Portmanteaus.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'6e6389a913cc01020d03ac16217bc1c63c9d0e16b78179b4c931741c0d5a69cf'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import functools\n",
    "import hashlib\n",
    "import io\n",
    "import pathlib\n",
    "import shutil\n",
    "import urllib.request\n",
    "import zipfile\n",
    "\n",
    "URL = 'https://zenodo.org/record/841982/files/xflr6/portmanteaus-v1.0.zip'\n",
    "\n",
    "CSV = pathlib.Path('esm3-analyses.csv')\n",
    "\n",
    "\n",
    "def sha256sum(filename, bufsize=32768) -> str:\n",
    "    s = hashlib.sha256()\n",
    "    with io.open(filename, 'rb') as fd:\n",
    "        for data in iter(functools.partial(fd.read, bufsize), b''):\n",
    "            s.update(data)\n",
    "    return s.hexdigest()\n",
    "\n",
    "\n",
    "if not CSV.exists():\n",
    "    with io.BytesIO() as b:\n",
    "        with urllib.request.urlopen(URL) as u:\n",
    "            shutil.copyfileobj(u, b)\n",
    "        with zipfile.ZipFile(b) as z:\n",
    "            i, = (i for i in z.infolist() if i.filename.endswith(CSV.name))\n",
    "            i.filename = CSV.name\n",
    "            z.extract(i)\n",
    "\n",
    "sha256sum(CSV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import types\n",
    "import warnings\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import count_nonzero as ntrue\n",
    "import pandas as pd\n",
    "import scipy.stats\n",
    "\n",
    "\n",
    "def crosstab(df, index, columns, tests=False, margins=True, **kwargs):\n",
    "    idx = [df[i] for i in index] if isinstance(index, list) else df[index]\n",
    "    cols = [df[c] for c in columns] if isinstance(columns, list) else df[columns]\n",
    "    result = pd.crosstab(idx, cols, margins=margins, **kwargs)\n",
    "    if tests:\n",
    "        result = types.SimpleNamespace(df=result)\n",
    "        values = result.df.iloc[:-1, :-1] if margins else result.df\n",
    "        testres = chi2_contingency(values), fisher_exact(values)\n",
    "        label = tests if isinstance(tests, str) else f'{index} & {columns}'\n",
    "        testres = [tr.to_frame(label).T for tr in testres]\n",
    "        result.expected = pd.DataFrame(\n",
    "            testres[0].at[label, 'expected'],\n",
    "            index=values.index, columns=values.columns)\n",
    "        result.chi2, result.fisher_exact = testres\n",
    "    return result\n",
    "\n",
    "\n",
    "def chi2_contingency(x, func=scipy.stats.chi2_contingency):\n",
    "    try:\n",
    "        chi2, p, dof, expected = func(x)\n",
    "    except ValueError:\n",
    "        return\n",
    "    result = chi2, p, dof, expected, (expected >= 5).all()\n",
    "    return pd.Series(result, index=('chi2', 'p', 'dof', 'expected', 'usable'))\n",
    "\n",
    "\n",
    "def fisher_exact(x, func=scipy.stats.fisher_exact):\n",
    "    return pd.Series(func(x), index=('odds', 'p'))\n",
    "\n",
    "\n",
    "def pearsonr(df, left, right, func=scipy.stats.pearsonr):\n",
    "    df = df[[left, right]].dropna()\n",
    "    name = f'{left} & {right}'\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter('ignore', scipy.stats.ConstantInputWarning)\n",
    "        with np.errstate(invalid='ignore'):\n",
    "            result = func(df[left], df[right])\n",
    "    return pd.Series(result, index=('r', 'p'), name=name)\n",
    "\n",
    "\n",
    "plt.style.use('classic')\n",
    "plt.rcParams.update({'figure.figsize': (6, 4), 'figure.facecolor': 'w',\n",
    "                     'figure.subplot.bottom': .125, 'font.size': 10, 'savefig.dpi': 72})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2818 entries, 0 to 2817\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Language  2818 non-null   object\n",
      " 1   Cell      2818 non-null   object\n",
      " 2   Position  2818 non-null   int64 \n",
      " 3   Form      2818 non-null   object\n",
      " 4   Meaning   2818 non-null   object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 110.2+ KB\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Cell</th>\n",
       "      <th>Position</th>\n",
       "      <th>Form</th>\n",
       "      <th>Meaning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s</td>\n",
       "      <td>-1</td>\n",
       "      <td>ku</td>\n",
       "      <td>SA[+1 +sg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1p</td>\n",
       "      <td>1</td>\n",
       "      <td>as</td>\n",
       "      <td>S[+1 +pl]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>2s</td>\n",
       "      <td>-1</td>\n",
       "      <td>e</td>\n",
       "      <td>SAP[-3 +sg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>2p</td>\n",
       "      <td>-1</td>\n",
       "      <td>eci</td>\n",
       "      <td>SAP[+2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>x</td>\n",
       "      <td>1</td>\n",
       "      <td>an</td>\n",
       "      <td>S[-1 -2 -3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;2s</td>\n",
       "      <td>-1</td>\n",
       "      <td>eci</td>\n",
       "      <td>SAP[+2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;2p</td>\n",
       "      <td>-1</td>\n",
       "      <td>eci</td>\n",
       "      <td>SAP[+2]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;3s</td>\n",
       "      <td>-1</td>\n",
       "      <td>ku</td>\n",
       "      <td>SA[+1 +sg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;3p</td>\n",
       "      <td>-1</td>\n",
       "      <td>ku</td>\n",
       "      <td>SA[+1 +sg]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;x</td>\n",
       "      <td>-2</td>\n",
       "      <td>ku</td>\n",
       "      <td>SA[+1 +sg]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language    Cell  Position Form      Meaning\n",
       "0     Ainu      1s        -1   ku   SA[+1 +sg]\n",
       "1     Ainu      1p         1   as    S[+1 +pl]\n",
       "2     Ainu      2s        -1    e  SAP[-3 +sg]\n",
       "3     Ainu      2p        -1  eci      SAP[+2]\n",
       "4     Ainu       x         1   an  S[-1 -2 -3]\n",
       "5     Ainu  1s->2s        -1  eci      SAP[+2]\n",
       "6     Ainu  1s->2p        -1  eci      SAP[+2]\n",
       "7     Ainu  1s->3s        -1   ku   SA[+1 +sg]\n",
       "8     Ainu  1s->3p        -1   ku   SA[+1 +sg]\n",
       "9     Ainu   1s->x        -2   ku   SA[+1 +sg]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RENAME = {'Quechua (Ayacucho)': 'Ayacucho',\n",
    "          'Tlachichilco Tepehuan': 'Tepehua',\n",
    "          'Lakhota': 'Lakota'}\n",
    "\n",
    "\n",
    "df = pd.read_csv(CSV, encoding='utf-8')\n",
    "\n",
    "df['Language'] = df['Language'].replace(RENAME)\n",
    "\n",
    "df.info()\n",
    "assert df.set_index(['Language', 'Cell', 'Position']).index.is_unique\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Language</th>\n",
       "      <th>Cell</th>\n",
       "      <th>Trans</th>\n",
       "      <th>Local</th>\n",
       "      <th>Direct</th>\n",
       "      <th>Position</th>\n",
       "      <th>Slot</th>\n",
       "      <th>Form</th>\n",
       "      <th>Meaning</th>\n",
       "      <th>Portmx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>prefix</td>\n",
       "      <td>ku</td>\n",
       "      <td>SA[+1 +sg]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1p</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>suffix</td>\n",
       "      <td>as</td>\n",
       "      <td>S[+1 +pl]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>2s</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>prefix</td>\n",
       "      <td>e</td>\n",
       "      <td>SAP[-3 +sg]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>2p</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>prefix</td>\n",
       "      <td>eci</td>\n",
       "      <td>SAP[+2]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>x</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>suffix</td>\n",
       "      <td>an</td>\n",
       "      <td>S[-1 -2 -3]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;2s</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>prefix</td>\n",
       "      <td>eci</td>\n",
       "      <td>SAP[+2]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;2p</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>prefix</td>\n",
       "      <td>eci</td>\n",
       "      <td>SAP[+2]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;3s</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>prefix</td>\n",
       "      <td>ku</td>\n",
       "      <td>SA[+1 +sg]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;3p</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-1</td>\n",
       "      <td>prefix</td>\n",
       "      <td>ku</td>\n",
       "      <td>SA[+1 +sg]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Ainu</td>\n",
       "      <td>1s-&gt;x</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>-2</td>\n",
       "      <td>prefix</td>\n",
       "      <td>ku</td>\n",
       "      <td>SA[+1 +sg]</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Language    Cell  Trans  Local  Direct  Position    Slot Form      Meaning  \\\n",
       "0     Ainu      1s  False  False    True        -1  prefix   ku   SA[+1 +sg]   \n",
       "1     Ainu      1p  False  False    True         1  suffix   as    S[+1 +pl]   \n",
       "2     Ainu      2s  False  False    True        -1  prefix    e  SAP[-3 +sg]   \n",
       "3     Ainu      2p  False  False    True        -1  prefix  eci      SAP[+2]   \n",
       "4     Ainu       x  False  False    True         1  suffix   an  S[-1 -2 -3]   \n",
       "5     Ainu  1s->2s   True   True    True        -1  prefix  eci      SAP[+2]   \n",
       "6     Ainu  1s->2p   True   True    True        -1  prefix  eci      SAP[+2]   \n",
       "7     Ainu  1s->3s   True  False    True        -1  prefix   ku   SA[+1 +sg]   \n",
       "8     Ainu  1s->3p   True  False    True        -1  prefix   ku   SA[+1 +sg]   \n",
       "9     Ainu   1s->x   True  False    True        -2  prefix   ku   SA[+1 +sg]   \n",
       "\n",
       "   Portmx  \n",
       "0   False  \n",
       "1   False  \n",
       "2   False  \n",
       "3   False  \n",
       "4   False  \n",
       "5   False  \n",
       "6   False  \n",
       "7   False  \n",
       "8   False  \n",
       "9   False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "SEP = '->'\n",
    "\n",
    "\n",
    "def is_local(cellkey, sep=SEP, persons=('1', '2')) -> bool:\n",
    "    subj, _, obj = cellkey.partition(sep)\n",
    "    return any(p in subj for p in persons) and any(p in obj for p in persons)\n",
    "\n",
    "\n",
    "def is_inverse(cellkey, sep=SEP, hierarchy=('1', '2', '3', '4', 'x')) -> bool:\n",
    "    subj, _, obj = cellkey.partition(sep)\n",
    "    for high, low in itertools.combinations(hierarchy, 2):\n",
    "        if low in subj and high in obj:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "\n",
    "def is_inverse_restricted(cellkey, sep=SEP, high=('1', '2'), low=('3', '4', 'x')) -> bool:\n",
    "    subj, _, obj = cellkey.partition(sep)\n",
    "    return any(l in subj for l in low) and any(h in obj for h in high)\n",
    "\n",
    "\n",
    "def is_inverse_relaxed(cellkey, sep=SEP, high=('1', '2')) -> bool:\n",
    "    subj, _, obj = cellkey.partition(sep)\n",
    "    return any(h in obj for h in high)\n",
    "\n",
    "\n",
    "def get_slot(cellpos) -> bool:\n",
    "    return 'prefix' if cellpos < 0 else 'suffix' if cellpos > 0 else 'stem'\n",
    "\n",
    "\n",
    "df.insert(2, 'Trans', df['Cell'].str.contains(SEP))\n",
    "df.insert(3, 'Local', df['Cell'].map(is_local))\n",
    "df.insert(4, 'Direct', ~df['Cell'].map(is_inverse))\n",
    "df.insert(6, 'Slot', df['Position'].map(get_slot))\n",
    "df.insert(9, 'Portmx', df['Meaning'].str.contains(SEP))\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Language'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trans</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>270</td>\n",
       "      <td>0</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>1963</td>\n",
       "      <td>585</td>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2233</td>\n",
       "      <td>585</td>\n",
       "      <td>2818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Local  False  True   All\n",
       "Trans                   \n",
       "False    270     0   270\n",
       "True    1963   585  2548\n",
       "All     2233   585  2818"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table('Position', 'Trans', 'Local', aggfunc=len, fill_value=0, margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trans</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>890</td>\n",
       "      <td>241</td>\n",
       "      <td>1131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>1076</td>\n",
       "      <td>241</td>\n",
       "      <td>1317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Local  False  True   All\n",
       "Trans                   \n",
       "False    186     0   186\n",
       "True     890   241  1131\n",
       "All     1076   241  1317"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(['Language', 'Cell'])[['Trans', 'Local']].first()\n",
    " .pivot_table(index='Trans', columns='Local', aggfunc=len, fill_value=0, margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Cell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Cell\n",
       "Language         \n",
       "Ainu           42\n",
       "Aleut          72\n",
       "Ayacucho       39\n",
       "Bella Coola    34\n",
       "Chuckchi       33\n",
       "Darai          34\n",
       "Fox            49\n",
       "Hixkaryana     35\n",
       "Jaqaru         14\n",
       "Jumjum         44\n",
       "Karuk          34\n",
       "Ket           102\n",
       "Kunama         86\n",
       "Lakota         37\n",
       "Maricopa       28\n",
       "Maung         115\n",
       "Mordvin        34\n",
       "Nocte          34\n",
       "Reyesano       33\n",
       "Sahu          115\n",
       "Siuslawan      85\n",
       "Tepehua        37\n",
       "Thangmi        33\n",
       "Turkana        34\n",
       "Wardaman       42\n",
       "Yimas          72"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Language')['Cell'].nunique().to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>marker/cell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.975917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.659278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.443182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.911855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.460084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.361111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       marker/cell\n",
