{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# tools for handling files\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# pandas/numpy for handling data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# seaborn/matplotlib for graphing\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# statistics\n",
    "from statistics import mean \n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from scipy import stats\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "from statsmodels.stats.multicomp import MultiComparison\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "# regex\n",
    "import re\n",
    "\n",
    "# for reading individual telomere length data from files\n",
    "from ast import literal_eval\n",
    "\n",
    "# for grabbing individual cells\n",
    "import more_itertools\n",
    "\n",
    "# my module containing functions for handling/visualizing/analyzing telomere length/chr rearrangement data\n",
    "import telomere_methods_rad_patient as trp\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from scipy.stats import zscore\n",
    "from scipy.stats import ks_2samp\n",
    "from scipy.stats import mannwhitneyu\n",
    "\n",
    "# incase reloading modules is required\n",
    "import importlib\n",
    "%load_ext autoreload\n",
    "%autoreload \n",
    "\n",
    "# setting darkgrid style for seaborn figures\n",
    "sns.set_style(style=\"darkgrid\",rc={'patch.edgecolor': 'black'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    " \n",
    "\n",
    "...\n",
    "\n",
    " \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Telomere Length Data from TeloFISH\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Telomere Length analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_patients_df = pd.read_csv('../data/compiled patient data csv files/all_patients_df.csv')\n",
    "all_patients_df['telo data'] = all_patients_df['telo data'].map(literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mmzORFAUhgHYd0thZrPLk9Odq22Z/dUsTRtTyVJZXcOOHN/q0q6rKyo9XYhtrq1PwO7RDKCmTUnAvctPrul7UlNew4bsNSMkShg4GPBUeXBtdJLRLILl38jnFNu/P885p//OFxhO4566XZbxOZyNG0/ACJsVx48ad8RJRWVlZvQckCE2tU8cEyk7q3Vj61U2Ev/dCbFYLu0VP4w8pzilG21nrdwUMbZiWSqkSt8PNuq/WYb3Jimw4dqfHEGsgtGMoR346Qkh2CNEdoxs79POOJzISb1kpGj/5YIfbRYc+/ZogqoYTMCn26hXcQIIhQ4bUWzCC0ByUlVdQsnDWGR8XceWURoimdaooqkCfFLgaliZSQ+7aXPS99bUJ8WSWSyzsnb9XJMVGMPLu+/niiUe44ZR2j6rytTWc58aOa5K4GkrApPjwww83ZhyC0CRUVWX//n2oqkJKSrtWN7y8uQqLCKOooAhDnMHvdm+pl1JXKSED/S8WLMkSHq2nIUMUjutz4UUcnHAPf//PvxleXk4bnY611VV8WF7BXxf+r3aRiNZC3FMUzlsL5n/Ixh8/4ALDETSSylxHG9IuubmpwzovRHeKZvd/d6NmqD63abwOLwa3AZ1Bh+pWkfT+b+NIihgB3FjG/PkOLrvuRtLbx/P4uNvomt6dtY89RsfOXZo6tHonkqJwXvp27vuYfnuR13pUndS6l282ziYmVHzZNjRJkugysAvb52/HeqUVbdixryJnvpOqH6voM6YPjlIHO1fvxHKR70hSxalgkFtXD6W5CwsLw66q3N/K17EVSVE47yiKwrbFHzK7Z5XPttGpNbwXr8Hl9qDXiY9HQ4pMjaS3rTfZS7OpdFWCArZIG93/1B2tQYvBbECbpcV5xFnnMqvqUSn9opTMy1rPNACh+TjrT72iKOK+i9Ci7dy5g96hgUeN/r9MHW1GT6v9OTpEQpYgv8p3ftyp8+HEiNSzYwo3kT4qPeD2zOsz2f7jdkpXlCJHyShVCrpqHT2G9CA0KrQRIxU6dUwEIHJA/zptu7MPNlVIDSLopPjpp5/ywQcfcPjwYRYsWMC//vUvIiMjefDBB0V1D6FF8Xo96CUl4PZQvcxnk69i7dYcYtV8bujkQSvD1zkadjgieOTmoRj1/j86sbf8s6HCPi/JsswFV16A4lVwVjjRGrXoTLqmDuu8VFZezqH58+u0JVx/fRNF03CC6vJ98MEHvPXWW0yYMKG2l9i/f38+/fRTXn/99QYNsDWy2yt54+13uPeJqTz57CwOHNgf8LFFlY6A24Q/Ji2tC6vtbQNu//mIkU27DjAu6QgTeyskWGTahMnc3UPl0W6F/H3+skaMVgCQNTKmcJNIiEKDCyopzpkzhxkzZnD99dfXJsURI0bw0ksv8eWXX571QV0uFyNHjmTlypW1bdu2bWPcuHH07NmTIUOG8Pbbb6MoJ87md+7cyU033URGRgZjxoxh8+bNZ33c5mDZiuWMumcS/841szbmchbJPbl55ts896rvycXWg0Xc+tYPbD1U1ASRtl56vZ7oHiP49YjvF+z6PAWNtS3eijy6RfteAUm0yIR7iyiqECcrgtAaBXX5NC8vj3bt2vm0x8fHU15eflYHdDqdPProo2RnZ9e2lZWVceedd3LllVcyY8YM9u/fz+TJkwkJCWHcuHFUV1czYcIERowYwaxZs/j000+5++67Wbx4MWFhYWd1/KZUWVnB9Lf+g73HWLTHLznLhlDcXa7k632ryfjhB0ZecQUul4s33nmXj5asxqVqGLviS564dRRjr79eXKo+C506xFNWURlwe7xZYnCyhnt669BpJP651s3P+z3klm/l36ONgP/J5YPjnKTf9yHeACU4g6m7abOY2Z3TPBcqbt8hnsrT/N7OVnMupxbMexUss8XMnmb6ngrBCyopZmRk8NVXX/HAAw/UtqmqynvvvUd6euCb5KfKycnh0UcfRVXrfpssW7YMrVbL1KlTkWWZ1NRUbr/9dr799lvGjRvHggUL0Ol0TJ58bO2uKVOmsGzZMhYuXMgNN5xaZ6H5evfjOZSnDKlNiCdTkvvw8TdfcfnQodz2wER2Rl6IYdAd/D7m7pXlG9m8/TlenP5U4wbdgpVVVHL4b6cv3l3l9LAqpxhVVZl8SySzTDr2F1axbdO6gPuU1EjMf7AffTtE/uHY4h9Y8If3bWiVFZVc+MaFTR1Gi7PywZVnfpDQ7AWVFKdOncqECRNYunQpLpeLZ555hv3791NdXc277wa/ltbq1avp168fEydOpEePHrXtffv25dVXX60zqlWSJCoqjtWf3LRpE5mZmbXbJUkiMzOTDRs2tKikuGt/LtoY/5NdJUmi3C3x7n8+Zld4X3SRiXW2a5N68NPun9m0ZTMZ3YM/ERFOL9SgZUi32DptyVEhfFqiB/xXTPnpkI6J/SIaIbrWz+PwkL8mH2eVE2TQSBra9GmDIVzMQWwOOnVIpKzixNVAfwNrYmIs2CxWdue0jlGoQSXFTp06sWjRIr755hv27NmD1+vlkksu4Zprrjmry5djx4712962bVvatj0x8KGmpoZ58+YxePBgAAoLC0lNTa2zT2RkJDt37gz62M2BJcSE4qxGNvgvXWWQFV58812ib37e73a13UCuvP5GQpRqcrJzGzLU85okSVzSox0vZe1iUj9qL1mrqsp7m6Fr+0Q0fgpZC2fHVeHiwJIDhI8KxxxpBsBb7eXg9weJ7RyLOdncxBEKZRXlZAcxmLJjKyoLGlRSvPrqq3n55Ze56aabGjoevF4vkyZNwuFwcO/xygkOhwO9vu79Hb1ej8vlOuPzaTQSNpv/JNTYHrt3PMufeht32nCfbe7SI2T9+AX6Nu0D7i9p9Ug6AxUFR3zuhVisNooKxYCc+tK/UwwGnYaHl+3FqnEiS1DiNnBhtyQu7tqmXo7RXP4um8qhXw4RfXN0nYLfmhAN0ddHk/efPMKSwpAkCcWrULK1BPtRO1qTlpgeMeitgYuJN6Xz+T1tLa89qKRYUlKC5pRVlxuCy+XiscceY/ny5XzwwQdERx+rgG8wGHwSoMvlwmg0nvE5vV6VsrLqBon3bMXEJDC8g5WFuetQEjNreyDusjy0W7/lrlf+y5LPPsSleJFk39+3x15C+qDLGHCl7yXjv08Y1mxeZ2vRMzWSnqmRuL0KiqJi0NXvZ+B8fr88Dg+E4ncFDEmSMHU3UbGvAq1Jy+Gsw1gGWgi/KBxvpZdDvx7CgIH4QfFNEPnpnc/vaUt67dHRga9CBJUUr7nmGiZMmMDo0aOJj4/3qYp+zTXXnFuEHLtkev/997Nx40beffddMjIyarfFxsZSWFhY5/FFRUW1SbMleebxx8j45lvmLvySrM07iI5LJD46kl633oNWb6Bb7/6s2vULhi6X+uzr3fw9Pca0nHuorYVOI0PDnxOeVzxVHrThgb9+9DF6nLuc5OfmE3tbbO26i1qLlqiroqhcV0nxtmIiu/3xwU6C4E9QSfHbb78F4Ouvv/bZJklSvSTFxx57jM2bN/Pvf//bZ0RrRkYGb731Fqp6rKK+qqqsX7+eO++885yP2xTGjB7FmNGjiImxMPqJmXW2Jaalk39wP/vXfYm223A0xjA85fl4t/5AZq9emMy2JopaEOqPzqLDXewOuN150AmlYBlk8bsQcVhmGEWfFImkKNS7oJLismUNW8FjwYIFLF68mJdffpm2bdvW9go1Gg0RERFcccUVzJ49m5kzZzJ27FjmzZtHVVUVI0acfrh9S9V72GjSivPZtHIJNQ4H1vBw0m+8BUNIy5mTKQino9Fr0Lg1eCo8aC11v4ZUj4pz17GSbtb2Vr/7S5IEYoCq0ACCrn1aUlLC/PnzOXDgAI888ghr166lffv2dOjQ4ZyD+OGHHwCYNGlSnfbY2Fh++eUXwsLCePvtt5k+fTqfffYZaWlpvPPOOy1q4v7ZMkfGctGoPzV1GOeFKqeH0io3kWF6TPrgrpO6vQo/bjzMrtwCdJKK1mDimv7tiAs3NXC0rUfiJYns/Xwv5ovMmDqYkCQJ51EnZYvLiL8onuLNxXirvGhCA7wn3saNVzg/BJUUt23bxvjx4+nUqRObNm3innvuYfny5Tz++OP885//ZMCAAWd94F27dtX+/4033jjj49PT0/9QSTlBCKSo0sm/l2zHKlURF6pw0K7BqQ1jwvBuhBkDfzQcLi8vfr6OO7o4uGvQsS/sEoeD2T+V0Kt7Gv07xTTWS2jRNEYNHa7pQPG2YkrWlYAMRouRdle2Q2PQIPeQyVueR8TlvnNCPZUedDpRB1Wof0ElxRdeeIHbb7+d++67j549ewIwc+ZMIiMjefnll/niiy8aNEhBqG/2Gg9//2Ydr17qJUwvcawMsEphVTlTvljHUzf1OTbAxo8PftrFU31rSLKe6MFEmCSevVjlwf9lk9kuCr1WLK8WDEkjEZUeRVR6lM82Y6QRrUOLfaudsAtOXBXy2D0UzS+i3ZW+pScF4VwF9cndunUrI0eO9Gm/7rrr2LNnT70HJQgN7fPf9jKln+d4QjwhOlTmjq41LN50xO9+qqpSXVlGktX/VIJbu3hYstn/vsLZSxiSgK5ER+F/CymcX0jh3ELsP9pJvSIVbYhYBFqof0H9VUVERLBv3z6SkpLqtK9fv75FTotoyWrsFezemIXbWUNyWneiElLPvJPgo6SsnJTu/s8J+8fLfL6igBGZCT7b3F4Vqy7wWoxdomR+2G6vtzgFiO4RTXQP8T0jNI6gkuKECRN46qmnuOeee1BVlaysLL788ks+/PBDHnrooYaOscVp3yGRyorgVg/5+4RhQT+vxhyJqV0fLH2uQQoxsmPpL1RtexZ34QEguIr/ZouVPc20RuGZVrX4IwIV3r40RQOD/K/cLkkSy3YUMPt/CzDroV24jCzB/jKF0hoY0UEDF/nfd2uBl9k/7OXpb/aeMbb6WqGhOa+4IbQsHTskUH685nRtW5Al3E79e7ZaLGTnHKq32BpLUEnx5ptvJioqivfeew+dTsesWbNITU3l6aefZtSoUQ0dY4tTWVHO1c+fflBQVWkhO1f+gBM9kiEUqkuIjIqmY//LkWTfHszetUsp1EVjSD5RSN3abwxh6cNQ18yl9+jxQcX29ZPXntVraUxlFZUceOaCRjnWm0uPUFbjxmb0nQOXU6Iw4oJIMuId3JWh0jf+2L3DLQVe/rZWJdSo52CFh0SL7/v06XZY90RXDI14TzF5+tZGO5bQupVXVLD5sUlnfmAQ0l95OeA2RVHqLADRnAR9UX748OEMH+5bs1M4e9VlxWxc+i0hg+4gRHtiBF1Zfg6bF88j4/K6UzFUVaXgSC7Gi31//xqTBYctmYr8g1hiE322C/5d0zOK51cc4oUhmjprVHoUlddWKyiSk1eGSNiMJz643WM0vHGZyoOLPcxYITEh3Uu/4wmz1KHySpaXQZ2jGzUhCkJzoagqK3Ky2ZabS4jBtwSnqqp88tbf2bFoIYbyctx6Hca0Ltw1bSZRUb4DrZpKUEnR6XTy5ZdfsnfvXr9FuJ9++un6jqtV271qMaaBtyFp6w4p18d2oDovm8qCw5hjTtR1dDvsKCGBK3foUnpQsH+tSIpnIc6q56LOsdz/YwEj2qt0CJfYUqCy5IDMVemxbNmXj83PtAyjViIzxkvH5DjW5zuYu6sSnQwajY7RvdsS10wLVQvCmZSWlgBQVGUnKvTs5oAfKS/jg0WLGA7cazBSUVFJSXg4f31qMg/OfB5Jknh50sP0Wrmcy3+vo+1xY1+7mlm33shTn3xGRETzqE4UVFJ85JFHyMrKonfv3j51T4WzV+NWMOr9T/I2dLmEA1u+4oKhJ9Ytk7U68NQEfD7F6UCrF+/L2cpIDKV7fAprDlSxJM9FUriBKSNC2J5XQ9ppPp89YmFXmYuruodD9/DGC1gQGkBpaQlvTJqIaW8Oz7dpy4/ffU++ycifBg8mznrmspKKqvLBosVMNhjRH7/qEq7V8lhMLGt+XsK891LIvPhSLFkryThlYYkwjYb7Ksr594vP8eiLrzbI6ztbQSXFlStX8s4779CnT5+Gjue8oPpZAeN3ks6I11N3cVut3ojOXYWqKnG6Zh0AACAASURBVEiS76U5756VxF9yZb3HeT6QZYl+qXXPiqPDtGzbF3ifnFJoEyt6hELL53K5eHb8LfxfYQFmjQasx8rquRSFl3/4gbtHX43NZDpWbzo3l5Xbt6HxKuhMJkb07k281crqffu4RFVrE+LJ+sgyr333DQezd3ODqoKfx9g0Giq2b2vw1xqsoJJiamoqqqo2dCznDY2npra4+alcuZuJj09h5/LvKC8rQ5V1yF4XZrOFkhX/JWTgLXUSo3PfOiLMIeiM/kdDCmcvxqzjQKUWp0fFoK37HimqyorDMk9mnHnZMkFo7r6bN4fR+XmYT6kOpJdl7tIb+GbtGsZdNIg3Fy+mS2kpD5pMaCSJispKPl24gNT0DPYVFnLLaa4gGsvLcdrthJ5mYI3mlI5AUwoqKc6aNYtHH32UUaNG0bZtW59RQ2IE6tlJ7prJ3m0/YbpgaJ121ePCm/0rhyQJTea1GC6Iq91WcXAL+tJf8a74AJfGCFoDsqOUNontSBnYOgujN6Wb+8YycfFhpg2SaRN27O+9xKEya4WXa3q2beLoBOHsdGyfQHllhU97msHAlyn+5zpHa7Us27yZt1ev5tX4eAaedJ/RotFwV2gYTy7/lWVVVVyVnEyo7P/qyc4D+/ht+1b6JibS0+S7ELGqqrjCm89tiKCXjtqzZw/vvvuu34V9W3pSPJt5hcE609QHyWTBtGctlr5j0JgjcexdR+WG7/GUHaXNzS+gC4+r83hDYncq8vdR+tO7oJ6YPJ7/G2yad3bX4utrflxznvN4rhLCDdx9aRJvri/CUeMEQKvTcWP/ONpYRM1NoWF1bB9PeWX9ztn150zjpGUk2hkMdRLiySZGR7Pcbued4mKeaeN7suhQFLKdTuyqymuFhbyfmIT2lCtkn5SV8lH2bt6ph+8lq9lM9p5zm7MbVFKcM2cOL7zwQr2sm9gcVVaUk/nYh41+XK/LSdnejXiObCIqJpHkW54id/lX6GP8n7mZe1yJtrqIxIFXN3Kk/q1/5c9NHUKDCg/RMuGiNk0dhnAeKq+sZOmgixv8OD8cOUK200lHP5c/y7xeekZF4vUz4+B3MVodV7dpQ7jByBK7nWEnrVxUpSjMLi3hlR49iTIaOVhdxcwDB7jSFEK60UiRx8PXVXasZjM/pHWul9dzya+/nPNzBJUUbTYb3bp1O+eDCXVp9AYiO/er06bKgXshklaHiu99SEEQhD/ikthY3tu9iyk6HYaTbospqsrbZWWMbt+Ob/bvD7h/qceDUatjVEICKwsLmVVSQggqHgCtjhvatSfyeMJNDAnl3s5dWFNSzLuVdix6HSPatfe5n9nUgkqK06ZN45lnnuH+++8nISEBzSnDauPi4gLsKZwtSXGhKl4kPyNUPZVF6I1ivT5BEOqHUaPhhtR2vHTgAGmyTCe9noNuNxs9HoYnJhKpN9AmzMz2mhq6+rl1NreykiEpKQBcGB3NhWeohS1LEv0io+gX2Xwm658qqKR4zz33AHD77bfXtkmSVDuCcseOHQ0T3Xkool13Srf9RGh33+o11eu/IbHXUD97CcL5SfEqqG4V2SD7Hc0tnFmM0cjdaWkccjg46KgmSm9gQmgomuO/z8vatuXd7GyqFYVepmOLQbtUlc8rKjBbzES1srnrQSXFRYsWNXQcwnHm+I44Sn6lMmseIemXowmx4i45TPWmhUSmdEFrOrtKE4LQGtWU1HD0t6MoegXZKOOt8BISHkLbAW1FcvyDQjUadpWWsdPtZo0EVUC82czlbeOY0LEjKwoLWVJehh7wSDID27Sho9nc1GHXu6CS4u9LRq1YsYI9e/agKAqpqalceOGFf2j1a5fLxZgxY5gyZQoXXnghAGVlZUybNo3ly5djs9l44IEHuPbaEyM4d+7cyfTp09m5cyft27fn6aefJj09/ayP3RLEdB+E215G0ZbvcTprMJhtJPe/Ak2AKjiC0NJV7KugcGshGAAVJLdEbGYsoXG+829rSmvI/TWXmD/FIOtO3Adz7HWQuyiX5MuTGzHy1qHM7eaTPTk8Hh5O6Em3bjbU1PDf/fu4NbUdF8fEcHFMTBNG2TiCSop5eXncd9997Nmzh+TkZBRFITc3l8TERN5//31iY2ODPqDT6eTRRx8lOzu7TvvkyZOprq5mzpw5bNmyhWnTppGcnExmZibV1dVMmDCBESNGMGvWLD799FPuvvtuFi9eTFhY6+w56cJstO19eVOHcd7Lq3CzbHc5qqoyoJ2F5MjWdamoOSjeXkxlRSVRN0chycd6eapXJe/rPGLcMZiT6/ZG8lblEXNj3YQIYGpnomZvDY5CB6ZocQJ5NhYcOsREW92ECNDTaGRnRTmHqqtJCPGdY+jP74VeWmqPPahy/s888wzh4eEsXbqUb775hu+++46lS5fSpk0bZs2aFfTBcnJyuPHGG8nNza3Tnpuby88//8zMmTNJS0vj+uuvZ/To0XzyyScALFiwAJ1Ox+TJk2nfvj1TpkzBbDazcOHCs3ipghA8RVH5+89HWLblIDe0s3Nzhyo25Rxm9uJDOD2BFxkWzo6qqJTuLSXisojahAggaSSiro2iYFOBzz5evMgG/19dloEWijYXNVi8rZXH5cKm8V9+cnSYmV8LfN+HU20tK+Nfu3cxZ9cuPtm9i3ezd7PLT8GA5i6onmJWVhZz584l/KSqAxEREUyaNIlbbrkl6IOtXr2afv36MXHiRHr0OLEu4KZNm4iOjiY5+cRlj169evHmm2/Wbs/MzKytpCNJEpmZmWzYsIEbbrgh6OMLQrA++C2fWzs7SY898RH5vz4yB8o9/O2Xozw4JP40e7dc+/btJSwsDGe2s1GOV3m0EmNn/yXzJElCE6OhYl0FBsuJHrrqDVxyUjbIuIvdjRb/yfT6llsPN3A1ZgiRJNxe72n3X1VcRHlJCVOsttoeoqKq/OvIUZwxXtKbUcWaMwkqKVosFioqfDN+ZWXlWd1THDt2rN/2wsJCYk65Vh0ZGUleXl7t9tTUVJ/tO3fuDPrYghCsGrdCjcNBeqzvV0WyVcamdVJS5SEiNOjlSIUAvC4vWkvg36PGosFbXvcLWbWrAWsHV22rIiyqdd5SaUgOiYC/0801NSSFhbGsoIC95WXoODbQ5sLYWDqZzSiqytbCIp6MiKiznyxJ3GWzMasgv/UlxREjRvCXv/yFadOm1Q5u2bx5MzNmzGDEiHOvu+lwOHzOsvR6PW63G1VVA273t7bjqTQaCZstuGvhwtlrjb/bfcUuMtsE7o0MSYbNh6u5pFP9lMurD/X1PvTseQF2ux1Dx8a5dxoeE87hbYcJ6eA/ftdhF+aLzVQdqsJ+xI4uREdEtwjKV5ZjG1h3WSPFpWBfa6fjtR3rXIptLC6Xq8V+HjKjovm2tJTRp4wm9agqn1dXoampYZTRwPUWa+2UjC/y8thfWUmiOYzeAXrJkiSRptFyyOEgwdQ493nP9T0IKik+/PDDFBYWcscdd9S2ybLMDTfcwOOPP35OAQAYDAafBOdyuTAajUiSdNrtZ+L1qpSVVZ9zjIJ/rfF3a9JJHDzN1bfSGggJcE+rqbTU90Fv1UMpeCo8Pj1GZ74T2SWz7/t9mC4wETIoBE+Zh7LVZVAFhXmFmPuY0Vg0OLIdOLY7SBqa1CQJ8Xf18T78fgl7TY2jHiIKUoiJrfZKdpcUc0OYmSitlg0OB19VV6E1GrlNp68zeV8vSfzJauVvJSUcULxcfpoVMCySxJrqKo42wtui1+uDeg+iowNPJQkqKRoMBl555RWmTp3Kvn37MBqNJCUl1dvIz9jYWIqK6t4cLyoqIvp4dYTY2FgKCwsDbheE+pQcoWf+msAf8v8dkPi/oWKprvqSNDSJfV/uw5BmwJxpRlVUKlZV4D3kxV3jJva22NqRplqLFmOSkbJfywjThuHZ48HpcGKJtxB/TXyLHfHYHGTExFDt8fCfkhJqqmqICg1lcJtYVh84QFez/6si46xWnrdXkqUo9A4wOnWj20UHo5Himhp2FxWBoiBrtHSOjsLaDO/DBkyKa9asCbhTVVVVnSo257r4cI8ePcjPz+fQoUMkJCQAsG7dOjIyMgDIyMjgrbfeqr3mraoq69ev58477zyn4wqCP5IkMbBTJK9kFfJoP02dL9oPN3tIi7Oh1Ygv3/qiMWrocG0HKg9UUvptKZIsEdU1ihptDa5Il8/UCwDrRVaKPimi/aj2TRBxw0pNbYfdbqdPE5V0HBxWtxeVc5peoE2jIVqWqZZlCjxuYrR1x5gccLnQG4xUlJWjVtmZbLYQptFQ5vUy5+hRtLZwLjmLKX1nEswttTMJmBTHjRsX1BPUR5m3xMRELrroIp544gn+8pe/sG3bNr799ls++ugjAK644gpmz57NzJkzGTt2LPPmzaOqqqpe7mc2R6qiULQji6qSfJB1SF4ntqTOWFNEUfbGMqCdGZ1G4sElxUQZPMgSFDg09G0XyZWdrE0dXqtkTjbXmZNYuLGQ8Av9D9CQJOnYRH+hwZ1uHG+Bx41Zb+DKuDj+tWcPXWWZYaGhKMCPdjt7JYl+0dEUFRZwg+3Ee2nTaLg3PIJ/lZVy2GwmPsg5kI0hYFLctm1bY8bBSy+9xNSpU7nxxhuJiori2WefpWfPngCEhYXx9ttvM336dD777DPS0tJ45513WuXEfVVRyF32GbquwzCnH1unUlVVKnavoHrtItr2vqyJIzx/9E4Oo3dyGDVuBUWFEH3zuo/Y2slaGdWlIhkC9MpPP0tA+AOqPB4qPR4i9Hr0x3uIqVYrqx0O+voZKPNJRSUj2rfHqNEwoWNHlhcV8WpZKVatjovi4rgkNJQPcnKYGODy681mC//Kz+OW1HYN+rrORsCkeOpKGC6XiyVLlrB//35uueUWdu/eTbt27YiMjPxDB961a1ednyMjI/nnP/8Z8PHp6el8+eWXf+hYLUnxzlXoug7DENeptk2SJELSLsK+4Xuqiw4TEtU658g1V0Y/l++EhheVHkXhykLCL/XtLXodXrSSmBJTX/IdDr49dJBwVSValsn1eJENBq5PTuaSmFg+2reXkspKhoWFoZUkyr1ePqkoJyUyAptOx5ayUrLy8+mm1TJEq2Wr28XSvDxuSklBqyroAtzrDdNoUM4wB7KxBfVXdfDgQf7852MLyubn5zNq1Cg+/vhjVq1axfvvv0/Xrl0bNMjzib04D3P3kX63hVwwlJJVn4qkKJwXQmJDUNerOPY7MKWc6KUoLoWiz4pIujSpCaNrPUpdLr7cv4/J4RG1vUOAw24372Znc0+nTtyW2o6tFeW8WliEBhWtRsuQ5BRijUb22O3szs9nii289v77ICDP7eadPTnoT1pR6VQeVcXbzNaIDeoU+Nlnn2Xw4MEsWbKkdr7gq6++ytChQ3nuuecaNMDzzmkWGZZ1RhQl8Pw5QWhtki9PxrvDS8EnBRR9X0TB/AJKPy8lcXAiBqu4qVgffjh8mAdt4XUSIkC8TscgvY7N5eVIkkR3q43xHTowrkNHbk5NJfb4FI2lR49w50kJ8XdtdDp6arRYjEZWOfxPk/jRbqdPM5tFEFRPcd26dUyePLm2zBocu7x69913c/XVVzdYcOcjyetEVRUkyfd8xVtVhq4ZDmFurhRF5X+7ytl6qAK9BrzIXHlBFB1jzjy/VWgeJFkibmAcqqqiuBRkrYwkRv7WK7fbhS3U/xSjwSGhvFZcTIbNdqxyTXk55W4XySGhJB3fx+BV0Aa4PHp5aCh/r67iJ0XB4HDQ8/h9SVVVWe6oZpcscau1eQ1cCyopmkwmioqKfEqt5eTkYG6F62k1pfDkzpTvWkFI50E+26o2fEdC+oAmiKrl8Soqryw6yPWdvNwx5NgCtA63l3+sO0JOgY0rL4g485MIzYYkSWgMp6vQ2bpYzWYu+fWXRjnWxaGhYPM/ylcjSSwrKmTO0SNkGE38KdxGV52e5UVFzKqsZKfTyeVhp88B3+fnc9Tj4WtJop3BgE2jodjjYa/LRbWq8u6hQ/X2Wqz1kI+CSoo33XQT06dPr61es2/fPlavXs1rr70mCnLXM0tyN6rWLca+/jtCug9D1hnxVpVRteE7bNFt0IXazvwkAvPXF3NnupcebU58kZp0Eo/11/KXpWUUV1mIFLVLhWYqe8/hen2+mBgLq/50s99t76xehUtRfC6fwrG6p6PSOqMWFXG35cQI0i5GI7eER/CS3Y5Rr8Orqmj89BYXOxw8f+kQupxmQGa/T+dQUNB8VtMI6lvh/vvvJywsjKeeegqHw8Fdd92F1Wrl9ttvFxPoG0DbXsNxFB+heNWnKIqKTm8gIX1Aq06Iv5e2Wrq/fkai7TpqZ1Km/57Ffb1kpv1axKDOjXMvQ1FV9uRXY3e6ibEaibfV7+Xblrw6g9D0hnVK44NNm7jLUnfahFNR+MLlxGSvZKKfHliILDNSp2Oj1ca7hYXcZTbXua9Y6PGwQZZ46A/OUGgqAZPikSNHaNu2LZIkIUkS48ePZ/z48VRWVuLxeOosIyXUP1NkHAkD4po6jBYrRBd4QFJbs4zT7T7nY3gVlTV7S3A6qgnVqVS6JMzmMHqlnFg+JyffTm5eCWM6qaTYJLIOl/PDBpm+nWKJCBXJTGh67Ww2ijt0YFZODoO1GtpotGxzu9kkwa29e/Pd+g3o9f4HNfUyGvmlyk7vtDRezM6mGxAhSWxVFKpMJu7u269xX0w9CJgUhw4dyvLly33mIYp7iEJD+L201SUp9XPfaMPuwAOrD1UodIoOoX+8xK85ldidHtLjQ2kfHXwPzquovLzoIHedcol2xcFK5mfX8PDQePYWOdlTXcy/R54oFZceC7elqzzwYx5PXJmMXnvucyDro7SVcH7rEx9PZtu2rM/PZ6OjmhRbOJceXwpKPc24JoVj93t7tmlDj9hY9paXU+FycZXVitXQMkcHB0yKqiqG/gstV2JUKGuOVNInzjfJvrlOISxM4r1lBxiTBhFR8POBcj5bp+XewfFYTXX3cbgUFFUl9KSBHot3lPGnznUTIsDARA0FVW5W7bOzal85zw3S+AxVN2ol7uoJi3eUc1V3ccVFaB40skyftm19200h2L1ewjS+n6Wljmp6d+gIHEuO7W0t/xbPae8piorzQks1pkckry5xUFDl5soOGmRJosKp8vc1XjyykSSjnVv7nPiQd4yEshqVJ34+xNQRyQCs2W9n6a5iYoxeZAnyq2X6tAtncCcrO45UcucQ/7280Z1kJi0tQyd5MQboCfaJ0zB3VxVwfiVFV4WLku0lqIqKraMNU3TTFL0WgndV5868sWY1kyzWOpVpjrrdrJQkHjplgfiW7rRJ8f7770enCzyZ/He/F+4WjqkqyKVk9wYUSQbFjTWhI9aUC8RJRiOSZYlHhyewPKeSx34uRyuryLKOq7q3Yd7aPG7t7nvWazNKXJroYePBKqpcXo7kF/HGMA3SSeXEPtxczIItXvSy/wodABpZQiurKErg+BRV5Xy6FqMqKgf/dxCv0UtYnzAkjUTBxgK8q7ykXJ7idyWM33mdXpBAoz9/pmQ0JzGhoVyT2YvZW7cS43ETJcvsVxQIM3Nvr16t7nvttEkxPT0dUyOtltxaFG5dQbVXJnTgOCStDlVVse9dS+WKr0gYeE2j/QGpqkpl7nbKDu4GSYNGlojs0g+jrXWd1Z2OJEkM6mhhUMcTo+oURcWq8xLoT/+qDhpeWltJeVUNb1zm+5g/p2t5ZEk5yDqcHi8Gre/7WVajotXoQAMlDjcRJt/H/LDHS++U86eXeGT5EQy9DHXKtemH6nEVuchdkkvKlSk++xRtKaJsfxmyRQYvqFUqMekxdVbSEBpHosXC/RdeSJnTSYXTyYCQEIza1jml6bSv6q677vrDBb/PR87yQqpqXJj7XV/bJkkSpvZ9qNEaKNm1hsjOfevlWK6KYop2rsbrdqHRGYjq2g992LEvWVVVOPjrl8iJGYRcfAeSJKM4q8nb8B3W8HDCO2TWSwwtkSSB+zQ9uCq3So1HIT1aIVAVxGEpKjvsJt7ZUM4DfXw/Qn9b42VkRlsMWpmnluby+mUa9CdVYTlcqfBVtoapI86PL3fFo+CochCT4ntCpo/So4apuCpc6C0nRuPmr8nHbXYTc8uJfVRVpfC7QlRFxZLqf9UFoWHZDAZsLXQATbBaZ6pvIkU7VxOaOcbvNkNSd+xLVxHZuS+q10NpzgaqCo+ABNakNMwJaUH3Igu3raSq2kFIj6sxmMx4q8s5vHEBZquNqM59KdqehTZtMIa4tNp9ZEMI5v43UvbLh5jjO6E1Nb9lt2wWM8nTtzb4cbpFyzx3SSha2ff3/XqWi1dW2Hl/dOCRqDYDPPHJUcJNsC1fx4xLDcSEyhyqUHjyfzV8u8vDtJ921z5+xX6ZQckaOkTIrDnsJeuwl72lKv9aXT/Ls9kszTu51hTVYEgM/EVq6mLCnmsn4niVIcWtYC+2Ez207jxSSZKIHBlJ4SeFIikKDSZgUoyLi6tT61Q4M6/Hi2z0n2wkSULV6HFWlnB41UKMF1yGqcvloCpU7FlDyc+fknTxdcja089dqy7MpdoD5v431rZpQqxYLryZytWfE1qSR1VxXu1ajKcK6XEVRTt+ok3m0D/+QhvI7pz6r+Jx+G++C1Hn5FUw9ZdNPD9YRT7pRGTNEQXJHMueV7vyzjerODbg3NfSg1o2zhpGRJieggon/1y9D3t1DbawEJ4Ym8KLfuYflla5KKt2M8pqxKTXEP/AgmZVxaMhyXoZxRG4e644FOST1qosyy4jJMP/orOSJKGJ1vj0LIXTs5ot9Pt0TlOH4Zc1wFqLTSVgUvzpp58aM44m83slFffh7ef8XJKzEq+jEo3J98xdVVXU6hIOr/ga82UPIOuOnzlLGkwd++OJacfhZXNo0znTZ786VSJ2rCV06P1+jx/aYwSFS99G1QZexVpricJecqReXm9LraTSoY2FywekM3FZNrF6B1Yj7C3XEBMdw/+N6IgkSYRaI9iUX0BGbN0Tw/1lCmWSlYiwY689xmJgwrDOZzxmeKie8PN0sr4xwoh7ReBiCdVbq2kzvE3tz4pLQTYFPiGXjTKK6zTXwAUf2XuCqy8aE2Nh82OT6uWY6a+83CJP/MTl03oUkdCegvXfYhk41mdbzf4N6HUavG27nEiIJ9FaY6jWhqB4PaiKl8I923BLOtAZwFWNUachKrUbqkaPFKA3KetNKJIWPDUBY/TaS9G20hvkZ6NzvJXO1/emwuHG4fIy2mxAc9Ll1NuHpPGPhW5+2FfGDWkqWhm+ypbYX2PmgasuaMLIW6aozlEULygm4sqIOid55b+VE2IO4eiKo3hqPJgTzIQlhZG3Iw9Tkv9Bfu4jbgzd636GqguqKd5SjOJRsLazYu1gbXWjIoXG0ay+HcvLy5k5cya//PILBoOBq6++mokTJ6LRaCgrK2PatGksX74cm83GAw88wLXXXnvOx/y9koou/twXStYB5u2/YV/zJSE9rkTWGVEVL46cVZC3E2NsB9yxgXsVmugUvKYI8jYtI+yiPxNyUq1TV8E+8rYuQtLqAy7YqSpeZL0RoyUSV/5e9LHtfB5TtWkBCX2uQhd67pcsWkMlFYtJh8XkO+1IliUeuKo7+eU1fL31CIqqMrB3W66PCtwLFwKzdbQha2UKPylECpVAC0q5Ak7QtNFgvdiKJkxD9e5qin4uQpZl3GVudLa67031nmpMFlPt8lGqonLgxwNIURKWKyzIehn7FjuFXxSSclkKOvOZp5QJwsmaVVJ85plnKCws5OOPP6akpITHHnsMm83GhAkTmDx5MtXV1cyZM4ctW7Ywbdo0kpOTycxsXiMpo7oOILT4CMVZc1BUQPViS+qM+cLRVB7cSU1lMUSn+N1XqSql3J5HSL8b0ZxS/Fsfk4onvhv6slxq9q3H1K6Xz/41OauOHSshjdxf5qPUDMSQ1B1JklDcTqo3LcRsC6+XhHi+iLUauWmg78lFa2a2mFn54MoGP44cIhM3Pg5b3xN/66FdQgnpGMKeWXsoe7EM2wAb4YPCUdwKxYuKqdxQibvYzf55+wHQWDUkP5xMSOqJkxVzDzMhnUPY/NxmnAedDf46ao/bzAc8CcFpVklx2bJlvPjii3Tq1AmAkSNHkpWVxWWXXcbPP//MokWLSE5OJi0tjQ0bNvDJJ580u6QIx4t5Xzjap92ckEbxL1/4TWiq143kKMUlawmztfHZDmDqOIDqX3fBgXU49SEYErrUbqs5sBn16A7MA69GkiSSBl9Pac5GKpetAlmHrHqJ7tybkKiE+nuhQqu0px4HPMXEWLjxwxv9bvvt09+w9vVdYFbSSrS9oS1tXW2xxFrYv2I/5QfLCQ0PxTrAStsObYnPiEdF5bfPfquTEH+nMWqIvTyWzimdsSX6Lz0278/zWuQ9L6FhBZUUDx48yGuvvcaWLVvweDw+dVGXLl1aL8HYbDa++eYbBg4cSEVFBb/++ivDhg1j06ZNREdHk5ycXPvYXr168eabb9bLcRuLJMtEtOtG6W9zCeszBkl77NKOUmOncvnHxPW8hLxtq06zvwYkmcRBoynZuYrK7OWg0SN5XZhjEwk/nhABJEkmomMmER2b30lDc6GqKlsOVlDpcNMl3lI7eEZoHIo+8GCZkE4hFH1XhKqoVFJJ5PhINCYNqqpydMdRjsw9QrfLuyFHBR6QY+hkoGh3UcCkKAj+BJUUH3/8ccrLy7ntttsIC2u4+W2/L2ScmZmJoij079+fBx54gI8++oiYU+rrRUZGkpeX12CxNBRrUhd0IWaKVnx4bFCMqqLTyiT2HY4u1IasulG9HiSN71vjLj2KITQMSZKJ7DKAyC5+DiAEZdn2PH7buo9BbV1EGlW+2a2lRDFz75UXYBLlxOqN2Wph3p/n+d/W00x4gNqvnkoPe/63cTF4YwAAIABJREFUh/wu+STem1jbLkkSoV1DqQ6pZuFfFmLtbcWKb28TwFPuYcNHG1j/7vqAsQm+Pvr1V+zlZXiR6JyayqVduqA9j6bnBZUUt23bxhdffEGHDh0aNJjc3Fy6du3K/fffj91uZ+bMmbz44ovYbDaf4f96vR632x1w0ElzFhKVQNJF/i9jRnbMpGjjAsJ61b38qqoq1eu/IXnAVY0RYqu2fm8xR/dn88YQCTiWAIekwqGKcl78ZiNPXn/s8vbhkmrmr8gBdzUS4JINXNO/Ax3aiHtHwdqTHXgqwEPT72OjYx0ak+9JSE1WDb1H96Y0o9TvviEpIaQOSUWLFtWr1g68OVn4ngiW5azCaKzfRZ1bq01rVzM0NIyRRYXEaHWoqsLG7dt5efcuHh45ClMQdbBbg6CSYkpKCsXFxQ2aFHNzc5k1axY//fQTbdocu6dmMBi44447ePjhh31GOrpcLoxG4xkTokYjYbO1nBGDobHJ1JQVUvHLB5i6DkVjjcFdsJea7T8T27UvGkPzqkXbkn63v/tx3V7+eolve4JFpldEFTsOV6CV4etfN/PMQBWz4djfWI3HzazfNlGW3pne7aPOOY6W+LurTy9NeZExD12Dfbi9TmJ0bHEQZ4qjyl6FPjrwJW2vxstFwy7ipy9+wnyNubaouKqqsAZuH3Y7bdpENPjr+P/t3Xl4TGf7wPHvJJOFLLJIUImINGIJSRBLkFoSxC5+QuzU1oUqLbFkQSqopX0RGlxVYitRW5F6LUXTRqQRu0r0FWk1iwgJWSYz8/sDU9NMGJXd87mu/DHnPHPOPWcm5z7nOc9SHcjlcrYHz+U/9es/9whGgpuhIfWLZGw9fZpJ3V99wI+q+BvXKimOHz+ewMBAxowZg62tbbGZMzp06PDagVy+fBkTExNVQgRwdnZGLpdTWFhIZmamWvnMzEysrKz+uZli5HIl2dmPXzu+8mTp1AYz+zyyfoun8LeH1LCoQ713BmusUq1oVe3YKpVKjHUK1EayeZ6vk4Tll1O5/yCXL7oo1fouGkolLOikZOrxm7RuZPnaNRRV7diVNgMDE75euIPF4Qs4+PM+LBpaoCfTw7GZI80HNufc9+dIy0xDv7bmxKgr16W2TW28+3kTeziWXEUu6MBfv/7FxmXf0KOrzxt/jLV1KvoIHbPuqdo5PM9aqkf+vUzkCgW6r1iNWlmPv5VVybU9Wp1lAwICAFi0aFGxdRKJhGvXrv3L0P5mbW3Nw4cPSU9PVz0/TE5OBsDT05PVq1eTmpqKjc2Tasf4+HhcXFxee78AJqa1+HX5mFLZVln5X0UHoIGJqeZnOZWFmakJ9aceLrZ8WHMpoPkKVqGETT/eZryrHro6xavdJBIJbWrnYTPtyGvHJjz5v/8iZC2HHPdzO+E2AEmHkjjKUQBqta+F7RTbYu/Lu53H/07+jxUHVhRbZ1rLlB5dfco28Grm1pXLtH/BekslPCosxPQNqIrWKilev369rOPA1dWVxo0bM2vWLAICAsjPzycoKIgBAwbQsmVLOnXqxOzZswkMDOTKlSscPHiw1OZxTE66Uyrbecba2pQBYd+VyraKCvK4dOI78nVqQG17eJSFzv0UGrfxxLz+q/ef2z9n0BvTDL2ksVQ/m+qLQhmr8W5xzw0dQsd5YZL2S4nbdbLUYfsnveju1rDYujoj1r8xx7c0JWl49mhtbUpfn77ERMdg+I4huoZPWp/m38jH5LoJS75bgq5UlxldZohj/prqOzqSooSSJpa7D9SsosM6viqt6+NkMhnp6ekons6cqlQqKSws5OrVq/TvX7xP3isHIpUSERHB4sWLGTNmDHp6evTq1YtPPvkEgGXLljFv3jz8/PyoXbs2oaGhuLm5vfZ+KzOlUsmvh7eh29afGs915lcqFVz9KZKWBjUwqV2vAiOsmvqOm8XSdRMJcMlSqwL9PVvJ5ce1+bCFDduv6wFFGt9/PkOfDi3Fs6ry0Pqd1jRwaMCpo6d4JHuEjkKHVi1b4TLVBR3dN6dFZFnr7tOXeWu/pHVubrHHAtlyOUfu3mXvl1/goK+Psa4uabIibhUW8FhZ8lTZtUyrZuterZLiDz/8QGBgIA8fFr8aq1OnTqkkxWfb+vLLLzWus7S0ZP369aWyn8pIIZcjl+Uj1a+B5Gm9fcatKygbtC42uo1EokONdkNJituJm0/xcVaFF3Np1Z7Ho1YyPXIpDR8n4lhbj/MZBshqWvPJiE7oS3V5pFebtNw/qWOsfuLNKVDy22NThlpVzX/4qsjKxoohE4ZUdBjVmlQqZcDs+YQvCGRUYQGmuk8aPt3Mz2eHvh7t7BsysUiO7dO7RaVSyanCAu44OPBuv344Tp9ebe7WtUqKK1eupGfPnowdOxZ/f38iIiLIzs5m0aJFvPfee2UdY7VWkPuAa2cP87gIMDRGkveAWqYmNOnUh7tJl9F315z0dPQMyFdUra4olUkHT2/ad/bibYe3yMnNBXKBeyze//fz8cNxOgR6GvB/zaRIgOjkIuafKCD+bg5rjmu+QDMTfd+EKsqja3ds33Zk55crKEi9g1xHh6iYs8jy84lsYIftc5MLSyQSuhoYsvrCBRyPH8eskrcveBVaJcXU1FQiIiJo0KABzs7OZGRk4OXlha6uLkuWLGHw4MFlHWe1JMt7RHz0Lgw7jaOmwd8NPx5l/cGv329Fqm/4ZKp4oUxIJBKSb90tttza2pSsI4tRKpWcSbjB3POJgBIX56Yc9W+B7tNqOwufudXm6lgQAGxtG/Dp8r9r64KBEN++2GWkayw/3sKCWgN8mbGkeIOnqkqrpGhqakpeXh4A9vb2XLt2DS8vL+zt7UlN1W6eLqG4pHP/Rb+dPzoG6i0h9Szqk1+vBaYFf5H1ewKGmsZKLZJhIBFzypUliUSCZ6smeLZ6+XyJglBd6b5gNhwjHV3yHzwox2jKnlZJsWvXrixYsIAFCxbQrl07li5dyjvvvEN0dDR16tQp6xirrZzcR+gba26wYeDQlkfntkF6LPJ6jdUmLlYqleSd241z63fKK1RBKHemtWoxo8sMrcoJZUdmaooy+77GfrkpMhn1nKrXRaNWSXHu3LmEhYVx5coVBgwYQHR0NEOHDqVmzZp8/vnnZR1jtaWUlNx6TqKjixIJrXsP59Lx3eQZmCOxaoTyURaS9N9o7NYR0zpixguh+kq6+aSrlLW1KYEbArl27Rrmtczp7NWZ0R1Hi6rrctLZfyQnloTS/R9JUalUssfIiLkTJldQZGVDq6RoZGREaGio6vXy5csJCQnBwMCg2Og2gvb0kD+ZMkq3+DEszLxNbUsr9AyNaNVnFPkPs8hJ/wODOraYdOhY5cZ7FYR/43bK/6hhX4MdV3cgbSSl6GERp784ja6JGLS9vHj3G8j6xAtsiz5Mf7kcE11d/ldYSJSRMf0DF2BsXL0GopAo/zkPVAmuX79OZGQkt2/fZvny5Rw7dgw7Ozs6d+5c1jG+FplMXu5DDTm8bUvOQy3q2SU6mLgPxKLreLXFSoWc9G+DyL+dWOqxmZjWKvXBCqqbZw1tXkY0tClbRUVFDHjPh0f9ctCRqteq3PvhHssH/gfvLj0rKLo3z+3b/2PXkkVk/fwTDn0H4B8wv8omxNce5u3MmTNMnToVHx8fEhMTKSwsJCsriyVLlhAWFka/fv1KLdjqQNukY21tSoc2blz6aTM08kDXxIqijGR0bsdh06Ahj+2cUEoN0ZU9pm4dK9p6D0RHV/MV8poJXuIELVQrB47u46FzNnoaxuO08LZg84GNIimWIzu7hsxatwnZvUz0LF9/QPzKSqukuGrVKubMmcPQoUM5evTJmITTpk3DysqK8PBwkRRfQ+NWHXBo0ZqkC7/w8N7vWNapR+JtJfluw9B7rnHNn/fucGxHBD1GTBFVp2XMrJYpFj5ztSonlJ0f404gbav5FCWRSMiUZ5RzRAJQrRMiaJkUb926hYeHR7HlHTt2JCwsrNSDetPo6unj5O4JwLnoveD2f2qtTQH0LG3Jud+UP367jI1Ti4oI843x23PjcFpbm/IwdpvqtWm7EeKOvJzUNKyJokCBrqHm2pG8grxyjkh4E2g1eKCNjQ0XLlwotvzEiRPY2hYfwV7499Iz7yGtpXlYXv1G7lxPjCvniAShYoz7v4nwq+ZaEXmenJbWpTNLjiA8T6s7xenTpzNr1iwuXbqEXC4nKiqKO3fuEB0dLbpklDKlpORWdc+6aQjCm6CxoxOtpG2Iv30Oqd3fpypFoQLZwSIClxafyk4QXpdWd4peXl5s376d7OxsHB0dOXXqFAqFgm3btuHjI+YtK00GOgqUcpnGdbL7f2JhYVnOEQm7DvzA9u+OkJp2r6JDeeOsDF7NIN0hGB8x4f6h+2TsziDvQB67Qr8TA4cIZULrLhlVVUV0ydDW22/b8vCfXTckOpi2HYR5l3Fqi5VKBem7F5D/e7zGbZma1iJJdLUoNQqFgrWffUz2ue180EaKro6EDfGFHEqGH+L/RP8NmVuuslAqlfyaEs/Cn+YR5r2CZlbOFR2SUIW9qEtGiUmxpCmcNPnoo49ePapyUpmToibW1qYM/WQpP5//lSL7TuiaWiHP/B3927H0GzQYm0aNmT2onWjsUcY2rgykR/Y3tK6jPr5s8n0lmwr68fGi6juNWWV273EmljWrd+tHoez9q36K69atQ0dHh6ZNm2JkZERJN5Sie0Dpa9WxC85t2pMQc4qszN+wadCQ5oNmldhHUShdBQUFPLj6X1q7FR9w3cFcQs3EWLKy7omq7AogEqJQ1kpMisHBwRw/fpyEhATc3d3p3r073bt3x8JCzDheHvQNDGnXtVdFh/FGSkq6SSvjtBLXdzW/y4Vf4+jmJb4fQahuSkyK/v7++Pv7k5uby+nTpzl+/DjLly/H0dERLy8vvL29qV+/fnnGKgjlokYNQ3KLpIDmKXNyZDrUqGlUvkEJglAuXtr61NjYmN69e7NixQp++uknJk+ezO3btxk5ciSDBg1i7dq1pRaMTCYjLCyMdu3a0a5dO4KDgyl8OpfXH3/8wfjx43F1dcXHx4cff/yx1PYrCM+zt3fgYkHJ/W+PZzfAvW2HcoxIEITyolWXjGekUikdO3akd+/e9OzZk5SUFDZu3FhqwSxbtoxjx44RHh7OunXrOHPmDGvXrkWpVPL+++9jZmbGnj17GDRoENOmTePOHdHaUih9EomETkM+YvWV4vP07biuh2P3cUilWnXxFQShitGqS8azKtSTJ09y+vRppFIpXbp0oVu3bnTs2BFDQ8PXDuThw4d4eHjw1Vdf0bFjRwD27t3L4cOHeffdd5k8eTIxMTEYGxsDMHbsWFxcXPj4449fuN2q1vr0bccGPHyQ/cIyprXMSLqZUk4RvbliTh3l5K4vqZNzESly7uQZ0nZUKD0Hjqzo0ARBeA3/qvVpamoqJ0+e5MSJE5w/f5769evTrVs3wsPDadWqVam3Oo2Pj6dGjRpqY6z6+vri6+vL+vXradasmSohArRu3Zrz58+XagyVwbNkZ21tyo4z19TW+XduKrpilCOPLr3w6NKL+zfOkn9oJtbDI9CtJ4YWE4TqrMSk6O3tjVQqxd3dnYCAABo1agRAYWEhv/zyi1rZDh1e//lKSkoKb731FocOHWL9+vU8fvyYXr168fHHH5ORkYG1tfp4oJaWlvz111+vvd/KLO2PFLLvZVKnvi1mllYVHc4by9ypE8r6h5AYi+9AEKq7EpOiUqlEJpMRExNDTExMiRuQSCRcu3atxPXaevToEampqURGRrJgwQIePXrEggULKCoqIi8vDz099TnV9PX1kck0D4f2PF1dCWZmNV87vvJ08fJl9Os1ZsX2w8hqWGKQc5o60iczAlS1z1JtmNlVdASCIJSDEpPi9evXyzMOpFIpubm5fP755zRo0ACAWbNmMWvWLAYNGkRubq5a+cLCQq2eZcrlyir1TPGvv+4yOmAZdUd+jkRHFwMAWnG34BE16seSmflQNPIQBEF4DS96pvhKrU/LkrW1NVKpVJUQAezt7SkoKMDKyoqMDPUJRTMzM7Gyqn7VWcvXbSSnaX8kOuqj1+gYGGHZfSJRBw5UUGSCIAjVX6VJiq6urhQVFXHjxg3VsuTkZIyMjHB1deX69es8fvz3HV98fDyurq4VEWqZSkq7j46hscZ1OnWdOBZzrpwjEgRBeHNUmqTYsGFDunfvzpw5c7h8+TLnz59n+fLl+Pn50aFDB9566y0CAgK4efMmERERJCYmMmTIkIoOWxAEQahGKk1ShCed952cnBgzZgwffPAB3t7ezJgxA11dXcLDw8nKysLX15f9+/ezZs0abGxsKjrkUudgXQtFwSON6xRpv+HVwb2cIxIEQXhziPkUK5m7d/9k2KeLeOTip/ZcUVHwmHrX9/DdpnXFWuIKgiAI2vtX8ylWF1UtKQJcvnqFwJXh3JSZg4kVZN3GzRK+CJmHpaWYrkgQBOF1iKRYxZLiM0d/iiN013FCx/Wji1vzig5HEAShWhBJsYomRYDMnDxqm9So6DAEQRCqjSrRT1HQTCREQRCE8iOSoiAIgiA8JZKiIAiCIDwlkqIgCIIgPCWSoiAIgiA8JZKiIAiCIDxV7btkCIIgCIK2xJ2iIAiCIDwlkqIgCIIgPCWSoiAIgiA8JZKiIAiCIDwlkqIgCIIgPCWSoiAIgiA8JZKilgoLC+nbty8xMTEVHQoAsbGxODk5UVRUVGb7WLVqFaNGjSqTbaekpDBlyhTc3d3x9PRkyZIlFBQUaCy7evVq/P39yySOZ/z9/Vm9evVLy926dYugoCC8vb1xdnambdu2+Pn5sXXrVgoLC7Xe3+3bt3FxcSE2NvaF5fLy8lizZg19+vTBxcWFtm3bMnnyZBITE7XeV1WQnJzM2LFjcXNzo2vXrmzcuLHEsnv37sXJyUn117x5c3r27ElUVFQ5Rlz9zZ8/X6v//9OnTzNmzBjatGlDu3btmDx5MlevXi2HCMuGSIpaKCgoYMaMGdy8ebOiQ1Fxc3Pj7NmzSKXSig7llRUWFjJlyhT09fXZuXMny5cv57///S+rVq3SWH78+PGsW7eunKMs7ujRowwePJi8vDwCAwP5/vvviYyMZOjQoURFRTFs2DDS0tJeuh2lUsn8+fPJz89/Ybm8vDxGjBhBdHQ006dP5/vvv2fLli3Y2dkxcuRILl68WFofrULJZDImTpxIvXr12LdvH0FBQYSHh3PgwIES32NlZcXZs2c5e/Ys0dHRTJ48meDgYM6fP1+OkVdfP//8M7t3735pua1btzJ16lQ6d+7Mrl272LJlC5aWlowYMaLKJsaqd0YtZ0lJScycOZPKNsaBvr4+VlZWFR3Gv3Lx4kVSUlLYvXs3RkZGODg48NFHH7FkyRICAgKKlTcyMqqAKNXFxMSwcOFCIiIicHd3V1vXuHFjBgwYQEhICJ988gnffPMNOjolX2/u2LEDuVz+0n2uW7eOtLQ0Dh8+TK1atVTL586dS3Z2NuvWrasUFwuvKy0tjZYtWxIcHIyhoSF2dnZ4eHgQFxdH//79Nb5HR0dH7fdvY2PD999/z9GjR2nTpk15hV4tPX78mMDAQFq1avXCcnfu3GHp0qWEhoYycOBA1fLFixeTmprKypUrX3jHX1mJO8WXOHfuHO3atWPXrl0vLbt37178/f1Zs2YN7du3p3Xr1oSGhqJQKNTK9O7dm5YtW+Lr66tWfdatWzciIyMZNmwYLVq0oH///iXeDTxffZqamoqTkxNr167F3d2dOXPmsHr1aqZMmcKoUaNwd3fn9OnTpKenM23aNNzd3XF2dmbgwIHExcWptpmUlIS/vz8uLi6MGzeO7Ozs1zhyJWvUqBERERFqyU4ikfDw4UON5Z+vPt27dy9+fn5MmzaN1q1bs3v3bkaNGsXChQvx9vamc+fOZGVlkZCQwPDhw3FxccHV1ZV3331X7S7u2LFj9OzZE1dXV0JDQ1940VNUVERoaCifffYZ7u7unD9/Hl9fX9zc3AgLC1Md6+DgYLKysjh9+nSJ27p79y6rV69m0aJFLzxGCoWCqKgoxowZo5YQnwkICODzzz8HYMKECYSEhKitnzFjxkv3UVnY2NjwxRdfYGhoiFKpJD4+nri4ODp06PBK26lZs2YZRfhmWbVqFW3btqVt27YvLHfo0CHMzMw0XrgsXLiQefPmlVWIZUokxZcYPnw4c+fOpUYN7Sb7vXTpEsnJyWzfvp2goCC2bdvGmTNngCcn9IULFzJp0iT2799Px44dmTRpEn/++afq/WvWrGHChAkcOHAAU1PTVzqxnT9/nqioKCZNmgTAyZMn6dmzJ1u3bqVVq1bMmjWLoqIidu7cyb59+6hbty7BwcHAkyrNSZMmYWNjw969e/Hy8tKq+uTfsLCwwMPDQ/VaoVAQGRmptuxFEhMTsbOzY/fu3XTt2hV4cmzDwsIIDw9HX1+fyZMn4+HhwaFDh9i0aROpqamqu6qkpCSmT5+Ov78/UVFRFBYWkpCQUOL+Tp48iZWVFV27duXq1atMnDiRQYMGsWfPHlJTU9m8eTPNmzdHT0+PIUOGcOLEiRK3FRQUxNixY7Gzs3vhZ7xz5w6ZmZklnpgsLCwwNjYGoG/fvhw7dkx195mXl8fJkyfp27fvC/dRGXl6ejJ8+HDc3Nzo2bOn1u+Lj48nJiamSn7myiQhIYGjR48ye/bsl5a9fv06zs7OGmtFGjZsiL29fVmEWOZE9WkpKyoqYuHChZiYmNCoUSM2b97MpUuXeOedd9i6dSsjRoxQVTXMnDmTc+fOsXXrVtWPcODAgXh5eQEwbtw4PvzwQ633PXr0aBo0aKB6bWZmxsiRI1Wvu3btSo8ePahXrx4AI0aMYMKECSiVSmJiYrh//z4hISGqKs3Y2Fju37//2sfkZcLCwrh27Rp79uzR+j1TpkxRu9P09PRUVZtlZGQwefJkxo8fj0QiwdbWlh49eqgSX1RUFK1atWLs2LEABAYGvjCRnThxQnWyXbp0KX369FE1QJg9ezbe3t40bdoUAHt7e86ePatxO/v27SM9PZ133333pZ8vKysLePIdPnPx4kXGjBmjVi4hIQEvLy+Cg4OJi4ujffv2nDp1CnNzc9zc3F66n8omPDyc9PR0QkJCCAsLY/78+RrLpaenqz6fTCZDJpPh7e1Ns2bNyjPcaqWwsJB58+Yxd+5cjbUT/5STk4OFhUU5RFa+RFIsZebm5piYmKheGxsbq1qIJicn895776mVd3V15datW6rXtra2au9VKBTI5XJ0dXVfuu/69eu/8LW/vz+HDx/m119/5ffff+fy5csAyOVykpKSsLW1VUs0zs7OqrvcsqBUKvnss8/YsWMHX375JY6Ojlq9z8zMrNhzxuc/q5WVFYMGDWLz5s1cu3aNpKQkbty4QcuWLYEn34OTk5OqvJ6entrrf0pOTmbYsGFkZGQQGxvLzJkz1WIBVCfjR48eaaxVyMzMZOnSpURERCCVSl/aatjU1BRArUq5SZMm7Nu3D3iSDJ9dSBkbG9OlSxeOHDlC+/btOXLkCH369Hnh9iurFi1aAJCfn8/s2bOZNWsW+vr6xcrVrl2bbdu2AU8uRP/8809WrlzJ+++/XyWfY1UGa9euxc7ODh8fH63Km5ubl/jIoyoTSbGU6enpFVv27HmVoaFhsXVyuVyt0YWmE4C2jXwMDAxKfK1QKBg/fjwPHjygd+/edOvWDZlMpnYn+s/9lGXLVoVCwbx58zh48CCrVq1S3R1r45+fE9SPW1paGoMHD6Zp06Z06tQJPz8/Tp06RXx8fInb1PS9PfPgwQNMTExISUlBqVTy9ttvq9ZdunQJExMT1cXMmTNncHFxKbaNM2fOcP/+fUaPHq22fOLEibz//vtMmTJFbbmdnR1mZmYkJCSokrm+vr6q2vWPP/5QK9+vXz+CgoL49NNP+fHHH7V6Bl5ZpKWlcfnyZbp3765a5uDggEwmIzc3V+PdiI6OjloVtIODA8bGxgwbNoybN29qfYEl/O3gwYNkZGSo3YHL5XLc3Nw0Pl5o0aIFERERKJVKJBKJ2rpTp06xf/9+li1b9sL/rcpIJMVy1KhRIxITE+nRo4dqWWJiIq6urmW+76SkJOLi4jhz5gzW1tYAqittpVKJo6MjKSkpPHjwQFV1UpZNqpcsWcLBgwdZvXq16rlgaTl27BhGRkZs2LBBtWzr1q2qpO/o6KjWdF8ul3Pjxg2aN2+ucXvm5uZkZGRgbm4OwL1796hZsyZKpZL169fTpEkTJBIJV69e5cSJE3z66afFtuHt7a3Wmk8ul+Pj40NoaCienp7FykulUgYPHsw333yDr6+vWu0DUKzrh6enJzKZjA0bNmBjY0OTJk1edpgqjeTkZKZOncqZM2ewtLQE4MqVK1hYWLxS9dyz71eblr1CcVu3blWrwdi8eTOXL19m+fLlGsv36tWLlStXsn//frXWpwqFgk2bNiGVSqtcQgSRFMvVuHHjCAgIwNHRERcXF/bu3cv169dZvHhxme/b1NQUHR0dDh8+jLe3N5cuXVJ1Vi8sLMTDw4O33nqLuXPn8vHHH3PhwgWio6PLJGFfuHCBb775hpkzZ+Ls7ExGRoZqXWl0MzEzMyM9PZ2ffvqJBg0acOTIEX744QfVc78hQ4awZcsW1qxZQ+/evdm+fTt//fVXidtr1qwZcXFxfPDBB9jb2xMSEsLo0aOJioriwoULNGvWjH379rF06VKCgoI0nsiNjY1VDWMA1cmnTp06as8Nnzdt2jQSEhLw8/Pjww8/pGXLluTk5HDgwAEiIyNp3bq1qqy+vj7e3t58/fXXxe46Kzt3d3ccHBwICAggICCAlJQUVqxY8cLPoVAo1H43d+/eZdmyZTRq1IjGjRuXR9jVzj8ft5iamqq6yGhSt25dpk0nnXL7AAACTElEQVSbRmBgIFlZWXTr1o2cnBw2btzIlStX2LlzZ3mEXepE69Ny1LNnT2bOnMl//vMf+vfvT2xsLJs2bSqXqp66desSEhLC119/TZ8+ffjqq6+YP38+enp6XLt2DT09PSIiIsjNzcXX15dvv/2W4cOHl0ks0dHRAKxYsYJOnTqp/ZXGCD0+Pj7079+f6dOn4+vryy+//MKcOXP4/fffyc/Pp2HDhqxfv56jR48ycOBA7t+/T+fOnUvcXp8+fdi9ezc5OTl88cUXpKenM2PGDJo0aUJAQAC//fYbGzZsYNGiRXh5eXHr1q1S6ddqaGjIli1b8PPzY8OGDfTv359Ro0Zx9epVQkNDiYyMLBZnQUFBlXue+Oy3p6ury5AhQwgKCmLMmDHFqpqfl5GRofrNdO7cmYkTJ1K/fn0iIiJe2EdUKF0TJkwgLCxMNbDFxIkTKSwsZOfOnVX24kSirGy90gWhEpo5cyZZWVmsXLlSVY36T9nZ2Xz00UfUrl2bFStWlHOET1q3btu2rcy60gjCm0AkRUHQQkFBAbNnzyY2NpbBgwfTqVMn6tWrh46ODmlpaZw9e5Zvv/0WDw8PFi9erLHBVFm5c+cOly5dYtWqVUyaNIkhQ4aU274FoboRSVEQXsHPP//Mnj17SExMJD09HQBLS0tcXV3x8/N75VFYSium9957D09PT1atWqVV9x1BEDQTSVEQBEEQnhJPpAVBEAThKZEUBUEQBOEpkRQFQRAE4SmRFAVBEAThKZEUBUEQBOEpkRQFQRAE4an/B5g9ZNjrNqxBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(rc= {'patch.edgecolor': 'black'})\n",
