Supplementary_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "import time\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from mlxtend.evaluate import permutation_test\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import Ridge, RidgeClassifier,Lasso, LinearRegression\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import GridSearchCV, LeaveOneOut, train_test_split, cross_val_score, cross_validate,cross_val_predict, StratifiedKFold, KFold, GroupKFold, LeavePGroupsOut, ShuffleSplit\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import preprocessing \n",
    "from sklearn.feature_selection import SelectKBest, f_regression\n",
    "import warnings\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "from glob import glob\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "import networkx as nx\n",
    "from joblib import Parallel, delayed\n",
    "import argparse\n",
    "import os, subprocess\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.decomposition import PCA\n",
    "from scipy.spatial import distance\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "16/32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11538461538461539"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3/(58-32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(58 - len(emotion))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# phenotypes\n",
    "rootDir = '/Users/rk/Desktop/projects/replicability/in/'\n",
    "\n",
    "alertness = ['MMSE_Score','PSQI_Score']\n",
    "cognition = ['PicSeq_AgeAdj','CardSort_AgeAdj','Flanker_AgeAdj','PMAT24_A_CR','CogFluidComp_AgeAdj','CogCrystalComp_AgeAdj',\n",
    "'CogEarlyComp_AgeAdj','CogTotalComp_AgeAdj','ReadEng_AgeAdj','PicVocab_AgeAdj','ProcSpeed_AgeAdj','DDisc_AUC_200','VSPLOT_TC','SCPT_SEN','SCPT_SPEC']\n",
    "memory = ['IWRD_TOT','ListSort_AgeAdj']\n",
    "emotion = ['ER40ANG','ER40FEAR','ER40HAP','ER40NOE','ER40SAD','AngAffect_Unadj','AngHostil_Unadj','AngAggr_Unadj','FearAffect_Unadj',\n",
    "'FearSomat_Unadj','Sadness_Unadj','LifeSatisf_Unadj','MeanPurp_Unadj','PosAffect_Unadj','Friendship_Unadj','Loneliness_Unadj',\n",
    "'PercHostil_Unadj','PercReject_Unadj','EmotSupp_Unadj','InstruSupp_Unadj','PercStress_Unadj','SelfEff_Unadj']\n",
    "motor = ['Endurance_AgeAdj', 'GaitSpeed_Comp', 'Dexterity_AgeAdj', 'Strength_AgeAdj']\n",
    "personality = ['NEOFAC_A','NEOFAC_O','NEOFAC_C','NEOFAC_N','NEOFAC_E']\n",
    "sensory = ['Noise_Comp','Odor_AgeAdj','PainIntens_RawScore','PainInterf_Tscore','Taste_AgeAdj','Mars_Final']\n",
    "phenotype_names = alertness + cognition + memory + emotion + motor + personality + sensory + ['age_num' ,'Gender']\n",
    "groups = np.array([1, 1, 2,2,2,2,2,2,2,2,2,2,2,2,2,2,2, 3,3, 4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4, 5,5,5,5, 6,6,6,6,6, 7,7,7,7,7,7, 8,8])\n",
    "battery = ['alertness', 'cognition', 'memory', 'emotion', 'motor', 'personality', 'sensory', 'demography']\n",
    "sample_size = np.arange(25, 450, 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main_func(num, window_size, thresh):\n",
    "  conntype=num\n",
    "  replicability_all_phenotypes = []\n",
    "  for phen in phenotype_names:\n",
    "      with open(f'{rootDir}/replicability_{phen}_conntype_{conntype}.pickle', 'rb') as file:\n",
    "          data = pickle.load(file)\n",
    "          data = np.convolve(data, np.ones(window_size)/window_size, mode='same')\n",
    "          replicability_all_phenotypes.append(data)\n",
    "  \n",
    "  replicability_conn = pd.DataFrame(replicability_all_phenotypes)\n",
    "  replicability_conn.index = phenotype_names\n",
    "\n",
    "  val_all = np.zeros((replicability_conn.shape[0], 1))\n",
    "  sample_all = np.zeros((replicability_conn.shape[0]))\n",
    "  for i in range(replicability_conn.shape[0]):\n",
    "    temp = replicability_conn.iloc[i].max()\n",
