data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In this code we aim to extract relevant data for our analysis starting from the csv file obtained from the code \"tracking data\" containing alla data from all the h5 files obtained after the tracking. \n",
    "\n",
    "The output of this code will be a csv file with one row for each fly and the following columns: \n",
    "\n",
    "For each fly (maze) we want to know: \n",
    "- fly ide (arena,exp,maze)\n",
    "- starving_cond\n",
    "- habituation_cond\n",
    "- first_choice\n",
    "- tot_time_blue \n",
    "- tot_time_orange \n",
    "- tot_time_neutral area \n",
    "- rate_interactions_blue (number of interactions with blue/ total number of interactions of the fly)\n",
    "- rate_interactions_orange (number of interactions with orange/ total number of interactions of the fly)\n",
    "- max_consec_time_blue \n",
    "- max_consec_time_orange\n",
    "- tot_time_reinforc_obj (total time orange for flies used to orange, Na for unused flies, total time with blue for flies used to blue) \n",
    "\n",
    "We will also detect dead flies and remove them "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import sys\n",
    "import re\n",
    "import datetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the csv file saved here home/matthias/Videos/Alice_Samara_Videos2/ly_data_AliceSamara.csv and store it in a pentus dataframe\n",
    "# Specify the path to the CSV file\n",
    "file_path = \"/home/matthias/Videos/Alice_Samara_Videos2/fly_data_AliceSamara.csv\" #CHANGE PATH HERE\n",
    "\n",
    "# Read the CSV file and store it in a DataFrame\n",
    "flydata_AS_df = pd.read_csv(file_path)\n",
    "\n",
    "#create a new datadrame with one row for each maze of each arena and keep the column about arena, exp, maze, starving_cond, habituation_cond\n",
    "flydata_AS_resume_df = flydata_AS_df[['arena', 'exp', 'maze', 'starving_cond', 'habituation_cond']].drop_duplicates()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop dead flies from the flydata_AS_resume_df\n",
    "# for each row in the flydata_AS_resume_df we need to analyse all the columns in the flydata_AS_df with the same arena, exp, maze, starving_cond, habituation_cond and find the total time the fly spent in each area\n",
    "for index, row in flydata_AS_resume_df.iterrows():\n",
    "    arena = row['arena']\n",
    "    exp = row['exp']\n",
    "    maze = row['maze']\n",
    "    starving_cond = row['starving_cond']\n",
    "    habituation_cond = row['habituation_cond']\n",
    "    \n",
    "    # Create a mask that selects rows where 'arena', 'exp', 'maze', 'starving_cond', and 'habituation_cond' match the current row\n",
    "    \n",
    "    mask = (flydata_AS_df['arena'] == arena) & (flydata_AS_df['exp'] == exp) & (flydata_AS_df['maze'] == maze) & (flydata_AS_df['starving_cond'] == starving_cond) & (flydata_AS_df['habituation_cond'] == habituation_cond)\n",
    "\n",
    "    #check if the fly is dead\n",
    "    #check if the values of head_x\thead_y\tthorax_x\tthorax_y\tabdo_x\tabdo_y\tare the same in all the rows of the mask (with a margin of 3)\n",
    "    if (flydata_AS_df.loc[mask, 'head_x'].std() < 6) & (flydata_AS_df.loc[mask, 'thorax_x'].std() < 6) & (flydata_AS_df.loc[mask, 'abdo_x'].std() < 6):\n",
    "        flydata_AS_resume_df.drop(index, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#initialize the columns we want to add to the flydata_AS_resume_df\n",
    "flydata_AS_resume_df['first_choice'] = np.nan\n",
    "flydata_AS_resume_df['tot_time_blue'] = np.nan\n",
    "flydata_AS_resume_df['tot_time_oran'] = np.nan\n",
    "flydata_AS_resume_df['tot_time_neutral'] = np.nan\n",
    "flydata_AS_resume_df['rate_interactions_blue'] = np.nan\n",
    "flydata_AS_resume_df['rate_interactions_oran'] = np.nan\n",
    "flydata_AS_resume_df['max_consec_time_blue'] = np.nan\n",
    "flydata_AS_resume_df['max_consec_time_oran'] = np.nan\n",
    "flydata_AS_resume_df['tot_time_reinforc_obj'] = np.nan\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#column first_choice\n",
    "# for each row in the flydata_AS_resume_df we need to analyse all the columns in the flydata_AS_df with the same arena, exp, maze, starving_cond, habituation_cond and find the first choice of the fly\n",
    "#the first choice of the fly is the first element of the column \"area\" different from \"neutral\"\n",
    "#the first choice is stored in the column first_choice of the flydata_AS_resume_df\n",
    "for index, row in flydata_AS_resume_df.iterrows():\n",
    "    arena = row['arena']\n",
    "    exp = row['exp']\n",
    "    maze = row['maze']\n",
    "    starving_cond = row['starving_cond']\n",
    "    habituation_cond = row['habituation_cond']\n",
    "    \n",
    "    # Create a mask that selects rows where 'arena', 'exp', 'maze', 'starving_cond', and 'habituation_cond' match the current row and the area is not neutral\n",
    "    mask = (flydata_AS_df['arena'] == arena) & (flydata_AS_df['area'] != 'neutral') & (flydata_AS_df['exp'] == exp) & (flydata_AS_df['maze'] == maze) & (flydata_AS_df['starving_cond'] == starving_cond) & (flydata_AS_df['habituation_cond'] == habituation_cond)\n",