    "\n",
    "fig = plt.figure(figsize=(7,3.2))\n",
    "ax = sns.boxenplot(x='timepoint',y='telo means', data=all_patients_df,\n",
    "                  linewidth=1,)\n",
    "ax = sns.swarmplot(x='timepoint',y='telo means', data=all_patients_df, size=8,\n",
    "                  linewidth=.5, edgecolor='black')\n",
    "\n",
    "# ax.set_title(\"Mean Telomere Length (TeloFISH) per timepoint\") \n",
    "ax.set_ylabel('Mean Telomere Length', fontsize=14)\n",
    "ax.set_xlabel('', fontsize=14)\n",
    "ax.tick_params(labelsize=14)\n",
    "plt.savefig('../graphs/paper figures/main figs/all patient Mean telomere length means teloFISH.png', \n",
    "            dpi=400, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA, correlations, linear regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPEATED MEASURES ANOVA for telomere length: 0.05898000440859586\n"
     ]
    }
   ],
   "source": [
    "df = all_patients_df[all_patients_df['patient id'] != 13].copy()\n",
    "\n",
    "trp.telos_scipy_anova_post_hoc_tests(df0=df, time_col='timepoint', target='telo means',\n",
    "                                     sig_test=stats.f_oneway, post_hoc='tukeyHSD', repeated_measures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg_df = all_patients_df.pivot(index='patient id', columns='timepoint', values='telo means')\n",
    "lin_reg_df = lin_reg_df.drop(13)\n",
    "lin_reg_df['constant'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>timepoint</th>\n",
       "      <th>1 non irrad</th>\n",
       "      <th>2 irrad @ 4 Gy</th>\n",
       "      <th>3 B</th>\n",
       "      <th>4 C</th>\n",
       "      <th>constant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timepoint</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>1 non irrad</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.947341</td>\n",
       "      <td>0.509322</td>\n",
       "      <td>0.401291</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2 irrad @ 4 Gy</th>\n",
       "      <td>0.947341</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.620616</td>\n",
       "      <td>0.400194</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 B</th>\n",
       "      <td>0.509322</td>\n",
       "      <td>0.620616</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.534244</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4 C</th>\n",
       "      <td>0.401291</td>\n",
       "      <td>0.400194</td>\n",
       "      <td>0.534244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>constant</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "timepoint       1 non irrad  2 irrad @ 4 Gy       3 B       4 C  constant\n",
       "timepoint                                                                \n",
       "1 non irrad        1.000000        0.947341  0.509322  0.401291       NaN\n",
       "2 irrad @ 4 Gy     0.947341        1.000000  0.620616  0.400194       NaN\n",
       "3 B                0.509322        0.620616  1.000000  0.534244       NaN\n",
       "4 C                0.401291        0.400194  0.534244  1.000000       NaN\n",
       "constant                NaN             NaN       NaN       NaN       NaN"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression for ['1 non irrad'] vs. 4 C:\n",
      "R2 is 0.1610\n",
      "Linear regression for ['1 non irrad', '2 irrad @ 4 Gy'] vs. 4 C:\n",
      "R2 is 0.1649\n"
     ]
    }
   ],
   "source": [
    "x_names = [['1 non irrad'], ['1 non irrad', '2 irrad @ 4 Gy'],]\n",
    "#            ['1 non irrad', '2 irrad @ 4 Gy', '3 B']]   \n",
    "y_name = '4 C'\n",
    "\n",
    "mtl_df_list = []\n",
    "\n",
    "for x_name in x_names:\n",
    "    x = lin_reg_df[x_name].values.reshape(-1, len(x_name))\n",
    "    y = lin_reg_df['4 C'].values.reshape(-1, 1)\n",
    "    regression = LinearRegression().fit(x, y)\n",
    "    print(f\"Linear regression for {x_name} vs. {y_name}:\\nR2 is {regression.score(x, y):.4f}\")\n",
    "    \n",
    "    mtl_df_list.append([', '.join(x_name), '4 C', round(regression.score(x, y), 4)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "mtl_LM_metrics = pd.DataFrame(mtl_df_list, columns=['Model features', 'Target', 'Linear regression R2 score'])\n",
    "mtl_LM_metrics['Model features'] = mtl_LM_metrics['Model features'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Model features</th>\n",
       "      <th>Target</th>\n",
       "      <th>Linear regression R2 score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>4 C</td>\n",
       "      <td>0.1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 non irrad, 2 irrad @ 4 Gy</td>\n",
       "      <td>4 C</td>\n",
       "      <td>0.1649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model features Target  Linear regression R2 score\n",
       "0                  1 non irrad    4 C                      0.1610\n",
       "1  1 non irrad, 2 irrad @ 4 Gy    4 C                      0.1649"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mtl_LM_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(rc= {'patch.edgecolor': 'black'})\n",
    "sns.set_color_codes()\n",
    "\n",
    "fig = plt.figure(figsize=(6,4))\n",
    "ax = sns.lineplot(x='Model features', y='Linear regression R2 score', data=mtl_LM_metrics, \n",
    "                  linewidth=6, sort=False, hue='Target', palette={'4 C':'grey'}, legend=False, marker='o', \n",
    "                  **{'markersize':14, \n",
    "                     'mec':'black', \n",
    "                     'mew':1})\n",
    "                       \n",
    "ax.set_ylabel('Linear regression R2 score', fontsize=14)\n",
    "ax.set_xlabel('Model features', fontsize=14,)\n",
    "\n",
    "plt.xticks(fontsize=14,)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylim(0,1)\n",
    "plt.title('Predicting post-therapy mean telomere length', fontsize=14)\n",
    "\n",
    "plt.savefig('../graphs/paper figures/main figs/linear regression metrics Mean telomere length means teloFISH.png', \n",
    "            dpi=400, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# more indepth stats\n",
    "\n",
    "# target = lin_reg_df['4 C']\n",
    "# linear_m = sm.OLS(endog=target, exog=lin_reg_df[['1 non irrad', '2 irrad @ 4 Gy', 'constant']], missing='drop')\n",
    "# results = linear_m.fit()\n",
    "# results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "## Individual Telomere Length analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "exploded_telos_all_patients_df = pd.read_csv('../data/compiled patient data csv files/exploded_telos_all_patients_df.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ua6WGDx+OoKAgvPPOO3j11Vcxc+ZMAA8HbtrY2CAwMBBTpkxBu3btMGHCBPFbQb9+/aBQKDBy5EicP3++RpvDhg3De++9h1WrVmH48OFIT09HXFyc2GcqZcKECdi0aRNCQ0Nrvfb3yv/333+Hk5NTi7mn3s7ODmvWrMGhQ4cwYsQIrF69GgsXLsSYMWOapH2pv0lAQADmzJmD6OhoTJgwAYMHD67R502tmy6fq8ePH0dRURF++eUXDBgwoMZPQycWq4+dnR3Cw8Px1VdfYfjw4VizZg1CQ0PRrl07nD9/Hu3atcPatWtRUlKCUaNG4YcffsD48ePVtufl5YVLly4hIyMD7733Hp599lm88847uHbtGtauXQsDAwOEh4fjxRdfxOLFi5Gdnd1kVzNDQkKwdOlS/PLLLxg/fjwCAgKQlJSE1157DWlpaTXGowwdOhSCILSp7hoAkAlSnYrUoly/fh0+Pj7YvXt3jcu1RNSy8FxtnTZv3ozPP/8ca9euhYODQ53bVFZW4uOPP8bPP/+Mbdu21Ri7owk3btzASy+9hEOHDsHc3FyjsZtTy/iKSkRE1AKMHTsWxcXFGDNmDIYPHw5vb2/Y29vDxMQEd+7cwalTp/DDDz+gXbt2+OqrrzRajJSWluLAgQPYunUr/Pz82lQxArAgISIiqmHq1KkYMmQINm7ciM8++ww3btxAeXm5eNvv66+/jpEjR9Z5911zkslkWLRoEZ555hlERUVpNLYmsMuGiIiItI6DWomIiEjrWJAQERGR1rEgISIiIq1rc4Nai4ruQ6WSHhZjZWWKwsISDWTEWK0tlqbj6XosuVyGp55q/LwrPNcZq7XF0/VYUud6mytIVCqhQR9S1dtqCmO1rliajsdYj9c2z3XGam3xGEs9dtkQERGR1jVrQXLx4kVMmjQJAQEBGDVqlPhY6Pj4eLz44osYOnQoVq5cKT6N8c6dO5g6dSqGDRsGf39/nDx5sjnTIyIiohaiQQVJ9Vz9GRkZ2L59e60HPdWlrKwMb775JqZOnYrk5GQEBwdj3rx52L9/P3bu3ImkpCSkpaXh2LFj+OmnnwAAERER8PDwwI4dO7B8+XLMmTMHZWVlT3B4RERE1BpIFiSrVq3Chx9+iFu3biEoKAibNm1CRESEZMOHDx9Gly5dMGjQIACAj48PPv30U6Snp8Pf3x8mJiYwNDTEqFGjkJqaisrKSuzbtw//+te/AABOTk6wt7fHwYMHn/AQiYiIqKWTLEh+/vlnREdHY9euXRg2bBgSEhLEp1HWJzs7G9bW1vjggw8watQoTJkyBVVVVbh16xY6duwobmdnZ4e8vDwUFRVBpVKhQ4cO4mu2trbIzc19zENrHXr1doGNjXmNn169XbSdFhERkUY16C4bExMTHDlyRHwcu1KplNynsrIS+/fvx7fffgs3Nzfs2bMH06dPh0KhqLWtXC6HSqWqsx09Pb2GpCiysjJt8LbW1maNavtJqIt1PecaTj9y7D3l8ifKrSUcV2uPpel4jNV4re1cZ6yWGUvT8RhLPcmCxMLCAlFRUcjIyEBsbCxiY2NhbW0t2bCNjQ0UCgXc3NwAAL6+vggNDYVcLkdBQYG4XV5eHuzs7GBlZQUAuHv3LiwsLMTXbG1tG3VAhYUlDbr9yNraDAUFxY1q+3E9TqzHza2lH1driKXpeLoeSy6XNaq4qNZWznXG0l4sTcfT9VhS57pkl01MTAwsLCwQHx8PExMTVFRUICYmRjLwwIEDcePGDfHOmhMnTkAmk+G1115DamoqSktLoVQqkZSUBF9fX+jr62Pw4MHYvHkzAODChQvIyspC3759JWMRERFR6yZ5hWTz5s2YPXu2uLxgwQIsXboUISEh9e5nbW2NuLg4REREoKysDAYGBli5ciU8PDxw6dIljBkzBhUVFfDx8UFAQAAAICwsDKGhofD394dMJsOyZctgZqbZS3dERESkeWoLklWrVqG4uBjbt29HSclfU8JWVlZi9+7dkgUJAPTp0wdbtmyptT4oKAhBQUG11j/99NOIj49vaO5ERETURqjtsnF2doaxsTHkcjmMjY3Fn6eeegqffvqpJnMkIiKiNk7tFRJvb294e3tj0KBBcHd312ROREREpGMkx5CYm5sjNDQUd+/eFad4Bx526RARERE1BcmC5P3334eLiwsGDhyoiXyIiIhIB0kWJOXl5QgPD9dAKkRERKSrJOch6dKlC27fvq2JXIiIdNqjj5LgYyRIl0heIZHL5Rg+fDhcXV1hZGQkrucYEiKipvXooyR6yhv0QHaiNkGyIBk0aJD4xF4iIiKi5iBZkFQ/UI+IiIiouUgWJH369IFMJqu1/vjx482SEBEREekeyYJk69at4u8VFRXYvXs32rVr16xJERERkW6RLEi6du1aY3nmzJkYM2YMpk6d2mxJERERYGBoCBsb8xrrOnfpipO/ndVSRkTNR7IgedTVq1dRWFjYHLkQEdHfKMvLa9x1A/DOG2q7GjWGRKVS4cGDB3jvvfeaPTEiIiLSHY0aQyKTyWBubg4LC4tmTYqIiIh0S4PGkOzevRsHDhxAZWUl+vfvjxEjRmgiNyIiItIRkp2RX3/9NVauXInu3bujR48eWLt2LdasWaOJ3IiIiEhHSF4hSUpKwvfffw8zMzMAwNixYxEYGIgZM2Y0e3JERESkGxo0XLu6GAEAc3Nz6Os3+uYcIiIiIrUkC5JnnnkGCQkJqKqqQlVVFTZs2AA7OztN5EZEREQ6QrIgCQsLQ1paGtzc3ODm5obt27dj8eLFmsiN/j8+kpyIiNo6yb6Xjh07YuPGjSgpKYEgCDW6b0gz+EhyorapV28XXM+5pu00iFqEev9ni4uLw5EjRwAApqamWLJkCT7//HONJEZE1NZVf9mo/iHSZWoLkvXr12Pfvn3o2LGjuG7s2LHYu3cvvvnmG40kR0RERLpBbUGSkpKCL774Avb29uK6nj174vPPP0diYqImciMiajMeHQv26EPziHSd2jEkenp6sLS0rLXe2toaenp6zZoUEVFb8+hYMIDjwYj+Tu3ZIAgClEplrfVKpRIVFRXNmhQRERHpFrUFyaBBgxAbG1trfWxsLPr169esSek6A0NDXtYlojpVfz7IZDJOA0Btitoum+DgYAQFBWHo0KFwc3ODIAjIyMhA586dERcXp8kcdY6yvJy3+RJRnfj5QG2V2oLE0NAQX331FY4cOYKzZ89CLpdj3Lhx8PDw0GR+bU7v3g7IycnVdhpEREQtiuTEaJ6envD09NRELjohJycXVcJfXV56sqNazIaIiKhl4LU+IiIi0joWJERERKR1LEiIiIhI6yQLksLCQsycORPDhg1DYWEhpk+fjtu3b2siNyIiItIRkgVJREQEvLy8oK+vDzMzMygUCoSGhmoiNyIiItIRkgVJTk4Oxo8fD7lcDgMDAyxcuBA3btzQRG5tQu/eDuIERpzkjIiIqG6St/3KZDKo/jYJz/379yEIQrMm1ZbwNl8iIiJpkgWJr68vFixYgOLiYmzZsgVbtmzB0KFDNZEbERER6QjJgiQ4OBhbt25FeXk5fvnlFwQEBGDcuHGayI2IiIh0hGRBEhISgqVLl+LVV199rAB79uzBggULcPLkSQBAfHw8kpOTUVVVhZEjR2LWrFmQyWS4c+cOFixYgJs3b0IulyMyMhK9evV6rJhERETUukgOaj179uxjN3716lXExMSIY07279+PnTt3IikpCWlpaTh27Bh++uknAA/v5vHw8MCOHTuwfPlyzJkzB2VlZY8dm4iIiFoPySsktra2GDFiBHr27AkTExNxfUhISL37lZWVYf78+Vi4cCHmzZsHAEhPT4e/v7/YzqhRo5Camgo/Pz/s27cPYWFhAAAnJyfY29vj4MGD8PPze+yDIyIiotZBsiBxcXGBi4tLoxtevHgxxo4dC0dHR3HdrVu3ajyoz87ODnl5eSgqKoJKpUKHDh3E12xtbZGb2/in4lpZmTZ4W2trs0a331LUl7smj6utxtJ0PMZqvJZ6rmvjc0UTMdvye9hWj621xZIsSObOnQulUomcnBwoFAoolUoYGhrWu09CQgL09fUxevRoXL9+XVxf1+3Ccrm8xm3Ff6enpyeVXi2FhSVQqaRvS7a2NkNBQXGj228JDAwNIZPJaqzr3KUrTv52VqPH1VZjaTqerseSy2WNKi6qtcRzXRufK4btDGp9HnTp1Bm/nTrXZDHa8nvYVo+tJcaSOtclC5KMjAy89dZb0NPTw6ZNmxAQEID4+Hj07NlT7T7btm3DgwcP8PLLL6OiokL83dnZGQUFBeJ2eXl5sLOzg5WVFQDg7t27sLCwEF+ztbWVPEBdpCwvx+lHiriecj6WiEgXlVcoIez+rcY6mV9vLWVD9Pgk/xeLiYnBunXrYGlpCTs7O0RHRyMqKqrefRITE5GWloaUlBSsXbsWRkZGSElJwdChQ5GamorS0lIolUokJSXB19cX+vr6GDx4MDZv3gwAuHDhArKystC3b9+mOUoiIiJq0SSvkJSVldUYB+Lj44OVK1c+VjBvb29cunQJY8aMQUVFBXx8fBAQEAAACAsLQ2hoKPz9/SGTybBs2TKYmbXeMR5ERETUcJIFiZ6eHoqLi8U+yqtXrzYqQOfOnXHq1ClxOSgoCEFBQbW2e/rppxEfH9+otukvBoaGNZ6VUz2mhIiIqDWQLEhmzJiBiRMnIj8/H/Pnz8eBAwcQHh6ugdSoMR4dV8IxJURE1Jo06Fk2CoUChw8fRlVVFaZOnVqjC4eIiIjoSUkWJABgaWmJAQMGiMvZ2dno3r17syVFREREukWyIImJicG3336L9u3bi/OIyGQyHD9+vNmTIyIiIt0gWZDs2rULBw8erDGLKhEREVFTkhz52K1bN1haWmoiFyIiItJRkldIJk2ahMmTJ6Nfv37Q1/9r87pu3SUiIiJ6HJIFSVxcHCwsLGpM+f7ocxOIiIiInoRkQXL//n1s3bpVE7kQERGRjpIcQ9KjRw9cvnxZE7kQERGRjpK8QlJUVIRXXnkF3bp1g4GBgbh+27ZtzZoYERER6Q7JgmT27NmayIOIiIh0mGSXjaenJ0xNTXH69Gn8+uuvaNeuHTw9PTWRGxEREekIyYJk+/btCA4ORkFBAQoLCzF37lwkJiZqIjciIiLSEZJdNuvXr0diYiJsbW0BPJx/ZNq0aRg9enSzJ0dERES6QfIKiUqlEosRALCzs+M8JERERNSkJAsSCwsL7Nu3T1z+5ZdfYG5u3pw5ERERkY6R7LIJDQ1FcHAwIiMjxXWrV69u1qSIiIhIt0gWJI6Ojti1axeysrIgCAIUCkWN+UioZTJsZwAbm5pXsrp06ozfTp3TUkZERETqqS1Ili5dWu+OISEhTZ4MNZ3yCiWE3b/VWCfz662lbIiIiOqntiAxNjbWZB5ERG1K794OyMnJ1XYaRK2G2oJk7ty54u9KpRI5OTlQKBRQKpUwNDTUSHJERK1VTk4uqoR+4rKe7KgWsyFq+STvssnIyICPjw/efPNN5OXlYdCgQTh9+rQmciMiIiIdIVmQxMTEYN26dbC0tISdnR2io6MRFRWlidyIiIhIR0gWJGVlZXB0dBSXfXx8UFlZ2axJERERkW6RLEj09PRQXFwszs569erV5s6JiIiIdIzkPCQzZszAxIkTkZ+fj/nz5+PAgQMIDw/XQGpERESkKyQLEl9fXygUChw+fBhVVVWYOnVqjS4cIiIioieltiCZP38+li9fDgBQKBRQKBQaS4qIqLXhvCNET0ZtQZKZmanJPIiIWjXOO0L0ZCS7bIiIqHV59FlWfI4VtQZqC5KLFy+iV69etdYLggCZTIaTJ082a2KtES/ZElFL8OizrPgcK2oN1BYk3bt3x9q1azWZS6v36CVbgJdtiYiIGkJtQWJgYIBOnTppMhciIiLSUWonRuMD9IiIiEhT1F4h2bRpkybzICKiZsJBrtQa8C4bIqI2joNcqTWQfJYNERERUXNjQUJERERap7bLJioqqt4dQ0NDJRtPSUnB+vXrIZPJYGxsjA8//BCurq6Ij49HcnIyqqqqMHLkSMyaNQsymQx37tzBggULcPPmTcjlckRGRtY5Fw8eW8sAACAASURBVAoRERG1LWoLEktLyydq+MqVK1i+fDmSkpJgY2OD/fv3Y/bs2YiIiMDOnTuRlJQEPT09vPnmm+jRoweGDRuGiIgIeHh4ICgoCOfPn8f06dOxe/duGBsbP1EuRERE1LKpLUhmzZqldqfS0lLJhg0MDBAVFQUbGxsAgIuLC27fvo2dO3fC398fJiYmAIBRo0YhNTUVfn5+2LdvH8LCwgAATk5OsLe3x8GDB+Hn59eogyIiIqLWRfIumz179mDFihUoLS2FIAhQqVT4888/cerUqXr369y5Mzp37gzg4XTzS5cuhbe3N/Lz8zFgwABxOzs7O+Tl5aGoqAgqlQodOnQQX7O1tUVuLqdibyq89Y+IiFoqyYJk2bJlmDt3LjZu3Ihp06Zhz549aN++fYMDlJaWYuHChcjNzcW6deswd+7cWtvI5XKoVKo699fT02twLACwsjJt8LbW1maNaru1q+vWvyd9DzT5Hmr679VWj62txOK5/mQa+560lX832o7HWOpJFiTGxsYYNmwYzp8/D0NDQ4SHh+PVV19tUOM3b95EUFAQevTogW+//RZGRkbo2LEjCgoKxG3y8vJgZ2cHKysrAMDdu3dhYWEhvmZra9uoAyosLIFKJUhuZ21thoKC4ka13RY9yXugyfdQ03+vtnpsLTGWXC5rVHFRjef6k2nMe9IS/920xni6HkvqXJe87dfAwABKpRJdu3bF+fPnIZfLoVQqJQP/+eefmDhxIvz8/BAbGwsjIyMAgI+PD1JTU1FaWgqlUomkpCT4+vpCX18fgwcPxubNmwEAFy5cQFZWFvr27SsZi4iIiFo3ySskPj4+mD59Oj766CMEBgbit99+a9AdOBs3bsStW7eQnp6O9PR0cf3XX38NPz8/jBkzBhUVFfDx8UFAQAAAICwsDKGhofD394dMJsOyZctgZsZLrURERG2dZEESFBSEkSNHws7ODqtXr8aJEyfg7+8v2fDMmTMxc+ZMtW0GBQXVWv/0008jPj6+AWkTERFRWyJZkPz+++8AgKKiIgCAh4cHcnNzxTEfRERERE9KsiCZPXu2+HtFRQVu376N559/HomJic2aGDU/3gZMREQthWRB8vPPP9dYPn36NIuRNoJPACUiopai0Q/X69mzp9iNQ0RERNQUGjyGBHg44+rZs2fx4MGDZk2KiIiIdEujxpDIZDJ06NAB4eHhzZkTERER6ZhGjyEhIiIiampqC5JVq1bVu2N9TwMmIiIiagy1BUn1vCNXrlxBdna2OL373r174ejoqLEEiYioaT16yz/A2/5J+9QWJIsWLQIATJ48GUlJSejQoQOAhzOwBgcHayY7IiJqco/e8g/wtn/SPsnbfgsKCsRiBADMzc1RWFjYrEkRERGRbpEc1Oro6IiQkBC8/PLLEAQBiYmJcHNz00RuREREpCMkC5KoqCisWLEC0dHRkMlk8PLyqnErMBEREdGTkixITE1N8cEHH2giFyKiVqF3bwfk5ORqOw2iNkVtQTJu3Dhs3LgR7u7ukMlktV4/efJksyZGRNRS5eTkokroV2OdnuyolrJpGnzYJmmb2oLks88+AwCkpaVpLBkiItIOPmyTtE3tXTY2NjYAgHfffRdHjx5Fhw4d0KlTJ/GHiIiIqKlI3vY7a9YsHDp0CN7e3li8eDHOnDmjibyohejt7gwbG3Pxp7e7s7ZTIiKiNkhyUKuXlxe8vLxw7949bN++HYsXL4ZKpUJKSoom8iMty7lxnZdxiYio2UleIQGAyspKHD16FIcOHUJhYSE8PT2bOy8iIiLSIZJXSP79739jx44dcHJywujRo/HZZ5/BwMBAE7kRERGRjmjQPCQ//PADunTpool8iIiISAepLUiOHDkCT09PPP/88zh//jzOnz9f43U/P79mT46IiIh0g9qC5Mcff4Snpye+++67Wq/JZDIWJG1QXY8kJyIi0gS1BUlUVBQA1FmQUNvER5ITUTXO3EqaprYgCQkJqXfHpUuXNnkyRETUMnDmVtI0tbf9Pvfcc3juuedQXFyMixcvwtHREc7Ozrh69Sqqqqo0mSMRERG1cWqvkLzxxhsAgPT0dCQkJMDY2BgA8K9//QuTJ0/WTHZERESkEyQnRissLKwx74hMJkNRUVGzJkVERES6RXIeEk9PT0ydOhX+/v4QBAEpKSnw9vbWRG7UAnGgG5FuqusuPJ7/1JQkC5JFixYhISEB6enpkMlkeOmllxAYGKiJ3KgF4kA3It3Eu/CouUkWJPr6+hg3bhwCAgIgCAIA4N69e7C0tGz25IiIiEg3SBYk33zzDT7++GNUVFQAAARBgEwmqzVzqy7q3dsBOTm52k6DiIio1ZMsSL777jts3LgRzz//vCbyaVVycnJRJfQTl/VkR7WYjXawX5mIiJqCZEFibW3NYoTUYr8y6QJeDa0bB7lTU5IsSPr374/vv/8ePj4+MDQ0FNdzDAkR6QpeDa0bB7lTU5IsSNauXQulUonIyEhxHceQUH34rYmIiBpLsiDJyMjQRB7Uhjz6rclouCcLFCIiqpfaguT333+vd0eOK6GGkipQABYpRES6Tm1BMnv2bLU7yWQy7N27t1kS2rdvHz7++GMolUo4OjpiyZIlMDU1bZZYpB0cCEtERI9SW5D8/PPPmswDAHDnzh2EhIRg48aNsLe3x/Lly/Gf//wH4eHhGs+lLhxpT0SkHseP0ZOQfLieJh06dAiurq6wt7cHAIwbNw7bt28XZ4jVtuqR9tU/1HSqP8iqf3q7O2s7JSJqpOqrn9U/+fn5Nc5rnttUH8lBrZqUm5sLOzs7cdnOzg4lJSW4f/9+g7tt5HJZg+M1ZlsA6NatGwC7Bi83dJ92dcRp14hlddvAyKDWNn9fJ7WsyX3snumIq9+licv2k/xr/X0a+/d6UpqMp8uxHjefpjrXR4wYgps3C2qsMzbWQ1lZlbjMc/3x9nn0vAbqPrcfxXO9bcaS2kYmtJTLDwDi4+Nx8+ZN8RbjyspKPP/88zh16hRMTEy0nB0RERE1lxbVZdOxY0cUFPz1TSUvLw8WFhYsRoiIiNq4FlWQDBgwAP/9739x9epVAMCmTZvg4+Oj3aSIiIio2bWoLhsA2L9/v/h04a5duyImJobT1BMREbVxLa4gISIiIt3TorpsiIiISDexICEiIiKtY0FCREREWseChIiIiLSOBQkRERFpHQsSIiIi0joWJERERKR1LEiIiIhI61iQEBERkdaxICEiIiKtY0FCREREWseChB6bUqnEpk2bxOVJkyYhNja2Sdo+f/48fv3113q3+eijjxAfHw8ASEtLw6xZs2ptk5qainHjxtXbTklJCUJCQtC3b1+88MILWLRoEe7fv1/nttevX4ejo6P44+TkhAEDBiAmJgaVlZUNPDqi1qWtnOuPbu/o6Ci5XVZWFt577z30798f7u7uGD16NHbt2tXgONRwLEjosf34449YvXq1uLxy5UpMnz69Sdp+6623kJ2dXe82v//+O55//vlav1c7evQoFi9eLBkrPDwcly9fxldffYX169fjv//9L5YuXVrvPps3b8ahQ4fwyy+/YPny5dixYwe++OILyVhErVFbOderFRYWIjo6WnK7U6dOYcyYMTAyMsKaNWuQnJwMf39/vPvuu9iyZUuD41HDsCChx/bog6ItLS3Rvn17jcU+d+6c+MF09uxZODs7i6+vWrUK06ZNQ5cuXSTbMTQ0xOLFi+Hs7AxXV1eMHj0aJ06cqHe/p556CtbW1rCzs4OnpycmTJiAHTt2PPmBEbVAbeFc/7t///vf6NGjh2TchQsX4sUXX0R0dDRcXFzQrVs3vP7665g5cyb+85//oKys7PEOiurEgkSHVXc/pKamYuDAgfDw8EBkZCQqKirEbbZu3YqXXnoJLi4u6Nu3L8LCwlBZWYljx44hJCQEeXl5cHR0xPXr12tdxt28eTN8fHzg7u6OcePGISMjQ3zN29sbGzZsQGBgIFxdXTFy5Ejx9UmTJuHGjRsIDQ3FwoUL68z5H//4B0pKSuDp6QlHR0ccP34c06dPR1JSEgDg8OHDWL9+Pfz8/Op9D2QyGaKjo/HPf/5TbD8tLQ39+vVr1HtpbGzcqO2JNInn+l/27NmDS5cuYcaMGfVud/LkSVy9ehVvvvlmrdcmTZqEtWvXwtDQsEExqYEE0lk5OTmCg4OD4OfnJ5w4cUI4evSo4OXlJSxbtkwQBEE4ceKE4OrqKuzatUu4fv268NNPPwkuLi7Cjz/+KJSXlwtff/210L9/fyE/P1+orKwUJk6cKHzyySeCIAjC3r17BU9PTyE9PV3Izs4WVq9eLfTs2VPIy8sTBEEQhgwZIvTt21dIT08Xrly5IkyYMEEYPXq0IAiCUFRUJAwcOFD48ssvhXv37tXIubKyUsjPzxd++OEHYcqUKUJ+fr5w8uRJwdPTU8jPzxfKyspqbL9ixQohMDCwQe/Hu+++Kzg4OAhDhgwR8vPz633Prl69Kq67efOmMGLECGHt2rUNikOkaTzXH7p7967g5eUl/Pbbb8Lhw4cFBwcHtdtu2LBBcHd3b9wbTU+EV0gI8+bNg4eHB/r27Ys5c+YgMTERKpUKRkZGiI6Ohp+fHzp16oQXX3wRzs7OyMzMhIGBAczMzCCXy2FtbQ09Pb0aba5btw7Tp0+Hr68v7O3tMXPmTLi4uNTodw0ICICvry+6d++OKVOm4OzZswAeXg7W09ODqakpzMzMarSrp6cHa2tr5Ofnw9nZGdbW1igsLISDgwOsra1hZGT02O9DUFAQNm3aBDs7O0ybNg0qlUrtti+//DLc3d3h5uaGwYMHo7S0FC+//PJjxybSBF0/15cuXQofHx/06tVLctvi4mKYmpo2OgY9Pn1tJ0Da5+7uLv7u4uKCP//8E7dv34aLiwuMjIywYsUKZGZm4uLFi/jjjz8a1J2RlZWFTz75BJ999pm4TqlUws7OTlz+e5+vqakpVCoVqqqqan3g1SUzMxODBw8GAFy6dAnPPfdcQw61XtVtxMbGYtCgQThx4gT69u1b57aff/45nnnmGQiCgDt37iAhIQFjxoxBcnIynnrqqSfOhag56PK5fvjwYRw5cgRpaWkN2v6pp55CcXHxY8Wix8OChGp8KFRfFZDL5Th48CCCg4MREBAALy8vvPXWW4iIiGhQm1VVVXj//fcxYMCAGutNTEzE3w0MDGrtJzwyeO5RN2/exPDhw1FeXo69e/ciPDxc7AdPTExEREQERo4c2aAcAeDBgwfYt28fBg4cKOZma2sLc3NzFBUVqd3vmWeeQbdu3QAA9vb2Yr/7jh07MGHChAbHJ9IkXT7X09LSUFBQAC8vLzFv4GGR9sUXX8DDw6PG9q6urigtLcXly5drFUGFhYWYN28eFi1aBIVC0eAcqH7ssiFcuHBB/P3s2bN4+umnYWVlhS1btuCVV17Bv//9b4wZMwY9evTAtWvXxG1lMpnaNrt3747c3Fx069ZN/Pnyyy9x/PjxJ8rVxsYGCQkJ0NfXR0pKCpKTk2FlZYW4uDgkJyfD29u70W3OmzcPhw4dEpdzcnJw9+5dyVH4fyeXyyEIAucioRZNl8/1efPmYceOHUhOTkZycjIiIyMBAMnJyXBxcam1vbOzMxwcHPDll1/Wei0hIQFnzpxBx44dH+/gqE68QkJYsmQJlixZgpKSEqxYsQLjx4+HTCaDpaUlTp06hQsXLkBPTw9r1qxBQUEBlEolgIffgIqLi5GdnV3rlrspU6bggw8+gEKhQO/evZGamoqtW7ciMDCwQTm1b98eV65cwZ9//glLS0txvb6+PoqLi+Hk5ITu3bvj3r17uHfvHvr37w99/cb/czYyMsKrr76KZcuWwcrKCgYGBoiMjISvr2+9l4aLiorEb4AlJSVYv349qqqq4OPj0+gciDRFl891KysrWFlZics3btwAAPFKZ13CwsLwxhtvQF9fH+PHj4eBgQF27NiB+Ph4REdH8+66JsaChDB8+HAEBQWhqqoKgYGBmDlzJgBg1qxZCAkJQWBgIExNTeHl5YUJEybg3LlzAIB+/fpBoVBg5MiR+P7772u0OWzYMBQWFmLVqlXIz8+HQqFAXFwcnJycGpTThAkTEBMTg5ycHKxatarGa2fOnIGrqyuAh5MkOTk5PdYHVLWQkBD85z//wdtvv40HDx7Az88PH374Yb37jB07VvzdxMQErq6uWL9+PTp37vzYeRA1N10/1xvLw8MDGzZswOrVq/HGG2/gwYMHcHBwwMqVK/nloxnIBKmOPGqzrl+/Dh8fH+zevbvebwlE1LrxXKfWgGNIiIiISOtYkBAREZHWscuGiIiItI5XSIiIiEjrWJAQERGR1rEgISIiIq1rc/OQFBXdh0olPSzGysoUhYUlGsiIsVpbLE3H0/VYcrkMTz3VvtHt81xnrNYWT9djSZ3rba4gUamEBn1IVW+rKYzVumJpOh5jPV7bPNcZq7XFYyz12GVDREREWseChIiIiLSuQQVJaWkpACAjIwPbt29v8BNNL168iEmTJiEgIACjRo3C2bNnAQDx8fF48cUXMXToUKxcuVJ8DPWdO3cwdepUDBs2DP7+/jh58uTjHBMRERG1MpIFyapVq/Dhhx/i1q1bCAoKwqZNmxARESHZcFlZGd58801MnToVycnJCA4Oxrx587B//37s3LkTSUlJSEtLw7Fjx/DTTz8BACIiIuDh4YEdO3Zg+fLlmDNnDsrKyp78KImIiKhFkyxIfv75Z0RHR2PXrl0YNmwYEhISxCdA1ufw4cPo0qULBg0aBADw8fHBp59+ivT0dPj7+8PExASGhoYYNWoUUlNTUVlZiX379uFf//oXAMDJyQn29vY4ePDgEx4iERERtXQNusvGxMQER44cwZgxYwAASqVScp/s7GxYW1vjgw8+wIULF2Bubo758+fj1q1b8PT0FLezs7NDXl4eioqKoFKp0KFDB/E1W1tb5ObmNuqArKxMG7yttbVZo9p+EozVumJpOh5jNR7PdcZqjfEYSz3JgsTCwgJRUVHIyMhAbGwsYmNjYW1tLdlwZWUl9u/fj2+//RZubm7Ys2cPpk+fDoVCUWtbuVwOlUpVZzt6enoNOIy/FBaWNOj2I2trMxQUFDeq7cfFWK0rlqbj6XosuVzWqOKiGs91xmpt8XQ9ltS5LtllExMTAwsLC8THx8PExAQVFRWIiYmRDGxjYwOFQgE3NzcAgK+vL6qqqiCXy1FQUCBul5eXBzs7O1hZWQEA7t69W+M1W1tbyVhERETUukkWJJs3b8bs2bPFwmLBggVYt26dZMMDBw7EjRs3xDtrTpw4AZlMhtdeew2pqakoLS2FUqlEUlISfH19oa+vj8GDB2Pz5s0AgAsXLiArKwt9+/Z9kuMjIiKiVkBtl82qVatQXFyM7du3o6TkrylhKysrsXv3boSEhNTbsLW1NeLi4hAREYGysjIYGBhg5cqV8PDwwKVLlzBmzBhUVFTAx8cHAQEBAICwsDCEhobC398fMpkMy5Ytg5mZZvsSiYiISPPUFiTOzs7IyMiAXC6HsbHxXzvo6+PTTz9tUON9+vTBli1baq0PCgpCUFBQrfVPP/004uPjG9Q2ERERtR1qCxJvb294e3tj0KBBcHd312ROREREpGMk77IxNzdHaGgo7t69K86oCjzs0iEiIs3q1dsF13Ouicudu3TFyd/OajEjoqYhWZC8//77cHFxwcCBAzWRDxER1eN6zjWc/ts0CT3lfCQZtQ2SBUl5eTnCw8M1kAoRERHpKsnSukuXLrh9+7YmciEiIiIdJXmFRC6XY/jw4XB1dYWRkZG4nmNIiIiIqKlIFiSDBg0SH5BHRERE1BwkC5LqB+oREVHz4h00pMskC5I+ffpAJpPVWn/8+PFmSYiISFfxDhrSZZIFydatW8XfKyoqsHv3brRr165ZkyIiIiLdIll+d+3aVfzp0aMHZs6ciV27dmkiNyIiItIRjb4eePXqVRQWFjZHLkRERKSjGjWGRKVS4cGDB3jvvfeaPTEiIiLSHY0aQyKTyWBubg4LC4tmTYqIiIh0i2RB0rVrV+zevRsHDhxAZWUl+vfvjxEjRmgiNyIiItIRkmNIvv76a6xcuRLdu3dHjx49sHbtWqxZs0YTuREREZGOkLxCkpSUhO+//x5mZmYAgLFjxyIwMBAzZsxo9uSIiIhINzToLpvqYgQAzM3Noa8vWccQERERNZhkQfLMM88gISEBVVVVqKqqwoYNG2BnZ6eJ3IiIiEhHSBYkYWFhSEtLg5ubG9zc3LB9+3YsXrxYE7kRERGRjpDse+nYsSM2btyIkpISCIJQo/uGiIiIqCnUe4UkLi4OR44cAQCYmppiyZIl+PzzzzWSGBEREekOtQXJ+vXrsW/fPnTs2FFcN3bsWOzduxfffPONRpIjIiIi3aC2IElJScEXX3wBe3t7cV3Pnj3x+eefIzExURO5ERGRBMN2BrCxMa/x09vdWdtpETWa2jEkenp6sLS0rLXe2toaenp6zZoUERE1THmFEsLu32qsk/n11lI2RI9PbUEiCAKUSiUMDAxqrFcqlaioqGj2xIiIdJ2BoSFsbMy1nQaRRqjtshk0aBBiY2NrrY+NjUW/fv2aNSkiIgKU5eU4rVLV+CFqq9ReIQkODkZQUBCGDh0KNzc3CIKAjIwMdO7cGXFxcZrMkYiIiNo4tQWJoaEhvvrqKxw5cgRnz56FXC7HuHHj4OHhocn8iIiISAdITozm6ekJT09PTeRCREREOqpBD9cjIiIiak4sSIiIiEjrWJAQERGR1kkWJIWFhZg5cyaGDRuGwsJCTJ8+Hbdv39ZEbkRERKQjJAuSiIgIeHl5QV9fH2ZmZlAoFAgNDdVEbkRERKQjJAuSnJwcjB8/HnK5HAYGBli4cCFu3LihidyIiIhIR0gWJDKZDKq/zQ54//59CILQrEkRERGRbpGch8TX1xcLFixAcXExtmzZgi1btmDo0KGayI0aoVdvF1zPuSYumxgZo/RBWY1tunTqjN9OndN0akRERJIkC5Lg4GBs3boV5eXl+OWXXxAQEIBx48ZpIjdqhOs512o856KnXM4ngBK1cI9+kSDSZZIFSUhICJYuXYpXX331sQLs2bMHCxYswMmTJwEA8fHxSE5ORlVVFUaOHIlZs2ZBJpPhzp07WLBgAW7evAm5XI7IyEj06tXrsWISEbUGdX2RINJVkv/6z549+9iNX716FTExMeKYk/3792Pnzp1ISkpCWloajh07hp9++gnAw7t5PDw8sGPHDixfvhxz5sxBWVlZfc0TERFRGyF5hcTW1hYjRoxAz549YWJiIq4PCQmpd7+ysjLMnz8fCxcuxLx58wAA6enp8Pf3F9sZNWoUUlNT4efnh3379iEsLAwA4OTkBHt7exw8eBB+fn6PfXBERETUOkgWJC4uLnBxcWl0w4sXL8bYsWPh6Ogorrt161aNB/XZ2dkhLy8PRUVFUKlU6NChg/iara0tcnNzGx2XiKgl4ngRovpJFiRz586FUqlETk4OFAoFlEolDA0N690nISEB+vr6GD16NK5fvy6ur+t2YblcXuO24r/T09OTSq8WKyvTBm9rbW3W6PYflyZjqWPYzgA2NubicrcuXXH12h9P1GZbfg/b6rG1lVit7Vx/dLwI0LxjRpr6mFvCe9gW4jGWepIFSUZGBt566y3o6elh06ZNCAgIQHx8PHr27Kl2n23btuHBgwd4+eWXUVFRIf7u7OyMgoICcbu8vDzY2dnBysoKAHD37l1YWFiIr9na2jb6gAoLS6BSSc+TYm1thoKC4ka3/zg0Gas+5RXKGnfeyPx6P1Febfk9bKvH1hJjyeWyRhUX1Xiu168p82jL72FbPbaWGEvqXJcsz2NiYrBu3TpYWlrCzs4O0dHRiIqKqnefxMREpKWlISUlBWvXroWRkRFSUlIwdOhQpKamorS0FEqlEklJSfD19YW+vj4GDx6MzZs3AwAuXLiArKws9O3bV/IAiYiIqPWTvEJSVlZWYxyIj48PVq5c+VjBvL29cenSJYwZMwYVFRXw8fFBQEAAACAsLAyhoaHw9/eHTCbDsmXLYGam/W4OIiIian6SBYmenh6Ki4shk8kAPLyVtzE6d+6MU6dOictBQUEICgqqtd3TTz+N+Pj4RrVNREREbYNkQTJjxgxMnDgR+fn5mD9/Pg4cOIDw8HANpEZERES6okHPslEoFDh8+DCqqqowderUGl04RERERE9KsiABAEtLSwwYMEBczs7ORvfu3ZstKaof5zMgIqK2RrIgiYmJwbfffov27duL84jIZDIcP3682ZOjuml6PgMiIqLmJlmQ7Nq1CwcPHqwxiyq1DY9OlNalU2f8duqcFjMiIiJdJVmQdOvWDZaWlprIhTSsronSiIiItEGyIJk0aRImT56Mfv36QV//r83runWXiIiI6HFIFiRxcXGwsLCoMeV79ZwkpBkcxEpEjcHuWGqNJAuS+/fvY+vWrZrIhdR4dBArB7ASUX3YHUutkeT/bD169MDly5c1kQsRERHpKMkrJEVFRXjllVfQrVs3GBgYiOu3bdvWrIkRERGR7pAsSGbPnq2JPIiIiEiHSXbZeHp6wtTUFKdPn8avv/6Kdu3awdPTUxO5ERERkY6QLEi2b9+O4OBgFBQUoLCwEHPnzkViYqImciMiIiIdIdlls379eiQmJsLW1hbAw/lHpk2bhtGjRzd7ckRERKQbJK+QqFQqsRgBADs7O85DQkRERE1KsiCxsLDAvn37xOVffvkF5ubm6negNqW3uzNsbMzFn97uztpOiYiI2iDJLpvQ0FAEBwcjMjJSXLd69epmTYq049HZHatxgiUiImpukgWJo6Mjdu3ahaysLAiCAIVCUWM+Emo7Hp3dEWABQkREmqG2IFm6dGm9O4aEhDR5MkREgmpy5AAAHjBJREFU1PT4bBtqDdQWJMbGxprMg4iImgmfbUOtgdqCZO7cueLvSqUSOTk5UCgUUCqVMDQ01EhyREREpBsk77LJyMiAj48P3nzzTeTl5WHQoEE4ffq0JnIjIiIiHSFZkMTExGDdunWwtLSEnZ0doqOjERUVpYnciIiISEdIFiRlZWVwdHQUl318fFBZWdmsSREREZFukSxI9PT0UFxcLM7OevXq1ebOiYiIiHSM5DwkM2bMwMSJE5Gfn4/58+fjwIEDCA8P10BqREREpCskCxJfX18oFAocPnwYVVVVmDp1ao0uHCIiIqInpbYgmT9/PpYvXw4AUCgUUCgUGkuKiIiIdIvaMSSZmZmazIOIiIh0mOSgViIiIqLmprbL5uLFi+jVq1et9YIgQCaT4eTJk82aGBEREekOtQVJ9+7dsXbtWk3mQkRERDpKbUFiYGCATp06aTIXIiIi0lFqx5DwAXpERESkKWoLkk2bNmkyDyIiItJhkhOjUfPq1dsF13Ou1VjXuUtXnPztrJYyIqKm0Lu3A3JycrWdBlGrwYJEy67nXMNplarGup5y3o1N1Nrl5OSiSugnLuvJjmoxG6KWjwVJC2RgaAgbG3Ntp1Enw3YGNXLr0qkzfjt1TosZERFRW6C2IImKiqp3x9DQUMnGU1JSsH79eshkMhgbG+PDDz+Eq6sr4uPjkZycjKqqKowcORKzZs2CTCbDnTt3sGDBAty8eRNyuRyRkZF1zoXS1inLy2tcNWlJV0zKK5QQdv8mLsv8emsxGyIiaivUFiSWlpZP1PCVK1ewfPlyJCUlwcbGBvv378fs2bMRERGBnTt3IikpCXp6enjzzTfRo0cPDBs2DBEREfDw8EBQUBDOnz+P6dOnY/fu3TA2Nn6iXIiIiKhlU1uQzJo1S+1OpaWlkg0bGBggKioKNjY2AAAXFxfcvn0bO3fuhL+/P0xMTAAAo0aNQmpqKvz8/LBv3z6EhYUBAJycnGBvb4+DBw/Cz8+vUQdFRETqPdr1CrD7lbRPcgzJnj17sGLFCpSWlkIQBKhUKvz55584depUvft17twZnTt3BvBwuvmlS5fC29sb+fn5GDBggLidnZ0d8vLyUFRUBJVKhQ4dOoiv2draIjeXo9RbMn6wEbU+j3a9Aux+Je2TLEiWLVuGuXPnYuPGjZg2bRr27NmD9u3bNzhAaWkpFi5ciNzcXKxbtw7/r717D4uqzh84/j5cRsRrkIjugqk/RSO6ua2SUirKJoFjXgi29NE0L212sS1vgKR4bc3E1lzLnkzxFrogURboo6lYj2tlIOJuKF5SLhJIXnCY4fz+8GGSyzAgMwMyn9fz+DzOmTPn8z3fM5/hM3PO+X5fe+21Gus4ODhQUe1Ok0qOjo71jgXg7t623ut26tSuQdtuDFvGsiVTH2zW2F9b92FLfX+0lFiS65ZXV9tbch+21H2722KZLUhat25NcHAwJ0+epFWrVsTExDBmzJh6bfzixYtMnz6dnj178umnn+Li4kKXLl0oLCw0rpOfn4+npyfu7u4AXLlyhQ4dOhif69y5c4N2qKjoKhUVqtn1OnVqR2Hhbw3a9p2yZazmwtL7a+s+bKnvj+YYy8FBaVBxUUly3fJMtb0l92FL3bfmGMtcrpu9fUOj0aDT6fD29ubkyZM4ODig0+nMBi4pKeH