USV_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn\n",
    "from collections import OrderedDict\n",
    "import matplotlib.ticker as plticker\n",
    "import scipy\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.9.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.version.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe 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>ID</th>\n",
       "      <th>group</th>\n",
       "      <th>time_interval</th>\n",
       "      <th>number_of_episodes</th>\n",
       "      <th>total_duration</th>\n",
       "      <th>mean_episode_duration</th>\n",
       "      <th>mean_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KT</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.035000</td>\n",
       "      <td>64.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KT</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KT</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KT</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>first_3_minutes</td>\n",
       "      <td>25</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.011667</td>\n",
       "      <td>64.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KT</td>\n",
       "      <td>CTRL</td>\n",
       "      <td>total</td>\n",
       "      <td>25</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.035000</td>\n",
       "      <td>64.558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID group    time_interval  number_of_episodes  total_duration  \\\n",
       "0  KT  CTRL              0-1                  25            0.87   \n",
       "1  KT  CTRL              1-2                   0            0.00   \n",
       "2  KT  CTRL              2-3                   0            0.00   \n",
       "3  KT  CTRL  first_3_minutes                  25            0.87   \n",
       "4  KT  CTRL            total                  25            0.87   \n",
       "\n",
       "   mean_episode_duration  mean_frequency  \n",
       "0               0.035000          64.558  \n",
       "1               0.000000           0.000  \n",
       "2               0.000000           0.000  \n",
       "3               0.011667          64.558  \n",
       "4               0.035000          64.558  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ini = pd.read_excel('USV_summarized.xlsx')\n",
    "df_ini.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_x/ry9s55s15p7fz95r041pw6fc0000gp/T/ipykernel_3495/2859662110.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_selected['Index'] = range(1, len(df_selected) + 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>ID</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean_episode_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>CTRL</th>\n",
       "      <th>EXP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Index  ID mean_episode_duration       \n",
       "group                            CTRL    EXP\n",
       "0         1  KT                 0.035  0.000\n",
       "1         2  KT                 0.000  0.000\n",
       "2         3  KT                 0.000  0.000\n",
       "3         4  KT                 0.000  0.017\n",
       "4         5  KT                 0.000  0.000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a table with 0-1, 1-2 and 2-3 time intervals only (first 3 minutes)\n",
    "\n",
    "df_selected = df_ini[(df_ini[\"time_interval\"] == \"0-1\") | (df_ini[\"time_interval\"] == \"1-2\") | (df_ini[\"time_interval\"] == \"2-3\")]\n",
    "df_selected['Index'] = range(1, len(df_selected) + 1) \n",
    "\n",
    "df_selected_mean_episode_duration = df_selected[['Index', 'ID', 'group', 'mean_episode_duration']]\n",
    "df_selected_mean_episode_duration = pd.pivot_table(df_selected_mean_episode_duration, \n",
    "               index=['Index', 'ID'], \n",
    "               columns=['group'], \n",
    "               values=['mean_episode_duration'])\n",
    "df_selected_mean_episode_duration = df_selected_mean_episode_duration.reset_index()\n",
    "df_selected_mean_episode_duration = df_selected_mean_episode_duration.fillna(0)\n",
    "df_selected_mean_episode_duration.mean_episode_duration[[\"CTRL\", \"EXP\"]].describe()\n",
    "df_selected_mean_episode_duration.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=546.5, pvalue=0.042192116417599845)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.mannwhitneyu(df_selected_mean_episode_duration.mean_episode_duration.CTRL, df_selected_mean_episode_duration.mean_episode_duration.EXP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>ID</th>\n",
