source_plots_2019-05-15.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting the sources of different bacteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, seaborn as sns, matplotlib.pyplot as plt, numpy as np\n",
    "import scipy.spatial.distance as dist, itertools\n",
    "import skbio.diversity as div, scipy.stats as stats, skbio.stats.ordination as ordination\n",
    "from io import StringIO\n",
    "from skbio import TreeNode\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "sns.set_style('white')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get patient colors\n",
    "dd_pat_col = {'0044': (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
    "              '0073': (0.7686274509803922, 0.611764705882353, 0.5803921568627451),\n",
    "              '001': (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
    "              '004': (1.0, 0.4980392156862745, 0.054901960784313725),\n",
    "              '005': (1.0, 0.7333333333333333, 0.47058823529411764),\n",
    "              '006': (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
    "              '007': (0.596078431372549, 0.8745098039215686, 0.5411764705882353),\n",
    "              '008': (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
    "              '009': (1.0, 0.596078431372549, 0.5882352941176471),\n",
    "              '010': (0.7725490196078432, 0.6901960784313725, 0.8352941176470589),\n",
    "              '011': (0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
    "              '012': (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
    "              '013': (0.9686274509803922, 0.7137254901960784, 0.8235294117647058),\n",
    "              '014': (0.6823529411764706, 0.7803921568627451, 0.9098039215686274)}\n",
    "dd_pat_col_name = {'patient_' + str(k): v for k, v in dd_pat_col.items()}\n",
    "\n",
    "#get patient colors on treatment\n",
    "dd_pat_treat = {'0044': (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
    "              '0073': (0.7686274509803922, 0.611764705882353, 0.5803921568627451),\n",
    "              '001': sns.color_palette()[0], '004': sns.color_palette()[1],\n",
    "              '005': sns.color_palette()[1], '006': sns.color_palette()[1],\n",
    "              '007': sns.color_palette()[0], '008': sns.color_palette()[0],\n",
    "              '009': sns.color_palette()[1], '010': sns.color_palette()[0],\n",
    "              '011': sns.color_palette()[0], '012': sns.color_palette()[1],\n",
    "              '013': sns.color_palette()[1], '014': sns.color_palette()[0]}\n",
    "dd_pat_treat_name = {'patient_' + str(k): v for k, v in dd_pat_treat.items()}\n",
    "\n",
    "#get patient dashes on responder\n",
    "dd_pat_dash = {'0044': (), '0073': (),\n",
    "               '001': (), '004': (), '005': (),\n",
    "               '006': (), '007': (3, 2), '008': (3, 2),\n",
    "               '009': (), '010': (), '011': (),\n",
    "               '012': (), '013': (), '014': (3, 2)}\n",
    "dd_pat_dash_name = {'patient_' + str(k): v for k, v in dd_pat_dash.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in metadata\n",
    "md = pd.read_csv('/Users/ndchu/Documents/uc_fmt/metadata/metadata_stool_dna_NV022018.tsv',\n",
    "                 sep='\\t', dtype={'patient': 'object',\n",
    "                                  'fmt': 'object',\n",
    "                                  'fmt_induction': 'object'})\n",
    "md.loc[:, 'patient_plt'] = 'patient_' + md['patient']\n",
    "\n",
    "#read in patient metadata\n",
    "md_pat = pd.read_csv('/Users/ndchu/Documents/uc_fmt/metadata/metadata_patients_2019-04-10.tsv',\n",
    "                     sep='\\t', dtype={'patient': 'object',\n",
    "                                      'fmt': 'object',\n",
    "                                      'fmt_induction': 'object'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get labels for publication\n",
    "pat_id_dict = md_pat.set_index('patient').to_dict()['label_pub']\n",
    "pat_id_plt_dict = {f'patient_{k}': v for k, v in pat_id_dict.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in 16S data\n",
    "otu = pd.read_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/dd_asv_tab_f.tsv',\n",
    "                  sep='\\t', index_col=0).astype(int)\n",
    "otu_rare = pd.read_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/dd_asv_tab_f_rare8500.tsv',\n",
    "                       sep='\\t', index_col=0).astype(int)\n",
    "otu_ra = otu.div(otu.sum())\n",
    "#read in taxa\n",
    "taxa = pd.read_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/taxonomy.tsv',\n",
    "                   sep='\\t')\n",
    "taxa[['domain', 'phylum', 'class',\n",
    "      'order', 'family', 'genus',\n",
    "      'species']] = taxa['Taxon'].str.split(';', expand=True)\n",
    "\n",
    "#read in meatgenomics\n",
    "mtphl = pd.read_csv('/Users/ndchu/Documents/uc_fmt/metagenomics/metaphlan/deepdive_metaphlan_tax_abun.tsv.gz',\n",
    "                    sep='\\t', index_col=0)\n",
    "#add taxa level\n",
    "mtphl.loc[:, 'taxa_level'] = mtphl.index.str.count('\\|') + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relative_abundance_tidy(df, sample_col, value_col,\n",
    "                            rel_abun_name='rel_abun'):\n",
    "    \"\"\"\n",
    "    Take a tidy table, where the samples are in sample_col and the values\n",
    "    to be converted to relative abundance are in value_col.\n",
    "    \n",
    "    sample_col can also be a list of columns\n",
    "    \"\"\"\n",
    "    #define relatibe abundance calculation\n",
    "    rel_abun = lambda x: (x / x.sum())\n",
    "    #calculate new values\n",
    "    values = df.groupby(sample_col)[value_col].transform(rel_abun)\n",
    "    values = values.rename(rel_abun_name)\n",
    "    #add to df and return\n",
    "    df = pd.concat([df, values], axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "otu_tidy = pd.melt(otu.reset_index(), id_vars='#OTU ID', var_name='sample_id', value_name='count')\n",
    "otu_tidy = relative_abundance_tidy(otu_tidy, 'sample_id', 'count')\n",
    "otu_tidy = otu_tidy.merge(md)\n",
    "otu_tidy = otu_tidy.merge(taxa, left_on='#OTU ID', right_on='Feature ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a dictionary of colors for bacterial sources\n",
    "dd_otu_set = {'shared_otus': (0.878, 0.878, 0.878),\n",
    "              'patient_otus': (0.909, 0.470, 0.470),\n",
    "              'env_otus': (0.933, 0.674, 0.349),\n",
    "              'donor0044_otus': (0.305, 0.749, 0.776),\n",
    "              'donor0073_otus': (0.643, 0.450, 0.850),\n",
    "              'donor0044_0073_otus': (0.376, 0.470, 0.803),\n",
    "              'donor_any_otus': (0.376, 0.470, 0.803)}\n",
    "\n",
    "dd_otu_set_tint = {k: tuple([x + ((255 - 255*x) * 0.8)/255 for x in v]) for k, v in dd_otu_set.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list donor samples\n",
    "sam_0044 = md.loc[md['patient'] == '0044']['sample_id'].tolist()\n",
    "sam_0073 = md.loc[md['patient'] == '0073']['sample_id'].tolist()\n",
    "\n",
    "#get donor otus\n",
    "otu_0044 = set(otu[sam_0044].loc[otu[sam_0044].astype(bool).any(axis=1)].index)\n",
    "otu_0073 = set(otu[sam_0073].loc[otu[sam_0073].astype(bool).any(axis=1)].index)\n",
    "\n",
    "donor_otus_dict = {'0044': otu_0044, '0073': otu_0073}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make a tidy dataframe\n",
    "otu_df = otu_ra.reset_index().melt(id_vars='#OTU ID', \n",
    "                                   var_name='sample_id', \n",
    "                                   value_name='rel_abundance')\n",
    "otu_df_md = otu_df.merge(md)\n",
    "#remove zeroes\n",
    "otu_df_md = otu_df_md.loc[otu_df_md['rel_abundance'] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#source track bacteria for each patient\n",
    "csource_abun = [] #have this be in format [[sample, recipient, donor0044, donor0073, shared, environment]]\n",
    "csource_ct = [] #also track the otus counts\n",
    "\n",
    "for patient, pat_df in otu_df_md.groupby('patient'):\n",
    "    if patient in ['0044', '0073']:\n",
    "        continue\n",
    "    #get patient baseline otus\n",
    "    bl_sam = md.loc[(md['patient'] == patient) & (md['days_past_fmt'] < -5)]['sample_id'].tolist()\n",
    "    bl_otus = set(pat_df.loc[pat_df['sample_id'].isin(bl_sam)]['#OTU ID'])\n",
    "    \n",
    "    #get patient abx otus\n",
    "    abx_sam = md.loc[(md['patient'] == patient) & \\\n",
    "                     (md['days_past_fmt'] > -5) & \\\n",
    "                     (md['days_past_fmt'] < 1)]['sample_id'].tolist()\n",
    "    abx_otus = set(pat_df.loc[pat_df['sample_id'].isin(abx_sam)]['#OTU ID'])\n",
    "    \n",
    "    #subtract the abx otus from the donor otus, since they came from before FMT\n",
    "    otu_0044_noabx = otu_0044 - abx_otus\n",
    "    otu_0073_noabx = otu_0073 - abx_otus\n",
    "    \n",
    "    #source track bacteria for each sample\n",
    "    for sample, sam_df in pat_df.groupby('sample_id'):\n",
    "        otus = set(sam_df['#OTU ID'])\n",
    "        \n",
    "        # for placebo patients\n",
    "        if sam_df['treatment'].iloc[0] == 'placebo':\n",
    "            shared = set()\n",
    "            src_0044 = set()\n",
    "            src_0073 = set()\n",
    "            src_0044_0073 = set()\n",
    "            src_pat = otus & bl_otus\n",
    "            env = otus - bl_otus\n",
    "        \n",
    "        # in cases of only 0044\n",
    "        elif set(pat_df['fmt_past']) == set([np.nan, '0044']):\n",
    "            shared = (otus & otu_0044_noabx & bl_otus)\n",
    "            src_0044 = (otus & otu_0044_noabx) - bl_otus - abx_otus\n",
    "            src_0073 = set()\n",
    "            src_0044_0073 = set()\n",
    "            src_pat = (otus & bl_otus) - shared\n",
    "            env = otus - bl_otus - otu_0044_noabx\n",
    "            \n",
    "        # in cases of only 0073\n",
    "        elif set(pat_df['fmt_past']) == set([np.nan, '0073']):\n",
    "            shared = (otus & otu_0073_noabx & bl_otus)\n",
    "            src_0044 = set()\n",
    "            src_0073 = (otus & otu_0073_noabx) - bl_otus\n",
    "            src_0044_0073 = set()\n",
    "            src_pat = (otus & bl_otus) - shared\n",
    "            env = otus - bl_otus - otu_0073_noabx\n",
    "        \n",
    "        #in cases of two donors\n",
    "        elif (set(pat_df['fmt_past']) == set(['0044', '0044, 0073', np.nan])) | \\\n",
    "             (set(pat_df['fmt_past']) == set(['0073', '0044, 0073', np.nan])):\n",
    "            shared = (otus & otu_0044_noabx & bl_otus) | (otus & otu_0073_noabx & bl_otus)\n",
    "            src_0044 = (otus & otu_0044_noabx) - bl_otus - otu_0073_noabx\n",
    "            src_0073 = (otus & otu_0073_noabx) - bl_otus - otu_0044_noabx\n",
    "            src_0044_0073 = (otus & otu_0044_noabx & otu_0073_noabx) - bl_otus\n",
    "            src_pat = (otus & bl_otus) - shared\n",
    "            env = otus - bl_otus - otu_0044_noabx - otu_0073_noabx\n",
    "        \n",
    "        otu_sets = [shared, src_0044, src_0073, src_0044_0073, src_pat, env]\n",
    "        abun = [sam_df.loc[sam_df['#OTU ID'].isin(x)]['rel_abundance'].sum() \\\n",
    "                    for x in otu_sets]\n",
    "        cts = [len(x) for x in otu_sets]\n",
    "        \n",
    "        \n",
    "        csource_abun.append([sample] + abun)\n",
    "        csource_ct.append([sample] + cts)\n",
    "\n",
    "#make into a dataframe\n",
    "#CUMULATIVE ABUNDANCE\n",
    "csource_abun_df = pd.DataFrame(csource_abun,\n",
    "                               columns=['sample_id', 'shared_otus', 'donor0044_otus',\n",
    "                                        'donor0073_otus', 'donor0044_0073_otus',\n",
    "                                        'patient_otus', 'env_otus'])\n",
    "#COUNTS OF OTUS\n",
    "csource_ct_df = pd.DataFrame(csource_ct,\n",
    "                             columns=['sample_id', 'shared_otus', 'donor0044_otus',\n",
    "                                      'donor0073_otus', 'donor0044_0073_otus',\n",
    "                                      'patient_otus', 'env_otus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt these tables into tidy tables\n",
    "csource_abun_df = csource_abun_df.melt(id_vars='sample_id', \n",
    "                                       var_name='otu_src', \n",
    "                                       value_name='cum_rel_abun')\n",
    "\n",
    "csource_ct_df = csource_ct_df.melt(id_vars='sample_id', \n",
    "                                   var_name='otu_src', \n",
    "                                   value_name='otu_count')\n",
    "#merge these tables\n",
    "csource_df = csource_abun_df.merge(csource_ct_df)\n",
    "\n",
    "#add metadata\n",
    "csource_df = csource_df.merge(md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relative_abundance_tidy(df, sample_col, value_col,\n",
    "                            rel_abun_name='rel_abun'):\n",
    "    \"\"\"\n",
    "    Take a tidy table, where the samples are in sample_col and the values\n",
    "    to be converted to relative abundance are in value_col.\n",
    "    \n",
    "    sample_col can also be a list of columns\n",
    "    \"\"\"\n",
    "    #define relatibe abundance calculation\n",
    "    rel_abun = lambda x: (x / x.sum())\n",
    "    #calculate new values\n",
    "    values = df.groupby(sample_col)[value_col].transform(rel_abun)\n",
    "    values = values.rename(rel_abun_name)\n",
    "    #add to df and return\n",
    "    df = pd.concat([df, values], axis=1)\n",
    "    return df\n",
    "\n",
    "#for otu counts, also add a frequency column\n",
    "csource_df = relative_abundance_tidy(csource_df, 'sample_id', 'otu_count', rel_abun_name='frac_otus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write this to a table\n",
    "csource_df.to_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/csource_df_2019-05-16.tsv',\n",