5558nKCiIVatW4eLiAkBgYCC7d+/m+vXr6HQ6du3axbBhw3BycmLw4MFs374dgOzsbHJycujfv7/ZWEIIIYS4u5n9hSQwMJCpU6eybNkywsPDOXbsWL3uwNm6dSuXLl0iNTWV1NRU4/JPPvmEoKAgxo0bR3l5OYGBgYwaNQqABQsWEBkZSUhICIqisGLFCtq1u3t//hRCCCFE/ZgtSKZPn87IkSPx9PRk7dq1HD16lJCQELMbnjFjBjNmzDC5zenTp9dYfu+997Ju3bp6NFsIIYQlyaCHoqmZLUhOnDgBQHFxMQB/+tOfyMvLM17zIYQQ4u4ngx6Kpma2IJk5c6bx/+Xl5Vy+fBlfX18SEhKs2jAhhBBC2A+zBcm+ffuqPP7xxx+lGBFCCCGERTV4kpSHH37YeBpHCCGEEMIS6n0NCdwacTUzM5OysjKrNkoIIYQQ9qVB15AoioKbmxsxMTHWbJMQQggh7EyDryERQgghhLA0kwXJ+++/X+cL65oNWAghhBCiIUwWJJXjjpw+fZozZ84Yh3ffu3cvPj4+NmuguPvIAEtCCCEaymRBEhUVBcCECRPYtWsXbm5uwK0RWF966SXbtE7clWSAJSGEEA1l9rbfwsJCYzEC0L59e4qKiqzaKCGEEELYF7MXtfr4+DB37ly0Wi2qqpKQkMBDDz1ki7YJIYQQwk6YLUhiY2OJi4tj8eLFKIpCQEBAlVuBhRBCCCEay2xB0rZtW+bNm2eLtgghhBDCTpksSCIiIti6dSuPPPIIiqLUeP7777+3asOEEEIIYT9MFiSrV68G4PPPP7dZY+xBv369OX8+r6mbIYQQQjQrJgsSDw8PAGbNmkVYWBjBwcG0bt3aZg1rqc6fz8OgDjA+dlS+bcLWCCGEEM2D2dt+X375ZQ4dOsTQoUOJjo4mIyPDFu0SQgghhB0xe1FrQEAAAQEBlJaWkpycTHR0NBUVFSQlJdmifaIFkJFbhRBCmGO2IAHQ6/V8++23HDp0iKKiIoKDg63dLtGCVB+51eVp/yoFCkiRIu5+cn2YEI1jtiBZtGgRX3zxBX379mXs2LGsXr0ajUZji7aJFqp6gQIyvLy4+8n1YUI0Tr3GIdmxYwdeXl62aI8QQohmQE61ClszWZAcOXIEf39/fH19OXnyJCdPnqzyfFBQkNUbJ4QQomnIJJnC1kwWJCkpKfj7+7Np06YazymKIgWJEEIIISzGZEESGxsLUGtBIoQQQghhSSYLkrlz59b5wqVLl1q8MUIIIYSwTyYHRuvVqxe9evXit99+49SpU/j4+HD//feTm5uLwWCwZRvvav369UZRFDw82te41VUIIe4WlRe53v6v3yP3N3WzRAti8heSF154AYDU1FTi4+ONw8aHhYUxYcIE27SuBZBbAYUQLYHcri+szezQ8UVFRVXGHVEUheLiYqs2SgghhBD2xew4JP7+/kyZMoWQkBBUVSUpKYmhQ4faom1CCCGEsBNmC5KoqCji4+NJTU1FURRGjBhBeHi4Ldom7IgMwiSEEPbNbEHi5OREREQEo0aNQlVVAEpLS+nYsaPVGyfsh7n5brp5eXP0WGZTNE0IIYQNmC1INm7cyMqVKykvLwdAVVUURakxcqsQliSjRAohhH0xW5Bs2rSJrVu34uvra4v2CCGEEMIOmS1IOnXqJMWIaHLVrzEBuc5EiKZWPS9dXVy4XlZmfCw5KhrCbEEycOBAtmzZQmBgIK1atTIul2tIhC3JGAhCND+1nVqVU63iTpktSNavX49Op2PhwoXGZXINiWgO5M4c0VT69evN+fN5Td0MIVoUswXJTz/9ZIt2CNFgcuGraCrVR2AGGYW5NnKqVTSEyYLkxIkTdb5QriupSb41CSHE7+RUq2gIkwXJzJkzTb5IURT27t1rlQbdTWorQORbU9Op7duYXGQnRPMip1qFKSYLkn379tmyHUb79+9n5cqV6HQ6fHx8WLJkCW3btm2StpgjE+c1L6a+jclpHSGaDznVKkwxO7meLf3666/MnTuXNWvW8NVXX+Hl5cU//vGPpm6WEMLO9evXGw+P9sZ/QgjLM3tRqy0dOnQIPz8/7rvvPgAiIiLQarUsWLAARVHqtQ0Hh/qt19B1AUJDh3DxYqHxcbdu3QBPk4/rs063bt1wrhan+jJzj02tg4umxjq3LzP3+G5/TW3Lev9fLx57zM/4uKtnF5JTUqlNQ98fjWHPse60PdbM9Zqvb4VBHWV83OO+H5Bct8xrqufkH7v+gaTkr7Cl5pYTLTWWuXUUtXKCmmZg/fr1XLhwwXiLsV6vx9fXl2PHjjXb0zZCCCGEaLxmdcqmoqKi1uUODs2qmUIIIYSwsGb1l75Lly4UFv5+SiQ/P58OHTrg6urahK0SQgghhLU1q4Jk0KBBHD9+nNzcXAC2bdtGYGBg0zZKCCGEEFbXrK4hAThw4AArV66kvLwcb29vli9fLvPmCCGEEC1csytIhBBCCGF/mtUpGyGEEELYJylIhBBCCNHkpCARQgghRJOTgkQIIYQQTa5ZDR1vC9aevG/ZsmXs2bOHDh06ANC9e3fee+891q1bR2JiIgaDgZEjR/Lyyy/Xezj826mqyty5c+nVqxeTJ0/GYDCwdOlSDh06hMFg4IUXXiAiIgKA3Nxc5s2bR0lJCa6urixfvpyePXs2Kh7AgAED6Ny5s3GdyZMnM3LkSH799VfeeustLl68iIODAwsXLuTRRx+tV5ykpCQ2bNiAoii0bt2a+fPn4+fnZ7LfrBFr9OjRlJWV4ex8a2Du0NBQpkyZwo0bN4iMjCQrK4uKigrefPNNhg0bVq9YmzdvZuvWrSiKgpeXF7GxsXTs2NFqx6y2eO7u7lY5ZgBpaWm89dZbfP/99wBWOV53SnK9/u8bW+U5SK5LrtdBtSNFRUXqgAED1DNnzqiqqqorVqxQFyxYYNEYYWFh6rFjx6os279/v6rVatVr166pZWVl6nPPPaempKQ0eNs///yzOn78ePXBBx9UP/roI1VVVXXz5s3qlClT1PLycrWkpET9y1/+oh4/flxVVVUdM2aMunv3bmMbgoOD1YqKikbFy8nJUYOCgmpd/5VXXlE/+OADVVVVNSsrSx00aJB6/fp1s3FycnLUgQMHqvn5+ca2Pvnkk3X2m6VjXbt2Te3Xr5+q0+lqvGb58uVqZGSkqqqq+ssvv6gDBw5UL126ZDZWRkaGOmTIELW0tFRVVVVdtmyZGhUVZbVjZiqeNY6ZqqrqmTNn1GHDhqkPP/ywsb2WPl53SnK9/u8bW+V55XYl1yXXTbGrUza1Td6XnJyMaqE7n3U6HVlZWXz88ceMHDmSmTNncvHiRVJTUwkJCcHV1ZVWrVoxevRodu/e3eDtx8fHM3r0aEaMGGFclpaWxujRo3FycqJDhw48/fTT7N69m/z8fE6fPs3TTz8NwJNPPsmNGzfIyspqVLwffvgBBwcHxo8fT2hoKO+//z4GgwG9Xs/+/fsJCwsDoG/fvtx3330cPHjQbByNRkNsbCweHh4APPDAA1y+fJk9e/bU2m/WiHXs2DFcXV2ZNm0aoaGhLFmyhLKyMmMfjxs3DoCuXbsyaNAgvvzyS7OxHnjgAb766ivatWvHzZs3yc/Pp2PHjlY7ZqbiWeOY3bhxgzfffJM5c+YYl5l6nzcmzp2SXK//+8ZWeQ6S65LrdbOrgiQvLw9Pz99n3/T09OTq1atcu3bNItvPz89nwIABzJo1i6SkJB566CFeeuklLl26RJcuXarEzc/Pb/D2o6OjGTVqVJVltW07Ly+PS5cu4eHhUWUeoM6dO5OXl9eoeAaDgYEDB7Jhwwbi4+M5dOgQmzZtori4mIqKCtzc3Boc749//CODBw8Gbv10vHTpUoYOHUpBQUGt/WaNWDqdjv79+xMXF0dCQgKXLl1i5cqVQM0+bkg/Ojs7k5aWxhNPPMHRo0cZPXq0VY9ZbfGsccyio6N59tln8fHxMS4z9T5vTJw7Jble//61VZ6D5Lrket3sqiCx9uR9Xl5efPjhh/To0QNFUZg8eTLnzp2rNa6lYtb2jc/BwcHkvjo6OjYqXlhYGJGRkWg0Gtq3b8+kSZNIS0uzSLzr16/z6quvcu7cOWJjY626b9VjBQYG8s4779C2bVtatWrFtGnTSEtLA0z3cX0NGzaM7777jpkzZzJ58mSrH7Pq8caOHWvRYxYfH4+TkxNjx46tstzW78W6SK43rn+tmecguS65Xju7KkisPXlfdnY2iYmJVZapqkrXrl1rxL3921tj1LZPnp6edO3alcuXL1d541gibmJiItnZ2cbHqqri5OSEu7s7AFeuXKkS7/aLq+py8eJFwsPDcXR05NNPP6V9+/Ym980asfbt28fRo0dr7BfU7OOCgoJ69ePZs2f5z3/+Y3w8ZswYLl68iIeHh1WOmal4SUlJFj1m//73v8nIyECr1TJ16lTKysrQarV07tzZKsfrTkiuNy6utfIcJNcl102zq4LE2pP3OTg4sHjxYs6fPw/Ali1b8PHxITAwkN27d3P9+nV0Oh27du2q95Xb5gQGBrJz5070ej2lpaWkpKQwbNgwPD098fb25osvvgDg4MGDODg40Lt370bF+9///kdcXBwGg4GysjLi4+MJDg7GycmJwYMHs337duDWB3ZOTg79+/c3u82SkhKef/55goKCWLVqFS4uLsZ9q63frBErLy+P5cuXU1ZWhsFg4JNPPiE4ONjYjspYeXl5HDx4kCFDhpiNVVhYyKxZs/j1118BSE5OplevXgQFBVnlmJmK9/PPP1v0mCUkJPD555+TlJTE+vXrcXFxISkpieHDh1v8eN0pyfXG5bo18hwk1yXX62Z3c9lYe/K+pKQkPvzwQwwGA56enixevJiuXbuybt06kpOTKS8vJzAwkLfeeuuObgUEmDNnjvH2PL1ez/Lly0lPT6e8vJxnn33WeNtebm4uUVFRFBcXo9FoWLRoEb6+vo2Kd+PGDRYuXMjx48fR6/U89dRTvP766yiKwuXLl4mMjOTChQsoisLs2bMZNGiQ2e1/8MEHxMXF1UjGTz75hO3bt9fab9aItX79evbv34/BYKB///5ERUWh0Wi4du0aMTExZGVlYTAYmDFjBlqttl59t2XLFrZs2YKjoyMeHh5ER0fTpUsXqx2z2uLde++9Fj9mlS5cuEBoaCg//PADgMn3eWPj3AnJ9YblurXzHCTXJdfrZncFiRBCCCGaH7s6ZSOEEEKI5kkKEiGEEEI0OSlIhBBCCNHkpCARQgghRJOTgkQIIYQQTU4Kkka6cOECffv2RavVotVqCQ0NZdy4cRw7dgyAjIwMXnnlFau3Y9euXQwePNh4S9nthg4dSkZGRoO2t2HDBuPcBfPnzyc9Pd3kuvn5+YSHh9f63MKFC1mzZk2DYt9uzZo1LFy4sMby8PBwtFotwcHBVfr/jTfeqHN7q1atYvHixXfcnqb2xhtvkJOTw9mzZ6vst1arZfjw4YwfP54LFy4A8Nlnn9GvX78q62i1Wg4cOIBer8fHx4fS0lL0ej1Tp041jmkgaie5LrluS/aY605N3YCWoHKwmEpffPEFc+fO5euvv8bPz4+4uDirtyExMZHXX3+93vfMN4S5pO7cuTPbtm2zeNy6VMarvDf+9v5vqZKTk3Fzc6Nnz56cPXuWNm3aVNlvVVWJiYlh9erVvPPOOwD079+ftWvX1tiWXq83/t/JyYlJkyaxaNEiVq1aZf0duYtJrkuu24K95roUJFZQUlJCp06dAPjuu+9YtGgRn3/+OXPmzKFt27acOnWKvLw8evTowbvvvkubNm2Ii4sjNTUVZ2dn7rnnHpYuXWqcpbLSb7/9xttvv012djaKohAQEMCsWbNYsWIFGRkZXLhwgeLiYiZOnGiybX5+fkydOpXDhw9TUFDAhAkTmDhxIuXl5cTGxpKeno67uzvu7u60a9cOgPHjx/Pcc8+RlZXF1atXiY6OBuCbb75hzZo1rFq1yjhgztWrV5k/fz7Z2dl4eHjg6OhIv379gFvf3lavXo2fn1+Nx+vWrSMtLY2bN29y48YNZs+ezfDhw+/4GKSlpbFu3Tr0ej2tW7dmzpw5PPTQQ1XWOXXqFLGxsZSUlKAoClOmTGHkyJGkp6ezZs0a7rnnHnJycmjTpg1/+9vf2LRpE7m5uYwYMYLZs2fXGWfVqlVkZmZSUFDA/fffz7Jly1i7dq1xXgkvLy8WLFhAp06d+PLLL/nXv/6Fg4MDTk5OzJ4929hnlVRV5f3336/1A6dSWVkZhYWFdO3atcH95e/vT0xMDP/9738bPZqvPZFcl1yXXLccKUgsoHJ8f4DS0lIKCwv55z//Weu6mZmZfPrppyiKQlhYGHv27OHxxx9n48aNHDlyBI1Gw8cff8xPP/1UY8jp2NhYOnbsaBwdb8aMGXz88cfMmzePkydP8txzz/HUU0/V2VadTsc999zDtm3byMzMJCIigoiICLZt20Zubi4pKSno9Xqef/5544dUpXHjxjFu3DjmzJmDRqNh165dxmmmK8XFxeHi4sKePXsoLi7mmWeeqZFw1f3yyy+kp6ezefNmXFxcSElJIS4u7o4/pHJycoiLi2PTpk106NCB7OxspkyZwt69e43rVPbf/PnzCQwMJC8vj7Fjxxqnq//pp5/YuXMnffr0YdKkSWzYsIGNGzdSWlpKQEAAU6ZMoaSkpM44eXl57N69G0dHRxISEjh9+jSfffYZTk5OxMfHExUVxbp161ixYgVxcXH4+flx4MABjh49WqPPsrOzqaiooGfPnsZl165dQ6vVUlFRQVFRER07diQoKIipU6ca1/nuu++qfJN+9NFHWbBgQa399uSTT5KamtrsPqSaE8n130muS65bmhQkFlD9Z9zvv/+eF198scbkWwABAQFoNBoAevfuzZUrV+jcuTN9+vThmWee4YknnuCJJ57A39+/xmu/+eYbtm7diqIoaDQawsPD2bhxY5U3ZX1Uzunh6+uLTqfj+vXrHDlyhJCQEDQaDRqNhtDQUE6dOlXldV5eXvTp04d9+/bh7+/PkSNHWLx4McXFxcZ1jhw5wrx581AUBTc3t3p90PzhD39g+fLlJCcnc/bsWY4fP96oaeIPHz5Mfn4+EyZMMC5TFIVz584ZH+fk5KCqqrEvPD09GT58OAcPHuSRRx7B29ubPn36GPf73nvvxdnZGXd3d1xdXSkpKTEb5+GHHzbOcrl//36ysrIYM2YMcGs2Wp1OB0BwcDAzZsxg8ODBPP7447zwwgs19un06dN4e3tXWXb7z7gHDhwwDtF8+wRypn7GrY23tzc//vhjvda1V5Lrkuu1xZFctwwpSKzg0UcfpXv37mRkZBhnP6xUOcET3HpDq6qKg4MDmzdvJiMjgyNHjrBkyRL69+9PZGRklddWn9q5oqKiyvnB+mrVqpUxPtQ+lbSp6aLHjRtHYmIiRUVFDB8+nDZt2lT5kKq+verbuf25yiQ9ceIEL730EhMnTmTgwIE89thjvP322w3er0oVFRUMGjSIlStXGpddunSpyoyTte3z7f1Z+YekUuVsoA2Jc/uHhcFgYPr06cZvmTdv3qS0tBSAN998k7CwMA4fPszOnTv56KOP2LlzZ5X5TxRFMTm1N9z6xjN+/HjeeOMNUlJSaNu2rcl1TTEYDI2ast4eSa5LroPkuqXIXTZWcObMGXJzc+nbt2+91s/OziYkJISePXsybdo0Jk6cWOMbC9yawTQ+Ph5VVdHpdOzYsYPHH3/cIm0OCAggMTGRmzdvcvPmTeMslNUNHz6cEydOsGPHjho/4VZuJyEhgYqKCq5cuVLlp1M3NzcyMzMB+PHHH43TVx89epQHHniASZMm8ec//5m9e/diMBjueF8GDBjAwYMHOXPmDAB79+5l1KhR3Lx507hOz549UVW1yk+uaWlpDerP+sSpNGjQIHbs2MHVq1eBW3cAzJ07l/LycoYMGYJer+evf/0rUVFR5OTk1Pjj0717d+PMsqa8+OKLuLi4mDyFYM758+fp0aPHHb3WXkmuS65XJ7l+5+QXEgu4/bwy3KqmFy5cSPfu3SkoKDD7+j59+jBixAjGjBmDq6srLi4uNb4xAURGRhIbG0toaCjl5eUEBAQwffp0i+xDeHg4586dIyQkhI4dO9KtW7da19NoNAQHB5Oens6DDz5Y4/mZM2eyYMECRowYgZubW5VzlH//+9+JiYlh+/bt+Pr6Gme2DAkJ4euvvyY4OBhnZ2f8/f25cuWKMaEbqk+fPsTExPDaa6+hqipOTk6sXbuW1q1bV9mPtWvXsnjxYt577z0qKip49dVXeeyxx+q87bGhcSpFRERQUFDAs88+C9z66XrJkiU4OzszZ84cXnvtNZycnFAUhaVLl+Ls7Fzl9X379kVRFHJzc43nvqvTaDRER0czbdo0xo4dW8/e+t3hw4fr/ZOvvZJc/53kuuS6pclsv0LcJRITE8nMzKz1D1hjpaenk5CQwLvvvmvxbQshGsZec11O2Qhxl9BqtRQUFJCTk2PR7er1euMdHEKIpmevuS6/kAghhBCiyckvJEIIIYRoclKQCCGEEKLJSUEihBBCiCYnBYkQQgghmpwUJEIIIYRoclKQCCGEEKLJ/T+vyduanCtchwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphing individual telomeres per individual per timepoint, personal telomere length dynamics as fxn of radiotherapy\n",
    "patient_ids = list(exploded_telos_all_patients_df['patient id'].unique())\n",
    "trp.histogram_plot_groups(x='individual telomeres', \n",
    "                          data=exploded_telos_all_patients_df.copy(), \n",
    "                          groupby='patient id', \n",
    "#                           iterable=[patient_ids[1]],\n",
    "                          iterable=patient_ids,\n",
    "                          n_bins=50,\n",
    "                          znorm=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true,
    "toc-nb-collapsed": true
   },
   "source": [
    "### Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Mixed Effect Modeling of telomere means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing spaces from column names for linear model (doesn't work w/ spaces)\n",
    "df = exploded_telos_all_patients_df.copy()\n",
    "df.rename({'individual telomeres': 'individual_telomeres',\n",
    "           'patient id': 'patient_id'}, axis=1, inplace=True)\n",
    "\n",
    "# encoding timepoint as numerical, log normalizing individual telos\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "df['timepoint_encoded'] = label_encoder.fit_transform(df['timepoint'])\n",
    "df['log_individ_telos'] = np.log(df['individual_telomeres'])\n",
    "\n",
    "# df = df.groupby(by=['patient_id', 'timepoint']).agg('mean').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPEATED MEASURES ANOVA for telomere length: 0.05898000440859586\n"
     ]
    }
   ],
   "source": [
    "df = all_patients_df[all_patients_df['patient id'] != 13].copy()\n",
    "\n",
    "trp.telos_scipy_anova_post_hoc_tests(df0=df, time_col='timepoint', target='telo means',\n",
    "                                     sig_test=stats.f_oneway, post_hoc='tukeyHSD', repeated_measures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept            4.887136e-108\n",
       "timepoint_encoded     0.000000e+00\n",
       "Group Var             6.910804e-03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# regressing time onto individual telos w/ random intercepts for patients\n",
    "\n",
    "model = smf.mixedlm(\"individual_telomeres ~ timepoint_encoded\", \n",
    "                    df, \n",
    "#                     re_formula='timepoint_encoded',\n",
    "                    groups=df['patient_id']) \n",
    "\n",
    "results = model.fit(method='powell')\n",
    "results.summary()\n",
    "results.pvalues[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Post-hoc Tukey HSD "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD,FWER=0.05</caption>\n",
       "<tr>\n",
       "      <th>group1</th>         <th>group2</th>     <th>meandiff</th>  <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>1 non irrad</td>  <td>2 irrad @ 4 Gy</td>   <td>8.297</td>  <td>7.7796</td>  <td>8.8144</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>1 non irrad</td>        <td>3 B</td>       <td>14.4682</td> <td>13.9509</td> <td>14.9856</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>1 non irrad</td>        <td>4 C</td>       <td>8.7314</td>  <td>8.2049</td>   <td>9.258</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>2 irrad @ 4 Gy</td>       <td>3 B</td>       <td>6.1713</td>  <td>5.6539</td>  <td>6.6886</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>2 irrad @ 4 Gy</td>       <td>4 C</td>       <td>0.4344</td>  <td>-0.0921</td>  <td>0.961</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "        <td>3 B</td>            <td>4 C</td>       <td>-5.7368</td> <td>-6.2633</td> <td>-5.2103</td>  <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj = pairwise_tukeyhsd(df['individual_telomeres'], df['timepoint'])\n",
    "obj.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_df = exploded_telos_all_patients_df.copy()\n",
    "# for col in test_df.columns:\n",
    "#     test_df.rename({col: col.replace(' ', '_')}, axis=1, inplace=True)\n",
    "# test_df = test_df[test_df['patient_id'] != 13].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphing all patients overall individual telomere length distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graphing individual telomeres per timepoint, overall cohort telomere length dynamics as fxn of radiotherapy\n",
    "df = exploded_telos_all_patients_df.copy()\n",
    "\n",
    "patient_ids = list(df['patient id'].unique())\n",
    "trp.histogram_plot_groups(x='individual telomeres', \n",
    "                          data=df, \n",
    "                          groupby='timepoint', \n",
    "                          n_bins=50,\n",
    "                          znorm=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true,
    "toc-nb-collapsed": true
   },
   "source": [
    "### Graphing cluster 1 patients' individual telomere length distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parsing overall individual telomere dist for cluster 1 \n",
    "df = exploded_telos_all_patients_df[exploded_telos_all_patients_df['patient id'].isin([7,10,12])].copy()\n",
    "\n",
    "patient_ids = list(df['patient id'].unique())\n",
    "trp.histogram_plot_groups(x='individual telomeres', \n",
    "                          data=df, \n",
    "                          groupby='timepoint', \n",
    "                          n_bins=50,\n",
    "                          znorm=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphing cluster 2 patients' individual telomere length distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parsing overall individual telomere dist for cluster 2\n",
    "df = exploded_telos_all_patients_df[~exploded_telos_all_patients_df['patient id'].isin([7,10,12])].copy()\n",
    "\n",
    "patient_ids = list(df['patient id'].unique())\n",
    "trp.histogram_plot_groups(x='individual telomeres', \n",
    "                          data=df, \n",
    "                          groupby='timepoint', \n",
    "                          n_bins=50,\n",
    "                          znorm=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kruskal wallis + Z-norm + KS-test w/ bonferroni correction for shape differences between timepoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x396 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = exploded_telos_all_patients_df[exploded_telos_all_patients_df['patient id'] != 13].copy()\n",
    "z_norm = trp.z_norm_individual_telos(exploded_telos_df=df)\n",
    "\n",
    "# z-norming distributions of individual telomeres per timepoint to enable statistical analysis between\n",
    "# shapes of overall cohort telomere length dynamics\n",
    "time_points = list(z_norm['timepoint'].unique())\n",
    "trp.histogram_plot_groups(x='z-norm_individual_telos', \n",
    "                          data=z_norm, \n",
    "                          groupby='timepoint', \n",
    "                          n_bins=50, \n",
    "                          znorm=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KRUSKAL-WALLIS TEST FOR SIG BETWEEN DISTRIBUTIONS: H STAT: 4417.2500567902225 P VALUE: 0.0\n"
     ]
    }
   ],
   "source": [
    "df = exploded_telos_all_patients_df[exploded_telos_all_patients_df['patient id'] != 13].copy()\n",
    "target='individual telomeres'\n",
    "\n",
    "g_1 = df[df['timepoint'] == '1 non irrad'][target]\n",
    "g_2 = df[df['timepoint'] == '2 irrad @ 4 Gy'][target]\n",
    "g_3 = df[df['timepoint'] == '3 B'][target]\n",
    "g_4 = df[df['timepoint'] == '4 C'][target]\n",
    "statistic, p_value = stats.kruskal(g_1, g_2, g_3, g_4)\n",
    "\n",
    "print(f'KRUSKAL-WALLIS TEST FOR SIG BETWEEN DISTRIBUTIONS: H STAT: {statistic} P VALUE: {p_value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kolmogorov-Smirnov | 1 non irrad vs 2 irrad @ 4 Gy P-VALUE: 0.07238870179647408 KS-STAT 0.007173913043478253\n",
      "Kolmogorov-Smirnov | 1 non irrad vs 3 B P-VALUE: 2.3151814439595848e-05 KS-STAT 0.013276397515527949\n",
      "Kolmogorov-Smirnov | 1 non irrad vs 4 C P-VALUE: 8.402756230273283e-16 KS-STAT 0.023431677018633537\n",
      "Kolmogorov-Smirnov | 2 irrad @ 4 Gy vs 3 B P-VALUE: 6.233890108982835e-06 KS-STAT 0.014021739130434793\n",
      "Kolmogorov-Smirnov | 2 irrad @ 4 Gy vs 4 C P-VALUE: 5.758230093941806e-19 KS-STAT 0.025729813664596257\n",
      "Kolmogorov-Smirnov | 3 B vs 4 C P-VALUE: 1.341631992664129e-27 KS-STAT 0.031149068322981366\n"
     ]
    }
   ],
   "source": [
    "# we see a diff. in shape between all timepoints, as well we see a sig diff between irrad @ 4 Gy & 4 C shapes,\n",
    "# though mean was the same (ANOVA)\n",
    "\n",
    "test = ks_2samp\n",
    "test_name = 'Kolmogorov-Smirnov'\n",
    "timept_pairs, row = trp.eval_make_test_comparisons(df=z_norm, target='z-norm_individual_telos',\n",
    "                                                   test=test, test_name=test_name,)\n",
    "\n",
    "KS_stats_df = pd.DataFrame(row, columns=[test_name, 'timepoint 1', 'timepoint 2', \n",
    "                                         'p value', 'KS statistic'])\n",
    "\n",
    "# iterate for between patients as well? would just be loop using above fxn, passing df per patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bonferroni pvalue correction to KS-tests\n",
    "bonferroni_corr = multipletests(KS_stats_df['p value'], method='bonferroni')\n",
    "\n",
    "# assigning elements of correction to objects\n",
    "true_false_reject_hyp, pval_corr = bonferroni_corr[0], bonferroni_corr[1]\n",
    "alpha_c_sidak, alpha_c_bonf = bonferroni_corr[2], bonferroni_corr[3]\n",
    "\n",
    "# df from vals\n",
    "bonf_corr_df = pd.DataFrame({'Reject null?': true_false_reject_hyp, 'Bonferroni corrected p values': pval_corr,\n",
    "                             'Sidak corrected alpha': alpha_c_sidak, 'Bonferroni corrected alpha': alpha_c_bonf})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "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>Kolmogorov-Smirnov</th>\n",
       "      <th>timepoint 1</th>\n",
       "      <th>timepoint 2</th>\n",
       "      <th>p value</th>\n",
       "      <th>KS statistic</th>\n",
       "      <th>Bonferroni corrected p values</th>\n",
       "      <th>Reject null?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kolmogorov-Smirnov</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>2 irrad @ 4 Gy</td>\n",
       "      <td>7.238870e-02</td>\n",
       "      <td>0.007174</td>\n",
       "      <td>4.343322e-01</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kolmogorov-Smirnov</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>3 B</td>\n",
       "      <td>2.315181e-05</td>\n",
       "      <td>0.013276</td>\n",
       "      <td>1.389109e-04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kolmogorov-Smirnov</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>4 C</td>\n",
       "      <td>8.402756e-16</td>\n",
       "      <td>0.023432</td>\n",
       "      <td>5.041654e-15</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kolmogorov-Smirnov</td>\n",
       "      <td>2 irrad @ 4 Gy</td>\n",
       "      <td>3 B</td>\n",
       "      <td>6.233890e-06</td>\n",
       "      <td>0.014022</td>\n",
       "      <td>3.740334e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolmogorov-Smirnov</td>\n",
       "      <td>2 irrad @ 4 Gy</td>\n",
       "      <td>4 C</td>\n",
       "      <td>5.758230e-19</td>\n",
       "      <td>0.025730</td>\n",
       "      <td>3.454938e-18</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kolmogorov-Smirnov</td>\n",
       "      <td>3 B</td>\n",
       "      <td>4 C</td>\n",
       "      <td>1.341632e-27</td>\n",
       "      <td>0.031149</td>\n",
       "      <td>8.049792e-27</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Kolmogorov-Smirnov     timepoint 1     timepoint 2       p value  \\\n",
       "0  Kolmogorov-Smirnov     1 non irrad  2 irrad @ 4 Gy  7.238870e-02   \n",
       "1  Kolmogorov-Smirnov     1 non irrad             3 B  2.315181e-05   \n",
       "2  Kolmogorov-Smirnov     1 non irrad             4 C  8.402756e-16   \n",
       "3  Kolmogorov-Smirnov  2 irrad @ 4 Gy             3 B  6.233890e-06   \n",
       "4  Kolmogorov-Smirnov  2 irrad @ 4 Gy             4 C  5.758230e-19   \n",
       "5  Kolmogorov-Smirnov             3 B             4 C  1.341632e-27   \n",
       "\n",
       "   KS statistic  Bonferroni corrected p values  Reject null?  \n",
       "0      0.007174                   4.343322e-01         False  \n",
       "1      0.013276                   1.389109e-04          True  \n",
       "2      0.023432                   5.041654e-15          True  \n",
       "3      0.014022                   3.740334e-05          True  \n",
       "4      0.025730                   3.454938e-18          True  \n",
       "5      0.031149                   8.049792e-27          True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading page (1/2)\n",
      "Rendering (2/2)                                                    \n",
      "Done                                                               \n"
     ]
    }
   ],
   "source": [
    "# merging bonferroni pvals w/ original KS stats & saving to file\n",
    "KS_stats_df[['Bonferroni corrected p values', 'Reject null?']] = bonf_corr_df[['Bonferroni corrected p values', \n",
    "                                                                               'Reject null?']]\n",
    "display(KS_stats_df)\n",
    "\n",
    "trp.df_to_png(df=KS_stats_df, \n",
    "              path='../graphs/paper figures/supp figs/KS test between overall shapes of individ telo dist.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering Short/Long Individual Telomeres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "melted_all_patients_df = pd.melt(\n",
    "    all_patients_df,\n",
    "    id_vars = [col for col in all_patients_df.columns if col != 'Q1' and col != 'Q2-3' and col != 'Q4'],\n",
    "    var_name='relative Q',\n",
    "    value_name='Q freq counts')\n",
    "\n",
    "melted_all_patients_df['Q freq counts'] = melted_all_patients_df['Q freq counts'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_qcol_descrip(row):\n",
    "    if row == 'Q1':\n",
    "        return 'short'\n",
    "    elif row == 'Q2-3':\n",
    "        return 'medium'\n",
    "    elif row == 'Q4':\n",
    "        return 'long'\n",
    "\n",
    "melted_all_patients_df['relative Q'] = melted_all_patients_df['relative Q'].apply(lambda row: change_qcol_descrip(row))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(rc= {'patch.edgecolor': 'black'})\n",
    "palette ={\"Q1\":\"#fdff38\",\"medium\":\"#d0fefe\",\"long\":\"#ffbacd\"}\n",
    "\n",
    "fig = plt.figure(figsize=(7,3.2))\n",
    "ax = sns.boxenplot(x='timepoint',y='Q1', data=all_patients_df,\n",
    "                  linewidth=1, color=\"#fdff38\", saturation=.95)\n",
    "ax = sns.swarmplot(x='timepoint',y='Q1', data=all_patients_df, size=8,\n",
    "                  linewidth=.5, edgecolor='black', color=\"#fdff38\")\n",
    "\n",
    "# ax.set_title(\"Mean Telomere Length (TeloFISH) per timepoint\") \n",
    "ax.set_ylabel('Number of short telomeres', fontsize=14)\n",
    "ax.set_xlabel('', fontsize=14)\n",
    "ax.tick_params(labelsize=14)\n",
    "plt.savefig('../graphs/paper figures/main figs/all patient Number of short telomeres teloFISH.png', \n",
    "            dpi=400, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>patient id</th>\n",
       "      <th>timepoint</th>\n",
       "      <th>telo data</th>\n",
       "      <th>telo means</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q2-3</th>\n",
       "      <th>Q4</th>\n",
       "      <th>Q1_sqrt</th>\n",
       "      <th>Q4_sqrt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>[70.74301669292741, 91.86033510872664, 59.1284...</td>\n",
       "      <td>84.796483</td>\n",
       "      <td>1195</td>\n",
       "      <td>2225</td>\n",
       "      <td>1180</td>\n",
       "      <td>34.568772</td>\n",
       "      <td>34.351128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2 irrad @ 4 Gy</td>\n",
       "      <td>[91.86033510872664, 119.31165592077517, 99.251...</td>\n",
       "      <td>90.975826</td>\n",
       "      <td>724</td>\n",
       "      <td>2350</td>\n",
       "      <td>1526</td>\n",
       "      <td>26.907248</td>\n",
       "      <td>39.064050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3 B</td>\n",
       "      <td>[191.10982054566642, 141.48603338585482, 114.0...</td>\n",
       "      <td>116.779989</td>\n",
       "      <td>231</td>\n",
       "      <td>1457</td>\n",
       "      <td>2912</td>\n",
       "      <td>15.198684</td>\n",
       "      <td>53.962950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4 C</td>\n",
       "      <td>[86.58100550477684, 139.3729078012595, 99.2504...</td>\n",
       "      <td>99.346299</td>\n",
       "      <td>372</td>\n",
       "      <td>2241</td>\n",
       "      <td>1987</td>\n",
       "      <td>19.287302</td>\n",
       "      <td>44.575778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>[149.47242207268806, 104.23734697174298, 171.1...</td>\n",
       "      <td>119.773675</td>\n",
       "      <td>1166</td>\n",
       "      <td>2270</td>\n",
       "      <td>1164</td>\n",
       "      <td>34.146742</td>\n",
       "      <td>34.117444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient id       timepoint  \\\n",
       "0           1     1 non irrad   \n",
       "1           1  2 irrad @ 4 Gy   \n",
       "2           1             3 B   \n",
       "3           1             4 C   \n",
       "4           2     1 non irrad   \n",
       "\n",
       "                                           telo data  telo means    Q1  Q2-3  \\\n",
       "0  [70.74301669292741, 91.86033510872664, 59.1284...   84.796483  1195  2225   \n",
       "1  [91.86033510872664, 119.31165592077517, 99.251...   90.975826   724  2350   \n",
       "2  [191.10982054566642, 141.48603338585482, 114.0...  116.779989   231  1457   \n",
       "3  [86.58100550477684, 139.3729078012595, 99.2504...   99.346299   372  2241   \n",
       "4  [149.47242207268806, 104.23734697174298, 171.1...  119.773675  1166  2270   \n",
       "\n",
       "     Q4    Q1_sqrt    Q4_sqrt  \n",
       "0  1180  34.568772  34.351128  \n",
       "1  1526  26.907248  39.064050  \n",
       "2  2912  15.198684  53.962950  \n",
       "3  1987  19.287302  44.575778  \n",
       "4  1164  34.146742  34.117444  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_df_cts = all_patients_df.copy()\n",
    "stats_df_cts['Q1_sqrt'] = np.sqrt(stats_df_cts['Q1'])\n",
    "stats_df_cts['Q4_sqrt'] = np.sqrt(stats_df_cts['Q4'])\n",
    "stats_df_cts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPEATED MEASURES ANOVA for telomere length: 0.041484514376121384\n",
      "      Multiple Comparison of Means - Tukey HSD,FWER=0.05      \n",
      "==============================================================\n",
      "    group1         group2     meandiff  lower    upper  reject\n",
      "--------------------------------------------------------------\n",
      " 1 non irrad   2 irrad @ 4 Gy -6.4897  -16.0887  3.1094 False \n",
      " 1 non irrad        3 B        -8.561   -18.16   1.038  False \n",
