NfdRteJn9/f10AABqNRqqpqUn09PRaZs+eXTF37tyqWbNmiRV/Yd0XDAaDPHbs2CN3d/eKwMBAQXJyMvvOnTtqbW1tBI/Ha7O0tKzfsWNHpeyA7/4SiUQtd+/e/Xf//v38M2fO8OLj47m1tbVUNpstMTc3b/T29i5cvnx5BYPB6HqvJwUmJiat7u7uJQcPHux0a7dJkybVZ2dn//fHH38UxMbGcq9cuaIhFoupLBZLamRk1Lx06dISLy+vMnNz82cmpZ2dnWszMzPTv//+e+24uDiNqKgofnNzM0VTU7Pt/fffr/Hw8CifPXt2nycwDx8+LAQAcHd373I7qalTp9YNGDCgOSMjQ/XatWssOzu7Lg+419DQkHp7exesW7duQFfpbt26pa7sEHgAAD09vRZlAaHLly9rdlbe6tWrS0QiUVdVQlZWFiMqKoov/1plZaWKwmt5XRbyFvoycOL/1BjXV1ZWVk1Hjx7NuXz5MictLU0tJiaGV1NT0/5ZXr58efGGDRtK9PT0ehww37x5cymLxZJ6e3sPmDFjhmlkZGT21KlT+3Qm2IuWnp7OuHXrljqPx2tzcXHpMtjk6elZdubMGf6RI0eEGzZsKAN4EihKSEjIiYqK4oSFhfHT0tLUb9y4wSFJEoRCYaudnZ3Yzc2tYu7cuf0OzChat25dYWJiIuf27dtqV65c0Xh6Fl7r5MmTqz/77LPyOXPmPJc6b9++rao4xjx+/Jjx+PFjBsCTsQ0A+hUQIrrbk/BVIAhiLgBMAICRADACANgAcIwkyU+6yDMWAHwBwBYAmABwHwBCAeBnkiQlneRxA4AvAWAoAEgA4DYA7CFJ8kJP2/rOO++QKSkpPU2OEEIIIYQQQgihl4QgiFSSJN950fXcvXs3b8SIEd2ufEAIIYQQetHu3r0rGDFihLGya6/r4Wy+ALAcngSECrpLTBDEDAD4EwDGA0A0ABwAADoA+ANARCd59gDAYQDQBYBfACAcACwBIIYgiOX97gFCCCGEEEIIIYQQQgghhNBr4nUNCK0EAFMA4ADAsq4SEgTBgScBHQkAvE+SpDtJkmvhSTDpBgDMJQjCRSHPWABYDQA5ADCcJMmVJEl+CQCjAaASAPYQBGH8XHuEEEIIIYQQQgghhBBCCCH0iryWASGSJJNIkswme7af3VwAEAJABEmS7Xu3kSTZBE9WGgF0DCp9/vT5W5Ikq+Ty5MGT1UUMAFjcx+YjhBBCCCGEEEIIIYQQQgi9Vl7LgFAvTXz6fEnJtT8BoAEAxhIEwehhnosKaRBCCCGEEEIIIYQQQgghhN5oKq+6Ac+B2dPne4oXSJJsIwgiFwAsAGAQAPxLEIQaAOgDQB1JkkVKyst++mzaWYUEQSwFgKUAAEZGRt020PKIZbdpupLult6v/I/X/9Wv/AZ+773S+l+HNrzp9b8ObXjT638d2vCm1/86tOFNr/91aMObXv/r0IY3vf7XoQ1vev2vQxve9PqfRxv+1/8f/XVow5te/+vQhje9/telDQghhBBC/yvehhVCGk+fazq5Lnud28f0HZAkGUyS5DskSb4jFAp73FCEEEIIIYQQQgghhBBCCKFX4W0ICHWHePrck/OI5PU2PUIIIYQQQgghhBBCCCGE0GvpbQgIyVb0aHRynaOQrrv03a0gQgghhBBCCCGEEEIIIYQQeqO8DQGhrKfPHc78IQhCBQAGAkAbADwAACBJsh4ACgBAnSAIXSXliZ4+dziTCCGEEEIIIYQQQgghhBBC6E30NgSErjx9nqLk2ngAUAWA6yRJNvcwj5NCGoQQQgghhBBCCCGEEEIIoTfa2xAQOg0A5QDgQhDEO7IXCYJgAsCOp38GKOQJfPq8kSAITbk8xgDwJQA0A0DYC2ovQgghhBBCCCGEEEIIIYTQS6XyqhugDEEQMwFg5tM/dZ4+jyEI4vDT/y4nSXINAABJkmKCIDzgSWDod4IgIgCgEgCmA4DZ09dPypdPkuR1giD2AsAqAPgPQRCnAYAOAPMBgAcAXiRJ5r2g7iGEEEIIIYQQQgghhBBCCL1Ur2VACABGAoCbwmuDnj4AAB4CwBrZBZIkzxIEMQEANgLAHABgAsB9eBLw2UeSJKlYAUmSqwmC+A8ALAeApQAgBYA0ANhNkuSF59sdhBBCCCGEEEIIIYQQQgihV+e1DAiRJLkVALb2Ms81APiwl3mOAMCR3uRBCCGEEEIIIYQQ6pWtGqNfdRO6tLUm9VU3ASH05vph/rTXeoxbffICjnEIPfU2nCGEEEIIIYQQQgghhF4xgiBGyz/odPooTU3NEUOHDh0yf/78AZGRkZy2tjaleefMmWOsmF/+YWNjY6YsX1JSkupHH300wNjYeJiampoVnU4fpaenZzllypRBISEhmvL1ZWVl0QmCGK2vr2/Zl/61traCv7+/wM7OTsTj8UbQaLRRPB5vxNixY0V79+4VtLa2dtu3kydPaihLs2rVKj2CIEbv3btXoOy6WCymbN++XcvW1tZUVjebzR5paWk5xMvLS/+ff/6hK8tXVlZGXbNmja6lpeUQDoczkk6nj9LR0Rn+4YcfDoqOjuZ01ld9fX1L+fefQqGMZrPZI62srMx37twp7KyvLi4uAwiCGM1isazKy8upnZUv6y9BEKP9/PyEytLs27ePTxDE6BUrVuh1lren94usP1lZWUrfp54Qi8WUgIAAnrOz88CBAwdasFgsKzU1Nathw4YN2bJli3ZTUxPR17LRm0H+85CRkcHoLN27775rKku7b98+vuL1P/74Q3X69OkD9fT0LOl0+ih1dXUrQ0PDYRMnTjTx9fXVFovFz8zZd3X/FhYWqgwbNmwIQRCjZ8+ebdzZZxPg/8cixTbJj1ErV67U6yz/zz//zO/sM3bhwgW24ueQRqON0tbWHu7k5DQoISFBTVmZtbW1FDabPZIgiNHOzs4DO228nOjoaM706dMH6uvrW7JYLCsmkznKyMho2MyZMwdGRkZyOmtPd4/ejA+NjY2ESCSyIAhitLa29nBlaZKSklS//PJL/fHjx4sEAsGIrtLKy8nJoc2bN89YS0trOJ1OH6Wvr2+5ZMkSw7Kysk7H1N54LVcIIYQQQgghhBBCCKE308qVK4sAACQSCVRXV1OzsrJY0dHR/MjISIGFhUXD8ePHHwwfPrxZWd5JkyZVDx8+vFHxdWNj42fSNzc3E0uWLDE8fvy4kEqlgrW1de0HH3xQzWAwyIKCAvr169fZly9f1jx9+nTVpUuXHvS3Tzk5OTRnZ2eTf//9V5XP57dNnDixRkdHp7WkpIT2+++/c1avXs0JCQkRxsTE3B88eHCnM7K+vr4Gc+bMqVFR6fmUXGJiotrHH388uLS0lKatrd1qb29fo6ur21pfX09JT09XDQgI0AkMDNROSkrKHDduXIMs38WLF9U//vjjwdXV1SqDBg1qmjlzZiWbzZbk5OQwkpKSNC5evKg5Y8aMyhMnTuSxWKwOxy0AACxevLiUy+VKJBIJ5OXlMeLi4rg+Pj5GSUlJnLi4uBz5tFVVVZSYmBgeQRDQ1NRECQ4O5vn4+JR117/vv/9eb+nSpRU8Hk/a4zcFAKytrevGjRtXq+ya4v3yPMTFxal/8cUXAzU0NCS2trZiJyen6qqqKpWEhASN7du3G8TExGhevXo1S1VVVel7id4OVCqVlEgkREBAgGD//v0FitfT09MZt27dYsvSKV4/ePAgz8vLayBJkmBra1s7ZcqUaiqVSj58+JCRnp6umpSUpOHi4lI9bNiwbu/hzMxM+pQpU0wfPnzI8PT0LDl48OBjCqXv6z+oVCp54sQJwe7duwuVjVGHDx8WdNYvGT09vZb58+dXAADU19dTUlJS1C5duqQZFxenGRYWlvPpp59Wy6cPDQ3VrKuroxIEAXFxcZrFxcWPdHR0JMrKrqqqonz00UcDExISuAwGg7S1tRWLRKImGo1GPnz4kPH7779rnDt3jpeQkFCyevXqUtl3kUx1dTU1LCxMS11dXeLh4VGqWD6fz1darzIrVqzQLyws7DKA9Ouvv/LDwsK0VFRUyEGDBjVVVFR0O/BnZGQwxo8fb15ZWakyadKkalNT06a0tDS1sLAwraSkJM6NGzcyO3t/egoDQgghhBBCCCGEEELoudm7d2+h4mv5+fkqnp6eRhcvXtR0dHQ0TUlJ+VdfX7/DcqHp06dXr1ixoqK7OhYtWmQUEREhEIlEjadOncoZMWLEM5OnbW1tEBwczIuJieH2rzdPfsHu5OQkys7OZs2ePbvi8OHDj9hstlT++qJFi4yioqL4Tk5OotTU1Ez56zJGRkbN9+/fZ/7000+C1atXl/ek7tu3bzNnzpwpamhooPr4+BRs3bq1mEajPZMmMzOTvmrVKoPq6ur2X4+npqYy58yZI2pubqbs2LEjf8OGDaXyE8X379+nzZgxw+TcuXM8Nzc3aWRk5ENl9Xt7e5eYmZm1yP5OSUlhvvfee0Pi4+O5v/32m/rUqVPrZNdCQkJ4DQ0NFHd395KjR49qHTlyRNhdQMjIyKj50aNHjC1btuj+/PPPHSbXuzJu3LhaZffai6Knp9d68ODB3MWLF1cxmcz2oE9VVRVl3LhxZrdv31bbtWuX1rZt20peVpvQy8fn89uEQmHryZMn+f7+/gWKn8eDBw8KSJIEe3v7moSEhGfGn9raWoq3t7cRQRAQFRV1b8aMGR0CmvHx8Wo6OjrKl1LKuX79OmvGjBmiiooK2rZt2/I3b97cIcDRW7I2nz59WsPFxaVG/lpaWhozLS1NffLkydXx8fGdjqv6+votip/LlStX6v3444+6Pj4+hooBobCwMCGFQgEPD4+SoKAg7cDAQMHWrVs7fIYkEglMnz598NWrVznvvvtubURERK6xsfEzwffGxkZiz549wnv37jHNzMw6tCMrK4seFhamxeFwJP0ZOy5cuMA+dOiQtp+f3yNvb2+jztJ99tln5e7u7uWjR49uYjKZJEEQ3W6t6OnpaVRZWamyY8eO/I0bN7b/m3722WcGhw4d0l61apX+8ePHH/W17QC4ZRxCCCGEEEIIIYQQesEMDQ3bYmJiHtjY2NQWFxfTN23apNvXsuLj49UiIiIEGhoakvj4+GzFYBAAgIqKCnzxxReVUVFRuf1rOcA333yjnZ2dzbKysqqPjIzMUwz2sNlsaWRkZJ6VlVV9dnY2a8eOHVrKylm7dm0Rk8mU+vn56SluCdWZL7/80rCuro66bNmy4m+//bZDMAgAwNzcvCU2NvbBpEmT2oMzXl5eRo2NjZTPP/+8eOPGjaWKqwZMTExaY2Nj73M4HMmpU6cEcXFxSrdzUvTOO+802djY1AIA/P3338/kOXLkiJBCocD69etL7O3ta+7du8e6cuVKl+V6eHiUCoXC1pCQEK2cnJyOnXuNjB07tnHZsmWV8sEgAABNTU2pl5dXCQDAX3/9xX41rUMv06JFi8rKy8tpERERzwRGmpubiVOnTgmsrKzqhwwZ0mGlY0pKCrOuro5qYmLSqCwYBAAwefLkeoFA0OUKkJiYGLajo6NZdXW1SkBAQO7zCAYBAHz88ceVTCZTGhIS0mHryoMHDwoAANzd3XsUzJa3Zs2aUgCAgoICemFhYfsClVu3bjHv3r2rZmtrK966dWsRjUYjjx49qnTbzODgYN7Vq1c5RkZGzXFxcfcVg0EAACwWi9y0aVNpcHBwfm/b2FOVlZUUT09PY1tbW/G6deu6DHiPHTu20c7OrlFxzOjMP//8Q7927RpHT0+vZf369c/8m+7evbuQxWJJo6Oj+T39/ugMBoQQQgghhBBCCCGE0AtHpVLBx8enCADg3LlzPKm0VzuEtQsICBACACxYsKBswIABnR+YAU8mCPtUiZzw8HABAMD69esLqVTlRzhQqVTw9vYuAgD49ddflZ6JY2Bg0OLp6VlSXl5O27Jli0539WZmZtJv3LjBYTAY5NatW4u7Sy/r69N8bDqd3mW+AQMGtLq6upYDAAQGBiptszIkSRIAAATx/7tGXbt2jZWRkaFqa2srNjExaXVzc5OVq3RyV0ZVVVXq4+Pg+aCoAAAgAElEQVRT0NTURFm7dq1+T9vwuqHT6STAky23XnVb0Ivn7u5eyWKxpKGhoc/c3ydOnNCoqKhQWbRokdJAgZaWlgQAoLS0lNbXSf3Q0FDNuXPniqRSKXHq1KlsT0/Pyr6Uo4ympmabk5NT1e+//67x8OHD9gBtY2MjcebMGb61tXWdhYVFU2/LlR/r5ceNAwcOCAEAFi5cWKGjoyOxt7evyc3NZV66dEldsYzQ0FAhAMDy5cuLORxOl18ez2Pc74yHh4eRWCymHjlyROmqyv64dOkSBwBgwoQJYsXvGk1NTemoUaPqmpqaKElJST0K4HcGA0IIIYQQQgghhBBC6KVwcHCoo1KpZGVlpcq9e/c6nL9w/vx57qpVq/QUH+Xl5e2zY7du3VIHAJg8ebL4Rbf3/v37tKKiIjqVSiU//PBDpb/ol5k6daqYSqWSRUVF9M5Wu2zbtq2Yz+e3BQUFactPuCqTmJioDgBgYWHR7YqBTvI1CIXCLvM5OjqKAQBSUlI6TMAqk5KSwpS9/2PHjq2XvS6b2P30008rAADmzZtXw+fz2y5cuMCrrKzscv7Ry8urwtTUtPHcuXP869evs3rSDgCAq1evspXdK6tWrdJLTEzs14Rpbx0+fFgAAODg4FDTXVr05tPU1JQ6OztX/vXXXxryn/VDhw4J1dXVJYsXL65Slm/IkCHNw4YNa6iqqlKxsbEx37lzp/DatWuspqamTs/kkffjjz8KPTw8Bqmrq0suXbqUNXPmzC7HpL7w9PQsl0gkRGBgIF/22q+//sqtrq5WWbx4cbdnginzww8/aAEAGBgYNOvq6rYBADQ0NBDR0dF8dXV1ySeffFIFACAXSH4mQN3a2gp3795VAwBwcnJ67n3uqaNHj3KjoqL427dvfywSiVq