    "                  sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "csource_df = pd.read_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/csource_df_20190206.tsv',\n",
    "                         sep='\\t', dtype={'patient': 'object',\n",
    "                                          'fmt': 'object',\n",
    "                                          'fmt_induction': 'object'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OXxHnrgtTHOoosHXHAK+oDJGwYN35SS5bl2RZi0M+kwuC21CYl1SBF7YPsP9wASFg1bI4ew/k+Pd7D/H7H15EIj51VXnGzPJ9zfMv9/DwU0e5/vIGbrqycaaTZEyRORlQJC67jFBbG33V1RTr66mqu4jsa9+m6ro/oOfr38N7+TW4JuiMIoTAku/E/+Xn0QefQ7RdN+JYWmuyA1ls2yYcC1dcjeD7Ppm+DKVCCWGJoBFleaihWDJGODL6TVcIQSQR581DJZqbYtRVTayu2rKs4NjDejBqrdG+HgowPK8caBSK6NywdgiCUUs0LNs6pbrjXCGEIB6PE4/H5/2AXpHzz8cpd311LAtfazzfx88exUo0EV66FAD/UCfaKyLsMD19Jf79G4c42lUiEbO48aparr6sisa64Pd9+YXwjpsb2NOeY+srAxzqyFHKCHLpED0DPmiorfJZVOvRs9jhsnVJ1q+toirloPZk+cK3D/P5rx3k1+5qYsnCsfOC7/mUiiVKhRJu0Q1K58q/Xduxse0geJ7sasDT0Vqzpz1PJCJoaYzgOPP39zNRvq95YUcP9//8CIeP5VncHGNF27S3STKm0WkDCinlzcCnGHYLU0rdNJWJqoSwLBzbJlsq4WaOgB3BitYRX7+egccewz/cgdXaEuzbuCYopdjzCGLJNSPaUhTzRQrZoNohO5AdaqPghJ0xby5uySXdm8b3fOKpOJF4ZOiCp31NNDF+kPDwL7p55KkeLAuuv6KG226oO6N66KF/CyEQtsCyrVMCAt8/UZIx+HKL7qjdQQcbY56rzoVzt8tVOYONml3fx8scJdSwFru2FuJh/L4w9O6D+lVseXmAo10lfu2uJtavTRIKnXrDtizBirYYrfWaQjaE58O2XSUe/MUAsajFLddUsWZZiOsvCUrJwraL1jZyeZzffH8L3/rxUT735QNcf0UNd95UTzRiBSPfuh6lQolioYhXCtp7CEsQCofwfT8oqcuP7FothAAh0Ah8DZ4PridwPU2xBKlUmOamyem1o7XmRw93sem5oBuubQsWNoVZ3BplcUuEJa1RWhaEsW1ByfXp73fRhRyObWNZAssS2Fa56tISWIKh9XOZ72u27ezlx491cOhonoVNUX7vnmVcekHNnD83Y3yVlFD8f8AngQNTnJYJsZLJoYtisZAd6uERvfBCBjZtovTLrUTe+TagXEqx4vagLUXHi4jWS4HggpBL54baKRRyBYq5IsV8Ecu2iMQiRGIRLNvC8zSgKRWKZPuDOvdUXYpQOLiBDz7tn86+g3kefbqH9RckiUYsnny+l19u7+f2G+q59vJqbHtyM5xlWVhhayidg0Yr0ThdIGTMH0N5p5hDF/uxE01BYLqoFf1mDr9rF3b9KtoP5bj16gSXrIlij/LzHmx7k+nPoH1NJBYhlorx1laLjVcvwLKCm6TWmlKxRC6dI9ufJZfOEU/FWb0iwf/8RBs/+flxfvF8Ly/tTHPP2+tZWB8E7gBOyCGWjGE5Dh1dLgf2FehPe6QzLpmsB/iEbIiFIRm3SMYEsahFPCqIRy1iUUEsIoJHIp3j1R1pYskoS5ec2sunUlpr7nukk1y6wB/+Wh1OyOLAEY9X9+TZ9soAm7cEQ4k4tsCyoLXR5u63pLATBQYyPl29Hsd7Pbr6yn97PXoHfAZrLkcLNGxbYAkx9G8avCjvM8b68rYRxxvcTwhsu1xFetLnbFsQCVmEwxaRsEU4JEjFoa4mTlN9dNTAUmvNi6/18ePHOjjQkaOlMcLvvH8pl6+tNYHEOaKSgGK/UuqxKU/JBNlVVYTc4ILj+ppwMiiNsKJRxPkr8F55FX3zjYhytz/RfDEkmvB3P4hoWY8QgmK+iO/5JGuSOCEHJ+QQT8Up5ovkMnly6RyZgSwHj3o8uz3HBSsirF0eRgubqobUhC9GxZLPN390hJqUw/vevoBY1Oa6K2r48SOd/OChTp56oZe7bm5grUxM+dOysARO2MEO2fg++FrjOCPPp1Ty2bk7y0uvDpDN+yRiNtGopqG6RDLukIzbJOMOiWHLkfDY7TSM2cMZnA+nkEYAVrwp2LB4Mezah9+uECvvJBnVXHVhbKiXj+3YOGGHUDiEZVvkBnKUiqUgKK9J4IRPXFKGF/8LIQhHwoTCIdyiSzadJdOXwbItYtEQv3LnAjZclOTI4QGaqlzSaY0VjtDZp9mzP8++A33sP1zA9QYbekIiZpNK2CQSwV9t22jHpmRZWFhoT1AqWeQRhMOCsCM4fjxHbUKQiJbYves4vhVmxXmpU377YwkCKJddu/u46gKbkJPAciy0p1mz1GLN0jh2KEXJsznS7bFnf4lFjbC02aLganoyIRxL09Los6TFZ3jTKV9DoSgoFCFXhGxekC1ANqcpugLf1/i+xvMZthz0lvHK7/3yNq+87Ho+vqfLpTV66HOD+5283vODh42Sp8sPUWAJeN+tKRbVh8ml0zy6s8iBoz7Zok1jbZTmhgixqM2jm4/RfjhHU32E3/qVpWy4yAQS55pKAopjUsr/AF6kPKiVUuqLU5qqCg0+ZXlOAjtxoiGdWHs+esfruNu2E7ru6mCdsLCW34L/8jeC0TMbVpPP5LEdm1AkRL7gs/dAjt37cuxpz9F+KE9NyuLS1VEuWR3hfbem0FqzaUuWX2zNsXpFmmsuq2bNykTFmeanPz/OseMlPv7BhcSiQdoXNkf4vQ8uZOcbWX78SCdf+k4HK5fGeNetjSxsDpPJ+vT0l+jpc+ntc+npd8nlPTxP43nBxcArZ/6geFfje8EFxfUYthzs75f3d8v7+54eGqmsvsZh2eIYbQujHOjI8/LrGfIFn2Tcpq7G4VhXkUzOI19Ij3mOji3KQYZdDjqCYCMxtHwi+BjcLxFzJr1kxhifEALHsiiVSoQBOxEEFGJRCxrwDxzjSEeGJU0OJQ/qGlK4JZdSsTQ0NkrwAYaq/SoJJAfb6VSFqug73kemL0NVXRX5bJ6Ukye1JMSRHvjmT3pIZ4Nfpm0LFrdEuO6KapYtjtK2MEp1yjmjm9XClhjFoscbe/qJOtCQ8jiwr5tMwWbF8hThkDX6eCg6mNelkC3gez51Kejqg1Urg1JKrXXw71MI2nlYfpHWGmitCSKGcDRMIuHQmDzRIFFrje/7lEolSqUSruuOWB4+hqBlCUKhMKFQCMdxCIVCQ8tTFcB7nqZQ9Ojt7aZUzOETo+S5rGoTXLA8uNbsOZjn5df62NVeoioV4qPvaeOqi+tMfj5HVRJQ7C3/bZ7KhJwJSwhsPLzoAqxE3dB6UVeDtWwp7gvbcK6+ElF+DBALN4D6CXr3Q7ipFUF7AhHhX752iDf35/B1EI0vbo2y8apaVrTFWLY4SixqBRcJy2LjtVVE4v08u62PL/5XB7XVDmtWxCmUNLm8TyQseOs1dSxqGTnvwxv7sjz5XC/XXV6NXB4fsU0IwQWrEpy/PM4zW/t44Inj/OMX9uM4gpI7cnAn24J4zA6KUu2gKPPEclCE6TiCsGVh2wLHLhd92gKnvL9tB8Wbjl0uRi0/JR0+WmTX3ixbXhkgFrG4aHWS9WuTrFoWH7pAFNwCNdF6MlmXdDYock6Xl4OXV14fLHd05ofee+OMJB6P2qMEH8MCj8TwACXYFg6Z0pCzEbJtSkVBGIEVD250IhFH1CTw+3Ice3MPS5csHOrBFIqEiBGMp+KVPFzXJRwJn9EorsISJKoTDHQP0NsZtEEIR8PEUjHqmm3+qDnF63uytDaFWdQSIVRhCUIlwmGbC1bX4nk+e/am8YsFWus12d5+sqf5bM+Az5NbsrS2JLj1hroRXaVD4VBQtZgK2i4NNiB1wg7haJiiN7LdkhDlbt+2fcrQ7lrrUwKMUqlENps9ZUj+kwOMweXxenVVwrYFhfwApWKO6upqampqhtJWKBTIZrOsCWeRbWE0EI1EicdD+L6LPYm96Yy5o5KBrf5KSnknwWyjqsLZRqeN7eVwY43YyQUj1jsbLqP4ne/jqV04a84HyqNnLn8r3s4fkOvro+jafO7eDhzH4q3X1rJiaYxli2JEIqdevAa7U9aF4c6b6rnthjpeUWmefqGPF3emiUUsYlGb7t4SL+5Ic+mFKe68sZ762qD049s/Pkp9bYh33Nww9rnYguuuqOHSdSmefL6XfMGnpsqhrjpETbVDbZVDMmFPaTGi1prefpdk3B61nhSCAKY6FaI6VflFQ2tNvuCPEXiceJ/Ougxk3KFAJF8YOwpxHDFKicfI98MDlOqkQzw2Jzs2TQnHsshZEUSsHmGd+L+0zjsP76UB7EwH0chiEsmRPZaECKrLhldvnIlQOJjRtlgoEkvGRrTzqa8Ncc1lU9t917YtVq0IpqNvP5DmWGeeru4Sx7pL5AuakquxLEF9bZimhjAdnUWefTHNHTfWcdsN9eMe27JOtME6E0KIocDgZMNLNYYHG7nyYH/D0zBWsFFJoNHf309/fz/JZHJEV2ohBNFolGg0Sm1t7VBwkcvl6OnpoaenB8dxiMfjxGIxIpHKSq+Mua+SXh5/SzDD4dMEs41ep5T69JSnrEJ2oZtitAURHZnB7VUrEDXVuM9vGQooAMSS6ygd3YOnbX765ACLWqL8+t3N1FRN7OJo24KL16S4eM3IAZCyOY/Hnu7hyed7efHVNNdfUU0m69Hd6/KHH1lEJHz6J614zOb2jeNfsKaKEILa6sl/uhBCEIvaxKI2jXWVX2Rd1yeT804JRIaXggy+P3zs9KUhzQ0RVrQlWNGWZGVbguaG6KgBmtaa471Fdu1Ls2tfhr0HMtx63QKuvmRm/l+mQsi2wXIgtWzEenvlKrxtr9BYfmieyq7EgyPDzrS2xUnaFgcjo3qe5khnkfZDedoP5dl7KM8zL6bRGt7+lnpuvq7uNEebWpZlDY2jMtxgqcbwko1SqUQ+nyeTyYzY13GcEQHGYMBh2zZCCNLpND09PcTjcerq6sYMCIYHF8BQYJPL5YYCksE5dGKxGPF4fNq79hrTp5K76PVKqWsApJT/TDDs76whsochvgRPj5w6VVgWzuWXUnr0cfyjx7CaghKMji7Q9beSyHexstnj2o3nT2p9Xzxm846bG7juimoeeKKbTc/2ooG3XF3LeUumdZKpecFxLKpT1oRLQ3KDpSGZE4FHd1+R3fszvPR6P09v7QaChn0rliRYsTTJ0tY4x7oL5SAiTU9fMEFbLGKxoi1Jc8PM3/gm02DDTFJtI9bbS5eggXh9E7ki59wNwLYFC5sjLGyOcPWlwZN5oeCTzXtTEmxPluGlGidPaDdYqnFysFEoFE4ZFC8UClEsFolGozQ0NEyodGHw+6uqqvB9n3w+PxRgZLNZXNcdqjox5p+pmm102lj9e6HhSkqeN9RIc5BzyYWUnvgF7i+3EHrb7Ty7rZ+XX+3lV26pJrTvh1xb72Dbq8c48tmprQ7xa+9s4saratj5RoYbNphMNF2EEMSjNvGozYJRSkO01hztKvBGe4Y32tPs2Z9huzo8tL0mFWLl0gSrliZZtSzJoqbYvGyt7rjBU6s/2MOjTMRiFGur0HULiej8TCRt1olErFGrQueK8Uo1PM87JdiIx+PU19efVVWFZVkjBo0rlUo4jqlynM/OZLbR705tkiqn3QKibzfA0ERHw4l4HHvdBbgvv8r3s2t47rUSv//+GhCCSLIK3f4EWr4DEZu6IszWpgitTWdWjzqddKEfwslpm0BtJgkhaG6M0twY5brLgiqMdMZlf0eWhroIjbXnxgBfOn8MvCr88Km//2Otq2m0HRKZPUDr9CduBuhSDrQH2j/x8gffe+BEp/RaMROEEEPVH1prdK4bN38QQRjLqnyIbO2VwBq7x4kQYs4P2W+c3lTNNjotvOxRhJfF0u6oAQVAaMNleC9uJ/TaDt7/tquorxYkUnHs1Fvw2p/Af/NR7AveN80pnx20m0cf3oK//2no3Yu17tcQbdfPdLJmRDLhsKbcQO9coTNHsSjipRaesq23dgmNnof1xjZYPnWT6vkHn0Mfeh7RfAmi5VJEePqHZtaFAfyXvoruPP3Erdbq92Atv3kaUjW1tPbxBzrwevfh9rXj9e7D621HFweG9omsvIPYunvGfcjwi2lJO93yAAAgAElEQVTyO39A4c3HwA5jVy3Erlo04iWitedEgG6ME1BIKT+mlPpyuVHmYDXHeiklSqk/m57kjc/LdAAQssSYAYVoWsChUCPX+m8SX3od2teEY2GEqEcsvAK9/2n0yjsR4RmZqnraaa2hZw/+gc3ow1vBK0CyBSJV6CMvwjkaUJyLvOxRLNunlFo8Yn13b4nkgirE0aP47R1TOseJv+cRSHegO3fCju8gFqxFLNyAaLpwUifyG4s+/gbeti9DKY1YcTsiUgXCKr/sE8uWhT68Ff+174NXQKy887T/Jlpr9PFdiGQTIjpzVZ7aLeD1H8Drbcft3YfX147Xtx8Gu7FaDnbVIkKtl2JXt+HULKV48DkKbzyAn+kkccXHEfbI0gXtexT2/pz8zu+jixnCbdcj7DBe/0FKh7dS3LdpaF8RimNVLcJOLSS68g7sqlMDWGN+GK+EYnCo7ddPWj9r2lD46Q4QFqFQlEzJHfXC136owBOlFbx/0QFKrkfMOjFng7X8VryDz+HvfRxbvmMmTmHa6EI/+uCz+Ps3Q+Yo2BFE6+VYS66BmmX4O7+Hbn9yaFIoY/7zM0dxkinyvj8i7+zdn2Vpo4PY2YXusyHdAanJr/bQmU4YOIS15r2I+lX4h55HH3oBfXR7UL3QegXW6ncjQpPfmFlrH73nYXx1P8QbsDf8KaJq8fifaV6P//I3gpmL3UKQtjGCCt13AH/nf6OP74K6FdhXTU/HOL8wEJQ2lEsd3N52/IHDDF62RSiOXbOUyLK3YNe0YdcsxU61IqyRtwK7fhVWYgG5l7/JwC8+Q/LqT2NFghK80tFXyL78Dfz+gziNFxC76IM41UtGpiPfh9d/cOjlDxyi1LEVp3aZCSjmsTEDCqXUw+XFy5VSnxhcL6W8F7h3qhNWCS/TgRVfgO2E0MUSntY4J2XwzVv62OUswn2rhHQa/7//m9L11+BctQGRakU0XYTe9wR60ZXgu+Dmg7pUNw9uDtzcifdeCfBB65H1rIkFWCtun5YnqonQvofufBW9fzP62MtBWmuXY624NShedk70WhAL1qL3/hzdpRBN62Yw1dPPLwyQV/dT2Ps4oYbzia39Vezq8W8u84GXPYqTWg4EbZDC5QZz/X0FxAKHcDSEzobxD+3EPn8KAoqj2wEQTRchEo3Y1UvQq+9Gdyn0oefRB57G696FfdnvIZJNpznaBL63mMZ/8avozh2I1suw1n2goqBFWDbWRR/Ct8PoNx/B9wpYa391RJWALvTjv34f+sBmCMURLevRHduCEpi6FZN3DlrjZzvxetuHAgi3dx86130ivbE6nJqlhBdtwK4OggcrXlmvDSEE0ZW3Y8Xryfzy3xh44n8Rv/g3KLz5KKWObViJBSSu/L8ItV426vGsaDVWtJrQggsm7ZyN2W+8Ko+PA38O1Ekp311ebQGnr2icJl66AzvZgl3u3VHyvBNd4QjGhNi2Y4DbrqtGRx1iIQtr2VJKjz2Bu2MnkbvuxFpxG97m7XhP/D/jf5kTBSsUTCIwVCRqAQIOv4DX9TrW6g/hH+3DXrkCUcFEYVNFp4/iH3gGffBZKPRBOIVY9lasJdcgkqMPeCrqVoIdRh/bAedIQKHdPPk3HiS/66fg5gm1rMfteo3+x/6UcNt1xNa8Fys+f8adGE77Ln6ui1A5CHZ9n8FyKdvyKbma1OIllBB4u1/HPv+tk54G/8hLkFqESJxo/CeEhWhcDY2r0Yuvxtv6BbzNf4d1ycewJuHmpLt3B1UcxQGstfcg2q4/fdVFJovf1YXO5rDPW4a19v34dqQcVBSxLvwgaB+993H83Q+AV0Qsuwlr5Z3gRPB62/HVfei6uxjo3IqwbYRtw2h/HefU9Rb4hR78zBH8TAde+hB++hDay4LQIARWVQtOvQxKAKrbsGvahkoUzkZ44RVY19eSfuazpDf/PThRYmt/lciK20xJpnGK8Uoo/g34Nynlnyml/mYa01SR4ILYSbhp/YmpmD0Pho0st+XlATxPc/GqEJYtiNZVw6/ejfeaovjAw+S/+FWcqzdgX/hRhM5DKAZODOwouAJd0Oi8h84VIJ1FVKWw5SrESTN3+odfwHvhG+S/9CXI2YjqKpxrr8K55KLgAjFq+j2EVXnQoT0f3dmJfyjo3mi1LUHUnxhwRntFdMfWoEqj+w1ABPXRS65FLFh32u8SdghRL9GdO6a0znymadfFywxQeOMJCrt+js7lsKtW4DRcgnATJK78VUpHNlHY8wjFA88SWXEbUfl2rHnWxsbPdYH2CUWDgdkG2yCls5qWBptsQbBgaSslW+Af7Jz034QupqF7N2LlHWPuI+pXYV/7P/C2/G/8X/4LrH43LLkRfawTq7YGET8xhL3WPvQfQh9X6FI26JUxvIeG9oPSx8NbIFaPfc2fIIYV02tfo/v60F3H8Tu78LuOo7u68DuPw/ARKCMRnIsvxL58I5YTxd91P36hH505CtkuxIJ1QRXOsBIVa9WdlJ74Pv7P7yMrBOjJqDWuL79OKIgOsI8h7C2nBiWOM34gM2wfbBsrGsVpbsZpacGuX07qxr+meOAZIks3YsVqJyH9xnxUSbfRHVLKv1ZK/S8p5UPAPymlHpnqhJ2Onz0G2sdKtmALEcweOqxhptaazVv7uGlDAlto4qkTM3g6a87HXtZG8ZHHcTc/h7ezBtHYAOkMOp1GpzPgjzHUYjiMfcFqnIvWYS1ZjLAEovoC3P3nQ6EHe3E3fp9H6WcPU3riCUJXrse+8hpEoRvdswfd8ya6Zw+kj0LdcqzF15SrH07qHz6Qxtt/AP/gYfxDh/E7jkCpNDItiQR2az0iPgB6NyKURiQWYJ3/TsSiqybcEEwsWIs+9gpkjsEkFDFr3wfPQ3seuC7a89CuO7Q82jrtusFnhv0dvi+uO/664cc7aZ0ulaA8Q22gCqjCZYACvwj+DTbFqHrPe6i65RbyO79PYddPKe59nOj5dxFZfsu8eSrzMkcBCMUXYOsTjZr3d/q0LbbJeSGEbWO11OB3HsN75I8gWo0IpyBSDZEUIloNyVZE9ZJguUJaa3THNkBjNV887r4i3oB14cdxn/gmpZ/8Aj2wFQZnHK1OIWojiFgWYXUgQn0Ip3yzHmxQadnDGlfa0HIZYuHteB1p9MvPBMFDZxe6s2vkbyMew2powF4tsRobsBrqwbFxt23HfWEr7vMvYK1Yjt32VvSxxxBVrVgb/hCrcc0p5+AXW/D2NiCqfRo+9deIUBg8Dz/bjdu9D7enHa/3AF7fQXT6eBBvaIGw41iJZqxYE1asEStajwhXgeef+F2P9nf4737Y38HlofeFwujHcN1g2+A1MBQi1NyM3dSEd3ALVlUVViqFnUwGy8nkmA9Oxrmlkl/BXwK3lZffBzwIzHhA4aWDHh52oiUY3c2yRvT02HsgT3dPiSvXpXBCzinDB4tYjMhdd+Ktu4DSzzeh+wcQyQTWgkZEMlF+JU/8TSTwjxzF3f4K3quv4b24HVFTg33RWrxdu9G9A4Tf9y4s5wD+8d34+/bj7Y9Qevw5Sk9txl7UjVWXRYQTiNrzYME69NGX8bd/HV79LqL1csSiq/HTIbznXsB7TQVPMraN1dKMs/5irEWtWAtb0YU03stP47+5B6+9D4oOUA/RFqy2NuxoHXY8B01VQxOjVUIsWAuAPrYDkWxCuy5++wG83XvQ/QPgenhukR7s4KIzeNM/6SY+/OI0qSwLbAthi2CWNEsgLBHM6GYPLgMWCAdEGIQoD6EqBNotgteHlaolvOwqnKZVWPE4ViyGFYvhZ7P0ffe79H3jG8SvuYbkbb9JZOUd5HZ8h9wr30b7HrHz75rcc5ohfjYIKKzEApych1u+eWTyQdDdUB+0KbBXX4h/8ElKO1sgpBF2AeyDCJEHuwiOj7B9iMWxalugfhGiYRlWzRKI1QEa0kfQ/QfRfQeg/wC6/wAU0xCrhzEaQvo9vXgv78DbtXuoVI54NVZdN6IxClkf3ZfBPxyGYgioBqoRtdUQiwVzgQdzdAf5yPfB1+h0F3hfG/oeUVWFaGzAvmw9VmM9oiEIHkQiPlqysJctRd/yFtytL1Lasg1/dwZRsw7R0oKVPoBIdiMSCUgmgr+lEsXv/hBRk8Rpe53cS1/CL2bw+trR+d6h41rxRkJNS7Hltdg1S3Gq2xCxsYe8nmradXGPHcPt6MDt6KDU0UHx9dfxM5lRS1hELIYVjSIiEUQ4HLxGWY5efDFO/fysRjQqCyhKSqljAEqpPinlJN8lzkzQZVRgJ4I2ASHbJj/sCf6ZrX3ccFkMx4Z4VXzMjGmftxT7vA9X9J32sjbsZW3oO27Be20X7vZXcJ98GiyLyPvfi71yObAGa/mt6Mt9QukjeDu3Unp+F95eGy0WE3rHO7Brgqc5vfpu6NmDt+8p3Je24z/0BjobgbCNs+ES7HXrEFUO5Dohcwyd3o5WD6N79mD5LtbaNqwl16HjEn3oGF77Afx9+ympNygBhMNYSxZjL12C1bYYq7UlKNIcg4g3oONNuC+9iH6uG2/vvqBUxBaIuFO+MYNrCxAgLB2MnepoREiD8IMbuAiWwQfLBzyE8NB4gDdyP4vy3+C9GL5u2HpE0HzltCwnGGCn/BdhDy2LcILoincRWnjFqH3rrWSSut/9XQYeeIDs5s0U9+2j+p57SF37J7i9+7Dm0aBGXuYoIpTCCiUIFTNki0EXwnDEIVfQ1EaC34lz8cXonn70QBqdyaKzOXRfFvKjNUBOE3QKex1sDxwQtge2D7YflB7E4ojEUkSyDmvRuhH5Uvsaf/ceSi9sw38jGLDOWthK6MbrsVetRDQvQB/djq/uQ8SbEY3nIxpWo0UK3XEU/3AHfscRtOsiLCv4wVjWiZcQiEQcq7ERUS51ENGJDzonUklCG6/DufZqvNdex33pFfSxTty97ZA/dWRRUZUi8uEP4m3/F4oHn8euWkio6cKhhpJ29RKsGRh/YzzCcQi1thJqHdkYV3sefiaDPzCA39+Pl07j9/fjp9PoQgFdLA79HVznF4voYhFKJaxYDOfqq2forIypVklA8Usp5beBZ4ErgBenNkmV8TJHsGL1Q0XQIdsmUyzi+z7ZvObN9iy/+yvVhKNhnNDkFseJcBjnorU4F63F7+uHYhGrceQsokJYkGrF2dCKfbmP+/wWSo8/SeHfv0TorTfiXLYeclncV47gvjCATtciqmPYMocV34vw2tHbf4L2hxXDOjFINiHarg+qSqoWndjW2Ixz8YUA+P0D+O378dv34+3bT+mxPcE+oRDWooUnAoyFCxHD/m10JoO3qw7/SBZRcwx75QIEbyAiXYhoHCwHLSxsOzzypm0Nu2mPdiMfaz9hD9tuj1h/4jNO0P5jxHEcRPmzI7/z7KczF45D1TveQfi88+j/wQ/o/vznqbr7bqLr5ldDVT97FCsRzG8Tsix8rUnnSjQ3OGTyJ7pWi0SC8NtuP+Xz2vMgm0Pn8+hcDnJ5dC6PzqbRfZ3o/uPodB+4oP1Q0Cap6KH7C1AcAAZgSzv+9T2ErrgsCM63bEP39EIigXP9NTiXXoJVPbJhoWi++JRqEgGw4jzsFedNxT/VmIRj46y7AGfdicai2nWDwCudgUwGnc1iLVuKVV1F6co/oj5eN+t6g02EsG3sqirsqipYOLHun9r3g0DPmLcqudP+AXAXIIHvKaXun9okVcZPd2AnWobeDzbMLHkeW5XLxsuC+RfiqdGLLifLyRe80QjLInTVFdjnr6L4kwcoPfAw7gtb0d094HlYy88jfNedWMvPQ1gC3X8Q/2AwB5tINAUNvJLNQW+NCm6YVlUKa90FUL7Q6XQmaI+xbz9e+35KTwTtBbBtrIWtWEuXYNXUUHr8SXQuj73kONaiPkSxF6rbsFZ/EqshmLG14BaoS4w9Bft8EV27llBrK73/9V/0ffvbFK+8ktQddyBGmU56LvIyRwk1BFVcTjnvHOjKkUxY6OLpLwvCtiGVRKQm3lhV+z7kchQf24T7i824v9gMgLVkMaG3bMQ+X85oL6mzIRwHUV0Fo10XLHtOBxNnywQT818lAUUtEAM6gFop5f9QSv3t1CZrfFr7eNkjOPUnJvYaDCiKnsfeI5q7b44QTUSxJtCGYKpZtTVEPvh+vJdeofTs8ziXXIiz4fJTSzeqFmGvec+kfa9IJoIp3MvTuOtsLggw2g/gt+/HfeoZ0BrRUE/k/XejX/0ncEJYa38zaDA6T3t8nI5dV0fdb/826UceIfvUU1hVVSRvvHGmk3XWtJtDF/uxE0HD28G8o60SIGhqnNogXFgWJBJE7roT97yl+B1HcS5aOzQjsGEYc1MlAcX3gV3AOiAPZKc0RRXw88fBd7GTJ0ooBsefONZbZMOFMVwPYonZN124ECIIJC65cObSEI/hnL8Kzl8FgM4X8I91YjU3IcIh9ILPgB2eULfW+Uo4Dqk77iB60UVYVfNjro/BHh5WeZbRwbyTiAn60h51zdPXYt8ZVpJmGMbcVtGVQyn1O1LK/wQ+BuX+deOQUlrAvwMXAQXgY0qp3cO2/ybw24ALfEYp9dOJJNrLHAEYUeUhhCBk25REiUVNISKJWNDq3zgtEY1gLznRHmMqhjqe60ITrC8+U1Odd6Dc5RqGSiiEENjCwtM+6cysaHNtGMYcVFF9gJQyCiQIBoSvpNL0nUBUKXUV8KfA54Ydq5mgXcY1wK3A30opJ9TU2ssGAUVGNNKfcYdenicIhQT9aY94MnqaoxjGrDRlecfzPPr6+sh2twOCtK4dyju5QtAVMGyZgMIwjDNTSQnFvwGfJBh74gDwdAWfuRZ4CEAp9ZyU8rJh264ANiulCkBBSrkbuBB4odJEv7n/GNFCis/825ER669fH+OmK+I4unTO1vsbc96U5Z2Xf/IFlnpB9u3M1/C3/3JwaNtbN8S5+qIoC2ZuUkzDMOa4SgKKqFLq7wCklN9TSvVX8JkqoG/Ye09K6Sil3FG2DRCMSjOmV199tUtK2X7qlk0j3j3zQ/i7ChJnGJPoIaXUbaffrWLTlHcAfj609MwPzzC1hnHmJjvvGDOskoDit4BvAVQYTAD0A6lh763yBXG0bSmgl3EopRrH224Y84jJO4ZhzEmVBBQRKeWLgAJ8AKXUPaf5zGbg7cB/SymvBF4Ztu2XwP9bbpcRAVYDOyaacMOYp0zeMQxjTqokoPiTMzjuj4CbpZTPEAxk9xtSyk8Bu5VS90spPw88RdAo9H8qpU4dr9Ywzk0m7xiGMScJPcZUulLKP1dKfaa83KKU6pjWlBmGYRiGMWeM1230pmHL35rqhBiGYRiGMXeNF1CIMZYNwzAMwzBGGK8NhR5jedY63SiDc0W5Eexg98C9wBeAfyYYHfERpdRfzVTaKiWl3AD8vVJqo5RyBfA1gt/RDuDjSilfSvkXwJ0E5/VJpdQvZyzBp3HS+awHfgK8Ud78v5VS351L53Myk3dmD5N3Zvf5GGMbL6C4dFjDsDXDlrVSarZOaD80ymC5hfznCGZKnTPKLfhRSm0ctu4l4G7gTeBnUsr1SqltM5PC05NS/t/AB4FMedU/AX+ulNokpfwP4K7y2Ag3ABuAxcAPgMtnIr2nM8r5rAf+SSk1fBTL9cyR8xmDyTuzgMk7s/t8jPGNF1DM3OxVZ268UQbniouAuJTyEYL/n78EIkqpPQBSyoeBtwCz9qII7AHeDXyj/P5S4Mny8oPALQTdkB9RSmlgv5TSkVI2KqU6pz21pzfa+Ugp5V0ET1qfJPjtzZXzGY3JO7ODyTuz+3yMcYzZhkIp1T7WazoTOEGjjjI4U4k5Q1ngswRzNfwO8FVGzvB62tERZ5pS6gdAadgqUb5YwIn0T3jUx5kyyvn8EvhjpdT1BE++f8EcOp8xmLwzC5i8A8zi8zHGV9HkYHPIeKMMzhW7gG8qpbRSahdBRqsbtv20oyPOQv6w5cH0T3jUx1nkR0qprYPLwCXM7fMBk3dmK5N3jDljvgUUm4E7AEYZZXCu+AjlGSallK1AHMhIKZdLKQXB09dTM5i+M/GilHJjefl2gvRvBm6VUlpSyiUEN7CumUrgBD0spbyivPwWYCtz+3zA5J3ZyuQdY86Ya0Wap3PKKIMznJ4z8RXga1LKpwladn+E4CnlW4BNUNf4/Aym70z8EfAlKWUYeA34vlLKk1I+BTxLENh+fCYTOEG/C/yrlLIIHAF+SynVP4fPB0zema1M3jHmjDFHyjQMwzAMw6jUfKvyMAzDMAxjBpiAwjAMwzCMs2YCCsMwDMMwzpoJKAzDMAzDOGsmoDAMwzAM46yZgMIwDMMwjLNmAgrDMAzDMM6aCSgMwzAMwzhrJqAwDMMwDOOsmYDCMAzDMIyzZgIKwzAMwzDOmgkoDMMwDMM4a/NtttF5S0qpgR2AN2z1FqXUx6SU+4BGoEkplR72mQ8DXwXeSzCV86fKm5YAOaCz/P73lVJPDfvc68CfKKXuK7+/FXgI+G2l1BfL664A7gdalFJmhjlj1pvmPLQReBBQBDOfCsAF/kop9ZNJPjXDmBVMQDG33KiU6hpjWxfwbuDeYes+BBwFUErdO7hNSvk1YIdS6rNjHOtB4EbgvvL7twE/Ae4CvlhedxPwgAkmjDlmuvIQwB6l1MWDb6SUFwGbpZTLlFKd43zOMOYkU+Uxf3wT+MDgGyllG5AEXj+DYz0IbBz2/m3Ap4GrpZSJ8rq3AD87o5Qaxuw0mXnoFEqp7UAWaJuM4xnGbGMCirnlCSnlS8NeC4Zt+xlwkZSypfz+g4x80pqIJ4HlUso6KeU6oEcptQt4HrhFShkBrgAePcPjG8ZMma48dAop5bsBH9g5Wcc0jNnEVHnMLeMV1xaB7wP3AJ8D3gfcALxnol+ilCpIKZ8ErgfWAD8tb/opcAvQQ1D33D/RYxvGDJuWPFS2XEr5Unk5BBwA7lJKZc/weIYxq5mAYn65F/gPKeWzgFJKdUspz/RYDxIEFFcCnyyv+xnw2wQN0Ux1hzEfTWYeGtGGwjDmO1PlMY8opZ4HYsDfAF87y8M9SFAa0Qa8UD7+3vK2d2ICCmMemuQ8ZBjnlAkFFFJKE4DMft8AJEE3zzOmlHqToJj24ZN6cjwIJJRS6myObxiz2KTkIcM41witx+/1J6V8L2ADEeAfgX84TVcpwzAMwzDOMZWUOHyaoDX/B4DFwNunNEWGYRiGYcw5lQQU+fLfAaVUAUhNYXoMwzAMw5iDKgko9gJbgP+UUv4FwVgEhmEYhmEYQ8ZsQyGlXK6U2lNeTiql0lLKZqXUkWlNoWEYhmEYs954AcWLwHGCuRt+qJRypzNhw330ox/VX/nKVwDYuHEjeB6bfvSjMffv13mK8eg0pS5w1213AXDfQ/edZs+5afD8Hnz0QaqiVTOcmsps3LgRgE2bNk3l14ipPPjZMnln9njX7e/Ctuyp/j1OiSnKS7M67xgTN+bAVkqpS6SU64GPAH8lpbwf+JJSave0pa6sp6dn5Arfh0hkzP2LvceJVFVPcapGskRQexRxxk7XXDZ4fkWvOMMpMSbC5J3Zw9c+NvZMJ2NW27p16wLHcb4MrMWMkzQb+cAO13U/dumllx47eeO4I2UqpbYB26SUYeBdwGellFGl1G1Tk1bDMAzjXOU4zpebm5tXNzY29liWZWYynmV83xednZ1rjhw58mXgHSdvrzQCbAT+D3tvHl9FeS/+v2c7a87JHhICYREY9lVRXMBdFCuKFDeqt732ftXq/XldilpvW7toW4vU61JbcSkWRHHBFdyxogJVEUFhANmTANmXc3KWmXl+f8xJSCAJYQkEnffrdV4z88wzM5+ZM8/MZz7P5/l8+gD5pFL5uri4uLi4HGaG5ubm1rrKRNdElmWRm5tbg2NB2oc2LRS6rgdwkuJcA2QCTwATDcOo7gxBO8pPfvITqKs7miK0yvRrpu+/0jHMsXh+P/nJT462CF0Kt+0cXa68+kpC3mNz1P0RbEuyq0x0bVL/T6vGiPacMncCrwKzDcNY0Xni7Z8pU6aIl156aU9BeXm7/cDl1SV487ofAcm+f8TNODnBnKMtRleiSzuWuW2n6+C2nX3Yp+2sWrVqy4gRI9rKBtvpLFmyJPTzn/+8b1FRUUwIgWVZ0lVXXbXrhz/8YdX+tz50nn/++czf/e53vRctWrSmsLAwCRCNRqUZM2b0Ki8v1wDS0tKs++67b+sbb7yRsWLFitAjjzyypXH7lStX+n/zm98Uvfzyy52aGmHVqlU5I0aM6L13eXtdHv0Mw/ivRmVC1/Uhuq737ywBXVxcXFxcjjYjR46se+GFF4wXX3zReOaZZ9Y/9dRT+StXrvQfiWO/9NJLOVOmTNk9Z86c3Mayf/7znznZ2dnJZ599dsOzzz67YcSIEfUzZ87sPnXq1KovvvgiXF9f3/Qenz9/fs6ll1561BSy9pwyx+m6/gRwHPCfwO1Ama7rsw3DmH1EpHNxcXFx+V7y3qe7sz/+ovKwmnROGZ1Vfta4vIqO1g+FQvall15a9vrrr2eOGjWq4e677+6xatWqNICJEydW/uxnP9t900039dY0TZSWlnoqKyu1e++9d8uYMWOizz77bNbcuXO7aZpm9+jRIz5z5syt8+fPz3rllVdybNvmxhtvLDnrrLOa+iA3bdrkqa2tVW+++eadkydPHnTrrbeWejweUVhYmFi4cGHOa6+9Vj9+/Pi66667brcQAk3TOOWUU6oXLlyYOX369IpYLCYtX748/Ve/+tUOwzC8M2bM6KMoilAURTzwwAObe/TokTyc17I12rNQ3A6caBhGEpgBnAdMwFEuXFxcXFxcvvPk5uaa1dXV6uuvv55eUlLifeWVV9a9+OKLxuLFi7O++uorP0D37t3jzz777IbLLrts97x583LKy8uVv/3tb93nzZtnvPzyy0YoFLKefPLJHHJOxcsAACAASURBVIC0tDTz5ZdfNporEwBz587NmTx5cnlGRoY1ZMiQyMKFCzMAJk2aVPPTn/609MUXX8w566yzhl9++eX62rVr/QBXXHFF+euvv54F8Nprr2WMGzeuJhAIiCVLloR1XY88++yz66+77rrSqqqqIzJeuT0LhTAMo1TX9b5AsjH+hK7rRy3AlYuLi4vL94OzxuVVHIg1obPYsWOHp1u3bsmNGzf6Ro0aVSfLMh6PRwwdOjSybt06H8CQIUOiAN27d0+sXLkybdOmTd5evXo1hMNhG2Ds2LF1S5cuDQ8fPjzSq1ev+N7HME2Tt99+Ozs/Pz/+4YcfZtTV1anz58/PmzZtWtUnn3wSPOOMM+ouueSSatM0mTdvXvYdd9zR+80331w7ZsyYaH19vbpjxw5t4cKFOXfeeed2gKuvvrr8wQcfzJ8+fXr/tLQ06/bbby8+EteqPQuFquu6CkwC3gLQdT0DCB4JwVxcXI4cZmUlDatXH20xXFy6FLW1tfLChQtzLrroosp+/frFvvzyyxBAIpGQVq9enXbcccfFASSppX9pnz59Elu3bvU3+jesWLEi1KtXrxg0jZJowaJFi9J1XY8sWLBg/bx58za89tpra6uqqrRVq1b5X3311ayHHnqoG4CqqgwZMqTB4/E07WPy5Mnls2fPzovFYvLQoUNj4Fgrxo4dW//888+vP++886oeffTR/E66RC1oz0LxD2AtoABn67o+FJgL/N+REMzFxeXIIISg5pVXMEtLka+96miL4+JyVPnyyy9DU6dO1WVZFpZlSddff33JwIED4wMHDowvX748NHny5IHJZFI6++yzq8aMGRN9+umn99lHbm6ued1115VcccUVuiRJokePHvH//d//3bFgwYKs1o75wgsv5E6dOrWsedlFF11U9vTTT+fdc8892++6666iiRMnDvb5fLbf77fuvffeLY31pk2bVnH66acPv+WWW7Y3lo0ePTp666239nn44YeFJEncdddd2zkCtDlsFEDX9TBQbxiGret6AU5gq7WGYcTa3KgTcIe+dR3coW/7cMwPG21Ys4baV18FQJ56Ib6hw4+kiN8b3LazD11u2KhLxziYYaMYhlFrGIadWvQD04Gth1+8A8PqgsF5XFyORUQySf0HHyCHnIBLoqrmKEvk4uJyrLLf0Nu6rl+g6/qbwEqgHBjZ6VK1Q+TDD6l+5RVEwk1S5eJyqESWLcOuqyP9Bz8ASQJXoXBxcTlI2gu9fSvwH8AqYCYgG4Zx3xGSq03UggLMxYupef110i+5ZB9nGBcXl45h1dURWbYM78CBeHr3RsnMxKo6qpH1XVxcjmHas1DcBrwD/NEwjPdw0pYedbwDBuAfOZL4unVEPv74aIvj4nLMUr9kCdg2aWecAYCakwPVroXCxcXl4GhPoegNfAY8qOv6MqCnruvpR0Sq/RA44QR8Q4cS+de/iK1bd7TFcXE55kiWlhJbvZrACSegZmYCoGRnQ00twrKOsnQuLi7HIm0qFIZhxA3DmGcYxpk4zphvAKt0XX/hiEnXBmo4TPiCC9C6d6fmtddI7nIzqru4dBQhBHXvvosUCBA85ZSmcjU7G2yBcLs9XFxcDoL9OmUCGIax0TCMO3DyesztXJE6hqSqpE+diuzzUb1gAXYkcrRFcnE5JogbBsnt20kbPx652RBSNccZ0miXuaP2XL6fLFmyJPRf//VffZuX/frXvy6cM2dOdlvb3HTTTb0XL14c7nzp2mfixImDb7/99qLmZYsWLQpPmzZtwOWXX95/2rRpA5599tksy7IYP378sI0bN7YYP37NNdcc9+6774YORYYOKRSNGIZhGYbx8qEc8HCipKWRMXUqdjRK9YsvuqZaF5f9IEyT+vffR83NxT+y5YAtJdt5Zoryox7t2MXF5QBYunRpsG/fvg1ffPFFqLa2tum9/vvf/77X3/72t2/nz5+/Yfbs2Rv/+te/FpaXl6sXXnhh+YIFC5qUpJ07d6rbt2/3nXnmmYcUk6G9SJnHBFpBAekXXkjNwoXULl5M+IILjrZILi5dlujKlVjV1WRccQWS3PJ7QvZ6IRjAdhUKly5A7Nu3sxNb/3VYI4F5eo0v9x137kHd4EuWLAk9/vjj+aqq2jt37vSeffbZlbfffvvOxvXLli0L/v73v+/5yCOPfHv//fcXdjQD6cUXXzzwqaee2pCZmWmNHTt25JNPPrlu9OjRDRdccMGgBx54YNOMGTP65OXlJYqLi72DBw+O/PnPf962t2zPPfdc7sSJE6vy8/MT8+bNy77uuuvKADIzM82//e1veRdeeGHV4MGDY2+//fYan88nrrzyyvKrr75av/POO0sA5s+fnz1p0qQKWZb57W9/W/jZZ5+FbNvmvPPOq7zxxht3d/QatWmh0HV9cWr6qwO56EcD3+DBBE8+mdiqVTR89tnRFsfFpUti19cTWbYMT79+ePv0ab1SZrproXBx2YvG8AS7du3yzJ49+9sXX3xx3bx585ryY3z++edpf/jDH3rOnj17Y1FRURI6noF0/Pjx1e+++274448/TuvWrVv8ww8/DH/99de+Hj16xL1eryguLvbNnDlzy6uvvrp22bJl6aWlpS0MATU1NfLq1avTzj///Oorrrii/MUXX8xrXPf3v/99Q0NDg3zrrbf2HTdu3IhZs2YV2LZNjx49kj179owtXbo0CLB48eLsq666qrxxftasWZsWLFhg+Hy+Axrd2Z6FIqzr+gLgNF3X9eYrDMO48kAOciQITpiAWVZG3XvvIRdddrTFcXHpckQ++giRTBI688w260gZ6djrNyGEcGO8uBxVfMedW3Gw1oSDPqbPZyeTyRY3fjQaVRpfrH379m3QNA1N0+zmCbqWL18ebmhoUDRNayrraAbSa665ZvfDDz9ckJ+fn7jpppuK586dm2fbtnTuuedWARQUFMQat8nKykrGYrEWhoDnnnsu27Zt6ZprrukPUFlZqb333nuhUaNGRbdu3eq95557ioHiHTt2aDfccMNxb7zxRuQHP/hBzdSpU8teeumlHEVR6NGjRyw/P98E+OMf//jtH/7whx6VlZXaySeffEDjyNtTKM4HhgH9gMc4gJwFuq7LwKPACCAOXNuY/jy1/v+AU4DG/prJhmEc0gB4SZLwH3888Q0bYHcF9GzjC8zFpQvTmW1H69mT4NixTc6XrZKZDokEoq4eKXxI/lkuLsccgwYNati4cWOgpKRE6969e7KhoUFatWpV2k9/+tNdxcXFnra2u+GGG0pKS0s9d9xxR6+///3vm6D9DKRpaWl2YwbSYcOGxUpLS72VlZXaL3/5y+LZs2cX/Otf/8r45z//ub68vFzdn2K/cOHCnEcffXRDY6bRVLdK3uDBg7fedtttfefNm7euqKgoWVBQkMzKykp6vV4BcP7559c8/PDDhbIsi8suu6wcIBaLSW+++WbWY489tkkIwbnnnjtkypQplb179+5QaOo2FYrUQ2qprutjgXOAIcB6wzBe6cB+LwZ8hmGM03X9JJxIm5ObrR8NnGcYxmF1J9fyHQuU2O16qbscs3Ra2/ENHUrSbt+CKWVmIABRXg6uQuHyPSM9Pd2+5ZZbtl977bX9vF6vbZqmfNlll+3u379/vD2FAuDHP/5x+TvvvJM5f/78VjOKtpWBFGDUqFF1JSUlXkVRGDVqVN2mTZt8oVDILi9vv5l/8cUXfoBGZQLg4osvrpo1a1bPZDIpzZgxY9sNN9zQT1EUYdu2dMopp1Sfe+65teCkQj/ttNOq33///cw//elP2wB8Pp8Ih8Pm+eefPzgUClljx46tLSoq6nCei3azjQLoun4fMAD4CBgPbDYM49b9bPMAsMIwjPmp5WLDMApT8zJQCnwMdAOeMAzjyfb2dyDZRssfewwrPY3ANdPbPS+Xg8PNmLgPh7VfoLPbjrV5M0o7Foqy4o3Y/3ge7fxz0U48/pDPx2UPbtvZBzfb6DHKQWUbTTHeMIxLDcP4C3ApcGoHtgkDzc2wlq7rjdaQIPAQTrCsicANuq4ftnzJWkEBuBYKl2OXTm07Smg/VoeAH7xe1zHTxcXlgOmIQqGlvozA0SjbN2k41ALNn1yyYRhmaj4KPGgYRtQwjDrgfZz+4sOCVlAAkSh2rZvi3OWYpFPbTjKZbHe9JEnIOdnY+zG1uri4uOxNRxSK54CPdV2fBSwF5ndgm4+BCwBS/cCrm60bgOOboei6ruFYPL44IKnbwIrsxBKbAbBLSg/HLl1cjjSd1nYqKyup2I9CASDlZLsWChcXlwNmv4GtDMOYqev6W8BAnD7bNR3Y78vAObquf4Jj1fixruu3ABsNw3hV1/W5wDIgCcwxDOPrgz+FPcS3fUCy6kOQemEXl8DAAYdjty4uR5JObTtxIfY7JFTOzcFatRoRiyH5fAd/Ji4uLt8rOhQpM6VEdESRaKxvA9ftVbyu2fo/AX/q6P46ilm1AUkWEPZgF7sWCpdjj85sOz6fj7q6OuKmiU/T2qwn5TgRee3ySpQe3Q/mUC4uLt9DjvnQ243YyQhWfTEAcpqJXVLqBudxcWmGNzUyan8KhZzTmNOjHNpRKFprX4mkzeer6/j4sxqSSUFmukpGukpmWGs2r5KVoaEobtt0cfku8Z1RKMyqDQDIgW5InkqIpSMqq5CyWx0SDEAykSRaG8UX8OENtD4M9Vihcfivq0C5tIWiKGiSRMw0SW+nnpSZCbKMXbbHj0IIgWVamEkTK5mamhayIqN5NBKWzIqvIixZUUskatE9z0NOlkZVrcnWkjiRaMvEfekhlYvOzmbMsBCy/N25Z4UtsCwL27KdqWljWzYevwfa1uFcughLliwJ/fznP+9bVFQUkyRJxONx+fzzz6+8/vrr28xn8cQTT+T86Ec/qvj66699b731VsYdd9xxQObxDz/8MC0jI8MaMWJEw97rnn/++czf/e53vRctWrSmsLAwCRCNRqUZM2b0Ki8v1wDS0tKs++67b+sbb7yRsWLFitAjjzyypXH7lStX+n/zm98Uvfzyy8aByHSw7Feh0HX9HOAWoOmNaxhG27F7jxJm1QaQVXy9z8Usnw+kYxeXILeiUAghiEfjROuiAERqI0iyhMfXbtySLkHjg922bCzTcn6pB5cQAtWjompq01SWDyihrMt3HK+iEDHNdq13kiIjZWdhV1UTrYtiJk3MpNk0vkuSJFSPiubVqK9LEI/H0FSJEwbKDOgRxuPTyMn2oXm1pvsvkbSprjWprjGpqE7y8Wc1PPPyLj5cXs2Uibn0LfJ3+ByqapJYFkjSnp8sSUiyMy8ECBtsIRACbNtpN7YA2xakh1QCfuWAr50QAmELbMvGtu0mxaH5b++4PpIkgQTJmiSedB81dcm91rd9vL3/n32qNivYez+Ni0IIEMIJViYEQtipabN5nPNqXKaNeiDh9/vw+/34fL7v7MfLyJEj6xqjXcZiMemcc84Zevnll1dkZma2ms766aefLrjyyisrRo0a1TBq1Kh9lIL98cILL+RMmjSpsjWF4qWXXsqZMmXK7jlz5uQ2JvL65z//mZOdnZ186KGHtgA8/PDDeTNnzux+9913Fz/22GOFjZE4AebPn59z6aWXHrEhWx2xUMwCbga2d7Ish4RZtR41vS9azhAkfxIU2fGjGD60RT1hCyK1ERKxBJpXIxgOUldVR311PaGsEJrn6H9GCCGaHlCNCoNlpr529op0KMkSiqo4ypAEZsIkFolBxFmvqEoLJUNRDvxB2ohpmvuv5NKl8SoK9aZJ0rLwqG03fzknm3gwhBmJURsVVNfZVNYKyqpsaiI2pimorTcp2ZXA55U55+Qwowb7yckSJJNJIjXODdj8/svJ0MjLdpT2E0eG+eyrOl57r5y/PLmD0UPTuOTcXNLDrcuUSNh8vsbpStlWEj+kayBJUNTdx8DjAgw8zk/PbhpCtFQKGpWHvef33Zlj+ZFl2VHgFRlFVZAV2fnJMrZlU11ew/bNER5bUExbsQQVGTyatNcPPOreZc5PU1Prm5e3UvdAiCcEiaQgYTrTZDK1nBR4NInehQnq6uoQAoLBAGlpaZ2mXEQ//TS74YsvDmskMP/o0eWBceM6PISprq5OkWVZqKoqlixZkvboo492B4jFYvL999+/+dNPPw1VVVVp119/fd//+I//2D1v3rzcv//975teeOGFzGeeeaabLMtixIgR9b/+9a+L77vvvu7FxcWeqqoqbdeuXZ7bb799e3Z2trlixYr09evXBwYNGrSxV69eTVEpN23a5KmtrVVvvvnmnZMnTx506623lno8HlFYWJhYuHBhzmuvvVY/fvz4uuuuu263EAJN0zjllFOqFy5cmDl9+vSKWCwmLV++PP1Xv/rVDsMwvDNmzOijKIpQFEU88MADm3v06LH/IV8HSEcUim2GYbx7uA98KJimidosUqYwG7Bqt+PrewGyLxP82UhhyRnp0QzLtKivrscyLfxpfnxBpyGEMkPUVtZSX1VPODuMoh78S7c9Gh9ILR5UQmCb9h6lIaVEtCD10FI9atPDSlEVFEVBasVcLGzR9FWZTCSJN8SJR52HsCzLLRUMVenww8CyLOicS+NyhPClLAYx02xXoZBysrE1H3URi78uqEVTnReYqkqoqoymSqQFFC49P5cTR4bxefdYwoRI3X8J59fi/kt1kWhejRNGhBgxOI33Pq7i3aVVfL0+ygVnZHLS8ABmPIlAkEjY1NVbRBoswhpMnuAn4EvDEhLxpEQsCbEEJEywU5YJSQJZllLWC4FHkdA00BSBpkAy6bSzoF8Q9saor47tkR0JRZGQJAlJlpBl2ZlvXE4pCY0KQ+O69pAVmfXbLAb0VLj16hyUlGyyJJBlR0ZZat9asTd2k+VFapq3U/PCljBtSMQk7Cgt19s01bEFWM23E87+WkWFqqjJF+9HsK0E/Ys0hvYTRKNRFEUhGAySlpaG1o5vjhBOd5CidPyZczT48ssvQ1OnTtVlWRaKoogZM2ZsD4VC9vr16/2zZs3aXFhYmPzzn/+c/+qrr2befvvtO5944omCv/71r5uWL1+eBlBRUaE89thj3V955ZW1wWDQvuGGG/q88847YQBN08TcuXM3vPPOO+Gnnnqq27x58zaMHTu2ZtKkSZXNlQmAuXPn5kyePLk8IyPDGjJkSGThwoUZ06ZNq5o0aVJNIpGQXnzxxZx77rmnT+/evRt++ctfbhs+fHjDFVdcUX7//fcXTp8+veK1117LGDduXE0gEBBLliwJ67oe+e1vf7tj6dKlaVVVVcrRUih267r+GLCSlNHTMIy/H25BDgTLsloIblZ/CwjUzP4AiFBvJO9W7J0SwrKQFIVELNH01RTKDKF599z4siI3KRV1lXWEs8PISse7CmzLJhFPNJmF9/m6Sf32FxKsUVHQPBqy2lJpOJAGKMkSmtd5aPvx7+n/TuxRMhKx1L0rgaqpTceRZbnVaVd+ALh0HFWWUWSZeDIJ7QwJlXNzEJqf6so4f7zjuAM6hiRJjtKQsvY13n/JRBIzYZKIJYg3xFFUBX+an/NPz+KEESGWfFxBbtCkoS5KPAnVdRbRBhtbQFpAITNdJRhwNNpG5XvPQR1riKIqINjTDdFK7hJJklFUDYFERY3N1pIEX29sYPOOOEkT+vf2c/KYdIYPCqKph95luK0kxvw3q7jqwnQG9Qk4wcOaKSqN821N2yo7WiRNm6+MWp5dXEqa32LsMD89u1nU1tbi9XoJBoNIkoRpmnv9LCQJbPz06ZW33+MExo2rOBBrwuGieZdHc/Lz85O//OUvewYCAbusrEwbMWJEpLXtN27c6K2pqVEbs39Go1F569atHoBBgwZFAXr06JFIJBJt3lymafL2229n5+fnxz/88MOMuro6df78+XnTpk2r+uSTT4JnnHFG3SWXXFJtmibz5s3LvuOOO3q/+eaba8eMGROtr69Xd+zYoS1cuDDnzjvv3A5w9dVXlz/44IP506dP75+WlmbdfvvtxYfjWu1NRxSKzalpfru1jhB1dXXELIvcZmVm1QaQZNT0vk5BuA9SYC2YQezdZcTTwsQiMRRVIS0zrVWzv6Iqe5SKqjpCWaE2/Q+aHpBx58XcaFFofPG29nWDRIt1TfOS1OJrpzOQJMmxSGhqk/y2bTd9QZpJk4SZQNhtazxNsklQXF3ctNzRaeP8kV6vKMohdfN8F/GpKg3JZPujoHJyELIHeXfZIR+vxf0XdO6/RCxBQ30D9dX1KJqCX1E46wQvpg2LPomy/KsGcjI1Tjk+nRNHhkkL7vsfNrZDK2lhmmZTm2x88aoedU/XgyI3dU00t+qlZ0PfvnDGqVBWmWDlmno++aKGf7y4k2BA4cSRIU4ek97UVdNRLEvw7bYGvlpbzxdr6kkLKvQbEKIgZ/8v0q6OpsqMGZLBqEHpfP51NQvfLaWuvpbTRqcxerBMPF7ZVNe0JGrqbXZVJKmssaiptxiqB+jT6yiewEHy29/+ttd77723OhwO2zfeeGPv5o7wtm033VR9+vRJ5ObmJubNm7fe4/GIOXPmZA8bNiy6ePFiT2vtbe/tARYtWpSu63pk9uzZTYrNGWecMXTVqlX+V199Neu9996z/vd//7dEVVWGDBnS0DyV+uTJk8tnz56dF4vF5MakYa+99lrG2LFj6++4447S5557LuvRRx/Nb/TBOJx0JLDVPbquT8LJNmp0MNtop2FZFlEhMG0bNfXCT1ZtQAn3QlKdbhAR7o0cSGCqKvVxC0uK4fV7CYQD7b60VU0llBGirqqOuuItBEQ5otvIFv4MeztfKZrzleXxeTpVKTis2AJpdxnyjmLUHSXIjVFFNQ0CfkQg6Ex9fvD5EF4vwuPBlmWQJRTVA5KEkOUmrzi7uYecJDnGmCYlRGoyzjQvF9DkMNZZyLJMz549O/EIxx5eVSWSSGDaNlobyla9JwhmkmCk+rAfX5IkvH4vHp+HeEOcWH2MRDKBL+jDH/Qz9cIMJoxL0r2bp90RIM0VFS+HPkorN8vDueOzOPvUTIxNUT75vIYln1bz/ifV9O/tZ+zIMOkhBU2VHR8GTW7myyCDBOs3R/lqbYQ1Rj2RBhtNlRh4XIDzxmcR8B8Dz4YDQJYlThiWyZghGSxfVcUr75Xy5tI6hvYL0BC32FIcx7IhN9PD0AFhhvYPM+i4EH7fsangn3vuuRVTpkwZlJaWZmVlZSXLyso0gOHDh9ddc801/X/2s5+VAOTl5ZnTp0/fNW3aNN2yLAoKChKXXnpp1eLFi1vd7/Dhw+sffPDBwt69e8cHDx4cA3jhhRdyp06d2kKbv+iii8qefvrpvHvuuWf7XXfdVTRx4sTBPp/P9vv91r333rulsd60adMqTj/99OG33HJLk9/j6NGjo7feemufhx9+WEiSxF133dUpPpEdzTbaHyfs9nhgk2EYt3WGMG3RPGNiMpmkpKSEzECAsM+HsBJUv38z3l5nExgwBYDy6hLkbx6noWwY5mmnEwgH8AU6FvFPCEFsw4c0hIfvvQLJTCJHo0jV1cixGD79ONT8bof1XDsDUVePtaMYe0cJ9o5iJyx5YwjmYAC5sDuSqiKSSac8aTbNi9QyiQRtepMdLjkBZBkUBTQNKTVFVUFRkFQVVBVJURCqCrLsyK2qSLLcVA9ZdurIMlowSMbJJ3em2F36LdFapt6kqlJSU0NWIEBor26P8uoSvHnd2by5hnS/hfhgKdlXXNSpMjZ2B7bmD3S0qakzWb6ylk++qKGyumNOyT6vzNABQYYPCjKoXxCvx/nw+a5nG7UswScrK3nv091khDWG9g8zdECYbtnetj603GyjxyhtZRvtSJfHeMMwTgHQdf1BnLC/Rw1N09BkmUg8Ttjnw6zeBMJCS/lPNCLn6AgtBxJxvP7MDu9fbHgd5cu38IYqsS0v7NqNXFUHkQiSEODzpZInVZD4YAnW8KFoZ4xHzszo2P7jCURdnfOrrUfU1zsvQE0Dj+a8SFPzkubZU9Y43Y/5XpgW9s5d2Dt2NCkQojqVvFKWkQvyUUePQO5RiNyjECkjvcNWlWhFOXLQT7YvEywLYVmOt1ezeWFZznJj+V7r262713J7+xGW5Sg/8Xibx8SysLOzoHMVimMOVZaRJYm4aTZlIRNmA1a0jMZHgpkwScbrCdd0/rO9sUuwERGNYm0vRhnQ74ha/ERdPdbGTYhIJPWL4q2PcFokwqlmFOFJkMzKoyG3O5GsAurS84lLatNoCNMUFBV66d87gKp2PeWos1EUidOOz+a047OPtiguR4mOKBSarutyKiRwR7ONdipBRaE6mSRpWamAVhJqRr8WdaRsHVvzI+3cRUNlFb4e3ZAyM5HacLYUSRPzXy9hrlyDqO+OpGxAK+qBlOEDdQNSzwTK8Zci9z0JSZIQDQ0kly7DXP5vrDXfoJ4wGvWEMYh4HFGbUhjq6vfM1zrLxA9tyBuyDB5PqwoI8QR26U7nZQpI4TByj+7IY49H7lmInJ+PpO35y4UQEK9BNFSAJwyBbCSpbSc02SMhqSqy9/AHARNCgJVAmLGmH2ZDi2WRbEBYcUSyAcwkwow3W9+Q2qbZ9raJHPjufhEeLJIk4VNVYs2GAUeNBSRKlsHIWxBCEPBCQ0mEUEUFwhZHzHogkknic5/HLi5BPfF4tInndLpSIZIm5qfLSX70yR7LnaYhBYNIwQByejoUdkeSZZSSUjxr/k26ECBJSN3yUIp6Ihf1QC7qiRwOdqqsxzJCCOzqauRQyLE2unzn6Mi/2phtdBlwYmr5qBJUVaqTSaKJBHLVBpRQTyRtr8A4Wf0RxJA3fAGffUYMHLN5dhZybg5STrYzTQ9jrVuP+fnnEEsiBQKo50xAGzUCKRAAQDSch/X53