       "count    26.000000\n",
       "mean      1.975917\n",
       "std       0.659278\n",
       "min       1.000000\n",
       "25%       1.443182\n",
       "50%       1.911855\n",
       "75%       2.460084\n",
       "max       3.361111"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(['Language', 'Cell'])['Position'].size()\n",
    " .groupby(level='Language').mean()\n",
    " .to_frame('marker/cell')\n",
    " .sort_values(by='marker/cell')\n",
    " .describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6464749527797673"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(['Language', 'Cell'])['Position'].size()\n",
    " .groupby(level='Language').mean()\n",
    " .std(ddof=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learned lexemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slot</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prefix</th>\n",
       "      <td>132</td>\n",
       "      <td>50</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suffix</th>\n",
       "      <td>119</td>\n",
       "      <td>55</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>251</td>\n",
       "      <td>105</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Portmx  False  True  All\n",
       "Slot                    \n",
       "prefix    132    50  182\n",
       "suffix    119    55  174\n",
       "All       251   105  356"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assert (df.groupby(['Language', 'Form', 'Meaning'])[['Slot', 'Portmx']]\n",
    "        .filter(lambda g: (g.nunique() > 1).any())\n",
    "        .empty)\n",
    "\n",
    "(df.groupby(['Language', 'Form', 'Meaning'])[['Slot', 'Portmx']].first()\n",
    " .pivot_table(index='Slot', columns='Portmx', aggfunc=len, fill_value=0, margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>19</td>\n",
       "      <td>16</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>251</td>\n",
       "      <td>105</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Portmx       False  True  All\n",
       "Language                     \n",
       "Ainu             9     1   10\n",
       "Aleut            8     9   17\n",
       "Ayacucho         6     2    8\n",
       "Bella Coola      9     2   11\n",
       "Chuckchi        10     5   15\n",
       "Darai            9     2   11\n",
       "Fox             13     4   17\n",
       "Hixkaryana       7     2    9\n",
       "Jaqaru           4     5    9\n",
       "Jumjum          16     9   25\n",
       "Karuk           10     3   13\n",
       "Ket             12     0   12\n",
       "Kunama          11     6   17\n",
       "Lakota           7     1    8\n",
       "Maricopa         3     0    3\n",
       "Maung           19    16   35\n",
       "Mordvin         10     8   18\n",
       "Nocte            8     2   10\n",
       "Reyesano         4     0    4\n",
       "Sahu            19     0   19\n",
       "Siuslawan       10     6   16\n",
       "Tepehua          9     3   12\n",
       "Thangmi          7     3   10\n",
       "Turkana          6     1    7\n",
       "Wardaman         7     5   12\n",
       "Yimas           18    10   28\n",
       "All            251   105  356"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby(['Language', 'Form', 'Meaning'], as_index=False)['Portmx'].first()\n",
    " .pivot_table('Form', 'Language', 'Portmx', aggfunc=len, fill_value=0, margins=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>odds</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [False, True, odds, p]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llf = (df.groupby(['Language', 'Form', 'Meaning', 'Portmx'], as_index=False)['Local']\n",
    "       .agg(lambda x: True if x.all() else False if not x.any() else None)\n",
    "       .query('Local == Local')\n",
    "       .pivot_table('Form', ['Language', 'Local'], 'Portmx', aggfunc=len, fill_value=0, dropna=False))\n",
    "\n",
    "(llf.join(llf.groupby(level='Language').apply(fisher_exact))\n",
    " .rename_axis('Portmx', axis=1)\n",
    " .query('p <= .05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>odds</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th>Direct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [False, True, odds, p]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dlf = (df.groupby(['Language', 'Form', 'Meaning', 'Portmx'], as_index=False)['Direct']\n",
    "       .agg(lambda x: True if x.all() else False if not x.any() else None)\n",
    "       .query('Direct == Direct')  # filter out Nan\n",
    "       .pivot_table('Form', ['Language', 'Direct'], 'Portmx', aggfunc=len, fill_value=0, dropna=False))\n",
    "\n",
    "(dlf.join(llf.groupby(level='Language').apply(fisher_exact))\n",
    " .rename_axis('Portmx', axis=1)\n",
    " .query('p <= .05'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transitive occurrences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>1725</td>\n",
       "      <td>238</td>\n",
       "      <td>1963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>528</td>\n",
       "      <td>57</td>\n",
       "      <td>585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>2253</td>\n",
       "      <td>295</td>\n",
       "      <td>2548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Portmx  False  True   All\n",
       "Local                    \n",
       "False    1725   238  1963\n",
       "True      528    57   585\n",
       "All      2253   295  2548"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = crosstab(df[df['Trans']], 'Local', 'Portmx', tests=True)\n",
    "ct.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>1735.729592</td>\n",
       "      <td>227.270408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>517.270408</td>\n",
       "      <td>67.729592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Portmx        False        True\n",
       "Local                          \n",
       "False   1735.729592  227.270408\n",
       "True     517.270408   67.729592"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>chi2</th>\n",
       "      <th>p</th>\n",
       "      <th>dof</th>\n",
       "      <th>usable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Local &amp; Portmx</th>\n",
       "      <td>2.268065</td>\n",
       "      <td>0.132065</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    chi2         p dof usable\n",
       "Local & Portmx  2.268065  0.132065   1   True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.chi2.drop('expected', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Local &amp; Portmx</th>\n",
       "      <td>0.782444</td>\n",
       "      <td>0.122239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    odds         p\n",
       "Local & Portmx  0.782444  0.122239"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.fisher_exact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Markers</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Portmx</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ratio</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ratio (norm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.878049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>66</td>\n",
       "      <td>33</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>31.818182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>62</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6.451613</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>0.261905</td>\n",
       "      <td>0.738095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8.108108</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17.241379</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.340909</td>\n",
       "      <td>0.659091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>53</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7.547170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>115</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>12.173913</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>0.422111</td>\n",
       "      <td>0.577889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.631579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>55</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>21.818182</td>\n",
       "      <td>26.315789</td>\n",
       "      <td>0.453280</td>\n",
       "      <td>0.546720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>0.637681</td>\n",
       "      <td>0.362319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>196</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>139</td>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>26.618705</td>\n",
       "      <td>2.127660</td>\n",
       "      <td>0.925985</td>\n",
       "      <td>0.074015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>255</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "      <td>5</td>\n",
       "      <td>27.058824</td>\n",
       "      <td>27.777778</td>\n",
       "      <td>0.493445</td>\n",
       "      <td>0.506555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>32.258065</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>0.743802</td>\n",
       "      <td>0.256198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>195</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>178</td>\n",
       "      <td>61</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>6.741573</td>\n",
       "      <td>1.639344</td>\n",
       "      <td>0.804396</td>\n",
       "      <td>0.195604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>56</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>28.125000</td>\n",
       "      <td>0.112676</td>\n",
       "      <td>0.887324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11.940299</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.427350</td>\n",
       "      <td>0.572650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>153</td>\n",
       "      <td>76</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>9.803922</td>\n",
       "      <td>9.210526</td>\n",
       "      <td>0.515604</td>\n",
       "      <td>0.484396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Markers       Portmx            Ratio            Ratio (norm)  \\\n",
       "Local         False True   False True       False      True         False   \n",
       "Language                                                                    \n",
       "Ainu             41    12      2     0   4.878049   0.000000     1.000000   \n",
       "Aleut            66    33     21     0  31.818182   0.000000     1.000000   \n",
       "Ayacucho         62    22      4     4   6.451613  18.181818     0.261905   \n",
       "Bella Coola      37    21      3     0   8.108108   0.000000     1.000000   \n",
       "Chuckchi         29    12      5     4  17.241379  33.333333     0.340909   \n",
       "Darai            53    20      4     0   7.547170   0.000000     1.000000   \n",
       "Fox             115    24     14     4  12.173913  16.666667     0.422111   \n",
       "Hixkaryana       22     6      4     0  18.181818   0.000000     1.000000   \n",
       "Jaqaru            9     7      3     4  33.333333  57.142857     0.368421   \n",
       "Jumjum           55    19     12     5  21.818182  26.315789     0.453280   \n",
       "Karuk            25    11      4     1  16.000000   9.090909     0.637681   \n",
       "Ket             196    22      0     0        NaN        NaN          NaN   \n",
       "Kunama          139    47     37     1  26.618705   2.127660     0.925985   \n",
       "Lakota           42    20      0     2   0.000000  10.000000     0.000000   \n",
       "Maricopa         16    16      0     0        NaN        NaN          NaN   \n",
       "Maung           255    18     69     5  27.058824  27.777778     0.493445   \n",
       "Mordvin          31     9     10     1  32.258065  11.111111     0.743802   \n",
       "Nocte            22     8      4     2  18.181818  25.000000     0.421053   \n",
       "Reyesano         32    12      0     0        NaN        NaN          NaN   \n",
       "Sahu            195    24      0     0        NaN        NaN          NaN   \n",
       "Siuslawan       178    61     12     1   6.741573   1.639344     0.804396   \n",
       "Tepehua          56    32      2     9   3.571429  28.125000     0.112676   \n",
       "Thangmi          39    18      1     3   2.564103  16.666667     0.133333   \n",
       "Turkana          28    10      4     0  14.285714   0.000000     1.000000   \n",
       "Wardaman         67    25      8     4  11.940299  16.000000     0.427350   \n",
       "Yimas           153    76     15     7   9.803922   9.210526     0.515604   \n",
       "\n",
       "                       \n",
       "Local           True   \n",
       "Language               \n",
       "Ainu         0.000000  \n",
       "Aleut        0.000000  \n",
       "Ayacucho     0.738095  \n",
       "Bella Coola  0.000000  \n",
       "Chuckchi     0.659091  \n",
       "Darai        0.000000  \n",
       "Fox          0.577889  \n",
       "Hixkaryana   0.000000  \n",
       "Jaqaru       0.631579  \n",
       "Jumjum       0.546720  \n",
       "Karuk        0.362319  \n",
       "Ket               NaN  \n",
       "Kunama       0.074015  \n",
       "Lakota       1.000000  \n",
       "Maricopa          NaN  \n",
       "Maung        0.506555  \n",
       "Mordvin      0.256198  \n",
       "Nocte        0.578947  \n",
       "Reyesano          NaN  \n",
       "Sahu              NaN  \n",
       "Siuslawan    0.195604  \n",
       "Tepehua      0.887324  \n",
       "Thangmi      0.866667  \n",
       "Turkana      0.000000  \n",
       "Wardaman     0.572650  \n",
       "Yimas        0.484396  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "of = (df[df['Trans']]\n",
    "      .groupby(['Language', 'Local'])['Portmx']\n",
    "      .agg([('Markers', 'size'), ('Portmx', ntrue)]))\n",
    "\n",
    "of['Ratio'] = 100 * of['Portmx'] / of['Markers']\n",
    "of['Ratio (norm)'] = of['Ratio'] / of['Ratio'].groupby(level='Language').sum()\n",
    "of.loc[of['Ratio (norm)'].isnull(), 'Ratio'] = None\n",
    "\n",
    "ofp = of.reset_index('Local')\n",
    "\n",
    "of.unstack('Local')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Local &amp; Ratio</th>\n",
       "      <td>-0.040995</td>\n",
       "      <td>0.791617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local &amp; Ratio (norm)</th>\n",
       "      <td>-0.277682</td>\n",
       "      <td>0.068004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             r         p\n",