      " 1 non irrad        4 C       -6.0259  -15.6249  3.5731 False \n",
      "2 irrad @ 4 Gy      3 B       -2.0713  -11.6704  7.5277 False \n",
      "2 irrad @ 4 Gy      4 C        0.4638  -9.1353  10.0628 False \n",
      "     3 B            4 C        2.5351  -7.0639  12.1341 False \n",
      "--------------------------------------------------------------\n",
      "pvalues: [0.2877665730794384, 0.0963089267871261, 0.3521526580484169, 0.9, 0.9, 0.8917208985830509]\n"
     ]
    }
   ],
   "source": [
    "df = stats_df_cts[stats_df_cts['patient id'] != 13].copy()\n",
    "# df = all_patients_df[all_patients_df['patient id'] != 13].copy()\n",
    "\n",
    "trp.telos_scipy_anova_post_hoc_tests(df0=df, time_col='timepoint', target='Q1_sqrt',\n",
    "                                     sig_test=stats.f_oneway, post_hoc='tukeyHSD', repeated_measures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPEATED MEASURES ANOVA for telomere length: 0.054153500056322156\n"
     ]
    }
   ],
   "source": [
    "df = stats_df_cts[stats_df_cts['patient id'] != 13].copy()\n",
    "# df = all_patients_df[all_patients_df['patient id'] != 13].copy()\n",
    "\n",
    "trp.telos_scipy_anova_post_hoc_tests(df0=df, time_col='timepoint', target='Q4_sqrt',\n",
    "                                     sig_test=stats.f_oneway, post_hoc='tukeyHSD', repeated_measures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ax = sns.set(font_scale=1)\n",
    "fig = plt.figure(figsize=(7,3.2))\n",
    "sns.set_style(style=\"darkgrid\",rc= {'patch.edgecolor': 'black'})\n",
    "# palette ={\"Q1\":\"#fdff38\",\"Q2-3\":\"#d0fefe\",\"Q4\":\"#ffbacd\"}\n",
    "palette ={\"short\":\"#fdff38\",\"medium\":\"#d0fefe\",\"long\":\"#ffbacd\"}\n",
    "\n",
    "\n",
    "ax = sns.boxenplot(x='timepoint', y='Q freq counts', hue='relative Q', data=melted_all_patients_df, palette=palette,\n",
    "             linewidth=2, saturation=5, color=\"black\", )\n",
    "ax = sns.stripplot(x='timepoint', y='Q freq counts', hue='relative Q', data=melted_all_patients_df, palette=palette,\n",
    "             linewidth=1, color=\"black\", dodge=True, )\n",
    "\n",
    "ax=fig.gca()\n",
    "# ax.set_title('Changes in Distribution Individual Telos Relative to Pre-Rad Therapy Time point', fontsize=14)\n",
    "ax.set_xlabel('', fontsize=14)\n",
    "ax.set_ylabel('Individual Telomere Counts', fontsize=14)\n",
    "ax.tick_params(labelsize=14)\n",
    "\n",
    "ax.get_legend().remove()\n",
    "plt.ylim(0,4300)\n",
    "\n",
    "\n",
    "# handles, labels = ax.get_legend_handles_labels()\n",
    "plt.legend(handles[0:3], labels[0:3], bbox_to_anchor=(.99,0.75), loc=\"upper left\", \n",
    "#            borderaxespad=0)\n",
    "\n",
    "plt.savefig('../graphs/paper figures/supp figs/counting # short long telos.png', \n",
    "            dpi=400,\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# nb_regression = all_patients_df.copy()\n",
    "# nb_regression['constant'] = 1\n",
    "# label_encoder = preprocessing.LabelEncoder()\n",
    "# nb_regression['encoded_timepoint'] = label_encoder.fit_transform(nb_regression['timepoint'])\n",
    "\n",
    "# results = sm.GLM(nb_regression['Q4'], nb_regression[['encoded_timepoint', 'constant']], \n",
    "#           family=sm.families.NegativeBinomial()).fit()\n",
    "# results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression short telo counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>timepoint</th>\n",
       "      <th>1 non irrad</th>\n",
       "      <th>2 irrad @ 4 Gy</th>\n",
       "      <th>3 B</th>\n",
       "      <th>4 C</th>\n",
       "      <th>constant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient id</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>1</th>\n",
       "      <td>1195.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1166.0</td>\n",
       "      <td>481.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>1182.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1190.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1159.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1188.0</td>\n",
       "      <td>929.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "timepoint   1 non irrad  2 irrad @ 4 Gy    3 B     4 C  constant\n",
       "patient id                                                      \n",
       "1                1195.0           724.0  231.0   372.0         1\n",
       "2                1166.0           481.0  292.0  1182.0         1\n",
       "3                1190.0          1006.0  407.0   503.0         1\n",
       "5                1159.0           319.0  234.0   521.0         1\n",
       "6                1188.0           929.0  587.0   124.0         1"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q_lr_df = all_patients_df.pivot(index='patient id', columns='timepoint', values='Q1')\n",
    "q_lr_df.drop(13, inplace=True)\n",
    "q_lr_df['constant'] = 1\n",
    "\n",
    "q_lr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from statsmodels.genmod.generalized_linear_model import GLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = sm.GLM(q_lr_df['4 C'], q_lr_df[['1 non irrad', '2 irrad @ 4 Gy', 'constant']], \n",
    "          family=sm.families.NegativeBinomial()).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>4 C</td>       <th>  No. Observations:  </th>  <td>    14</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>    11</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>   <td>NegativeBinomial</td> <th>  Df Model:          </th>  <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>log</td>       <th>  Scale:             </th> <td>  1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -106.83</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>           <td>Thu, 20 Feb 2020</td> <th>  Deviance:          </th> <td>  3.8896</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>               <td>15:40:07</td>     <th>  Pearson chi2:      </th>  <td>  3.13</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1 non irrad</th>    <td>   -0.0359</td> <td>    0.016</td> <td>   -2.296</td> <td> 0.022</td> <td>   -0.067</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2 irrad @ 4 Gy</th> <td>    0.0011</td> <td>    0.001</td> <td>    0.881</td> <td> 0.378</td> <td>   -0.001</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>constant</th>       <td>   47.9832</td> <td>   18.544</td> <td>    2.587</td> <td> 0.010</td> <td>   11.637</td> <td>   84.330</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:                    4 C   No. Observations:                   14\n",
       "Model:                            GLM   Df Residuals:                       11\n",
       "Model Family:        NegativeBinomial   Df Model:                            2\n",
       "Link Function:                    log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -106.83\n",
       "Date:                Thu, 20 Feb 2020   Deviance:                       3.8896\n",
       "Time:                        15:40:07   Pearson chi2:                     3.13\n",
       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "1 non irrad       -0.0359      0.016     -2.296      0.022      -0.067      -0.005\n",
       "2 irrad @ 4 Gy     0.0011      0.001      0.881      0.378      -0.001       0.003\n",
       "constant          47.9832     18.544      2.587      0.010      11.637      84.330\n",
       "==================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression for ['1 non irrad'] vs. 4 C:\n",
      "R2 is 0.4328\n",
      "Linear regression for ['1 non irrad', '2 irrad @ 4 Gy'] vs. 4 C:\n",
      "R2 is 0.5536\n"
     ]
    }
   ],
   "source": [
    "x_names = [['1 non irrad'], ['1 non irrad', '2 irrad @ 4 Gy'],] \n",
    "y_name = '4 C'\n",
    "\n",
    "numst_df_list = []\n",
    "\n",
    "for x_name in x_names:\n",
    "    name = x_name[0]\n",
    "    \n",
    "    x = q_lr_df[x_name].values.reshape(-1, len(x_name))\n",
    "    y = q_lr_df['4 C'].values.reshape(-1, 1)\n",
    "    regression = LinearRegression().fit(x, y)\n",
    "    print(f\"Linear regression for {x_name} vs. {y_name}:\\nR2 is {regression.score(x, y):.4f}\")\n",
    "    \n",
    "    numst_df_list.append([', '.join(x_name), '4 C', round(regression.score(x, y), 4)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>Model features</th>\n",
       "      <th>Target</th>\n",
       "      <th>Linear regression R2 score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>4 C</td>\n",
       "      <td>0.4328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 non irrad, 2 irrad @ 4 Gy</td>\n",
       "      <td>4 C</td>\n",
       "      <td>0.5536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model features Target  Linear regression R2 score\n",
       "0                  1 non irrad    4 C                      0.4328\n",
       "1  1 non irrad, 2 irrad @ 4 Gy    4 C                      0.5536"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qLM_metrics = pd.DataFrame(numst_df_list, columns=['Model features', 'Target', 'Linear regression R2 score'])\n",
    "qLM_metrics['Model features'] = qLM_metrics['Model features'].astype('str')\n",
    "qLM_metrics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(rc= {'patch.edgecolor': 'black'})\n",
    "sns.set_color_codes()\n",
    "\n",
    "fig = plt.figure(figsize=(6,4))\n",
    "ax = sns.lineplot(x='Model features', y='Linear regression R2 score', data=qLM_metrics, \n",
    "                  linewidth=6, sort=False, hue='Target', palette={'4 C':'#fdff38'}, legend=False, marker='o', \n",
    "                  **{'markersize':14, \n",
    "                     'mec':'black', \n",
    "                     'mew':1})\n",
    "\n",
    "ax.set_ylabel('Linear regression R2 score', fontsize=14)\n",
    "ax.set_xlabel('Model features', fontsize=14,)\n",
    "# ax.tick_params(labelsize=14,)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylim(0,1)\n",
    "plt.title('Predicting post-therapy numbers of short telomeres', fontsize=14)\n",
    "\n",
    "plt.savefig('../graphs/paper figures/main figs/linear regression metrics numbers short telos teloFISH.png', \n",
    "            dpi=400, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression long telo counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>timepoint</th>\n",
       "      <th>1 non irrad</th>\n",
       "      <th>2 irrad @ 4 Gy</th>\n",
       "      <th>3 B</th>\n",
       "      <th>4 C</th>\n",
       "      <th>constant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>patient id</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>1</th>\n",
       "      <td>1180.0</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>2912.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1164.0</td>\n",
       "      <td>1537.0</td>\n",
       "      <td>2527.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1164.0</td>\n",
       "      <td>1350.0</td>\n",
       "      <td>2074.0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1166.0</td>\n",
       "      <td>2831.0</td>\n",
       "      <td>2893.0</td>\n",
       "      <td>2026.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1174.0</td>\n",
       "      <td>1515.0</td>\n",
       "      <td>2184.0</td>\n",
       "      <td>3806.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "timepoint   1 non irrad  2 irrad @ 4 Gy     3 B     4 C  constant\n",
       "patient id                                                       \n",
       "1                1180.0          1526.0  2912.0  1987.0         1\n",
       "2                1164.0          1537.0  2527.0   602.0         1\n",
       "3                1164.0          1350.0  2074.0  1808.0         1\n",
       "5                1166.0          2831.0  2893.0  2026.0         1\n",
       "6                1174.0          1515.0  2184.0  3806.0         1"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_lr_df = all_patients_df.pivot(index='patient id', columns='timepoint', values='Q4')\n",
    "long_lr_df.drop(13, inplace=True)\n",
    "long_lr_df['constant'] = 1\n",
    "\n",
    "long_lr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results = sm.GLM(long_lr_df['4 C'], long_lr_df[['1 non irrad', '2 irrad @ 4 Gy', 'constant']], \n",
    "#           family=sm.families.NegativeBinomial()).fit()\n",
    "\n",
    "# results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear regression for ['1 non irrad'] vs. 4 C:\n",
      "R2 is 0.0459\n",
      "Linear regression for ['1 non irrad', '2 irrad @ 4 Gy'] vs. 4 C:\n",
      "R2 is 0.2082\n"
     ]
    }
   ],
   "source": [
    "x_names = [['1 non irrad'], ['1 non irrad', '2 irrad @ 4 Gy'],] \n",
    "y_name = '4 C'\n",
    "\n",
    "long_df_list = []\n",
    "\n",
    "for x_name in x_names:\n",
    "    name = x_name[0]\n",
    "    \n",
    "    x = long_lr_df[x_name].values.reshape(-1, len(x_name))\n",
    "    y = long_lr_df['4 C'].values.reshape(-1, 1)\n",
    "    regression = LinearRegression().fit(x, y)\n",
    "    print(f\"Linear regression for {x_name} vs. {y_name}:\\nR2 is {regression.score(x, y):.4f}\")\n",
    "    \n",
    "    long_df_list.append([', '.join(x_name), '4 C', round(regression.score(x, y), 4)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model features</th>\n",
       "      <th>Target</th>\n",
       "      <th>Linear regression R2 score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>4 C</td>\n",
       "      <td>0.0459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 non irrad, 2 irrad @ 4 Gy</td>\n",
       "      <td>4 C</td>\n",
       "      <td>0.2082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model features Target  Linear regression R2 score\n",
       "0                  1 non irrad    4 C                      0.0459\n",
       "1  1 non irrad, 2 irrad @ 4 Gy    4 C                      0.2082"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "long_LM_metrics = pd.DataFrame(long_df_list, columns=['Model features', 'Target', 'Linear regression R2 score'])\n",
    "long_LM_metrics['Model features'] = long_LM_metrics['Model features'].astype('str')\n",
    "long_LM_metrics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(rc= {'patch.edgecolor': 'black'})\n",
    "sns.set_color_codes()\n",
    "\n",
    "fig = plt.figure(figsize=(6,4))\n",
    "ax = sns.lineplot(x='Model features', y='Linear regression R2 score', data=long_LM_metrics, \n",
    "                  linewidth=6, sort=False, hue='Target', palette={'4 C':'#ffbacd'}, legend=False, marker='o', \n",
    "                  **{'markersize':14, \n",
    "                     'mec':'black', \n",
    "                     'mew':1})\n",
    "\n",
    "ax.set_ylabel('Linear regression R2 score', fontsize=14)\n",
    "ax.set_xlabel('Model features', fontsize=14,)\n",
    "# ax.tick_params(labelsize=14,)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.ylim(-.05,1)\n",
    "plt.title('Predicting post-therapy numbers of long telomeres', fontsize=14)\n",
    "\n",
    "plt.savefig('../graphs/paper figures/main figs/linear regression metrics numbers long telos teloFISH.png', \n",
    "            dpi=400, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Chromosome Aberration Data from dGH\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_patients_df[['patient id', 'timepoint', 'telo means']].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1770, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient id</th>\n",
       "      <th>timepoint</th>\n",
       "      <th>cell number</th>\n",
       "      <th>total inversions</th>\n",
       "      <th>telo means</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.796483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>2</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>84.796483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.796483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.796483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient id    timepoint  cell number  total inversions  telo means\n",
       "0           1  1 non irrad            1          0.000000   84.796483\n",
       "1           1  1 non irrad            2          0.333333   84.796483\n",
       "2           1  1 non irrad            3          0.000000   84.796483\n",
       "3           1  1 non irrad            4          0.000000   84.796483"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# alteryx testing\n",
    "\n",
    "inter = pd.read_csv('../data/compiled patient data csv files/all_chr_aberr_df.csv')\n",
    "inter = inter[['patient id', 'timepoint', 'cell number', '# inversions', '# terminal inversions']].copy()\n",
    "inter['total inversions'] = inter['# inversions'] + inter['# terminal inversions']\n",
    "\n",
    "inter = inter.groupby(['patient id', 'timepoint', 'cell number']).agg('mean').reset_index()\n",
    "inter.drop(['# inversions', '# terminal inversions'], axis=1, inplace=True)\n",
    "merge = inter.merge(all_patients_df[['patient id', 'timepoint', 'telo means']], on=['patient id', 'timepoint'], how='inner')\n",
    "\n",
    "print(merge.shape)\n",
    "merge.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cleaned_chr_df.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_chr_aberr_df = pd.read_csv('../data/compiled patient data csv files/all_chr_aberr_df.csv')\n",
    "general_cleaner = Pipeline([('cleaner', trp.general_chr_aberr_cleaner(drop_what_timepoint=False, adjust_clonality=True))])\n",
    "cleaned_chr_df = general_cleaner.fit_transform(all_chr_aberr_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_chr_df['# inversions'] = cleaned_chr_df['# inversions'] + cleaned_chr_df['# terminal inversions']\n",
    "cleaned_chr_df.drop(['# terminal inversions'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "melt_aberrations = pd.melt(cleaned_chr_df, id_vars=['patient id', 'timepoint'],\n",
    "                           var_name='aberration type', value_name='count per cell')\n",
    "\n",
    "melt_aberrations['count per cell'] = melt_aberrations['count per cell'].astype('int64')\n",
    "melt_aberrations['aberration type'] = melt_aberrations['aberration type'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['# inversions', '# sister chromatid exchanges', '# dicentrics',\n",
       "       '# excess chr fragments', '# sat associations', '# terminal SCEs',\n",
       "       '# translocations'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melt_aberrations['aberration type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>patient id</th>\n",
       "      <th>timepoint</th>\n",
       "      <th>aberration type</th>\n",
       "      <th>count per cell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient id    timepoint aberration type  count per cell\n",
       "0           1  1 non irrad    # inversions               0\n",
       "1           1  1 non irrad    # inversions               1\n",
       "2           1  1 non irrad    # inversions               0\n",
       "3           1  1 non irrad    # inversions               0\n",
       "4           1  1 non irrad    # inversions               0\n",
       "5           1  1 non irrad    # inversions               0\n",
       "6           1  1 non irrad    # inversions               0\n",
       "7           1  1 non irrad    # inversions               0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "melt_aberrations.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Chromosome Rearrangements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# melt_aberrations_chr_only = melt_aberrations[~melt_aberrations['aberration type'].isin(['# sub-telo SCEs', 'tricentrics',\n",
    "#                                                                                         '# dicentrics', '# translocations',\n",
    "#                                                                                         '# sat associations', 'cell number'])].copy()\n",
    "\n",
    "# ax = sns.set(font_scale=2)\n",
    "# ax = sns.catplot(y='aberration type', x='count per cell', hue='chromosome', \n",
    "#                  col='timepoint', col_wrap=2, \n",
    "#                  data=melt_aberrations_chr_only, kind='bar', height=7, aspect=1.5, orient=\"h\",)\n",
    "\n",
    "# ax.set_ylabels('')\n",
    "# ax.set_xlabels('average count per cell')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = melt_aberrations[melt_aberrations['aberration type'] != '# terminal SCEs'].copy()\n",
    "df.rename({'timepoint':'Time point'}, axis=1, inplace=True)\n",
    "\n",
    "ax = sns.set_style(style=\"darkgrid\",rc= {'patch.edgecolor': 'black'})\n",
    "# ax = sns.set(font_scale=1.35)\n",
    "ax = sns.catplot(y='aberration type', x='count per cell',\n",
    "                 col='Time point', col_wrap=2,  \n",
    "                 data=df, \n",
    "                 kind='bar', height=3., aspect=1.5, orient=\"h\",)\n",
    "\n",
    "fontsize=14\n",
    "\n",
    "ax.set_ylabels('', fontsize=fontsize)\n",
    "ax.set_xlabels('', fontsize=fontsize)\n",
    "plt.xlabel('Average aberration frequency per cell', horizontalalignment='right', x=0.45,\n",
    "           fontsize=14)\n",
    "\n",
    "axes = ax.axes.flatten()\n",
    "axes[0].set_title(\"1 non irrad\", fontsize=fontsize)\n",
    "axes[1].set_title(\"2 irrad @ 4 Gy\", fontsize=fontsize)\n",
    "axes[2].set_title(\"3 B\", fontsize=fontsize)\n",
    "axes[3].set_title(\"4 C\", fontsize=fontsize)\n",
    "\n",
    "ax.savefig('../graphs/paper figures/main figs/all patients rearrangements.png', dpi=400,\n",
    "          bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>patient id</th>\n",
       "      <th>timepoint</th>\n",
       "      <th>aberration type</th>\n",
       "      <th>count per cell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td># inversions</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient id    timepoint aberration type  count per cell\n",
       "0           1  1 non irrad    # inversions               0\n",
       "1           1  1 non irrad    # inversions               1\n",
       "2           1  1 non irrad    # inversions               0\n",
       "3           1  1 non irrad    # inversions               0\n",
       "4           1  1 non irrad    # inversions               0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aberr = melt_aberrations[melt_aberrations['aberration type'] == '# inversions']\n",
    "aberr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD,FWER=0.05</caption>\n",
       "<tr>\n",
       "      <th>group1</th>         <th>group2</th>     <th>meandiff</th>  <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>1 non irrad</td>  <td>2 irrad @ 4 Gy</td>  <td>0.3595</td>  <td>0.2046</td>  <td>0.5144</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>1 non irrad</td>        <td>3 B</td>       <td>0.6262</td>  <td>0.4713</td>  <td>0.7811</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>1 non irrad</td>        <td>4 C</td>       <td>0.3405</td>  <td>0.1856</td>  <td>0.4954</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>2 irrad @ 4 Gy</td>       <td>3 B</td>       <td>0.2667</td>  <td>0.1117</td>  <td>0.4216</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>2 irrad @ 4 Gy</td>       <td>4 C</td>       <td>-0.019</td>  <td>-0.174</td>  <td>0.1359</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "        <td>3 B</td>            <td>4 C</td>       <td>-0.2857</td> <td>-0.4406</td> <td>-0.1308</td>  <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = pairwise_tukeyhsd(aberr['count per cell'], aberr['timepoint'])\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.001, 0.001, 0.001, 0.001, 0.9, 0.001]\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.libqsturng import psturng\n",
    "\n",