       "      <th>time_interval</th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean_frequency</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>CTRL</th>\n",
       "      <th>EXP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>KT</td>\n",
       "      <td>0-1</td>\n",
       "      <td>64.558</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>KT</td>\n",
       "      <td>1-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>KT</td>\n",
       "      <td>2-3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>KT</td>\n",
       "      <td>0-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>KT</td>\n",
       "      <td>1-2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Index  ID time_interval mean_frequency        \n",
       "group                                   CTRL     EXP\n",
       "0         1  KT           0-1         64.558     NaN\n",
       "1         2  KT           1-2            NaN     NaN\n",
       "2         3  KT           2-3            NaN     NaN\n",
       "3         4  KT           0-1            NaN  57.427\n",
       "4         5  KT           1-2            NaN     NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected_mean_frequency = df_selected[['Index', 'ID', 'time_interval', 'group', 'mean_frequency']]\n",
    "df_selected_mean_frequency = pd.pivot_table(df_selected_mean_frequency, \n",
    "               index=['Index', 'ID', 'time_interval'], \n",
    "               columns=['group'], \n",
    "               values=['mean_frequency'])\n",
    "df_selected_mean_frequency = df_selected_mean_frequency.replace(0, np.nan)\n",
    "df_selected_mean_frequency = df_selected_mean_frequency.reset_index()\n",
    "df_selected_mean_frequency.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=8.0, pvalue=0.3727272727272727)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.mannwhitneyu(df_selected_mean_frequency.mean_frequency.CTRL, df_selected_mean_frequency.mean_frequency.EXP, nan_policy='omit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>ID</th>\n",
       "      <th colspan=\"2\" halign=\"left\">total_duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>CTRL</th>\n",
       "      <th>EXP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Index  ID total_duration       \n",
       "group                     CTRL    EXP\n",
       "0         1  KT           0.87  0.000\n",
       "1         2  KT           0.00  0.000\n",
       "2         3  KT           0.00  0.000\n",
       "3         4  KT           0.00  0.017\n",
       "4         5  KT           0.00  0.000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected_total_duration = df_selected[['Index', 'ID', 'group', 'total_duration']]\n",
    "df_selected_total_duration = pd.pivot_table(df_selected_total_duration, \n",
    "               index=['Index', 'ID'], \n",
    "               columns=['group'], \n",
    "               values=['total_duration'])\n",
    "df_selected_total_duration = df_selected_total_duration.reset_index()\n",
    "df_selected_total_duration = df_selected_total_duration.fillna(0)\n",
    "df_selected_total_duration.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=541.5, pvalue=0.054135364340956546)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.mannwhitneyu(df_selected_total_duration.total_duration.CTRL, df_selected_total_duration.total_duration.EXP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>ID</th>\n",
       "      <th colspan=\"2\" halign=\"left\">number_of_episodes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>CTRL</th>\n",
       "      <th>EXP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>KT</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>KT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Index  ID number_of_episodes     \n",
       "group                         CTRL  EXP\n",
       "0         1  KT               25.0  0.0\n",
       "1         2  KT                0.0  0.0\n",
       "2         3  KT                0.0  0.0\n",
       "3         4  KT                0.0  1.0\n",
       "4         5  KT                0.0  0.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selected_number_of_episodes = df_selected[['Index', 'ID', 'group', 'number_of_episodes']]\n",