6z9E7WVlZTAAAkUikNOg2aNCgZgCAzMxMZn/qwTOEEEIIIYQQQgghhNBLwWKxSC6XK6moqFApKipSMTc3f2ZSLTExkZuYmNjhfApPT89yWVCkvLycBvBkhcuLbm9+fj4NAIDL5UrU1dW7/NW5urp6e98ePXpEGzx4cIf2aWhoSL29vQvWrVs3YN26dXonT57s9FfmRUVFNAAAXV3dXvVTlk9PT6/bCUtjY+MWAICysjKlwaldu3Zpc7lciUQigby8PEZcXBy3qamJMnny5OopU6bUAQCIxWJKTEwMT11dXbJw4cIqAAAajQazZs2qCAkJ0Q4JCeF3tbUSlUqF77777vHcuXNFa9asMbx+/fq9nvTz1q1b6rLglCIul9s2adKkemXXnrfvvvtO+Ndff3HMzc0be3L+FXo7eHp6lkdGRgoCAgIEe/bsKbp37x79+vXrHFdX1zJlZ4gBAFAoFIiKisr55JNPjJOTk9k+Pj5GAAAqKirkkCFDGpydnatXr15dyuPxlOYPDAzUAQA4duxYznvvvdfwIvrl6OhYN2jQoKbw8HDBt99+W0yhUCAsLEzI4XAkbm5uVQ8ePOgQyJdXUFBAX7VqlR4AQH19PSU1NVUtNTVVnUKhwLfffvtYli40NJQnFouprq6u5bKxdd68eTUrVqxou3TpkmZZWdkjWUC7tLRUpbW1lQAAGDRo0HMPxPREfn6+ytdffz1g/PjxNStXruz1tnk9IRaLqQAAGhoaSgP5stflz4vrCwwIIYQQQgghhBBCCKGXhiSfxFUUz7UBAPjpp5/yejqpTqFQ+r0tUHl5OfW7777TVnzdx8enRCAQSOS2R+tRXV31TWblypXlQUFB2qdPnxYkJyeX2tjYdDhr5GlZvaq7L/lk7e1MWFiY1tOyQFVVVWpqato4f/78irVr17YHeA4dOsSrq6ujurq6lqmqqrYXuHTp0vKQkBDtw4cPC7o7a2POnDliOzs78bVr1zgnT57UmD9/frcrbVauXFnUn4PhFXV3LyjLc+TIEe7mzZuNBAJB65kzZ+4zGAzcMu5/xMSJE+tFIlHjiRMnBLt27So6cOCAQCqVwrJly7q810UiUcvNmzfvpaWlMWNjYzmpqamqd+7cUUtPT1dLT09XCwsLE165ciVLMVgOADBu3Djx1atXOZ6ensaJiYlZJiYmzwSLt2/frlVdXf3MfP/cuXOrxo4dq3SM6czChQvLt23bZhATE8MePHhwy82bN9lubm6l8p/vzhQWFtL9/f11AZ5socjj8docHByq16xZUzx58uT2IG1YWFiHM4nkA8lBQUF8X1/fUoBnt5x7Ufbt28fPy8tjyL82ceLE2mnTptUCALi5uRm3tbURhw8ffu5bxfWUbLyW33avLzAghBBCCCGEEEIIIYReioaGBqKmpoYKAKCjo9PWlzIEAkHr48ePGXl5efQRI0Y096c9FRUVVNnkpTzZiiRDQ8NWAICqqiqVuro6oqtVQnV1de19MzAw6HRVj4qKCuzYseOxq6uryZo1awz+/PPPbGXpZCt8CgsLu/xFviLZiqKCggJGd2kfPnxIBwAQCoVK25uZmZluZmbW5S/y5SZ2nwnkWVtbN1lYWDRkZGSo/vnnn6rjx4/vckXD3r17H48ZM2aor6+vwZw5c1761mvd3QuKr//666/czz77bBCPx2uNj4/PGjp06CtZuYBeHTc3t3JfX1/D06dPa0RERAgsLCwa7OzsehR8GTVqVNOoUaPatwa7ffs2c8mSJcZ37txRW758uWFCQkKOYp5ffvnl4Y4dO3SOHTsmnDBhgrnifRcUFKStOF4YGxs39zYg5OnpWbFz5079kJAQgaGhYQtJkvDFF1/0aLs4a2vruuTk5Kyu0qSlpTHT0tLUBw4c2KS4kk8WSD569KhAFhDS0dFpo9FoZGtrK5Gbm0u3sLDo17ivTHh4uEDZisNp06bV7t+/n5+UlKTx888/5w0cOPCFrUzlcDgSAADZ94ii7lYQ9RSeIYQQQgghhBBCCCGEXoq4uDh1iURC8Pn8tu4CDZ2xtrauAwCIj4/n9Lc9ZmZmLSRJpio+ZG0TiUQtOjo6LRKJhIiNjWV3VVZsbCxbIpEQurq6Lcq2i5Pn4uJSY2trW/vXX39xoqOjlfZj0qRJdQAAGRkZahUVFT3eImjixIm1T/Opyp+9pExcXBwbAOCdd96p62n58m7evMlKT09XAwD44IMPzAmCGC3/yMjIUAUACAgIEHZdEoCNjU3j7NmzK+7fv8/86aefBH1pT390dy/ICw0N1Vy8ePEgPp/flpiYmNXfwCR6M3l6elYwmUzpV199NaC0tJS2aNGiPp2xAwBgZWXVFB4engsA8PfffysdEwiCgPDw8EefffZZSWFhId3e3t787t277YHfgoKCdMX7ty/bGOrq6rY5ODhUxcXFaUZERAhGjhxZb21trfRcm76QnTmWm5vLVBwzbGxsLAAAsrOzWfHx8WoAT1YOjRgxoh4A4OLFi12Ow32VnJycpfjeyVYgpqWlqQIAeHl5GSu2FwCgtLSUJvu7uzG3K2ZmZk0AANnZ2UrPCHrw4AEDAMDc3Lxf/xYYEEIIIYQQQgghhBBCL5xEIoGdO3fqAgDMnDmzz2etfP7552UAAMeOHRPk5+d3uftNY2Nj//bWAYAFCxaUAwDs2rVLt7OtiyQSCezatUsXAGDhwoU9mhTes2dPPkEQsGHDBgNl5Zqbm7eMGTNG3NzcTGzbtq3DVmaKZH0dOnRoy7vvvlvb0tJCbNu2Taez9Pn5+SonTpwQAvz/e9pbsoldGxub2o8++qhc2YPJZErPnz/Pq6mp6XYecvfu3QVMJlPq5+enV19f/1rOWwYGBvKWLl06SEtLq/XKlSuZlpaWGAz6HyUQCCRTpkypKikpobFYLKm7u3tlf8rjcrkSgO63cvzll18ee3l5FZWWltImTZpknpyczOpPvcp4eHiUt7S0EFVVVSr9CXQpamxsJKKiongUCgXmzZundMwYN26cGAAgKCioPZC8ZMmSMgCA/fv369TW1nY5NjyPcV/emDFj6job3wAAmEymVH6862s9jo6OtQAAf/zxB0cieXYRUFVVFSUtLU2dyWRK7e3t+3U+Gm4ZhxBCCCGEEEIIIYReqIKCAhUPDw+j5ORktq6ubss333xT3NeyHBwc6l1cXMojIiIEDg4OppGRkTmKk/ISiQRCQkJ4586d48bGxj7oT9s3bdpUEh0dzUtLS1N3cXEZEBoa+kh+67i6ujpiyZIlRmlpaeoikahRts1Rd+zs7BpnzJhRcfbsWX5jY6PSCc4DBw7kjx8/3vzAgQO6mpqaEl9f3xIajfZMmuzsbPrKlSsNPv/88zLZeRf79u3LnzBhgnlAQICOgYFBi7e39zMTurm5uTRnZ2eTmpoa6ty5cyscHBx6PcFYV1dHnD17lkelUuHkyZO5xsbGSldFzZw5k3Lu3DleSEgIb/Xq1V0exm5sbNzq6elZ8tNPP+kGBgZ2GwR72X7++Wf+119/bayrq9ty5cqVLFNTU9wm7n/c999/Xzh79uxqbW3tVk1NzS6DAZmZmfTTp09zly1bVsHn85+Z8ZdKpeDr66sLAGBtbV3bXb379u0rVFVVle7atUvf0dHRNCYmJnvcuHFdbsvYG9OmTasNDw+/L5VKiVmzZj23LRyPHDmiWV1drTJ+/PiayMhIpefxVFVVUfT19Uf89ttvmhUVFfl8Pl+ydOnSyuPHj/OvXr3KcXR0HHzixIm8AQMGPDPmNDU1Ef7+/oJ///2XdfTo0UfPq80eHh5VHh4eVcquRUZGCjgcjuTkyZP9PlvIwsKiWXaWmp+fn9bGjRvbv0vWrl2r19jYSHF1dS3jcDj9OlQJA0IIIYQQQgghhBBC6LlZtWqVHsCTCc7q6mpqVlYWKzU1Vb21tZWwtLSsP378eK6urm6fzg+SOXz48CMKhUIeP35caGVlNczGxqbWwsKigcFgkIWFhbTr169zSkpKaFOmTFE6idcbGhoa0kuXLmU7OzubnDp1SpCUlKRhb29fo62t3VZaWqqSlJSkUVZWRjM3N2+8cOFCNpvN7vFk3e7duwsuXrzIe/TokdLzfqysrJrOnj2b7erqOnj79u0GwcHBWnZ2drW6urqt9fX1lIyMDFZaWpr605VGRbJ8NjY2jZGRkfcXLlw4eP369UYhISFaY8eOFbPZbOmDBw8YSUlJGk1NTZTp06dXHj16tE8TmaGhobza2lqqvb19TWfBIAAADw+PsnPnzvHCwsKE3QWEAAC2bdtWHB4eLuzsPZG5evUqW3avKeJyuW2bN2/uEJj76quvDNTU1JT++/j5+RWKRKJOAzwxMTHsr7/+2lgqlcLYsWPFgYGBHba166xe9PYSiUQtXd038iorK6mbNm0y3L59u8GoUaPqzM3NG9lstrSsrEzl2rVr7MePHzN4PF6bv7//456U5+fnV6yqqirdsmWLoZOTk2l0dHT2Bx980K/VIzIUCgUWLFjw3M/yCg0NFQIALFmypNOxQFNTU/rhhx9WnTlzhh8cHMzbsGFDGZVKhfPnz+fMmzdvYGJiItfMzMxyzJgxYlNT0yYqlUrm5+fTr127xqmqqlJZunRpyfNud1/cvn2buWPHjmdWaYrFYuqcOXOMZX/v37//sfz3YVBQ0KPx48eb+/r6GiYlJbHNzMyaUlNT1W7evMkeMGBA8969ewv62y4MCCGEEEIIIYQQQgi9SFtrUl91E14mf39/XQAAGo1GqqmpSfT09Fpmz55dMXfu3KpZs2aJqdQ+H7HQjsFgkMeOHXvk7u5eERgYKEhOTmbfuXNHra2tjeDxeG2Wlpb1O3bsqFy8eHG/A0IATyZ97969++/+/fv5Z86c4cXHx3Nra2upbDZbYm5u3ujt7V24fPnyCgaD0fVeTwpMTExa3d3dSw4ePNjp1m6TJk2qz87O/u+PP/4oiI2N5V65ckVDLBZTWSyW1MjIqHnp0qUlXl5eZebm5s9MSjs7O9dmZmamf//999pxcXEaUVFR/ObmZoqmpmbb+++/X+Ph4VE+e/ZscV/fk8OHDwsBANzd3bvcTmrq1Kl1AwYMaM7IyFC9du0ay87OrssD7jU0NKTe3t4F69atG9BVulu3bqkrOwQeAEBPT69FWWDm8uXLmp2Vt3r16hKRSNRpfbm5uXTZ1n6nTp1SesZRZ/W+7VafvPA/Ncb1lZWVVdPRo0dzLl++zElLS1OLiYnh1dTUtH+Wly9fXrxhw4YSPT29HgfMN2/eXMpisaTe3t4DZsyYYRoZGZk9derUPp0J9qKlp6czbt26pc7j8dpcXFy6DDZ5enqWnTlzhn/kyBHhhg0bygCeBIoSEhJyoqKiOGFhYfy0tDT1GzducEiSBKFQ2GpnZyd2c3OrmDt3bp/HteepoKCAFhUVxZd/rampiSL/2nfffVeoq6vbft3CwqL55s2b/6xfv17/jz/+4Pzxxx8aQqGwddGiRaV+fn6F2traz+4l1wcYEEIIIYQQQgghhBBC/UaSZJ8nhc+cOZMHAHm9zTdx4sT6iRMn9ugX8WZmZi39aSODwSBXr15d3pNVLvK669uBAwcKDhw40OWvvjU0NKRbtmwp3bJlS6+CDTo6OpKnB6MX9iZfQUFBendp0tLSMntaXl5e3n/l/967d2+h7MB2ZdauXVu+du1ape9zd3mV6Ul/urNixYqKFStW9PnsK/Tm6834sW/fvsJ9+/Y9c5+yWCxy4cKF1QsXLqzuTb3d3b9dfV7kdTYW9Wb8HTZsWLOy92HatGm13b0/lpaWSvMqM3ny5PrO0s6ePVvcl2B2f78DlOmqvJ68J8qYmJi0nj59Oq9fDevCa3k4G0IIIYQQQgghhBBCCCGEEHp+MCCEEEIIIYQQQgghhBBCCCH0lsOAEEIIIYQQQgghhBBCCCGE0FsOA0IIIYQQQgghhBBCCCGEEEJvOQwIIYQQQgghhBBCCCGEEEIIveUwIIQQQgghhBBCCCGEEEIIIfSWw4AQQgghhBBCCCGEEEIIIYTQWw4DQgghhBBCCCGEEEIIIYQQQm85DAghhBBCCCGEEEIIIYQQQgi95TAghBBCCCGEEEIIIYQQQggh9JbDgBBCCCGEEEIIIYQQQgghhNBbDgNCCCGEEEIIIYQQQgghhBBCbzkMCCGEEEIIIYQQQgghhBBCCL3lVF51AxBCCCGEEEIIIYTeZpZHLEe/6jZ0Jd0tPfVVtwEh9OZ6vP6v13qMM/B7D8c4hJ7CFUIIIYQQQgghhBBCqN8Ighgt/6DT6aM0NTVHDB06dMj8+fMHREZGctra2pTmnTNnjrFifvmHjY2NmbJ8SUlJqh999NEAY2PjYWpqalZ0On2Unp6e5ZQpUwaFhIRoyteXlZVFJwhitL6+vmVf+tfa2gr+/v4COzs7EY/HG0Gj0UbxeLwRY8eOFe3du1fQ2trabd9OnjypoSzNqlWr9AiCGL13716BsutisZiyfft2LVtbW1NZ3Ww2e6SlpeUQLy8v/X/++YeuLF9ZWRl1zZo1upaWlkM4HM5IOp0+SkdHZ/iHH344KDo6mtNZX/X19S3l338KhTKazWaPtLKyMt+5c6ews766uLgMIAhiNIvFsiovL6d2Vr6svwRBjPbz8xMqS7Nv3z4+QRCjV6xYoddZ3p7eL7L+ZGVlKX2femrTpk3aEyZMMNHX17dUVVW1UldXtzI1NR362WefGeTk5ND6UzZ6/cl/HjIyMhidpXv33XdNZWn37dvHV7z+xx9/qE6fPn2gnp6eJZ1OH6Wurm5laGg4bOLEiSa+vr7aYrH4mTn7ru7fwsJClWHDhg0hCGL07NmzjTv7bAL8/1ik2Cb5MWrlypV6neX/+eef+Z19xi5cuMBW/BzSaLRR2traw52cnAYlJCSoKSuztraWwmazRxIEMdrZ2Xlgp42XEx0dzZk+ffpAfX19SxaLZcVkMkcZGRkNmzlz