    "print(list(psturng(np.abs(res.meandiffs / res.std_pairs), len(res.groupsunique), res.df_total)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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euXIFnTt3lip7/vy5zC8MLGvsIRIRURmZnENs0aIFJk2ahOXLl0vKBg0aBG9vb65RJCKiekFQQgwKCgIgfWmnyMhIFBcXIzQ0VD6RERERKZCgIVM7OztERUXB1NRUqjwjIwMjR47EhQsX5Bbg2+CQKRERlZHJkKm2tjbu3r1brvzBgwdQU1N78+iIiIjqCEGzTIcOHQp/f39Mnz4dHTt2BADcuHEDERER8PDwkGuAREREiiAoIU6bNg2lpaVYtmyZZBJNkyZNMHbsWEyePFmuARIRESlCjbdue/z4MdTV1aGjU/k4bF3Bc4hERFRGJgvzgdcTaK5du4bi4mL8N4f+e/YpERHRu0hQQly3bh3Cw8PRqFEjaGtrS92npKTEhEhERO88QUOmPXv2hJeXFyZNmqSImGSGQ6ZERFRGJssuioqKMGDAAJkFRUREVNcISoiffvopIiMjy507JCIiqi8EnUN88uQJjh07hl9//RWGhoblFuNHRkbKJTgiIiJFEZQQzczM4OPjI+9YiIiIak2N1yEWFxdDRUUFSkpK8opJZjiphoiIyshkUg3weljU2dkZNjY2uHv3LgICAhAeHo6SkhKZBEpERFSbBCXEzZs3Y8OGDfjyyy+hqvp6lLVXr16Ijo7GDz/8INcAiYiIFEFQQty1axdCQkLg7u4uGSr9+OOPsWTJEsTGxso1QCIiIkUQlBCzs7PRunXrcuXNmzfH8+fPZR0TERGRwglKiDY2NoiOjpYqKykpwYYNG9C5c2e5BEZEBACXL1/E/Pn+uHz5Ym2HQvWcoIQ4b948xMbGwt3dHSKRCEFBQXByckJCQgLmzp1b44OKRCK4ubnh9OnTlda5d+8eJk6cCBsbGwwaNAi///57jY9DRO++vXt3IjX1Ovbu3VnboVA9J2gdYtu2bXH06FHs378fmZmZEIvFcHZ2xuDBg8tt9l2dwsJC+Pn54datW5XWKS0txRdffAFzc3NERUUhLi4Ovr6+OHDgAFq1alWj4xHRu62g4JXUdyJ5EXz5Jw0NDQwbNuytDpaeng4/P79qt4A7e/Ys/vrrL0RGRkJHRwdt2rTB6dOnERUVha+//vqtYiAiIqqIoITYt2/fKhfix8fHCzrY+fPn0b17d3z99dewsbGptF5KSgo6dOggdRFiOzs7XLzIcwhERCQfghLijBkzpG4XFxfjzp072LdvX7n7qjJ69GhB9XJzc9G0aVOpMn19fWRnZws+FhERUU0ISogeHh4Vltva2mLdunUYOnSoTIMqKCgot4G4uro6ioqKatROVVv0ENG7QUVFSfLdwEC3lqOh+kzwOcSKmJmZ4fr167KKRUJDQwN5eXlSZSKRCJqamjVqh3uZEr37xOJSyffc3Be1HA29y6rby1RQQjxz5ky5spcvX2Lnzp1o27btm0dXiWbNmiEtLU2q7OHDhzAwMJD5sYiIiACBCXHChAnlytTU1NCpUyeEhobKPChra2v89NNPyM/Ph5aWFgDg0qVLVU7EISIiehuCEuJ/e2vy8PjxY2hoaEBbWxvdunVDy5YtMXv2bEybNg0nT55ESkoKvvvuO7nHQfS+6mxjgex/7td2GOX06NEDWlpayMxMR9OmDWs7nHKat2yBK8l/1nYYJAOCEuKdO3cEN/imC+c/++wzeHh4YNq0aVBRUcHq1avh7++PIUOGwNjYGCtXroSRkdEbtU1E1cv+5z7GbfKt7TDKuf/LTRS/EKFhs8Z1Mr6tE3+s7RBIRgQlRGdnZ8k6xLJF9f9dl1haWgolJSWkpqYKOvCff0r/RxUXFyd128TEBDt27BDUFhER0dsSlBB/+OEHRERE4Ntvv4WtrS3U1dVx/fp1fPfdd3Bzc8PHH38s7ziJiIjkSlBCDAsLQ3h4OGxtbSVlXbt2xYIFC+Dj44NJkybJLUAiIiJFEHS1i7y8PIjF4nLlz549q/FieSIiorpIUA/x008/xcyZM+Hr6wtLS0uUlpbi6tWriIiIELwdGxERUV0mKCHOmjUL6urqWLx4MZ4+fQoAaNGiBby9vTFmzBi5BkhERKQIghKimpoaZs6ciW+//RZPnjyBpqamZME8EZE8KakpS30nkhfBv2FpaWkICAjA9OnT8eLFC+zYsQMJCQnyjI2ICI06N4NGM2006tystkOhek5QQkxISMDIkSMhFouRkpICkUiEx48fY+rUqfj111/lHSMRvccaGOmiqbMpGhjxShckX4IS4vLlyzFnzhwsWrQIKioqAABfX1/4+/tj9erVcg2QiIhIEQQlxMzMTDg4OJQr79WrF+7duyfzoIiIiBRNUEI0MjJCcnJyufK4uLg33ruUiIioLhE0y3TGjBmYOXMmrl69CrFYjOjoaNy5cwdHjx7F999/L+8YiYiI5E5QD9HJyQk7d+7E06dP0bZtW8THx6OkpASRkZEYNGiQvGMkqjWXL1/E/Pn+uHz5Ym2HQkRyJqiH6O/vjylTpmDJkiXyjoeoTtm7dyf++isTr14VoEuXrrUdDhHJkaAe4vHjx6GszEWx9P4pKHgl9Z3offU+jJYI6iGOHz8ewcHBGDt2LAwNDaGhoSF1PyfWEBHVb+/DaImghPjjj6+vCP3HH38AgNTFgmtyUWAiIno3vQ+jJZUmxISEBHTv3h3q6ur47bffFBkTERGRwlV6YtDX1xePHj0CAIwdOxba2towNDSs8IuIiOhdV2kPsUmTJggICICVlRXu3buHtWvXokGDBhXWnT59utwCJCIiUoRKE+LSpUuxYcMGXL58GUpKSkhOToaamlq5emXnE4neRhcbC9z9535th1FOjx49oKWlhczMdDRt2rC2wynHqGULXE7+s7bDIKoXKk2Itra2WLVqFQCgf//+WLNmDfT09BQWGL1f7v5zHxfDJtV2GOUsSbiDh/nFMP6gIVbWwfi6ztpY2yEQ1RuCZpnGxcXJOw4iIgJg29kC97I5WlJThs1bIOnK242WCEqIRESkGPey7+Og5/jaDqOcTdn/4Im4GIa6DetkfK7bt7x1G9x+hoiICEyIREREAGqQEAsLC7F//378+OOPePr0Kc6ePYvc3Fx5xkZERKQwgs4h3r59G+PHj4eKigqys7Ph4eGBXbt24cyZM9i4cSOsrKzkHSdRrdBQVZb6TkT1l6C/8tDQUHz00Uc4fvy4ZC1ieHg4Bg4ciIULF8o1QKLaNKCNHsz0NDGgDZccEdV3ghJiUlISxowZI7UIX1lZGV5eXtzYm+q19gZa8OnWAu0NtGo7FCKSM0EJUUtLq8LzhTdv3kTDhnVvPQoREcmWmrKS1Pf6SFBCHDlyJAIDA3HixAkAQEZGBvbs2YPAwEB89tlncg2QiIhqX6+GjdFKXQO9Gjau7VDkRtCkmi+++AK6uroIDQ1FQUEBfHx8oK+vjwkTJmDSJOHbWYlEIixYsABHjhyBuro6xo8fj8mTJ1dYd9KkSUhMTJQqW7VqFZycnAQfj4iIZMNMswHMNCu+wEN9IXinGk9PT3h6eiI/Px9isRi6uro1PtiSJUuQnJyMzZs3Izs7GzNnzkTLli3h6uparu6tW7ewfPly2NvbS8oaNWpU42MSEREJISghxsbGVnqfuro6DAwMYG1tDXV19Urr5efnY8+ePVi7di2srKxgZWUFLy8v7Nixo1xCzMvLw4MHD9C5c2cYGBgIfCpERERvTlBCjImJwcWLF6GhoQFTU1OUlpbi9u3bKCgogJGREZ4+fQpdXV2sX78e5ubmFbaRlpYGkUgEOzs7SZmdnR1Wr14NsVgMFRUVSXl6ejo0NDTQsmXLt3x6REREwgiaVNOuXTv07dsXv//+O2JiYrBv3z6cOnUKAwYMgJOTE86ePYt+/fpVuSYxNzcXjRo1goaGhqTsgw8+QFFRER49eiRVNz09HQ0bNsTXX3+N3r1747PPPsPvv//+hk+RiIioeoKHTHfv3i21xEJHRwe+vr4YMWIEZs2ahbFjx8Ld3b3SNgoKCsoNqZbdFolEUuUZGRl4+fIl+vfvj6lTp+L48ePw8fHBrl27YG1tLfjJ6evrCK5L9K4yMKj5+XySLb4HdcPbvg+CEqKWlhZu3bpVbjg0PT1dktTy8/OhqalZaRsaGhrlEl/Z7QYNpGcu/e9//8PUqVMlCdjS0hLXr1+vcUJ89CgPJSWlgusTvYtyc1/UdgjvPb4HdUN174OyslKVHSVBCXHixInw9/dHWlqaZN/Sa9euITIyEpMmTUJ2djaCgoLQt2/fStto1qwZnj9/DpFIJEmiubm5UFdXLzd7VEVFpdyCfzMzM/z559td/JGIiKgyghLi+PHj0aRJE+zcuRNbt26Fqqoq2rRpg/nz58PFxQUXLlyAra0tpk+fXmkb7du3h5qaGpKSktC9e3cAwKVLl9CxY0eoqkqH4evriyZNmiA4OFhSlpqaWumEHSIiorcleB3i4MGDMXjw4Arvs7e3l1ovWJEGDRrA3d0d8+fPx+LFi5Gbm4tNmzZhwYIFAF73FnV1daGpqYn+/fsjICAAdnZ26NSpE/bv349Lly5h/vz5NXhqREREwglKiCUlJTh27BjS09MhFosBAKWlpRCJREhNTcXmzZsFHWzOnDkIDg7GuHHjoK2tjS+//BIuLi4AgN69e2PRokUYMmQI3N3dkZeXhx9//BHZ2dlo164dNm7cCGNj4zd8mkRERFUTlBBDQkIQExODDh064MqVK7C1tUVWVhYePnyIMWPGCD5YgwYNEBYWhrCwsHL3/ff84Oeff47PP/9ccNtERERvQ9A6xCNHjmDp0qXYtWsXjI2NERwcjPj4eLi6uqKgoEDeMRIREcmdoISYl5eHTp06AXi9SD8lJQUqKirw9vbGqVOn5Brg++ry5YuYP98fly9frO1QiIjeC4ISorGxMa5fvw4AaNu2La5cuQLg9bnFvLw8+UX3Htu7dydSU69j796dtR0KEdF7QdA5xEmTJsHPzw8LFy6Ei4sLPDw8oKSkhOTkZKm9SUl2CgpeSX0nIiL5EpQQhw4ditatW0NTUxPm5uZYtWoV9u7dC2tra0ybNk3eMRIREcldjXaqKVsY7+joCEdHR7kGRkREpEiCziGmpqaW202GiIioPhGU5UaOHCm5soWhoWG5q1b07NlTLsEpQmfr9si+f6+2wyinR48e0NLSQmZmOpo2bVj9AxSseQtDXElJre0wiIhkRlBCXLNmDYDXC/T/S0lJCamp7+4HY/b9exj17Y7aDqOch9eiIC58Dl295nUyvp+/56YJRFS/CEqIaWlp8o6DiIioVgk6hwgAhYWF2L9/PyIiIvD06VOcPXsWubm58oyNiIhIYQT1EG/fvo1x48ZBVVUV2dnZcHd3x65du3DmzBls3LhRco1EIiKid5WgHmJoaCicnJxw/PhxqKmpAQDCw8MxcOBALFy4UK4Bvq+UVNSkvhMRkXwJSohJSUkYM2YMlJSU/u+Bysrw8vJ6pyfU1GU6LbtATac5dFp2qe1QiIjeC4KGTLW0tJCbmwtTU1Op8ps3b6Jhw7q3JKA+0GjUChqNWtV2GERE7w1BPcSRI0ciMDAQJ06cAABkZGRgz549CAwMxGeffSbXAImIiBRBUA/xiy++gK6uLkJDQ1FQUAAfHx/o6+tjwoQJmDRpkrxjJCIikjtBCfHFixfw9PSEp6cn8vPzIRaLoaurK+/YiIiIFEbQkGnPnj3h4+ODAwcOAACTIRER1TuCEmJkZCRMTU0RHh6OXr16Yfr06Th27BhEIpG84yMiIlIIQQnR2toas2bNQlxcHLZu3QpDQ0MsW7YMDg4OmDVrlrxjJCIikjvBW7eVsbS0RNeuXWFvb4+SkhIkJyfLIy4iIiKFEjSppqCgAPHx8Th27BhOnToFXV1dDBo0CFu3bkWnTp3kHSMREZHcCUqI3bt3h46ODgYMGICffvoJXbt2ldyXnp6ONm3ayC1AIiIiRRCUEFetWgUHBweoqKgAAJ49e4YDBw5g3759uH79OrdvIyKid56ghOjo6AixWIy4uDjExsYiPj4eIpEItra2CAsLk3eMREREcldtQkxLS0NMTAwOHDiAJ0+ewMDAAMXFxVi3bh369OmjiBiJiIjkrtKEuGXLFuzbtw83b96EiYkJPDw8MK0UtT4AABJLSURBVGDAAHTu3BlWVlZo2bKlIuMkIiKSq0oT4uLFi2FiYoLvv/8eLi4uUFau8QoNIiKid0alWW7p0qUwMzPDnDlz0K1bN3zzzTc4dOgQ8vLyFBkfERGRQlTaQ3Rzc4ObmxuePXuGo0eP4sCBA/jf//4HFRUVlJSU4MyZMzA2Noa6uroi4yUiIpKLasdBGzVqhOHDh2Pbtm2Ij4/HN998g44dO+K7776Do6MjQkNDFREnERGRXNXoxGDTpk0xYcIEREVF4ejRo/D09MTp06cFP14kEiEgIAD29vbo1asX1q9fX2ndtLQ0jBgxAtbW1hgyZAiuXLlSk1CJiIhq5I1nypiYmOCrr77CoUOHBD9myZIlSE5OxubNmzF//nysWbMGBw8eLFcvPz8fXl5esLa2RkxMDOzs7ODt7c3zl0REJDcKmzqan5+PPXv2YM6cObCysoKTkxO8vLywY8eOcnUPHToENTU1zJ49G+bm5pg7dy50dXVx+PBhRYVLRETvGUE71chCWloaRCIR7OzsJGV2dnZYvXo1xGKxZFs4AEhJSUGXLl0kSz2UlJTQpUsXJCUlYdiwYYKPqaysVG0dExMTfKCnXYNnQsDr103I61uT9tQb6susvfeFLN8HExMTfKDTRCZtvU/k8bfQ4AP+LdSUkPehuvsVlhBzc3PRqFEjaGhoSMo++OADFBUV4dGjR2jatKlUXVNTU6nH6+vrIy0trUbH1BOQ6P7+++8atUmv/TjHXabt8X14M3/7yG7rRL4Hb2bpsCCZtsf34c38Hb70rdtQ2JBpQUFBuSUaZbdFIpGguv+tR0REJCsKS4gaGhrlElrZ7QYNGgiqq6mpKd8giYjovaWwhNisWTM8f/5cKtHl5uZCXV0djRo1Klc3NzdXquzhw4cwMDBQSKxERPT+UVhCbN++PdTU1JCUlCQpu3TpEjp27AhVVelTmdbW1khKSkJpaSkAoLS0FJcvX4aNjY2iwiUioveMwhJigwYN4O7ujvnz5+PKlSv47bffsGnTJowdOxbA697iq1evAAAff/wx8vPzsWDBAqSnp2PRokV4+fIlXFxcFBUuERG9Z5RKy7phClBQUIDg4GAcO3YM2tramDhxIiZOnAgAsLCwwKJFizBkyBAAwJUrVxAUFIT09HRYWFggODgYVlZWigqViIjeMwpNiERERHUVL3JIREQEJkQiIiIATIhEREQAmBAFEYlEcHNzq9GlruTp3LlzsLCwQHFxsdyOsXz5cnh6esq83aysLPj4+MDe3h59+vTB4sWLUVhYWGHdiIgIjBo1SuYx/NuoUaMQERFRbb3MzEwEBgbC2dkZVlZW6NatG4YPH47t27fXaAel27dvw9raGufOnauyXkFBAVauXAlXV1dYW1ujW7du8Pb2RkpKiuBjvSsyMjIwfvx42Nraol+/ftiwYUOldWNiYmBhYSH56tixIwYOHIjo6GgFRly/zZs3T9Df/qlTpzBu3Dh07doV3bt3h7e3N27cuKGACOWHCbEahYWF+Oabb3Dr1q3aDkXC1tYWiYmJ5dZv1nUikQg+Pj5QV1fHrl27sHTpUpw4cQLLly+vsP7EiROxZs0aBUdZ3pEjRzB06FAUFBQgICAABw8exI4dOzBixAhER0dj5MiRePDgQbXtlJaWYt68eZLlRZUpKCjAmDFjcPToUcyYMQMHDx7Etm3bYGJigs8//7xeXRu0qKgIkydPRosWLRAbG4vAwECsXr0a+/fvr/QxBgYGSExMRGJiIo4ePQpvb28EBQXh4sWLCoy8fjpz5gz27t1bbb3t27dj2rRpcHR0xO7du7Ft2zbo6+tjzJgx73RSfLc+URUsPT0dfn5+qGsTcdXV1d/JXXuuXLmCrKws7N27F9ra2jA3N8f06dOxePFizJ49u1x9be3avwrJ6dOnERISgnXr1sHe3l7qvnbt2uHTTz9FcHAw/ve//2Hr1q2SK7RU5Oeff4ZYLK72mGvWrMGDBw9w6NAhqV2c5s6di6dPn2LNmjV14h8FWXjw4AE6d+6MoKAgaGpqwsTEBA4ODrhw4QIGDx5c4WOUlZWlfv+NjIxw8OBBHDlyBF27dlVU6PVOfn4+AgIC0KVLlyrr3blzB2FhYQgNDYW7+/9t8r9w4ULcvXsX4eHhVfby6zL2EKtw/vx5dO/eHbt37662bkxMDEaNGoWVK1eiR48esLOzQ2hoKEpKSqTquLi4oHPnzhgyZIjUsFn//v2xY8cOjBw5Ep06dcLgwYMr7Qn8e8j07t27sLCwwKpVq2Bvb485c+YgIiICPj4+8PT0hL29PU6dOoWcnBz4+vrC3t4eVlZWcHd3x4ULFyRtpqenY9SoUbC2tsaECRPw9OnTt3jlKmZmZoZ169ZJJTolJSU8f/68wvr/HjKNiYnB8OHD4evrCzs7O+zduxeenp4ICQmBs7MzHB0d8fjxYyQlJWH06NGwtraGjY0NJk2aJNV7O378OAYOHAgbGxuEhoZW+c9OcXExQkND8d1338He3h4XL17EkCFDYGtri0WLFkle56CgIDx+/BinTp2qtK379+8jIiICCxYsqPI1KikpQXR0NMaNG1duS0MAmD17Nr7//nsAgJeXF4KDg6Xu/+abb6o9Rl1iZGSEH374AZqamigtLcWlS5dw4cIF9OzZs0btaGlpySnC98fy5cvRrVs3dOvWrcp6Bw4cQOPGjSv8hyUkJAT+/v7yClHumBCrMHr0aMydO7fc5uOVuXr1KjIyMrBz504EBgYiMjISCQkJAF5/oIeEhGDKlCn45Zdf0KtXL0yZMgX//POP5PErV66El5cX9u/fj4YNG9bog+3ixYuIjo7GlClTAAAnT57EwIEDsX37dnTp0gUzZ85EcXExdu3ahdjYWDRv3hxBQa8vWyMSiTBlyhQYGRkhJiYGTk5OgoZNaqpJkyZwcHCQ3C4pKcGOHTukyqqSkpICExMT7N27F/369QPw+nVdtGgRVq9eDXV1dXh7e8PBwQEHDhzAxo0bcffuXUlvKj09HTNmzMCoUaMQHR0NkUgktZXgf508eRIGBgbo168fbty4gcmTJ8PDwwNRUVG4e/cutmzZgo4dO0JNTQ3Dhg1DXFxcpW0FBgZi/PjxMDExqfI53rlzBw8fPqz0Q6lJkybQ0dEBALi5ueH48eOSXmdBQQFOnjwJNze3Ko9RV/Xp0wejR4+Gra0tBg4cKPhxly5dwunTp9/Z510XJCUl4ciRI5g1a1a1ddPS0mBlZVXhaEjr1q3LXbrvXcIhUxkqLi5GSEgIdHV1YWZmhi1btuDq1avo27cvtm/fjjFjxkiGGPz8/HD+/Hls375d8kvo7u4OJycnAMCECRPw1VdfCT722LFjYWxsLLnduHFjfP7555Lb/fr1w4ABA9CiRQsAwJgxY+Dl5YXS0lKcPn0aT548QXBwsGQo89y5c3jy5MlbvyZVWbRoEVJTUxEVFSX4MT4+PlI9zD59+kiGyXJzc+Ht7Y2JEydCSUkJrVq1woABAyRJLzo6Gl26dMH48eMBAAEBAVUmsbi4OMmHbFhYGFxdXSWTDWbNmgVnZ2e0b98eAGBqaorExMQK24mNjUVOTg4mTZpU7fN7/PgxgNfvX5krV65g3LhxUvWSkpLg5OSEoKAgXLhwAT169EB8fDz09PRga2tb7XHqotWrVyMnJwfBwcFYtGgR5s2bV2G9nJwcyXMsKipCUVERnJ2d0aFDB0WGW2+IRCL4+/tj7ty5FY5K/NeLFy/QpEn9vJA0E6IM6enpQVdXV3JbR0dHMhM0IyMDU6dOlapvY2ODzMxMye1WrVpJPbakpARisRgqKirVHtvQ0LDK26NGjcKhQ4dw+fJl/PXXX7h27RoAQCwWIz09Ha1atZJKNFZWVpLerayVlpbiu+++w88//4wVK1agbdu2gh7XuHHjcucV//08DQwM4OHhgS1btiA1NRXp6en4888/0blzZwCv3wMLCwtJfTU1Nanb/5WRkYGRI0ciNzcX586dg5+fn1QsACQfwi9fvqxwJOHhw4cICwvDunXroKqqWu3M4IYNGwKA1DCypaUlYmNjAbxOhGX/QOno6ODDDz/E4cOH0aNHDxw+fBiurq5Vtl+XderUCQDw6tUrzJo1CzNnzix3XVTg9YXFIyMjAbz+J/Sff/5BeHg4vvjii3f23FVtWrVqFUxMTDBo0CBB9fX09Co9zfGuY0KUITU1tXJlZeeoKrqWo1gslppkUdEfv9AJPRoaGpXeLikpwcSJE/Hs2TO4uLigf//+KCoqkuqB/vc48prBWlJSAn9/f/z6669Yvny5pEcsxH+fIyD9mj148ABDhw5F+/bt0bt3bwwfPhzx8fG4dOlSpW1W9J6VefbsGXR1dZGVlYXS0lK0adNGct/Vq1ehq6sr+ScmISEB1tbW5dpISEjAkydPJJvYl5k8eTK++OIL+Pj4SJWbmJigcePGSEpKkiRydXV1yVDrvXv3pOp/8sknCAwMxLfffovff/9d0PnuuuTBgwe4du0aPvroI0mZubk5ioqKkJeXV2FPRFlZWWro2dzcHDo6Ohg5ciRu3bol+B8seu3XX39Fbm6uVK9bLBbD1ta2wlMKnTp1wrp161BaWgolJSWp++Lj4/HLL79gyZIlVf5t1VVMiApiZmaGlJQUDBgwQFKWkpKikEtapaen48KFC0hISEDTpk0BQPIfdmlpKdq2bYusrCw8e/ZMMmQir6nTixcvxq+//oqIiAjJeUBZOX78OLS1tbF+/XpJ2fbt2yXJvm3btlJT88ViMf7880907Nixwvb09PSQm5sLPT09AMCjR4+gpaWF0tJSrF27FpaWllBSUsKNGzcQFxeHb7/9tlwbzs7OUrP2xGIxBg0ahNDQUPTp06dcfVVVVQwdOhRbt27FkCFDpEYcAJRb3tGnTx8UFRVh/fr1MDIygqWlZXUvU52SkZGBadOmISEhAfr6+gCA69evo0mTJjUalit7j4XM4iVp27dvlxq52LJlC65du4alS5dWWP/jjz9GeHg4fvnlF6lZpiUlJdi4cSNUVVXfyWQIMCEqzIQJEzB79my0bdsW1tbWiImJQVpaGhYuXCj3Yzds2BDKyso4dOgQnJ2dcfXqVclidJFIBAcHB7Rs2RJz587F119/jeTkZBw9elTmyTo5ORlbt26Fn58frKyspC4CLYtlJI0bN0ZOTg7++OMPGBsb4/Dhwzh27JjkPN+wYcOwbds2rFy5Ei4uLti5cyeys7Mrba9Dhw64cOECvvzyS5iamiI4OBhjx45FdHQ0kpOT0aFDB8TGxiIsLAyBgYEVfoDr6OhIJsEAkHzwNGvWTOo84b/5+voiKSkJw4cPx1dffYXOnTvjxYsX2L9/P3bs2AE7OztJXXV1dTg7O2Pz5s3lepvvAnt7e5ibm2P27NmYPXs2srKysGzZsiqfS0lJidTvzv3797FkyRKYmZmhXbt2igi7Xvnv6ZWGDRtKlsBUpHnz5vD19UVAQAAeP36M/v3748WLF9iwYQOuX7+OXbt2KSJsueAsUwUZOHAg/Pz88OOPP2Lw4ME4d+4cNm7cqJDhnebNmyM4OBibN2+Gq6srfvrpJ8ybNw9qampITU2Fmpoa1q1bh7y8PAwZMgR79uzB6NGjZR7H0aNHAQDLli1D7969pb5ksevOoEGDMHjwYMyYMQNDhgzB2bNnMWfOHPz111949eoVWrdujbVr1+LIkSNwd3fHkydP4OjoWGl7rq6u2Lt3L168eIEffvgBOTk5+Oabb2BpaYnZs2fj5s2bWL9+PRYsWAAnJydkZmbKZM2qpqYmtm3bhuHDh2P9+vUYPHgwPD09cePGDYSGhmLHjh3l4iwsLHwnzx+W/e6pqKhg2LBhCAwMxLhx48oNMf9bbm6u5PfG0dERkydPhqGhIdatW1flOlCSHS8vLyxatEiyacXkyZMhEomwa9eud/qfEl7+iagKfn5+ePz4McLDwyVDp//19OlTTJ8+HR988AGWLVum4Ahfz2KNjIyUy1IZovcJEyJRFQoLCzFr1iycO3cOQ4cORe/evdGiRQsoKyvjwYMHSExMxJ49e+Dg4ICFCxdWODFKXu7cuYOrV69i+fLlmDJlCoYNG6awYxPVR0yIRAKcOXMGUVFRSElJQU5ODgBAX18fNjY2GD58eI13VpFVTFOnTkWfPn2wfPlyQctziKhyTIhERETgpBoiIiIATIhEREQAmBCJiIgAMCESEREBYEIkIiICAPw/Ydt34jP9CYkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trp.graph_all_aberrations_freq(melt_aberrations=melt_aberrations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import importlib\n",
    "%load_ext autoreload\n",
    "%autoreload "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trp.graph_all_aberrations_freq(melt_aberrations=melt_aberrations, aberr_list=['# sister chromatid exchanges'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12c47d150>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "ax=sns.set(font_scale=1.5)\n",
    "ax = sns.lineplot(x='timepoint', y='count per cell', data=melt_aberrations[melt_aberrations['aberration type'] != '# terminal SCEs'],\n",
    "                  hue='aberration type', \n",
    "#                   palette=sns.color_palette(\"terrain\", melt_aberrations['aberration type'].nunique()),\n",
    "                  )\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics Chromosome Rearrangements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = preprocessing.LabelEncoder()\n",
    "melt_aberrations['timepoint_encoded'] = label_encoder.fit_transform(melt_aberrations['timepoint'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# inversions 2.8793128491455715e-15\n",
      "        Multiple Comparison of Means - Tukey HSD, FWER=0.05        \n",
      "===================================================================\n",
      "    group1         group2     meandiff p-adj  lower   upper  reject\n",
      "-------------------------------------------------------------------\n",
      "   1 non irrad 2 irrad @ 4 Gy   0.3595 0.001  0.2046  0.5144   True\n",
      "   1 non irrad            3 B   0.6262 0.001  0.4713  0.7811   True\n",
      "   1 non irrad            4 C   0.3405 0.001  0.1856  0.4954   True\n",
      "2 irrad @ 4 Gy            3 B   0.2667 0.001  0.1117  0.4216   True\n",
      "2 irrad @ 4 Gy            4 C   -0.019   0.9  -0.174  0.1359  False\n",
      "           3 B            4 C  -0.2857 0.001 -0.4406 -0.1308   True\n",
      "-------------------------------------------------------------------\n",
      "# inversions pvalues: [0.001, 0.001, 0.001, 0.001, 0.9, 0.001]\n",
      "\n",
      "\n",
      "# sister chromatid exchanges 0.06700814306980919\n",
      "\n",
      "\n",
      "# dicentrics 3.774831915280363e-21\n",
      "        Multiple Comparison of Means - Tukey HSD, FWER=0.05         \n",
      "====================================================================\n",
      "    group1         group2     meandiff p-adj   lower   upper  reject\n",
      "--------------------------------------------------------------------\n",
      "   1 non irrad 2 irrad @ 4 Gy    0.669  0.001  0.5682  0.7699   True\n",
      "   1 non irrad            3 B     0.35  0.001  0.2492  0.4508   True\n",
      "   1 non irrad            4 C   0.2357  0.001  0.1349  0.3365   True\n",
      "2 irrad @ 4 Gy            3 B   -0.319  0.001 -0.4199 -0.2182   True\n",
      "2 irrad @ 4 Gy            4 C  -0.4333  0.001 -0.5341 -0.3325   True\n",
      "           3 B            4 C  -0.1143 0.0189 -0.2151 -0.0135   True\n",
      "--------------------------------------------------------------------\n",
      "# dicentrics pvalues: [0.001, 0.001, 0.001, 0.001, 0.001, 0.01885686962035671]\n",
      "\n",
      "\n",
      "# excess chr fragments 5.420958629361807e-13\n",
      "        Multiple Comparison of Means - Tukey HSD, FWER=0.05        \n",
      "===================================================================\n",
      "    group1         group2     meandiff p-adj  lower   upper  reject\n",
      "-------------------------------------------------------------------\n",
      "   1 non irrad 2 irrad @ 4 Gy     0.45 0.001  0.3478  0.5522   True\n",
      "   1 non irrad            3 B   0.2095 0.001  0.1073  0.3117   True\n",
      "   1 non irrad            4 C   0.1881 0.001  0.0859  0.2903   True\n",
      "2 irrad @ 4 Gy            3 B  -0.2405 0.001 -0.3427 -0.1383   True\n",
      "2 irrad @ 4 Gy            4 C  -0.2619 0.001 -0.3641 -0.1597   True\n",
      "           3 B            4 C  -0.0214   0.9 -0.1236  0.0808  False\n",
      "-------------------------------------------------------------------\n",
      "# excess chr fragments pvalues: [0.001, 0.001, 0.001, 0.001, 0.001, 0.9]\n",
      "\n",
      "\n",
      "# sat associations 2.5079736386352722e-05\n",
      "        Multiple Comparison of Means - Tukey HSD, FWER=0.05         \n",
      "====================================================================\n",
      "    group1         group2     meandiff p-adj   lower   upper  reject\n",
      "--------------------------------------------------------------------\n",
      "   1 non irrad 2 irrad @ 4 Gy   0.0595 0.4338 -0.0419   0.161  False\n",
      "   1 non irrad            3 B   0.2143  0.001  0.1128  0.3157   True\n",
      "   1 non irrad            4 C   0.1024 0.0469  0.0009  0.2038   True\n",
      "2 irrad @ 4 Gy            3 B   0.1548  0.001  0.0533  0.2562   True\n",
      "2 irrad @ 4 Gy            4 C   0.0429 0.6754 -0.0586  0.1443  False\n",
      "           3 B            4 C  -0.1119 0.0238 -0.2133 -0.0105   True\n",
      "--------------------------------------------------------------------\n",
      "# sat associations pvalues: [0.4337670435960369, 0.001, 0.04694268219187914, 0.001, 0.6754137733207328, 0.02383659311705344]\n",
      "\n",
      "\n",
      "# translocations 1.9719725719939544e-10\n",
      "        Multiple Comparison of Means - Tukey HSD, FWER=0.05         \n",
      "====================================================================\n",
      "    group1         group2     meandiff p-adj   lower   upper  reject\n",
      "--------------------------------------------------------------------\n",
      "   1 non irrad 2 irrad @ 4 Gy   0.2071  0.001   0.148  0.2663   True\n",
      "   1 non irrad            3 B   0.0738 0.0074  0.0146   0.133   True\n",
      "   1 non irrad            4 C   0.0738 0.0074  0.0146   0.133   True\n",
      "2 irrad @ 4 Gy            3 B  -0.1333  0.001 -0.1925 -0.0742   True\n",
      "2 irrad @ 4 Gy            4 C  -0.1333  0.001 -0.1925 -0.0742   True\n",
      "           3 B            4 C      0.0    0.9 -0.0592  0.0592  False\n",
      "--------------------------------------------------------------------\n",
      "# translocations pvalues: [0.001, 0.007448304853158949, 0.007448304853158949, 0.001, 0.001, 0.9]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trp.chr_scipy_anova_post_hoc_tests(df0=melt_aberrations[melt_aberrations['aberration type'] != '# terminal SCEs'],\n",
    "                                   post_hoc='tukeyHSD', repeated_measures=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_chr_df['aberration index'] = (cleaned_chr_df['# inversions'] + \n",
    "                                      cleaned_chr_df['# translocations'] + cleaned_chr_df['# dicentrics'] + \n",
    "                                      cleaned_chr_df['# excess chr fragments'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pivoting out aberrations for linear regression\n",
    "\n",
    "group_chr = cleaned_chr_df.groupby(['patient id', 'timepoint']).agg('mean').reset_index()\n",
    "# pivot_chr = group_chr.pivot(index='patient id', columns='timepoint', values='# inversions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "row = []\n",
    "aberr_types = [col for col in group_chr.columns if col != 'patient id' and col != 'timepoint']\n",
    "\n",
    "for aberr in aberr_types:\n",
    "    pivot_chr = group_chr.pivot(index='patient id', columns='timepoint', values=aberr)\n",
    "    x_name2 = ['1 non irrad']\n",
    "    x_name3 = ['1 non irrad', '2 irrad @ 4 Gy']\n",
    "    y_name = '4 C'\n",
    "\n",
    "#     print(f'ABERRATION TYPE | {aberr}')\n",
    "    for x_name in [x_name2, x_name3]:\n",
    "        x = pivot_chr[x_name].values.reshape(-1, len(x_name))\n",
    "        y = pivot_chr['4 C'].values.reshape(-1, 1)\n",
    "\n",
    "        regression = LinearRegression().fit(x, y)\n",
    "#         print(f\"Linear regression for {x_name} vs. {y_name}:\\nR2 is {regression.score(x, y):.4f}\")\n",
    "#         print('\\n')\n",
    "        row.append(['Linear Regression', aberr, x_name, y_name, f'{regression.score(x, y):.4f}'])\n",
    "    \n",
    "LM_aberr_r2 = pd.DataFrame(data=row, columns=['Model', 'Aberration type', 'Features', 'Target', 'Linear regression R2 score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "LM_aberr_r2['Features'] = LM_aberr_r2['Features'].apply(lambda row: ', '.join(row))\n",
    "LM_aberr_r2['Linear regression R2 score'] = LM_aberr_r2['Linear regression R2 score'].astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "chr_interest = ['# inversions', \n",
    "                '# translocations', '# dicentrics', '# excess chr fragments',\n",
    "                'aberration index']\n",
    "\n",
    "chr_df = LM_aberr_r2[LM_aberr_r2['Aberration type'].isin(chr_interest)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "chr_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1298fdb90>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x495 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path=f'../graphs/paper figures/supp figs/linear regression chr aberr models.png'\n",
    "trp.render_mpl_table(chr_df, col_width=3.5, path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12b2a8750>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x135 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtl_LM_metrics = pd.DataFrame(mtl_df_list, columns=['Features', 'Target', 'Linear regression R2 score'])\n",
    "mtl_LM_metrics['Features'] = mtl_LM_metrics['Features'].astype('str')\n",
    "mtl_LM_metrics['Model'] = 'Linear Regression'\n",
    "mtl_LM_metrics['Telomeric outcome'] = 'Mean Telomere Length'\n",
    "mtl_ordered = mtl_LM_metrics[['Model', 'Telomeric outcome', 'Features', 'Target', 'Linear regression R2 score']]\n",
    "\n",
    "path=f'../graphs/paper figures/supp figs/linear regression mean telomere length models.png'\n",
    "trp.render_mpl_table(mtl_ordered, col_width=3.5, path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12bb661d0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x135 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qLM_LM_metrics = pd.DataFrame(numst_df_list, columns=['Features', 'Target', 'Linear regression R2 score'])\n",
    "qLM_LM_metrics['Features'] = qLM_LM_metrics['Features'].astype('str')\n",
    "qLM_LM_metrics['Model'] = 'Linear Regression'\n",
    "qLM_LM_metrics['Telomeric outcome'] = 'Number of short telomeres'\n",
    "qLM_ordered = qLM_LM_metrics[['Model', 'Telomeric outcome', 'Features', 'Target', 'Linear regression R2 score']]\n",
    "\n",
    "path=f'../graphs/paper figures/supp figs/linear regression number of short telomeres models.png'\n",
    "trp.render_mpl_table(qLM_ordered, col_width=3.5, path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a37eed0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x135 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "long_LM_metrics = pd.DataFrame(long_df_list, columns=['Features', 'Target', 'Linear regression R2 score'])\n",
    "long_LM_metrics['Features'] = long_LM_metrics['Features'].astype('str')\n",
    "long_LM_metrics['Model'] = 'Linear Regression'\n",
    "long_LM_metrics['Telomeric outcome'] = 'Number of long telomeres'\n",
    "long_ordered = long_LM_metrics[['Model', 'Telomeric outcome', 'Features', 'Target', 'Linear regression R2 score']]\n",
    "\n",
    "path=f'../graphs/paper figures/supp figs/linear regression number of long telomeres models.png'\n",
    "trp.render_mpl_table(long_ordered, col_width=3.5, path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353),\n",
       " (0.5490196078431373, 0.33725490196078434, 0.29411764705882354),\n",
       " (0.8901960784313725, 0.4666666666666667, 0.7607843137254902),\n",
       " (0.4980392156862745, 0.4980392156862745, 0.4980392156862745),\n",
       " (0.7372549019607844, 0.7411764705882353, 0.13333333333333333),\n",
       " (0.09019607843137255, 0.7450980392156863, 0.8117647058823529)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette(\"tab10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
       " (0.17254901960784313, 0.6274509803921569, 0.17254901960784313),\n",
       " (0.8392156862745098, 0.15294117647058825, 0.1568627450980392),\n",
       " (1.0, 0.4980392156862745, 0.054901960784313725),\n",
       " (0.5803921568627451, 0.403921568627451, 0.7411764705882353)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blue = sns.color_palette(\"tab10\")[0]\n",
    "red = sns.color_palette(\"tab10\")[1]\n",
    "orange = sns.color_palette(\"tab10\")[2]\n",
    "green = sns.color_palette(\"tab10\")[3]\n",
    "purple = sns.color_palette(\"tab10\")[4]\n",
    "\n",
    "# blue orange green red purple\n",
    "\n",
    "palette_choice = [blue, orange, green, red, purple]\n",
    "palette_choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 876.96x417.6 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.set(font_scale=1.2)\n",
    "col_order=['# inversions', '# translocations', '# dicentrics', '# excess chr fragments', 'aberration index']\n",
    "ax = sns.relplot(x='Features', y='Linear regression R2 score', data=chr_df, \n",
    "                 col='Aberration type', hue='Aberration type', kind='line', col_order=col_order, \n",
    "                 col_wrap=3, legend=False, height=2.9, aspect=1.40, palette=palette_choice,\n",
    "                 facet_kws={'sharey':True, 'sharex':True},\n",
    "                 **{'linewidth':3, 'marker':'o', 'markersize':10,\n",
    "                    'mec':'black', 'mew':0.5, })\n",
    "\n",
    "ax.set_xlabels('Model features')\n",
    "\n",
    "# ax.set_xticklabels(ha='center')\n",
    "# ax[0].set_title('# inversions')\n",
    "ax_list = ax.axes\n",
    "ax_list[0].set_title('inversions')\n",
    "ax_list[1].set_title('translocations')\n",
    "ax_list[2].set_title('dicentrics')\n",
    "ax_list[3].set_title('excess chr fragments')\n",
    "ax_list[4].set_title('aberration index')\n",
    "\n",
    "plt.ylim(-0.05,0.6)\n",
    "\n",
    "plt.tight_layout(pad=.7)\n",
    "plt.savefig(f'../graphs/paper figures/supp figs/linear regression performance for all chr aberr types.png',\n",
    "           dpi=400, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "UFuncTypeError",
     "evalue": "ufunc 'multiply' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUFuncTypeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-0fd50c5976da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfont_scale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcol_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'# inversions'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'# translocations'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'# dicentrics'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'# excess chr fragments'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'aberration index'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m ax = sns.lmplot(x='Features', y='Linear regression R2 score', data=chr_df, \n\u001b[0m\u001b[1;32m      4\u001b[0m                  \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Aberration type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Aberration type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcol_order\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                  \u001b[0mcol_wrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2.9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.40\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mlmplot\u001b[0;34m(x, y, data, hue, col, row, palette, col_wrap, height, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws, size)\u001b[0m\n\u001b[1;32m    614\u001b[0m         \u001b[0mscatter_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscatter_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mline_kws\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    615\u001b[0m         )\n\u001b[0;32m--> 616\u001b[0;31m     \u001b[0mfacets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mregplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mregplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    618\u001b[0m     \u001b[0;31m# Add a legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_dataframe\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    826\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m             \u001b[0;31m# Draw the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_facet_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m         \u001b[0;31m# Finalize the annotations and layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m_facet_plot\u001b[0;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[1;32m    844\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;31m# Draw the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mplot_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0;31m# Sort out the supporting information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mregplot\u001b[0;34m(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)\u001b[0m\n\u001b[1;32m    815\u001b[0m     \u001b[0mscatter_kws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"marker\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m     \u001b[0mline_kws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mline_kws\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 817\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscatter_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    818\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    819\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, scatter_kws, line_kws)\u001b[0m\n\u001b[1;32m    367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    368\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_reg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 369\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m         \u001b[0;31m# Label the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(self, ax, kws)\u001b[0m\n\u001b[1;32m    410\u001b[0m         \u001b[0;34m\"\"\"Draw the model.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    411\u001b[0m         \u001b[0;31m# Fit the regression model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 412\u001b[0;31m         \u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myhat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr_bands\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_regression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    413\u001b[0m         \u001b[0medges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mfit_regression\u001b[0;34m(self, ax, x_range, grid)\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m                     \u001b[0mx_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_xlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m             \u001b[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_min\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    201\u001b[0m         \u001b[0mci\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mci\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mlinspace\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/site-packages/numpy/core/function_base.py\u001b[0m in \u001b[0;36mlinspace\u001b[0;34m(start, stop, num, endpoint, retstep, dtype, axis)\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;31m# Convert float/complex array scalars to float, gh-3504\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \u001b[0;31m# and make sure one can use variables that have an __array_interface__, gh-6634\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m     \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[0mstop\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;34m*\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUFuncTypeError\u001b[0m: ufunc 'multiply' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32')"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 876.96x417.6 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.set(font_scale=1.2)\n",