       "Local & Ratio        -0.040995  0.791617\n",
       "Local & Ratio (norm) -0.277682  0.068004"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([pearsonr(ofp, 'Local', c) for c in ['Ratio', 'Ratio (norm)']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p</th>\n",
       "      <th>dof</th>\n",
       "      <th>expected</th>\n",
       "      <th>usable</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Aleut</th>\n",
       "      <th>False</th>\n",
       "      <td>45</td>\n",
       "      <td>21</td>\n",
       "      <td>11.491071</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[[52.0, 14.0], [26.0, 7.0]]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>11.491071</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[[52.0, 14.0], [26.0, 7.0]]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Kunama</th>\n",
       "      <th>False</th>\n",
       "      <td>102</td>\n",
       "      <td>37</td>\n",
       "      <td>11.496919</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[[110.60215053763442, 28.397849462365592], [37...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>11.496919</td>\n",
       "      <td>0.000697</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[[110.60215053763442, 28.397849462365592], [37...</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Portmx          False  True       chi2         p  dof  \\\n",
       "Language Local                                          \n",
       "Aleut    False     45    21  11.491071  0.000699  1.0   \n",
       "         True      33     0  11.491071  0.000699  1.0   \n",
       "Kunama   False    102    37  11.496919  0.000697  1.0   \n",
       "         True      46     1  11.496919  0.000697  1.0   \n",
       "\n",
       "Portmx                                                   expected usable  \n",
       "Language Local                                                            \n",
       "Aleut    False                        [[52.0, 14.0], [26.0, 7.0]]   True  \n",
       "         True                         [[52.0, 14.0], [26.0, 7.0]]   True  \n",
       "Kunama   False  [[110.60215053763442, 28.397849462365592], [37...   True  \n",
       "         True   [[110.60215053763442, 28.397849462365592], [37...   True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctl = (df[df['Trans']]\n",
    "       .pivot_table('Position', ['Language', 'Local'], 'Portmx', aggfunc=len, fill_value=0))\n",
    "\n",
    "(ctl.join(ctl.groupby(level='Language').apply(chi2_contingency))\n",
    " .rename_axis('Portmx', axis=1)\n",
    " .query('usable == 1 & p <= .05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>odds</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Aleut</th>\n",
       "      <th>False</th>\n",
       "      <td>45</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Kunama</th>\n",
       "      <th>False</th>\n",
       "      <td>102</td>\n",
       "      <td>37</td>\n",
       "      <td>0.059929</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059929</td>\n",
       "      <td>0.000102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tepehua</th>\n",
       "      <th>False</th>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>10.565217</td>\n",
       "      <td>0.001467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>23</td>\n",
       "      <td>9</td>\n",
       "      <td>10.565217</td>\n",
       "      <td>0.001467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Portmx          False  True       odds         p\n",
       "Language Local                                  \n",
       "Aleut    False     45    21   0.000000  0.000062\n",
       "         True      33     0   0.000000  0.000062\n",
       "Kunama   False    102    37   0.059929  0.000102\n",
       "         True      46     1   0.059929  0.000102\n",
       "Tepehua  False     54     2  10.565217  0.001467\n",
       "         True      23     9  10.565217  0.001467"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ctl.join(ctl.groupby(level='Language').apply(fisher_exact))\n",
    " .rename_axis('Portmx', axis=1)\n",
    " .query('p <= .05'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transitive cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Language</th>\n",
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       "      <th rowspan=\"5\" valign=\"top\">Ainu</th>\n",
       "      <th>1s-&gt;2s</th>\n",
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       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>1s-&gt;3p</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1s-&gt;x</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Yimas</th>\n",
       "      <th>3p-&gt;2d</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3p-&gt;2p</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3p-&gt;3s</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3p-&gt;3d</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3p-&gt;3p</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1131 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Local  Direct  Markers  Portmx   Hasp      Ratio\n",
       "Language Cell                                                    \n",
       "Ainu     1s->2s   True    True        1       0  False   0.000000\n",
       "         1s->2p   True    True        1       0  False   0.000000\n",
       "         1s->3s  False    True        1       0  False   0.000000\n",
       "         1s->3p  False    True        1       0  False   0.000000\n",
       "         1s->x   False    True        2       0  False   0.000000\n",
       "...                ...     ...      ...     ...    ...        ...\n",
       "Yimas    3p->2d  False   False        4       0  False   0.000000\n",
       "         3p->2p  False   False        3       0  False   0.000000\n",
       "         3p->3s  False    True        4       1   True  25.000000\n",
       "         3p->3d  False    True        4       1   True  25.000000\n",
       "         3p->3p  False    True        3       1   True  33.333333\n",
       "\n",
       "[1131 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cells = (df[df['Trans']]\n",
    "         .groupby(['Language', 'Cell', 'Local', 'Direct'], sort=False)['Portmx']\n",
    "         .agg([('Markers', 'size'), ('Portmx', ntrue), ('Hasp', 'any')])\n",
    "         .reset_index(['Local', 'Direct']))\n",
    "\n",
    "cells['Ratio'] = 100 * cells['Portmx'] / cells['Markers']\n",
    "\n",
    "cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Ratio &amp; Markers</th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>-0.173851</td>\n",
       "      <td>0.303450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>0.035474</td>\n",
       "      <td>0.782539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>-0.216970</td>\n",
       "      <td>0.232945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>0.240767</td>\n",
       "      <td>0.217134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>-0.024049</td>\n",
       "      <td>0.905222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>-0.036824</td>\n",
       "      <td>0.852418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>-0.132580</td>\n",
       "      <td>0.414771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>-0.167380</td>\n",
       "      <td>0.643943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>-0.192715</td>\n",
       "      <td>0.260133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>-0.034922</td>\n",
       "      <td>0.859967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>-0.175439</td>\n",
       "      <td>0.132188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>-0.230396</td>\n",
       "      <td>0.212423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>-0.238243</td>\n",
       "      <td>0.014874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>0.006371</td>\n",
       "      <td>0.974333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>-0.144841</td>\n",
       "      <td>0.462110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>0.426728</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>0.675099</td>\n",
       "      <td>0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>0.587354</td>\n",
       "      <td>0.001016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>-0.019087</td>\n",
       "      <td>0.923202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>0.023560</td>\n",
       "      <td>0.893134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>0.411838</td>\n",
       "      <td>0.000798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Ratio & Markers         r         p\n",
       "Language                           \n",
       "Ainu            -0.173851  0.303450\n",
       "Aleut            0.035474  0.782539\n",
       "Ayacucho        -0.216970  0.232945\n",
       "Bella Coola      0.240767  0.217134\n",
       "Chuckchi        -0.024049  0.905222\n",
       "Darai           -0.036824  0.852418\n",
       "Fox             -0.132580  0.414771\n",
       "Hixkaryana            NaN       NaN\n",
       "Jaqaru          -0.167380  0.643943\n",
       "Jumjum          -0.192715  0.260133\n",
       "Karuk           -0.034922  0.859967\n",
       "Ket                   NaN       NaN\n",
       "Kunama          -0.175439  0.132188\n",
       "Lakota          -0.230396  0.212423\n",
       "Maricopa              NaN       NaN\n",
       "Maung           -0.238243  0.014874\n",
       "Mordvin          0.006371  0.974333\n",
       "Nocte           -0.144841  0.462110\n",
       "Reyesano              NaN       NaN\n",
       "Sahu                  NaN       NaN\n",
       "Siuslawan        0.426728  0.000135\n",
       "Tepehua          0.675099  0.000031\n",
       "Thangmi          0.587354  0.001016\n",
       "Turkana         -0.019087  0.923202\n",
       "Wardaman         0.023560  0.893134\n",
       "Yimas            0.411838  0.000798"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cells.groupby(level='Language').apply(lambda x: pearsonr(x, 'Ratio', 'Markers'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hasp</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>686</td>\n",
       "      <td>204</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>196</td>\n",
       "      <td>45</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>882</td>\n",
       "      <td>249</td>\n",
       "      <td>1131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Hasp   False  True   All\n",
       "Local                   \n",
       "False    686   204   890\n",
       "True     196    45   241\n",
       "All      882   249  1131"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = crosstab(cells, 'Local', 'Hasp', tests=True)\n",
    "\n",
    "ct.df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Hasp</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>694.058355</td>\n",
       "      <td>195.941645</td>\n",
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       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>187.941645</td>\n",
       "      <td>53.058355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Hasp        False        True\n",
       "Local                        \n",
       "False  694.058355  195.941645\n",
       "True   187.941645   53.058355"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chi2</th>\n",
       "      <th>p</th>\n",
       "      <th>dof</th>\n",
       "      <th>usable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Local &amp; Hasp</th>\n",
       "      <td>1.754557</td>\n",
       "      <td>0.185305</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  chi2         p dof usable\n",
       "Local & Hasp  1.754557  0.185305   1   True"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.chi2.drop('expected', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Local &amp; Hasp</th>\n",
       "      <td>0.772059</td>\n",
       "      <td>0.162222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  odds         p\n",
       "Local & Hasp  0.772059  0.162222"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct.fisher_exact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Local &amp; Hasp</th>\n",
       "      <td>-0.041992</td>\n",
       "      <td>0.158161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local &amp; Ratio</th>\n",
       "      <td>-0.052016</td>\n",
       "      <td>0.080369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      r         p\n",
       "Local & Hasp  -0.041992  0.158161\n",
       "Local & Ratio -0.052016  0.080369"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([pearsonr(cells, 'Local', c) for c in ['Hasp', 'Ratio']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hasp</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>chi2</th>\n",
       "      <th>p</th>\n",
       "      <th>dof</th>\n",
       "      <th>expected</th>\n",
       "      <th>usable</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Kunama</th>\n",
       "      <th>False</th>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>8.513763</td>\n",
       "      <td>0.003525</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[[35.72, 21.28], [11.28, 6.72]]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>8.513763</td>\n",
       "      <td>0.003525</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[[35.72, 21.28], [11.28, 6.72]]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Hasp            False  True      chi2         p  dof  \\\n",
       "Language Local                                         \n",
       "Kunama   False     30    27  8.513763  0.003525  1.0   \n",
       "         True      17     1  8.513763  0.003525  1.0   \n",
       "\n",
       "Hasp                                   expected usable  \n",
       "Language Local                                          \n",
       "Kunama   False  [[35.72, 21.28], [11.28, 6.72]]   True  \n",
       "         True   [[35.72, 21.28], [11.28, 6.72]]   True  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctl = cells.pivot_table('Markers', ['Language', 'Local'], 'Hasp', aggfunc=len, fill_value=0)\n",
    "\n",
    "(ctl.join(ctl.groupby(level='Language').apply(chi2_contingency))\n",
    " .rename_axis('Hasp', axis=1)\n",
    " .query('usable == 1 & p <= .05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hasp</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>odds</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th>Local</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Aleut</th>\n",
       "      <th>False</th>\n",
       "      <td>29</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Kunama</th>\n",