    "    if temp > thresh:\n",
    "      val_all[i] = np.argmax(np.array(replicability_conn.iloc[i])>thresh)\n",
    "      sample_all[i] = int(sample_size[int(val_all[i])])\n",
    "    else:\n",
    "       val_all[i] = np.nan\n",
    "       sample_all[i] = np.nan\n",
    "       \n",
    "  return replicability_conn, (np.count_nonzero(~np.isnan(val_all))/replicability_conn.shape[0] )*100, sample_all\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31.03448275862069, 24.137931034482758, 29.310344827586203, 25.862068965517242, 27.586206896551722]\n"
     ]
    }
   ],
   "source": [
    "replicability_0, percent_replicability_0, samples_needed_0 = main_func(0, 4, 0.8)\n",
    "replicability_1, percent_replicability_1, samples_needed_1 = main_func(1, 4, 0.8)\n",
    "replicability_2, percent_replicability_2, samples_needed_2 = main_func(2, 4, 0.8)\n",
    "replicability_3, percent_replicability_3, samples_needed_3 = main_func(3, 4, 0.8)\n",
    "replicability_4, percent_replicability_4, samples_needed_4 = main_func(4, 4, 0.8)\n",
    "\n",
    "print([percent_replicability_0, percent_replicability_1, percent_replicability_2, percent_replicability_3, percent_replicability_4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_sample_size = pd.DataFrame([samples_needed_0, samples_needed_1, samples_needed_2, samples_needed_3, samples_needed_4]).T\n",
    "results_sample_size.index = phenotype_names\n",
    "results_sample_size.columns = ['Streamlines', 'FA', 'RD', 'AD', 'ADC']\n",
    "results_sample_size.dropna(how='all', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Streamlines</th>\n",
       "      <th>FA</th>\n",
       "      <th>RD</th>\n",
       "      <th>AD</th>\n",
       "      <th>ADC</th>\n",
       "      <th>groups</th>\n",
       "      <th>battery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CardSort_AgeAdj</th>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flanker_AgeAdj</th>\n",
       "      <td>325.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMAT24_A_CR</th>\n",
       "      <td>150.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CogFluidComp_AgeAdj</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CogCrystalComp_AgeAdj</th>\n",
       "      <td>125.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Streamlines     FA     RD     AD    ADC  groups  \\\n",
       "CardSort_AgeAdj              400.0    NaN    NaN    NaN    NaN       2   \n",
       "Flanker_AgeAdj               325.0    NaN  375.0    NaN    NaN       2   \n",
       "PMAT24_A_CR                  150.0  300.0  325.0  325.0  300.0       2   \n",
       "CogFluidComp_AgeAdj          300.0    NaN  375.0  400.0  400.0       2   \n",
       "CogCrystalComp_AgeAdj        125.0  275.0  250.0  275.0  275.0       2   \n",
       "\n",
       "                         battery  \n",
       "CardSort_AgeAdj        cognition  \n",
       "Flanker_AgeAdj         cognition  \n",
       "PMAT24_A_CR            cognition  \n",
       "CogFluidComp_AgeAdj    cognition  \n",
       "CogCrystalComp_AgeAdj  cognition  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_labels = []\n",
    "battery_labels = [] \n",
    "for i in results_sample_size.index:\n",
    "    group_labels.append(groups[phenotype_names.index(i)])\n",
    "    battery_labels.append(battery[groups[phenotype_names.index(i)]-1])\n",
    "results_sample_size['groups']=group_labels\n",
    "results_sample_size['battery'] = battery_labels\n",
    "results_sample_size.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MMSE_Score</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PSQI_Score</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PicSeq_AgeAdj</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.130682</td>\n",
       "      <td>0.130682</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.139610</td>\n",
       "      <td>0.133929</td>\n",