5sDIyEhOZ+3p7tGb8aGxsZEQiUQWBEGM1tbWHt5V2qysLPqCBQuMDAwMLBkMxigulzty+PDh5lu2bNFWlj4iIkLDzs5OpK2tPZzJZI4yMDCw7Oo97C1cIYQQQgghhBBCCCGEnpuVK1cWAQBIJBKorq6mZmVlsaKjo/mRkZECCwuLhuPHjz8YPnx4s7K8kyZNqh4+fHij4uvGxsbPpG9ubiaWLFliePz4cSGVSgVra+vaDz74oJrBYJAFBQX069evsy9fvqx5+vTpqkuXLj3ob59ycnJozs7OJv/++68qn89vmzhxYo2Ojk5rSUkJ7ffff+esXr2aExISIoyJibk/ePDgTmdkfX19DebMmVOjotLzKbnExES1jz/+eHBpaSlNW1u71d7evkZXV7e1vr6ekp6erhoQEKATGBionZSUlDlu3LgGWb6LFy+qf/zxx4Orq6tVBg0a1DRz5sxKNpstycnJYSQlJWlcvHhRc8aMGZUnTpzIY7FYpLK6Fy9eXMrlciUSiQTy8vIYcXFxXB8fH6OkpCROXFxcjnzaqqoqSkxMDI8gCGhqaqIEBwfzfHx8yrrr3/fff6+3dOnSCh6PJ+3xmwIA1tbWdePGjatVdk3xfnlejhw5IlRVVZW+++67tVpaWm2tra1Eenq66qFDh7RPnjwpuHTpUpadnV2H+xe9PahUKimRSIiAgADB/v37CxSvp6enM27dusWWpVO8fvDgQZ6Xl9dAkiTB1ta2dsqUKdVUKpV8+PAhIz09XTUpKUnDxcWletiwYd3ew5mZmfQpU6aYPnz4kOHp6Vly8ODBxxRK39d/UKlU8sSJE4Ldu3cXKhujDh8+LOisXzJ6enot8+fPrwAAqK+vp6SkpKhdunRJMy4uTjMsLCzn008/rZZPHxoaqllXV0clCALi4uI0i4uLH+no6EiUlV1VVUX56KOPBiYkJHAZDAZpa2srFolETTQajXz48CHj999/1zh37hwvISGhZPXq1aWy7yKZ6upqalhYmJa6urrEw8OjVLF8Pp+vtF5lVqxYoV9YWNhtAOnMmTOcTz75ZLBEIiHs7e1rTExMmurq6ij3799nXrhwgbtt27YS+fTLli3TDwwM1OFyuW0ODg7VfD6/7cGDB4yEhATu5cuXNffv35/7xRdfVPa0ncpgQAghhBBCCCGEEEIIPTd79+4tVHwtPz9fxdPT0+jixYuajo6OpikpKf/q6+t3WC40ffr06hUrVlR0V8eiRYuMIiIiBCKRqPHUqVM5I0aMeGbytK2tDYKDg3kxMTHc/vXmyS/YnZycRNnZ2azZs2dXHD58+BGbzZbKX1+0aJFRVFQU38nJSZSampopf13GyMio+f79+8yffvpJsHr16vKe1H379m3mzJkzRQ0NDVQfH5+CrVu3FtNozy5EyczMpK9atcqgurq6fUVOamoqc86cOaLm5mbKjh078jds2FAqP1F8//592owZM0zOnTvHc3Nzk0ZGRj5UVr+3t3eJmZlZi+zvlJQU5nvvvTckPj6e+9tvv6lPnTq1TnYtJCSE19DQQHF3dy85evSo1pEjR4TdBYSMjIyaHz16xNiyZYvuzz//3GFyvSvjxo2rVXavvUiZmZkZqqqqHYJnP/zwg2DNmjUDfHx89P/444/7L7NN6OXi8/ltQqGw9eTJk3x/f/8Cxc/jwYMHBSRJgr29fU1CQsIz409tbS3F29vbiCAIiIqKujdjxowOAc34+Hg1HR0d5Usp5Vy/fp01Y8YMUUVFBW3btm35mzdv7hDg6C1Zm0+fPq3h4uJSI38tLS2NmZaWpj558uTq+Pj4TsdVfX39FsXP5cqVK/V+/PFHXR8fH0PFgFBYWJiQQqGAh4dHSVBQkHZgYKBg69atJaBAIpHA9OnTB1+9epXz7rvv1kZEROQaGxs/E3xvbGwk9uzZI7x37x7TzMysQzuysrLoYWFhWhwOR9KfsePChQvsQ4cOafv5+T3y9vY26izdP//8Q//0008Hc7nctsuXL99T/CFEc3PzM4G1R48eqQQHB+vw+fy2u3fvZsh/R8bExLCnT59u+t133+n3NyCEW8YhhBBCCCGEEEIIoRfK0NCwLSYm5oGNjU1tcXExfdOmTbp9LSs+Pl4tIiJCoKGhIYmPj89WDAYBAKioqMAXX3xRGRUVldu/lgN888032tnZ2SwrK6v6yMjIPMVgD5vNlkZGRuZZWVnVZ2dns3bs2KGlrJy1a9cWMZlMqZ+fn57illCd+fLLLw3r6uqoy5YtK/722287BIMAAMzNzVtiY2MfTJo0qT044+XlZdTY2Ej5/PPPizdu3FiquGrAxMSkNTY29j6Hw5GcOnVKEBcX16OtiN55550mGxubWgCAv//++5k8R44cEVIoFFi/fn2Jvb19zb1791hXrlzpslwPD49SoVDYGhISovUmbLmmLBgEALBo0aIqAIC8vDzmy20RehUWLVpUVl5eTouIiHgmMNLc3EycOnVKYGVlVT9kyJAOK8VSUlKYdXV1VBMTk0ZlwSAAgMmTJ9cLBIIuV6rExMSwHR0dzaqrq1UCAgJyn0cwCADg448/rmQymdKQkJAOW1cePHhQAADg7u7eo2C2vDVr1pQCABQUFNALCwvbF6jcunWLeffuXTVbW1vx1q1bi2g0Gnn06FGl22YGBwfzrl69yjEyMmqOi4u7rxgMAgBgsVjkpk2bSoODg/N728aeqqyspHh6ehrb2tqK161b12XAe+PGjXoNDQ0Uf3//R8pWxTIYjGfGk/v37zOkUimMGDGiTvEHE87OzrVqamrSqqqqfi/wwYAQQgghhBBCCCGEEHrhqFQq+Pj4FAEAnDt3jieV9mqHsHYBAQFCAIAFCxaUDRgwoPMDM+DJBGGfKpETHh4uAABYv359IZWq/FgcKpUK3t7eRQAAv/76q9IzcQwMDFo8PT1LysvLaVu2bNHprt7MzEz6jRs3OAwGg9y6dWtxd+llfX2aj02n07vMN2DAgFZXV9dyAIDAwEClbVaGJEkCAIAg/v/H7deuXWNlZGSo2traik1MTFrd3A3uzjAAACAASURBVNxk5Sqd3JVRVVWV+vj4FDQ1NVHWrl2r39M2vG5kZ0OZm5vjdnH/A9zd3StZLJY0NDT0mfv7xIkTGhUVFSqLFi1SGijQ0tKSAACUlpbSehoUVhQaGqo5d+5ckVQqJU6dOpXt6enZr9Ui8jQ1NducnJyqfv/9d42HDx+2B2gbGxuJM2fO8K2tressLCyaeluu/FgvP24cOHBACACwcOHCCh0dHYm9vX1Nbm4u89KlS+qKZYSGhgoBAJYvX17M4XC6/PJ4HuN+Zzw8PIzEYjH1yJEjSldVyjQ3NxOXLl3S5PF4bR999FFNUlKS6rZt27Q2bdqkfeLECY2mpqYO2+5ZWFg00Wg08j//+Y9aUVHRM4GfixcvqtfX11PGjh0r7m8fcMs4hBBCCCGEEEIIIfRSODg41FGpVLKyslLl3r17dHNz8xb56+fPn+fm5eV1OKzdx8enRPar+Vu3bqkDAEyePLnfE2PduX//Pq2oqIhOpVLJDz/8UOkv+mWmTp0qplKpZFFRET0nJ4em7Cyhbdu2FYeHhwuDgoK0v/766y4DWomJieoAABYWFt2uGOgkX4NQKOwyn6OjozgoKEg7JSWlwwSsMikpKUzZ+z927Nh62euyid1PP/20AgBg3rx5NStWrGi7cOECr7KyMr+r84G8vLwqAgICtM+dO8e/fv16ydixY3sUVLl69Sp71apVesquTZ06tWbSpEn1yq49D3v37hU8fvyYXldXR/nnn39YN27c4Ojp6bXs2bPn8YuqE70+NDU1pc7OzpVnzpwRyH/WDx06JFRXV5csXry4auPGjR2CvkOGDGkeNmxYw3//+19VGxsbczc3t7Lx48fXjR49uonJZHYbxPjxxx+FwcHBOlwut+3s2bPZ7733XkN3eXrL09OzPDo6mh8YGMjfuXNnMQDAr7/+yq2urlZZvHhxt2eCKfPDDz9oAQAYGBg06+rqtgEANDQ0ENHR0Xx1dXXJJ598UgUA4ObmVh4XF8cNDAwUTpkypX3FY2trK9y9e1cNAMDJyanLcfhFOnr0KDcqKoq/d+/ehyKRqKWrtCkpKcympibKyJEj652dnQfFxsZqyl/X1dVtOXHiRM6ECRPa/w21tbUlvr6+j7du3WpoYWFh4ejoWM3j8dpyc3MZiYmJ3LFjx4oPHz7cZSCqJzAghBBCCCGEEEIIIYReChaLRXK5XElFRYVKUVGRimJAKDExkZuYmNjhfApPT89yWVCkvLycBvBkhcuLbm9+fj4NAIDL5UrU1dW7nLBVV1dv79ujR4+UBoQ0NDSk3t7eBevWrRuwbt06vZMnT3Y6uVdUVEQDANDV1e1VP2X59PT0upywBAAwNjZuAQAoKytTul3brl27tLlcrkQikUBeXh4jLi6O29TURJk8eXK1bMJWLBZTYmJieOrq6pKFCxdWAQDQaDSYNWtWRUhIiHZISAi/q62VqFQqfPfdd4/nzp0rWrNmjeH169fv9aSft27dUpcFpxRxudy2FxkQOnLkiOA///lP+3Z4w4YNazhx4sSDYcOGddgWCr2dPD09yyMjIwUBAQGCPXv2FN27d49+/fp1jqura5myM8QAACgUCkRFReV88sknxsnJyWwfHx8jAAAVFRVyyJAhDc7OztWrV68u7SyAGhgYqAMAcOzYsZwXEQwCAHB0dKwbNGhQU3h4uODbb78tplAoEBYWJuRwOBI3N7eqBw8e0LvKX1BQQJcFauvr6ympqalqqamp6hQKBb799tv2gGloaChPLBZTXV1dy2VjqyyQfOnSJc2ysrJHsoB2aWmpSmtrKwEAMGjQoG7HtRchPz9f5euvvx4wfvz4mpUrV3a7bV5xcTENAODWrVtsBoMh9ff3z3N1da0Wi8XUvXv3CgMDA3Vmzpwp+ueffzJkQTIAgM2bN5cOHDiwZfny5cYRERHtK9CMjIyaFy5cWKHs7L3ewoAQQgghhBBCCCGEEHppSPJJXEXxXBsAgJ9++ilvxYoVFT0ph0Kh9HtboPLycup3332nrfi6bEWS3PZoPaqrq77JrFy5sjwoKEj79OnTguTk5FIbGxulK2J6W3df8sna25mwsDCtp2WBqqqq1NTUtHH+/PkVa9eubQ/wHDp0iFdXV0d1dXUtkz9jZ+nSpeUhISHahw8fFnR31sacOXPEdnZ24mvXrnFOnjypMX/+/Jqu0gMArFy5sqg/B8Mr6u5ekH/t7t27mQAAxcXF1Bs3bqht3rxZf8yYMUPCwsIezJ0794WvXEOv3sSJE+tFIlHjiRMnBLt27So6cOCAQCqVwrJly7q810UiUcvNmzfvpaWlMWNjYzmpqamqd+7cUUtPT1dLT09XCwsLE165ciVLMVgOADBu3Djx1atXOZ6ensaJiYlZJiYmzwSLt2/frlVdXf3MfP/cuXOrerrqTmbhwoXl27ZtM4iJiWEPHjy45ebNm2w3N7fSzs7QkldYWEj39/fXBQCgUqkkj8drc3BwqF6zZk3x5MmT24O0YWFhHc4kkg8kBwUF8X19fUsBnt1y7kXZt28fX3F16sSJE2unTZtWCwDg5uZm3NbWRvR0hU5bWxsBACCRSGDjxo2Pv/766wqAJ6uAAgICCh48eMCMi4vj7tu3TyBbiQUA4Ovrq+3n52ewaNGiklWrVpUaGhq23b17l7l+/Xr9ZcuWDbxz545qYGBgv1YiYkAIIYQQQgghhBBCCL0UDQ0NRE1NDRUAQEdHp0+/dBYIBK2PHz9m5OXl0UeMGNGvFRkVFRVU2eSlPNmKJENDw1YAgKqqKpW6ujqiq1VCdXV17X0zMDDodFWPiooK7Nix47Grq6vJmjVrDP78889sZelkK3wKCwu7/EW+ItmKooKCgg5b7yl6+PAhHQBAKBQqbW9mZma6mZlZl7/Il5vYfSaQZ21t3WRhYdGQkZGh+ueff6qOHz++yxUNe/fufTxmzJihvr6+BnPmzOk2IPS8dXcvKMujo6MjmTVrlvi9996rNzc3H7Z06dKBU6ZM+U93q8nQ28HNza3c19fX8PTp0xoRERECCwuLBjs7ux4FX0aNGtU0atSo9vN4bt++zVyyZInxnTt31JYvX26YkJCQo5jnl19+ebhjxw6dY8eOCSdMmGAeHx+fNXTo0PbPZ1BQkLbieGFsbNzc24CQp6dnxc6dO/VDQkIEhoaGLSRJwhdffNGj7eKsra3rkpOTs7pKk5aWxkxLS1MfOHBgk+JKPlkg+ejRowJZQEhHR6eNRqORra2tRG5uLt3CwuK5r8QLDw8XKFtxOG3atNr9+/fzk5KSNH7++ee8gQMH9mjFJp/Pb/9+c3V1rVa8PmPGjKq4uDhuSkpK+0rDCxcusL/99luDyZMnV4eEhLQHfcaNG9dw8eLFHBMTk2G//PKL9ooVK0rl/917q0+HVyGEEEIIIYQQQggh1FtxcXHqEomE4PP5bd0FGjpjbW1dBwAQHx/P6W97zMzMWkiSTFV8yNomEoladHR0WiQSCREbG8vuqqzY2Fi2RCIhdHV1W5RtFyfPxcWlxtbWtvavv/7iREdHK+3HpEmT6gAAMjIy1CoqKqg97dPEiRNrn+ZTLS8v7zJfXFwcGwDgnXfeqesqXWdu3rzJSk9PVwMA+OCDD8wJghgt/8jIyFAFAAgICBB2V5aNjU3j7NmzK+7fv8/86aefBN2lf966uxe6IhAIJFZWVnVVVVUqaWlprJfRXvTqeXp6VjCZTOlXX301oLS0lLZo0aI+nbEDAGBlZdUUHh6eCwDw999/Kx0TCIKA8PDwR5999llJYWEh3d7e3vzu3bvtgd+CgoJ0xfu3pysu5enq6rY5ODhUxcXFaUZERAhGjhxZb21t3dR9zp6RnTmWm5vLVBwzbGxsLAAAsrOzWfHx8WoAT1YOjRgxoh4A4OLFi12Ow32VnJycpfjeyVYgpqWlqQIAeHl5GSu2FwCgtLSUJvtbNuZaWlq2v198Pr9DQJnH40kAAJqamtrjM+fPn9cAAJgwYUKHc5LYbLZ0xIgR9VKpFJKTk1X701cMCCGEEEIIIYQQQgihF04ikcDOnTt1AQBmzpzZ60lKmc8//7wMAODYsWOC/Pz8Lne/aWxsJPpaj8yCBQvKAQB27dql29nWRRKJBHbt2qULALBw4cIeTQrv2bMnnyAI2LBhg4Gycs3NzVvGjBkjbm5uJrZt29ZhKzNFsr4OHTq05d13361taWkhtm3b1uFge5n8/HyVEydOCAH+/z3tLdnEro2NTe1HH31UruzBZDKl58+f59XU1HQ7D7l79+4CJpMp9fPz06uvr3+j5i2Li4vpAE/Og3nVbUEvh0AgkEyZMqWqpKSExmKxpO7u7pX9KY/L5UoAut/K8Zdffnns5eVVVFpaSps0aZJ5cnLycw9Cenh4lLe0tBBVVVUq/Ql0KWpsbCSioqJ4FAoF5s2bp3TMGDdunBgAICgoqD2QvGTJkjIAgP379+vU1tZ2OTY8j3Ff3pgxY+o6G98AAJhMplR+vAN4sjWcubl5IwBASkpKh3+f9PR0FgCAoaFh+2qn5uZmAgCgrKxM6fdaRUWFCgAAnU7v1xjzRg2sCCGEEEIIIYQQQujNU1BQoOLs7DwoOTmZraur2/LNN98Ud59LOQcHh3oXF5fy6upqFQcHB9P09PQOW6NJJBIICgrizZkzZ2D/Wg6wadOmkkGDBjWlpaWpu7i4DKirq3tmsrGuro5wdXUdkJaWpi4SiRpl2xx1x87OrnHGjBkVWVlZrOjoaJ6yNAcOHMhXV1eXHDhwQHfLli3ara0dFx5lZ2fTp02bNigxMbF9u6N9+/blM5lMaUBAgM6uXbs6rM7Jzc2lOTk5iWpqaqhz586tcHBwqFdM0526ujri7NmzPCqVCidPnsw9efLkQ2UPR0fH6oaGBkpISIjSPsozNjZu9fT0LCkvL6cFBgZ2GwR7me7du0f/559/lG7ft3v3bsF///tfVR0dnZbOzoRCb6fvv/++8OjRozlnz569p6mp2eVhN5mZmfQdO3ZoKVvxJ5VKwdfXVxcAwNrausMKEUX79u0r9Pb2LqioqFBxdHQ0vXr1ar9WjSiaNm1abXh4+P2jR4/m9DfQJe/IkSOa1dXVKuPGjauJjIxUOmacP38+h8ViSX/77TdN2Xu1dOnSynHjxokfPnzIcHR0HPzw4UOaYtlNTU3Ezp07hZ6enobPq70AAB4eHlWdjW8AABwORyL7W367SA8Pj1IAAF9fX/2Ghob2742cnBxaUFCQNgDAxx9/XCV7/b333qsDAAgPDxfm5uY+07/IyEjO7du31RkMBjlx4sQ+reiUwTOEEEIIIYQQQgghhNBzs2rVKj2AJxOc1dXV1KysLFZqaqp6a2srYWlpWX/8+PFcXV3dPp0fJHP48OFHFAqFPH78uNDKymqYjY1NrYWFRQODwSALCwtp169f55SUlNCmTJlS1X1pXdPQ0JBeunQp29nZ2eTUqVOCpKQkDXt7+xptbe220tJSlaSkJI2ysjKaubl544ULF7LZbHaPT0DfvXt3wcWLF3mPHj1Set6PlZVV09mzZ7NdXV0Hb9++3SA4OFjLzs6uVldXt7W+vp6SkZHBSktLU3+60qhIls/GxqYxMjLy/sKFCwevX7/eKCQkRGvs2LFiNpstffDgASMpKUmjqamJMn369MqjR4/26JB0RaGhobza2lqqvb19jbGxcadb5Hl4eJSdO3eOFxYWJly9enV5Z+lktm3bVhweHi7s7D2RuXr1Klt2rynicrltmzdv7hCY++qrrwzU1NSU/vv4+fkVikSiTreHu3nzpqqbm9tgKyurukGDBjVraWm1VlZWqqSmpqplZ2ezVFVVpSEhIbkqKjjd+r9EJBK1dHXfyKusrKRu2rTJcPv27QajRo2qMzc3b2Sz2dKysjKVa9eusR8/fszg8Xht/v7+j7svDcDPz69YVVVVumXLFkMnJyfT6Ojo7A8++KDXwV1lKBQKLFiw4Lmf5RUaGioEAFiyZEmnY4Gmpqb0ww8/rDpz5gw/ODiYt2HDhjIqlQrnz5/PmTdv3sDExESumZmZ5ZgxY8SmpqZNVCqVzM/Pp1+7do1TVVWlsnTp0pLn3e6++Oqrr8ovXryokZCQwB06dKjF+++/X9PQ0ECJi4vTrKmpoS5atKh02rRp7cG/xYsXV4WGhopv3LjBsbS0HObg4FClra3dmpWVxfr99981SJKEjRs3PtbR0VF6pllP4QiFEEIIIYQQQggh9AKlu6Wnvuo2vEz+/v66AAA0Go1UU1OT6OnptcyePbti7ty5VbNmzRJTqT0+DqdTDAaDPHbs2CN3d/eKwMBAQXJyMvvOnTtqbW1tBI/Ha7O0tKzfsWNH5eLFi/sdEAJ4Mul79+7df/fv388/c+YMLz4+nltbW0tls9kSc3PzRm9v78Lly5dXMBiMXm3lY2Ji0uru7l5y8ODBTrd2mzRpUn12dvZ/f/zxR0FsbCz3ypUrGmKxmMpisaRGRkbNS5cuLfHy8iozNzd/ZlLa2dm5NjMzM/3777/XjouL04iKiuI3NzdTNDU1295///0aDw+P8tmzZ4v7+p4cPnxYCP/H3r3HaVnW+QP/XszAADKAyMhBAjNQSrEIJZXIRPOYmUKJ9XO1JbStdEsElFSy3JJqMd1lPUTrITXBU2ahYkhreVZaMxQEhFTEQc7n08z9+4MZd8IZGOaZGer2/X69ntftc9/X4Xs/Poy+5sN13RExYsSInW4ndcopp6zr1avX5tmzZ7d94okn2gwaNGinK2g6dOhQOXbs2MVjxozptbN2zz33XLvaHgIfEdG9e/cttQVCjzzyyN51jTdq1KjyPn361DnfEUccsWHEiBHlTz/9dOljjz3WYfXq1UUlJSVZjx49No8cObJ8zJgx5b17967XA+fzpsfVg99XP+Maqn///ptuu+22BY888kj7WbNm7fXggw92Wr169bt/lr/5zW++femll5Z379693oH5FVdcsbRNmzaVY8eO7XXaaacdOHXq1HmnnHJKQStImspLL71U8txzz7Xr1KnTtuHDh+80bDr//PPfuffee/e59dZbyy699NJ3IrYHRb/73e8W3Hfffe1vvvnmfWbNmtXuqaeeap9lWZSVlW0dNGjQmnPOOWf5sGHDGvxzrTEVFRXFtGnTFlx99dX73nHHHZ2nTJnSOaUUffv23fjVr3516de//vUVO7afOXPm/AkTJpTde++9naZPn773pk2bWrRv337b0UcfvfqCCy5YWsjP7GoCIQAAAAAKlmVZg38pfO+99y6KiEW722/IkCHrhwwZUq+/EX/QQQdtKaTGkpKSbNSoUcvqs8qlpl3d26RJkxZPmjRp8c7G6NChQ+X48eOXjh8/vl7b0VXr2rVrRdWD0d/anX6LFy9+aVdtZs2aNae+4y1atOgvNd9PnDjxreoHttdm9OjRy0aPHl3r57yrvrWpz/3sSp8+fbb87Gc/q9fKDfJpd35+XHfddW9dd911f/M9bdOmTXb22WevOvvss1ftzry7+v7u7M9LTXX9LNqdn7+HHHLI5to+h89+9rNrd/X59OvXr9a+tfnMZz6zvq62Z5xxxpqGBCOF/jegNrsar2XLlnH55Zcvvfzyy+v1s7ukpCS74oorltYWaDcWzxACAAAAAADIOYEQAAAAAABAzgmEAAAAAAAAck4gBAAAAAAAkHMCIQAAAAAAgJwTCAEAAAAAAOScQAgAAAAAACDnBEIAAAAAAAA5JxACAAAAAADIOYEQAAAAAABAzgmEAAAAAAAAck4gBAAAAAAAkHMCIQAAAAAAgJwr3tMFAAAAAOTZK30/PGBP17AzH57zygt7ugYAoOlZIQQAAABAwVJKA2q+WrVq9fG99977ox/5yEc+fOaZZ/aaOnVq+23bttXad+jQofvv2L/ma+DAgQfV1m/mzJltv/jFL/baf//9D9lrr736t2rV6uPdu3fvd+KJJx4wefLkvWvON3fu3FYppQH77bdfv4bc39atW+Oaa67pPGjQoD6dOnX6aMuWLT/eqVOnjx511FF9Jk6c2Hnr1q27vLcpU6Z0qK3NRRdd1D2lNGDixImda7u+Zs2aFt/73vf2PeKIIw6snru0tPRj/fr1+/AFF1yw38svv9yqtn7vvPNO0cUXX9ytX79+H27fvv3HWrVq9fGuXbseevLJJx9w//33t6/rXvfbb79+NT//Fi1aDCgtLf1Y//79+/7whz8sq+tehw8f3iulNKBNmzb9ly1bVlTX+NX3m1IacPXVV5fV1ua6667bJ6U04MILL+xeV9/6fl+q72fu3Lm1fk71dfnll3c5+uije++333792rZt279du3b9DzzwwI989atf7bFgwYKWuzPW/fff337kyJE9jjzyyAM7dOjwsZTSgAEDBtT6PQdoLFYIAQAAANBovv3tby+JiKioqIhVq1YVzZ07t83999+/z9SpUzsffPDBG+68887XDj300M219T322GNXHXrooRt3PL///vv/TfvNmzenf/7nf/7AnXfeWVZUVBSHH3742uOOO25VSUlJtnjx4lZPPvlk6SOPPLL3Pffcs/Lhhx9+rdB7WrBgQctTTz219yuvvNJ2n3322TZkyJDVXbt23VpeXt7y97//fftRo0a1nzx5ctmDDz44/0Mf+lDtaUlEXHbZZT2GDh26uri4/r+SmzFjxl5f+tKXPrR06dKWXbp02XrMMces7tat29b169e3eOmll9pef/31XW+44YYuM2fOnPPJT35yQ3W/hx56qN2XvvSlD61atar4gAMO2PT5z39+RWlpacWCBQtKZs6c2eGhhx7a+7TTTlvxy1/+clGbNm2y2ub+yle+srRjx44VFRUVsWjRopLp06d3HDduXM+ZM2e2nz59+oKabVeuXNniwQcf7JRSik2bNrW46aabOo0bN+6dXd3fj370o+7nnXfe8k6dOlXW+0OJiMMPP3zdJz/5ybW1Xdvx+9JYbr311rK2bdtWfuITn1i77777btu6dWt66aWX2v785z/vMmXKlM4PP/zw3EGDBr3n+1ubSZMmlc2YMaNjSUlJ1rNnz01r1qxp0xQ1A9QkEAIAAACg0UycOPGtHc+98cYbxeeff37Phx56aO8TTjjhwOeff/6V/fbb7z3LhT73uc+tuvDCC5fvao5zzz2351133dW5T58+G+++++4FH/3oR/8mANi2bVvcdNNNnR588MGOhd1NxNq1a1ucdNJJfebNm9fmjDPOWH7LLbe8XlpaWlnz+rnnntvzvvvu2+ekk07q88ILL8ypeb1az549N8+fP7/1tdde23nUqFHL6jP3n/70p9af//zn+2zYsKFo3Lhxi7/73e++3bLl3y5EmTNnTquLLrqox6pVq95dkfPCCy+0Hjp0aJ/Nmze3uOqqq9649NJLl7Zo8X8bBc2fP7/laaed1vuBBx7odM4551ROnTr1r7XNP3bs2PKDDjpoS/X7559/vvXgwYM//Oijj3b87W9/2+6UU05ZV31t8uTJnTZs2NBixIgR5bfddtu+t956a9muAqGePXtufv3110vGjx/f7T/+4z8W1+czqfbJT35ybW3ftaY0Z86c2W3btn1PePbv//7vnS+++OJe48aN2+9//ud/5tdnrEsuueTtq6++evHHPvaxTQsWLGjVt2/fBq1cA9gdtowDAAAAoEl94AMf2Pbggw++NnDgwLVvv/12q8svv7xbQ8d69NFH97rrrrs6d+jQoeLRRx+dt2MYFBFRXFwcX//611fcd999CwurPOL73/9+l3nz5rXp37//+qlTpy7aMewpLS2tnDp16qL+/fuvnzdvXpurrrpq39rGGT169JLWrVtXXn311d3XrFlTr9/JfeMb3/jAunXriv7lX/7l7X/7t397TxgUEdG3b98t06ZNe+3YY499N5y54IILem7cuLHF1772tbe/853v/E0YFBHRu3fvrdOmTZvfvn37irvvvrvz9OnT96pPPYcddtimgQMHro2IePrpp/+mz6233lrWokWLuOSSS8qPOeaY1a+++mqbxx57bKfjjhw5cmlZWdnWyZMn77u7W67tCbWFQRER55577sqIiEWLFrWu71jHHXfc+sMOO2zT7qwWAyiUQAgAAACAJldUVBTjxo1bEhHxwAMPdKqs3K0dwt51/fXXl0VEfPnLX36nV69edW7PFhFR11Zou+P222/vHBFxySWXvFVUVPtjcYqKimLs2LFLIiJ+8Ytf1PpMnB49emw5//zzy5ctW9Zy/PjxXXc175w5c1o99dRT7UtKSrLvfve7b++qffW9VvUrbdWq1U779erVa+tZZ521LCLihhtuqLXm2mRZliIiUkrvnnviiSfazJ49u+0RRxyxpnfv3lvPOeec6nFrfSZStbZt21aOGzdu8aZNm1qMHj16v/rW8Pem+tlQffv2rdd2cQB7iggaAAAAgGZx/PHHrysqKspWrFhR/Oqrr7bq27fvlprXf/3rX3dctGhRyY79xo0bV965c+eKiIjnnnuuXUTEZz7zmTVNXe/8+fNbLlmypFVRUVF28skn1/q8mmqnnHLKmqKiomzJkiWtFixY0LK2ZwldeeWVb99+++1lN954Y5dvfetbOw20ZsyY0S4i4uCDD15ffe/1UaPfhrKysp32O+GEE9bceOONXZ5//vl29Rn7+eefb139+R911FHrq89PmjSpLCLin/7pn5ZHRHzhC19YfeGFF277zW9+02nFihVv7Oz5QBdccMHy66+/vssDDzywz5NPPll+1FFH1StU+eMf/1h60UUXda/t2imnnLL62GOPXV/btcYwceLEzm+++WardevWtXj55ZfbPPXUU+27d+++5Sc/+cmbTTUnQGMQCAEAAADQLNq0aZN17NixYvny5cVLliwp3jEQmjFjRscZM2a857k/559//rLqUGTZsmUtI7avcGnq0YA1BgAAIABJREFUet94442WEREdO3asaNeu3U5XG7Vr1+7de3v99ddrDYQ6dOhQOXbs2MVjxozpNWbMmO5Tpkyp9dk9ERFLlixpGRHRrVu33brP6n7du3ffsqu2+++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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = comb_panel_comp_pct_dict['prim'].plot(kind='bar', stacked=True, figsize=(30, 15))\n",