       "      <th>False</th>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>0.065359</td>\n",
       "      <td>0.001533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0.065359</td>\n",
       "      <td>0.001533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tepehua</th>\n",
       "      <th>False</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>0.000921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>0.000921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Hasp            False  True       odds         p\n",
       "Language Local                                  \n",
       "Aleut    False     29    16   0.000000  0.002852\n",
       "         True      18     0   0.000000  0.002852\n",
       "Kunama   False     30    27   0.065359  0.001533\n",
       "         True      17     1   0.065359  0.001533\n",
       "Tepehua  False     21     2  31.500000  0.000921\n",
       "         True       2     6  31.500000  0.000921"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ctl.join(ctl.groupby(level='Language').apply(fisher_exact))\n",
    " .rename_axis('Hasp', axis=1)\n",
    " .query('p <= .05'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cells with portmanteau vs. Occurrences vs. Mean cell ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cells</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Hasp</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Perc</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Markers</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Portmx</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ratio</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cell ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6.896552</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.878049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.896552</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>35.555556</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66</td>\n",
       "      <td>33</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>31.818182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.888889</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>24</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>62</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6.451613</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>4.513889</td>\n",
       "      <td>31.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8.108108</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>26.315789</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17.241379</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>21.052632</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7.547170</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.916667</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>115</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>12.173913</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>12.552083</td>\n",
       "      <td>17.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>42.857143</td>\n",
       "      <td>55.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>39.285714</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>55</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>21.818182</td>\n",
       "      <td>26.315789</td>\n",
       "      <td>27.023810</td>\n",
       "      <td>34.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>6.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>84</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>47.368421</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>139</td>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>26.618705</td>\n",
       "      <td>2.127660</td>\n",
       "      <td>28.654971</td>\n",
       "      <td>1.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>60.416667</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>255</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "      <td>5</td>\n",
       "      <td>27.058824</td>\n",
       "      <td>27.777778</td>\n",
       "      <td>29.774306</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>32.258065</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>12.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>96</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>195</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>15.789474</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>178</td>\n",
       "      <td>61</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>6.741573</td>\n",
       "      <td>1.639344</td>\n",
       "      <td>4.970760</td>\n",
       "      <td>1.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8.695652</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>56</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>28.125000</td>\n",
       "      <td>2.536232</td>\n",
       "      <td>24.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.564103</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>9.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>29.629630</td>\n",
       "      <td>37.500000</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>11.940299</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>12.037037</td>\n",
       "      <td>15.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>26.666667</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>153</td>\n",
       "      <td>76</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>9.803922</td>\n",
       "      <td>9.210526</td>\n",
       "      <td>8.851852</td>\n",
       "      <td>5.707672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cells        Hasp             Perc             Markers        \\\n",
       "Local       False True  False True       False       True    False True    \n",
       "Language                                                                   \n",
       "Ainu           29     8     2     0   6.896552    0.000000      41    12   \n",
       "Aleut          45    18    16     0  35.555556    0.000000      66    33   \n",
       "Ayacucho       24     8     4     4  16.666667   50.000000      62    22   \n",
       "Bella Coola    20     8     3     0  15.000000    0.000000      37    21   \n",
       "Chuckchi       19     8     5     4  26.315789   50.000000      29    12   \n",
       "Darai          20     8     4     0  20.000000    0.000000      53    20   \n",
       "Fox            32     8    14     4  43.750000   50.000000     115    24   \n",
       "Hixkaryana     22     6     4     0  18.181818    0.000000      22     6   \n",
       "Jaqaru          7     3     3     3  42.857143  100.000000       9     7   \n",
       "Jumjum         28     8    11     4  39.285714   50.000000      55    19   \n",
       "Karuk          20     8     4     1  20.000000   12.500000      25    11   \n",
       "Ket            84     8     0     0        NaN         NaN     196    22   \n",
       "Kunama         57    18    27     1  47.368421    5.555556     139    47   \n",
       "Lakota         23     8     0     2   0.000000   25.000000      42    20   \n",
       "Maricopa       16     8     0     0        NaN         NaN      16    16   \n",
       "Maung          96     8    58     3  60.416667   37.500000     255    18   \n",
       "Mordvin        20     8     9     1  45.000000   12.500000      31     9   \n",
       "Nocte          20     8     4     2  20.000000   25.000000      22     8   \n",
       "Reyesano       20     8     0     0        NaN         NaN      32    12   \n",
       "Sahu           96     8     0     0        NaN         NaN     195    24   \n",
       "Siuslawan      57    18     9     1  15.789474    5.555556     178    61   \n",
       "Tepehua        23     8     2     6   8.695652   75.000000      56    32   \n",
       "Thangmi        20     8     1     2   5.000000   25.000000      39    18   \n",
       "Turkana        20     8     4     0  20.000000    0.000000      28    10   \n",
       "Wardaman       27     8     8     3  29.629630   37.500000      67    25   \n",
       "Yimas          45    18    12     4  26.666667   22.222222     153    76   \n",
       "\n",
       "            Portmx            Ratio            Cell ratio             \n",
       "Local        False True       False      True       False      True   \n",
       "Language                                                              \n",
       "Ainu             2     0   4.878049   0.000000   6.896552   0.000000  \n",
       "Aleut           21     0  31.818182   0.000000  28.888889   0.000000  \n",
       "Ayacucho         4     4   6.451613  18.181818   4.513889  31.250000  \n",
       "Bella Coola      3     0   8.108108   0.000000   5.833333   0.000000  \n",
       "Chuckchi         5     4  17.241379  33.333333  21.052632  25.000000  \n",
       "Darai            4     0   7.547170   0.000000   7.916667   0.000000  \n",
       "Fox             14     4  12.173913  16.666667  12.552083  17.708333  \n",
       "Hixkaryana       4     0  18.181818   0.000000  18.181818   0.000000  \n",
       "Jaqaru           3     4  33.333333  57.142857  42.857143  55.555556  \n",
       "Jumjum          12     5  21.818182  26.315789  27.023810  34.375000  \n",
       "Karuk            4     1  16.000000   9.090909  17.500000   6.250000  \n",
       "Ket              0     0        NaN        NaN        NaN        NaN  \n",
       "Kunama          37     1  26.618705   2.127660  28.654971   1.851852  \n",
       "Lakota           0     2   0.000000  10.000000   0.000000  18.750000  \n",
       "Maricopa         0     0        NaN        NaN        NaN        NaN  \n",
       "Maung           69     5  27.058824  27.777778  29.774306  25.000000  \n",
       "Mordvin         10     1  32.258065  11.111111  33.333333  12.500000  \n",
       "Nocte            4     2  18.181818  25.000000  20.000000  25.000000  \n",
       "Reyesano         0     0        NaN        NaN        NaN        NaN  \n",
       "Sahu             0     0        NaN        NaN        NaN        NaN  \n",
       "Siuslawan       12     1   6.741573   1.639344   4.970760   1.111111  \n",
       "Tepehua          2     9   3.571429  28.125000   2.536232  24.375000  \n",
       "Thangmi          1     3   2.564103  16.666667   1.250000   9.375000  \n",
       "Turkana          4     0  14.285714   0.000000  15.000000   0.000000  \n",
       "Wardaman         8     4  11.940299  16.000000  12.037037  15.625000  \n",
       "Yimas           15     7   9.803922   9.210526   8.851852   5.707672  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf = (cells.reset_index()\n",
    "      .groupby(['Language', 'Local'])\n",
    "      .agg({'Cell': 'size', 'Hasp': ntrue, 'Markers': 'sum',\n",
    "            'Portmx': 'sum', 'Ratio': 'mean'})\n",
    "      .rename(columns={'Cell': 'Cells', 'Ratio': 'Cell ratio'}))\n",
    "\n",
    "cf.insert(2, 'Perc', 100 * cf['Hasp'] / cf['Cells'])\n",
    "cf.insert(5, 'Ratio', 100 * cf['Portmx'] / cf['Markers'])\n",
    "nf = cf[['Perc', 'Ratio', 'Cell ratio']]\n",
    "nf /= nf.groupby(level='Language').sum()\n",
    "cf = cf.join(nf, rsuffix=' (norm)')\n",
    "cf.loc[cf['Perc (norm)'].isnull(), ['Perc', 'Ratio', 'Cell ratio']] = None\n",
    "\n",
    "cfp = cf.reset_index('Local')\n",
    "\n",
    "cf = cf.unstack('Local')\n",
    "\n",
    "cf.loc[:, :'Cell ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Local &amp; Perc</th>\n",
       "      <td>0.021124</td>\n",
       "      <td>0.891740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local &amp; Ratio</th>\n",
       "      <td>-0.040995</td>\n",
       "      <td>0.791617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local &amp; Cell ratio</th>\n",
       "      <td>-0.069615</td>\n",
       "      <td>0.653413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           r         p\n",
       "Local & Perc        0.021124  0.891740\n",
       "Local & Ratio      -0.040995  0.791617\n",
       "Local & Cell ratio -0.069615  0.653413"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Point-biserial correlation = Pearson r\n",
    "pd.DataFrame([pearsonr(cfp, 'Local', c) for c in ['Perc', 'Ratio', 'Cell ratio']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Perc</th>\n",
       "      <th>Ratio</th>\n",
       "      <th>Cell ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Perc</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.893704</td>\n",
       "      <td>0.873536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratio</th>\n",
       "      <td>0.893704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.956063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell ratio</th>\n",
       "      <td>0.873536</td>\n",
       "      <td>0.956063</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Perc     Ratio  Cell ratio\n",
       "Perc        1.000000  0.893704    0.873536\n",
       "Ratio       0.893704  1.000000    0.956063\n",
       "Cell ratio  0.873536  0.956063    1.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfp[['Perc', 'Ratio', 'Cell ratio']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (cf.sort_values(by=('Cell ratio (norm)', True), kind='mergesort')['Cell ratio']\n",
    "      .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray))\n",
    "\n",
    "for x, lang in [(1, 'Aleut'), (6, 'Kunama'), (20, 'Tepehua')]:\n",
    "    ax.annotate('*', (x - .05, cf.loc[lang, 'Cell ratio'].sum() + .5))\n",
    "\n",
    "#ax.get_figure().savefig('results_local.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(cells.groupby(level='Language').apply(lambda x: pearsonr(x, 'Local', 'Ratio'))['r']\n",
    " .sort_values(kind='mergesort')\n",
    " .plot.bar(figsize=(15, 5)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = cf['Cell ratio'].dropna().T.plot(figsize=(8, 6), legend=False)\n",
    "ax.legend(bbox_to_anchor=(1.35, 1.15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf['Cell ratio'].boxplot(return_type='axes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.violinplot(cf['Cell ratio'].dropna().values, showmedians=True, bw_method=.25);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Markers</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Portmx</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ratio (norm)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cell ratio (norm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>66</td>\n",
       "      <td>33</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>62</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.261905</td>\n",
       "      <td>0.738095</td>\n",
       "      <td>0.126214</td>\n",