       "      <td>0.133929</td>\n",
       "      <td>0.175595</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.181090</td>\n",
       "      <td>0.139423</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.076923</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CardSort_AgeAdj</th>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>0.043403</td>\n",
       "      <td>0.098958</td>\n",
       "      <td>0.161458</td>\n",
       "      <td>0.133681</td>\n",
       "      <td>0.143056</td>\n",
       "      <td>0.087500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flanker_AgeAdj</th>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.149306</td>\n",
       "      <td>0.177083</td>\n",
       "      <td>0.227083</td>\n",
       "      <td>0.239710</td>\n",
       "      <td>0.229293</td>\n",
       "      <td>0.284848</td>\n",
       "      <td>0.293672</td>\n",
       "      <td>0.371324</td>\n",
       "      <td>0.407038</td>\n",
       "      <td>0.465596</td>\n",
       "      <td>0.555421</td>\n",
       "      <td>0.541167</td>\n",
       "      <td>0.609619</td>\n",
       "      <td>0.652728</td>\n",
       "      <td>0.710975</td>\n",
       "      <td>0.579397</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0         1         2         3         4         5   \\\n",
       "MMSE_Score       0.000000  0.000000  0.000000  0.000000  0.125000  0.125000   \n",
       "PSQI_Score       0.000000  0.000000  0.050000  0.050000  0.050000  0.050000   \n",
       "PicSeq_AgeAdj    0.000000  0.062500  0.062500  0.130682  0.130682  0.068182   \n",
       "CardSort_AgeAdj  0.083333  0.083333  0.083333  0.083333  0.000000       NaN   \n",
       "Flanker_AgeAdj   0.055556  0.149306  0.177083  0.227083  0.239710  0.229293   \n",
       "\n",
       "                       6         7         8         9         10        11  \\\n",
       "MMSE_Score       0.187500       NaN       NaN       NaN       NaN  0.000000   \n",
       "PSQI_Score       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "PicSeq_AgeAdj    0.139610  0.133929  0.133929  0.175595  0.166667  0.104167   \n",
       "CardSort_AgeAdj       NaN       NaN       NaN  0.083333  0.027778  0.043403   \n",
       "Flanker_AgeAdj   0.284848  0.293672  0.371324  0.407038  0.465596  0.555421   \n",
       "\n",
       "                       12        13        14        15        16  \n",
       "MMSE_Score       0.000000  0.000000  0.000000       NaN       NaN  \n",
       "PSQI_Score       0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "PicSeq_AgeAdj    0.181090  0.139423  0.076923  0.076923  0.000000  \n",
       "CardSort_AgeAdj  0.098958  0.161458  0.133681  0.143056  0.087500  \n",
       "Flanker_AgeAdj   0.541167  0.609619  0.652728  0.710975  0.579397  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "replicability_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Streamlines</th>\n",
       "      <th>FA</th>\n",
       "      <th>RD</th>\n",
       "      <th>AD</th>\n",
       "      <th>ADC</th>\n",
       "      <th>groups</th>\n",
       "      <th>battery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CardSort_AgeAdj</th>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flanker_AgeAdj</th>\n",
       "      <td>325.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMAT24_A_CR</th>\n",
       "      <td>150.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CogFluidComp_AgeAdj</th>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CogCrystalComp_AgeAdj</th>\n",
       "      <td>125.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CogEarlyComp_AgeAdj</th>\n",
       "      <td>175.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CogTotalComp_AgeAdj</th>\n",
       "      <td>150.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ReadEng_AgeAdj</th>\n",