    "col_order=['# inversions', '# translocations', '# dicentrics', '# excess chr fragments', 'aberration index']\n",
    "ax = sns.lmplot(x='Features', y='Linear regression R2 score', data=chr_df, \n",
    "                 col='Aberration type', hue='Aberration type', col_order=col_order, \n",
    "                 col_wrap=3, legend=False, height=2.9, aspect=1.40, \n",
    "#                  facet_kws={'sharey':True, 'sharex':True},\n",
    "#                  **{'linewidth':3, 'marker':'o', 'markersize':10,\n",
    "#                     'mec':'black', 'mew':0.5, }\n",
    "               )\n",
    "\n",
    "ax.set_xlabels('Model features')\n",
    "\n",
    "# ax.set_xticklabels(ha='center')\n",
    "# ax[0].set_title('# inversions')\n",
    "ax_list = ax.axes\n",
    "ax_list[0].set_title('inversions')\n",
    "ax_list[1].set_title('translocations')\n",
    "ax_list[2].set_title('dicentrics')\n",
    "ax_list[3].set_title('excess chr fragments')\n",
    "ax_list[4].set_title('aberration index')\n",
    "\n",
    "plt.ylim(-0.05,0.6)\n",
    "\n",
    "plt.tight_layout(pad=.7)\n",
    "# plt.savefig(f'../graphs/paper figures/supp figs/linear regression performance for all chr aberr types.png',\n",
    "#            dpi=400, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing Complete Blood Count Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading TeloFISH, chromosome aberration, CBC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading TeloFISH data\n",
    "\n",
    "all_patients_df = pd.read_csv('../data/compiled patient data csv files/all_patients_df.csv')\n",
    "all_patients_df['telo data'] = all_patients_df['telo data'].map(literal_eval)\n",
    "all_patients_df.drop(['telo data', 'Q1', 'Q2-3', 'Q4'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading chromosome aberration data\n",
    "\n",
    "all_chr_aberr_df = pd.read_csv('../data/compiled patient data csv files/all_chr_aberr_df.csv')\n",
    "\n",
    "general_cleaner = Pipeline([('cleaner', trp.general_chr_aberr_cleaner(drop_what_timepoint=False, adjust_clonality=True))])\n",
    "cleaned_chr_df = general_cleaner.fit_transform(all_chr_aberr_df)\n",
    "cleaned_chr_df['# inversions'] = cleaned_chr_df['# inversions'] + cleaned_chr_df['# terminal inversions']\n",
    "cleaned_chr_df.drop(['# terminal inversions'], axis=1, inplace=True)\n",
    "\n",
    "grp_chr_aberr = cleaned_chr_df.groupby(['patient id', 'timepoint']).agg('mean').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading qPCR telo data\n",
    "\n",
    "all_qPCR_df = pd.read_csv('../data/qPCR telo data/all_qPCR_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading cbc counts data\n",
    "\n",
    "cbc_data = pd.read_csv('../data/compiled patient data csv files/cleaned cbc data.csv')\n",
    "cbc_data.drop(['date', 'on AA 0=no 1=yes', 'LN 0=n 1=y', '  dose   0=78  1=70   2= implant 3=70/28 4=1800/3 bst'], \n",
    "              axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging teloFISH + CBC, qPCR + CBC, chr aberrations + CBC, & merging all\n",
    "\n",
    "merge_telofish_cbc = all_patients_df.merge(cbc_data, on=['patient id', 'timepoint'])\n",
    "\n",
    "merge_qpcr = all_qPCR_df.merge(cbc_data, on=['patient id', 'timepoint'])\n",
    "\n",
    "merge_chr_cbc = grp_chr_aberr.merge(cbc_data, on=['patient id', 'timepoint'])\n",
    "\n",
    "merge_all = merge_telofish_cbc.merge(grp_chr_aberr, on=['patient id', 'timepoint'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TeloFISH vs. CBC data"
   ]
  },
  {
   "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></th>\n",
       "      <th>patient id</th>\n",
       "      <th>telo means</th>\n",
       "      <th>WBC</th>\n",
       "      <th>RBC</th>\n",
       "      <th>Hgb</th>\n",
       "      <th>Hct</th>\n",
       "      <th>mcv</th>\n",
       "      <th>mch</th>\n",
       "      <th>mchc</th>\n",
       "      <th>rdw</th>\n",
       "      <th>...</th>\n",
       "      <th>cd3 abs</th>\n",
       "      <th>cd4%</th>\n",
       "      <th>cd4 abs</th>\n",
       "      <th>cd 8%</th>\n",
       "      <th>cd 8 abs</th>\n",
       "      <th>cd 19%</th>\n",
       "      <th>cd 19 abs</th>\n",
       "      <th>cd4/cd8</th>\n",
       "      <th>NK %</th>\n",
       "      <th>NK abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>patient id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.482406</td>\n",
       "      <td>0.045076</td>\n",
       "      <td>0.056003</td>\n",
       "      <td>0.216487</td>\n",
       "      <td>0.086520</td>\n",
       "      <td>0.151196</td>\n",
       "      <td>0.270799</td>\n",
       "      <td>0.347655</td>\n",
       "      <td>-0.220234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.047696</td>\n",
       "      <td>-0.345496</td>\n",
       "      <td>-0.060331</td>\n",
       "      <td>0.377962</td>\n",
       "      <td>0.201079</td>\n",
       "      <td>0.113894</td>\n",
       "      <td>0.093738</td>\n",
       "      <td>-0.419719</td>\n",
       "      <td>-0.240242</td>\n",
       "      <td>-0.141554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>telo means</th>\n",
       "      <td>-0.482406</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.126405</td>\n",
       "      <td>-0.063405</td>\n",
       "      <td>-0.048631</td>\n",
       "      <td>0.009699</td>\n",
       "      <td>0.117051</td>\n",
       "      <td>0.054275</td>\n",
       "      <td>-0.145268</td>\n",
       "      <td>-0.007756</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.265807</td>\n",
       "      <td>0.281594</td>\n",
       "      <td>-0.190007</td>\n",
       "      <td>-0.250713</td>\n",
       "      <td>-0.331702</td>\n",
       "      <td>-0.288298</td>\n",
       "      <td>-0.304504</td>\n",
       "      <td>0.429559</td>\n",
       "      <td>0.408281</td>\n",
       "      <td>0.118537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WBC</th>\n",
       "      <td>0.045076</td>\n",
       "      <td>-0.126405</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>0.220666</td>\n",
       "      <td>0.227359</td>\n",
       "      <td>0.271600</td>\n",
       "      <td>0.260500</td>\n",
       "      <td>0.054146</td>\n",
       "      <td>-0.372158</td>\n",
       "      <td>...</td>\n",
       "      <td>0.539096</td>\n",
       "      <td>0.306203</td>\n",
       "      <td>0.639397</td>\n",
       "      <td>-0.225986</td>\n",
       "      <td>0.340598</td>\n",
       "      <td>0.170815</td>\n",
       "      <td>0.437403</td>\n",
       "      <td>0.267823</td>\n",
       "      <td>-0.185876</td>\n",
       "      <td>0.289421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RBC</th>\n",
       "      <td>0.056003</td>\n",
       "      <td>-0.063405</td>\n",
       "      <td>0.001271</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.330533</td>\n",
       "      <td>0.325687</td>\n",
       "      <td>-0.551065</td>\n",
       "      <td>-0.448978</td>\n",
       "      <td>0.063059</td>\n",
       "      <td>0.378307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.407696</td>\n",
       "      <td>-0.037649</td>\n",
       "      <td>0.386152</td>\n",
       "      <td>0.158299</td>\n",
       "      <td>0.333421</td>\n",
       "      <td>-0.075314</td>\n",
       "      <td>0.126309</td>\n",
       "      <td>-0.060543</td>\n",
       "      <td>-0.103375</td>\n",
       "      <td>0.323583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hgb</th>\n",
       "      <td>0.216487</td>\n",
       "      <td>-0.048631</td>\n",
       "      <td>0.220666</td>\n",
       "      <td>0.330533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921122</td>\n",
       "      <td>0.374157</td>\n",
       "      <td>0.494990</td>\n",
       "      <td>0.439348</td>\n",
       "      <td>-0.352577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361162</td>\n",
       "      <td>0.053451</td>\n",
       "      <td>0.324169</td>\n",
       "      <td>0.382794</td>\n",
       "      <td>0.368324</td>\n",
       "      <td>-0.038466</td>\n",
       "      <td>0.089672</td>\n",
       "      <td>-0.257840</td>\n",
       "      <td>-0.265620</td>\n",
       "      <td>0.210809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hct</th>\n",
       "      <td>0.086520</td>\n",
       "      <td>0.009699</td>\n",
       "      <td>0.227359</td>\n",
       "      <td>0.325687</td>\n",
       "      <td>0.921122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.383547</td>\n",
       "      <td>0.350255</td>\n",
       "      <td>0.056114</td>\n",
       "      <td>-0.230922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.369794</td>\n",
       "      <td>0.032424</td>\n",
       "      <td>0.346737</td>\n",
       "      <td>0.253293</td>\n",
       "      <td>0.343606</td>\n",
       "      <td>0.072362</td>\n",
       "      <td>0.176192</td>\n",
       "      <td>-0.152870</td>\n",
       "      <td>-0.256686</td>\n",
       "      <td>0.213496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mcv</th>\n",
       "      <td>0.151196</td>\n",
       "      <td>0.117051</td>\n",
       "      <td>0.271600</td>\n",
       "      <td>-0.551065</td>\n",
       "      <td>0.374157</td>\n",
       "      <td>0.383547</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918503</td>\n",
       "      <td>0.106445</td>\n",
       "      <td>-0.582201</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.118412</td>\n",
       "      <td>0.056052</td>\n",
       "      <td>-0.117447</td>\n",
       "      <td>0.046161</td>\n",
       "      <td>-0.052865</td>\n",
       "      <td>0.130086</td>\n",
       "      <td>0.044068</td>\n",
       "      <td>-0.050749</td>\n",
       "      <td>-0.095841</td>\n",
       "      <td>-0.169225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mch</th>\n",
       "      <td>0.270799</td>\n",
       "      <td>0.054275</td>\n",
       "      <td>0.260500</td>\n",
       "      <td>-0.448978</td>\n",
       "      <td>0.494990</td>\n",
       "      <td>0.350255</td>\n",
       "      <td>0.918503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.489273</td>\n",
       "      <td>-0.661689</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.078138</td>\n",
       "      <td>0.083939</td>\n",
       "      <td>-0.091850</td>\n",
       "      <td>0.193523</td>\n",
       "      <td>0.008687</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.155774</td>\n",
       "      <td>-0.118079</td>\n",
       "      <td>-0.130829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mchc</th>\n",
       "      <td>0.347655</td>\n",
       "      <td>-0.145268</td>\n",
       "      <td>0.054146</td>\n",
       "      <td>0.063059</td>\n",
       "      <td>0.439348</td>\n",
       "      <td>0.056114</td>\n",
       "      <td>0.106445</td>\n",
       "      <td>0.489273</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.394782</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066935</td>\n",
       "      <td>0.089432</td>\n",
       "      <td>0.028703</td>\n",
       "      <td>0.397382</td>\n",
       "      <td>0.143590</td>\n",
       "      <td>-0.265709</td>\n",
       "      <td>-0.178768</td>\n",
       "      <td>-0.299353</td>\n",
       "      <td>-0.106153</td>\n",
       "      <td>0.023885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rdw</th>\n",
       "      <td>-0.220234</td>\n",
       "      <td>-0.007756</td>\n",
       "      <td>-0.372158</td>\n",
       "      <td>0.378307</td>\n",
       "      <td>-0.352577</td>\n",
       "      <td>-0.230922</td>\n",
       "      <td>-0.582201</td>\n",
       "      <td>-0.661689</td>\n",
       "      <td>-0.394782</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.078095</td>\n",
       "      <td>-0.075194</td>\n",
       "      <td>-0.119642</td>\n",
       "      <td>0.022586</td>\n",
       "      <td>-0.052700</td>\n",
       "      <td>-0.114159</td>\n",
       "      <td>-0.089429</td>\n",
       "      <td>-0.001233</td>\n",
       "      <td>-0.051859</td>\n",
       "      <td>-0.198498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plt</th>\n",
       "      <td>-0.412318</td>\n",
       "      <td>0.172354</td>\n",
       "      <td>0.243272</td>\n",
       "      <td>-0.074261</td>\n",
       "      <td>0.049405</td>\n",
       "      <td>0.040060</td>\n",
       "      <td>0.055999</td>\n",
       "      <td>0.066981</td>\n",
       "      <td>0.028868</td>\n",
       "      <td>-0.220423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408450</td>\n",
       "      <td>0.197672</td>\n",
       "      <td>0.410429</td>\n",
       "      <td>0.046549</td>\n",
       "      <td>0.358229</td>\n",
       "      <td>-0.147660</td>\n",
       "      <td>0.042702</td>\n",
       "      <td>0.075712</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.289992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mpv</th>\n",
       "      <td>-0.258088</td>\n",
       "      <td>-0.000113</td>\n",
       "      <td>0.169130</td>\n",
       "      <td>0.225262</td>\n",
       "      <td>0.234935</td>\n",
       "      <td>0.392096</td>\n",
       "      <td>0.139594</td>\n",
       "      <td>0.006954</td>\n",
       "      <td>-0.288044</td>\n",
       "      <td>0.203591</td>\n",
       "      <td>...</td>\n",
       "      <td>0.268907</td>\n",
       "      <td>0.082519</td>\n",
       "      <td>0.290063</td>\n",
       "      <td>-0.089949</td>\n",
       "      <td>0.183797</td>\n",
       "      <td>0.216494</td>\n",
       "      <td>0.284548</td>\n",
       "      <td>0.109532</td>\n",
       "      <td>-0.146806</td>\n",
       "      <td>0.078580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gran%</th>\n",
       "      <td>-0.265054</td>\n",
       "      <td>0.180082</td>\n",
       "      <td>0.009123</td>\n",
       "      <td>-0.559908</td>\n",
       "      <td>-0.411022</td>\n",
       "      <td>-0.424544</td>\n",
       "      <td>0.206388</td>\n",
       "      <td>0.156887</td>\n",
       "      <td>-0.056742</td>\n",
       "      <td>-0.062757</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.714490</td>\n",
       "      <td>0.252633</td>\n",
       "      <td>-0.595861</td>\n",
       "      <td>-0.415881</td>\n",
       "      <td>-0.760667</td>\n",
       "      <td>-0.166623</td>\n",
       "      <td>-0.422975</td>\n",
       "      <td>0.364609</td>\n",
       "      <td>0.293757</td>\n",
       "      <td>-0.377525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lmp%</th>\n",
       "      <td>0.027142</td>\n",
       "      <td>-0.196474</td>\n",
       "      <td>0.088627</td>\n",
       "      <td>0.559527</td>\n",
       "      <td>0.375069</td>\n",
       "      <td>0.414239</td>\n",
       "      <td>-0.273757</td>\n",
       "      <td>-0.244848</td>\n",
       "      <td>-0.013491</td>\n",
       "      <td>0.060101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.801260</td>\n",
       "      <td>-0.197470</td>\n",
       "      <td>0.716819</td>\n",
       "      <td>0.283195</td>\n",
       "      <td>0.786176</td>\n",
       "      <td>0.213352</td>\n",
       "      <td>0.513762</td>\n",
       "      <td>-0.262537</td>\n",
       "      <td>-0.262625</td>\n",
       "      <td>0.501336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mono%</th>\n",
       "      <td>0.547701</td>\n",
       "      <td>0.015066</td>\n",
       "      <td>-0.153847</td>\n",
       "      <td>-0.176707</td>\n",
       "      <td>-0.027397</td>\n",
       "      <td>-0.054069</td>\n",
       "      <td>0.331129</td>\n",
       "      <td>0.321180</td>\n",
       "      <td>0.066422</td>\n",
       "      <td>-0.099505</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.348530</td>\n",
       "      <td>-0.200644</td>\n",
       "      <td>-0.394392</td>\n",
       "      <td>0.152340</td>\n",
       "      <td>-0.217742</td>\n",
       "      <td>0.023082</td>\n",
       "      <td>-0.179080</td>\n",
       "      <td>-0.149404</td>\n",
       "      <td>0.054731</td>\n",
       "      <td>-0.315797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eos%</th>\n",
       "      <td>0.206229</td>\n",
       "      <td>0.119629</td>\n",
       "      <td>-0.121313</td>\n",
       "      <td>0.079285</td>\n",
       "      <td>0.145357</td>\n",
       "      <td>0.107267</td>\n",
       "      <td>0.007345</td>\n",
       "      <td>0.054057</td>\n",
       "      <td>0.125795</td>\n",
       "      <td>-0.066832</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.258075</td>\n",
       "      <td>0.203747</td>\n",
       "      <td>-0.239980</td>\n",
       "      <td>-0.027444</td>\n",
       "      <td>-0.256531</td>\n",
       "      <td>-0.285941</td>\n",
       "      <td>-0.343021</td>\n",
       "      <td>0.210397</td>\n",
       "      <td>0.066135</td>\n",
       "      <td>-0.135670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baso%</th>\n",
       "      <td>0.010755</td>\n",
       "      <td>0.301144</td>\n",
       "      <td>0.013914</td>\n",
       "      <td>-0.005616</td>\n",
       "      <td>-0.001234</td>\n",
       "      <td>0.094450</td>\n",
       "      <td>0.073392</td>\n",
       "      <td>-0.015497</td>\n",
       "      <td>-0.204428</td>\n",
       "      <td>-0.250134</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.041744</td>\n",
       "      <td>0.346744</td>\n",
       "      <td>0.016373</td>\n",
       "      <td>-0.219396</td>\n",
       "      <td>-0.118437</td>\n",
       "      <td>-0.178747</td>\n",
       "      <td>-0.176549</td>\n",
       "      <td>0.465297</td>\n",
       "      <td>0.098092</td>\n",
       "      <td>0.027343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nrbc</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gran#</th>\n",
       "      <td>-0.118379</td>\n",
       "      <td>0.016935</td>\n",
       "      <td>0.876199</td>\n",
       "      <td>-0.243316</td>\n",
       "      <td>0.031462</td>\n",
       "      <td>0.036594</td>\n",
       "      <td>0.352784</td>\n",
       "      <td>0.316216</td>\n",
       "      <td>0.017757</td>\n",
       "      <td>-0.354281</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119143</td>\n",
       "      <td>0.397890</td>\n",
       "      <td>0.262774</td>\n",
       "      <td>-0.377870</td>\n",
       "      <td>-0.079640</td>\n",
       "      <td>0.065011</td>\n",
       "      <td>0.170038</td>\n",
       "      <td>0.405330</td>\n",
       "      <td>-0.031604</td>\n",
       "      <td>0.064017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lymph#</th>\n",
       "      <td>0.076382</td>\n",
       "      <td>-0.293638</td>\n",
       "      <td>0.586373</td>\n",
       "      <td>0.391279</td>\n",
       "      <td>0.336236</td>\n",
       "      <td>0.357343</td>\n",
       "      <td>-0.104188</td>\n",
       "      <td>-0.080423</td>\n",
       "      <td>0.026185</td>\n",
       "      <td>-0.118091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.948706</td>\n",
       "      <td>-0.033763</td>\n",
       "      <td>0.928520</td>\n",
       "      <td>0.105314</td>\n",
       "      <td>0.847489</td>\n",
       "      <td>0.278416</td>\n",
       "      <td>0.673670</td>\n",
       "      <td>-0.110802</td>\n",
       "      <td>-0.308250</td>\n",
       "      <td>0.546756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mono#</th>\n",
       "      <td>0.501696</td>\n",
       "      <td>-0.094394</td>\n",
       "      <td>0.618297</td>\n",
       "      <td>-0.153716</td>\n",
       "      <td>0.145985</td>\n",
       "      <td>0.101529</td>\n",
       "      <td>0.448138</td>\n",
       "      <td>0.460818</td>\n",
       "      <td>0.158968</td>\n",
       "      <td>-0.360540</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129578</td>\n",
       "      <td>0.072046</td>\n",
       "      <td>0.151939</td>\n",
       "      <td>0.016709</td>\n",
       "      <td>0.102851</td>\n",
       "      <td>0.116811</td>\n",
       "      <td>0.174917</td>\n",
       "      <td>0.020066</td>\n",
       "      <td>-0.128706</td>\n",
       "      <td>-0.043695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eos#</th>\n",
       "      <td>0.214030</td>\n",
       "      <td>0.023208</td>\n",
       "      <td>0.563596</td>\n",
       "      <td>0.073564</td>\n",
       "      <td>0.307509</td>\n",
       "      <td>0.278495</td>\n",
       "      <td>0.200661</td>\n",
       "      <td>0.236600</td>\n",
       "      <td>0.154073</td>\n",
       "      <td>-0.290308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.116345</td>\n",
       "      <td>0.401192</td>\n",
       "      <td>0.206862</td>\n",
       "      <td>-0.177053</td>\n",
       "      <td>-0.028970</td>\n",
       "      <td>-0.157342</td>\n",
       "      <td>-0.065269</td>\n",
       "      <td>0.372025</td>\n",
       "      <td>-0.074399</td>\n",
       "      <td>0.078128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baso#</th>\n",
       "      <td>-0.001150</td>\n",
       "      <td>0.278747</td>\n",
       "      <td>0.409894</td>\n",
       "      <td>0.070176</td>\n",
       "      <td>0.166313</td>\n",
       "      <td>0.276380</td>\n",
       "      <td>0.195955</td>\n",
       "      <td>0.099034</td>\n",
       "      <td>-0.187635</td>\n",
       "      <td>-0.342541</td>\n",
       "      <td>...</td>\n",
       "      <td>0.118416</td>\n",
       "      <td>0.433728</td>\n",
       "      <td>0.231272</td>\n",
       "      <td>-0.331775</td>\n",
       "      <td>-0.063651</td>\n",
       "      <td>-0.088281</td>\n",
       "      <td>-0.019791</td>\n",
       "      <td>0.566157</td>\n",
       "      <td>0.059975</td>\n",
       "      <td>0.150053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wbc</th>\n",
       "      <td>0.032423</td>\n",
       "      <td>-0.115737</td>\n",
       "      <td>0.998091</td>\n",
       "      <td>-0.003767</td>\n",
       "      <td>0.211040</td>\n",
       "      <td>0.221447</td>\n",
       "      <td>0.272310</td>\n",
       "      <td>0.257000</td>\n",
       "      <td>0.043900</td>\n",
       "      <td>-0.371530</td>\n",
       "      <td>...</td>\n",
       "      <td>0.540795</td>\n",
       "      <td>0.309680</td>\n",
       "      <td>0.643888</td>\n",
       "      <td>-0.235111</td>\n",
       "      <td>0.339412</td>\n",
       "      <td>0.172790</td>\n",
       "      <td>0.441893</td>\n",
       "      <td>0.277237</td>\n",
       "      <td>-0.178744</td>\n",
       "      <td>0.295736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lym%</th>\n",
       "      <td>-0.009158</td>\n",
       "      <td>-0.182016</td>\n",
       "      <td>0.058556</td>\n",
       "      <td>0.547182</td>\n",
       "      <td>0.338682</td>\n",
       "      <td>0.376962</td>\n",
       "      <td>-0.296733</td>\n",
       "      <td>-0.267865</td>\n",
       "      <td>-0.020465</td>\n",
       "      <td>0.091123</td>\n",
       "      <td>...</td>\n",
       "      <td>0.804096</td>\n",
       "      <td>-0.206268</td>\n",
       "      <td>0.717933</td>\n",
       "      <td>0.291912</td>\n",
       "      <td>0.792425</td>\n",
       "      <td>0.210414</td>\n",
       "      <td>0.518444</td>\n",
       "      <td>-0.282559</td>\n",
       "      <td>-0.258935</td>\n",
       "      <td>0.503031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Abs lym</th>\n",
       "      <td>0.045180</td>\n",
       "      <td>-0.265932</td>\n",
       "      <td>0.568844</td>\n",
       "      <td>0.386510</td>\n",
       "      <td>0.313005</td>\n",
       "      <td>0.341351</td>\n",
       "      <td>-0.111267</td>\n",
       "      <td>-0.095086</td>\n",
       "      <td>0.005047</td>\n",
       "      <td>-0.114043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.958922</td>\n",
       "      <td>-0.025345</td>\n",
       "      <td>0.944371</td>\n",
       "      <td>0.087354</td>\n",
       "      <td>0.851973</td>\n",
       "      <td>0.278000</td>\n",
       "      <td>0.683801</td>\n",
       "      <td>-0.088489</td>\n",
       "      <td>-0.290770</td>\n",
       "      <td>0.569064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd3%</th>\n",
       "      <td>0.020425</td>\n",
       "      <td>-0.001117</td>\n",
       "      <td>0.051438</td>\n",
       "      <td>0.165321</td>\n",
       "      <td>0.336405</td>\n",
       "      <td>0.239413</td>\n",
       "      <td>0.005171</td>\n",
       "      <td>0.130770</td>\n",
       "      <td>0.327250</td>\n",
       "      <td>0.036569</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270924</td>\n",
       "      <td>0.651917</td>\n",
       "      <td>0.217464</td>\n",
       "      <td>0.638208</td>\n",
       "      <td>0.319912</td>\n",
       "      <td>-0.654257</td>\n",
       "      <td>-0.358162</td>\n",
       "      <td>-0.088182</td>\n",
       "      <td>-0.630943</td>\n",
       "      <td>-0.314475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd3 abs</th>\n",
       "      <td>0.047696</td>\n",
       "      <td>-0.265807</td>\n",
       "      <td>0.539096</td>\n",
       "      <td>0.407696</td>\n",
       "      <td>0.361162</td>\n",
       "      <td>0.369794</td>\n",
       "      <td>-0.118412</td>\n",
       "      <td>-0.078138</td>\n",
       "      <td>0.066935</td>\n",
       "      <td>-0.078095</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103674</td>\n",
       "      <td>0.964798</td>\n",
       "      <td>0.239357</td>\n",
       "      <td>0.919864</td>\n",
       "      <td>0.085409</td>\n",
       "      <td>0.503935</td>\n",
       "      <td>-0.116753</td>\n",
       "      <td>-0.396157</td>\n",
       "      <td>0.430248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd4%</th>\n",
       "      <td>-0.345496</td>\n",
       "      <td>0.281594</td>\n",
       "      <td>0.306203</td>\n",
       "      <td>-0.037649</td>\n",
       "      <td>0.053451</td>\n",
       "      <td>0.032424</td>\n",
       "      <td>0.056052</td>\n",
       "      <td>0.083939</td>\n",
       "      <td>0.089432</td>\n",
       "      <td>-0.075194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.103674</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.239781</td>\n",
       "      <td>-0.148355</td>\n",
       "      <td>-0.082118</td>\n",
       "      <td>-0.593569</td>\n",
       "      <td>-0.337081</td>\n",
       "      <td>0.636785</td>\n",
       "      <td>-0.193412</td>\n",
       "      <td>-0.050243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd4 abs</th>\n",
       "      <td>-0.060331</td>\n",
       "      <td>-0.190007</td>\n",
       "      <td>0.639397</td>\n",
       "      <td>0.386152</td>\n",
       "      <td>0.324169</td>\n",
       "      <td>0.346737</td>\n",
       "      <td>-0.117447</td>\n",
       "      <td>-0.091850</td>\n",
       "      <td>0.028703</td>\n",
       "      <td>-0.119642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.964798</td>\n",
       "      <td>0.239781</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032233</td>\n",
       "      <td>0.789152</td>\n",
       "      <td>0.060347</td>\n",
       "      <td>0.482205</td>\n",
       "      <td>0.095431</td>\n",