       "      <td>0.873786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>37</td>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>29</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.340909</td>\n",
       "      <td>0.659091</td>\n",
       "      <td>0.457143</td>\n",
       "      <td>0.542857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>53</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>115</td>\n",
       "      <td>24</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0.422111</td>\n",
       "      <td>0.577889</td>\n",
       "      <td>0.414802</td>\n",
       "      <td>0.585198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.435484</td>\n",
       "      <td>0.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>55</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0.453280</td>\n",
       "      <td>0.546720</td>\n",
       "      <td>0.440136</td>\n",
       "      <td>0.559864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>25</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.637681</td>\n",
       "      <td>0.362319</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>196</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>139</td>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>0.925985</td>\n",
       "      <td>0.074015</td>\n",
       "      <td>0.939297</td>\n",
       "      <td>0.060703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>255</td>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "      <td>5</td>\n",
       "      <td>0.493445</td>\n",
       "      <td>0.506555</td>\n",
       "      <td>0.543582</td>\n",
       "      <td>0.456418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.743802</td>\n",
       "      <td>0.256198</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>32</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>195</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>178</td>\n",
       "      <td>61</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.804396</td>\n",
       "      <td>0.195604</td>\n",
       "      <td>0.817308</td>\n",
       "      <td>0.182692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>56</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.112676</td>\n",
       "      <td>0.887324</td>\n",
       "      <td>0.094244</td>\n",
       "      <td>0.905756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>39</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>28</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.427350</td>\n",
       "      <td>0.572650</td>\n",
       "      <td>0.435146</td>\n",
       "      <td>0.564854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>153</td>\n",
       "      <td>76</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>0.515604</td>\n",
       "      <td>0.484396</td>\n",
       "      <td>0.607977</td>\n",
       "      <td>0.392023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Markers       Portmx       Ratio (norm)            \\\n",
       "Local         False True   False True         False     True    \n",
       "Language                                                        \n",
       "Ainu             41    12      2     0     1.000000  0.000000   \n",
       "Aleut            66    33     21     0     1.000000  0.000000   \n",
       "Ayacucho         62    22      4     4     0.261905  0.738095   \n",
       "Bella Coola      37    21      3     0     1.000000  0.000000   \n",
       "Chuckchi         29    12      5     4     0.340909  0.659091   \n",
       "Darai            53    20      4     0     1.000000  0.000000   \n",
       "Fox             115    24     14     4     0.422111  0.577889   \n",
       "Hixkaryana       22     6      4     0     1.000000  0.000000   \n",
       "Jaqaru            9     7      3     4     0.368421  0.631579   \n",
       "Jumjum           55    19     12     5     0.453280  0.546720   \n",
       "Karuk            25    11      4     1     0.637681  0.362319   \n",
       "Ket             196    22      0     0          NaN       NaN   \n",
       "Kunama          139    47     37     1     0.925985  0.074015   \n",
       "Lakota           42    20      0     2     0.000000  1.000000   \n",
       "Maricopa         16    16      0     0          NaN       NaN   \n",
       "Maung           255    18     69     5     0.493445  0.506555   \n",
       "Mordvin          31     9     10     1     0.743802  0.256198   \n",
       "Nocte            22     8      4     2     0.421053  0.578947   \n",
       "Reyesano         32    12      0     0          NaN       NaN   \n",
       "Sahu            195    24      0     0          NaN       NaN   \n",
       "Siuslawan       178    61     12     1     0.804396  0.195604   \n",
       "Tepehua          56    32      2     9     0.112676  0.887324   \n",
       "Thangmi          39    18      1     3     0.133333  0.866667   \n",
       "Turkana          28    10      4     0     1.000000  0.000000   \n",
       "Wardaman         67    25      8     4     0.427350  0.572650   \n",
       "Yimas           153    76     15     7     0.515604  0.484396   \n",
       "\n",
       "            Cell ratio (norm)            \n",
       "Local                   False     True   \n",
       "Language                                 \n",
       "Ainu                 1.000000  0.000000  \n",
       "Aleut                1.000000  0.000000  \n",
       "Ayacucho             0.126214  0.873786  \n",
       "Bella Coola          1.000000  0.000000  \n",
       "Chuckchi             0.457143  0.542857  \n",
       "Darai                1.000000  0.000000  \n",
       "Fox                  0.414802  0.585198  \n",
       "Hixkaryana           1.000000  0.000000  \n",
       "Jaqaru               0.435484  0.564516  \n",
       "Jumjum               0.440136  0.559864  \n",
       "Karuk                0.736842  0.263158  \n",
       "Ket                       NaN       NaN  \n",
       "Kunama               0.939297  0.060703  \n",
       "Lakota               0.000000  1.000000  \n",
       "Maricopa                  NaN       NaN  \n",
       "Maung                0.543582  0.456418  \n",
       "Mordvin              0.727273  0.272727  \n",
       "Nocte                0.444444  0.555556  \n",
       "Reyesano                  NaN       NaN  \n",
       "Sahu                      NaN       NaN  \n",
       "Siuslawan            0.817308  0.182692  \n",
       "Tepehua              0.094244  0.905756  \n",
       "Thangmi              0.117647  0.882353  \n",
       "Turkana              1.000000  0.000000  \n",
       "Wardaman             0.435146  0.564854  \n",
       "Yimas                0.607977  0.392023  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf[['Markers', 'Portmx', 'Ratio (norm)', 'Cell ratio (norm)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Local &amp; Perc (norm)</th>\n",
       "      <td>-0.291594</td>\n",
       "      <td>0.054795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local &amp; Ratio (norm)</th>\n",
       "      <td>-0.277682</td>\n",
       "      <td>0.068004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Local &amp; Cell ratio (norm)</th>\n",
       "      <td>-0.306743</td>\n",
       "      <td>0.042843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  r         p\n",
       "Local & Perc (norm)       -0.291594  0.054795\n",
       "Local & Ratio (norm)      -0.277682  0.068004\n",
       "Local & Cell ratio (norm) -0.306743  0.042843"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([pearsonr(cfp, 'Local', f'{c} (norm)') for c in ['Perc', 'Ratio', 'Cell ratio']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Perc (norm)</th>\n",
       "      <th>Ratio (norm)</th>\n",
       "      <th>Cell ratio (norm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Perc (norm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993548</td>\n",
       "      <td>0.983565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratio (norm)</th>\n",
       "      <td>0.993548</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell ratio (norm)</th>\n",
       "      <td>0.983565</td>\n",
       "      <td>0.988801</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Perc (norm)  Ratio (norm)  Cell ratio (norm)\n",
       "Perc (norm)           1.000000      0.993548           0.983565\n",
       "Ratio (norm)          0.993548      1.000000           0.988801\n",
       "Cell ratio (norm)     0.983565      0.988801           1.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfp[[f'{c} (norm)' for c in ['Perc', 'Ratio', 'Cell ratio']]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (cf['Cell ratio (norm)'].sort_values(by=True, kind='mergesort')\n",
    "      .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray))\n",
    "\n",
    "#ax.get_figure().savefig('results_local_norm.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Local</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cell ratio (norm)</th>\n",
       "      <td>13.337538</td>\n",
       "      <td>8.662462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Local                  False     True \n",
       "Cell ratio (norm)  13.337538  8.662462"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf['Cell ratio (norm)'].sum().to_frame('Cell ratio (norm)').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = cf['Cell ratio (norm)'].dropna().T.plot(figsize=(8, 6), legend=False)\n",
    "\n",
    "ax.legend(bbox_to_anchor=(1.35, 1.15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf['Cell ratio (norm)'].boxplot(return_type='axes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.violinplot(cf['Cell ratio (norm)'].dropna().values, showmedians=True, bw_method=.25);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Direct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Portmx</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>odds</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th>Direct</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Aleut</th>\n",
       "      <th>False</th>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Fox</th>\n",
       "      <th>False</th>\n",
       "      <td>53</td>\n",
       "      <td>14</td>\n",
       "      <td>0.222689</td>\n",
       "      <td>0.010231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>68</td>\n",
       "      <td>4</td>\n",
       "      <td>0.222689</td>\n",
       "      <td>0.010231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Jumjum</th>\n",
       "      <th>False</th>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "      <td>3.455357</td>\n",
       "      <td>0.037495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>3.455357</td>\n",
       "      <td>0.037495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yimas</th>\n",
       "      <th>False</th>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>14.925620</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>121</td>\n",
       "      <td>21</td>\n",
       "      <td>14.925620</td>\n",
       "      <td>0.000332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Portmx           False  True       odds         p\n",
       "Language Direct                                  \n",
       "Aleut    False      42     0        inf  0.000002\n",
       "         True       36    21        inf  0.000002\n",
       "Fox      False      53    14   0.222689  0.010231\n",
       "         True       68     4   0.222689  0.010231\n",
       "Jumjum   False      43     8   3.455357  0.037495\n",
       "         True       14     9   3.455357  0.037495\n",
       "Yimas    False      86     1  14.925620  0.000332\n",
       "         True      121    21  14.925620  0.000332"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctd = (df[df['Trans']]\\\n",
    "       .pivot_table('Position', ['Language', 'Direct'], 'Portmx', aggfunc=len, fill_value=0))\n",
    "\n",
    "(ctd.join(ctd.groupby(level='Language').apply(fisher_exact))\n",
    " .rename_axis('Portmx', axis=1)\n",
    " .query('p <= .05'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cells</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Hasp</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Perc</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Markers</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Portmx</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ratio</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cell ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.526316</td>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>27</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.444444</td>\n",
       "      <td>42</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.842105</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>28.571429</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8.510638</td>\n",
       "      <td>10.810811</td>\n",
       "      <td>7.738095</td>\n",
       "      <td>13.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.896552</td>\n",
       "      <td>3.448276</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>3.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>34.782609</td>\n",
       "      <td>4.545455</td>\n",
       "      <td>34.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>26</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7.692308</td>\n",
       "      <td>4.255319</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>3.645833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>77.777778</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>67</td>\n",
       "      <td>72</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>20.895522</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>22.314815</td>\n",
       "      <td>6.439394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>55.555556</td>\n",
       "      <td>28.571429</td>\n",
       "      <td>63.333333</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>51</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>15.686275</td>\n",
       "      <td>39.130435</td>\n",
       "      <td>17.604167</td>\n",
       "      <td>37.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>18.750000</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>18.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>27.272727</td>\n",
       "      <td>45.238095</td>\n",
       "      <td>64</td>\n",
       "      <td>122</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>17.187500</td>\n",
       "      <td>22.131148</td>\n",
       "      <td>18.181818</td>\n",
       "      <td>25.396825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.764706</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.060606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>12</td>\n",
       "      <td>49</td>\n",