       "      <td>150.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PicVocab_AgeAdj</th>\n",
       "      <td>125.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VSPLOT_TC</th>\n",
       "      <td>125.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>2</td>\n",
       "      <td>cognition</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AngAggr_Unadj</th>\n",
       "      <td>350.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>4</td>\n",
       "      <td>emotion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LifeSatisf_Unadj</th>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>emotion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SelfEff_Unadj</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>4</td>\n",
       "      <td>emotion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Endurance_AgeAdj</th>\n",
       "      <td>150.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5</td>\n",
       "      <td>motor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dexterity_AgeAdj</th>\n",
       "      <td>250.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>motor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strength_AgeAdj</th>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>5</td>\n",
       "      <td>motor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taste_AgeAdj</th>\n",
       "      <td>275.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>275.0</td>\n",
       "      <td>7</td>\n",
       "      <td>sensory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_num</th>\n",
       "      <td>175.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>8</td>\n",
       "      <td>demography</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>8</td>\n",
       "      <td>demography</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Streamlines     FA     RD     AD    ADC  groups  \\\n",
       "CardSort_AgeAdj              400.0    0.0    0.0    0.0    0.0       2   \n",
       "Flanker_AgeAdj               325.0    0.0  375.0    0.0    0.0       2   \n",
       "PMAT24_A_CR                  150.0  300.0  325.0  325.0  300.0       2   \n",
       "CogFluidComp_AgeAdj          300.0    0.0  375.0  400.0  400.0       2   \n",
       "CogCrystalComp_AgeAdj        125.0  275.0  250.0  275.0  275.0       2   \n",
       "CogEarlyComp_AgeAdj          175.0  325.0  275.0  325.0  300.0       2   \n",
       "CogTotalComp_AgeAdj          150.0  225.0  225.0  225.0  225.0       2   \n",
       "ReadEng_AgeAdj               150.0  300.0  275.0  325.0  300.0       2   \n",
       "PicVocab_AgeAdj              125.0  275.0  250.0  275.0  275.0       2   \n",
       "VSPLOT_TC                    125.0  275.0  250.0  275.0  275.0       2   \n",
       "AngAggr_Unadj                350.0  275.0  350.0  275.0  325.0       4   \n",
       "LifeSatisf_Unadj             400.0    0.0    0.0    0.0    0.0       4   \n",
       "SelfEff_Unadj                  0.0    0.0  400.0    0.0  400.0       4   \n",
       "Endurance_AgeAdj             150.0  200.0  175.0  175.0  200.0       5   \n",
       "Dexterity_AgeAdj             250.0  275.0  300.0  300.0  300.0       5   \n",
       "Strength_AgeAdj               75.0   75.0   75.0   75.0   75.0       5   \n",
       "Taste_AgeAdj                 275.0  275.0  275.0  275.0  275.0       7   \n",
       "age_num                      175.0  325.0  350.0  300.0  300.0       8   \n",
       "Gender                        75.0   75.0   75.0   75.0   75.0       8   \n",
       "\n",
       "                          battery  \n",
       "CardSort_AgeAdj         cognition  \n",
       "Flanker_AgeAdj          cognition  \n",
       "PMAT24_A_CR             cognition  \n",
       "CogFluidComp_AgeAdj     cognition  \n",
       "CogCrystalComp_AgeAdj   cognition  \n",
       "CogEarlyComp_AgeAdj     cognition  \n",