    "chartBox = ax.get_position()\n",
    "ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height])\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=2, prop={'size':20})\n",
    "plt.title(\"Panel Composition for Primary Pancreas Tumor Mutations\", size=35)\n",
    "plt.xlabel('Gene', size=25)\n",
    "plt.ylabel('Percent', size=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = comb_panel_comp_pct_dict['met'].plot(kind='bar', stacked=True, figsize=(30, 15))\n",
    "chartBox = ax.get_position()\n",
    "ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height])\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=2, prop={'size':20})\n",
    "plt.title(\"Panel Composition for Metastatic Pancreas Tumor Mutations\", size=35)\n",
    "plt.xlabel('Gene', size=25)\n",
    "plt.ylabel('Percent', size=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "from adjustText import adjust_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_prim = np.log10(comb_pval_dict['prim'][['Black_White_fdr', 'Black_Asian_fdr', 'Asian_White_fdr']])\n",
    "x_met = -np.log10(comb_pval_dict['met'][['Black_White_fdr', 'Black_Asian_fdr', 'Asian_White_fdr']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_outliers(x, y, labels, x_min, y_min):\n",
    "    # label points that pass thresholds\n",
    "    x_pass = []\n",
    "    y_pass = []\n",
    "    genes_pass = []\n",
    "    for a, b, c in zip(x, y, labels):\n",
    "        if abs(a) >= x_min and abs(b) >= y_min:\n",
    "            x_pass.append(a)\n",
    "            y_pass.append(b)\n",
    "            genes_pass.append(c)\n",
    "            \n",
    "    return list(zip(x_pass, y_pass, genes_pass))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_y_prim_AW = comb_pm_mut_freq_dict['prim']['Asian'] - comb_pm_mut_freq_dict['prim']['White']\n",
    "split_y_prim_BW = comb_pm_mut_freq_dict['prim']['Black'] - comb_pm_mut_freq_dict['prim']['White']\n",
    "split_y_prim_BA = comb_pm_mut_freq_dict['prim']['Black'] - comb_pm_mut_freq_dict['prim']['Asian']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_y_met_AW = comb_pm_mut_freq_dict['met']['Asian'] - comb_pm_mut_freq_dict['met']['White']\n",
    "split_y_met_BW = comb_pm_mut_freq_dict['met']['Black'] - comb_pm_mut_freq_dict['met']['White']\n",
    "split_y_met_BA = comb_pm_mut_freq_dict['met']['Black'] - comb_pm_mut_freq_dict['met']['Asian']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split y-axis\n",
    "\n",
    "fig = plt.figure(figsize=(14, 12))\n",
    "# fig.patch.set_facecolor('white')\n",
    "\n",
    "plt.scatter(x_prim['Black_White_fdr'], split_y_prim_BW, label='Black vs. White', s=230, c='tab:olive', edgecolors='black')\n",
    "plt.scatter(x_prim['Asian_White_fdr'], split_y_prim_AW, label='Asian vs. White', s=230, c='tab:pink', edgecolors='black')\n",
    "plt.scatter(x_prim['Black_Asian_fdr'], split_y_prim_BA, label='Black vs. Asian', s=230, c='tab:cyan', edgecolors='black')\n",
    "\n",
    "plt.scatter(x_met['Black_White_fdr'], split_y_met_BW, s=230, c='tab:olive', edgecolors='black')\n",
    "plt.scatter(x_met['Asian_White_fdr'], split_y_met_AW, s=230, c='tab:pink', edgecolors='black')\n",
    "plt.scatter(x_met['Black_Asian_fdr'], split_y_met_BA, s=230, c='tab:cyan', edgecolors='black')\n",
    "\n",
    "plt.axvline(x=0, c='black')\n",
    "plt.axhline(y=0, c='black')\n",
    "\n",
    "# plot significance lines\n",
    "plt.axvline(x=-np.log10(0.05), c='red', linestyle='dashed', linewidth=3.5)\n",
    "plt.axvline(x=np.log10(0.05), c='red', linestyle='dashed', linewidth=3.5)\n",
    "\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('Pancreatic Cancer', size=30, **hfont)\n",
    "plt.xlabel('-log10(FDR)', size=30, **hfont)\n",
    "plt.ylabel('Difference in Mutation Frequency (%)', size=30, **hfont)\n",
    "plt.xlim(-2, 2)\n",
    "plt.ylim(-15, 25)\n",
    "\n",
    "plt.xticks(np.arange(-2, 3, 1))\n",
    "plt.xticks(fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "plt.tick_params('both', length=10, width=2, which='major')\n",
    "\n",
    "plt.legend(\n",
    "#     bbox_to_anchor=(1.05, 1), \n",
    "    loc='upper right', prop={\"family\":\"Helvetica\", 'size':20})\n",
    "\n",
    "x = pd.concat([x_prim['Black_White_fdr'], x_prim['Asian_White_fdr'], x_prim['Black_Asian_fdr'],\n",
    "          x_met['Black_White_fdr'], x_met['Asian_White_fdr'], x_met['Black_Asian_fdr']]).to_list()\n",
    "\n",
    "y = pd.concat([split_y_prim_BW, split_y_prim_AW, split_y_prim_BA, \n",
    "               split_y_met_BW, split_y_met_AW, split_y_met_BA]).to_list()\n",
    "\n",
    "labels = split_y_prim_BW.index.to_list() + split_y_prim_AW.index.to_list() + split_y_prim_BA.index.to_list() +\\\n",
    "            split_y_met_BW.index.to_list() + split_y_met_AW.index.to_list() + split_y_met_BA.index.to_list()\n",
    "\n",
    "outliers = label_outliers(x, y, labels, 0, 6)\n",
    "\n",
    "texts = [plt.text(x=a, y=b, s=label, size=30, **hfont) for a,b,label in outliers]\n",
    "adjust_text(texts, arrowprops=dict(arrowstyle=\"-\", color='k', lw=1.75))\n",
    "\n",
    "# plt.savefig(\"figures/pancreas/pancreas_FDR_scatter_v2.pdf\", bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rearrange order of columns\n",
    "pval_columns = ['Black_White_pval', 'Black_Asian_pval', 'Asian_White_pval',\n",
    "                'Black_White_fdr', 'Black_Asian_fdr', 'Asian_White_fdr',\n",
    "                'Black_White_odds_ratio', 'Black_Asian_odds_ratio', 'Asian_White_odds_ratio',\n",
    "                'Black_White_CI', 'Black_Asian_CI', 'Asian_White_CI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Mutation counts and sequenced counts\n",
    "# comb_pm_mut_seq_dict['prim'].to_csv(\n",
    "#     'data_tables/pancreas/pancreas_mut_seq_prim.txt', sep='\\t')\n",
    "\n",
    "# comb_pm_mut_seq_dict['met'].to_csv(\n",
    "#     'data_tables/pancreas/pancreas_mut_seq_met.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Mutation frequency\n",
    "# comb_pm_mut_freq_dict['prim'].to_csv(\n",
    "#     'data_tables/pancreas/pancreas_mut_freq_prim.txt', sep='\\t')\n",
    "\n",
    "# comb_pm_mut_freq_dict['met'].to_csv(\n",
    "#     'data_tables/pancreas/pancreas_mut_freq_met.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # p-values and fdr\n",
    "# comb_pval_dict['prim'][pval_columns].to_csv(\n",
    "#     'data_tables/pancreas/pancreas_pval_prim.txt', sep='\\t')\n",
    "\n",
    "# comb_pval_dict['met'][pval_columns].to_csv(\n",