       "      <td>-0.284642</td>\n",
       "      <td>0.540355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd 8%</th>\n",
       "      <td>0.377962</td>\n",
       "      <td>-0.250713</td>\n",
       "      <td>-0.225986</td>\n",
       "      <td>0.158299</td>\n",
       "      <td>0.382794</td>\n",
       "      <td>0.253293</td>\n",
       "      <td>0.046161</td>\n",
       "      <td>0.193523</td>\n",
       "      <td>0.397382</td>\n",
       "      <td>0.022586</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239357</td>\n",
       "      <td>-0.148355</td>\n",
       "      <td>0.032233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.509250</td>\n",
       "      <td>-0.240891</td>\n",
       "      <td>-0.109063</td>\n",
       "      <td>-0.785708</td>\n",
       "      <td>-0.615321</td>\n",
       "      <td>-0.363972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd 8 abs</th>\n",
       "      <td>0.201079</td>\n",
       "      <td>-0.331702</td>\n",
       "      <td>0.340598</td>\n",
       "      <td>0.333421</td>\n",
       "      <td>0.368324</td>\n",
       "      <td>0.343606</td>\n",
       "      <td>-0.052865</td>\n",
       "      <td>0.008687</td>\n",
       "      <td>0.143590</td>\n",
       "      <td>-0.052700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.919864</td>\n",
       "      <td>-0.082118</td>\n",
       "      <td>0.789152</td>\n",
       "      <td>0.509250</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.097894</td>\n",
       "      <td>0.462219</td>\n",
       "      <td>-0.406645</td>\n",
       "      <td>-0.504392</td>\n",
       "      <td>0.201142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd 19%</th>\n",
       "      <td>0.113894</td>\n",
       "      <td>-0.288298</td>\n",
       "      <td>0.170815</td>\n",
       "      <td>-0.075314</td>\n",
       "      <td>-0.038466</td>\n",
       "      <td>0.072362</td>\n",
       "      <td>0.130086</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>-0.265709</td>\n",
       "      <td>-0.114159</td>\n",
       "      <td>...</td>\n",
       "      <td>0.085409</td>\n",
       "      <td>-0.593569</td>\n",
       "      <td>0.060347</td>\n",
       "      <td>-0.240891</td>\n",
       "      <td>0.097894</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.811833</td>\n",
       "      <td>-0.229258</td>\n",
       "      <td>-0.088258</td>\n",
       "      <td>0.074464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd 19 abs</th>\n",
       "      <td>0.093738</td>\n",
       "      <td>-0.304504</td>\n",
       "      <td>0.437403</td>\n",
       "      <td>0.126309</td>\n",
       "      <td>0.089672</td>\n",
       "      <td>0.176192</td>\n",
       "      <td>0.044068</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.178768</td>\n",
       "      <td>-0.089429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.503935</td>\n",
       "      <td>-0.337081</td>\n",
       "      <td>0.482205</td>\n",
       "      <td>-0.109063</td>\n",
       "      <td>0.462219</td>\n",
       "      <td>0.811833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177355</td>\n",
       "      <td>-0.288432</td>\n",
       "      <td>0.284034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cd4/cd8</th>\n",
       "      <td>-0.419719</td>\n",
       "      <td>0.429559</td>\n",
       "      <td>0.267823</td>\n",
       "      <td>-0.060543</td>\n",
       "      <td>-0.257840</td>\n",
       "      <td>-0.152870</td>\n",
       "      <td>-0.050749</td>\n",
       "      <td>-0.155774</td>\n",
       "      <td>-0.299353</td>\n",
       "      <td>-0.001233</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.116753</td>\n",
       "      <td>0.636785</td>\n",
       "      <td>0.095431</td>\n",
       "      <td>-0.785708</td>\n",
       "      <td>-0.406645</td>\n",
       "      <td>-0.229258</td>\n",
       "      <td>-0.177355</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.401723</td>\n",
       "      <td>0.272365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NK %</th>\n",
       "      <td>-0.240242</td>\n",
       "      <td>0.408281</td>\n",
       "      <td>-0.185876</td>\n",
       "      <td>-0.103375</td>\n",
       "      <td>-0.265620</td>\n",
       "      <td>-0.256686</td>\n",
       "      <td>-0.095841</td>\n",
       "      <td>-0.118079</td>\n",
       "      <td>-0.106153</td>\n",
       "      <td>-0.051859</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.396157</td>\n",
       "      <td>-0.193412</td>\n",
       "      <td>-0.284642</td>\n",
       "      <td>-0.615321</td>\n",
       "      <td>-0.504392</td>\n",
       "      <td>-0.088258</td>\n",
       "      <td>-0.288432</td>\n",
       "      <td>0.401723</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.458031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NK abs</th>\n",
       "      <td>-0.141554</td>\n",
       "      <td>0.118537</td>\n",
       "      <td>0.289421</td>\n",
       "      <td>0.323583</td>\n",
       "      <td>0.210809</td>\n",
       "      <td>0.213496</td>\n",
       "      <td>-0.169225</td>\n",
       "      <td>-0.130829</td>\n",
       "      <td>0.023885</td>\n",
       "      <td>-0.198498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.430248</td>\n",
       "      <td>-0.050243</td>\n",
       "      <td>0.540355</td>\n",
       "      <td>-0.363972</td>\n",
       "      <td>0.201142</td>\n",
       "      <td>0.074464</td>\n",
       "      <td>0.284034</td>\n",
       "      <td>0.272365</td>\n",
       "      <td>0.458031</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            patient id  telo means       WBC       RBC       Hgb       Hct  \\\n",
       "patient id    1.000000   -0.482406  0.045076  0.056003  0.216487  0.086520   \n",
       "telo means   -0.482406    1.000000 -0.126405 -0.063405 -0.048631  0.009699   \n",
       "WBC           0.045076   -0.126405  1.000000  0.001271  0.220666  0.227359   \n",
       "RBC           0.056003   -0.063405  0.001271  1.000000  0.330533  0.325687   \n",
       "Hgb           0.216487   -0.048631  0.220666  0.330533  1.000000  0.921122   \n",
       "Hct           0.086520    0.009699  0.227359  0.325687  0.921122  1.000000   \n",
       "mcv           0.151196    0.117051  0.271600 -0.551065  0.374157  0.383547   \n",
       "mch           0.270799    0.054275  0.260500 -0.448978  0.494990  0.350255   \n",
       "mchc          0.347655   -0.145268  0.054146  0.063059  0.439348  0.056114   \n",
       "rdw          -0.220234   -0.007756 -0.372158  0.378307 -0.352577 -0.230922   \n",
       "plt          -0.412318    0.172354  0.243272 -0.074261  0.049405  0.040060   \n",
       "mpv          -0.258088   -0.000113  0.169130  0.225262  0.234935  0.392096   \n",
       "gran%        -0.265054    0.180082  0.009123 -0.559908 -0.411022 -0.424544   \n",
       "lmp%          0.027142   -0.196474  0.088627  0.559527  0.375069  0.414239   \n",
       "mono%         0.547701    0.015066 -0.153847 -0.176707 -0.027397 -0.054069   \n",
       "eos%          0.206229    0.119629 -0.121313  0.079285  0.145357  0.107267   \n",
       "baso%         0.010755    0.301144  0.013914 -0.005616 -0.001234  0.094450   \n",
       "nrbc               NaN         NaN       NaN       NaN       NaN       NaN   \n",
       "gran#        -0.118379    0.016935  0.876199 -0.243316  0.031462  0.036594   \n",
       "lymph#        0.076382   -0.293638  0.586373  0.391279  0.336236  0.357343   \n",
       "mono#         0.501696   -0.094394  0.618297 -0.153716  0.145985  0.101529   \n",
       "eos#          0.214030    0.023208  0.563596  0.073564  0.307509  0.278495   \n",
       "baso#        -0.001150    0.278747  0.409894  0.070176  0.166313  0.276380   \n",
       "wbc           0.032423   -0.115737  0.998091 -0.003767  0.211040  0.221447   \n",
       "Lym%         -0.009158   -0.182016  0.058556  0.547182  0.338682  0.376962   \n",
       "Abs lym       0.045180   -0.265932  0.568844  0.386510  0.313005  0.341351   \n",
       "cd3%          0.020425   -0.001117  0.051438  0.165321  0.336405  0.239413   \n",
       "cd3 abs       0.047696   -0.265807  0.539096  0.407696  0.361162  0.369794   \n",
       "cd4%         -0.345496    0.281594  0.306203 -0.037649  0.053451  0.032424   \n",
       "cd4 abs      -0.060331   -0.190007  0.639397  0.386152  0.324169  0.346737   \n",
       "cd 8%         0.377962   -0.250713 -0.225986  0.158299  0.382794  0.253293   \n",
       "cd 8 abs      0.201079   -0.331702  0.340598  0.333421  0.368324  0.343606   \n",
       "cd 19%        0.113894   -0.288298  0.170815 -0.075314 -0.038466  0.072362   \n",
       "cd 19 abs     0.093738   -0.304504  0.437403  0.126309  0.089672  0.176192   \n",
       "cd4/cd8      -0.419719    0.429559  0.267823 -0.060543 -0.257840 -0.152870   \n",
       "NK %         -0.240242    0.408281 -0.185876 -0.103375 -0.265620 -0.256686   \n",
       "NK abs       -0.141554    0.118537  0.289421  0.323583  0.210809  0.213496   \n",
       "\n",
       "                 mcv       mch      mchc       rdw  ...   cd3 abs      cd4%  \\\n",
       "patient id  0.151196  0.270799  0.347655 -0.220234  ...  0.047696 -0.345496   \n",
       "telo means  0.117051  0.054275 -0.145268 -0.007756  ... -0.265807  0.281594   \n",
       "WBC         0.271600  0.260500  0.054146 -0.372158  ...  0.539096  0.306203   \n",
       "RBC        -0.551065 -0.448978  0.063059  0.378307  ...  0.407696 -0.037649   \n",
       "Hgb         0.374157  0.494990  0.439348 -0.352577  ...  0.361162  0.053451   \n",
       "Hct         0.383547  0.350255  0.056114 -0.230922  ...  0.369794  0.032424   \n",
       "mcv         1.000000  0.918503  0.106445 -0.582201  ... -0.118412  0.056052   \n",
       "mch         0.918503  1.000000  0.489273 -0.661689  ... -0.078138  0.083939   \n",
       "mchc        0.106445  0.489273  1.000000 -0.394782  ...  0.066935  0.089432   \n",
       "rdw        -0.582201 -0.661689 -0.394782  1.000000  ... -0.078095 -0.075194   \n",
       "plt         0.055999  0.066981  0.028868 -0.220423  ...  0.408450  0.197672   \n",
       "mpv         0.139594  0.006954 -0.288044  0.203591  ...  0.268907  0.082519   \n",
       "gran%       0.206388  0.156887 -0.056742 -0.062757  ... -0.714490  0.252633   \n",
       "lmp%       -0.273757 -0.244848 -0.013491  0.060101  ...  0.801260 -0.197470   \n",
       "mono%       0.331129  0.321180  0.066422 -0.099505  ... -0.348530 -0.200644   \n",
       "eos%        0.007345  0.054057  0.125795 -0.066832  ... -0.258075  0.203747   \n",
       "baso%       0.073392 -0.015497 -0.204428 -0.250134  ... -0.041744  0.346744   \n",
       "nrbc             NaN       NaN       NaN       NaN  ...       NaN       NaN   \n",
       "gran#       0.352784  0.316216  0.017757 -0.354281  ...  0.119143  0.397890   \n",
       "lymph#     -0.104188 -0.080423  0.026185 -0.118091  ...  0.948706 -0.033763   \n",
       "mono#       0.448138  0.460818  0.158968 -0.360540  ...  0.129578  0.072046   \n",
       "eos#        0.200661  0.236600  0.154073 -0.290308  ...  0.116345  0.401192   \n",
       "baso#       0.195955  0.099034 -0.187635 -0.342541  ...  0.118416  0.433728   \n",
       "wbc         0.272310  0.257000  0.043900 -0.371530  ...  0.540795  0.309680   \n",
       "Lym%       -0.296733 -0.267865 -0.020465  0.091123  ...  0.804096 -0.206268   \n",
       "Abs lym    -0.111267 -0.095086  0.005047 -0.114043  ...  0.958922 -0.025345   \n",
       "cd3%        0.005171  0.130770  0.327250  0.036569  ...  0.270924  0.651917   \n",
       "cd3 abs    -0.118412 -0.078138  0.066935 -0.078095  ...  1.000000  0.103674   \n",
       "cd4%        0.056052  0.083939  0.089432 -0.075194  ...  0.103674  1.000000   \n",
       "cd4 abs    -0.117447 -0.091850  0.028703 -0.119642  ...  0.964798  0.239781   \n",
       "cd 8%       0.046161  0.193523  0.397382  0.022586  ...  0.239357 -0.148355   \n",
       "cd 8 abs   -0.052865  0.008687  0.143590 -0.052700  ...  0.919864 -0.082118   \n",
       "cd 19%      0.130086  0.001873 -0.265709 -0.114159  ...  0.085409 -0.593569   \n",
       "cd 19 abs   0.044068 -0.034821 -0.178768 -0.089429  ...  0.503935 -0.337081   \n",
       "cd4/cd8    -0.050749 -0.155774 -0.299353 -0.001233  ... -0.116753  0.636785   \n",
       "NK %       -0.095841 -0.118079 -0.106153 -0.051859  ... -0.396157 -0.193412   \n",
       "NK abs     -0.169225 -0.130829  0.023885 -0.198498  ...  0.430248 -0.050243   \n",
       "\n",
       "             cd4 abs     cd 8%  cd 8 abs    cd 19%  cd 19 abs  cd4/cd8   \\\n",
       "patient id -0.060331  0.377962  0.201079  0.113894   0.093738 -0.419719   \n",
       "telo means -0.190007 -0.250713 -0.331702 -0.288298  -0.304504  0.429559   \n",
       "WBC         0.639397 -0.225986  0.340598  0.170815   0.437403  0.267823   \n",
       "RBC         0.386152  0.158299  0.333421 -0.075314   0.126309 -0.060543   \n",
       "Hgb         0.324169  0.382794  0.368324 -0.038466   0.089672 -0.257840   \n",
       "Hct         0.346737  0.253293  0.343606  0.072362   0.176192 -0.152870   \n",
       "mcv        -0.117447  0.046161 -0.052865  0.130086   0.044068 -0.050749   \n",
       "mch        -0.091850  0.193523  0.008687  0.001873  -0.034821 -0.155774   \n",
       "mchc        0.028703  0.397382  0.143590 -0.265709  -0.178768 -0.299353   \n",
       "rdw        -0.119642  0.022586 -0.052700 -0.114159  -0.089429 -0.001233   \n",
       "plt         0.410429  0.046549  0.358229 -0.147660   0.042702  0.075712   \n",
       "mpv         0.290063 -0.089949  0.183797  0.216494   0.284548  0.109532   \n",
       "gran%      -0.595861 -0.415881 -0.760667 -0.166623  -0.422975  0.364609   \n",
       "lmp%        0.716819  0.283195  0.786176  0.213352   0.513762 -0.262537   \n",
       "mono%      -0.394392  0.152340 -0.217742  0.023082  -0.179080 -0.149404   \n",
       "eos%       -0.239980 -0.027444 -0.256531 -0.285941  -0.343021  0.210397   \n",
       "baso%       0.016373 -0.219396 -0.118437 -0.178747  -0.176549  0.465297   \n",
       "nrbc             NaN       NaN       NaN       NaN        NaN       NaN   \n",
       "gran#       0.262774 -0.377870 -0.079640  0.065011   0.170038  0.405330   \n",
       "lymph#      0.928520  0.105314  0.847489  0.278416   0.673670 -0.110802   \n",
       "mono#       0.151939  0.016709  0.102851  0.116811   0.174917  0.020066   \n",
       "eos#        0.206862 -0.177053 -0.028970 -0.157342  -0.065269  0.372025   \n",
       "baso#       0.231272 -0.331775 -0.063651 -0.088281  -0.019791  0.566157   \n",
       "wbc         0.643888 -0.235111  0.339412  0.172790   0.441893  0.277237   \n",
       "Lym%        0.717933  0.291912  0.792425  0.210414   0.518444 -0.282559   \n",
       "Abs lym     0.944371  0.087354  0.851973  0.278000   0.683801 -0.088489   \n",
       "cd3%        0.217464  0.638208  0.319912 -0.654257  -0.358162 -0.088182   \n",
       "cd3 abs     0.964798  0.239357  0.919864  0.085409   0.503935 -0.116753   \n",
       "cd4%        0.239781 -0.148355 -0.082118 -0.593569  -0.337081  0.636785   \n",
       "cd4 abs     1.000000  0.032233  0.789152  0.060347   0.482205  0.095431   \n",
       "cd 8%       0.032233  1.000000  0.509250 -0.240891  -0.109063 -0.785708   \n",
       "cd 8 abs    0.789152  0.509250  1.000000  0.097894   0.462219 -0.406645   \n",
       "cd 19%      0.060347 -0.240891  0.097894  1.000000   0.811833 -0.229258   \n",
       "cd 19 abs   0.482205 -0.109063  0.462219  0.811833   1.000000 -0.177355   \n",
       "cd4/cd8     0.095431 -0.785708 -0.406645 -0.229258  -0.177355  1.000000   \n",
       "NK %       -0.284642 -0.615321 -0.504392 -0.088258  -0.288432  0.401723   \n",
       "NK abs      0.540355 -0.363972  0.201142  0.074464   0.284034  0.272365   \n",
       "\n",
       "                NK %    NK abs  \n",
       "patient id -0.240242 -0.141554  \n",
       "telo means  0.408281  0.118537  \n",
       "WBC        -0.185876  0.289421  \n",
       "RBC        -0.103375  0.323583  \n",
       "Hgb        -0.265620  0.210809  \n",
       "Hct        -0.256686  0.213496  \n",
       "mcv        -0.095841 -0.169225  \n",
       "mch        -0.118079 -0.130829  \n",
       "mchc       -0.106153  0.023885  \n",
       "rdw        -0.051859 -0.198498  \n",
       "plt         0.000616  0.289992  \n",
       "mpv        -0.146806  0.078580  \n",
       "gran%       0.293757 -0.377525  \n",
       "lmp%       -0.262625  0.501336  \n",
       "mono%       0.054731 -0.315797  \n",
       "eos%        0.066135 -0.135670  \n",
       "baso%       0.098092  0.027343  \n",
       "nrbc             NaN       NaN  \n",
       "gran#      -0.031604  0.064017  \n",
       "lymph#     -0.308250  0.546756  \n",
       "mono#      -0.128706 -0.043695  \n",
       "eos#       -0.074399  0.078128  \n",
       "baso#       0.059975  0.150053  \n",
       "wbc        -0.178744  0.295736  \n",
       "Lym%       -0.258935  0.503031  \n",
       "Abs lym    -0.290770  0.569064  \n",
       "cd3%       -0.630943 -0.314475  \n",
       "cd3 abs    -0.396157  0.430248  \n",
       "cd4%       -0.193412 -0.050243  \n",
       "cd4 abs    -0.284642  0.540355  \n",
       "cd 8%      -0.615321 -0.363972  \n",
       "cd 8 abs   -0.504392  0.201142  \n",
       "cd 19%     -0.088258  0.074464  \n",
       "cd 19 abs  -0.288432  0.284034  \n",
       "cd4/cd8     0.401723  0.272365  \n",
       "NK %        1.000000  0.458031  \n",
       "NK abs      0.458031  1.000000  \n",
       "\n",
       "[37 rows x 37 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_telofish_cbc.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Longitudinal correlations between mean telomere length & CBC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parsing high correlation data\n",
    "\n",
    "df = merge_telofish_cbc.copy()\n",
    "\n",
    "hi_r_variables = []\n",
    "\n",
    "for col in df.columns:\n",
    "    if col != 'patient id' and col != 'telo means' and col != 'timepoint':\n",
    "        r2_value = df[['telo means', col]].corr().iloc[0][1]\n",
    "        if abs(r2_value) > 0:\n",
    "            hi_r_variables.append([col, r2_value])\n",
    "#             print(col, r2_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "r2_telos_corr = pd.DataFrame(hi_r_variables, columns=['marker', 'R2 correlation'])\n",
    "r2_telos_celltype = r2_telos_corr[r2_telos_corr['marker'].isin(['lymph#', 'mono#', 'gran#', 'eos#', 'baso#'])].copy()\n",
    "r2_telos_lymph_percent = r2_telos_corr[r2_telos_corr['marker'].isin(['NK %', 'cd4%', 'cd 8%', 'cd 19%'])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_r2_telos_celltype = r2_telos_celltype.copy()\n",
    "\n",
    "m_r2_telos_celltype['marker'].iloc[0] = 'granulocyes'\n",
    "m_r2_telos_celltype['marker'].iloc[1] = 'lymphocytes'\n",
    "m_r2_telos_celltype['marker'].iloc[2] = 'monocytes'\n",
    "m_r2_telos_celltype['marker'].iloc[3] = 'eosinophils'\n",
    "m_r2_telos_celltype['marker'].iloc[4] = 'basophils'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = m_r2_telos_celltype.copy()\n",
    "x = df['R2 correlation']\n",
    "df['colors'] = ['black' if x < 0 else 'green' for x in df['R2 correlation']]\n",
    "df.sort_values('R2 correlation', inplace=True)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "plt.figure(figsize=(7,3.2))\n",
    "plt.hlines(y=df.index, xmin=0, xmax=df['R2 correlation'], color=df['colors'], alpha=0.6, linewidth=14,)\n",
    "\n",
    "# Decorations\n",
    "plt.yticks(df.index, df['marker'], fontsize=14)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.xlabel('Correlation (R) values with Mean Telomere Length', fontsize=14)\n",
    "plt.ylabel('Counts of peripheral WBC types', fontsize=14)\n",
    "\n",
    "plt.grid(linestyle='-', alpha=.2, color='black')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('../graphs/paper figures/supp figs/correlations between WBC cell type counts and telomere length.png', dpi=600,\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "m_r2_telos_lymph_percent = r2_telos_lymph_percent.copy()\n",
    "\n",
    "m_r2_telos_lymph_percent['marker'].iloc[0] = 'CD4 %'\n",
    "m_r2_telos_lymph_percent['marker'].iloc[1] = 'CD8 %'\n",
    "m_r2_telos_lymph_percent['marker'].iloc[2] = 'CD19 %'\n",
    "m_r2_telos_lymph_percent['marker'].iloc[3] = 'NK %'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = m_r2_telos_lymph_percent\n",
    "x = df['R2 correlation']\n",
    "df['colors'] = ['black' if x < 0 else 'green' for x in df['R2 correlation']]\n",
    "df.sort_values('R2 correlation', inplace=True)\n",
    "df.reset_index(inplace=True, drop=True)\n",
    "\n",
    "plt.figure(figsize=(7,3.2))\n",
    "plt.hlines(y=df.index, xmin=0, xmax=df['R2 correlation'], color=df['colors'], alpha=0.6, linewidth=14,)\n",
    "\n",
    "# Decorations\n",
    "plt.yticks(df.index, df['marker'], fontsize=14)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.xlabel('Correlation (R) values with Mean Telomere Length', fontsize=14)\n",
    "plt.ylabel('Percentage of lymphocytes', fontsize=14)\n",
    "\n",
    "plt.grid(linestyle='-', alpha=.2, color='black')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('../graphs/paper figures/supp figs/correlations between proportions of lymphocytes and telomere length.png', \n",
    "            dpi=600,\n",
    "            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>patient id</th>\n",
       "      <th>timepoint</th>\n",
       "      <th>telo means</th>\n",
       "      <th>WBC</th>\n",
       "      <th>RBC</th>\n",
       "      <th>Hgb</th>\n",
       "      <th>Hct</th>\n",
       "      <th>mcv</th>\n",
       "      <th>mch</th>\n",
       "      <th>mchc</th>\n",
       "      <th>...</th>\n",
       "      <th>cd3 abs</th>\n",
       "      <th>cd4%</th>\n",
       "      <th>cd4 abs</th>\n",
       "      <th>cd 8%</th>\n",
       "      <th>cd 8 abs</th>\n",
       "      <th>cd 19%</th>\n",
       "      <th>cd 19 abs</th>\n",
       "      <th>cd4/cd8</th>\n",
       "      <th>NK %</th>\n",
       "      <th>NK abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>84.796483</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.86</td>\n",
       "      <td>14.7</td>\n",
       "      <td>43.6</td>\n",
       "      <td>89.7</td>\n",
       "      <td>30.2</td>\n",
       "      <td>33.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1740</td>\n",
       "      <td>50</td>\n",
       "      <td>1175</td>\n",
       "      <td>23</td>\n",
       "      <td>541</td>\n",
       "      <td>17</td>\n",
       "      <td>400</td>\n",
       "      <td>2.17</td>\n",
       "      <td>7</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3 B</td>\n",
       "      <td>116.779989</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.79</td>\n",
       "      <td>14.7</td>\n",
       "      <td>43.0</td>\n",
       "      <td>89.8</td>\n",
       "      <td>30.7</td>\n",
       "      <td>34.2</td>\n",
       "      <td>...</td>\n",
       "      <td>528</td>\n",
       "      <td>64</td>\n",
       "      <td>404</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4 C</td>\n",
       "      <td>99.346299</td>\n",
       "      <td>5.9</td>\n",
       "      <td>4.58</td>\n",
       "      <td>14.8</td>\n",
       "      <td>43.9</td>\n",
       "      <td>95.9</td>\n",
       "      <td>32.3</td>\n",
       "      <td>33.7</td>\n",
       "      <td>...</td>\n",
       "      <td>431</td>\n",
       "      <td>50</td>\n",
       "      <td>294</td>\n",
       "      <td>21</td>\n",
       "      <td>124</td>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>2.38</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient id    timepoint  telo means  WBC   RBC   Hgb   Hct   mcv   mch  \\\n",
       "0           1  1 non irrad   84.796483  8.7  4.86  14.7  43.6  89.7  30.2   \n",
       "1           1          3 B  116.779989  5.7  4.79  14.7  43.0  89.8  30.7   \n",
       "2           1          4 C   99.346299  5.9  4.58  14.8  43.9  95.9  32.3   \n",
       "\n",
       "   mchc  ...  cd3 abs  cd4%  cd4 abs  cd 8%  cd 8 abs  cd 19%  cd 19 abs  \\\n",
       "0  33.7  ...     1740    50     1175     23       541      17        400   \n",
       "1  34.2  ...      528    64      404     19       119       4         27   \n",
       "2  33.7  ...      431    50      294     21       124      11         64   \n",
       "\n",
       "   cd4/cd8   NK %  NK abs  \n",
       "0      2.17     7     158  \n",
       "1      3.40     9      57  \n",
       "2      2.38    14      83  \n",
       "\n",
       "[3 rows x 38 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_telofish_cbc.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trp.graph_cyto_cbc_data(plot_cyto_y='telo means', plot_cbc_y='WBC', df=merge_telofish_cbc,\n",
    "                        cyto_name='Mean Telomere Length', cbc_name='Total WBC counts',\n",
    "                        ylim1=(80, 120),\n",
    "                        ylim2=(3.25, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chromosome aberrations vs. CBC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge_chr_cbc.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "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>patient id</th>\n",
       "      <th>timepoint</th>\n",
       "      <th># inversions</th>\n",
       "      <th># sister chromatid exchanges</th>\n",
       "      <th># dicentrics</th>\n",
       "      <th># excess chr fragments</th>\n",
       "      <th># sat associations</th>\n",
       "      <th># terminal SCEs</th>\n",
       "      <th># translocations</th>\n",
       "      <th>WBC</th>\n",
       "      <th>...</th>\n",
       "      <th>cd3 abs</th>\n",
       "      <th>cd4%</th>\n",
       "      <th>cd4 abs</th>\n",
       "      <th>cd 8%</th>\n",
       "      <th>cd 8 abs</th>\n",
       "      <th>cd 19%</th>\n",
       "      <th>cd 19 abs</th>\n",
       "      <th>cd4/cd8</th>\n",
       "      <th>NK %</th>\n",
       "      <th>NK abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>8.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1740</td>\n",
       "      <td>50</td>\n",
       "      <td>1175</td>\n",
       "      <td>23</td>\n",
       "      <td>541</td>\n",
       "      <td>17</td>\n",
       "      <td>400</td>\n",
       "      <td>2.17</td>\n",
       "      <td>7</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3 B</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>5.7</td>\n",
       "      <td>...</td>\n",
       "      <td>528</td>\n",
       "      <td>64</td>\n",
       "      <td>404</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4 C</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>5.9</td>\n",
       "      <td>...</td>\n",
       "      <td>431</td>\n",
       "      <td>50</td>\n",
       "      <td>294</td>\n",
       "      <td>21</td>\n",
       "      <td>124</td>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>2.38</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>5.3</td>\n",
       "      <td>...</td>\n",
       "      <td>740</td>\n",
       "      <td>42</td>\n",
       "      <td>472</td>\n",
       "      <td>25</td>\n",
       "      <td>275</td>\n",
       "      <td>15</td>\n",
       "      <td>170</td>\n",
       "      <td>1.72</td>\n",
       "      <td>15</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3 B</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>203</td>\n",
       "      <td>53</td>\n",
       "      <td>147</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2.43</td>\n",
       "      <td>24</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient id    timepoint  # inversions  # sister chromatid exchanges  \\\n",
       "0           1  1 non irrad      0.233333                      0.633333   \n",
       "1           1          3 B      1.266667                      0.700000   \n",