       "CogTotalComp_AgeAdj     cognition  \n",
       "ReadEng_AgeAdj          cognition  \n",
       "PicVocab_AgeAdj         cognition  \n",
       "VSPLOT_TC               cognition  \n",
       "AngAggr_Unadj             emotion  \n",
       "LifeSatisf_Unadj          emotion  \n",
       "SelfEff_Unadj             emotion  \n",
       "Endurance_AgeAdj            motor  \n",
       "Dexterity_AgeAdj            motor  \n",
       "Strength_AgeAdj             motor  \n",
       "Taste_AgeAdj              sensory  \n",
       "age_num                demography  \n",
       "Gender                 demography  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_sample_size.fillna(0, inplace=True)\n",
    "results_sample_size\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "list = ['Streamlines', 'FA', 'RD', 'AD', 'ADC']\n",
    "replicabilities = [replicability_0, replicability_1, replicability_2, replicability_3, replicability_4]\n",
    "\n",
    "colormaps = ['Greys', 'Reds', 'Greens', 'Blues', 'cividis']\n",
    "for enu, df in enumerate(replicabilities):\n",
    "    colormap = plt.get_cmap(colormaps[enu % len(colormaps)])\n",
    "    c = 0.2\n",
    "    line_width = 1\n",
    "    fig, ax = plt.subplots(figsize=(1,1))\n",
    "    for i in results_sample_size[list[enu]].index:\n",
    "        df = df.fillna(0)\n",
    "        plt.plot(df.loc[i][:-1], linewidth = line_width, color = colormap(c))\n",
    "        c=c+0.075\n",
    "    ax.set_title(list[enu], fontsize = 7)\n",
    "    ax.set_xticks(range(len(sample_size)-1))\n",
    "    ax.set_xticklabels(sample_size[:-1], rotation=90, fontsize=3)  \n",
    "    ax.set_yticks(np.arange(0, 1.2, .2))\n",
    "    ax.set_yticklabels(np.around(np.arange(0, 1.2, .2), decimals=1), fontsize=3)\n",
    "    ax.set_ylabel('$P_{\\mathrm{replicability}}$', fontsize=7)\n",
    "    ax.set_xlabel('Sample size', fontsize=8)\n",
    "    ax.axhline(y=0.8, color='black', linestyle='--', linewidth=0.5)\n",
    "    #plt.savefig(f'./images/line_{list[enu]}.svg', dpi=1200, bbox_inches='tight')\n",
    "    #plt.savefig(f'../fig/line_{list[enu]}.jpeg', dpi=1200, bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormaps = ['Greys', 'Reds', 'Greens', 'Blues', 'cividis']\n",
    "for idx, modality in enumerate(list):\n",
    "    colormap = plt.get_cmap(colormaps[idx % len(colormaps)])\n",
    "    #colormap = plt.cm.Pink\n",
    "    c = 0.2\n",
    "    fig, ax = plt.subplots(figsize=(1,3))\n",
    "    for enu, i in enumerate(results_sample_size[modality]):\n",
    "        ax.barh(enu, int(i), color=colormap(c))\n",
    "        c=c+0.075\n",
    "    ax.set_yticks(range(len(results_sample_size.index)))\n",
    "    ax.set_yticklabels(results_sample_size[modality].index, fontsize=5)\n",
    "    ax.set_xticks(sample_size)\n",
    "    ax.set_xticklabels(sample_size, rotation=90, fontsize=3)  \n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.set_xlabel('Sample size', fontsize=9)\n",
    "    #plt.savefig(f'./images/bar_{modality}_bar.svg', dpi=1200, bbox_inches='tight')\n",
    "    #plt.savefig(f'../fig/bar_{modality}_bar.jpeg', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 200x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming replicability_vals are defined\n",
    "replicability_vals = [percent_replicability_0, percent_replicability_1, percent_replicability_2, percent_replicability_3, percent_replicability_4]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(2, 1))\n",
    "\n",
    "colormaps = ['Greys', 'Reds', 'Greens', 'Blues', 'cividis']\n",
    "\n",
    "for enu, (i, cmap_name) in enumerate(zip(replicability_vals, colormaps)):\n",
    "    colormap = plt.get_cmap(cmap_name)\n",
    "    ax.bar(enu, i, color=colormap(0.5))  # Using a mid-point value for the color\n",
    "\n",
    "ax.set_xticks(range(len(replicability_vals)))\n",
    "ax.set_xticks([])\n",