    "#     'data_tables/pancreas/pancreas_pval_met.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DDR Genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load DDR genes list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using these genes that were sequenced by all 7 panels\n",
    "DDR_genes = pd.DataFrame(['ATM', 'BRCA1', 'BRCA2', 'MSH2', 'MSH6'], columns=['Hugo_Symbol'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove DDR genes not in dataset, if any\n",
    "prim_DDR_genes = DDR_genes[DDR_genes['Hugo_Symbol'].isin(mut_seq_dict['prim'].index)]\n",
    "met_DDR_genes = DDR_genes[DDR_genes['Hugo_Symbol'].isin(mut_seq_dict['met'].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 5 5\n"
     ]
    }
   ],
   "source": [
    "# check if number of genes changed\n",
    "print(len(DDR_genes), len(prim_DDR_genes), len(met_DDR_genes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>Hugo_Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hugo_Symbol\n",
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prim_DDR_genes == met_DDR_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from here assuming prim_DDR genes are the same as met_DDR\n",
    "# assuming both prim and met have the same DDR genes\n",
    "# if not all True in printouts above, need to modify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in mut_dict.items():\n",
    "    DDR_mut_dict[key] = value[value['Hugo_Symbol'].isin(prim_DDR_genes.iloc[:,0])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by variants of interest (use this for all genes calculation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_var_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in DDR_mut_dict.items():\n",
    "    DDR_var_mut_dict[key] = value[value['Variant_Classification'].isin(variants)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate mutation frequency (all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using all patients as common denominator\n",
    "# use this mutation frequency for the combined graphs\n",
    "# using variant filtered mutations\n",
    "# count the number of unique patients with mutations in the DDR genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_all_mut_cnt_dict = {}\n",
    "DDR_all_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prim prim\n",
      "met met\n"
     ]
    }
   ],
   "source": [
    "for (k1, v1), (k2, v2) in zip(DDR_var_mut_dict.items(), {k: race_demo_dict[k] for k in ['prim', 'met']}.items()):\n",
    "    # checking order of prim and met is consistent\n",
    "    print(k1, k2)\n",
    "    mut_cnt = v1.groupby('PRIMARY_RACE')['PATIENT_ID'].nunique()\n",
    "    mut_cnt = mut_cnt.rename('DDR Mutations')\n",
    "    DDR_all_mut_cnt_dict[k1] = mut_cnt\n",
    "    \n",
    "    DDR_all_mut_freq_dict[k1] = (mut_cnt / v2.values * 100).to_frame().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher's Test and FDR (all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: p-values and FDR are the same here because only testing on hypothesis\n",
    "# we will not adjust p-values for combined genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_all_pval_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prim prim\n",
      "met met\n"
     ]
    }
   ],
   "source": [
    "for (k1, v1), (k2, v2) in zip(DDR_all_mut_cnt_dict.items(), {k: race_demo_dict[k] for k in ['prim', 'met']}.items()):\n",
    "    print(k1, k2)\n",
    "    df = v1.append(v2).to_frame().transpose()\n",
    "    df.columns = ['Asian_mut', 'Black_mut', 'White_mut', 'Asian_seq', 'Black_seq', 'White_seq']\n",
    "    df.index = ['DDR Mutations']\n",
    "    \n",
    "    DDR_all_pval_dict[k1] = fisher_fdr_all_groups(df, groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prim':                Black_White_odds_ratio  Black_White_pval  Black_White_fdr  \\\n",
       " DDR Mutations                0.392799          0.314075         0.314075   \n",
       " \n",
       "               Black_White_CI  Black_Asian_odds_ratio  Black_Asian_pval  \\\n",
       " DDR Mutations  (0.094, 1.64)                0.340278          0.274388   \n",
       " \n",
       "                Black_Asian_fdr Black_Asian_CI  Asian_White_odds_ratio  \\\n",
       " DDR Mutations         0.274388  (0.065, 1.77)                1.154349   \n",
       " \n",
       "                Asian_White_pval  Asian_White_fdr Asian_White_CI  \n",
       " DDR Mutations          0.644795         0.644795  (0.483, 2.76)  ,\n",
       " 'met':                Black_White_odds_ratio  Black_White_pval  Black_White_fdr  \\\n",
       " DDR Mutations                0.890449               1.0              1.0   \n",
       " \n",
       "                Black_White_CI  Black_Asian_odds_ratio  Black_Asian_pval  \\\n",
       " DDR Mutations  (0.308, 2.578)                   1.025               1.0   \n",
       " \n",
       "                Black_Asian_fdr  Black_Asian_CI  Asian_White_odds_ratio  \\\n",
       " DDR Mutations              1.0  (0.254, 4.131)                0.868731   \n",
       " \n",
       "                Asian_White_pval  Asian_White_fdr  Asian_White_CI  \n",
       " DDR Mutations               1.0              1.0  (0.334, 2.257)  }"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_pval_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim_met_DDR_pval = pd.concat([DDR_all_pval_dict['prim'], DDR_all_pval_dict['met']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim_met_DDR_pval.index = ['Primary', 'Metastasis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prim_met_DDR_pval[pval_columns].to_csv('tables/pancreas/pancreas_DDR_all_pval.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Significant mutations (all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_all_sig_pval_dict = {}\n",
    "DDR_all_sig_freq_dict = {}\n",
    "DDR_all_sig_genes_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(DDR_all_pval_dict.items(), DDR_all_mut_freq_dict.items()):\n",
    "    DDR_all_sig_genes_dict[k1], DDR_all_sig_pval_dict[k1], DDR_all_sig_freq_dict[k1] = find_sig_genes_groups(groups, v1, v2, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prim': {('Black',\n",
       "   'White'): Empty DataFrame\n",
       "  Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "  Index: [],\n",
       "  ('Black',\n",
       "   'Asian'): Empty DataFrame\n",
       "  Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       "  Index: [],\n",
       "  ('Asian',\n",
       "   'White'): Empty DataFrame\n",
       "  Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       "  Index: []},\n",
       " 'met': {('Black',\n",
       "   'White'): Empty DataFrame\n",
       "  Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "  Index: [],\n",
       "  ('Black',\n",
       "   'Asian'): Empty DataFrame\n",
       "  Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       "  Index: [],\n",
       "  ('Asian',\n",
       "   'White'): Empty DataFrame\n",