       "      <td>35.294118</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>83</td>\n",
       "      <td>190</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>19.277108</td>\n",
       "      <td>30.526316</td>\n",
       "      <td>18.627451</td>\n",
       "      <td>34.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>43.750000</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>28.571429</td>\n",
       "      <td>26.923077</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>31.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>22.222222</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>33</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>12.121212</td>\n",
       "      <td>14.285714</td>\n",
       "      <td>108</td>\n",
       "      <td>131</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>6.870229</td>\n",
       "      <td>2.979798</td>\n",
       "      <td>4.880952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>21.428571</td>\n",
       "      <td>29.411765</td>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>13.043478</td>\n",
       "      <td>11.904762</td>\n",
       "      <td>8.571429</td>\n",
       "      <td>7.843137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>1.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>47.368421</td>\n",
       "      <td>47</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6.382979</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>4.687500</td>\n",
       "      <td>19.736842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>27</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>3.703704</td>\n",
       "      <td>41.666667</td>\n",
       "      <td>87</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1.149425</td>\n",
       "      <td>14.788732</td>\n",
       "      <td>0.925926</td>\n",
       "      <td>13.224206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cells        Hasp             Perc            Markers        \\\n",
       "Direct      False True  False True       False      True    False True    \n",
       "Language                                                                  \n",
       "Ainu           18    19     0     2   0.000000  10.526316      29    24   \n",
       "Aleut          27    36     0    16   0.000000  44.444444      42    57   \n",
       "Ayacucho       14    18     4     4  28.571429  22.222222      47    37   \n",
       "Bella Coola    12    16     2     1  16.666667   6.250000      29    29   \n",
       "Chuckchi       11    16     1     8   9.090909  50.000000      18    23   \n",
       "Darai          12    16     2     2  16.666667  12.500000      26    47   \n",
       "Fox            18    22    14     4  77.777778  18.181818      67    72   \n",
       "Hixkaryana     10    18     2     2  20.000000  11.111111      10    18   \n",
       "Jaqaru          5     5     4     2  80.000000  40.000000       9     7   \n",
       "Jumjum         16    20     7     8  43.750000  40.000000      51    23   \n",
       "Karuk          12    16     2     3  16.666667  18.750000      18    18   \n",
       "Ket            28    64     0     0        NaN        NaN      76   142   \n",
       "Kunama         33    42     9    19  27.272727  45.238095      64   122   \n",
       "Lakota         14    17     0     2   0.000000  11.764706      29    33   \n",
       "Maricopa       12    12     0     0        NaN        NaN      20    12   \n",
       "Maung          34    70    12    49  35.294118  70.000000      83   190   \n",
       "Mordvin        12    16     3     7  25.000000  43.750000      14    26   \n",
       "Nocte          12    16     2     4  16.666667  25.000000      12    18   \n",
       "Reyesano       12    16     0     0        NaN        NaN      24    20   \n",
       "Sahu           34    70     0     0        NaN        NaN      73   146   \n",
       "Siuslawan      33    42     4     6  12.121212  14.285714     108   131   \n",
       "Tepehua        14    17     3     5  21.428571  29.411765      46    42   \n",
       "Thangmi        12    16     2     1  16.666667   6.250000      25    32   \n",
       "Turkana        12    16     0     4   0.000000  25.000000      18    20   \n",
       "Wardaman       16    19     2     9  12.500000  47.368421      47    45   \n",
       "Yimas          27    36     1    15   3.703704  41.666667      87   142   \n",
       "\n",
       "            Portmx            Ratio            Cell ratio             \n",
       "Direct       False True       False      True       False      True   \n",
       "Language                                                              \n",
       "Ainu             0     2   0.000000   8.333333   0.000000  10.526316  \n",
       "Aleut            0    21   0.000000  36.842105   0.000000  36.111111  \n",
       "Ayacucho         4     4   8.510638  10.810811   7.738095  13.888889  \n",
       "Bella Coola      2     1   6.896552   3.448276   5.555556   3.125000  \n",
       "Chuckchi         1     8   5.555556  34.782609   4.545455  34.375000  \n",
       "Darai            2     2   7.692308   4.255319   8.333333   3.645833  \n",
       "Fox             14     4  20.895522   5.555556  22.314815   6.439394  \n",
       "Hixkaryana       2     2  20.000000  11.111111  20.000000  11.111111  \n",
       "Jaqaru           5     2  55.555556  28.571429  63.333333  30.000000  \n",
       "Jumjum           8     9  15.686275  39.130435  17.604167  37.500000  \n",
       "Karuk            2     3  11.111111  16.666667   8.333333  18.750000  \n",
       "Ket              0     0        NaN        NaN        NaN        NaN  \n",
       "Kunama          11    27  17.187500  22.131148  18.181818  25.396825  \n",
       "Lakota           0     2   0.000000   6.060606   0.000000   8.823529  \n",
       "Maricopa         0     0        NaN        NaN        NaN        NaN  \n",
       "Maung           16    58  19.277108  30.526316  18.627451  34.642857  \n",
       "Mordvin          4     7  28.571429  26.923077  22.222222  31.250000  \n",
       "Nocte            2     4  16.666667  22.222222  16.666667  25.000000  \n",
       "Reyesano         0     0        NaN        NaN        NaN        NaN  \n",
       "Sahu             0     0        NaN        NaN        NaN        NaN  \n",
       "Siuslawan        4     9   3.703704   6.870229   2.979798   4.880952  \n",
       "Tepehua          6     5  13.043478  11.904762   8.571429   7.843137  \n",
       "Thangmi          3     1  12.000000   3.125000   6.250000   1.562500  \n",
       "Turkana          0     4   0.000000  20.000000   0.000000  18.750000  \n",
       "Wardaman         3     9   6.382979  20.000000   4.687500  19.736842  \n",
       "Yimas            1    21   1.149425  14.788732   0.925926  13.224206  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd = (cells.reset_index()\n",
    "       .groupby(['Language', 'Direct'])\n",
    "       .agg({'Cell': 'size', 'Hasp': ntrue,\n",
    "             'Markers': 'sum', 'Portmx': 'sum', 'Ratio': 'mean'})\\\n",
    "       .rename(columns={'Cell': 'Cells', 'Ratio': 'Cell ratio'}))\n",
    "\n",
    "cfd.insert(2, 'Perc', 100 * cfd['Hasp'] / cfd['Cells'])\n",
    "cfd.insert(5, 'Ratio', 100 * cfd['Portmx'] / cfd['Markers'])\n",
    "nfd = cfd[['Perc', 'Ratio', 'Cell ratio']]\n",
    "nfd /= nfd.groupby(level='Language').sum()\n",
    "cfd = cfd.join(nfd, rsuffix=' (norm)')\n",
    "cfd.loc[cfd['Perc (norm)'].isnull(), ['Perc', 'Ratio', 'Cell ratio']] = None\n",
    "\n",
    "cfdp = cfd.reset_index('Direct')\n",
    "\n",
    "cfd = cfd.unstack('Direct')\n",
    "\n",
    "cfd.loc[:, :'Cell ratio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Direct &amp; Perc</th>\n",
       "      <td>0.178565</td>\n",
       "      <td>0.246163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct &amp; Ratio</th>\n",
       "      <td>0.215627</td>\n",
       "      <td>0.159804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct &amp; Cell ratio</th>\n",
       "      <td>0.242860</td>\n",
       "      <td>0.112182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            r         p\n",
       "Direct & Perc        0.178565  0.246163\n",
       "Direct & Ratio       0.215627  0.159804\n",
       "Direct & Cell ratio  0.242860  0.112182"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([pearsonr(cfdp, 'Direct', c) for c in ['Perc', 'Ratio', 'Cell ratio']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (cfd.sort_values(by=('Cell ratio (norm)', True), kind='mergesort')['Cell ratio']\n",
    "      .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray))\n",
    "\n",
    "for x, lang in [(19, 'Aleut'), (1, 'Fox'), (13, 'Jumjum'), (17, 'Yimas')]:\n",
    "    ax.annotate('*', (x - .05, cfd.loc[lang, 'Cell ratio'].sum() + .5))\n",
    "\n",
    "#ax.get_figure().savefig('results_direct.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ly5fPtYYaAAAAAIDsYhiGLly4oMKFC2f4vFdGolNTU9WrVy/99NNPcjgc6tWrl15++WVJ0vz58zV//nxNnDhRkrR//3516tRJp0+fVq5cufTWW2+pVatWNxsBAAAAAIBs55UiGgAAAACAnMAr3bkBAAAAAMgJKKIBAAAAAPCQVxqL4caKFy+eYWO0/fv3m5qjdu3a+vXXX1WwYEG3PIZhyOFwKD4+3tQ88MyxY8f0xx9/KDEx0fVYs2bNLMly+fJljRkzRlu2bHHLM3fuXEvyADfLuaKJ5pW4Vf3000/au3evUlJSXI/16dPHsjwnT57U22+/ra1bt7q9TmzatMmyTHYRFxenBx54QL6+vvr999+1efNmPf/888qXL5/V0QD8Q7d1Eb13716VLFnS7bHVq1crIiLC1BxpO5UnJiZq2rRpuueee0zNIEkzZ86UJG3ZssX0a+PfmTx5st59913Fx8erZMmS2rp1q6pXr25ZER0TEyN/f3+tWrVKffv21ZQpU1SrVi1LsqR1/PhxHThwwO1NpB1ywb6OHz+uTp06aenSpXI4HKpfv74mTJigQoUKWR0N18mVK1eGNzlSU1MtSGMvzz33nHbu3KmwsDDX1qFW3xDq1KmTatasqSVLlmjEiBEaN26cwsLCLM20du1a7du3z+01okOHDqbnaN68uVatWqWjR4+qTZs2qlmzpn799Vd98803puaw66BKixYtNG/evCwfM9OLL76ozz//PMvHYK0VK1Zk+Hh2vhe8rRuLFS1aVCNGjNCzzz4rSfroo480YcIE7d271+JkUo0aNbRq1SqrY1jOrn/IJXuMAJcvX14rVqxQvXr1tHnzZq1YsUJTpkzR5MmTTc2RNs/27dv1yCOPaNu2bbpw4YKaNm2a6R8vMwwZMkTDhg1TiRIl3N5Erlu3zpI8cXFx6t69u44cOaItW7Zoy5YtWrZsmXr37m1JHkn673//m272wMiRI03PcfDgQX344Yfp3swuXbrU9CxPPvmkIiIi9OKLL0qSxo4dq99//13ff/+96VmcpkyZoujoaLfH+vXrpw8//NCaQJKSk5N14MABt5+dRx55xNQM//vf/1wfX758WVOnTlVqaqpee+01U3OkldEMswIFCigiIkLvvfeeAgICTMlRunRp7dixw/W3zw4qVqyoLVu2uF4vkpKSVLt2ba1evdqSPN27d9eiRYtUsWJFt9eI//73v6ZnqVSpkjZt2qTx48fr5MmTGjBggCpUqKCtW7eamuP48eMqVKiQDh06lOHzQUFBpuZxcv77pOV8v2GVjDI5f8bNtnfvXvXq1SvdLA8r3yuvW7dOixcvliQ1bNhQ4eHhluRIe93ExETt3r1b5cqVy9YZMLf1SPTy5cvVunVrLV26VEePHpV07W6k1c6cOaMTJ05Ydv0VK1bo1Vdf1V9//aWUlBRXwZqQkGB6FruOjttlBNjX11cFCxZ0FRy1atXSK6+8YmqGtO68805Jko+Pj/73v/8pX758OnXqlGV5pGvfq3379lkyuyMjXbt2Vbt27TRs2DBJUrly5dS+fXvLiuhevXrpwIED2rhxo9q2batvvvlGDRs2tCTLs88+q/r166tHjx6Wv+mPi4tzK5jfeOMNVaxY0bpAkj7//HMVLVpU9evXlyS99957lr55XLBggWJiYnT27FndfffdOnfunIoVK6YDBw6YmuPuu+92+7hPnz6qVq2apUV0VFSUjh49qk6dOkmSvvzySxUoUECGYahbt26mFWjBwcG6cuWK7rrrLlOu5wlfX19Jkp+fn86cOaOCBQvq9OnTluVZvHixdu7cKT8/P8syOF25ckVXrlzRL7/8YulruXPGjVXF8vXGjRunsWPHas+ePapUqZLr8fPnz6ts2bKWZJo1a5ZmzpypAwcOqGXLlm6Z8ubNa0mmmJgYde/eXe+++65mzpypMWPGKDg42JIskjR+/HgNHjxYLVu2lMPh0NNPP62BAweqc+fOpmdZv3692+fr1q3TlClTsvWat3UR/dBDD2nEiBFq1KiR7r33Xm3evNm0u8NphYWFue5Yp6am6tChQ3r99ddNz+EUExOjIUOGqGrVqpa/kbXbH3KnUaNGafPmzapXr542btzoGgE2W548eWQYhkJDQ/Xxxx8rKChIFy9eND2HU0BAgM6ePasmTZooMjJS9957r4oUKWJZHkl64IEHbFNAS9fWA0ZFRWnEiBGSrt1w8PGx7k/tsmXLtHXrVoWFhWnEiBF67bXX9Pzzz1uSJTExUe+//74l176eYRg6ceKEAgMDJUknTpyQ1ROz5s2bp0aNGmnGjBlasmSJlixZooULF1qWZ+DAgVqzZo1atGihzZs3KzY21vQRs4zs2rXL0qJMkn7++We3m/I1atRQtWrVtG7dOpUpU8a0HCNGjFCDBg1Up04dtyLxrbfeMi3D9UJDQ3XmzBlFRUWpWrVq8vf3V+XKlS3LU6hQIeXJk8ey66fVtm1bBQYGKjQ0VDVq1NDx48ctvQGyadMm9e/fX/v373ebHWR2z57GjRurVKlS6t69u0aNGuV63N/f3/SZL06lS5dW8+bNtWnTJjVv3twtk/NGp9kSEhLUunVrDR48WOXLl9e4ceNUrVo19e/f35I8n376qTZu3Kj77rtPktS/f3/Vr1/fkiL6elWrVlXXrl2z9Rq3dRE9YsQIff7551q2bJk2bdqk6tWr68svv1Tt2rVNzfHxxx+7Pvbx8VGJEiUsXXfn7++vp59+2rLrZ8RujUjsMgI8ePBgJSQk6KOPPlK3bt107tw5S9fh/PDDD8qdO7fee+89ff311zp37pwl68rSatiwoV555RW1a9fO7U2kVS+8Pj4+bsXY2bNnLS3O/Pz8XGtKk5OTFRgYqGPHjlmSpVy5cjp8+LCKFStmyfXTevXVVxUWFqbHH39ckrRw4ULX7AGrFC5cWDNnzlSzZs0UGBioX375xdLRs1y5cikoKMj1dzAqKsrtDa5Z0i73SU1NlWEYGjNmjOk50oqPj9elS5dcBdClS5d07tw5STL1e/af//xHvr6+SkxMVHJysmnXvZHY2FhJ0ssvv6zKlSvr3Llzaty4sWV5qlWrpqefflqtW7d2+95Y0VtkwIAB6tGjh/z9/eVwOJQvXz7Nnj3b9BxOzz//vHr06KGIiAhLB1WCgoIUFBSkP//807IM16tQoYIqVKigpk2b6r777tOVK1csvxlzxx13SJLy5cungwcPKjAw0PIbis4C+vqPzZZ21lZqaqrWrl2b7X8Tb+sievHixVq3bp3uuece1ahRQzVq1FC7du20a9cu0zKkpqZq4sSJmjZtmmnXzEqrVq00bdo0tW7d2jXtymp2a0RilxHgevXqSZLy58+vX375xfTrXy/terKoqCiL01wzdepUSdJ3333neszhcJh+J93pmWeeUdeuXZWQkKCJEydq7Nixlt6VzZcvny5duqSaNWsqKipKgYGBlo18nDp1ShUqVFBERITbm1kruru3b99eYWFhWr58uSSpb9++lk0bfOqpp9zW1/r4+MjX11ft27eXZF33e+cbtiJFiujbb79VcHCwzp49a3qOtMt9fHx8FBgYaPksqnbt2ql69ep65plnJElz5sxR27ZtdfHiRVOnV+7evVu7d+827Xr/VM2aNa2OoA0bNkiSvvjiC9djDofDsgadd999t/766y+3AYMHH3zQkiy5c+fO9tG6f6Ju3boZNsazom+G04kTJ1S3bl2dO3dOR44c0caNGzVr1ix99NFHpmepVauWzpw5ox49eqhy5cry9fVVmzZtTM/hVLJkSb355puun6EJEyaka+hslrSzBXx8fFSyZEl99dVX2XrN27qxmHOtr3PUpXDhwrpw4YLpWwlUq1bNFmuxnb777jtFRUXp0qVLkv7/v5OVnU7t1ohk6dKlqly5sk6fPu0aAX7//ffVoEEDU67/ySef3PD5Xr16mZLjenbZrs3uZsyYoXnz5skwDLVo0ULt2rWzLMvff/+tAgUK6OrVqxoxYoTOnTunl19+WUWLFjU9S2YvaFZNL7eLrF7orfr3mTFjhho3bqz9+/erTZs2OnfunD7++GM999xzluSxmx9++MH15r5evXpq2rSp6RmaNWum2NhY+fv7m37tzCxcuFCvvPKK9u/f75o5YPV7DLuwS58Bp5deekkdO3ZUlSpVLLn+9X744QfXx4mJiZo+fbpCQ0MtXQZUp04dDR48WD179tTmzZtlGIbKlSunHTt2WJZJutbX4/z58ypXrpxlGU6dOqWePXu6NRYbPXq07r//fssymem2LqJ37dqlVq1auYroIkWKaPbs2SpVqpSpOd544w3Fx8crOjrarRmBVdNNS5QooYkTJ6pKlSpud/PTNm8xW9WqVbVu3TqFh4dr4cKFKliwoEqVKmWLTupW6NixY6bPORwOy7pzp33RSLtd28CBAy3Jk5azYYuTVW8qFy5cmG7qYkaPwVp2WQtoZ6dPn9a9996b5WPZ7frvlbMo43sltW7dWhs3blSjRo3cZndY0X3fKTQ0VGPGjEk3RdjK9xh26DIvXeuRM2/evHR9BsxeSuLs1ZOcnKzdu3crJCTE7efHLnt6p6SkqF69epbuAFKlShVt2LBBYWFh2rx5syS5fWy2NWvWaPHixa6tGatXr25JDju6evWqTpw44faanp3Lx27r6dzdu3fXm2++6RoFmjlzprp166Zly5aZmmPWrFmS5DYd18o3APfff79rmrBd2K0RSUpKiubMmZNuKx6zmrV8+eWXplznn7p+umvlypVVo0YNS4voNWvWqGPHjtqzZ4/b41aNevTv3z9dwZzRY2bZuXOn3nnnHVc3ficzuz6PGDFCffv2VZ8+fTJ83oo3/HZZC5jWCy+8kOHjVt00a9SoUbo30xk9lt3s+L2yy3ZtZcqUMbWRmSf8/f0VGRlpdQyX60d/z549q6CgIEtGf+3SZyBtrx47S01NtayHh5OPj4+Sk5Nds/Di4uIs+zs0fPhwjRkzxtUtvE2bNurVq1emr61msMse7FOmTFGvXr10xx13KFeuXJKu1VonT57Mtmve1kX02bNn3aZRtmnTRh988IHpOayappOZZs2a6dNPP9Wzzz7rdufRyulgaRuRVKlSRWfPnrV05K5NmzY6ceKE5R3MU1JSNGrUKNcNmMjISL388suWdntOy+rt2qRrPzNTpkxRt27dtGLFCn3yySeWNGPas2ePdu3apfPnz2v+/Pmux8+fP+9aOmGFNm3aqEOHDpZuK+WcgZM/f35Lrp8Ru60FlOR24zAxMVFz5sxx2+7FLElJSUpMTFRqaqouXLjgaox3/vx5tz2bzWLH75Vdtmt7++23Lbt2Zp544gnXaKsd2KnLvF36DDgb7CYmJipPnjyuAvHq1atKSkoyPY9T2v4Qqamp2rZtm5o0aWJJlpSUFPn4+KhHjx5q0aKFTp06pQEDBmjatGl68cUXLck0fvx4bdq0ybUjycCBA1W9enXLiujM9mC3ooh+7733tH79elNnG9/W07krV66sadOmue7S7ty5U+3bt9fGjRstyWOX6abOOzTStR92q9crpaamqnz58tq5c6cl189IqVKltGvXrgzX/5qpV69e2rdvn2JiYuRwODRx4kQVL148yzXT2SWz7dqs2l5BkipVqqRNmza51tNLUnh4eLo9A7PbV199pSlTpmjDhg1u68v8/f3VpUsXS9ZLStZOO7teamqqbUYS7bYWMCOXLl1Ss2bNXOvNzDJo0CANGjTI9frg5O/vr759+5o+88SO36tHHnnE0j28nd59990MH7dyi6uCBQvq/PnzuvPOO11NOh0Oh+Lj4y3JU7lyZW3cuNHtNcL5mNns1megRo0a+umnn1w3OM+fP6+mTZtq5cqVluRJ2x/Cx8dHISEhqlatmiVZWrdu7ZpJumrVKn333XcyDENPPPGEoqOjLZlN6pxantVjZilZsqS2b99uiz3Yreg/ZY/hrGwydOhQ1apVy7XuZfv27fr6669Nz2G36aZXr1615LqZyZ07t+677z637UKsVrRoUSUlJVm+ncHy5cu1ZcsW142Ppk2bWjIy5WS37dqk/39n/5577tGmTZtUtGhRnTp1yvQczz//vJ5//nlNmjRJnTp1Mv36malbt65WrFihWrVqWR1FRYoUUfv27dWxY0c9/PDDlmRIuxZwwoQJtl0LKF3bKunIkSOmX/ftt9/W22+/re7du7t1NLbKihUrbPe9sst2bRcuXHB9nJiYqB9//FEREREWJnLvpm4Hdhn9la7tEy1dK+Lt0PPl0qVLbjOE8ufPb8lOJE7OJop22E7qypUrrr+Bzh1+4uLiVLduXctGfuvVq6fo6GjXe4wpU6aoQYMGrht6Zq/zt8Me7AkJCZKkFi1a6OOPP0633Wl2DljelkV0ly5dNH78eEVGRurzzz93FWbVq1c3vSGKZJ/ppnYWEhKiRx99VM8884xb8zWrulCHhISoTp06euqpp9y+V2bnMQxDV69edRXRhmFYuuew2Xuse6JNmzY6c+aM+vfvr9q1ays5OVmDBw+2LE9qaqri4+MVEBAg6dqU97lz5yomJsaSPE8//bQiIyOVL18++fn5WdqUafXq1ZoyZYpr380XXnhBbdu2NXVWjp3XAvbu3dttpseGDRss7bz6xRdfKC4uTr/99puka7//VmzF8+mnn5p+zazYZbu26xtSvfPOO4qOjjY1w/WCgoIsvf71Xn75ZZ09e1aDBw92G/21yk8//aS9e/e6rSG1qii7evWqLl686HrflZCQ4JbLbNu3b1fbtm1tsZ3UrFmz9Pjjj+vNN9/UkCFDdOTIEdWrV089e/ZUjx49TM8jSd98840k6ddff3V7fNasWZa8rtthD/YCBQq4zZrq06ePabNsb8vp3M7pndd/bHUeq6ebOuXKlSvdNOX8+fMrIiJCn332mal7XDpl1I3ayi7Udsnz2muvaePGja43RVOnTlWlSpUseUGR7NNMJzPJyclKTEw0fRu7tJzbtaVl5ZTqUqVKqV+/fum68Vu1J7LT8uXLNXbsWH3//feWrLPNbC2glTc4Bw0a5PrYOZWxZcuWrpE0s3333Xfq1KmTatasKYfDod9//12TJk3Sk08+aUkeO7Hzdm1ly5a1dPudkydP6u2339bWrVvdumFb/V7MDp577jnt3LlTYWFhbmtIx48fb0meDz/8UN9++626desmSRo7dqxatWql1157zZI8dttO6sKFC6pfv74aNmyo2bNnq1u3burdu7clWeyobt266R5zOBy2eU+Y3W7Lkei07HCPwC7TTZ3effddpaSkuEbGJk2apCtXruiBBx5Q165dtWjRItMz2a0btV3yfPjhhxo3bpyrUdXTTz+tLl26WJbHLs100urcubMaNGigevXq6f7777es4HDK6G+Olfuj5s2bN9Ouz1bZuHGjvvnmGy1dujTDF2Ez1KtXz20t4IULFyxdC5iamqqCBQtaNvsmI4MGDdKaNWsUEhIiSfrrr7/07LPPmlZE9+3bVyNGjHBrNpSW2aO+admhWJbk1h8jNTVV69atU2BgoIWJ5LrxsmTJEo0YMULjxo1TWFiYpZnsMvq7ceNG7dixwzavn/369VNgYKBrf+YePXooKirKsjwXL15UzZo1XZ87HA75+vpaksU5RfqDDz5Q69at9cQTT6h+/fqWTZ12WrdunatPRqNGjSztFWH2bkd2c1sW0ZcvX9b27dtlGIYSExNdHzuZ/YNvt+mm8+bNc2tC8NZbb7kaE1h1N1SSjh07pj/++MPtzrWZU0LsmCdXrlzq3r276y6x1Y3OEhMT9f7771ua4Xq1a9fWjz/+qFdffVUBAQFq0KCBGjRoYFlHz0KFCum///2vnn32WUnXpllZuW68adOm+v77720xejhixAhNmTJFqamp6tixo7Zu3WrZv43d1gLmzp1bU6dOtVURnZqa6iqgpWvLXMzsqVGnTh1JStflOTk52dLvlWSf7cjSznDx8fFRxYoVLb3RKl3bAqhfv36KjY3Vk08+qcjISNWuXVvvvfeeJXkyG/21QnBwsK5cuWKb/i9nzpxx9fNw2rNnj0JDQy3JY6ftpJo3b+76OG/evFq+fLmWL18uybptasePH6/BgwerZcuWcjgcatWqlQYOHKjOnTubnsXJLnuw7927V7169Uo3AyZbGxoat6GgoCAjODg4w/+KFy9uabakpCQjISHB0gyhoaHGyZMnXZ+fPHnSCA0NNQzDMCpWrGhJpkmTJhlBQUFGvnz5jEqVKhm5c+c2Hn30UUuy2CnP0aNHjccff9zw8/Mz/Pz8jKZNmxrHjh0zPYdT27ZtjUOHDll2/Ru5evWqMXXqVCM4ONjIlSuXZTn+/PNPIzQ01AgKCjKCgoKMhx9+2NizZ49leQoUKGA4HA7jrrvuMgoWLGgUKFDAKFiwoCVZYmJijFWrVlly7euVL1/euHDhguvz8+fPG2XLlrUwkWH069fPiI2NtTRDWg0aNDAmTJhgpKamGqmpqcbEiRONBg0aWJZn165dxquvvmo88MADRuXKlS3LYRiG8emnn7r+Gz58uBEREWG89NJLlmayi/DwcMMwDKNKlSrG6dOnjdTUVCMkJMSyPKVKlTJSUlIsu35af/zxhxEREWH85z//MQYNGuT6zyo1a9Y0rly54vr8wIEDrveDVpg2bZrRpEkTo0iRIsabb75pBAUFGbNmzbIsj92UL18+3fv38uXLW5bn+++/NwIDA408efIYAQEBhsPhMIKDgy3JUrt2bWPmzJlGmTJljG3bthkxMTHGkCFDsvWat+VI9MGDB62O4GbFihU3fN7srrl9+vRRhQoV9Pjjj0uSFi1apAEDBujixYt69NFHTc3iNGrUKG3evFn16tXTxo0btWLFCk2ZMsWSLHbK07VrV9WsWVPTp0+XdG29UpcuXfT999+bmsM5nfLChQu2aKaT1sSJE7V48WJX34HXXntNDRs2tCxP6dKltXPnTu3evVvStTXJVk7ds1OnXCtnulzvueeeU4MGDdzWAlo9RXfcuHE6f/68OnXqpLvuusvyrYHGjh2r5557Ti+99JKka/09zN7h4tKlS5o1a5YmTZqk/fv36/Lly1q9erVKly5tao7rOf9NnLp3727qTKVff/1VtWvXdtuT3snhcOjee+9V9erVLRlxDQ0N1ZkzZxQVFaVq1arJ39/fbQ90s9lp9Pc///mPfH19lZiYqOTkZKvj6Nlnn1Xbtm01Z84cHT9+XI8//rhGjRplWZ6oqCiVKFFC3333nZKSkhQbG+s2vRvSfffdl+HHVrDTHuwJCQlq3bq1Bg8erPLly2vcuHGqVq1atm7Bels2FrObsLAwbdu2TcWLF3dNAalQoYJy584th8OhdevWmZ7pjz/+cK1lqFu3rqUdYKWM93G0simcXfJk1KQqo8eyW2ZNdJysLD58fX1VtWpVvfHGG6pfv77uvPNOy7I42WnNkh04//ZlxoppcdK1n2vnWsBmzZpZuhZQkg4dOpTh41Z1Oz5z5ozuuece19TpvHnzmjrVMyYmRnPnzlWtWrX0wgsv6PHHH1fJkiV14MABU67/T1y9elVlypTRrl27TLleTEyMJkyYkGlPgVOnTqlUqVKaM2eOKXky8/vvv+vs2bN6/PHHLbuZuGPHDsXExKhOnTpuN3+t2Eu7VKlSrhusdtG3b19dvHhRa9eu1cCBA9WqVSurIyETrVq1UunSpdW1a1dJ0oQJE7Rz507Lfs/ttAe7c5/oGjVqaPr06QoMDFSZMmWy9f3FbTkSbTdhYWH66KOPXKNjixcv1syZMzVx4kTLMpUrV87ywjmtPHnyyDAMhYaG6uOPP1ZQUJCla97skscwDJ04ccLVKObEiROWNMu7vki2wx6OTmfOnNGyZcv0888/a+DAgSpQoIAaNGigN99805I8dluzZIdOuQsWLJAkzZw5UwcPHnR7A2DldjjXrwW0mt22BmrRooWWLFni2v7m0KFDevLJJ00rAmbOnKkqVaqoa9euioyMlMPhsLwvhFPaplRWbEc2YcIESTdu7GPVaP3gwYM1YMAASdKjjz4qwzAUFRVl+iwGJzuN/pYqVUoJCQmmbuuXEWdzLOna6G+XLl3UsGFDlSxZUtu2bTN9TWtmzQOdrJztZidjx45Vz549ValSJTkcDjVo0EBffPGFZXnstAd7rVq1dObMGfXo0UOVK1eWr6+vWrduna3XZCTaBI888ojbHyxJqlChgmVTHjZt2qT+/ftr//79bp0qrRoNkq5tkVS5cmWdPn1a3bp107lz5/T++++rQYMGOTrPtGnT9Prrr7um3i9cuFDDhg3Tc889Z2oOJzvt4ZhWcnKyVq5cqcWLF2v69OlKSEjQmTNnLMnyyCOPaMmSJa5pVqdOnXLr6Gm2J598UjVr1tSkSZPcOuVa0eTn+q39DMNQ1apVLdnur2PHjhm+abNqWz3JHjc80hozZoyWL1/umupZr149jRgxwrSmfRcvXnRN5T5y5Ig6dOigr776SnFxcaZc/0bstB3Zn3/+ma7zdMuWLU3P4RQZGakOHTq4Xqe6dOmilJQUy3637DT627p1a23cuFGNGjVyGxUfOXKkqTmKFy+e6XNWNM3KbLabs4ngK6+8YmoeeGbGjBlq3Lix9u/f79qDffTo0WrXrp1lmfbv36+vv/5aBQoUUM+ePbP1WhTRJggLC9PIkSNd065+/fVXvfLKK5btG1u+fHn16NFDERERttg3dv369Ro2bJh27twp6dooed++fRUeHm5JHuna1LxcuXJZdv20duzY4Tb13sr9fe22h6N07Q3bxo0bVbFiRTVo0ED169dXlSpVLBuxyuimWUaPmcU5/d853SopKUm1a9fW6tWrTc8SGhqqzZs36+6775Z0rUiqVKmS9uzZY3qWzz77zPVxYmKi5syZo0qVKunTTz81PYuTnW54ONllqufOnTs1efJkTZs2TQ899JCioqL04osvWpLFTvr27auvv/5aZcqUces8/fPPP1uWKSEhQfXq1dOwYcP0/fff69ixY5o+fbplr6nNmjVTbGys5aO/kvvNl7Tefvttk5PY2+7duzVx4kRNmzZNRYoUcdtRJid7++231atXL91zzz2SpNOnT+uzzz7L0T8/DRo00PDhw1WxYkUdO3ZM5cqVU7Vq1XTgwAF17NhR/fr1y7ZrU0SbYNWqVWrTpo3rDnVKSopmzZql6tWrW5LHijW1mVm9erWaNGmibt26qVq1ajIMQ+vXr9fYsWP1008/qVq1apbkKlasmLp166aYmBhLGjccOXJEJ06cSLeWdsOGDSpUqJAefPBB0zNJcm2FFhYW5roJlPZjK/zyyy967LHH3O7qW+n6NUvjx4/Xn3/+admapapVq2rdunUKDw/XwoULVbBgQZUqVUp79+41Pcs777yjOXPmuLb/+uabb/T0009bsjbxepcuXVKzZs1ca9mtYJcbHmlv+KSmpqpLly6qU6eO2rdvL8m6/VGla6+f3333nSZPnuxaz26VdevWacuWLW6zBszeoiwkJETbtm2zRdOstI4cOaJatWqpfPnymjt3rqXNFe0y+mtH33//vR577DEVKFBAknT27FmtWrVKTZs2NT2LXZsI2klG79+t7B80btw4tWnTRvnz51ePHj20Zs0ajRw50tSGyQ8//LD+/PNPSdLw4cP1+++/69tvv9XZs2dVu3btbB3AYE20CWrUqKF9+/a5Go6ULl3akilfTo8++qg2bNhgi2ZHH330kSZPnqynnnrK9dhTTz2latWq6f3339e8efMsyfXLL7/oiy++ULly5dSoUSP16NHD1IK+X79+Ga6hvXjxomv/TSvYaQ9HJ2evgStXrujKlSuux60adchozdLYsWMtySLZq1PuO++8o/DwcC1dulSS9OGHH7qWKljNz89PR44csTSDr6+vK8uZM2dUsGBBnT592vQcafdHdZo9e7Zmz55t2f6oTj4+PmrVqpXlzY+GDh2q2bNn6/Dhw6pdu7Z++eUX1a9f3/QiumjRora5gRgWFuY2AygxMVF79+51zSqz6o1+mTJlVKZMGUuu7TRjxgy1bdtWn3zySYbPW7U//MCBA92KsgIFCmjgwIGmF9Fpmwj269fP1USQAtrd1atX0z2WlJRkQZJrPvvsM3Xt2lW///67tm/friFDhujVV181tWFy2mayq1atci03KliwoHx8srfMZSTaRHZ5k1++fHnt3r1bISEhbi++VrzAhYaGZjqV80bPmeXSpUuKjY3VkCFDdP/996t3795q27Zttk8Vdo74ZqRcuXL6448/svX6mYmNjdWMGTO0bds2Pf/884qNjdVHH33kGlm0wtq1axUdHZ3uZyU1NdXUHNdvZef80+r8WTF7K7uMrFy5UufOnVPjxo2z/cXleqmpqSpfvrxr2YbVMmoMVahQIc2ePdv0LCkpKfLx8VFUVJRGjx6t2NhYjRkzRv7+/ipVqpRmzJhheibcWLly5bRhwwZVr15dW7Zs0e7du9W/f3/TZ5ysX79eH3zwgSIjI91ezzt06GBqDunaUrUbqV27tklJ7Cdv3ry6ePGiOnbsmO45h8Nh2XrxjEY2rejZky9fPlWpUkX9+vVzNREsUaKEpTfs7OiZZ55ReHi4+vbtK8MwNGLECK1fv96S1y3p/4+Cv/feeypcuLA6depk+sh4lSpVNG/ePBUoUEBBQUFavXq1aweJ0qVLZ+uOCYxEm2DNmjXq2LGj5W/ynaxc83e9G01Bc66btIphGPr55581a9Ys5c2bV23bttWMGTP0zTff6Ntvv83Wa1++fPmGuaxixz0ce/XqpSlTpqhbt25asWKFPvnkE0tGZvr27ZvuMYfDoWPHjun48eOW/b6nZeX3Knfu3Lrvvvt06dIlW0w9zZ8/v+tjHx8f9erVy7JmTM8995xmzZrlmmHy8ssvq0qVKtq3b59lXebTsssNYDvx8/OTn5+frl69KsMwVKpUKe3bt8/0HGPHjtW2bdtkGIbbmmgriujatWsrNTVV0dHRmjZtmunXvxGrp97Xrl1btWvX1ldffaXg4GDTrpuVfPnyadWqVapRo4aka1uS5cuXz/Qcx48f16xZs/Tuu++qS5cu6tChg+Wd1O1o9OjRioqK0oABA+RwOFSrVi1NnTrVsjy5cuXSrFmzNGvWLNfyGrNHxvv376+wsDD5+Piobt26rgJ61apV2f+7ZiDbVa1a1VizZo1RsWJFIyEhwRg8eLAxfPhwq2PZQunSpY1t27YZW7duTfdf6dKlLcs1dOhQIzg42GjatKmxaNEit+dCQkKy/frlypUzjh07lu7xo0ePGuXKlcv262clLi7OOHLkiNUxDMMwjLCwMMMwDLd/lypVqlgVx+XMmTNG7969jfvuu8947733LMvx008/GaVKlTLuuOMOI1euXIbD4TBy5cplSZYXXnjBqFixojFkyBBj9OjRrv9yuubNmxvdunVze+zw4cNGiRIljDFjxliUyjBWr15tlC5d2siVK5fbfzCMmjVrGklJSUb79u2NPn36GCNHjrTkb3OJEiWM5ORk0697I1WrVrU6gpshQ4YYYWFhxj333GO0bNnSyJcvn9GiRQvTc0ycONEoUqSIMWHCBNOvnZlVq1YZDzzwgFG7dm2jdu3aRuHChY21a9dammnHjh1G3759jfvvv9+IiIgwPvvsM0vz2NHFixeNixcvWh3DWLNmjdG8eXPj448/NgzDMHbv3m307NnT9BzHjx83tmzZYly9etX12NGjR41Dhw5l63WZzm0C59SGtJuRX7/Vi5kuX76sMWPGpLsra8U+fMHBwZlOjbZy7V3Pnj3Vs2dP1x2ttDZu3Jjta0o/++wzTZ8+XZMnT1apUqUkSbt27VJMTIyeffbZbG/bn5mtW7eqTZs2+vvvvyVJgYGBmjFjhipUqGBJHkmqVq2a1q5dqzp16mjkyJEqWrSowsPDdfDgQUvyJCYmatSoUa5tHt58801XJ00rhIaGasyYMem68Vsx08NOUxk7d+6sDz/80K3L6Ztvvqlx48aZnuXKlSt6/PHHFRERoSFDhujIkSOqW7euevTooZdfftn0PE7VqlXTJ598km6WR0azLnKaP/74Q8WLF9elS5fUv39/nTt3TgMGDDD9b2HDhg31448/Wtpn5XpvvPGG4uPjFR0d7dpjXLKuIZ1dpt5L0l9//aXw8HAZhqFcuXLJMAw5HA7Fx8ebnsXp7NmzruaFNWrUcDUZs5qdmghabe/eva49vDNiZbNH6dprWJ48eSzNYAWmc5vAufbwnnvu0aZNm1S0aFGdOnXKsjwxMTHy9/fXqlWr1LdvX02ZMsWy9ZpWFTo3kpqaqj///DPDAlqSKU2ZXnrpJZ08eVJhYWGuP0xJSUl69dVXLSugpWuFx7vvvqtnnnlG0rVmQ507d7bshpAktWnTRmfOnFH//v1Vu3ZtJScna/DgwabnuHr1qiZOnKj33ntP9erV09q1axUUFGR6juv5+/srMjLS6hiSpC+//NLqCC4bN250u7lx7733WvZznCdPHn333XeqX7++pGu/Vy+++KKlBbR0bY/WatWqKSUlRfny5dObb77pWo+XU6VdS++UL18+5c2bV1999ZXpHZ9LlCihOnXqqHnz5m7LWKxqVCVJs2bNknStQaeTlTfF7TL1fuPGjYqOjlabNm302muvWd6U06lgwYKm7f3+T9iliaAd9O7dWwsWLMiw6aOVv1vbtm1Tu3btdO7cOR05ckQbN27UrFmz9NFHH1mSx2yMRJtg1KhR6tChgzZu3KhWrVq53uS/+uqrluRxjog79669cOGCmjZtmq4xUk5Wo0YNrVy50vK9oi9duuTag7lMmTKWrxNPO5vCyco9kK+XnJysxMRES9Z0lSlTRleuXNE777yT4WiUVXeK33nnHVWsWFEtWrSw5PpppaSkaNSoUa4315GRkXr55ZdNb3Impf9ZNgxD5cuXt6Rpn/P35/Tp02rdurWeeOIJ9e7d2/W8VT87dpvlYQe5cuVSeHi4GjdunOHrg9n7tdppdoddPfbYY1q6dKk6deqk++67T0WKFNHkyZPTvZZlpzfffFPTp0/X2LFjbXNTU7r285zRbEA79PCAO8MwdPz4cRUuXNjqKC516tTR4MGD1bNnT23evFmGYahcuXKu9623O0ais5HzjVH9+vV19OhRBQYGaunSpUpMTNQDDzygxMRESxogOdvB+/j46H//+5/y5ctn6ci4HYWHh+uJJ55QVFSU23S0Zs2amZrjrrvucm0PYgeVKlXS8uXLVadOHUnXurFatV1SVoW72YXHpUuX5HA4Mtzz2Mo7xaNHj9b58+d15513Kk+ePJZOH+zTp4/27dunF198UQ6HQxMnTtShQ4cy3fYlO1WvXl09evTQa6+9JsMwNHz4cFWvXt30HJL7llJ58+bV8uXLtXz5cknW/uxkNMtjwIABlmSxiyVLlmjy5MmaPn26nn32Wb3wwgt66KGHLMtjp9kdTocPH87w8WLFipmc5JovvvhCSUlJGjFihPr376/ff//d9MZnhw4d0ubNm20zVdrpwoULro8vX76sqVOnUkDbWKNGjSzbnSUjFy9edGtY6nA4XFs15gSMRGej4sWLp3vMeccvOTlZFy5c0IgRI9SpUydTczVu3FgzZszQiBEjtHz5ct1777363//+5zb1KqerW7duusccDodrf9ucyrlFkbPj4cGDB1WmTBnXejwztzXI6PfLyeq9bO3k0KFDGT5uxVTzRx55RFu2bHGN4KWkpKhSpUqWzGRISEjQK6+8ogULFsjhcKhZs2YaOXKkJbMYbgXJycm6fPmyypUrl2mRlJMkJCRoxowZ+vLLL3XnnXfqgw8+ULVq1UzPkVlnXiu6czvdd999cjgcMgxDiYmJunTpku655x6dPHnSskzwnHMGCuyncePGio2N1b333mt1FEnXbkb/9ttvqlatmjZt2qS4uDg99dRTmW7RerthJDobHThw4IbPHz9+XPXq1TO9iP7hhx+UO3duvffee/r666917tw5S19w7WjZsmVWR7AlO22PltXvF66xw7psJ8MwdPXqVVcRbRiGZVu2+fv7M+X1H7jjjjt0xx13WLrFnp34+/urefPmio+P1yeffKJdu3ZZUkR///33ro8TExO1cuVKVa9e3dLX9Otnts2dO9f0fYela1PdM2tcKonf/wzs2rVLp0+ftjoGMpE3b15VrFhRTZo0cZslaXYvBqcePXqoRYsWOnXqlAYMGKDY2Ngcsx5aooi2VKFChdSlSxfTr5t2L8moqCjTr3+rSE5O1oEDB9w6mFvdAdFqzimdhw8ftnQKY1rr169X2bJlddddd+m///2v1q1bpz59+thq3ZCVihcvnuEbSStG6hs3bqxGjRopOjpa0rVRtMaNG5uew+nYsWP6448/3H7HzV6ycau5UVGSE6Smpmr+/PmaNGmSDh06pPbt22vTpk0qVKiQJXm++eYbt88PHDhgi73F02rZsqWGDh2qQYMGmXrdKlWqSJK2b9+uFStWqF27dnI4HJoxY4Yee+wxU7PYVcGCBV2/06mpqTIMQ2PGjLE4FTJTvnx5lS9f3uoYLlFRUSpRooS+++47JSUlKTY21m169+2O6dw50KZNm9S/f3/t379fKSkprseZ/vr/LViwQDExMTp79qzuvvtunT17VkFBQZaMfv73v/9Ntx2ZVXcdly9frnbt2snHx0eHDx/W+vXrNXr0aMXGxlqSR5IqVKigTZs2af/+/WrSpImefvppbdq0SYsWLbIsk52kbfCRmJioadOm6Z577tHAgQNNz3L16lWNHz9eixcvlsPhUIMGDdSlSxdLCrPJkyfr3XffVXx8vEqWLKmtW7eqevXqWrlypelZ7OZG0+sjIyN1/PhxE9PYS2BgoIoVK6aOHTvq0UcfTfe8HW60VqhQwZKRX6eEhATXx6mpqVq7dq169eqlPXv2WJKnVq1aWrBggfz9/V35nnjiCZqpyn25j4+PjwIDA23TNRz2t2vXLpUuXdrqGJahiM6Bypcvrx49eqTbN7Zs2bIWprKXsLAwzZs3Ty1atNDmzZsVGxurrVu3atiwYabm6NWrlw4cOKCNGzeqbdu2+uabb9SwYUNNmjTJ1BxO1atX19dff62nn35amzdvlnTt58bKTozOfdg/+eQTpaSkqE+fPgoLC3PlQ3o1atTQqlWrTLve9dsCOV92nMWzFTeFypcvrxUrVqhevXravHmzVqxYoSlTpjDFU/QbuJHg4GDXz61z3a+TFf828+fPd32cmpqqNWvWaNmyZVq3bp2pOSSpbdu2mjFjhqvjs2EYyp07t0qWLKmRI0daNuvk4Ycf1p9//pnlY8CtYN26dekGVqza0i44OFglS5ZUz5499eSTT+a4mUpM586BcufOra5du1odw9Zy5cqloKAg10h9VFSURo0aZXqOZcuWaevWrQoLC9OIESP02muv6fnnnzc9h1Nqamq6adxWd2K8cuWK/v77b33//ff68MMPJbE9x42cOXNGJ06cMPWaH3/88Q23BbKCr6+vChYs6Podr1Wrll555RVrQ9kE/QYyZ7ftvdK+Lvn4+CgkJMS1T7PZdu3aJenajBM7eeSRRxQdHe3qP/Pll19muA1hTnT9zETn7g05+UaZnQ0dOlSzZ8/W4cOHVbt2bf3yyy+qX7++ZUX0/v379d1332nMmDF65ZVX1K1bN3Xu3FkBAQGW5DEbRXQO9Oijj2rDhg2u9UJIz9ltukiRIvr2228VHByss2fPmp7Dz8/PdVc/OTlZgYGBOnbsmOk50ua5ePGi627j9u3bXVumWaV3794qVaqUGjRooEqVKmnfvn0qWLCgpZnsICUlRT4+PgoLC3Nb83bo0CG99tprpmax27ZAklzbfYWGhurjjz9WUFCQLl68aGkmwFPOKfejR49O91xycrIlW2jadRRq0qRJevfdd103yRo0aGDJchY7ev755zOcmQh7mj59ujZs2KDq1atrzpw52r17t/r3729Znly5cumpp57SU089pY0bN+qpp57SO++8o+eee06DBg267XvTMJ07B3G+mU5OTtbu3bsVEhLi9iJr5vZEdjdjxgw1btxY+/fvV5s2bXTu3DmNHj1a7dq1MzVHvXr1tGDBAr3++us6deqUAgMDtWbNGtO3n3BO01u0aJEGDRqkffv2qWHDhlq8eLGmT5+uevXqmZrnRq5evaqUlBTLR8it1rp1a82aNUu//vqr6zEfHx8VLVpUderUsWSkwS7bAknS0qVLVblyZZ0+fVrdunXTuXPn9P7776tBgwaW5AH+CeeU++sLV8MwlJKSYskWmj4+Pq51x9dnsmpv+tTUVEVHR5u+L/StomLFitqyZYvVMeChKlWqaMOGDXrkkUe0detWORwOy7+He/fu1WeffaZvvvlGzZo1U0xMjJYuXaqpU6dasn2lmRiJzkE+/vhjqyPcMho1aqSCBQuqcuXK2rt3ryRZ0hRlxowZyp07t4YNG6aRI0fq7Nmzmj17tuk5nNP0IiMjFRoaqoULF8owDA0aNMjyEUU77pNqB1euXFH37t31xRdfuB6Li4tT3bp1061RNotdtgWSpDp16ihXrlzKnz+/fvnlF0syAP+WHbfQLFWqlH788UfTrueJ3LlzW9bQ7FbAzMRby5133qnk5GRVrFhRr776qooUKWLp8rXIyEjt3btX3bt3144dO1SgQAFJ13rVTJkyxbJcZmEkOgdxjiYia4899piWLFniGs08dOiQGjVqpN27d1uczBrO5l129Mwzz7g+TrtP6k8//WRhKutduXJFjz/+uCIiIjRkyBAdOXJEdevWVY8ePfTyyy+bmiWjbYHat29v2bZAklSsWDF169ZNMTExuu+++yzLAWSXUaNGqXfv3qZdz66vE2+88Ybi4+MVHR3ttreuHTqpW4WZibemP/74Q8WLF9elS5fUv39/nTt3TgMGDLBsjf+cOXP01FNP2abXidkoonMQu77A2dGYMWO0fPlyzZkzx3VHf8SIEWrSpIkp13/qqaduuL5s7ty5puRwsuM0vcw490mdPn261VEsd+HCBdWvX18NGzbU7Nmz1a1bN1PfVDvZcVug3bt364svvtCMGTPUqFEj9ejRw7JRceB2YNddETLqNp/Tm2d99913io+PV4kSJdwe379/vwICAtS8eXOLkiEjmc0ec74Hs2rbU0lua7OPHj2q+Ph4W+1lnZ0oonMQiuh/pm/fvrp48aLWrl2rgQMHqlWrVqZd+6uvvrrh82Z36C5btuwNp+kFBQWZmCZrVu+TagfOtUinT59W69at9cQTT7gV0GYWrnbbFiitS5cuKTY2VkOGDNH999+v3r17q23btrZtkgQAN6tbt25q2LBhuvc1c+fO1S+//OK2DAjWy5Ur1w13uHj77bctSCW99dZbWr9+vfbt26c9e/bo+PHjatWqlalbaFqJIjoHuZVGE62StglCamqqunTpojp16qh9+/aScu70LzvfgLl+n9S1a9dq6dKlluyTaifs9Zs1wzBc23OcOHFCnTp10rJly+Tj46Nvv/3W6ngAvOjKlSu6cuWK6/OM3g/lFDd6TS9btqx27NhhciLcyLJlyzR58mStWbPGNjtcSNcGLDZt2qQqVaq4ZqE88sgjt31DMScai+Ugdmz6YTcZTWGaPXu2Zs+ebWrhkVXjJ7On7tj5Xpud9km1E/b6vbH3339f48ePV9myZfXGG2+obt26WrBggfr06aOSJUtaHQ+Al6xZs0YdO3ZM12DMyoZMVktJScn0uZy6vtXO6tatq7p167p2uHjuuecs3+FCutbo7Pqt0ez8ftHbKKJzkDx58thu2q3d2KXwyJ8/v9UR3NhxnZvTsmXLrI6AW9CxY8f0888/KzU1VZMnT1aHDh304IMPqkWLFpo5c6bV8QB4ycsvv6wpU6aoW7duWrFihT755BPT99C2m+TkZCUkJKQbjT9//rySk5MtSoWs2GmHC+naUr7ffvtNDodDSUlJev/991WxYkXL8piN6dw5iF2bftjRypUrVbNmTbfHvvnmG7dO0LDW3r17VbJkyUynDeXUqffI2qVLlzRz5kxNnjxZ+/fv1+XLl7V69WqVLl3a6mgAvMw5dbl8+fLavn27JCk8PFzr16+3OJl1Bg0apE2bNmnKlCkqWLCgJOns2bPq1KmTHnnkEb3zzjvWBoQbO+5wIUl///23nn/+eS1ZskQOh0N169bV4MGDFR4ebmkus1BEAxkICQlRly5d9Prrrys5OVkvv/yy1q9fb/qLblxcnLp3764jR45oy5Yt2rJli5YtW2ZJh2W7adq0qX744Qe3tb/OplWs+UVmYmJiNHfuXNWqVUsvvPCCHn/8cZUsWdI2s1AAeFfVqlW1bt061alTRyNHjlTRokUVHh6ugwcPWh3NMqmpqXrhhRc0Z84c1/KVvXv3qlWrVpo8eXK6Kbqwlh13uNiwYYMOHTqkOnXq6M4779T+/fs1YMAA/f777zp16pTpeaxAEQ1kwLmnZEpKik6ePKnq1atr5MiRrn2jzdKkSRO1a9dOw4YN09atW5WSkqKwsDDX3fSczFksO+3bt0/z589XSEiInnzySQuTwc7y5cunKlWqqF