       "2           1          4 C      0.566667                      0.933333   \n",
       "3           2  1 non irrad      0.300000                      0.533333   \n",
       "4           2          3 B      1.066667                      0.700000   \n",
       "\n",
       "   # dicentrics  # excess chr fragments  # sat associations  # terminal SCEs  \\\n",
       "0      0.000000                0.000000            0.133333         0.566667   \n",
       "1      0.366667                0.433333            0.766667         0.800000   \n",
       "2      0.066667                0.266667            0.366667         0.766667   \n",
       "3      0.000000                0.033333            0.333333         0.766667   \n",
       "4      0.300000                0.166667            0.466667         1.000000   \n",
       "\n",
       "   # translocations  WBC  ...  cd3 abs  cd4%  cd4 abs  cd 8%  cd 8 abs  \\\n",
       "0          0.033333  8.7  ...     1740    50     1175     23       541   \n",
       "1          0.100000  5.7  ...      528    64      404     19       119   \n",
       "2          0.133333  5.9  ...      431    50      294     21       124   \n",
       "3          0.033333  5.3  ...      740    42      472     25       275   \n",
       "4          0.033333  5.6  ...      203    53      147     22        61   \n",
       "\n",
       "   cd 19%  cd 19 abs  cd4/cd8   NK %  NK abs  \n",
       "0      17        400      2.17     7     158  \n",
       "1       4         27      3.40     9      57  \n",
       "2      11         64      2.38    14      83  \n",
       "3      15        170      1.72    15     171  \n",
       "4       2          6      2.43    24      67  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 705,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_chr_cbc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14d47d1d0>"
      ]
     },
     "execution_count": 707,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='lymph#', y='# inversions', lowess=True, data=merge_chr_cbc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Longitudinal correlations between chromosome aberrations & CBC data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# inversions lymph# -0.7522869776095474\n",
      "# dicentrics lymph# -0.7509787076100194\n",
      "# excess chr fragments lymph# -0.6717065000901444\n",
      "# translocations lymph# -0.6015085894070261\n"
     ]
    }
   ],
   "source": [
    "chr_corr_df = pd.DataFrame()\n",
    "\n",
    "for aberr in grp_chr_aberr.columns:\n",
    "    if aberr != 'patient id' and aberr != 'timepoint':\n",
    "        test = grp_chr_aberr[['patient id', 'timepoint', aberr]].merge(cbc_data, on=['patient id', 'timepoint']) \n",
    "\n",
    "        df = test.copy()\n",
    "\n",
    "        hi_r_variables = []\n",
    "\n",
    "        for col in df:\n",
    "            if col == 'lymph#':\n",
    "                r2_value = df[[aberr, col]].corr().iloc[0][1]\n",
    "                if abs(r2_value) > 0.60:\n",
    "                    hi_r_variables.append([col, r2_value])\n",
    "                    print(aberr, col, r2_value)\n",
    "                    \n",
    "#         single_aberr_df = pd.DataFrame(hi_r_variables, columns=['CBC data', f'R2 correlation {aberr}'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>CBC data</th>\n",
       "      <th>R2 correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lymph#</td>\n",
       "      <td>-0.601509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CBC data  R2 correlation\n",
       "0   lymph#       -0.601509"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbc_corr_telo = pd.DataFrame(hi_r_variables, columns=['CBC data', 'R2 correlation'])\n",
    "cbc_corr_telo.sort_values(by='R2 correlation', ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trp.graph_cyto_cbc_data(plot_cyto_y='# inversions', plot_cbc_y='lymph#', df=merge_chr_cbc,\n",
    "                    cyto_name='# inversions', cbc_name='Lymphocyte cell counts',\n",
    "                    ax_color1='teal'\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trp.graph_cyto_cbc_data(plot_cyto_y='# translocations', plot_cbc_y='lymph#', df=merge_chr_cbc,\n",
    "                    cyto_name='# translocations', cbc_name='Lymphocyte cell counts',\n",
    "                    ax_color1='red',\n",
    "                    ylim1=(0, 0.15)\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trp.graph_cyto_cbc_data(plot_cyto_y='# dicentrics', plot_cbc_y='lymph#', df=merge_chr_cbc,\n",
    "                    cyto_name='# dicentrics', cbc_name='Lymphocyte cell counts',\n",
    "                    ax_color1='orange'\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trp.graph_cyto_cbc_data(plot_cyto_y='# excess chr fragments', plot_cbc_y='lymph#', df=merge_chr_cbc,\n",
    "                    cyto_name='# excess chr fragments', cbc_name='Lymphocyte cell counts',\n",
    "                    ax_color1='purple',\n",
    "                    ylim1=(0, 0.35)\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>CBC data</th>\n",
       "      <th>R2 correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cd4/cd8</td>\n",
       "      <td>0.429559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NK %</td>\n",
       "      <td>0.408281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baso%</td>\n",
       "      <td>0.301144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cd4%</td>\n",
       "      <td>0.281594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baso#</td>\n",
       "      <td>0.278747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cd 8%</td>\n",
       "      <td>-0.250713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cd3 abs</td>\n",
       "      <td>-0.265807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Abs lym</td>\n",
       "      <td>-0.265932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>cd 19%</td>\n",
       "      <td>-0.288298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lymph#</td>\n",
       "      <td>-0.293638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cd 19 abs</td>\n",
       "      <td>-0.304504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cd 8 abs</td>\n",
       "      <td>-0.331702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CBC data  R2 correlation\n",
       "0    cd4/cd8         0.429559\n",
       "1        NK %        0.408281\n",
       "2       baso%        0.301144\n",
       "3        cd4%        0.281594\n",
       "4       baso#        0.278747\n",
       "5       cd 8%       -0.250713\n",
       "6     cd3 abs       -0.265807\n",
       "7     Abs lym       -0.265932\n",
       "8      cd 19%       -0.288298\n",
       "9      lymph#       -0.293638\n",
       "10  cd 19 abs       -0.304504\n",
       "11   cd 8 abs       -0.331702"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cbc_corr_telo = pd.DataFrame(hi_r_variables, columns=['CBC data', 'R2 correlation'])\n",
    "cbc_corr_telo.sort_values(by='R2 correlation', ascending=False).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# inversions lymph# -0.7522869776095474\n",
      "# dicentrics lymph# -0.7509787076100194\n",
      "# excess chr fragments lymph# -0.6717065000901444\n",
      "# translocations lymph# -0.6015085894070261\n"
     ]
    }
   ],
   "source": [
    "chr_corr_df = pd.DataFrame()\n",
    "\n",
    "for aberr in grp_chr_aberr.columns:\n",
    "    if aberr != 'patient id' and aberr != 'timepoint':\n",
    "        test = grp_chr_aberr[['patient id', 'timepoint', aberr]].merge(cbc_data, on=['patient id', 'timepoint']) \n",
    "\n",
    "        df = test.copy()\n",
    "\n",
    "        hi_r_variables = []\n",
    "\n",
    "        for col in df:\n",
    "            if col == 'lymph#':\n",
    "                r2_value = df[[aberr, col]].corr().iloc[0][1]\n",
    "                if abs(r2_value) > 0.60:\n",
    "                    hi_r_variables.append([col, r2_value])\n",
    "                    print(aberr, col, r2_value)\n",
    "                    \n",
    "#         single_aberr_df = pd.DataFrame(hi_r_variables, columns=['CBC data', f'R2 correlation {aberr}'])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Addressing whether recovery fraction of lymphocytes (pre vs. post) correlates with chromosome aberrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parsing out time / lymph#\n",
    "time_cbc = merge_chr_cbc[['patient id', 'timepoint', 'lymph#']]\n",
    "recovery_percent = time_cbc.pivot_table(index=['patient id'], values='lymph#', columns='timepoint').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature engineering lymph# percent recovery\n",
    "recovery_percent['pre vs. post recov %'] = (recovery_percent['4 C'] / recovery_percent['1 non irrad']) * 100\n",
    "recovery_percent['pre vs. immed post %'] = (recovery_percent['3 B'] / recovery_percent['1 non irrad']) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0.0%'),\n",
       " Text(0, 0, '10.0%'),\n",
       " Text(0, 0, '20.0%'),\n",
       " Text(0, 0, '30.0%'),\n",
       " Text(0, 0, '40.0%'),\n",
       " Text(0, 0, '50.0%'),\n",
       " Text(0, 0, '60.0%')]"
      ]
     },
     "execution_count": 488,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2AN4jRsO9zOwb4mSd8WmP7abAUVn6KyIi0lRZp38vBg4iRpQldwKHmtnZc9DueCJoDgHuAzoAdRXXTAJWaOD1s7v+BOAm4D/Ahe5eB5yU/q5RqoiIFCLrQqXdiSQPQ0sF7n6jmTlwD9WnaBuzHbHA6BrgMqA18E3FNd8Qq4urafR6d3/QzJYBWrn7FDPrTCyA6m9m5xJpF18E9nP3rxrraMuWLWjbtnXmNyYyv5nbv//6vMm8bHa/n1mD6mLA11XKPwfaNLFPuPurAGbWmlhwdBOwZMVlrYCGAt7XzBpwZ7o+3bctJao4GTgfWJ/4gtCdyBDVH7igsb5On17PlCmNxl2R+drc/v3X503mZaXfz3btqoe+rEH1MeAKM9vb3T8AMLOViVHm41kqSPc0e7v7I2XFI4FFiWnlX1a8pD0zTzeXm5Cen+31aavO+kA/4AjgJXf/Ii2S2jJL36X29ei5BnUTJhZSd8dO7Xl9+DuF1C0itSVrUO0PPESs2P08lS0BPEksNMqiK/CAmXUsyxXcG/iIuL96nJkt7u5fpuc2JhL3VzM0PX8DQJreXbGB608FznP3GWZWz4/3kRdGiSsWGHUTJnL+nb8rpO4Tdn+ikHpFpPZkDaodiJWzXYFuRHald919VBPaGkyMTAea2dHAasSU7DnpuTHpuTOAbYgVvvsDmNmiwNLAR2mh0TXAYDN7gQikA4BB7v5ueYNpC1AvoG8qehU4wczWBnYgvhSIiIjkImtQfQb4g7sPIwJjk7n7d2a2NXAV8BLwBfBX4HJ3rzez7YAbgWHEqt0d0jYciO03zxAJ/Ue7+xAz6wucBSxDZFQ6sEqzpwLnunt96sO/zOxO4DkikF8xJ+9FZHbW6mlMnNDQ3Yvmad+pAyOGeyF1i0jzZA2q44nkDcOa05i7jyOS81d77j1iNFztuWepmKpNaQYbTTXo7vtWKTuCuLcqUpiJEz7k9w/tPfsL58Dj2986+4tE5CeRNaiOINIGDgdGU7ES2N2L+ddDRESkhmQNqvUoX66I/ETW6tmNiRPyP86rfacVGDF8ju5oiVSVNffvLNOoIiJzy8QJ4/njvZl27zXJI7v8Pvc6ZcGWdaRKWmR0BNCFuPd5ADDO3a8rqG8iIiI1JWtC/b2Ay4nVuhsBLYGJwKVpb+llxXVxwdWrhzG+Lv8VpCt07MBrr2v1qIhI3rKOVI8HDnL3e83sWAB3v8rMJgEXEZmVJGfj6z5k/JX9cq93hcOuzr1OERHJHlRXIxInVHqdWdMFzrd69+jKuLoJudfbuWMnhr3elDwaIiIyL8oaVN8EtgauTI/r05/7EdttFgjj6iYw+erbcq93uX7/k3udIiIy92UNqkcDj5nZ5kQC/NPMrAvQk0gpKCIissDLuqXmeTMz4qSX74G2RBL8Pdx9bIH9ExGRGte7R3fG1Y3Lvd7OHTsz7PW3c6+3OTJvqXH3icQIdXlghrt/VFy3RERkfjGubhwfXjg693o7HLdy7nU2V9YtNS2Bs4m9qUunsknAle5+bnHdExERqR0Lzf4SIPan7gUcC6xNHKd2GtAvHdUmIiKywMs6/bsXsJ27P1dW9oaZfQDcAZyRd8dERERqTdaR6lRigVKlKcCM/LojIiJSu5qSUelGMzseGEIE2B5EJqUBZrZq6UJ3fz/3XoqIiNSArEG1dOzbQ/yY+KF0aPhawDnpcT2RF1hERGSBkzWorlJoL0REROYDWZM/jCm6IyIiIrUu60IlERERmQ0FVRERkZxkTlOYBzNbjUgksTHwJXA3cLK7f21mKwHXE4egjwWOcvdBjdS1K3Au0BH4J9DX3Sen5zYCbgWWAE5y9+vLXncJMMLdbyngLYqIyAIsc1A1s8WAnYA1gAFEZqVRKSdwltcvCvwDGAlsCCwH3JSeOwZ4GBgFrAv8EbjfzLq7+wdV6loXuAU4BHgt9edW4PfpkiuJAP0K8KiZPejuH5tZe2Bz4Lis71tERCSrTNO/ZrY64MBZwInEKTUHA2+bWe+Mba0HrA7s4+6j3H0wcCqwJ7AZYMCB7j7S3c8HXgT2b6Cu/sD97j7Q3UcAewNbpn4CrAk84O5PEQkqSvtoTwQudvfpGfssIiKSWdZ7qpcTI8nVgW9S2e7AfcR0bhYObOXuU8vK6okA3QcY7u5flD33PLBBA3X1AX5Imeju44AxZdePBXqlKeWlgPFm1gnYFLgzY39FRESaJOv074bAEe5eH8eqgrvPMLMLgTeyVJCOinuy9NjMFgIOS2UdgLqKl0wCVmigutldfwLwd+JA9XPdvc7MrgIubOootWXLFrRt27opL5kjc6ONn7K9+d38/P9vfn5vP0V7kq957fcla1CdSgSydyrKfwF82vRuAXAp0JO4h3oUP46AS74BWjXw2taNXe/uD5rZMkArd59iZp2JBVD9zexc4oCAF4H93P2rxjo5fXo9U6Y0ekku5kYbs2uvZ481mFCX6RZ5k3Tq2J7hr1f+6sxf5oX/f/NDWwtCe9X06Nmdugn5H+LdsVNnXh8+bx3inbef6velXbs2VZ/PGlT/Blybcv+2ALqa2ebEGavXNKVDZtaCmDLuB+zs7m+b2dfAkhWXtgIa+q/1NbMG3Jmud/dpwLT08GTgfGB9Ytq6O3AzcW/2gqb0f342oW4iz123de71bnLgY7nXKTI/qZswjoH3T8693n12Wi73OqVxWTMqnW1mnwFXEKPEfwCTgUuAi7M2lqZ8byQWJ+3m7g+npyYQq4nLtQc+bKCqCen52V6f7quuTwTxI4CX3P0LM3sS2DJr3yVfPXqsQV0Bo2KAjh3b8/p8PjKWYq3VszsTCxg5tu/UmRHz+chxQZcpqJrZisBV7n6FmS0OLOzun5lZS+K0mtcytncJsAewo7s/WlY+FDjJzBZ39y9T2capvJqh6fkbUv86Ays2cP2pwHnpHnA9Py7OWpgfDwWQuayubiJ33Py7QureY98nCqlXFhwTJ4xjl/tG5V7vvTt3zb1Ombdknf79gBgJflQW9CC2qjxPjF4bZWZ9iJHiicCrac9oyWBi9e5AMzsD2IZY4bt/eu2iwNKp/enElPNgM3uBCKQDgEHu/m5Fm6sCvYC+qehV4AQzWxvYgbKFUyIiIs3VYFA1swOBU9LDFsBwM6s8kHxJ4K2Mbe2c/jwv/ZRbBNiOmBoeBvwH2MHdR6fnNwSeIU7LGe3uQ8ysL7Fvdhkio9KBVdo8lVj9Ww/g7v8yszuJ7TiDielsERGRXDQ2Ur2ZWBC0EJH56ELgs7Ln64lVwU9nacjdjwGOaeSS94h9pNVe+ywVU7UpzWCjqQbdfd8qZUcQI2YREZFcNRhU3f07IvUfZrYUcLW7fz+3OiYiIlJrsmZUOhd408xOK0sFKCIiImWyBtXliPug6xH5fl81s6NT6j8REREh+z7VL4ip4FvNrC2wPbA1cIaZvQbcAdzp7p8X1lMREZF53JwcUt4F6EqkKKwHxgG7AmPM7E859k1ERKSmZE3+sA4ROHcmDgUfBJwOPOLuX6drTgCuAu4qpqsiIiLztqzJH4YS+zrPAe5z98+qXDMEuD+vjomIiNSarNO/nd19c+CeUkA1s27lF7j7YHevloBBRERkgZA1qC5hZqOIDEUlT5nZCDNbpYB+iYiI1JysQfUq4GXiqLeS1YkDyq/Ou1MiIiK1KGtQ7QOcWb5lJiXWP5M4/FtERGSBlzWoTgLWqVK+FjAlv+6IiIjUrqyrf/8KXGdm3fnx7NSewP8SKQxFREQWeFkzKl1hZl8BBwFHA98C7wKHufsdBfZPRERy1qtHd8bXjcu93hU6dua119/Ovd5aknWkirvfSJx3KiIiNWx83TiG3Dwp93o32Hf53OusNZmDqpntBBwHrJle58AV7n5zQX0TERGpKZkWKplZP+JA8KeBvYA9gSeBy83sgOK6JyIiUjuyjlSPAfq5+61lZQ+Z2VvAycANufdMRESkxjTlPNUXq5QPAVbMrzsiIiK1K2tQHQ78uUr5PsCCvdRLREQkyTr9exyR63cL4KVUtj7wS+Kw8iYxs1bAMOAId38ylS0NXAtsCfwXON3db2mkjs2AAUS6xJeBA9z9vfTcmsDdxCh6gLufUfa6/sDS7n5mU/stIiLSmEwjVXcfAvQmpnvXADoTi5bM3Qc3pUEzWwy4E+he8dRAYBki7eFZwLVmtmEDdXQGHgFuJzI9TQQeNrPS+zkPeA7YHDjSzNYqa/sgIpmFiIhIrpqyT3UUcFRzGkvHxd0BtKgoXw3YFuiSRptvpoDaj+r3cvsCb7j7hen1+xGB9TfEquQ1gWvc/TUzG5kejwAOAe5q4DxYERGRZskUVM1sKeBEYG1gMSqCortvkrG9TYFniBXDX5aVrw98WJq+TZ5n5qPmyvUhRqKl9r8ys9eADYigOhboZWYvA6sBY82sNRGM+2Tsq4iISJNkHaneSuT6vQeY41Geu19T+ruZlT/VAairuHwSsEIDVc3u+jOBR4FzgNvdfaiZHZv+/jkiIiIFyBpUNwc2dfdXCupHa+CbirJvgEXNrIW712e8vhWAu79oZu2BNu7+iZktDuwHrGdmhxH7bv8N7O3ukxvrWMuWLWjbtvUcvammmBttqD21N6+3pfbUXq23lzWoTgBmNLs3DfuaFBDLtAKmVQmojV3/SemBu39b9rg/kRFqSeKg9TWJFc1nEvdZGzR9ej1TpnyV7V00w9xoQ+2pvXm9LbWn9mqlvXbt2lR9vsGgamarlj28GrjJzI4D3geml1/r7u83s58TgPYVZe2BD5t4/VuVF5pZG2I/7TrA74B/u/tEM3uCWCUsIiKSi8ZGqu8BpVFiaWHSoLLn61N5PdCymf0YCnQys5XdfXQq2ziVN3T9D4uj0iKknsQotNLhwE3uPtXM6vlxG9HCVCy4EhERaY7Gguoqc6sT7v6+mf0/4NZ0z7M3kbR/MwAzawm0A/6bpnVvAo41s5OBB4lVwmOBp8rrNbMlgf8BeqWi4cAv0nad3fgxkYWIiEizNRhU3X1MZVlKotCVuL/6hru/k2Nf9iYS879E7Dk9ICWdgEg28QERZJ9199FmtiNwGbE9ZyiwnbtX3vc9Arje3b9M72m0mZ1OrAx+G9g1x/6LiMgCLus+1Q7AA8R+0v8S071LmNlTwK7uPqWpDbt75V7XycAfG7h2NLPujR3EzNPR1V43SypCd78IuKiJ3RUREZmtrAn1bwSmAau6+7LuvhRgxIrbq4vqnIiISC3JGlQ3AQ4rW0REyn7UH9imgH6JiIjUnKxB9V2gR5XylYHReXVGRESklmVN/nALcJWZrUOcVPM9EWT7AwNTQnsA3P2m3HspIiJSA7IG1cOBKcAO6afks4qyemK7i4iIyAInU1B197m2Z1VERKRWZT5P1cy6EzlzK3Pu1rv7nbn2SkREpAZl3ad6DnGe6ufE1ppy9YCCqoiILPCyjlQPAQ5x92uL7IyIiEgty7ql5gvg+SI7IiIiUuuyjlSPIbbUnEYkrp8px667j827YyIiIrUma1BdmDg55pmK8ryOfhMREal5WYPqJcT+0+uZdaGSiIiIkD2oLgYMcPf3i+yMiIhILcu6UOki4BQza11kZ0RERGpZ1pHqVsC6wF5m9jGR+/cH7r5i3h0TERGpNVmD6g3pR0RERBqQNaguDdzl7h8W2RkREZFalvWe6i7AWDN72swOMLOliuyUiIhILcoUVN19Q2B14P+Ag4APzewfZraHmS1eZAdFRERqRdaRKu4+xt0vdvd1idNq3iD2rU4yszvNbLOiOikiIlILMh/9BmBmKwG7AjsDvYDBwF1AB+AeM7vN3Y+a086YWSvgCmK6+RvgUne/sIFr1wb+BqwNjAIOdvdX0nPtgXvTc3cBB7l7fXpuW2BHd993TvspIiJSTdaj344hgmlv4FXgDmA7d59Yds1o4GpgjoMqsR92A2ALYAXgNjMb6+53VfRncWAQcDewHzEl/ZiZrebuXwDHAx8BfYDHgG2BR9LLTwF2b0YfRUREqso6/bsf8A9gDXdf390HlAfUZDhw6Jx2JAXKvsCR7j7M3R8GLgQOq3L5bsB3wNHuPgo4EvgslUNMTz/u7iOBoekxZrYD8KYyQ4mISBEyjfe7kmYAAA5QSURBVFTdvVu1cjNrA3Rx99fc/S3grWb0ZW2gFTMfMfc8cKqZtXT36WXlfYAX3H1G6l+9mb1AjHJvIE7S6WlmiwHdgYfNrAVwMjF1LSIikrvMC5UasCHwSh4dIe7L/tfdvy4rmwQsCixX5dq6irJJxJQxwMXA1sCXwGTgfiKYvubuo3Pqr4iIyEyatFCpYK2JxUnlSo9bZby2FYC7v2tmqwDLuPtkM1sIOBHYwcx2Bs4DPgb2dvd3G+vUIou0/LhduzZjAOrr65v4lrKpP2Sv6uVFtXfoVXO3vb5zry2A3feZu+0d/6e52x71t8y99uqPqF5cWHt7zuX2qtc7v7T357ncXv0+c7m9Y2ett8jPXv3/zlT3StWumZeC6tfMGjxLj7/KeO0P16Xp4snp4a7Ay+nxdcCmwGbA5cAfZtOvdhn6LiIi0uzp3zxNAJYys0XLytoTI9D/Vrm2fUVZe2CWNIpplHoCcC7QFfjW3d8EniDuzYqIiOSiwZGqmf0mw+t75NiX14Fvifu0z6ayjYFh7v59xbVDiaPoWqRFSi2AjYALqtS7B/Ciu481s2X48YvEwkCLHPsvIiILuMamf5/MWEcuE9ju/pWZ3QJcbWb7ECPPY4htNqWEDp+5+zTgPuB84Aozuzpd04ZI9PADM2tJ7FktTfG+A7Qys+2IUepLefRdREQEGgmq7v5TTA0fBVwDPA18Dpzl7vek5z4E9gUGuvvnZrY1cC1wADAC2Colfii3FzDY3ccDuPuXZtaPSK84kR/3tYqIiDRbi0JXKYqIiCxA5qWFSiIiIjVtXtpSM19JhwMMA45w96z3p5vaxmrAX4kFXV8SuZBPrkigkWd7awJXEvejPwGudPeLimgrtbc7kWe63MPuvn1B7S1CLHbbm1jEdg/x/69yT3Qebe0D3NzA0yu5+9gC2lyKOLBiK2AacBvx+zK90RfOeXvLEr8vvwO+AP7q7pcV0M4snzUzW5q4PbQlsXvgdHefdYNvTu2VPbcsccDHunklmmng/fUCLiXysX9MbBW8oJRlLu/2Kp5/Aqhz932KaMvMTiR2a5Qb4O7VN003v70liO2VOxCfi+uB00qHsDSVgmoBUnrEO4gUiUW1sSiRj3kksWJ6OeCm9PTRBbS3CHGIwTPAwUQ+5TvMrM7d/553e0l34EGgX1lZIV8YkouA7YHtiAV4dxBfHk4poK27gcfLHi8EPAq8X0RATa4mspFtQuy/Lr2/or4YPUgkavkdsZDwFjOb4e4D8mqgkc/aQODnxK6AdYFrzexdd3+xoPZKgfwfwLLNaWN27aV2BhFf+g4E1iDe71TiS1Ou7VU8vx/wW6DZX1Aaaas7EeTOKyv7ssD2bgU6E/kL2qdrxhApb5tMQTVnZtaN+J9S9Had9YiD49dz96nAKDM7lfj2mntQBToRCTQOTSuw3zOzJ4lfxKKCajdgRJXDG3JnZm2BQ4Bt3P2FVHYGBS1mS/8Np5W1fxjxwd68iPaSrYA/pzzdmNkdwG8oIKiaWW9iBmVNd/dUdhzx+5lLUG3os5ZmcLYl8pK/B7xpZhsSX87mOKg29tk2s02IQPP5nNbfhPa2Ig4UOTyNTN8xs0uBPWlGUJ3dv11m1oEYQTY7Ne1s2uoGXJ7n576R35VuxO9Kt7Lf00uB9ZnDoKp7qvnblBjNbVBwO06seJ5aVlYPtC2kMffR7r6bu08zsxZmthEx4nmqiPaSbsT7nBs2JjJy/TDd5e4D3X12GbeaLR1McTox5fRpgU19AuxpZq3NrCPwe2IqrAirAp+W/qFK3gA6mNnKObXR0GdtfeDDFFBLnq9yXV7tQfy3vI7I3paXhtobDPypYqo3j8/+7P7tuga4itia2FxV20rJeoz8P/cNvbffAG+V/566+9nuXiW5ajYaqebM3a8p/d3MimznI8oCQPplPIzs+4ubYzzQkZiuvK+IBtL09mrANmb2F+Ib5r3EvbHc73GmtsYAu5vZycTU4b3ASe7+bQHtlTuIyBw2R9+Mm6AfcR/1C+IL9dPAGQW1NQlYwszalG11K+VKXRYY3dwGGvmsze7Ajbzbw91PSuWrN6eNLO25+zhgXNlzPyP26v9fEe2lx7sRn5FdaHgtQB5trUzcMuhrZncRX3RvAi5pzv3iRtpbDfjAzI4A+hNfTm4g7k/P0T1VjVTnH5cCPYlkF0XbLv30AnJfeJJ0Ib70fQnsBBxLTG9dWlB7bYBViA/WQcR9452JM30Lk7KBHQRc4e7fFdkWcbtgODHDsBXxD9jFBbX1EvEP/zVm1sbMOvFjAF+0wVflo6EDNxZN/73nGynBze3A4sDZBbWxLLEg8oC58DvaNf05AdiGSPJzMsXc0oL43P8a2AL4E3HwyrHA4XNaoUaqNS79I/FXYhSys7u/XXSb7v5qars1sfjkmLxHc+7+tpkt6+6fpKI30nu908wOr5K6srm+B5YA9nL3/wCY2THAbWZ2VB6rKhvQi/i2fFtB9QMzrRRfuZQMxcwOAP5pZue5+6Q823P3b8xsJyLL2RTgMyIH9/rkeN+xAQ0duDFtTkcf86I0m3MHsRBsiwLXHgwA7nH3wjPQuftjFZ/7N1NQP5RiFtR9T3zJ2z3NqLxiZisR6yv+OicVKqjWsDTleyMxgtvN3R8usK1OQG93f6SseCTxC7kEsaw/V2UfrJJRwCLEytVZDk9opjrg+1JALXUBWCy1l2vQKfMH4CV3r5yuzFtvYEopoCbDgJbEtGzu78/dXwPWMLPlgU+JkfIMoKjVzSWZD9yoVWnK90HiHuHvCw54ewDTzGz/9LhV6sO67p77DocGPvcd824nqQMmVGTjc2DFOa1Q07+17RLiF35Hd3+g4La6Ag+YWfmB8b2Bj9w994BqZjua2aSKU4t6EqOeIr6RDwEWNrNflpV1I+4/Vn7I89SHWHhStDriFKgOZWWlqbb3827MzJYys+fNbDl3n5RmMv4IvObuRY9UhwKdKhZEbZzK5xd/J0b9W5RWqxeoC7AWcYBKD+Ax4BHiFkKuzOxwMxtRUdyT4hYsDgFWsjhspaQbzbjnr5FqjTKzPsARxD2AVy0OHACgoGmgwcTIdKCZHU1MWZ4PnFNAW6X2WgDXmdm5xAf7IuCiIqbw0sH2DwM3m9lBxH2584HrC5hqLvcLYs9q0YYCb5Kms4n3dy1wWxFfitz903R74JK0NakXcBpx36pQ7v6+mf0/4Na0Vak3MZuzWdFtzw1p0dAORG7zcWWf/elpAWOuKlZRY2ZTiVmdMXm3RezdPt/MziNm4dYj1okcXEBbEIv13gRuT7d7VgGOoxkL+DRSrV07pz/PI6a1fvgxs9y/LKUFClsT9yBeIv5B/iuxSTt3aQpoS2Jq8jViu8LfmHlDeN7+hzic4WngIWJ67cQC2wNYnlnPC85d+mKwVWrraeAB4ovLQQU2uxuxv3kEsYimb8XtgyLtTcxqvEQE8wPcfchcartou6Q/b2fmz/7wn6xHOUlbW7YhFg6Vfm+Od/fKzGp5tTc9tTeN2Id/PTEDeOWc1qmE+iIiIjnRSFVERCQnCqoiIiI5UVAVERHJiYKqiIhIThRURUREcqKgKiIikhMlfxBZAKTMVPu6+7Xp8bPA8+7e7APYzawH0Mbd/9XA8/XAb939STPbh5lPOfmeSJH4AHByRbo4kZqjkarIgmF34NSyxzsSGaPy8CBxBmZWHxLHs3Ug8gEfSORAfjjlsxapWRqpiiwYZjryzN3zzOLU1OPUZlSk0hxjZv8B3iLS792fW89E5jIFVZEakJLDf0Dke72AOET9duDI0hmXZrYvkbd0NeJ4tXuJ82E3Jk25pqnYVYCBlE3/mtmBxNFsywGvp3pfSc+NJs5d3YPI4etE2r9X0jTySsD1Zraxu+8zJ+/P3d3MniMFVTNbkkhN+Vvi36kngX4FHm8mkgtNtYjUltOIqdzt08/ZAGa2MXA1caBzFyIB+b7ENO+LxOELpWnXceUVmtm2wF+AI4kTQQYBT1ecaHM6cWD7WkRO3VJu1B2B8cQh0nN8sHMykjghBOAs4hD1TYmTfJYDLmtm/SKFU1AVqS0nuPu/3P1Z4h7pAek+5DRgf3d/wN3HuPt9RIL17unYtc9I064piXi544Dz3f1hd3/X3c8BXgUOKLvmVnd/yN3fIRKOrwM/TCNPBz5398+a+d4+A9qkv68MTAU+cPeRxGEHRRxSLZIrTf+K1JYXy/7+KrA0sLy7DzOzaWZ2JtAd+CUxYn0qQ51dgXPN7C9lZa2IEWhJ+eHtnwMLmVnLKgG6OZZIdUOMSh8BPjKzZ4jFULfm2JZIITRSFakt5We7tkx/zjCzLYkj8joQZ1LuDGQ9vHphYvq2R9lPV+DYsmu+rfK6pi5Qmp21iMVKpJH4CsRo+VNidPx4zu2J5E5BVaS29Cj7+zrEHs/JQF/gFnc/0N1vAEYRC5ZKga+xMx4d6Ozu75V+iCD764x9avb5kWbWBfgVsbgKMzsCWN/d/+7uexJnwf7azJZvblsiRdL0r0htuczM9gOWBM4ErnL3ejP7BNjAzNYi7nGeSIxaW6XXTQWWNLM1gPcr6rwUuMnM/g08T6ww3o84iD6LqcCaZrZ0xq06C5lZ+/T3RYkR6oXAU+7+aCrvDByc3utEYE9gLPBxxj6J/CQUVEVqy13Ao8TU79+Ac1L5GcQ2mSHEfclBwFXEal6Ap4F/AyOILTY/cPe7zWw5YoVvx3Td9u7+esY+XUlsuVmVWA08Ox2IlcgAXwNjgLuJwFpyKnGP9SFi8dJLwLY538MVyV2L+vpmz9yISMHK9ql2SdOzIjIP0j1VERGRnCioioiI5ETTvyIiIjnRSFVERCQnCqoiIiI5UVAVERHJiYKqiIhIThRURUREcvL/AQDtfQsY8sD0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,3.2))\n",
    "\n",
    "fontsize=14\n",
    "\n",
    "ax = sns.barplot(x='patient id', y='pre vs. immed post %', data=recovery_percent)\n",
    "\n",
    "ax.set_ylabel('Lymphocyte percent recovery', fontsize=fontsize)  \n",
    "ax.set_xlabel('patient IDs', fontsize=fontsize)\n",
    "\n",
    "# enforcing tick sizes\n",
    "ax.tick_params(labelsize=14)\n",
    "\n",
    "labels = ax.get_yticks().tolist()\n",
    "labels_percent = []\n",
    "for l in labels:\n",
    "    labels_percent.append(str(l) + '%')\n",
    "ax.set_yticklabels(labels_percent)\n",
    "\n",
    "# plt.savefig('../graphs/paper figures/supp figs/lymphocyte percent recovery.png', dpi=400,\n",
    "#            bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging lymphocyte percent recovery w/ chr aberr & cbc dataframe\n",
    "\n",
    "merge_recov_percent_chr = merge_chr_cbc.merge(recovery_percent[['patient id', \n",
    "                                                                'pre vs. post recov %',\n",
    "                                                                'pre vs. immed post %']],\n",
    "                                                                on=['patient id'])\n",
    "\n",
    "merge_recov_percent_chr = merge_recov_percent_chr[merge_recov_percent_chr['timepoint'] == '4 C'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating R2 correlation values between aberrations of interest & pre/post lymphocyte recovery\n",
    "\n",
    "metric = 'pre vs. post recov %'\n",
    "\n",
    "aberrations = ['# inversions', '# dicentrics', '# translocations', '# excess chr fragments']\n",
    "r2_list = []\n",
    "\n",
    "for aberr in aberrations:\n",
    "    r2_value = merge_recov_percent_chr[[aberr, metric]].corr()[metric][0]\n",
    "    r2_list.append([aberr, round(r2_value, 3)])\n",
    "\n",
    "r2_df = pd.DataFrame(r2_list, columns=['Aberration', 'R2 value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x230.4 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,3.2))\n",
    "\n",
    "fontsize=14\n",
    "\n",
    "ax = sns.barplot(y='Aberration', x='R2 value', data=r2_df, orient='h')\n",
    "\n",
    "ax.set_ylabel('Average frequencies', fontsize=fontsize)  \n",
    "ax.set_xlabel('R2 Correlation with post-therapy lymphocyte recovery', fontsize=fontsize)\n",
    "# plt.xticks(rotation=30)\n",
    "\n",
    "# enforcing tick sizes\n",
    "ax.tick_params(labelsize=14)\n",
    "\n",
    "# plt.savefig('../graphs/paper figures/supp figs/correlation aberrations lymphocyte percent recovery.png', dpi=400,\n",
    "#            bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "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>patient id</th>\n",
       "      <th>timepoint</th>\n",
       "      <th># inversions</th>\n",
       "      <th># sister chromatid exchanges</th>\n",
       "      <th># dicentrics</th>\n",
       "      <th># excess chr fragments</th>\n",
       "      <th># sat associations</th>\n",
       "      <th># terminal SCEs</th>\n",
       "      <th># translocations</th>\n",
       "      <th>WBC</th>\n",
       "      <th>...</th>\n",
       "      <th>cd3 abs</th>\n",
       "      <th>cd4%</th>\n",
       "      <th>cd4 abs</th>\n",
       "      <th>cd 8%</th>\n",
       "      <th>cd 8 abs</th>\n",
       "      <th>cd 19%</th>\n",
       "      <th>cd 19 abs</th>\n",
       "      <th>cd4/cd8</th>\n",
       "      <th>NK %</th>\n",
       "      <th>NK abs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>0.233333</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>8.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1740</td>\n",
       "      <td>50</td>\n",
       "      <td>1175</td>\n",
       "      <td>23</td>\n",
       "      <td>541</td>\n",
       "      <td>17</td>\n",
       "      <td>400</td>\n",
       "      <td>2.17</td>\n",
       "      <td>7</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3 B</td>\n",
       "      <td>1.266667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>5.7</td>\n",
       "      <td>...</td>\n",
       "      <td>528</td>\n",
       "      <td>64</td>\n",
       "      <td>404</td>\n",
       "      <td>19</td>\n",
       "      <td>119</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>3.40</td>\n",
       "      <td>9</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4 C</td>\n",
       "      <td>0.566667</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>5.9</td>\n",
       "      <td>...</td>\n",
       "      <td>431</td>\n",
       "      <td>50</td>\n",
       "      <td>294</td>\n",
       "      <td>21</td>\n",
       "      <td>124</td>\n",
       "      <td>11</td>\n",
       "      <td>64</td>\n",
       "      <td>2.38</td>\n",
       "      <td>14</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1 non irrad</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>5.3</td>\n",
       "      <td>...</td>\n",
       "      <td>740</td>\n",
       "      <td>42</td>\n",
       "      <td>472</td>\n",
       "      <td>25</td>\n",
       "      <td>275</td>\n",
       "      <td>15</td>\n",
       "      <td>170</td>\n",
       "      <td>1.72</td>\n",
       "      <td>15</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3 B</td>\n",
       "      <td>1.066667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>5.6</td>\n",
       "      <td>...</td>\n",
       "      <td>203</td>\n",
       "      <td>53</td>\n",
       "      <td>147</td>\n",
       "      <td>22</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2.43</td>\n",
       "      <td>24</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient id    timepoint  # inversions  # sister chromatid exchanges  \\\n",
       "0           1  1 non irrad      0.233333                      0.633333   \n",
       "1           1          3 B      1.266667                      0.700000   \n",
       "2           1          4 C      0.566667                      0.933333   \n",
       "3           2  1 non irrad      0.300000                      0.533333   \n",
       "4           2          3 B      1.066667                      0.700000   \n",
       "\n",
       "   # dicentrics  # excess chr fragments  # sat associations  # terminal SCEs  \\\n",
       "0      0.000000                0.000000            0.133333         0.566667   \n",
       "1      0.366667                0.433333            0.766667         0.800000   \n",
       "2      0.066667                0.266667            0.366667         0.766667   \n",
       "3      0.000000                0.033333            0.333333         0.766667   \n",
       "4      0.300000                0.166667            0.466667         1.000000   \n",
       "\n",
       "   # translocations  WBC  ...  cd3 abs  cd4%  cd4 abs  cd 8%  cd 8 abs  \\\n",
       "0          0.033333  8.7  ...     1740    50     1175     23       541   \n",
       "1          0.100000  5.7  ...      528    64      404     19       119   \n",
       "2          0.133333  5.9  ...      431    50      294     21       124   \n",
       "3          0.033333  5.3  ...      740    42      472     25       275   \n",
       "4          0.033333  5.6  ...      203    53      147     22        61   \n",
       "\n",
       "   cd 19%  cd 19 abs  cd4/cd8   NK %  NK abs  \n",
       "0      17        400      2.17     7     158  \n",
       "1       4         27      3.40     9      57  \n",
       "2      11         64      2.38    14      83  \n",
       "3      15        170      1.72    15     171  \n",
       "4       2          6      2.43    24      67  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_chr_cbc.head()"
   ]
  }
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