       "  Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       "  Index: []}}"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DDR Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): Empty DataFrame\n",
       " Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       " Index: [],\n",
       " ('Asian',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       " Index: []}"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black, White]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): Empty DataFrame\n",
       " Columns: [Black, Asian]\n",
       " Index: [],\n",
       " ('Asian',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Asian, White]\n",
       " Index: []}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_freq_dict['prim']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Asian vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       "Index: []"
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     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs Asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DDR Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): Empty DataFrame\n",
       " Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       " Index: [],\n",
       " ('Asian',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       " Index: []}"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black, White]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): Empty DataFrame\n",
       " Columns: [Black, Asian]\n",
       " Index: [],\n",
       " ('Asian',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Asian, White]\n",
       " Index: []}"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_freq_dict['met']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Asian vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Asian_White_pval, Asian_White_fdr, Asian_White_odds_ratio, Asian_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs Asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_Asian_pval, Black_Asian_fdr, Black_Asian_odds_ratio, Black_Asian_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutation frequency plots (all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim_met_DDR = pd.concat([DDR_all_mut_freq_dict['prim'], DDR_all_mut_freq_dict['met']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim_met_DDR.index = ['Primary', 'Metastasis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prim_met_DDR.to_csv('tables/pancreas/DDR_all_mut_freq.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (prim_met_DDR).plot(kind='barh', figsize=(8, 5), width=0.5)\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('DDR Mutation Frequency in Pancreas Cancer', fontsize=20, **hfont)\n",
    "plt.xlabel('Patients with Mutation (%)', fontsize=15, **hfont)\n",
    "plt.ylabel('DDR Mutations', fontsize=15, **hfont)\n",
    "plt.xticks(size=15, **hfont)\n",
    "plt.yticks(size=15, **hfont)\n",
    "plt.xlim((0, 100))\n",
    "ax.invert_yaxis()\n",
    "ax.set_yticklabels(['Primary', 'Metastasis'])\n",
    "plt.legend(prop={'family': 'Helvetica', 'size': 15}, loc='lower right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/Pancreas/DDR_Actionable/pancreas_DDR_Action_all_prim.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total mutation counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count mutations in each patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find genes that were sequenced by all panels, using this as our driver gene list\n",
    "common_genes = combined_panels[combined_panels['SEQ_ASSAY_ID'].apply(len) == 7].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(common_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count number of mutations (specified by list) in each patient, separated by primary/metstastasis and by race\n",
    "\n",
    "# removing duplicates:\n",
    "# first choose sample sequenced by later panel\n",
    "# then if still duplicates, choose the sample with the largest mutation count\n",
    "\n",
    "# mut_tbl: mutation table in MAF format\n",
    "# driver_genes: list of driver genes to be counted\n",
    "# races: list of races to separate by\n",
    "def count_mutations(mut_tbl, genes, races):\n",
    "    race_dict = {}\n",
    "    mut = mut_tbl[mut_tbl['Hugo_Symbol'].isin(genes)]\n",
    "    # remove assay duplicates\n",
    "    mut = remove_duplicates(mut)\n",
    "    \n",
    "    for race in races:\n",
    "        # these are patients with >= 1 count\n",
    "        race_mut = mut[mut['PRIMARY_RACE'] == race]\n",
    "        race_mut_cnt = race_mut.groupby(['PATIENT_ID', 'SAMPLE_ID'])['SAMPLE_ID'].count()\n",
    "        \n",
    "        # for remaining duplicates, choose sample with highest mutation count\n",
    "        race_mut_cnt = race_mut_cnt.groupby('PATIENT_ID').max()\n",
    "        \n",
    "        # find patients with zero counts and add them back in\n",
    "        zero_IDs = mut_tbl[(mut_tbl['PRIMARY_RACE'] == race) & \n",
    "                           ~(mut_tbl['PATIENT_ID'].isin(race_mut_cnt.index))]['PATIENT_ID'].unique()\n",
    "        zero_mut_cnt = pd.Series(index=zero_IDs, data=0)\n",
    "        \n",
    "        total_cnt = pd.concat([race_mut_cnt, zero_mut_cnt], axis=0)\n",
    "        total_cnt = total_cnt.rename('Mutation Count')\n",
    "\n",
    "        race_dict[race] = total_cnt\n",
    "    \n",
    "    return race_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in var_mut_dict.items():\n",
    "    driver_dict[key] = count_mutations(value, common_genes, races)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean and Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in driver_dict.items():\n",
    "    means = []\n",
    "    medians = []\n",
    "    for race in races:\n",
    "        medians.append(np.median(value[race]))\n",
    "        means.append(np.mean(value[race]))\n",
    "    df = pd.DataFrame(np.array([means, medians]), index=['Mean', 'Median'], columns=races)\n",
    "    median_dict[key] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>3.436364</td>\n",
       "      <td>3.06</td>\n",
       "      <td>3.39795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Asian  Black    White\n",
       "Mean    3.436364   3.06  3.39795\n",
       "Median  3.000000   3.00  3.00000"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>3.673913</td>\n",
       "      <td>3.388889</td>\n",
       "      <td>3.644537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Asian     Black     White\n",
       "Mean    3.673913  3.388889  3.644537\n",
       "Median  3.000000  3.500000  3.000000"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# median_dict['prim'].to_csv('tables/pancreas/pancreas_prim_median_count.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# median_dict['met'].to_csv('tables/pancreas/pancreas_met_median_count.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate significance (Wilcoxon Rank Sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate if distribution of mutation counts is different between races, including outliers\n",