    "df_selected_number_of_episodes = pd.pivot_table(df_selected_number_of_episodes, \n",
    "               index=['Index', 'ID'], \n",
    "               columns=['group'], \n",
    "               values=['number_of_episodes'])\n",
    "df_selected_number_of_episodes = df_selected_number_of_episodes.reset_index()\n",
    "df_selected_number_of_episodes = df_selected_number_of_episodes.fillna(0)\n",
    "df_selected_number_of_episodes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=538.0, pvalue=0.06406357017650396)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.mannwhitneyu(df_selected_number_of_episodes.number_of_episodes.CTRL, df_selected_number_of_episodes.number_of_episodes.EXP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>time_interval</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_episode_duration</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group time_interval               variable  value\n",
       "0   CTRL           0-1  mean_episode_duration   35.0\n",
       "1   CTRL           1-2  mean_episode_duration    0.0\n",
       "2   CTRL           2-3  mean_episode_duration    0.0\n",
       "3    EXP           0-1  mean_episode_duration   17.0\n",
       "4    EXP           1-2  mean_episode_duration    0.0\n",
       "5    EXP           2-3  mean_episode_duration    0.0\n",
       "6   CTRL           0-1  mean_episode_duration   33.0\n",
       "7   CTRL           1-2  mean_episode_duration   44.0\n",
       "8   CTRL           2-3  mean_episode_duration   34.0\n",
       "9    EXP           0-1  mean_episode_duration    0.0\n",
       "10   EXP           1-2  mean_episode_duration    0.0\n",
       "11   EXP           2-3  mean_episode_duration    0.0\n",
       "12  CTRL           0-1  mean_episode_duration   29.0\n",
       "13  CTRL           1-2  mean_episode_duration    0.0\n",
       "14  CTRL           2-3  mean_episode_duration   31.0\n",
       "15   EXP           0-1  mean_episode_duration   41.0\n",
       "16   EXP           1-2  mean_episode_duration    0.0\n",
       "17   EXP           2-3  mean_episode_duration    0.0\n",
       "18  CTRL           0-1  mean_episode_duration   35.0\n",
       "19  CTRL           1-2  mean_episode_duration    0.0\n",
       "20  CTRL           2-3  mean_episode_duration    0.0\n",
       "21   EXP           0-1  mean_episode_duration    0.0\n",
       "22   EXP           1-2  mean_episode_duration    0.0\n",
       "23   EXP           2-3  mean_episode_duration    0.0\n",
       "24  CTRL           0-1  mean_episode_duration   54.0\n",
       "25  CTRL           1-2  mean_episode_duration  323.0\n",
       "26  CTRL           2-3  mean_episode_duration    0.0\n",
       "27   EXP           0-1  mean_episode_duration   30.0\n",
       "28   EXP           1-2  mean_episode_duration    0.0\n",
       "29   EXP           2-3  mean_episode_duration    0.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a table that will be used for the plot\n",
    "\n",
    "df_mean_episode_duration = df_ini[['group', 'time_interval', 'mean_episode_duration']]\n",
    "df_mean_episode_duration = df_mean_episode_duration[(df_mean_episode_duration['time_interval'] == '0-1') | (df_mean_episode_duration['time_interval'] == '1-2') | (df_mean_episode_duration['time_interval'] == '2-3')]\n",
    "df_mean_episode_duration = pd.melt(df_mean_episode_duration, id_vars= ['group', 'time_interval'], value_vars=['mean_episode_duration'])\n",
    "df_mean_episode_duration.value = df_mean_episode_duration.value *1000 # convert to ms\n",
    "df_mean_episode_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_x/ry9s55s15p7fz95r041pw6fc0000gp/T/ipykernel_3495/2349238555.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_mean_episode_duration.groupby(['group']).mean()\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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CTRL</th>\n",
       "      <td>41.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXP</th>\n",
       "      <td>5.866667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           value\n",
       "group           \n",
       "CTRL   41.200000\n",
       "EXP     5.866667"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_episode_duration.groupby(['group']).mean() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           value\n",
      "group           \n",
      "CTRL   20.726840\n",