    "\n",
    "ax.set_ylabel('Replicability (%)', fontsize=7)\n",
    "ax.set_xlabel('Connectome type', fontsize=7)\n",
    "\n",
    "ax.set_yticks(range(0, 40, 5))\n",
    "ax.set_yticklabels(range(0, 40, 5), fontsize=6)\n",
    "\n",
    "labels = ['Streamlines', 'FA', 'RD', 'AD', 'ADC']\n",
    "for enu, label in enumerate(labels):\n",
    "    ax.text(enu, 14.5, label, ha='center', va='center', rotation=90, fontsize=7)\n",
    "\n",
    "#plt.savefig('./images/main_replicability_bar.svg', dpi=1200, bbox_inches='tight')\n",
    "#plt.savefig('../fig/main_replicability_bar.png', dpi=1200, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import pearsonr\n",
    "mean_r2_all = []\n",
    "sample_all = []\n",
    "for i, phen in enumerate(results_sample_size.index):\n",
    "    for j, model in enumerate(list):\n",
    "        with open(f'{rootDir}/y_hat_rep_{phen}_conntype_{j}.pickle', 'rb') as file:\n",
    "            y_hat_rep = pickle.load(file)\n",
    "            try:\n",
    "                index = np.where(sample_size==int(results_sample_size.iloc[i,j]))[0][0] \n",
    "                var1 = y_hat_rep[index]\n",
    "            except IndexError:\n",
    "                pass\n",
    "        with open(f'{rootDir}/y_true_rep_{phen}_conntype_{j}.pickle', 'rb') as file:\n",
    "            y_true_rep = pickle.load(file)\n",
    "            try:\n",
    "                index = np.where(sample_size==int(results_sample_size.iloc[i,j]))[0][0] \n",
    "                var2 = y_true_rep[index]\n",
    "            except IndexError:\n",
    "                pass\n",
    "        r2_all = np.zeros(100)\n",
    "        for k in range(100):\n",
    "            correlation = []\n",
    "            correlation, _ = pearsonr(var1[:,k], var2[:,k])\n",
    "            r2_all[k] = correlation*correlation\n",
    "        mean_r2 = r2_all.mean()\n",
    "        mean_r2_all.append(mean_r2)    \n",
    "        sample_all.append(results_sample_size.iloc[i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 80/80 [00:05<00:00, 13.71it/s]\n"
     ]
    }
   ],
   "source": [
    "# Reshape data into (19, 5)\n",
    "r2 = np.array(mean_r2_all).reshape((19, 5))\n",
    "s = np.array(sample_all).reshape((19, 5))\n",
    "\n",
    "# Concatenate r2 and s along the columns\n",
    "empirical = pd.DataFrame(np.concatenate((r2, s), axis=1))\n",
    "\n",
    "# Set index\n",
    "empirical.index = pd.Index(range(19))\n",
    "\n",
    "# Reshape the DataFrame\n",
    "reshaped_r2 = empirical.iloc[:, :5].values.flatten()\n",
    "reshaped_s = empirical.iloc[:, 5:].values.flatten()\n",
    "reshaped_data = np.column_stack((reshaped_r2, reshaped_s))\n",
    "\n",
    "reshaped_df = pd.DataFrame(reshaped_data, columns=['R2', 'n'])\n",
    "\n",
    "# Remove rows where either 'R2' or 'n' is zero\n",
    "reshaped_df = reshaped_df[~((reshaped_df['R2'] == 0) | (reshaped_df['n'] == 0))]\n",
    "\n",
    "import numpy as np\n",
    "from scipy.stats import t\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Functions\n",
    "def r2t(r, n):\n",
    "    return (r * np.sqrt(n - 2)) / np.sqrt(1 - r ** 2)\n",
    "\n",
    "def p_rep(t_stat, df):\n",
    "    return t.cdf(np.abs(t_stat) / np.sqrt(2), df)\n",
    "\n",
    "def ps_rep(t_stat, df, alpha):\n",
    "    term1 = np.abs(t_stat) / np.sqrt(2)\n",
    "    #term2 = t.ppf(1 - alpha / 2, df) / np.sqrt(2)\n",
    "    term2 = t.ppf(1 - alpha, df) / np.sqrt(2) # one tailed\n",
    "    return t.cdf(term1 - term2, df)\n",
    "\n",
    "def r2psrep(r, n):\n",
    "    return ps_rep(r2t(r, n), n - 2, 0.05)\n",
    "\n",
    "# Initial values\n",
    "n = 1000\n",
    "theory_res=[]\n",
    "for i in tqdm(reshaped_df.iloc[:,0]):\n",
    "    r2 = i\n",
    "    # Calculate t_obs and ps_rep for initial values\n",
    "    tobs = r2t(np.sqrt(r2), n)\n",
    "    ps_rep_val = ps_rep(tobs, n - 2, 0.05)\n",