xBrn8aOFyMPvATJ78dzzhmoY8eQ/HAp5orPMZd/1lIGSUIK6VByzgAAIABJREFUpSGFQs6x+vZxlsMhpFAIQmlIXhmQQEiQdL66RSLV3ZBI7Ol2aCpLQjKBSHVDNK2LxUFVUE88vsn6IIdDTUoDkd2I0mXYkTJnProbImVgNVNwZBWCeUjBfEjrhpSWj5SWD8FuSJofKVkL/jTnmggbWnmht3ipJxtaURDiLeo2r4/Y1yO/VWQVSfUhqT5Q/al5P7Iv05FT8Tr3g+pDCbv+E63h1TSiySSmZSHbMRKlK0BYSFVrMXP74PdJ1MdtSCaxvvgSZczIzn+xC0Hi1TedgHT9+zntybTQJk3sFIVGCIH1zTqS77yPqK5BGTgA7fTxSFkZSJ62nTBFPIG9oxhr23bsbTswV66CFU7bl7sXoIwYijp0MFLwu6lcCNsmuWULKApKONymgiASCZI7dpDcto3Etm0kt21DRCIExo8ndP75R15wl06ns7KNdiqqLDuOZfE4/ppNeHuM36eOJXlBSlCt7WJ59wtoKCmjlxphiNpAqKQU8fXaPZUlCSk9gjIkHfX8/w9ZbfkwkfxZKONuw/7mBcSmd7CqN6OM/AlSIBsrmMbXfU/jm929CJSXkJ6fTn7fbHoPzCGQEwJsiFYgomUQLUdESyBahthe7kybv9AVD6g+ULzOVPUiKT7H2TToBSVVpvpACYCaBaoXlFSZsBCRMkRkLWL9h5itKQ2SDIFcpGAeZOtIwVzw5ziWivpdENmJqCuGXV86SkMj3nQUSUYIiyoz3nKf+0PxNCkAUqMC4AkhB3JbKACOguDbt25jeaqOJLtfN4eKN/UCiJsmSuknYCeRtCBS5TeUVZyBTwIzpwC5Ty8Sry9C2bIVz4UTOzX7qLn0U6zVX6OdOQH1tJNJvvch5tJPEKaJZ/Ikx0/mMGGX7iKx+B3srduQ8nLxXn0lSt/eHdpW8npQjuuDclwfAIRlYe/cjb15C+aab0gueofkW+8h9+uLOnwoit7fsSJ+BxC2Te1LLxH7/PMW5ZLfjxwOo4RCSIEAVnk55s6dTjcnoOTm4tV1tKIifMOHt7Zrl+8AbT6ZdV2/1jCM2SmnzMZujtG6rmMYxl1HRry2CXg8VEWjeDyZqFn7pik3E445N9/+iqnnFFJS4eGTryS+rEwnO2cAZ5zfje4BG1GyCVH6MlJmLsq4G5HU1r9MJEVDGXYFdtZx2F89g/X+XcQJUhLJoi6SRaaci79fmHjNeqq+rUbeUU23YA0hpQ6peS+RrEEgBymQA9kDkAJZTuwKM+a8pM0YNL6wzRgiUQ8N5U5Z49f8/nqdWigNA5xpIM+Z+rOQ5P17WQvbhEgZIrIL6nci6nciYtUIXyY+X/qel3uTEuBvpgikfprfUXY6cDyXI4tHUZCAWDKJuv1D1Ix+qJn9aNj8FpHaCFqaSn5hCO+QKzCXfkryg38R21GCZ+pklB6Fh3RsZ3iyaKEgmOvWk3xvCcrQwainnezEsjhrApKmkvzgXyRME8+Ui/brQ9TucS0bUV2N+ckyzM+/BL8fbdJE1NEj2+wK7QiSoqAUFqAUFqCdOg57127Mr9ZgfbWGxPqN4PWinToOcdLogz5GV0AIQf0bbxD7/HMCEybg6d0bu64Oq7YWu66u6WdVVKBkZRGcMAGtqAitZ0/k76i1xqUl7X3qNQ4rWbdX+VH3oQAIejxURSIks0bs4z8BUF+fIFoXJ82KIL5+jgLg0jygcRj4+j0nIoXyUE787327TfZCCMG66BBW7L6WUPRr8v2V9Mms5qTQehR7lVPJB5YaptbOoLi+iG3VISriGTRIWWQX5tO3XzcG9ks7qFwCjTJgJ/coGI2Khxl3LC2B3A4rDe0hySqECpBCBU1lZvVmRFp3At9hT/XvC5Ik4dU0YvEIgYYy/P0mIwfziW1ejI8IZVVpDOquOS/28acg9+5F4sVXiD/5jGNBOPmkDndDiIYYdrHjIGxtL3Yi2CYSSFmZyNlZSJkZmF+sQu5e4FgimsUS0SacCqpK8p33SVgWnqkXt9v/LkwTUVWNXVmFqKxCVFWl5qsR1dXOF7Mso550AtqEU5H8Hc8h0lHkbnl4zjkTcdbp2Ju3Yq74jOR7S5BKS7Gn/NAZidQaB9mldFBdUQexTWTJEqKffELglFNIO++8Y2OIvMsRpc2WaRjGW6nZEwzDuLGxXNf1OcCczhZsfyiyjBrbiZkzGknbV/u1TIstOyF40kyyQyYko04ffjJKsiHCunUVbNlciSqZxHNPZFipSr9erQf6sW3Bl9/U887SKop3xskMh5lw0vmMGOEE3HH8FGohGQF/NqrqJRfIBXrXm6z9NsraDRE+Wx/lX6t2I0m76d3Dx+B+QQb1C5CVoeH3yR1K5yxJktM1onjAG9pTfigX0+V7iU9VqU5qCH83tG6jQFKwvVn4gwF27mz5olKKeuC77j9JvPYmyXc/wNq0Be8lP0AKpXxqTNNxQq6vd6Z19anRRsWIsj0jRaS8XNQhA8HnR1RWYldUIjZuQgqH8Vw+tdWuAe2Uk0BVSC56h/j8F/FeNgVJ05wItNU12NuLneHQO4qxd+5qMrMD4PU6ikt+HtLggchZGci9ilpNGni4kWQZ5bg+yH17O1ae95ZQ9s3vOv24nYlvzBjSLrjgO61MzJw5M3/FihVhWZYFwK233lp8/PHHR6dOnar/9re/3Tpo0KDY/vZxoDz++OO55eXlWmMCsOY88MAD+c8//3zeBx98sNrv9wuA3bt3q3feeWevaDQqA+Tn5yd+//vfb5s1a1YBwC9+8Yum/SxcuDDj7bffznz00Uc3773vw017XR4/A+4GsnRdn5IqloGvO1uojiBsC3X3CmK9JpOwrKY+YXBi1XtUqKq1yc5OJa5JvXwlnLSpI4qgqCbJOx9V8dnqOpasKiYnU+PEUWHGjgiRma5hmoJ/f1XLu0urKKtMkpetceXkPI4fFm4xLEySJPClO7+9CKWpjB0RZuyIMLYt2Foc45sNUdZujPDGBxW88cGeyLIeTcLnlfH7ZHxeBb/Pmfd7ZXw+Gb9P2TPvTS375GbbyIc9FXQkarHu2yjGpii1tRKSUoZPq0WWncA2iiw1zcuS5ExlJ/ujojjXpkWdxm2kPcst9tVeueIcQ5GdzJJKY72mbfY9RsCvNsUBcGmJZkUAGbno3Ca/lHj2WGTVg0fZ10dG8vvw/PASrM+/JLH4HRoefRwpLeiMXoq18oz1+5F7dEcdOthxEi7sjuTbd4RQa10g+8h64glIqkritUXE/zEPKRjE2lEMkVT0Y01DLixAPflE5LxcR4nIzISA/6i//CRJQjvtZKzueQQq6g98Bwcb/+VgtmtnGykYxD9mTLv/kxCCZMm/UTP7HpMjrL7++mvfRx99lPHSSy+tk2WZlStX+u+4444+b7311jdHS6a33nor68wzz6xasGBB1tVXX10B8PDDD+efdNJJtT/96U/LAO66666eTzzxRO706dPLr7nmmgF33nlnSWOk55deeinnP//zP3ceCVnbs1A8Ajyi6/pdhmHceySEORCsuu1olV8SK7qIaCLRQqGwUgluVI/a7sMkM11j2oV5XHxuDl+urWfZylreeL+CNz+oYECfALvKE1TXmvQo8PLjH+YzYlDaIb2wZVmiT08/fXr6mXRmNrX1Jhu3NFBXb9EQt4jFbBriNg2xxp9FZXWSWKosabb/gFBk55yyM1WyMzWyMzRnmpoPBvYfydOyBFt2xFj3raP0bC+JI4CATyYrU8OyTWQEti2cREO2SCUZaixrXi6c5EO26OyYWG2SGdZ44M5hR+fgXRxR8hFknYyVObiprEIdSBjoJn8LFOyzjSRJqMePQi7qQfL9D52y3kVIaWnOCKZmU9KCHXuZx6sRkd1IOXq71dQxo0BVSby+GCmU5nz99yxE6dEDKS/3kPwgjgRSr54EBx97L9mOIoSgYdUc4t++BYoX/+CpePtNPOju19ra2uxIJHJYL1gwGCwPh8Nt5gfJy8szd+/e7ZkzZ07O2WefXTNq1KiGV155pcmD/y9/+UtBVVWV1tDQIP/lL3/Z1KtXr8Rtt93Wa9euXZ7a2lp13LhxNXfffXfJTTfd1Lumpkatra1Vn3zyyQ0PPfRQ/sqVK0NCCH70ox/tmjp1atVHH32Udt999/UMh8OmLMsMGzZsn9wgS5YsCXXv3j1+9dVX777tttv6NioUhYWF8XfffTezT58+sZNPPrn+nnvu2S5JEqqqUlhYGP/oo4/SJkyYUF9aWqru3LnTM2HChPqlS5cG//SnP/VUVVWEQiHroYce2hQOhzs4tK5jdMRdfo2u678xDOOXuq4vBh4wDOPtwynEwWBWbUCyYvhUiUgiQYZ/z5dIXUxG8whyczoWL8HjkZusCGWVCVZ8Wcfnq+vIydS44qI8Bh7XfsjugyWcpjJ6aGj/FVOYpthH8XDmLRpiNvURi4rqJBVVSb5aG6E+2jJrqdcjk52pkpOpkZVSNnIyNcJpCttK4qzdGGH95gZicRtJgt49fEw8PYtB/YIUdfciy9JBR/tz8ofsq2g0KiBWKpmaZYlUdkRnXYt6HShv3JedOl5OxoHlYPi+IKwkyeKPUNMGkdD2KA71ZBAy42RF/g2c2ub2cl4u3sunHrociXqsT/8M0QoY8/+QC0a1W1/Ri/AGhzuOzooH5ChYmxG7diBSXYFSqDuSt+PtyuXQEcKm4cuniW96F2/fs7GjFTSsnkti21ICo/8TNWtfP7euSG5urvnQQw9t/Mc//pH3+OOPF3i9XvvGG28snjJlSjXAhAkTaq688srK++67r/urr76aefHFF1eNGDEi8uMf/3hrQ0ODdNpppw2/++67SwBOOOGE2ptuumn3okWLwsXFxd6FCxeua2hokC655JJBZ511Vu0f//jHnrNmzdqk63r8tttuK2pNnueffz5n6tSp5QMHDoxrmmYvW7YseNJJJ0WuvfbaMp/PZz/xxBP5P//5z4NDhw6t/93vfre1qKgoOXXq1LKFCxdmT5gwoX7+/PnZP/jBD8oB3n777cwzzjij6r//+793vf766xlVVVVqOBxOtHbcg6UjCsWvgYmp+cuARUCXUCjkQB5pviDlkQhx08SX6n+NmxKVdRb9eocPeL+5WR4mnZnNpDO7XrQ3VZUIqSqhDjpMx+I2ldVJyqscJaOiKklFtUlZRZK1G6P7WDwy01VGD0ljYL8AA/oEDtpxtDWkVBdIR/xEXDqfxK7PEckIPm+QesvCFgJZkvB6FKK1VaRXrUfE6zr1xSxsC/uLxyFWDaEC7JVPIPlvR8rYN3OUMGOITe9gf/sOWPH9e4aHeyLlDETKGYSU3R9J6QKKpW1hx2udkVGydtS7Yg4XQthEv3iCxJYP8A74Af6hlwOQLPk30VVzqPvgV3j7noVvyGXIno6P9giHwxXtWRM6gw0bNnjD4bD1f//3f1sA/v3vfwd+9rOf9Z8wYUIdwMiRI6MAOTk5yfLyci0rK8tcvXp18Prrrw8Fg0ErmUw2mcmOO+64OMC6dev869evD0ydOlUHsCxL2rp1q6e8vFzTdT0OMHr06PqtW7e2GJNdWVmpLF++PL2qqkqdO3duXiQSUebMmZN30kknbV6yZEnoyiuvrLjmmmsqYrGY9OCDD+bfc889RU888cS3kyZNqnnooYcKo9Go9Pbbb2c/88wzBsDNN99cOmvWrILLL798QG5ubvKEE05oNVvqodARhSJpGMZuAMMwanRdt/a3QWcjhI1ZtQGt2yj8Hg9SJEIkkcCXctTyeRW2liYYPPT7Ha/A55Xp3s1L926t9FsLQV3EoqIqSXWtSfc8L3k5352HnEv7xLcvQQ7m4w11o76+nrhp4lEUMkIKu3Z7KRA2YudKpF77xng5XNhrX0CUr0MecTVS3jCspX/A+vcjKKfeiZQKly9sC7F9Kfb61yFei1QwGlmfDL5MZ7STlQQrAbbpRFq1YlC1GVG+FrHlA8Smd5xAaJl9HeUiZxBk9Go3ImxnIGq2I//7UWpijZk4pdRwa68Th6Up7ow3Ney6Md5Ms+VUfJrGdc7ynvmmfRzBcxPCJvr54yS2fohv4MX4Bv+w6RniKRyLljeMhm8WEN/4FomSzwgMn47WY1yXfc6sWbPG/9xzz+U+9dRTG/1+vxgwYEAsEAhYjRmL95Z77ty5OaFQyHzggQeKN2zY4H3jjTdy7ZRTcKNTZ79+/WKjRo2qmzVr1lbLsrj//vsL+vbtG8/Kykp+8803vsGDB8dWr14dDIfDLd6tzz33XPakSZPKf/Ob3+wAiEQi8hlnnDFs9+7d6pw5c7pt3brV+5Of/KTc5/OJAQMGNGzevNkH4PF4xPjx46tnzpzZvXfv3g05OTkWwPPPP581bdq08mHDhsVmzpyZP2fOnJw77rij9HBev468cVfouj4P+BQYC6w8nAIcDFakFGFGUTMHIEsSfo+HaCJBViBA0rLwaBJCUrrsTdsVkCSJcJpKOO37rXR9HzFrt2LVbMY/8DI8jVa9ZJKK6qRjQfJmQDAPUfoFtKFQCCuJKP8GKZAHafkH3Nbs7R8jNr+P1OdM5J6nAKCc8DOsT/7kKBUn34YoW4u97mWI7IKsfijHX4+U2bfZXvZVlCWAnEHQ/wInKmvlRke5KF+HMF4B4xXwZSL3OQup6NT9DhVv9dxbi9FSvxOiZU6cGW/Ysex4QuANg6w6io3qxz/sKmd7K74ndHyzod/CbMCOVe2JQmvFHYXpQJC1JiWEJmXj4BQUqXngPLmlT5oQNtHPHiOxbSm+QVPwDbp0n/tA0vwERlyNp+hUoiufJLLiYfzRCnz6Dw74uh8JLrnkkuqNGzf6Lr744kF+v98WQvA///M/OzIyMlr9kB4/fnztLbfc0vfiiy8O+Xw+q6CgIFZSUtJiqNKkSZNqli1bFpoyZYre0NCgTJgwoSocDtv333//5hkzZvTx+/1WIBCwwuFwQ/PtFi5cmPOnP/2paWRGMBi0J0yYUDVnzpyce++9d+svfvGLogULFuR5vV47PT09ee+9925rrHvVVVeVX3zxxUMeeeSR9Y1lI0eOjKaOZ2uaZt97771bD9+Vc+hItlEJmAzowFrDMF493ELsj70zJsZWvUTDxhcIn3Yvij+baCJBWX09eWlplNcmsOUEW8pkRg/rWMIul47zXc+YeBB0aa21tWyjkY3Pkdj5GRnj/4ik+SmtqUGSJErLLLIyBUklQE7FYsTGxSjn3N+i20OYccS2fzldD/FU0jlfJlLuYKTcIU43QxtmbWElnWixNVuxv3oGKasf8tj/buG0Z+9eg73iYdACzjDstAIn1H234Yf8gSDidY5yse0jRMV652VadBpynzOR/K0PIxXChtodznaV3+5RHFpEkc1ASusGwTzHUhKvQyRqnaHk8ToQFmQehzXyx+Rk9W31OO3K3Rjm3moMdR9PKSQpJaSFQtKooOyZF6lgec23bwqk19Fw94CkBfD2vwBf/wtA1oh+9lcS2z/BN/iH+Add0qHzSGz/BCXcAzWjN7jZRo9ZDiXbaCbgB0qBTF3X7zQM477DLN8BYVZvRPZlofgdPwe/5pjqI4kElXUJ/H5Br8LA0RTRxaVLYicjJEpX4Ok+runr3Kuq1MXjmLZNXQR69vEiecdgbVzU1O0hklHEliXYm9+DRD1Sto40/CqI1SDKvkGUfoHY/jEgQUZvR8GQVUS0HBEpc17CsWqawskF8pBH/3SfEQBy3lAYdhX2pnccRaLnyYct0qrkDSEVjoXCsYjqLdib3kVsfg9r83tI3Y9H7nsOhHtCZJdj0Shfh6gwnBg24OS0CRdCweh98ty0hRDCCUCn+rAO1NLQKLckg+Y/KGtKezhB8swm5ULsRwmxqr4l9s0LxL99GyXcA7PsG/xDL8endyy9vSTJeIvadvR1OfbpiELxArAeGAbEgGinSrQfhBCYNd+i5QxpKpMkiUAq2ZGs2OyutBje87sRO9/F5XCS2Lkc7CS+nqc3lfk0jbp4nJxMieLdSXrJEiLUA4J52DuWIRoqEVs+ADOGlDcUud8FSFnH7dlpr/EI24Lqzdhl3yDKvkZseNMJEe8NO2Hgs3WkYE4qJHyu4zTZhqOk3Os05F6ndep1kDJ6o4y+FhG9GHvz+4htS7GKV4AnDRKpWBH+LKT8kUjZAx3LSytxZvZ7HEmCw6wIHC6cIHkakqIBHXO+NSs30rBmvqNMDLsS34ALO1dIl2OKDnWgG4Zxna7rTwLXAv/aX31d12XgUWAEEAeuNQxjY7P1PwX+H2ACvzMM4/WOCmzXlSCSdaiZ/VuUB71eIokE4TSZXRUJ13/C5ZikM9uOEDbxkqWoGf1QQnvycTTGcJFlCctyTOCSJCEVjEFsXISo2oRUMAq53/lI6a2ObnOsCFn9ULL6gX6Rk2Jekp0++C6MFMhBGTINMeBCxNaPEHXFSFn9kHIGOsqP+xxpgZrVj7TTfoFI1CF7D3wUnct3mw4pFLqu+4Agjr0yrQObXAz4DMMYp+v6ScBMHD8MdF3PB/4bOB7wAUt1XX/HMIwOpa80y53UIlJ6Pyx7j/+HJiuOdBL4tC6RbsTF5WDotLaT3PkVdqwcb7/JLdoOSFiWhKIIgp495XKfM7ElCbn72BY5XTrC4TbPdzaSFkDqd97RFuOYQJIkpM5TJmzbtqXGERIuXQ/btiWgVeebjigUjwA348Se2A4s7cA2pwKLAQzDWKbr+vHN1o0FPk49BOO6rm8EhgP/7sB+2bR2LYFEkFseqgXqWqy74NQgY4f6yDtwy6SLS1eh09rOhhUfkJYMcPs/wlhiY4t1k04LcvxgL7nN2o7kDaPokw/pZFxcDpA1ZWVlg3Nzc2tcpaLrYdu2VFZWlg6saW19RxQKn2EYfwDQdX2BYRi1HdgmDNQ0W7Z0XVcNwzBbWVcHtKsCfP311+W6ru81xGXfnpdPXtqnyMWls1lsGMbE/VfrMEeg7Xy0Tz237bgcBfZpO6ZpXrtz587ZO3fuHIqTO8qla2EDa0zTvLa1lR1RKP4LmAvQQWUCoJaWXj5y6oHY2roQUN3ezgzDyO3gcV1cjnXctuPyvWXMmDG7gY4NG3HpcnREofDqur4SMEj1mxiGceV+tvkY+AHwfKofeHWzdSuA36f8MrzAINown7i4fA9x246Li8sxSUcUihkHsd+XgXN0Xf8EJ3jJj3VdvwXYaBjGq7qu/x+O3VUGfmEYxmHPL+/icozith0XF5djkjYjZeq6frdhGL9LzRcYhnFYY367uLi4uLi4fHdoz+nlzGbzcztbEBcXFxcXF5djl/YUCqmNeRcXFxcXFxeXFrSnUIg25l1cXFxcXFxcWtCeD0UN8DWOdWJws3lhGMbJR0zCA2B/YYuPFVKjahrjDWwG/gY8iBNu+W3DMO45WrJ1FF3XTwT+aBjG6bqu9wOexlFM1wA/MwzD1nX9V8AknPO62TCMFUdN4P2w1/mMBl4DNqRW/9UwjOeOpfPZG7ftdB3cttO1z8elbdob5TH8iElx+GgzbPGxQmpIIIZhnN6s7EvgUmAT8Iau66MNw/ji6Ei4f3Rd/znwIyCSKnoAuNswjCW6rj8GTE4FW5oAnAj0BF4ETjga8u6PVs5nNPCAYRgzm9UZzTFyPm3gtp0ugNt2uvb5uLRPmwqFYRhb21rXhWkvbPGxwgggoOv62zj/z68Br2EY3wLouv4WcBbQZR+KwLfAFOCZ1PIY4MPU/CLgXJy4Jm8bhiGAbbquq7qu5xqGUXbEpd0/rZ2Pruv6ZJwvrZtx7r1j5Xxaw207XQO37XTt83Fph+9aaNNWwxYfLWEOkijwZ+A84DrgKVqmjN9vuOWjjWEYLwLJZkVS6mEBe+Q/4DDSR4tWzmcFcLthGONxvnx/xTF0Pm3gtp0ugNt2gC58Pi7t811TKNoLW3yssB74p2EYwjCM9TgNLavZ+v2GW+6CNM9M1yj/AYeR7kK8bBjG543z/P/svXeYXeV1t30/u5w6vXd1bQl1CSEkg+jdgCk2xRQbY8Bx7PhNe/MleZM4X7448RvHcVxiY2NjTDGmY2NEbwKEhCSEUNnqGkkz0vR26i7P98eeGc0wVWWatO/rOtc5Z7ezzsx59v7ttdazFixiYn8f8MfOeMUfOz4ThlNNULwLXAnQT9niicJdePFrDMMoAyJAzDCMaYZhCLy7r77dncY3Gw3DOL/z9RV49r8LXGYYhmIYRhXeBaxhrAw8Rl4yDOOsztcXAeuZ2N8H/LEzXvHHjs+EYaK5NIeiT9niMbbneHgAeNAwjNV4md134d2lPAKoeLHGD8bQvuPhL4CfG4YRALYBT5qm6RiG8Q7wPp6w/fpYGniMfA34kWEYaeAwcI9pmm0T+PuAP3bGK/7Y8ZkwDDht1MfHx8fHx8dnuJxqIQ8fHx8fHx+fMcAXFD4+Pj4+Pj4njC8ofHx8fHx8fE4YX1D4+Pj4+Pj4nDC+oPDx8fHx8fE5YXxB4ePj4+Pj43PC+ILCx8fHx8fH54TxBYWPj4+Pj4/PCeMLCh8fHx8fH58TxhcUPj4+Pj4+PieMLyh8fHx8fHx8TphTrTnYKY9hGBL4BHB6LP7QNM27DcPYBxQCxaZpdvTY50vAr4DP43Vg/PPOVVVAAqjvfP8N0zT7dGM0DKMAOAA8aJrm107m9/HxGS1Gc+wYhjEZ2E3frq3Pm6b5DyfnG/n4jC98QTExuWCQ9r4NwPXAQz2W3QEcATBN86GudYZhPAh8YprmfwzxeV8BngNuNQzj70zTbDoB2318xpLRHDsJ0zQXnrDFPj4TBD/kcerxMHBb1xvDMCYBGcD24zmYYRgKcC/wIPAOcM+Jm+jjMy45qWPHx+d0w/dQTEzeMAyjp9v2UtM06zpfvwDcbRhGqWmatcDteHdVNx7nZ12O5+p9FcgEvm8Yxn+Ypmkf5/F8fMaS0Rw7YcMwPurx3jZN88zjPJaPz7jXjuPfAAAgAElEQVTHFxQTk8HctmngSeBW4HvATcB5HP9J8WvAI6Zp2oZhPA/8DC+e/NhxHs/HZywZzbHjhzx8Tiv8kMepyUPAbYZhrADM48156HT5Xgnc3Jm0ZuKJ0P91kuz08RlvnJSx4+NzOuILilMQ0zQ/AMLAv+LlPhwv9wKrTdMsN01zsmmak4ElwOLOE66PzynFSRw7Pj6nHcckKDoT9HwmBr8BDGDV8exsGEYAb3bHd3suN01zJ164w/dS+JyqnNDY8fE5XRFSykE3MAzj84AKBIH/C3x3GNMMfXx8fHx8fE4jhuNx+EvgFbzpVJXA1SNqkY+Pj4+Pj8+EYziCItn53G6aZgpv6qCPj4+Pj4+PTzfDERR7gQ+BXxqG8Y/AByNrko+Pj4+Pj89EY8AcCsMwppmmubvzdYZpmh2GYZSYpnl4VC308fHx8fHxGfcMJig2Ao3A/cDTfmVEHx8fHx8fn4EYdJaHYRiLgbuAi4DngZ+bprlrlGzr5itf+Yp84IEHADj//PPBcXjzmWcG3L5NJklHQqNknce1l18LwHOrnhvVzx1trrviOlRF5c033xxrUwbl/PPPBxgNO8VIf8CJ4I+d8UNADXDN5dcAo/K7PCFGafyM67Hjc+wMWnrbNM0NwIbOmgTXAf9hGEbINM3LR8W6Tpqbm3svcF0IBgfcPt3SSDAre4St6o0ivHSUoDawXacCrnRRUcfaDJ9h4o+d8UPKTo21CT4+I8pwC1UVAlOAEjpb+fr4+Pj4+Pj4dDGgh8IwjAheU5w7gVzgAeBy0zRbRsm2frnrrrugvX0sTeiX2+68beiNTgFuveNWMoPjf+bwXXfdNdYmjDv8sTP2TJTf5USx02d8MVhS5mG8vIlfmKa5dlSt+hTXX3+9fPrpp48uaGgY1G3b0FJDsKhsFCw7/UjZKQqiBWNtxnhiXMeB/bEzfvDHTh/G9djxOXYGy6GYbppmR9cbwzDmAOnOXg4+A7Du4zaefamBqy/OZ9nCLITwx4yPj4+Pz6nPYDkUyw3DqDYMQzcM4z48b8VvDMO4e5Rsm5C88V4LsbjDo8/V8T8P19Dcao21ST4+Pj4+PiPOYILir4BlpmlawP8GLgPOw+tA6dMPB2qTHDyc4rrLCrnxikL2VCf41x9X8976VoZqwubj4+Pj4zORGSzkIU3TrDUMYypgddWfMAzDL3A1AB9sbENVBWfOzyQaUTljRpTHnj/Cb39fx8YtHdxyTRF5OXqf/SzbpaHJorHZorggQGF+YAys9/Hx8fHxOX4GExSaYRgacBXwEoBhGDlAdDQMm2hYtsuHm9tZMCtKNOLVaSjI0/n6HeW8t76V515p4Ds/2c8V5+cTDCgcaUhT15imrsGiscWipwOjtDDAvFlR5s3KoKos6Odh+Pj4+PiMewYTFL8GtgEqcLFhGHOBR4D/Hg3DJhrrN7cTT7gsW5TVa7miCM5ZmtPtrXj25QYAdE1QlK9TURpkybxMivJ18nN1qmuSfLw9xqurm3n5nWayMzVPXBhRZkyOoGm+uPDx8fHxGX8MKChM03zIMIxngQ7TNF3DMEqBL+GJDJ8evLO2haderKeyNIgxNdLvNnk5On9yezkHa1NEIyo5WRqK0lccTK0Kc/7ZucTiDlt2xNhsdrD2ozZWr2sloAumTw5jTI0wa1qEksKA773wGTZuRwcyFkMdZNroeEJKSSotCQWHW3/Px8dnLBmq9HZbj7dh4LbOR/FIGjVRcBzJMy/V8/baVubMjHLnDSX9ioQuhBBUlg2vT0I0onLWwizOWphF2nLZsSfOtl1xzD1xtu70vBzZmSrG1AjGtAhnTD8aavHx6Y/2F17Arqkh/+6xmahl25K9BxJs2xVn++442Vkad1xfTDjU93e7pzrBc680UF2T4ltfrmBSxej2F/Hx8Tl2BhUUAIZhXAn8KfAZ4N+AhSNt1EQgkXT41ROH2b47zoXLc7jmkoJBxcSJENAV5hoZzDUyAGhqsdi+2xMXn+yIsXZTO7lZGn//jUnoun8359M/an4+yY8+wk0mUUJHL9BW0w6s+k2EZ95w0j+zrcNm07YOtu2Ks3NvnFRaoigwuSLEtl0xfvpIDV+7rbzbC1Fbl+IPrzWy2YyRlaESDSv85pnD/PW9VQQC/m/bx2c8M1jp7b/AC3FsAr4HKKZpfmeU7BrX1Deluf/RWuqb0txyTRHLF49uM6W8HJ0VS7JZsSQb15V8tLWDB588zIeb20fdFp+JQ2DKFGKAdfAgwenTu5ena94nXfMearQEMqadtM9LWy7fu/8AzW02+TkaS+dnMWt6hJlTIoSCCh9tbefBJw7zs0cOccs1xbz6bjMffNRGQFe46sJ8zj87h/0Hk/zooUP8/rVGbrii8KTZ5uPjc/IZzEPxl8BjwK9M09zcKTDGBU57+5jFgXftS/DA4zVI4Ou3lzNjSv85E6OFoggWzcng5XcCvLWmhbMX+dU5ffpHr6wERSFdXd1LULjxOgASO56GuV87aZ/37oetNLfZ3HtrGWfMiPT5XS48I5M7boBfP3WYf/nRflQFzluWw6Xn5pER9cIgM6dGOG9ZDm990MJcIzpgjtJYkU67PP9qA0X5AebPziAna0in7ymJZbvomu9BOt0Z7Nc/GbgB+EFno7CoYRjZpmm2joplA5DcsoX0xx+T9dnPjvpnr9nYyuO/ryM/V+feW8vGTb0IIQTnn53Do8/VsWNvYtyddH3GByIQQC8pwaqu7rXcSTSg5UzHbt2HqH4JKmae8GelUi6vrm5m5pQwc2YOPNN88dxMFAXMPQku/kwu+bl967RcfXE+23bFeOTZI/zN16qIhMdPrtBzrzTwzjrvlPjki/VMrgixYHYGC2ZnUJDX97ucirzxQT2P/P4g5yzO44tXV/ph19OYAf/zpmmmTNN81DTNC/ESMV8ANhmG8eSoWdcPTlMTiY8/JrV796h9putKnnu5gUefq2P65DB/fnfluBETXSyZm0lGROXNNWPaDNZnnBOorMSqrcVNpwGQThqZakErOIPQ1MtRmj7BrfvkhD/n7bUttMccrrowf8htF56RyU2fLepXTICXQ3T7dSW0tds8var+hG07WWzbFeOdda2cf3YOf/f1SXz2wnxsR/LcKw3883/v49//Zz+r3mrkYG0K1z31KuW6ruS3LxzkoWcPUFIQ5K11jfzLT03qmlJjbZrPGDEsKWma5i7TNP8GmIZXi2LMiCxfjpKVRduqVcjOk+JIkkq5/OLxWl57r5lzlmZz3xfLx9UdUhe6rnDO0my27IhR1zjyfxefiYleUQFSYh08CIAb9y7QariI0JTLkaF83E8eQzrH/xtKJB1ee6+ZM2ZEmFIZPil2T6oIcenKPNZuamfT1o6hdxhhYnGHR549QklhgKsvyqe4MMClK/P463ur+Mc/m8x1lxUQDCi8+EYT3/1ZNf/ne3t59NkG3tvQSF3jxBcYyZTDDx/ew0ur67h4RSHf/sZs/uyOaTQ0pfmnH25n41b/xuZ05JgCfqZpOsAzI2TLsBCaRnTpUtpfe42Od94h86KLRuyzmlosfv5YDTV1aW68opCVy3JG7LNOBuecmc0rq5t5+4MWbryyaKzN8RmH6GVlIARWdTXBqVNxEp6gUCKFCEXHnfxZ1O2/xt3xe9TZxzfr4801LcQTLldeMLR34li4bGUen+yI8ds/1JFMucyeHiErc2xyFp54oY5Y3OHeL5b1cfHn5+pcsDyXC5bn0tZus323N0122+4YGz/ZD3iF7UoKQpQWhSgtDFFW5L0uyQ+O+5BBU2uaH/x6NwcOJ/ji1RVcvMI71yycnc0/fWMWP3l0L//9mz1csbKYGy4tQ1X9nK7ThQmZQaQXFRFeuJD42rWE5sxBLyk56Z+x72CSnz9Wg2VL7vtiGbOnj/+K41mZGkvmZrBmYxtXXpA/Lj0pPmOLEgiglZaS7syj6PJQKJHOGRRZkxGVn0HueRVZvgyRVXFMx48nHN58v4X5s6JUDbPmynBRVcEd15fwPw8f4pHnjgBQURrkjOkRzpgRZXJFaMSmbvdk/eZ2Nmzp4LMX5lNZOvh3zMrUuuvJJKwkibYo+w/FqalPcrguyd6DMdZtbu4uvS8EFOYGKS0KeiKjMNwtNiL91OsYbfYdivODh3aTTDl8685pzDd6zyorzAvyt/fO5LEXDvLi20fYcyDGfTdPISfr9MgnOd0ZbNroKtM0LzcM4x9N0/z2aBo1FEpmJhkXXkhq507a/vhH8r70JYRy8lT9+s3tPPLsEbKzNP70zlJKiyZGZUGA887OYe2mdtZsbOPCFbljbY7POCRQVUV83TqkZeHG6xFaBEU/KpiV2TfgHPkY5+OHUT/z1wgx/LH1+nvNJFIuV5x/cr0TXZQUBvinb03m0OE0W3fF2LrzaJn6SEhh1jRPXMyaHiEr4+TfLzW3WvzuhTqmVIa46DPHNr4UIZhUFmFSWe+k6bTlcrghSW1dkpq6JLX13mPLznZs52hoJDtTO+rN6OHVyMnUR2Vm14YtLfzs8X1kRjX+7j6DipL+w1m6rnDH56qYPinKr585wD/+cBv33TyF2dMyR9xGn7FlsBGXZRjGE8C5hmEYPVeYpnnryJo1OGpmJgSDZF56Ka3PPEN83Tqiy5Yh7QR2WzUox/fDlVLy4ptNrHqriWlVIb5yU1n39LWJQmVpiOmTwrz9QQvnLcs5IXfjO2tbqCgNnrQ4uM/4IFBZSXzNGqyaGpxE/VHvRCciEEU540bcj36F3P8WYvIFwzpue8zmzTUtLJ6TQXnJyIlwIQQVpUEqSoNcem4e8YTjhRR2xdi2K86GLV6ORVVZkNnTo5wxI8Kk8hP3Xriu5JFnj+A4ktuuKz5prvyArlBVGqGqtLfQcF1JfVOK2vqjQqOmLsn7G5tIpNzu7cJBhdLCzvBJUYiyzteFucGTYqOUklXv1PHEqkNMKY/wzTumkZ05tMdhxaJ8JpVF+NEje/i/D+zk+kvLuHJl8ah4kXzGhsEExRXAPGA68FNg2L8CwzAU4CfAAiAF3N3V/rxz/X/jVd5s71x07fFMRw3OmkVg+nQ63n6bkGGQbnyH5O4XYNFfHuuhSFsujzx7hI1bOli2MJMvfLZows6rPv/sHH7xeC0fb+9g0ZzjE1f7DiZ54o/1VJQG+at7Kv3aFqPEaIwdvbISgHR1NS51qNlT+mwjypchDq7B3f4somQRIjR0/tBr7zZj2XLEvBMDEQmrLJ6byeK5mbiu5NDhFFt3xdm6M8bL7zTx0ttNnLcs54QLY72ztpUdexPc9NkiCvNGfpaXogiKC0IUF4RYOPvociklre02NfW9vRpbdrbz7oam7u00VVBcEOzj1SgpDBEYZp6G7Uh+81w1b69rZOm8HO7+/ORh7wtQXhzmH74+iwefruapl2rYta+Du78wmYzIhIy2+wzBYM3BWoHVhmGcBVwCzAF2mKb53DCO+zkgZJrmcsMwzsartHltj/WLgctM02w4ftO9O5Wsyy6j8f77aVu1CnVGHSAh2Ying4ZHa7vNzx+r4UBNimsvKeDCFTkT+gI614iSn6Px1pqW4xYUf3jN+9ccrE2x/1CKyX4vhdFixMeOEgqhFReT3r8fUdxEoGRpn22EECjzvojz1rdxP3kc9cx7Bz1mW7vNO2tbOXNeJsWFYzelWlG8fjmVZSEuW5lHLO7w2PNH+OCjNq65JP+4bxIO16d5/tUG5syIsGJJ1tA7jCBCCHKydHKydM74VBghnnSo7eHNqK1PUl2bYP2Wll55GgU5ge6E0J5ejZ4X+ljC5seP7GXb7nauvqCEz11celzehXBQ5b6bJzNjUpTf/vEQ3/7Rdv7k1ilMqRj/eWk+x8ZwZOI/AzOBd4A7DcNYaZrmUFUzzwFWAZimucYwjDO7VnTegc0A7jcMoxh4wDTNXx6X9YCanU3GeefR/uqraGoHSjaIZOOw9z9Qm+Tnj9USTzh85eZS5s/KOF5Txg2KIli5LIdnXmpg/6Ekk8qPTQyYe+Ls2JvgqgvyefXdJlava2FyxclPfPXpl1EZO4GqKuIbNqAXuH1CHl2IaCHKjCtxzedwj2xCKV4w4PFeWd2M40guPy/veMwZMaIRlRVLsvl4e4ztu+PMM/qO79Y2mxffbGTX/gTRiEpmVCUzQ/OeoyoZUZVXVzcT0AW3XFM8rm82IiGVaVVRplX1vlhblsuRxh7hk7okNfVJtu1ux7KP5mlkZXh5GqWFIbbvbae+Kc3dn5/EZxafmNdJCMHFK4qYUhHlJ4/u4V9/uoOf/8uiEzqmz/hjOIJipWmanwEwDOMHwJph7JMF9HTDOoZhaKZp2kAU+CHwn4AKvGEYxoemaX58bKYfJXzmmSQ2b8Le66DPVTo9FEPz8bYOHnr6MNGwyrfuqqSidOIkXw7F2Yuy+OMbTby1poU7bhi+GJBS8ofXGsjN0rhwRQ6tHTZrNrTxuUudCZdPMkEZlbGjV1XBunXIWBAlMvAUYzHtUqhZh7v5MUS+gdD6itPmVovVH7aybFHWuCv4BmBMjRAJKXy0paOXoEimXF57t5k33vfE0OwZUVIpl/pGi93VSeJxh57VIr7yhdIxm6Z6oui6QkVJuE8ipetKGlrSfbwa6zY3o2mCv/rKdIwpJy+ZclpVlG9/Yzb3/27fSTumz/hhOKNDNwxDMU3TxcujGE5Fljag569Q6TwhAsSBH5imGQcwDON1vHjxcQsKoSiEl8+g/dk6nIO5qIVNg24vpeTVd5v5w6uNVJUH+erNZRP2RDEQ4ZDK8sVZvL22hWsvKSB7mD0GPt4eY/+hFLdcU4SuK5y7NJvV61r54KO2Y85q9zkuRmXsBDrzKGRHEDU8cG6BUDTUebfhvPddXPN51Dlf6LPNy283gZRctnJ8eSe6UFXB/NkZfLS1A8tyURTB+xtaefHNJtpjDovnZnD1RQV9KnU6jiQWd2iPOWiaoLhg/ImlE0VRBEV5QYrygiyYdXQKqJQSKRmRBMqMqMa37jx5Teh8xg/DCSg+DrxrGMb3gdXAb4exz7vAlQCdceDNPdbNxMvNUA3D0PFcvBuOyep+EHozSnEHbkMGsm1gD4Vle8mXv3+1kUVzM/jGlypOOTHRxcqzcpAuvLNueFXrXFfywuuNFOXrnLXAixOXFgWZPinM6nUtE7663wRhVMaOEomgZAVwO8KI4OAdakXeNMSklci9ryNb9vda19hs8f7GNpYvySYvZ/zWGlg0J4NkyuWF1xv5t//Zz+9eqKeoIMBf3F3Jl24s7bfst6oKsjI1ykuCp6SYGAwhxIjOxvBnepyaDCkoTNP8HvBVvBPdPaZp/tcwjvsMkDQM4z3g+8D/Mgzjzw3DuMY0zW145bvXAG8BD5mmueW4v0Endtt+tDIvbiibYkjp9tmmPWbz44cOsXZTO1ecn8edN5QcU8byRKMgT2ferCjvfthKLO4Muf2HH7dzuD7NVRfm95puds7SbBpbbLbtio+kuT4eozZ2lBzPQ9GdrTfYtrOug2AmzuaHke7R39Kqt5pQhODSc8end6KLmVMiRMIKr7/vieuv3lzKN79UziQ/2djH56QxrFtz0zQ/AYbdMagzPHLfpxZv77H+u8B3h3u8oZBS4rTuRy9bSPqjGmS7BskWCB89ydXWpbj/0RraOhy+dGMJi+eeHkVWLjk3j607D/KDXx3k67eXDxj6sG3JH99spKI0yILZvRPX5s/KICtDZfW6FqZPLRgNs09bRnPsiGgcHA378GGvJPdg2+oRlDk34W74OXLfG4ipF1PXkGbdpjZWLssZ9227VVVw67XFxOIOZy3I8stB+/iMABPy9ty27V7v3UQD0o6j509BLchGxoLIjiPd67fujPH9Bw5i2ZJvfqnitBETAJPKQ9x3WxnNrRb/9csDNDRZ/W733vpWmlpsrr4ov487UtMEK5Zks3VnnKZmu9/9fSYWUroQ9Gaepj/VznwgROkSRNFcXPN5ZKKJF99qQtMEF58zMXJr5s/KYPnibF9M+PiMEBNSUDhOb/e907YPADVrMnpZBTIWQLYfRkrJW2ta+NmjNeTn6vzFVytPSxfnzCkR/vSOChIpl//65QFqjvRuL5xKu7z0dhPTJoWZNS3S7zGWL85CCHh/Q3u/630mFjLVitBSKFmh4QsKIVDm3gJI4usfYcPmNlaelTMiJa59fHwmHkMKCsMwLjEM40XDMF7veoyGYceC3bofFA01o4xA1RRwFdzaA5h74jy1qp55RpRvfbmC3OzxmzQ20kyqCPHNL3mNnv77wYPsP5jsXvfWBy20xxyuvih/wDn2udlePsbaj2JYVt/8FJ+JhdPZFEwvK8Y6cADpDu9/KiIFKDOvJtjyCYsKdk+omT+y4wgyfkK19Hx8fAZhOB6K7+NV6/taj8eY0draSksfD8V+1IwKhKKhV3gXTVlTz/6D3p347deVEAxOSGfMSaWsOMi37qokHFT40UMH2bE3Tjzh8Nq7zcyZGWVq1eA9O85ZmkM84fLO+sZeTYt8Jh5uZ9tyffJUZCqFXVc37H1rwudwMF7EjVNeJ6KnR8rEk46z/mc47/8n0pk4Nvv4TCSG46usNk3z1RG3ZJhIKUlKieO6qIqClC52WzXBsmUAqDk5oIPbEOOwSJGXo/liogcFeTrfuquSn/zmED99uIbpk8Mkki6fvXDoSngzp4QpLtD5zXMHePj5A+RlByjI7XoEKcwLUJgbJD83QG6W7k8NG8e48XoQCsGps+jgDazqavSS4RVA++ObLSTrL+Nr0x7G3f4s6rxbRtjaE0emY9B+yHu95xXEjKvG2CIfn1OP4QiKOsMwfgpspLOolWma94+oVYMQiURobW0lYVlkBIO48TpwkqhZkwAvzivywritbdSRYObkEFLKcV0ud7TJztL45pcreOrxDzkn8ALG/AsoL5kx5H5CCO69OZOmvUdo7pA0tqWpb5Uc2Cf56GNJ2tWwXQ2JQFUF+TkBCrsER16w83WQgtwAWRma/z8ZQ5x4HUq4AC0nFzUnh3R1NZGzzhpyv/0Hk3yyI8ZVF56Bknc+ct+byIqzEbl9G4yNJ2TzHu9FpAh354uIiuWI8PCmukorgazb7CWlKn61WB+fgRiOoNjb+Twumjnouo4KxNNpMoJB7Dav0I6WNbl7G1GYizySYMqkdi48O4fWhlaC4SDBSBBF8b0VAJHEbm4pfRRhJ6lUnkI2VyFyB69eJ+0k2Zu+R06is3CYCuR1PnrgouKiY0kNy1VJJTVSB1Ss/RqNrsphV8NFR9EDaHoQPRgkGA4SCoeIRMJEM0IEQyFQdIQaALXzufN9f8tQdYTw/7fDxU3Uo4S9KcB6VRWp7dtJHzgAmYNfMF94o5FoWOG8ZTkoyrU4hzfifPww6rl/O64vtrJ5NwgFdemf4Lzz/+FufRJ1yT1D7yddb6ps/RaU2BHEzKtHwVofn4nJkILCNM1vG4ZxFV63UXOY3UZHDCEEYU0jZlnd9SdQdJRoD71TWgqf1GAUObgSNEUh0ZEg0ZEgEAoQioRQdfW0vUN2j2zGXf8zRCQfddHdOOvvx1n7Y9TP/G9ERvHA+219AhJNRM68DyWQiXTSSNcCJ+3FpR3Le3a9Z+l0rnMtXCtFOpXCSqVw7DSuHQcnjZA2imWh2TaBuA2N4ODVmD5mFK2H6NBRc6eScfa3jvfPdMphWRbSddGlxI3XEyj1vArRFSuwDhyg+ZFHECuWIi8q7Xds7N6fYPvuONdeUkAoqABhlLm34H74P8g9ryKmXzbK3+gYaN4NWZWIzFKU6Zfj7vg9boOJUmAMupvc+SKyfgtklOLueAFRMBuRN/xOxj4+pxNDCgrDML6D1+FwNV630XNN0/zLEbdsECKqSodtk7Qs7LZ9qFlVve+OyicDH5KfF0KikJWfhW3ZpBIpUokU6WQaVVMJRoIEQ0HEaRTrd2vW4W78JWRVoJ71TUQwE3XZN3De/XectT/0REWwb50Ot+4TZPVq5JSLCE5aedLtiiVsGppS1DfGaG6O09wco601TltbnPaOBLgWurDRFRtNsckMSXIzIDsqyQpLMsIu0aAkEnAIaQ6KtFEyxoVTbdzQ2tpKMpGgVLGRdqK7KZiWl0fel79M6+9/T3r1B6RbOwhcfSUi0HtW1AtvNJIZVTl36dFS3UrJQmTxAtwdv0eULUFExl/hM+k6yJZ9iMpzgM6GZwfew93yW8S5fz+gZ8Wt3+p9r/JlKPNuxXn7X3A2/AJ15f9BBPzW2z4+n2akuo2OKCFVReCFPZS2AwQrzum1XkQykWGHYF4OWtA7WWi6hqZrRDIipJIpUvEU8bY4ifYEgXCn10Ibvy7bk4G7/23czY9C3nTUpV9H6N6sDhEtQl36dZz3/xNn3Y9Rl/+5F0boRKZjuJt+A5llyOlXjoht0bBGtFxjUnnfE7WUkrYOm4bmNA3NKeqb0zQ0pdjbnKb+QJrGljSOI1FxKaWFKpqYpjeTkd3OWbNHxNwJSTAYJBaLkU54zfOUHk3BlFCInBtvpP61VThrN5Ksqyd40w0oed600B174uzal+CGywsJBHqHlpS5N+O8+U+4mx9BOeub48/z13bA84blTQVAqAGUM27EXf8z5P63EFMu7LOLTDTjbnwAMktR5n0RoQVRF9+N8+6/4378MMqSe8bf9/TxGWNGqtvoiCKEIKTrJNJJIm66OyGzJ6nCbEQwRDDY+y5LKIJQJEQwHPS8FvFU90MLaIQiIfSgfsqdLNxdq3C3P4MomuedDNUAUkqslIUW0FByp6Is+gru+p/hbngA5cx7u3MS3C2PQ7rNiz+ro1/LQwhBdqZOdqbOtKrOfi1S4jY3Yx04QLr6IIl91cgjtYjOKcVJGaEx6LumexKJRGhqaiKetlAANdK7y6gQAuXMBWgzZpJ66jmS9/+K4PXXoMyYxgtvNJKTpWl0Ht0AACAASURBVLFiSVaf44pwHsqsz+FueRxZ8yGifOkofaPh0ZWQKXKP/h5EySJEwexOz8rSXl456do4G+4Hx0Jdci9CC3r75ExGMT6Hu/1pZPVqxKRzj9kWu2kXdtPurk/q7KMie/RT6bEM73f+6WV09SnqZ50c7HjyU/sjB91e6BH04gVoRXMQil+8zGdohvMr6eo2ugZY1vl+zAkHAiQsCzdcgpY9uc/6WEEFUUCJdUC0b3VMIQR6QEcP6LiO64VD4ik6WjoQivCSBCMhFHViJ/pJKXG3P4vcvQpRthRl4ZcRiopt2cRaYzi2g6IqZORkoJUugjlfwN3yOO6W36HMuQl5eCPy0AcoMz+LyJkEdmroDx0B3GQS6+BBrAMHsKqrvWJMsZi3UvPqj+grVqBXVqJXVqJkZzPpFBOFJ4qqqgQVhQRBb2yE+w9PqDOmEbrny6R+9zSpR39H87xl7DtQyeev8lra94eYfD4cXIO75XFE4RnjJiQgpYtsMCGUiwgfLcIlhECZ8wWct/9fXPNZ1Pm3d69ztz0FzXtQFt+D+FTYTEy7BNGwzfueedMRmaXDtEMi9r5Gu/k8J/eeTIAQ3jNAV2Jy17Ie60SvZZ/at591Mt1Oatcq0MLoJQsIlC5BK1mIMk7+tz7jj+EkZX7PMIyXgFnAA52NwsaciK7TBNi587pjwV24UtKeWUbUdeHgfigaPK6rqArhjDChaAgrZZGKp0jGkiRjSfSgTigSQgtMvGmOUkrcbU96CXNVK1Hm3QJSEGuLkYqnEIogkhkhGUvS1thGJCtCcPIFiEQjcs+ruFoIWb0asqsQIxTq6Ndux8E+csQTD50Pp76++y5KLSwkaBjd4kErKUGop3a46mQR0TSa3Shu5uReYa1Po+TlErrrDtIvvEjupg+4WzvArFlfGHB7IRTU+bfhrP4O7vZnUOffNhLmDwvpWMiG7cgjHyGPfAypNkTVOX22E5lliMkXeG3Zq1Yicibh1qxH7n0dMeVClLIlffcRCsrCL+O8/c9ePsU5f4MYwmsn7RTuxw+h1HyIXr6MyILbvQTiHhdyMeBFnqMiod/tRw7ppLGObMaqXY9VuwHr4BoQKlrhbPTSJQTKlqAcY86MdNK4yVZkqhXNT2495RhQUBiGcbdpmr/oTMrsktSLDcPANM2/HR3zBkZVFNRkHXbevD7TBVvaJTKrCNHSjNy3G87se2LoDyEEgVCAQCiAYzvdXov2VDuKqhCKhAiEAxNi6qmUEtd81hMTky9AmXMTdtom1hbDdVyC4SDhzDCKohAIB4i1xoi3xbHTNhHjekg0I3e96JU0X/jnIzol0GltPSoeqquxDh0Cy2tiJqJR9IoKQvPnH/U+hAev6OkzMBFVpRlw8hcPua0I6JizLuCTTTrXOpuwf/kg6k03opQU9b99dhViykXIPa94tSlG8YIhrTjyyGZPRNRtAScFWghRNBdRvBBRsqDf/ZSZV+McWouz5beoC+7E3fRryJ2KMvuGAT9LhLJRFnwJd92PcLc9jTr3poHtitXhfPgzaD+EO/MaonNvmjA3JkINECjzhIOULk7TLtI167FqPiSx6dckNv0aNWcyeukS9JKFALipVmSy9ehzsgWZau0WEdI6On8r94ZHx+qr+YwQg3koDnQ+b//U8jHPoQAvc1tr2kyq7KLuqpldHGl2yc/PRDmyA7fm8HEdX9VUIpkRwhlh0sk0yXiSeHuceEecYMiraaHp4zeuKHf+EblrFaJqJcz+PLHWGOlkGkVVyMzLRO+Rwa8oXsgjGUuS6EhgWzYZc+9AKBqiYBYi02tt7TgOVsKiOd2MoijdDyHEgK8/jZtKYR861Mv74La1eStVFb2sjPDSpZ54qKpCzc2dMCfgiYCmKCiJw1hZg9ccAXBdyR/fbMLKm03gmrnYTz5D8hcPErjmSrT5c/vdR5l5NU7teq82xcq/H1bsXUoJ0j1m0SoTzcgjm5CHP0I2ml5uQDALUb4MUbIAkW8M6T0Qehhl9nU4mx7Cfvc/vOnGi+8Z0m6leB5yyoXIva/jFs5GKZ7vdXBtr0E27+l+EDsCegRl2TdxcqdN2N+yEApa/ky0/Jkw7xac9hqsmvWka9eT3PY0yW1P9d1HjyCC2SihHNScSSjBbE+MdT77nHoMOGpM03yp8+VS0zT/tGu5YRgPAQ+NtGFD4XTUoDVvIVV2UXfVzC4a26CqUkPpqEU2dWCbO1GnTEIEBnbxDoQQXj5FryTOzumnqq56XotQYERPFFJKpCuRyAEv1D1xdr2MfWAd7oybcUqWYjW2IV1JKBoinBHud38hBOGMMFpAo6Olg7bmOFHji6i6SqojQTqZxrG9hMc0w++FoAA4DiKVQsbjEItBOo1Ip1EKC1EnTULLzETLy0PLzUXRtG5BghCdSWNM2BPxeEM6KfSmzaTKL8F2XbRBvG2btnVQcyTN7dcXo0/OQrv3LlJPPEP66edxD9WgX3pRn1CT0IIoc2/FXfcj5O6XETOGDpW5259FHngX9cz7hvRqSMdC7n0Nt3YjtO7zFkaLEVMvQSlZCDmTj7nAmSxbRtzKwNVziAbTaOHhNTxTZl2P07gT96MHkdmVyJZ9YHc23QtkIHKnIiqXI8rOQkTyxyz/aCRQM8tQjTJCxtW4yVbshm2gBlCC2SihbEQwe0gx53PqMVjI4+vA3wN5hmFc37lYAbaMhmFD4bTtQ0nUoiK7q2Z2YTnexUfTG5GaIP3YE6CqKJOqUGdMQ50xDZGfd8wXKU3X0LI1wplh0gnPa9EVKghGPNFxolNPpZQ4tuM9LAfbtnEsp/vCCl7OR9dDVdXuxFHbsrHbm3AiC2DOmQCIpIWmq4QzI8PyqOgBnez8bDpaO4i1xXp993BGGDQozCr0Qiqu2/3sui5OPI7V2Ijd0oLT1oaTSCBVFanryFAIkZkJxcUQCCBVFUtKrKNfHJqaBrSrp+ejpydkoOWKoqDrOpo2fr1IY4GTaEBr/oRU+SUk0mkyQ30TlqHTO/FGEyWFAZbM9WZAiMwMgnfeivXK69hr1uHWHib4+esRmRm99lWK5yFLl+DufMErVz1IsTSZjiH3vQ6OhbPm+ygL7kQp778EuLTiOOt+Ak07IWcKyqzrECUL+yROHgu2ZdPR0oEbqUTg0iEzyUimCYSGvvnwvBl347z/fWQ65nlGcqcicqdCpPC0EcFKKJtAxdljbYbPOGAwD8WPgR8bhvG3pmn+6yjaNCzstv0oWoRwMEQslerVr6PrAqtHXOS5LkrFrTi7duPs3I310qtYL72KyMlBnT4VZca0Y/ZeKIpCKBoiGAlip22S8WR3EmdXcqfovLuW7tELrnSOXoS7H67snKklu8VET1RNJRAKeEJFgOu43Q8rZZF2e3gLpIMaP0IAC718IVoggPP+B9hvvoO7Yhly5TmIYYgKRVXIzM0klfDuqALBQPffNGWnvL+zbePW1PTKfXBbWrr+QOglJUQqK9HLy9ErK1Hz8xH93A33FCTH+tpxnF7LP42qqlR0dp/18XCTDSjJOlRc4oMIivWb2znSkObLny/p1eRNqCqByy9BKS8j/fwfSfzsAYJfuB61qrLX/sqcm0i31pI8uB1RluGJ8YDWx8Mmq98BJ426/C9wzOe92g+xesSMK3tvl2jGWftD3EQbctE30YpnIdShvXWDkU6m6WjtQAhBZl4mqqrS3txOR0sH0awowUhwyGOIjBK0S/79uG0Yr8h0Gum6KAP8Pk4Et6OD2IYNZK48+QXyfMaW4dy+fWIYxj+bpvkPhmGsAv7TNM2XR9qwoXBa96NmTSKo63SkUiQti3DAq62QEVGIJyXZkTxoMlGmTkadNgUuuxi3uQVn1x7cXbuxN22GDzd0ei8qUad3ei8K8od1ohJCoAd19KA39TTeHifRkSAZ99ye0h043USIzkxt4dXG6Lqz1gM6qq6iat5jKDuklJ7AqN0Amx5AKZqLcuZ9CEXDen8t9mtvIgrysd95D2ebSeDqK1EnVQ56zC77QpHeJxNp27irP6Bx517s2lpwvfnsSk6Ol/PQNW2zvByhD8/dKYRAVVXUE5yl0SXIeoqOiZA8O9q4iQYEEAnotKftPvlHAI4jWfVWE+XFARbMzuj3ONq8OShFhaR++xSpBx9Bv+witLM8r5iVtkglFKwzvgGuDbEEqc4whFBEt7jQdQX2vuGVs86fibrsz3A/fhh3x/OIeB3KvNsQqo7TfpjUzjewSq/AyZoKCGhoRQiBFtC6j6fpR2diyUQSt7YWt+Ywbu1h1OlT0RZ5iZlSyu58IVVTiWoCufEj7PYY0ZnTiGdke8nLrtt9c3AqIS0Lp7UVt7UVp9Ob6La0HF3W2opMJEBRCM6dS2T5cvRJk07o7+A4DrGaGmK1tVjBIHLSJPrW4/WZ6AxHUPwTcHnn65uAF4ExFRTStXA6DhGafAkhXfeqZnYKiuZ2m+J8DcsRiGgx0k56JXYzSiFahIgUoi1ZgFi62LtAVh/A2bkHZ9durJdfw3r5NUROtue9mD4NdcpkRHBo70VXLYd0Mk06mfYEgyK80ISieK+7cwNOYk6AdBD7X0dsexpRMAtlyb0IRcP+cCPWS6+injGLwA2fw927j/QfXiT1q9+gLV2MftEFiNDQd2BdOHv3k/7Di8jGJsTkyURWruyedaFmjv2poUug+SJicJxEA0KPEglFaU+39ck/Ali7qY36Jouv3lw6aAt6pbjIq1fxzPOkX32TlAP29Om4rvQKyEVD6Nt/A3Uf4RYsxJ1yGU6gANuysVIWCekS0bMJTL0E8EIIysIvIaNFODtfIC1ysIrPxpY6lF2GIlxCkbAn4G0Xy7Kw096xAJASpaMdUVuLsm8/yuHDiHgcQiGcrdsRmZkoU6cQa/MSlLWOdvTVq0nv8xoMIgSsfg81KxMuvYwEBTi2QzQ7Ouh4HU95PtKyPIHQKRbctjZPNHSKBbu1FZlOg6YhdR00DXQdMjJQsrMRxcWIjAyUaBSZSpHYto3Ek0+iRyKEzzyTyMKFw/Lm2rZNKpUiGY+TaG/H6bphyM1FSySI+KHIU5Lh/Fct0zTrAEzTbDUMwxlqh5HG6ajx3PtZk45WzexsFlbTmCI3WyVmaYjcMyCrEln9rtfEqguhQKQAESmCaCHazCK0hcuRdhi3pgV31z7sj7fAhxs970VV5dHciyG8F13TTkcaKSXy8EbcbU9DvB5RPB9l8VcRqo696RPSf3gRZcZ0Atdfi1AV1OlTCf3JV7Fefxv7g3U45k4CV12OagzetlzG46Rffh3no48ROTkot9xA3vwzR/z7+YwMbqIBJVxIQFVRhSDxqfwj25G89FYTVWVB5hpDFzBydR3nqqtIdcRBVRENjUQK8wgW5HhhvzO/6lXP3P4M6trvECicizL7ehw1h/b6I8RnfBk1p4AuGei6LqnSC0hlLkUKDZFsJNC+k+CkpWiZBWDZuLWHoaYWvaYWtaYWtyOGW1yMW1KCLC/HmTYdZ6bX9EsRAlVTkes34KzdgKOHcANBtDVrUDdtgqJC9AtWop4xC5GZgbNjN8627fDcc8hFi0gvXoxTc5ioKlEnT0LqGrbl5TXZlo1jO7iOC4LufKbu3CZNQZESRdePqUDep71t3WE+28aNxbDjcdxEAjeVwkmlkJblrXNdbwpep1CQmgbZ2VBQ4OUtaRocqyewzJvhlQZi6TRi61Y0TUPPzSWQkYGqqmiahqqqpNNpkskkyWQS27a9/S0LpaYGvaWFaEkJGfPmofrTvk9ZhiMo1hqG8SjwPnAWsHFkTRoap6MaoLvkdjgQIBGLYTkOzR1pMrMhKzOAyMhCW/n33h1EqhVidchYPTJeB7F6ZKwOmnYinaPZ1wKBWpyPOqkAmSjGbRS4tQ1YL+/zvBfZ2T1yL4bnvTjZyOY9OFuf9DooZpahnPUNROEchBDYW7eTfvb3KFMmEfzC9YgeSaIiECBw+cWoc88g/fwLpB57AvWMWSiVA+QZpNNYH6yDZArtnOXoK88hrbj9b+szIXCT9Wg5071ZPYEAsVQKV0qUTpG8bptNU6vNFz5bNKBwllJ6YY1YCivtCfVANIze2IDz/PO4gHv9NagzpyOEgig/C1GyCLnvdZytq7A2/hC3PotAKErqc5+jvbaBwJ7dOCWlODk5AGiOJJCqRan9ABGeh1y9gdSBQ7hH6rpDbSIzE6WsFG3+XJSyUpSyEkQ02p2LZKdtzxuStpDz53vGp1IE3l1NsCAP9ev3oBT2LsykzZ+DNn8OgbSFs3sPyd27SE+dRntbG/LAYcg4GgJSFAUtoKGGVe+C77g4aQvLdXsUo/IQjg2aRl3M7f4b9gnTOZ3J10N5OhQFolHvAd7fw3EQUiI67RKK4s2Y0nUUTevlwRvua/A8DY7jYFkWVnMzKdfFTiax2tuJd4mGnt8TUJub0UwT5dAhgnl5RJcvJ3DOOf3mUPmcWgxHUHwTuBYwgCdM03x+ZE0aGrv9AELPQAnlAUerZsYti7TlACoZmUdj+EIICOVAKAeRP7PXsaSUkG73BEbcExxdwkPY1aiROOo0kCkVty2M225hf9QK6zeCIlBKclGmT0GdPRelpGxw16iTRjbu9Cr0fWpqmmdHGhQVBsidkLF63O3PIGvXQzALZf7tiIrl3fP3nR27SD/5LEpFOcGbPz9gAqZaUUbonruw330f6+13cbZ+utTIUZSKcgJXX4FS3FnM6BSa+na6IV0bN9mM0tnDIxIIdOcfRQIBLNvltfUWUypDzJ4e6Wd/SSqRIhlP4jouQvGmGgfDQe8OPCcD954vk3r8KVKP/g79/HPRVp7jdfONp7B3qtibSsG2UfLiiLwYak01icnTSc8+A5JJ1I0bUbdtQ2lv94QJAIdB11HKy9BWnI1SUYZaXtZndkkXQojuZoBduK6L3dyKouhoN1475N9KBHS02QYZQCqWIOk6iPZ2xJZPYM9elPp6hJSe97KyHLe5Bbf6AGpzi+clyMqCaVOgvBw3nsC1bGRGBumCAs+DYNveeE+lIJmERALFssC2EV3PgBIIoAQCqOEwSjiMGo2iRqNoWVmoWVmooZHN8eiV25SdDZMn47S1kVi7lvj69TiAKC1FmzEDd9cu3G3bUAIBwkuWEL7jDrSC8dd91mfkGI6gyAXCQC2QaxjG/2Oa5ndG1qzBcdqru8Md4FXNDKgqiXQaVZPEEi55w3TtCSEgmOUVxMnrW+xHpmOdAqMOtVNwuO1HkEdacBtANqew327Cfns9REC/4BK0xYtBSmR7B7KjA9nShFv9EW7tTkjaSEcBGQLC4GpIy4Vk6miDHkWBQMBrH60BqgtYCNmMCDuIKStR516EKCjqVv3Onn2kHn8KpbiI4Be/MKTnRGgq+nnnoK042zu5DUQoOC5iwz4njhurB2R3l9GQpqEIQTydJhII8MaGVlpjkttv7B3Wc2yHZDzpzfqRoOoq0Yxov/VXlLxcQl+5k/QfXsR68x2cQ7UouTnY6zeC66LOn4t+7gpEVIDrIKKF6J3TpPVgLpQXIs9e4o2b9g5Ip1FKShBFhYgT6KujKAqB/OHVl/g0wWiYYDQMFMGsacgVS3H2VePu3YezZx/W7j0QiaBWVaCcdSZKVQVKSXGvGh1ufQOptevg9deRqRQiEEDJzkbNzkbJyUHNykIpLkbtWpadPSIzLE4GalYWGRdfTPT880lt3Ur8/fexnn8etbCQjKuvJrR4MUpw+PlZPqcOwxEUTwI7gHlAEogPvvnIIu0UTuwwoeJFvZaHAwFaEwkK8xRi8ZOX5iECUQhMQeRO6V7WdZqQVhxi9bh11Tg7d+Ns2Yv1witYL70xwEU6DJEgQhegWCDbEJqNCEkoyURkF4FtQawNGW8G2/HEhyVA6rhWJjS4cGA/9tu/BE1D5OehFOTj7NiFyM8jePvNiGM4EQldg3Fc8dPn5OHGjgBHu4wKIQh35h8l0w4vvN/MtHKFmVMi/Yc1QgFC0dCQ9UxEQCdw3dXY5WVYL72KC6gL5qGfu6K7HXpPumY0AaDriLxc6Ge78YIIh9FmGzDby9OQyRQEBy9upxQWoFx2IflXX4e0bcQIexZGA6FphObPJzR/Pm5HByI6ePKqz6nPsK4kpmneZxjGL4G7gbeH2t4wDAX4CbAASAF3m6a5q8f6rwL3AjbwL6Zp/mG4Bjut+wG3T8vyiK7TmkiQEVFo77D63/kkI/QI5ExCzZmEOvNc7KlP465/G6kvAJGAxD6EmkaUzUSddRGiZEavOKJ0bWjeg9uwHdmwDVrWQ1CHkjJE5kxEVjlkliOyyhEBz70rEwnc+kZkQwNufSNuQwPuoVqUokKCN9+IiPR1VftMHEZ07HR4gkLp0bY8EggQS6dZu62VtpjD7ZeFvJoqA4U1hokQAn3ZmahTJ3vhipxTt9TyscyWEro+7CnVEwklo//wk8/pxbAEhWEYISCK18djOL+czwEh0zSXG4ZxNvA9vDwMDMMowcvLOBMIAasNw3jFNM1hBeft5j2e4Z8SFLqq4rqd0QJ1bNqNqMY1UL8F2ryufMJYjjLt0gErBQpFg/yZqPkzwbgG6aRB0QYtHSzCYdSqCqjyCzadoozY2HFjdaAEEIGs7mWhzotb0knzxSuyKSxSibfHuyujnmhZ+U8nPfr4+Jy6DEdQ/Bj4Fl7tiQPA6mHscw6wCsA0zTWGYfScZ3gW8G7nSTBlGMYuYD6wbjgGV+/YSdCK8rc/agAaeq27eFmEM+eEyI6MjaAQioa65D5kzTovWXKYPQG69x+knbTPacOIjZ1D+w9hxbL52+/t7rX8cxdkMGtKAClBwyGSnzuuG9/5+PiMT4Zz1giZpvlvAIZhPGGaZtsw9skCWnu8dwzD0EzTtPtZ1w4M6g/dsmVLg2EY+3sv7Rt5ef23w7DMx+fksso0zcuH3mzYjMLYWdXrnT9ufMaIkz12fMaY4QiKe4BHAIYpJgDaoFdlVaXzhNjfukygZbCDmaZZONh6H59TCH/s+Pj4TEiGIyiCxv/f3v0HWVnVcRx/r4AUY06pTIqRTtl8s9SKBqgmYkGFIW2YMEsSGdQiyEqGMsZgwhCcaUorxgQHEUal+JGjYWXgTKGExs4gkgR+NMssAifEfkhTFNAf59y4u957l92be++zfF7/8Oz9cZ7vubvfh3Oe5z7nG7EVEPm2cEmf7OQ9m4CPAKvzdeAny55rAxbk72X0B84Gtnc1cLNeyrljZoV0NAOKWd1o9z7gwoh4lLR42pURMRP4jaS1EbEQ2Egqhz5b0j+7sQ+z3si5Y2aF1FKp7DNARMyRND9vnyZpd49GZmZmZoVR68by0WXbK17tQMzMzKy4ag0oWqpsm5mZmbVTa0BxuMq2mZmZWTu1vkPxV+DXpLMT7yjbPizpAz0WYRd0tmxxUeS7akrrDfwOuB34Dmm55fWSvtao2I5WRAwHvi6pNSLOApaTBqbbgWskHYqIucBFpH7NkNTWsIA70aE/Q4AHgGfy04skrSpSfzpy7jQP505z98eqq3WXx3k9FsX/T9Vli4si3xKIpNayx54ALgF+C/w4IoZIerwxEXYuIr4MXAHszw/dAsyRtCEiFgPj82JLI4HhwGDgXmBoI+LtTIX+DAFukXRz2WuGUJD+VOHcaQLOnebuj9VWdUAh6ffVnmtitZYtLop3AQMiYj3p93MD0F/SswARsQ44H2jagyLwLDABuDv//F7g4bz9IDCGtK7JekmHgecjom9EDJT05x6PtnOV+hMRMZ4005pB+tsrSn8qce40B+dOc/fHajj68oHFUHHZ4kYF003/AL4JjAWmActoXzK+0+WWG03SvUB5ydeWfLCAI/F3eRnpRqnQnzbgOkkfIs1851Kg/lTh3GkCzh2giftjtfW2AUWtZYuL4mngHkmHJT1NSrSTyp7vdLnlJnSobLsUf5eXkW4i90naUtoG3kOx+wPOnWbl3LHC6G0Dik3AhwEqLFtcFFeRrl8TEYOAAcD+iHhrRLSQZl8bGxhfd2yNiNa8PY4U/yZgbEQcFxFvJv0HtrdaA01mXUQMy9vnA1sodn/AudOsnDtWGEU7pdmZVyxb3OB4umMpsDwifkH6ZvdVpFnKCqAP6Vrj5gbG1x1fBJZExPHATuAHkg5GxEbgMdLA9ppGBthF04FbI+IAsAeYKulvBe4POHealXPHCqPqbaNmZmZmR6u3XfIwMzOzBvCAwszMzOrmAYWZmZnVzQMKMzMzq5sHFGZmZla33nbbaLfle71XAztIt831A74taXUP7X8B6T75LwHnSLq1G20MI60OuFbS9XXGswGYJumpssfOBd4g6ZF62u5CDMuBlZJ+2sX3TQWWSfp3py+2ujl3XtHWBpw7dgzyGYr2fiapVdJI0pr5syLi3T20708AoyRtAOZ0s40xwOJ6D4g1XEKqPNvsvkJad8B6jnOnNueO9Xo+Q1GFpJcj4nbgYxHxJKkM8mDgZFKRnrmkpX6HSdoXEdOBE0jFcGaR1q9/Dpgs6X/L50bEyPxeSCv5TQYuA95Eqoa4DjgpIm4DrgUWA28jDf5KVQe3533/S9LE3O4w4FPAgYj4I7APWAAczDF9hvT7XgacQZpFfp5Ulv4O4PXAKcASSYs6fh4RcTowJbf/OHBnKQbgOmAR8Jr8+cyTdH9E/IpU2Og80kJD44HjgVW5P/1INRf+DqwBdufP4UFJs8v2fWKlGPNM8AngHFI9gEuBC4BTgZWkCprWw5w77Tl37FjhMxS1vUBKwsHALyWNJVXGm54PdCtIBzRIJXrvAiYC35L0QWA9KVnLvROYJGk0sBa4VNI80qpxYyQtAPZJ+izpILc3F9IZD3w3t3ECcGPpgAggqQ1YTip3fD+wBJiQZ4y7SAe0acBzkt6ffx4OnEU6NToGuBiYWemDkLSr1H7eV3kMbwdulnQh8DmOrHR3IvD9shjG/7HlOgAAApdJREFUAcNINRbGAV8o+3zOzDENBUbnksYltWJsk3QB8BAwUdLS/FlehjWSc+dI+84dOyb4DEVtZwClGcvQiBhFKmTTPz+/FFgVEY8AeyS9EBEzgevzrGsn6QBVbhewMCJeBk4nrWNfzbnAiIgYnn/uGxEn523VeN9A4DRgdUQAvJZ0gB5ImiEiaTuwPdc8mBERE3Lf+tVot6NSDLuBORFxNWk2Vd7G1vzvH0izsNWkWeMPSTPR+fn5bZL2AUTEZiDK2thTI8by9k/tQuz26nLu1ObcsV7HZyiqiIjXAZ8mnU6cAvxF0uWk4kMDIqJF0vOkqnizSQdIgKnADXlm0QJ8tEPTdwBXSpoC/Cm/pqPSY0+RZimtpFnJGuCl/NyhCu8r2Us6mI/P710A/Jx0kB6a+/eWiPge6Ytsj0malNuvFE/JIdr/zZRiuBG4S9IVeT/lbXRc270V2J1nTPOBm/LjZ0fEgIjoQ5r97Sh7T60YK60d3zFO60HOnYqcO9br+QxFe6PztcWDpM9mriRFRF9gZUSMAPYDzwCDSDOmJcBCYFJuow14KCJeJF3f/FGHfdwNbI6Il0inhQdViGNHRNwDXE0qDPQw6fTmbZIO5ZlTVfk115KuKx9HmplMBh4F7szt9QFmkEoFL4qIy4EXgf9ERP8qTW8BvhEROzs8voY0c9xDmu2cUiO8baSZ6QzS5zwvP34gt/NGUgGkbWX9fKALMUKqyPiTiBglycVqeoZzx7ljxzgXB6tTRHycdKvaVxsdS1FFxJmk67zva3Qs1nOcO/Vz7lgz8RmKOkTETcAI0pe+zOwoOXfMeh+foTAzM7O6+cs3ZmZmVjcPKMzMzKxuHlCYmZlZ3TygMDMzs7p5QGFmZmZ1+y9nDGL8UCwRWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 540x324 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot OTU counts abundance for only placebo\n",
    "#collapse all donor categories\n",
    "csource_df.loc[:, 'otu_src_bin'] = csource_df['otu_src'].replace({'donor0044_otus': 'donor_any_otus',\n",
    "                                                                        'donor0073_otus': 'donor_any_otus',\n",
    "                                                                        'donor0044_0073_otus': 'donor_any_otus'})\n",
    "#make a new dataframe with these labels\n",
    "csource_ct_bin_df = csource_df.groupby(['sample_id', \n",
    "                                           'otu_src_bin']).sum()\n",
    "csource_ct_bin_df = csource_ct_bin_df.reset_index()[['sample_id', \n",
    "                                                     'otu_src_bin',\n",
    "                                                     'otu_count',\n",
    "                                                     'frac_otus']]\n",
    "csource_ct_bin_df = csource_ct_bin_df.merge(md)\n",
    "\n",
    "fig = plt.figure(figsize = (7.5,4.5))\n",
    "\n",
    "counter = 1\n",
    "for patient, sub_df in csource_ct_bin_df.groupby('patient'):\n",
    "    if patient in ['001', '007', '008', '010', '011', '014']:\n",
    "        ax = fig.add_subplot(3,2,counter)\n",
    "        sns.lineplot(x='days_past_fmt', y='frac_otus',\n",
    "                 hue='otu_src_bin', data=sub_df, palette=dd_otu_set)\n",
    "        \n",
    "        ax.axvspan(-8, 0, alpha=0.05, color='red') #antibiotics\n",
    "        ax.axvspan(0, 84, alpha=0.05, color='green') #capsules\n",
    "        \n",
    "        ax.set_title(pat_id_dict[patient])\n",
    "        \n",
    "        #set axes\n",
    "        ax.set_ylim(0, 0.9)\n",
    "        ax.set_xlim(-12, 150)\n",
    "        \n",
    "        #add lines at each of the IBDQ timepoints\n",
    "        screen_time = min(sub_df['days_past_fmt'].tolist(), \n",
    "                          key=lambda x:abs(x - -7))\n",
    "        week4_time = min(sub_df['days_past_fmt'].tolist(), \n",
    "                          key=lambda x:abs(x - 28))\n",
    "        week12_time = min(sub_df['days_past_fmt'].tolist(), \n",
    "                          key=lambda x:abs(x - 84))\n",
    "        week18_time = min(sub_df['days_past_fmt'].tolist(), \n",
    "                          key=lambda x:abs(x - 126))\n",
    "        \n",
    "        ax.plot([screen_time, screen_time], \n",
    "                [0.85, 1], color='black')\n",
    "        ax.plot([week4_time, week4_time], \n",
    "                [0.85, 1], color='black')\n",
    "        ax.plot([week12_time, week12_time], \n",
    "                [0.85, 1], color='black')\n",
    "        ax.plot([week18_time, week18_time], \n",
    "                [0.85, 1], color='black')\n",
    "        \n",
    "        \n",
    "        if counter in [1, 3, 5]:\n",
    "            ax.set_ylabel('Fraction of ASVs')\n",
    "        else:\n",
    "            ax.set_ylabel('')\n",
    "        \n",
    "        if counter in [5, 6]:\n",
    "            ax.set_xlabel('Days after fecal transplant')\n",
    "        else:\n",
    "            ax.set_xlabel('')\n",
    "        \n",
    "        if counter != 4:\n",
    "            ax.legend_.remove()\n",
    "        else:\n",
    "            ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),\n",
    "                     labels=['Donor ASVs', 'Unknown ASVs',\n",
    "                             'Patient ASVs', 'Shared ASVs'])\n",
    "    \n",
    "        counter += 1\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('/Users/ndchu/Documents/uc_fmt/figures/soure_plot_16S_otufrac_fmt_simple_2019-05-22.png',\n",
    "            dpi=300)\n",
    "plt.savefig('/Users/ndchu/Documents/uc_fmt/figures/soure_plot_16S_otufrac_fmt_simple_2019-05-22.pdf',\n",
    "            dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MGX Species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get species\n",
    "mtphl_sp = mtphl.loc[mtphl['taxa_level'] == 7].reset_index()\n",
    "\n",
    "#add metadata\n",
    "mtphl_sp_md = mtphl_sp.merge(md, left_on='sample', right_on='sample_id')\n",
    "\n",
    "#get donor taxa\n",
    "taxa_0044 = set(mtphl_sp_md.loc[mtphl_sp_md['patient'] == '0044']['taxa'].unique())\n",
    "taxa_0073 = set(mtphl_sp_md.loc[mtphl_sp_md['patient'] == '0073']['taxa'].unique())\n",
    "\n",
    "donor_taxa_dict = {'0044': taxa_0044, '0073': taxa_0073}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#source track bacteria for each patient\n",
    "csource_abun_mtphl = [] #have this be in format [[sample, recipient, donor0044, donor0073, shared, environment]]\n",
    "csource_ct_mtphl = [] #also track the otus counts\n",
    "\n",
    "for patient, pat_df in mtphl_sp_md.groupby('patient'):\n",
    "    if patient in ['0044', '0073']:\n",
    "        continue\n",
    "    #get patient baseline otus\n",
    "    bl_sam = md.loc[(md['patient'] == patient) & (md['days_past_fmt'] < -5)]['sample_id'].tolist()\n",
    "    bl_taxa = set(pat_df.loc[pat_df['sample_id'].isin(bl_sam)]['taxa'])\n",
    "    \n",
    "    #get patient abx otus\n",
    "    abx_sam = md.loc[(md['patient'] == patient) & \\\n",
    "                     (md['days_past_fmt'] > -5) & \\\n",
    "                     (md['days_past_fmt'] < 1)]['sample_id'].tolist()\n",
    "    abx_taxa = set(pat_df.loc[pat_df['sample_id'].isin(abx_sam)]['taxa'])\n",
    "    \n",
    "    #subtract the abx otus from the donor otus, since they came from before FMT\n",
    "    taxa_0044_noabx = taxa_0044 - abx_taxa\n",
    "    taxa_0073_noabx = taxa_0073 - abx_taxa\n",
    "    \n",
    "    #source track bacteria for each sample\n",
    "    for sample, sam_df in pat_df.groupby('sample_id'):\n",
    "        taxa = set(sam_df['taxa'])\n",
    "        \n",
    "        # for placebo patients\n",
    "        if sam_df['treatment'].iloc[0] == 'placebo':\n",
    "            shared = set()\n",
    "            src_0044 = set()\n",
    "            src_0073 = set()\n",
    "            src_0044_0073 = set()\n",
    "            src_pat = taxa & bl_taxa\n",
    "            env = taxa - bl_taxa\n",
    "        \n",
    "        # in cases of only 0044\n",
    "        elif set(pat_df['fmt_past']) == set([np.nan, '0044']):\n",
    "            shared = (taxa & taxa_0044_noabx & bl_taxa)\n",
    "            src_0044 = (taxa & taxa_0044_noabx) - bl_taxa\n",
    "            src_0073 = set()\n",
    "            src_0044_0073 = set()\n",
    "            src_pat = (taxa & bl_taxa) - shared\n",
    "            env = taxa - bl_taxa - taxa_0044_noabx\n",
    "            \n",
    "        # in cases of only 0073\n",
    "        elif set(pat_df['fmt_past']) == set([np.nan, '0073']):\n",
    "            shared = (taxa & taxa_0073_noabx & bl_taxa)\n",
    "            src_0044 = set()\n",
    "            src_0073 = (taxa & taxa_0073_noabx) - bl_taxa\n",
    "            src_0044_0073 = set()\n",
    "            src_pat = (taxa & bl_taxa) - shared\n",
    "            env = taxa - bl_taxa - taxa_0073_noabx\n",
    "        \n",
    "        #in cases of two donors\n",
    "        elif (set(pat_df['fmt_past']) == set(['0044', '0044, 0073', np.nan])) | \\\n",
    "             (set(pat_df['fmt_past']) == set(['0073', '0044, 0073', np.nan])):\n",
    "            shared = (taxa & taxa_0044_noabx & bl_taxa) | (taxa & taxa_0073_noabx & bl_taxa)\n",
    "            src_0044 = (taxa & taxa_0044_noabx) - bl_taxa - taxa_0073_noabx\n",
    "            src_0073 = (taxa & taxa_0073_noabx) - bl_taxa - taxa_0044_noabx\n",
    "            src_0044_0073 = (taxa & taxa_0044_noabx & taxa_0073_noabx) - bl_taxa\n",
    "            src_pat = (taxa & bl_taxa) - shared\n",
    "            env = taxa - bl_taxa - taxa_0044_noabx - taxa_0073_noabx\n",
    "        \n",
    "        taxa_sets = [shared, src_0044, src_0073, src_0044_0073, src_pat, env]\n",
    "        abun = [sam_df.loc[sam_df['taxa'].isin(x)]['rel_abundance'].sum() \\\n",
    "                    for x in taxa_sets]\n",
    "        cts = [len(x) for x in taxa_sets]\n",
    "        \n",
    "        \n",
    "        csource_abun_mtphl.append([sample] + abun)\n",
    "        csource_ct_mtphl.append([sample] + cts)\n",
    "\n",
    "#make into a dataframe\n",
    "#CUMULATIVE ABUNDANCE\n",
    "csource_abun_mtphl_df = pd.DataFrame(csource_abun_mtphl,\n",
    "                                     columns=['sample_id', 'shared_taxa', 'donor0044_taxa',\n",
    "                                              'donor0073_taxa', 'donor0044_0073_taxa',\n",
    "                                              'patient_taxa', 'env_taxa'])\n",