      "EXP     3.347873\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_x/ry9s55s15p7fz95r041pw6fc0000gp/T/ipykernel_3495/2538545603.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sem is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  sem = df_mean_episode_duration.groupby(['group']).sem()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 3.34787305],\n",
       "       [20.7268402 ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem = df_mean_episode_duration.groupby(['group']).sem()   \n",
    "print(sem)\n",
    "sem = sem.reindex(['EXP', 'CTRL'], level='group')\n",
    "sem = sem.values\n",
    "sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2, 4), dpi=500)\n",
    "seaborn.set_context('paper')\n",
    "pal_1 = seaborn.color_palette(['#E0326A', '#3BAF87'])\n",
    "\n",
    "ax = seaborn.barplot(x=\"variable\", y=\"value\", hue=\"group\", hue_order=['EXP', 'CTRL'],\n",
    "                     data=df_mean_episode_duration, errorbar = None,\n",
    "                     palette=pal_1, width = 0.6, linewidth = 0.8)\n",
    "\n",
    "# get x & height for each container going from left to right\n",
    "n_containers = len(ax.containers)\n",
    "n_bars = len(ax.containers[0])\n",
    "x = []\n",
    "h = []\n",
    "for i in range(n_bars):\n",
    "    for j in range(n_containers):\n",
    "        x.append(ax.containers[j][i].get_x() + ax.containers[j][i].get_width()/2)\n",
    "        h.append(ax.containers[j][i].get_height())\n",
    "\n",
    "ax.errorbar(x, h, sem, fmt=' ', ecolor='dimgray')\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "plt.xticks(ticks = [0], labels= [\"\"], fontsize = 13)\n",
    "#plt.legend(loc = 'upper left', fontsize = 13)\n",
    "plt.yticks(ticks = [0, 20, 40, 60, 80], labels= ['', '', '', '', ''], fontsize=13)\n",
    "ax.get_legend().remove()\n",
    "\n",
    "plt.plot([-0.1,-0.1,0.1,0.1], [55,55,55,55], linewidth=1, color='k') # 0 i 1 to pierwsze dwa elementy na osi x; 80 to wartość osi y\n",
    "plt.text(-0.05, 55+1, \"* p = .042\", ha = 'center', va='bottom', color='k', fontsize=12)\n",
    "\n",
    "plt.savefig('wykresy/mean_episode_duration_0_3_barplot.tiff')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>time_interval</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>64.558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>57.427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>56.218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>54.855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>55.312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>59.196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>66.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>58.560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>65.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>53.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>37.624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CTRL</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>EXP</td>\n",
       "      <td>0-1</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>66.410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>EXP</td>\n",
       "      <td>1-2</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>EXP</td>\n",
       "      <td>2-3</td>\n",
       "      <td>mean_frequency</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group time_interval        variable   value\n",
       "0   CTRL           0-1  mean_frequency  64.558\n",
       "1   CTRL           1-2  mean_frequency     NaN\n",
       "2   CTRL           2-3  mean_frequency     NaN\n",
       "3    EXP           0-1  mean_frequency  57.427\n",
       "4    EXP           1-2  mean_frequency     NaN\n",
       "5    EXP           2-3  mean_frequency     NaN\n",
       "6   CTRL           0-1  mean_frequency  56.218\n",
       "7   CTRL           1-2  mean_frequency  54.855\n",
       "8   CTRL           2-3  mean_frequency  55.312\n",
       "9    EXP           0-1  mean_frequency     NaN\n",
       "10   EXP           1-2  mean_frequency     NaN\n",
       "11   EXP           2-3  mean_frequency     NaN\n",
       "12  CTRL           0-1  mean_frequency  59.196\n",
       "13  CTRL           1-2  mean_frequency     NaN\n",
       "14  CTRL           2-3  mean_frequency  66.024\n",
       "15   EXP           0-1  mean_frequency  58.560\n",
       "16   EXP           1-2  mean_frequency     NaN\n",