    "# using Wilcoxon because mutation counts of each race is not normally distributed (tested using shapiro-wilk test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_pval_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculates the confidence interval of difference of medians for wilcoxon rank sum test\n",
    "# taken from here: https://gist.github.com/meetbash/aa5dbbeae746a173c5421ac57b45fe70#file-non_param_paired_ci-py\n",
    "\n",
    "def non_param_unpaired_CI(sample1, sample2, conf):\n",
    "    n1 = len(sample1)  \n",
    "    n2 = len(sample2)  \n",
    "    alpha = 1-conf      \n",
    "    N = stats.norm.ppf(1 - alpha/2) \n",
    "    # The confidence interval for the difference between the two population\n",
    "    # medians is derived through the n x m differences.\n",
    "    diffs = sorted([i-j for i in sample1 for j in sample2])\n",
    "    # the Kth smallest to the Kth largest of the n x m differences then determine \n",
    "    # the confidence interval, where K is:\n",
    "    k = np.math.ceil(n1*n2/2 - (N * (n1*n2*(n1+n2+1)/12)**0.5))\n",
    "    CI = (round(diffs[k-1],3), round(diffs[len(diffs)-k],3))\n",
    "    return CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in driver_dict.items():\n",
    "    df = pd.DataFrame(index=['Rank Sum Stat', 'p-value', 'CI', 'Mean Percent Difference'])\n",
    "    for group in groups:\n",
    "        pct_diff = (np.mean(value[group[0]]) - np.mean(value[group[1]])) / np.mean(value[group[1]]) * 100\n",
    "        CI = non_param_unpaired_CI(value[group[0]], value[group[1]], 0.95)\n",
    "        df[group[0] + '_' + group[1]] = list(stats.ranksums(value[group[0]], value[group[1]])) + [CI] + [pct_diff]\n",
    "    driver_pval_dict[key] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primary Stats:\n"
     ]
    },
    {
     "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>Black_White</th>\n",
       "      <th>Black_Asian</th>\n",
       "      <th>Asian_White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank Sum Stat</th>\n",
       "      <td>-0.757684</td>\n",
       "      <td>0.433087</td>\n",
       "      <td>-1.43825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.44864</td>\n",
       "      <td>0.664952</td>\n",
       "      <td>0.150363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CI</th>\n",
       "      <td>(-1, 0)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(-1, 0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Percent Difference</th>\n",
       "      <td>-9.94569</td>\n",
       "      <td>-10.9524</td>\n",
       "      <td>1.1305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Black_White Black_Asian Asian_White\n",
       "Rank Sum Stat             -0.757684    0.433087    -1.43825\n",
       "p-value                     0.44864    0.664952    0.150363\n",
       "CI                          (-1, 0)      (0, 1)     (-1, 0)\n",
       "Mean Percent Difference    -9.94569    -10.9524      1.1305"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Primary Stats:')\n",
    "driver_pval_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metastatic Stats:\n"
     ]
    },
    {
     "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>Black_White</th>\n",
       "      <th>Black_Asian</th>\n",
       "      <th>Asian_White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank Sum Stat</th>\n",
       "      <td>0.00662037</td>\n",
       "      <td>0.219578</td>\n",
       "      <td>-0.513061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.994718</td>\n",
       "      <td>0.8262</td>\n",
       "      <td>0.607909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CI</th>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(-1, 1)</td>\n",
       "      <td>(-1, 0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Percent Difference</th>\n",
       "      <td>-7.01455</td>\n",
       "      <td>-7.75805</td>\n",
       "      <td>0.806039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Black_White Black_Asian Asian_White\n",
       "Rank Sum Stat            0.00662037    0.219578   -0.513061\n",
       "p-value                    0.994718      0.8262    0.607909\n",
       "CI                           (0, 1)     (-1, 1)     (-1, 0)\n",
       "Mean Percent Difference    -7.01455    -7.75805    0.806039"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Metastatic Stats:')\n",
    "driver_pval_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "# driver_pval_dict['prim'].to_csv('tables/pancreas/pancreas_prim_count_stats.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# driver_pval_dict['met'].to_csv('tables/pancreas/pancreas_met_count_stats.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_count_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in driver_dict.items():\n",
    "    dfs = []\n",
    "    for race in races:\n",
    "        df = pd.DataFrame(value[race])\n",
    "        df['Race'] = race\n",
    "        if key == 'prim':\n",
    "            df['Sample Type'] = 'Primary'\n",
    "        else:\n",
    "            df['Sample Type'] = 'Metastasis'\n",
    "        dfs.append(df)\n",
    "    driver_count_dict[key] = pd.concat(dfs)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_df = pd.concat([driver_count_dict['prim'], driver_count_dict['met']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try a log scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding 1 to prevent infinity values\n",
    "driver_df['log10(Mutation Count)'] = np.log10(driver_df['Mutation Count'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 12))\n",
    "ax = sns.boxplot(data=driver_df[['log10(Mutation Count)', 'Race', 'Sample Type']], \n",
    "                 hue='Race', x='Sample Type', y='log10(Mutation Count)', notch=True, fliersize=12,\n",
    "#                 width=\n",
    "                )\n",
    "# plt.ylim(-0.1, 3)\n",
    "# ax.set_xlim(-0.55, 1.55)\n",
    "sns.despine(ax=ax)\n",
    "\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('Pancreatic Cancer', size=30, **hfont)\n",
    "plt.xlabel('Sample Type', size=30, **hfont)\n",
    "plt.ylabel('Mutation Count', size=30, **hfont)\n",
    "plt.xticks(fontsize=30, **hfont)\n",
    "ax.set_yticks([np.log10(1), np.log10(11), np.log10(101)])\n",
    "ax.set_yticklabels(['0', '$10^1$', '$10^2$'])\n",
    "plt.yticks(fontsize=30, **hfont)\n",
    "plt.tick_params('y', length=10, width=2, which='major')\n",
    "\n",
    "\n",
    "plt.legend(loc='upper right', prop={\"family\":\"Helvetica\", 'size':20})\n",
    "# plt.savefig(\"figures/pancreas/pancreas_boxplot_v3.pdf\", bbox_inches='tight')\n",
    ";"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "287.797px"
   },
   "toc_section_display": true,
   "toc_window_display": true
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 },
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}