    "#COUNTS OF OTUS\n",
    "csource_ct_mtphl_df = pd.DataFrame(csource_ct_mtphl,\n",
    "                                   columns=['sample_id', 'shared_taxa', 'donor0044_taxa',\n",
    "                                            'donor0073_taxa', 'donor0044_0073_taxa',\n",
    "                                            'patient_taxa', 'env_taxa'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt these tables into tidy tables\n",
    "csource_abun_mtphl_df = csource_abun_mtphl_df.melt(id_vars='sample_id', \n",
    "                                       var_name='taxa_src', \n",
    "                                       value_name='cum_rel_abun')\n",
    "\n",
    "csource_ct_mtphl_df = csource_ct_mtphl_df.melt(id_vars='sample_id', \n",
    "                                   var_name='taxa_src', \n",
    "                                   value_name='taxa_count')\n",
    "#merge these tables\n",
    "csource_mtphl_df = csource_abun_mtphl_df.merge(csource_ct_mtphl_df)\n",
    "\n",
    "#add metadata\n",
    "csource_mtphl_df = csource_mtphl_df.merge(md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relative_abundance_tidy(df, sample_col, value_col,\n",
    "                            rel_abun_name='rel_abun'):\n",
    "    \"\"\"\n",
    "    Take a tidy table, where the samples are in sample_col and the values\n",
    "    to be converted to relative abundance are in value_col.\n",
    "    \n",
    "    sample_col can also be a list of columns\n",
    "    \"\"\"\n",
    "    #define relatibe abundance calculation\n",
    "    rel_abun = lambda x: (x / x.sum())\n",
    "    #calculate new values\n",
    "    values = df.groupby(sample_col)[value_col].transform(rel_abun)\n",
    "    values = values.rename(rel_abun_name)\n",
    "    #add to df and return\n",
    "    df = pd.concat([df, values], axis=1)\n",
    "    return df\n",
    "\n",
    "#for otu counts, also add a frequency column\n",
    "csource_mtphl_df = relative_abundance_tidy(csource_mtphl_df, 'sample_id', \n",
    "                                           'taxa_count', rel_abun_name='frac_mgx_sp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write this to a table\n",
    "csource_mtphl_df.to_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/csource_mtphl_df_2019-05-16.tsv',\n",
    "                        sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read this to a table\n",
    "csource_mtphl_df = pd.read_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/csource_mtphl_df_2019-05-16.tsv',\n",
    "                               sep='\\t', dtype={'patient': 'object',\n",
    "                                                'fmt': 'object',\n",
    "                                                'fmt_induction': 'object'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd_taxa_set = {f'{k[:-5]}_taxa': v for k, v in dd_otu_set.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ndchu/programs/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 540x324 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot OTU counts abundance for only placebo\n",
    "#collapse all donor categories\n",
    "csource_mtphl_df.loc[:, 'taxa_src_bin'] = csource_mtphl_df['taxa_src'].replace({'donor0044_taxa': 'donor_any_taxa',\n",
    "                                                                              'donor0073_taxa': 'donor_any_taxa',\n",
    "                                                                              'donor0044_0073_taxa': 'donor_any_taxa'})\n",
    "#make a new dataframe with these labels\n",
    "csource_mtphl_bin_df = csource_mtphl_df.groupby(['sample_id', \n",
    "                                           'taxa_src_bin']).sum()\n",
    "csource_mtphl_bin_df = csource_mtphl_bin_df.reset_index()[['sample_id', \n",
    "                                                     'taxa_src_bin',\n",
    "                                                     'taxa_count',\n",
    "                                                     'frac_mgx_sp']]\n",
    "csource_mtphl_bin_df = csource_mtphl_bin_df.merge(md)\n",
    "\n",
    "fig = plt.figure(figsize = (7.5,4.5))\n",
    "\n",
    "counter = 1\n",
    "for patient, sub_df in csource_mtphl_bin_df.groupby('patient'):\n",
    "    if patient in ['001', '007', '008', '010', '011', '014']:\n",
    "        ax = fig.add_subplot(3,2,counter)\n",
    "        sns.lineplot(x='days_past_fmt', y='frac_mgx_sp',\n",
    "                 hue='taxa_src_bin', data=sub_df, palette=dd_taxa_set)\n",
    "        \n",
    "        ax.axvspan(-8, 0, alpha=0.05, color='red') #antibiotics\n",
    "        ax.axvspan(0, 84, alpha=0.05, color='green') #capsules\n",
    "        \n",
    "        ax.set_title(pat_id_dict[patient])\n",
    "        \n",
    "        ax.set_ylim(0, 0.9)\n",
    "        \n",
    "        if counter in [1, 3, 5]:\n",
    "            ax.set_ylabel('Fraction of species')\n",
    "        else:\n",
    "            ax.set_ylabel('')\n",
    "        \n",
    "        if counter in [5, 6]:\n",
    "            ax.set_xlabel('Days after fecal transplant')\n",
    "        else:\n",
    "            ax.set_xlabel('')\n",
    "        \n",
    "        if counter != 4:\n",
    "            ax.legend_.remove()\n",
    "        else:\n",
    "            ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),\n",
    "                     labels=['Donor species', 'Unknown species',\n",
    "                             'Patient species', 'Shared species'])\n",
    "    \n",
    "        counter += 1\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('/Users/ndchu/Documents/uc_fmt/figures/soure_plot_mgx_mgxfrac_fmt_simple_2019-05-16.png',\n",
    "            dpi=300)\n",
    "plt.savefig('/Users/ndchu/Documents/uc_fmt/figures/soure_plot_mgx_mgxfrac_fmt_simple_2019-05-16.pdf',\n",
    "            dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gut check source plots for placebo patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#source track bacteria for each placebo patient\n",
    "#let's first check with only donor 44\n",
    "plc_dsrc_chk_abun = [] #have this be in format [[sample, recipient, donor0044, donor0073, shared, environment]]\n",
    "plc_dsrc_chk_ct = [] #also track the otus counts\n",
    "\n",
    "for patient, pat_df in otu_df_md.groupby('patient'):\n",
    "    if patient in ['0044', '0073', '001', '007', '008',\n",
    "                   '010', '011', '014']:\n",
    "        continue\n",
    "    #get patient baseline otus\n",
    "    bl_sam = md.loc[(md['patient'] == patient) & (md['days_past_fmt'] < -5)]['sample_id'].tolist()\n",
    "    bl_otus = set(pat_df.loc[pat_df['sample_id'].isin(bl_sam)]['#OTU ID'])\n",
    "    \n",
    "    #get patient abx otus\n",
    "    abx_sam = md.loc[(md['patient'] == patient) & \\\n",
    "                     (md['days_past_fmt'] > -5) & \\\n",
    "                     (md['days_past_fmt'] < 1)]['sample_id'].tolist()\n",
    "    abx_otus = set(pat_df.loc[pat_df['sample_id'].isin(abx_sam)]['#OTU ID'])\n",
    "    \n",
    "    #subtract the abx otus from the donor otus, since they came from before FMT\n",
    "    otu_0044_noabx = otu_0044 - abx_otus\n",
    "    \n",
    "    #source track bacteria for each sample\n",
    "    for sample, sam_df in pat_df.groupby('sample_id'):\n",
    "        otus = set(sam_df['#OTU ID'])\n",
    "        \n",
    "        # for placebo patients with donor 0044\n",
    "        shared = (otus & otu_0044_noabx & bl_otus)\n",
    "        src_0044 = (otus & otu_0044_noabx) - bl_otus - abx_otus\n",
    "        src_0073 = set()\n",
    "        src_0044_0073 = set()\n",
    "        src_pat = (otus & bl_otus) - shared\n",
    "        env = otus - bl_otus - otu_0044_noabx\n",
    "        \n",
    "        otu_sets = [shared, src_0044, src_0073, src_0044_0073, src_pat, env]\n",
    "        abun = [sam_df.loc[sam_df['#OTU ID'].isin(x)]['rel_abundance'].sum() \\\n",
    "                    for x in otu_sets]\n",
    "        cts = [len(x) for x in otu_sets]\n",
    "        \n",
    "        \n",
    "        plc_dsrc_chk_abun.append([sample] + abun)\n",
    "        plc_dsrc_chk_ct.append([sample] + cts)\n",
    "\n",
    "#make into a dataframe\n",
    "#CUMULATIVE ABUNDANCE\n",
    "plc_dsrc_chk_abun_df = pd.DataFrame(plc_dsrc_chk_abun,\n",
    "                               columns=['sample_id', 'shared_otus', 'donor0044_otus',\n",
    "                                        'donor0073_otus', 'donor0044_0073_otus',\n",
    "                                        'patient_otus', 'env_otus'])\n",
    "#COUNTS OF OTUS\n",
    "plc_dsrc_chk_ct_df = pd.DataFrame(plc_dsrc_chk_ct,\n",
    "                             columns=['sample_id', 'shared_otus', 'donor0044_otus',\n",
    "                                      'donor0073_otus', 'donor0044_0073_otus',\n",
    "                                      'patient_otus', 'env_otus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt these tables into tidy tables\n",
    "plc_dsrc_chk_abun_df = plc_dsrc_chk_abun_df.melt(id_vars='sample_id', \n",
    "                                       var_name='otu_src', \n",
    "                                       value_name='cum_rel_abun')\n",
    "\n",
    "plc_dsrc_chk_ct_df = plc_dsrc_chk_ct_df.melt(id_vars='sample_id', \n",
    "                                   var_name='otu_src', \n",
    "                                   value_name='otu_count')\n",
    "#merge these tables\n",
    "plc_dsrc_chk_df = plc_dsrc_chk_abun_df.merge(plc_dsrc_chk_ct_df)\n",
    "\n",
    "#add metadata\n",
    "plc_dsrc_chk_df = plc_dsrc_chk_df.merge(md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relative_abundance_tidy(df, sample_col, value_col,\n",
    "                            rel_abun_name='rel_abun'):\n",
    "    \"\"\"\n",
    "    Take a tidy table, where the samples are in sample_col and the values\n",
    "    to be converted to relative abundance are in value_col.\n",
    "    \n",
    "    sample_col can also be a list of columns\n",
    "    \"\"\"\n",
    "    #define relatibe abundance calculation\n",
    "    rel_abun = lambda x: (x / x.sum())\n",
    "    #calculate new values\n",
    "    values = df.groupby(sample_col)[value_col].transform(rel_abun)\n",
    "    values = values.rename(rel_abun_name)\n",
    "    #add to df and return\n",
    "    df = pd.concat([df, values], axis=1)\n",
    "    return df\n",
    "\n",
    "#for otu counts, also add a frequency column\n",
    "plc_dsrc_chk_df = relative_abundance_tidy(plc_dsrc_chk_df, \n",
    "                                          'sample_id', 'otu_count', \n",
    "                                          rel_abun_name='frac_otus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write this to a table\n",
    "plc_dsrc_chk_df.to_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/csource_placebo_check_0044_df_2019-05-22.tsv',\n",
    "                  sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x324 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot OTU counts abundance for only placebo\n",
    "#collapse all donor categories\n",
    "plc_dsrc_chk_df.loc[:, 'otu_src_bin'] = plc_dsrc_chk_df['otu_src'].replace({'donor0044_otus': 'donor_any_otus',\n",
    "                                                                        'donor0073_otus': 'donor_any_otus',\n",
    "                                                                        'donor0044_0073_otus': 'donor_any_otus'})\n",
    "#make a new dataframe with these labels\n",
    "plc_dsrc_chk_bin_df = plc_dsrc_chk_df.groupby(['sample_id', \n",
    "                                           'otu_src_bin']).sum()\n",
    "plc_dsrc_chk_bin_df = plc_dsrc_chk_bin_df.reset_index()[['sample_id', \n",
    "                                                     'otu_src_bin',\n",
    "                                                     'otu_count',\n",
    "                                                     'frac_otus']]\n",
    "plc_dsrc_chk_bin_df = plc_dsrc_chk_bin_df.merge(md)\n",
    "\n",
    "fig = plt.figure(figsize = (7.5,4.5))\n",
    "\n",
    "counter = 1\n",
    "for patient, sub_df in plc_dsrc_chk_bin_df.groupby('patient'):\n",
    "    if patient in ['004', '005', '006', '009', '012', '013']:\n",
    "        ax = fig.add_subplot(3,2,counter)\n",
    "        sns.lineplot(x='days_past_fmt', y='frac_otus',\n",
    "                 hue='otu_src_bin', data=sub_df, palette=dd_otu_set)\n",
    "        \n",
    "        ax.axvspan(-8, 0, alpha=0.05, color='red') #antibiotics\n",
    "        ax.axvspan(0, 84, alpha=0.05, color='green') #capsules\n",
    "        \n",
    "        ax.set_title(pat_id_dict[patient])\n",
    "        \n",
    "        #set axes\n",
    "        ax.set_ylim(0, 0.9)\n",
    "        ax.set_xlim(-12, 150)\n",
    "        \n",
    "        if counter in [1, 3, 5]:\n",
    "            ax.set_ylabel('Fraction of ASVs')\n",
    "        else:\n",
    "            ax.set_ylabel('')\n",
    "        \n",
    "        if counter in [5, 6]:\n",
    "            ax.set_xlabel('Days after fecal transplant')\n",
    "        else:\n",
    "            ax.set_xlabel('')\n",
    "        \n",
    "        if counter != 4:\n",
    "            ax.legend_.remove()\n",
    "        else:\n",
    "            ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),\n",
    "                     labels=['Donor ASVs', 'Unknown ASVs',\n",
    "                             'Patient ASVs', 'Shared ASVs'])\n",
    "    \n",
    "        counter += 1\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('/Users/ndchu/Documents/uc_fmt/figures/source_placebo_chk44_16S_otufrac_2019-05-22.png',\n",
    "            dpi=300)\n",
    "plt.savefig('/Users/ndchu/Documents/uc_fmt/figures/source_placebo_chk44_16S_otufrac_2019-05-22.pdf',\n",
    "            dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#source track bacteria for each placebo patient\n",
    "#let's first check with only donor 44\n",
    "plc_dsrc_chk_abun = [] #have this be in format [[sample, recipient, donor0044, donor0073, shared, environment]]\n",