+/foqMjJTD4VCJEiW46QLcZt5++23Vq1dPq1evVkxMjDZt2qSWLVsqOTlZ7733nl577TWrI1pu3759rgZjlSpVskWzKqRntx0uPvzwQ73//vsqXbq04uPj1bNnT73xxhvq3r27BgwYoAIFCpiaxyoU0UAmZs+erc6dOytv3rxavHixJVM9q1Spog0bNrhNxWda/jUNGjTQ8OHDVbFiRR07dkzlypVTtWrVdPDgQUVHR6tfv35WR4QNXbx4UbNmzdKkSZN05MgRdejQQV999ZXi4uKsjgbAi7p3765ly5bp8OHDqlGjhurWratatWqpQoUKObozN3CzHn74YS1ZskSFCxfWrl27VK5cOS1atEj169e3OpqpaMEHZKBnz54aOnSoNmzYoLFjx6px48aKjY01PYePj4/bHcezZ8/mqM6HN3L06FFXA4vp06erdu3a+umnn7Rq1Sp9/fXX1oaDbeXNm1edOnXSqlWrtHDhQiUmJiopKUk1atTQ559/bnU8AF7yxRdfaNeuXdq/f786deqkw4cPq3PnzipSpIiaNm1qdTzgluXn56fChQtLkkqXLq3Q0NAcV0BLFNFAhq5cuaLVq1crJCRETzzxhFasWKEvvvjC9BzPPPOMunbtqoSEBE2cOFENGzZU586dTc9hR3feeafr41WrVqlJkyaSpIIFC8rHh40HkLUyZcpo+PDhOnr0qPr27asffvjB6kgAvCwwMFCtWrVS27Zt1aZNGwUGBmrLli1WxwJuWYmJidq+fbu2bdumbdu2yTAMt89zCqZzAx5KTU21pNnGjBkzNG/ePBmGoRYtWqhdu3amZ7CjKlWqaN68eSpQoICCgoK0evVqhYaGSrp2Z3TXrl0WJwQAWGXFihVavny5li1bpqNHj6p69eqqVauWateuzV7wwE1Iu0b7ejmpsStFNJDGjBkz1LZtW33yyScZPt+rVy+TEyEzc+fOVdeuXeXj46NHH31Us2fPlnRtVPrdd9/VwoULLU4IALBKrly5VL16db311ltq3Lix1XEA3GaY8wik4Ry9zKhxV2Z33bLD5cuXXfs3Pvvss+rXr58WLlyoUqVKafTo0XrwwQdNy2JXLVu2VI0aNfT333+7be8QHBys8ePHW5gMAGA150j08OHD1aNHD1WtWlV16tRRnTp1XLOWAODfYiQaSGP+/Pk3fL5Zs2am5IiKitK5c+d06dIl5c6dW0FBQWrVqpWWLl2qXbt26fvvvzclBwAAt7qkpCStXbtWy5Yt0/Tp03Xx4kUdOXLE6lgAbmEU0UAadevWdX28ceNGValSxdUN2+FwaOnSpabkKFOmjHbu3KnExEQVKlRIZ86cUa5c1/oAli9fnn2iAQDwwLFjx7Rs2TItX75cS5cu1cmTJ1WzZk399NNPVkcDcAtjOjeQxrJly1wfh4WFmVY0Xy9PnjySrm0jEBwc7CqgJemOO+6wJBMAALeKmJgY/frrrzp27JgiIiJUt25dxcbGKjw8nB0cANw0/ooAmTBzDfT1nNsHGIbh9rF0bb00AADIXNGiRTVp0iRVr16dm88AvI7p3EAmKlWqpE2bNllybbYPAAAAAOyJkWggjbSbxF++fNltBFiSWxfo7HTw4EFTrgMAAADgn2EkGkijePHimT7HCDAAAAAAimgAAAAAADyUK+tDAAAAAACARBENAAAAAIDHKKIBAAAAAPAQRTQAACYKDg7Wli1brI4BAAD+JYpoAAAAAAA8RBENAIDFRo4cqfDwcFWsWFHh4eFavXq167ng4GC99dZbioiIUPHixTV48GDXc7t27VJERITKli2rli1bqlGjRpoyZYokKTo6Wh9//LHr2FdffVXvvPOOJGnJkiWKiIhQWFiYypYtq0mTJrmOO378uBo1aqQyZcqoUaNGatOmjevrkpOT9cYbb6hq1aqqWLGinn32WZ09ezbb/l0AALAjimgAACzWvn17rV+/Xlu2bNGYMWPUsWNHt+fPnTun1atXa/369Ro2bJiOHj3q+rouXbpox44dGjJkiFasWOHR9SpVqqSVK1dq8+bN+u233/Tuu+/qyJEjkqRevXopIiJCO3fu1NSpU7V8+XLX1w0bNkx333231q1bpy1btqh8+fIaMGCAd/4RAAC4RfhYHQAAgJxu8+bNGjJkiM6cOSMfHx/t3r1bly9f1p133ilJateunSTp3nvvVYkSJXTgwAHly5dPW7ZsUYcOHSRJDz/8sGrWrOnR9c6cOaNOnTppz5498vHx0ZkzZ/THH3+oSJEiWrJkiYYPHy5JCgwM1BNPPOH6unnz5un8+fOaM2eOJCkpKUnBwcHe+mcAAOCWQBENAICFkpKS1LJlSy1btkzh4eFKSEhQ/vz5deXKFVcR7efn5zo+d+7cSklJyfBcDofD9bGPj49SU1NdnycmJipv3rySpG7duqlJkyaaM2eOHA6HKlWqpMTExCzPaRiGxowZo0aNGv37/2EAAG5xTOcGAMBCiYmJSkpKUrFixSRJY8aM8ejr/P39VaFCBcXGxkqSdu/erZUrV7qeDwkJ0bp16yRdG3n+8ccfXc+dPXtWQUFBcjgcWrFihbZu3ep6rl69eq511X///bcWLFjgeq5FixYaNWqULl26JEm6dOmSduzY8S/+rwEAuHUxEg0AgMkiIyN1xx13uD4fMGCAqlatqnvvvVdt2rTx+DxTp07VCy+8oGHDhikkJETh4eEqUKCAJKlLly56+umn9fDDD6tEiRKqXr266+s++OADvfjii3rvvfdUsWJFVatWzfXc6NGj9fzzz6tMmTIqXLiwqlWr5jpnv379dOXKFVWrVs01Qt2vXz+VLVv2Jv41AAC4tTgMwzCsDgEAAP65ixcv6u6775bD4dCBAwcUERGh9evXq2jRov/6nJcvX9Ydd9zhWitdvXp1xcbGuhXaAADkZIxEAwBwi1q1apVee+01SVJqaqpGjRp1UwW0JO3du1cdOnSQYRhKSkrSiy++SAENAEAajEQDAAAAAOAhGosBAAAAAOAhimgAAAAAADxEEQ0AAAAAgIcoogEAAAAA8BBFNAAAAAAAHqKIBgAAAADAQxTRAAAAAAB46P8BqvK2B8ONeDQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(cells.groupby(level='Language').apply(lambda x: pearsonr(x, 'Direct', 'Ratio'))['r']\n",
    " .sort_values(kind='mergesort')\n",
    " .plot.bar(figsize=(15, 5)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Markers</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Portmx</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ratio (norm)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Cell ratio (norm)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Language</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ainu</th>\n",
       "      <td>29</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleut</th>\n",
       "      <td>42</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ayacucho</th>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.440476</td>\n",
       "      <td>0.559524</td>\n",
       "      <td>0.357798</td>\n",
       "      <td>0.642202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bella Coola</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chuckchi</th>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.137725</td>\n",
       "      <td>0.862275</td>\n",
       "      <td>0.116788</td>\n",
       "      <td>0.883212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Darai</th>\n",
       "      <td>26</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.643836</td>\n",
       "      <td>0.356164</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fox</th>\n",
       "      <td>67</td>\n",
       "      <td>72</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>0.789969</td>\n",
       "      <td>0.210031</td>\n",
       "      <td>0.776054</td>\n",
       "      <td>0.223946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hixkaryana</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jaqaru</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.660377</td>\n",
       "      <td>0.339623</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.321429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jumjum</th>\n",
       "      <td>51</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0.286159</td>\n",
       "      <td>0.713841</td>\n",
       "      <td>0.319471</td>\n",
       "      <td>0.680529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Karuk</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>0.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ket</th>\n",
       "      <td>76</td>\n",
       "      <td>142</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kunama</th>\n",
       "      <td>64</td>\n",
       "      <td>122</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>0.437134</td>\n",
       "      <td>0.562866</td>\n",
       "      <td>0.417219</td>\n",
       "      <td>0.582781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lakota</th>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maricopa</th>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maung</th>\n",
       "      <td>83</td>\n",
       "      <td>190</td>\n",
       "      <td>16</td>\n",
       "      <td>58</td>\n",
       "      <td>0.387064</td>\n",
       "      <td>0.612936</td>\n",
       "      <td>0.349678</td>\n",
       "      <td>0.650322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mordvin</th>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0.514851</td>\n",
       "      <td>0.485149</td>\n",
       "      <td>0.415584</td>\n",
       "      <td>0.584416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nocte</th>\n",
       "      <td>12</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reyesano</th>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sahu</th>\n",
       "      <td>73</td>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Siuslawan</th>\n",
       "      <td>108</td>\n",
       "      <td>131</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0.350267</td>\n",
       "      <td>0.649733</td>\n",
       "      <td>0.379073</td>\n",
       "      <td>0.620927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tepehua</th>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0.522822</td>\n",
       "      <td>0.477178</td>\n",
       "      <td>0.522184</td>\n",
       "      <td>0.477816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thangmi</th>\n",
       "      <td>25</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.793388</td>\n",
       "      <td>0.206612</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Turkana</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wardaman</th>\n",
       "      <td>47</td>\n",
       "      <td>45</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0.241935</td>\n",
       "      <td>0.758065</td>\n",
       "      <td>0.191919</td>\n",
       "      <td>0.808081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yimas</th>\n",
       "      <td>87</td>\n",
       "      <td>142</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0.072118</td>\n",
       "      <td>0.927882</td>\n",
       "      <td>0.065436</td>\n",
       "      <td>0.934564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Markers       Portmx       Ratio (norm)            \\\n",
       "Direct        False True   False True         False     True    \n",
       "Language                                                        \n",
       "Ainu             29    24      0     2     0.000000  1.000000   \n",
       "Aleut            42    57      0    21     0.000000  1.000000   \n",
       "Ayacucho         47    37      4     4     0.440476  0.559524   \n",
       "Bella Coola      29    29      2     1     0.666667  0.333333   \n",
       "Chuckchi         18    23      1     8     0.137725  0.862275   \n",
       "Darai            26    47      2     2     0.643836  0.356164   \n",
       "Fox              67    72     14     4     0.789969  0.210031   \n",
       "Hixkaryana       10    18      2     2     0.642857  0.357143   \n",
       "Jaqaru            9     7      5     2     0.660377  0.339623   \n",
       "Jumjum           51    23      8     9     0.286159  0.713841   \n",
       "Karuk            18    18      2     3     0.400000  0.600000   \n",
       "Ket              76   142      0     0          NaN       NaN   \n",
       "Kunama           64   122     11    27     0.437134  0.562866   \n",
       "Lakota           29    33      0     2     0.000000  1.000000   \n",
       "Maricopa         20    12      0     0          NaN       NaN   \n",
       "Maung            83   190     16    58     0.387064  0.612936   \n",
       "Mordvin          14    26      4     7     0.514851  0.485149   \n",
       "Nocte            12    18      2     4     0.428571  0.571429   \n",
       "Reyesano         24    20      0     0          NaN       NaN   \n",
       "Sahu             73   146      0     0          NaN       NaN   \n",
       "Siuslawan       108   131      4     9     0.350267  0.649733   \n",
       "Tepehua          46    42      6     5     0.522822  0.477178   \n",
       "Thangmi          25    32      3     1     0.793388  0.206612   \n",
       "Turkana          18    20      0     4     0.000000  1.000000   \n",
       "Wardaman         47    45      3     9     0.241935  0.758065   \n",
       "Yimas            87   142      1    21     0.072118  0.927882   \n",
       "\n",
       "            Cell ratio (norm)            \n",
       "Direct                  False     True   \n",
       "Language                                 \n",
       "Ainu                 0.000000  1.000000  \n",
       "Aleut                0.000000  1.000000  \n",
       "Ayacucho             0.357798  0.642202  \n",
       "Bella Coola          0.640000  0.360000  \n",
       "Chuckchi             0.116788  0.883212  \n",
       "Darai                0.695652  0.304348  \n",
       "Fox                  0.776054  0.223946  \n",
       "Hixkaryana           0.642857  0.357143  \n",
       "Jaqaru               0.678571  0.321429  \n",
       "Jumjum               0.319471  0.680529  \n",
       "Karuk                0.307692  0.692308  \n",
       "Ket                       NaN       NaN  \n",
       "Kunama               0.417219  0.582781  \n",
       "Lakota               0.000000  1.000000  \n",
       "Maricopa                  NaN       NaN  \n",
       "Maung                0.349678  0.650322  \n",
       "Mordvin              0.415584  0.584416  \n",
       "Nocte                0.400000  0.600000  \n",
       "Reyesano                  NaN       NaN  \n",
       "Sahu                      NaN       NaN  \n",
       "Siuslawan            0.379073  0.620927  \n",
       "Tepehua              0.522184  0.477816  \n",
       "Thangmi              0.800000  0.200000  \n",
       "Turkana              0.000000  1.000000  \n",
       "Wardaman             0.191919  0.808081  \n",
       "Yimas                0.065436  0.934564  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd[['Markers', 'Portmx', 'Ratio (norm)', 'Cell ratio (norm)']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r</th>\n",
       "      <th>p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Direct &amp; Perc (norm)</th>\n",
       "      <td>0.406578</td>\n",
       "      <td>0.006168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct &amp; Ratio (norm)</th>\n",
       "      <td>0.416424</td>\n",
       "      <td>0.004928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Direct &amp; Cell ratio (norm)</th>\n",
       "      <td>0.457289</td>\n",
       "      <td>0.001804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   r         p\n",
       "Direct & Perc (norm)        0.406578  0.006168\n",
       "Direct & Ratio (norm)       0.416424  0.004928\n",
       "Direct & Cell ratio (norm)  0.457289  0.001804"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([pearsonr(cfdp, 'Direct', f'{c} (norm)') for c in ['Perc', 'Ratio', 'Cell ratio']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Perc (norm)</th>\n",
       "      <th>Ratio (norm)</th>\n",
       "      <th>Cell ratio (norm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Perc (norm)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.958776</td>\n",
       "      <td>0.955347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ratio (norm)</th>\n",
       "      <td>0.958776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cell ratio (norm)</th>\n",
       "      <td>0.955347</td>\n",
       "      <td>0.990275</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Perc (norm)  Ratio (norm)  Cell ratio (norm)\n",
       "Perc (norm)           1.000000      0.958776           0.955347\n",
       "Ratio (norm)          0.958776      1.000000           0.990275\n",
       "Cell ratio (norm)     0.955347      0.990275           1.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfdp[[f'{c} (norm)' for c in ['Perc', 'Ratio', 'Cell ratio']]].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (cfd['Cell ratio (norm)'].sort_values(by=True, kind='mergesort')\n",
    "      .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray))\n",
    "\n",
    "#ax.get_figure().savefig('results_direct_norm.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Direct</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cell ratio (norm)</th>\n",
       "      <td>8.075977</td>\n",
       "      <td>13.924023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Direct                False      True \n",
       "Cell ratio (norm)  8.075977  13.924023"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfd['Cell ratio (norm)'].sum().to_frame('Cell ratio (norm)').T"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}