       "17   EXP           2-3  mean_frequency     NaN\n",
       "18  CTRL           0-1  mean_frequency  65.841\n",
       "19  CTRL           1-2  mean_frequency     NaN\n",
       "20  CTRL           2-3  mean_frequency     NaN\n",
       "21   EXP           0-1  mean_frequency     NaN\n",
       "22   EXP           1-2  mean_frequency     NaN\n",
       "23   EXP           2-3  mean_frequency     NaN\n",
       "24  CTRL           0-1  mean_frequency  53.822\n",
       "25  CTRL           1-2  mean_frequency  37.624\n",
       "26  CTRL           2-3  mean_frequency     NaN\n",
       "27   EXP           0-1  mean_frequency  66.410\n",
       "28   EXP           1-2  mean_frequency     NaN\n",
       "29   EXP           2-3  mean_frequency     NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a table that will be used for the plot\n",
    "\n",
    "df_mean_frequency = df_ini[['group', 'time_interval', 'mean_frequency']]\n",
    "df_mean_frequency = df_mean_frequency[(df_mean_frequency['time_interval'] == '0-1') | (df_mean_frequency['time_interval'] == '1-2') | (df_mean_frequency['time_interval'] == '2-3')]\n",
    "df_mean_frequency = pd.melt(df_mean_frequency, id_vars= ['group', 'time_interval'], value_vars=['mean_frequency'])\n",
    "df_mean_frequency = df_mean_frequency.replace(0, np.nan)\n",
    "df_mean_frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_x/ry9s55s15p7fz95r041pw6fc0000gp/T/ipykernel_3495/398010462.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_mean_frequency.groupby(['group']).mean()\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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CTRL</th>\n",
       "      <td>57.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EXP</th>\n",
       "      <td>60.799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        value\n",
       "group        \n",
       "CTRL   57.050\n",
       "EXP    60.799"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean_frequency.groupby(['group']).mean()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          value\n",
      "group          \n",
      "CTRL   2.922875\n",
      "EXP    2.824501\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_x/ry9s55s15p7fz95r041pw6fc0000gp/T/ipykernel_3495/3708029456.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sem is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  sem = df_mean_frequency.groupby(['group']).sem()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[2.82450072],\n",
       "       [2.92287456]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem = df_mean_frequency.groupby(['group']).sem()   \n",
    "print(sem)\n",
    "sem = sem.reindex(['EXP', 'CTRL'], level='group')\n",
    "sem = sem.values\n",
    "sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2, 4), dpi=500)\n",
    "seaborn.set_context('paper')\n",
    "pal_1 = seaborn.color_palette(['#E0326A', '#3BAF87'])\n",
    "\n",
    "ax = seaborn.barplot(x=\"variable\", y=\"value\", hue=\"group\", hue_order=['EXP', 'CTRL'],\n",
    "                     data=df_mean_frequency, errorbar = None,\n",
    "                     palette=pal_1, width = 0.6, linewidth = 0.8)\n",
    "\n",
    "# get x & height for each container going from left to right\n",
    "n_containers = len(ax.containers)\n",
    "n_bars = len(ax.containers[0])\n",
    "x = []\n",
    "h = []\n",
    "for i in range(n_bars):\n",
    "    for j in range(n_containers):\n",
    "        x.append(ax.containers[j][i].get_x() + ax.containers[j][i].get_width()/2)\n",
    "        h.append(ax.containers[j][i].get_height())\n",
    "\n",
    "ax.errorbar(x, h, sem, fmt=' ', ecolor='dimgray')\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "plt.xticks(ticks = [0], labels= [\"\"], fontsize=13)\n",
    "#plt.legend(loc = 'lower left', fontsize = 13)\n",
    "plt.yticks(ticks = [0, 20, 40, 60], labels= ['', '', '', ''], fontsize=13)\n",
    "ax.get_legend().remove()\n",
    "\n",
    "#plt.plot([-0.1,-0.1,0.1,0.1], [0.05,0.05,0.05,0.05], linewidth=1, color='k') # 0 i 1 to pierwsze dwa elementy na osi x; 80 to wartość osi y\n",
    "#plt.text((-0.1+0.1)*.5, 0.05+0.001, \"p = .056\", ha='center', va='bottom', color='k', fontsize=12)\n",
    "\n",
    "plt.savefig('wykresy/mean_frequency_0_3_barplot.tiff')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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