    "plc_dsrc_chk_ct = [] #also track the otus counts\n",
    "\n",
    "for patient, pat_df in otu_df_md.groupby('patient'):\n",
    "    if patient in ['0044', '0073', '001', '007', '008',\n",
    "                   '010', '011', '014']:\n",
    "        continue\n",
    "    #get patient baseline otus\n",
    "    bl_sam = md.loc[(md['patient'] == patient) & (md['days_past_fmt'] < -5)]['sample_id'].tolist()\n",
    "    bl_otus = set(pat_df.loc[pat_df['sample_id'].isin(bl_sam)]['#OTU ID'])\n",
    "    \n",
    "    #get patient abx otus\n",
    "    abx_sam = md.loc[(md['patient'] == patient) & \\\n",
    "                     (md['days_past_fmt'] > -5) & \\\n",
    "                     (md['days_past_fmt'] < 1)]['sample_id'].tolist()\n",
    "    abx_otus = set(pat_df.loc[pat_df['sample_id'].isin(abx_sam)]['#OTU ID'])\n",
    "    \n",
    "    #subtract the abx otus from the donor otus, since they came from before FMT\n",
    "    otu_0073_noabx = otu_0073 - abx_otus\n",
    "    \n",
    "    #source track bacteria for each sample\n",
    "    for sample, sam_df in pat_df.groupby('sample_id'):\n",
    "        otus = set(sam_df['#OTU ID'])\n",
    "        \n",
    "        # for placebo patients with donor 0044\n",
    "        shared = (otus & otu_0073_noabx & bl_otus)\n",
    "        src_0044 = set()\n",
    "        src_0073 = (otus & otu_0073_noabx) - bl_otus\n",
    "        src_0044_0073 = set()\n",
    "        src_pat = (otus & bl_otus) - shared\n",
    "        env = otus - bl_otus - otu_0073_noabx\n",
    "        \n",
    "        otu_sets = [shared, src_0044, src_0073, src_0044_0073, src_pat, env]\n",
    "        abun = [sam_df.loc[sam_df['#OTU ID'].isin(x)]['rel_abundance'].sum() \\\n",
    "                    for x in otu_sets]\n",
    "        cts = [len(x) for x in otu_sets]\n",
    "        \n",
    "        \n",
    "        plc_dsrc_chk_abun.append([sample] + abun)\n",
    "        plc_dsrc_chk_ct.append([sample] + cts)\n",
    "\n",
    "#make into a dataframe\n",
    "#CUMULATIVE ABUNDANCE\n",
    "plc_dsrc_chk_abun_df = pd.DataFrame(plc_dsrc_chk_abun,\n",
    "                               columns=['sample_id', 'shared_otus', 'donor0044_otus',\n",
    "                                        'donor0073_otus', 'donor0044_0073_otus',\n",
    "                                        'patient_otus', 'env_otus'])\n",
    "#COUNTS OF OTUS\n",
    "plc_dsrc_chk_ct_df = pd.DataFrame(plc_dsrc_chk_ct,\n",
    "                             columns=['sample_id', 'shared_otus', 'donor0044_otus',\n",
    "                                      'donor0073_otus', 'donor0044_0073_otus',\n",
    "                                      'patient_otus', 'env_otus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#melt these tables into tidy tables\n",
    "plc_dsrc_chk_abun_df = plc_dsrc_chk_abun_df.melt(id_vars='sample_id', \n",
    "                                       var_name='otu_src', \n",
    "                                       value_name='cum_rel_abun')\n",
    "\n",
    "plc_dsrc_chk_ct_df = plc_dsrc_chk_ct_df.melt(id_vars='sample_id', \n",
    "                                   var_name='otu_src', \n",
    "                                   value_name='otu_count')\n",
    "#merge these tables\n",
    "plc_dsrc_chk_df = plc_dsrc_chk_abun_df.merge(plc_dsrc_chk_ct_df)\n",
    "\n",
    "#add metadata\n",
    "plc_dsrc_chk_df = plc_dsrc_chk_df.merge(md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relative_abundance_tidy(df, sample_col, value_col,\n",
    "                            rel_abun_name='rel_abun'):\n",
    "    \"\"\"\n",
    "    Take a tidy table, where the samples are in sample_col and the values\n",
    "    to be converted to relative abundance are in value_col.\n",
    "    \n",
    "    sample_col can also be a list of columns\n",
    "    \"\"\"\n",
    "    #define relatibe abundance calculation\n",
    "    rel_abun = lambda x: (x / x.sum())\n",
    "    #calculate new values\n",
    "    values = df.groupby(sample_col)[value_col].transform(rel_abun)\n",
    "    values = values.rename(rel_abun_name)\n",
    "    #add to df and return\n",
    "    df = pd.concat([df, values], axis=1)\n",
    "    return df\n",
    "\n",
    "#for otu counts, also add a frequency column\n",
    "plc_dsrc_chk_df = relative_abundance_tidy(plc_dsrc_chk_df, \n",
    "                                          'sample_id', 'otu_count', \n",
    "                                          rel_abun_name='frac_otus')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write this to a table\n",
    "plc_dsrc_chk_df.to_csv('/Users/ndchu/Documents/uc_fmt/16S/dada2_AG302018/csource_placebo_check_0073_df_2019-05-22.tsv',\n",
    "                  sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x324 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot OTU counts abundance for only placebo\n",
    "#collapse all donor categories\n",
    "plc_dsrc_chk_df.loc[:, 'otu_src_bin'] = plc_dsrc_chk_df['otu_src'].replace({'donor0044_otus': 'donor_any_otus',\n",
    "                                                                        'donor0073_otus': 'donor_any_otus',\n",
    "                                                                        'donor0044_0073_otus': 'donor_any_otus'})\n",
    "#make a new dataframe with these labels\n",
    "plc_dsrc_chk_bin_df = plc_dsrc_chk_df.groupby(['sample_id', \n",
    "                                           'otu_src_bin']).sum()\n",
    "plc_dsrc_chk_bin_df = plc_dsrc_chk_bin_df.reset_index()[['sample_id', \n",
    "                                                     'otu_src_bin',\n",
    "                                                     'otu_count',\n",
    "                                                     'frac_otus']]\n",
    "plc_dsrc_chk_bin_df = plc_dsrc_chk_bin_df.merge(md)\n",
    "\n",
    "fig = plt.figure(figsize = (7.5,4.5))\n",
    "\n",
    "counter = 1\n",
    "for patient, sub_df in plc_dsrc_chk_bin_df.groupby('patient'):\n",
    "    if patient in ['004', '005', '006', '009', '012', '013']:\n",
    "        ax = fig.add_subplot(3,2,counter)\n",
    "        sns.lineplot(x='days_past_fmt', y='frac_otus',\n",
    "                 hue='otu_src_bin', data=sub_df, palette=dd_otu_set)\n",
    "        \n",
    "        ax.axvspan(-8, 0, alpha=0.05, color='red') #antibiotics\n",
    "        ax.axvspan(0, 84, alpha=0.05, color='green') #capsules\n",
    "        \n",
    "        ax.set_title(pat_id_dict[patient])\n",
    "        \n",
    "        #set axes\n",
    "        ax.set_ylim(0, 0.9)\n",
    "        ax.set_xlim(-12, 150)\n",
    "        \n",
    "        if counter in [1, 3, 5]:\n",
    "            ax.set_ylabel('Fraction of ASVs')\n",
    "        else:\n",
    "            ax.set_ylabel('')\n",
    "        \n",
    "        if counter in [5, 6]:\n",
    "            ax.set_xlabel('Days after fecal transplant')\n",
    "        else:\n",
    "            ax.set_xlabel('')\n",
    "        \n",
    "        if counter != 4:\n",
    "            ax.legend_.remove()\n",
    "        else:\n",
    "            ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),\n",
    "                     labels=['Donor ASVs', 'Unknown ASVs',\n",
    "                             'Patient ASVs', 'Shared ASVs'])\n",
    "    \n",
    "        counter += 1\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('/Users/ndchu/Documents/uc_fmt/figures/source_placebo_chk73_16S_otufrac_2019-05-22.png',\n",
    "            dpi=300)\n",
    "plt.savefig('/Users/ndchu/Documents/uc_fmt/figures/source_placebo_chk73_16S_otufrac_2019-05-22.pdf',\n",
    "            dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counts of shared, distinct, and transferred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>sample_id</th>\n",
       "      <th>otu_src</th>\n",
       "      <th>cum_rel_abun</th>\n",
       "      <th>otu_count</th>\n",
       "      <th>storage</th>\n",
       "      <th>barcode_id</th>\n",
       "      <th>patient_donor</th>\n",
       "      <th>patient</th>\n",
       "      <th>approx_wk</th>\n",
       "      <th>days_past_fmt</th>\n",
       "      <th>...</th>\n",
       "      <th>fmt_current</th>\n",
       "      <th>fmt_past</th>\n",
       "      <th>clinical_intervention</th>\n",
       "      <th>responder_fmt</th>\n",
       "      <th>reads_16s</th>\n",
       "      <th>reads_mgx</th>\n",
       "      <th>sample_mimarks</th>\n",
       "      <th>notes</th>\n",
       "      <th>patient_plt</th>\n",
       "      <th>frac_otus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>03-133</td>\n",
       "      <td>shared_otus</td>\n",
       "      <td>0.848979</td>\n",
       "      <td>30</td>\n",
       "      <td>rna_later</td>\n",
       "      <td>BP-17.0622</td>\n",
       "      <td>patient</td>\n",
       "      <td>001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0044, 0073</td>\n",
       "      <td>True</td>\n",
       "      <td>nonresponder</td>\n",
       "      <td>200032.0</td>\n",
       "      <td>17985300.0</td>\n",
       "      <td>03.133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>patient_001</td>\n",
       "      <td>0.491803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03-133</td>\n",
       "      <td>donor0044_otus</td>\n",
       "      <td>0.050746</td>\n",
       "      <td>8</td>\n",
       "      <td>rna_later</td>\n",
       "      <td>BP-17.0622</td>\n",
       "      <td>patient</td>\n",
       "      <td>001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0044, 0073</td>\n",
       "      <td>True</td>\n",
       "      <td>nonresponder</td>\n",
       "      <td>200032.0</td>\n",
       "      <td>17985300.0</td>\n",
       "      <td>03.133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>patient_001</td>\n",
       "      <td>0.131148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-133</td>\n",
       "      <td>donor0073_otus</td>\n",
       "      <td>0.005543</td>\n",
       "      <td>4</td>\n",
       "      <td>rna_later</td>\n",
       "      <td>BP-17.0622</td>\n",
       "      <td>patient</td>\n",
       "      <td>001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0044, 0073</td>\n",
       "      <td>True</td>\n",
       "      <td>nonresponder</td>\n",
       "      <td>200032.0</td>\n",
       "      <td>17985300.0</td>\n",
       "      <td>03.133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>patient_001</td>\n",
       "      <td>0.065574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03-133</td>\n",
       "      <td>donor0044_0073_otus</td>\n",
       "      <td>0.073640</td>\n",
       "      <td>9</td>\n",
       "      <td>rna_later</td>\n",
       "      <td>BP-17.0622</td>\n",
       "      <td>patient</td>\n",
       "      <td>001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0044, 0073</td>\n",
       "      <td>True</td>\n",
       "      <td>nonresponder</td>\n",
       "      <td>200032.0</td>\n",
       "      <td>17985300.0</td>\n",
       "      <td>03.133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>patient_001</td>\n",
       "      <td>0.147541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-133</td>\n",
       "      <td>patient_otus</td>\n",
       "      <td>0.009509</td>\n",
       "      <td>4</td>\n",
       "      <td>rna_later</td>\n",
       "      <td>BP-17.0622</td>\n",
       "      <td>patient</td>\n",
       "      <td>001</td>\n",
       "      <td>12.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0044, 0073</td>\n",
       "      <td>True</td>\n",
       "      <td>nonresponder</td>\n",
       "      <td>200032.0</td>\n",
       "      <td>17985300.0</td>\n",
       "      <td>03.133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>patient_001</td>\n",
       "      <td>0.065574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  sample_id              otu_src  cum_rel_abun  otu_count    storage  \\\n",
       "0    03-133          shared_otus      0.848979         30  rna_later   \n",
       "1    03-133       donor0044_otus      0.050746          8  rna_later   \n",
       "2    03-133       donor0073_otus      0.005543          4  rna_later   \n",
       "3    03-133  donor0044_0073_otus      0.073640          9  rna_later   \n",
       "4    03-133         patient_otus      0.009509          4  rna_later   \n",
       "\n",
       "   barcode_id patient_donor patient  approx_wk  days_past_fmt    ...     \\\n",
       "0  BP-17.0622       patient     001       12.0           82.0    ...      \n",
       "1  BP-17.0622       patient     001       12.0           82.0    ...      \n",
       "2  BP-17.0622       patient     001       12.0           82.0    ...      \n",
       "3  BP-17.0622       patient     001       12.0           82.0    ...      \n",
       "4  BP-17.0622       patient     001       12.0           82.0    ...      \n",
       "\n",
       "  fmt_current    fmt_past  clinical_intervention responder_fmt  reads_16s  \\\n",
       "0        44.0  0044, 0073                   True  nonresponder   200032.0   \n",
       "1        44.0  0044, 0073                   True  nonresponder   200032.0   \n",
       "2        44.0  0044, 0073                   True  nonresponder   200032.0   \n",
       "3        44.0  0044, 0073                   True  nonresponder   200032.0   \n",
       "4        44.0  0044, 0073                   True  nonresponder   200032.0   \n",
       "\n",
       "    reads_mgx  sample_mimarks  notes  patient_plt frac_otus  \n",
       "0  17985300.0          03.133    NaN  patient_001  0.491803  \n",
       "1  17985300.0          03.133    NaN  patient_001  0.131148  \n",
       "2  17985300.0          03.133    NaN  patient_001  0.065574  \n",
       "3  17985300.0          03.133    NaN  patient_001  0.147541  \n",
       "4  17985300.0          03.133    NaN  patient_001  0.065574  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csource_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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