    "\n",
    "    # Calculate ps_rep values for different sample sizes\n",
    "    psrep_vals = [r2psrep(np.sqrt(r2), i) for i in range(1, 1001)]\n",
    "\n",
    "    # Minimum sample size for 80% power\n",
    "    n_vals = np.where(np.array(psrep_vals) > 0.8)[0]\n",
    "    n_min_80_power = n_vals[0] + 1 if n_vals.size > 0 else np.nan\n",
    "    theory_res.append([np.log10(i), n_min_80_power, i])\n",
    "theory_res = np.array(theory_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.017241379310344827"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/58"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sample size')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2,2))\n",
    "\n",
    "sns.scatterplot(x=np.log10(reshaped_df['R2']), y=np.log10(reshaped_df['n']))\n",
    "sns.regplot(x=theory_res[:,0], y=np.log10(theory_res[:,1]), color='black', scatter_kws={\"alpha\":0.0},  line_kws={\"linewidth\": 1})\n",
    "ax.set_xlabel(r'$\\mathrm{R}^2$', fontsize=10)\n",
    "ax.set_ylabel('Sample size', fontsize=10)\n",
    "\n",
    "#plt.savefig(f'./images/bar_kileens_plot.svg', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.162277660168379\n",
      "10.0\n",
      "31.622776601683793\n",
      "10\n",
      "31.622776601683793\n",
      "100\n",
      "316.22776601683796\n"
     ]
    }
   ],
   "source": [
    "print(10 ** -1.5 *100)\n",
    "print(10 ** -1 * 100)\n",
    "print(10 ** -.5 * 100)\n",
    "\n",
    "print(10 ** 1)\n",
    "print(10 ** 1.5)\n",
    "print(10 ** 2)\n",
    "print(10 ** 2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Data to plot\n",
    "labels = '', ''\n",
    "sizes = [31, 69]\n",
    "colors = ['red', 'green']\n",
    "\n",
    "# Create a doughnut plot\n",
    "fig, ax = plt.subplots(figsize=(1,1))\n",
    "ax.pie(sizes, labels=labels, colors=colors, startangle=90, wedgeprops=dict(width=0.3))\n",
    "\n",
    "# Draw center circle for doughnut shape\n",
    "centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
    "fig.gca().add_artist(centre_circle)\n",
    "\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle\n",
    "ax.axis('equal')  \n",
    "plt.savefig(f'./replicability_myst/images/pieplot_1.svg', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Data to plot\n",
    "labels = '', ''\n",
    "sizes = [50, 50]\n",
    "colors = ['red', 'green']\n",
    "\n",
    "# Create a doughnut plot\n",
    "fig, ax = plt.subplots(figsize=(1,1))\n",
    "ax.pie(sizes, labels=labels, colors=colors, startangle=90, wedgeprops=dict(width=0.3))\n",
    "\n",
    "# Draw center circle for doughnut shape\n",
    "centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
    "fig.gca().add_artist(centre_circle)\n",
    "\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle\n",
    "ax.axis('equal')  \n",
    "plt.savefig(f'./replicability_myst/images/pieplot_2.svg', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 100x100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Data to plot\n",
    "labels = '', ''\n",
    "sizes = [11.5, 100-11.5]\n",
    "colors = ['red', 'green']\n",
    "\n",
    "# Create a doughnut plot\n",
    "fig, ax = plt.subplots(figsize=(1,1))\n",
    "ax.pie(sizes, labels=labels, colors=colors, startangle=90, wedgeprops=dict(width=0.3))\n",
    "\n",
    "# Draw center circle for doughnut shape\n",
    "centre_circle = plt.Circle((0,0),0.70,fc='white')\n",
    "fig.gca().add_artist(centre_circle)\n",
    "\n",
    "# Equal aspect ratio ensures that pie is drawn as a circle\n",
    "ax.axis('equal')  \n",
    "plt.savefig(f'./replicability_myst/images/pieplot_3.svg', dpi=1200, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.16",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "d1415223e6cc21f91a568fa5baf9838b679db112332bb108f01418172f76d90f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}