    "\n",
    "    #now within these rows we want to extract the value of the first element of the column \"area\"\n",
    "    area_values = flydata_AS_df.loc[mask, 'area']\n",
    "    \n",
    "    if len(area_values) > 0:\n",
    "        first_choice = area_values.iloc[0]\n",
    "    else:\n",
    "        first_choice = None  \n",
    "        \n",
    "    #we save it in the new df\n",
    "    flydata_AS_resume_df.at[index, 'first_choice'] = first_choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#column tot time\n",
    "# for each row in the flydata_AS_resume_df we need to analyse all the columns in the flydata_AS_df with the same arena, exp, maze, starving_cond, habituation_cond and find the total time the fly spent in each area\n",
    "for index, row in flydata_AS_resume_df.iterrows():\n",
    "    arena = row['arena']\n",
    "    exp = row['exp']\n",
    "    maze = row['maze']\n",
    "    starving_cond = row['starving_cond']\n",
    "    habituation_cond = row['habituation_cond']\n",
    "    \n",
    "    # Create a mask that selects rows where 'arena', 'exp', 'maze', 'starving_cond', and 'habituation_cond' match the current row\n",
    "    \n",
    "    mask = (flydata_AS_df['arena'] == arena) & (flydata_AS_df['exp'] == exp) & (flydata_AS_df['maze'] == maze) & (flydata_AS_df['starving_cond'] == starving_cond) & (flydata_AS_df['habituation_cond'] == habituation_cond)\n",
    "\n",
    "    #now within these rows we want to count the total number of rows with the area corresponding to orange, blue or neutral\n",
    "    #we save it in the new df\n",
    "    flydata_AS_resume_df.at[index, 'tot_time_blue'] =0.03448 * len(flydata_AS_df.loc[mask & (flydata_AS_df['area'] == 'blue')])\n",
    "    flydata_AS_resume_df.at[index, 'tot_time_oran'] = 0.03448 * len(flydata_AS_df.loc[mask & (flydata_AS_df['area'] == 'orange')])\n",
    "    flydata_AS_resume_df.at[index, 'tot_time_neutral'] = 0.03448 * len(flydata_AS_df.loc[mask & (flydata_AS_df['area'] == 'neutral')])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No interactions found for arena: 7 exp: 5 maze: 2\n",
      "No interactions found for arena: 0 exp: 5 maze: 1\n",
      "No interactions found for arena: 8 exp: 5 maze: 1\n",
      "No interactions found for arena: 7 exp: 4 maze: 1\n"
     ]
    }
   ],
   "source": [
    "#column rate interactions = number of interaction with an object / total number of interaction\n",
    "\n",
    "def count_consecutive_frames(df, area_value, min_length=58):\n",
    "    count = 0\n",
    "    current_streak = 0\n",
    "\n",
    "    for value in df['area']:\n",
    "        if value == area_value:\n",
    "            current_streak += 1\n",
    "            if current_streak == min_length:\n",
    "                count += 1\n",
    "        else:\n",
    "            current_streak = 0\n",
    "\n",
    "    return count\n",
    "\n",
    "\n",
    "# for each row in the flydata_AS_resume_df we need to analyse all the columns in the flydata_AS_df with the same arena, exp, maze, starving_cond, habituation_cond \n",
    "for index, row in flydata_AS_resume_df.iterrows():\n",
    "    arena = row['arena']\n",
    "    exp = row['exp']\n",
    "    maze = row['maze']\n",
    "    starving_cond = row['starving_cond']\n",
    "    habituation_cond = row['habituation_cond']\n",
    "    \n",
    "    # Create a mask that selects rows where 'arena', 'exp', 'maze', 'starving_cond', and 'habituation_cond' match the current row \n",
    "    mask = (flydata_AS_df['arena'] == arena) & (flydata_AS_df['exp'] == exp) & (flydata_AS_df['maze'] == maze) & (flydata_AS_df['starving_cond'] == starving_cond) & (flydata_AS_df['habituation_cond'] == habituation_cond)\n",
    "    \n",
    "    #within the rows of the mask in the flydata_AS_df we want to count how many times the fly stays in the orange or blue area for at least 2 seconds (58 frames)\n",
    "    #to do that we need to count how many times there are at least 58 consecutive rows with the same area (orange or blue) and save this number in the new df\n",
    "    \n",
    "    filtered_df = flydata_AS_df[mask]\n",
    "\n",
    "    # Count consecutive frames for 'orange' and 'blue'\n",
    "    orange_count = count_consecutive_frames(filtered_df, 'orange')\n",
    "    blue_count = count_consecutive_frames(filtered_df, 'blue')\n",
    "    total_count = orange_count + blue_count\n",
    "    #print(orange_count, blue_count, total_count)\n",
    "\n",
    "    if total_count > 0:\n",
    "        flydata_AS_resume_df.at[index, 'rate_interactions_blue'] = blue_count / total_count\n",
    "        flydata_AS_resume_df.at[index, 'rate_interactions_oran'] = orange_count / total_count\n",
    "    else:\n",
    "        print('No interactions found for arena:', arena, 'exp:', exp, 'maze:', maze)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#max consecutive time in orange and blue\n",
    "# for each row in the flydata_AS_resume_df we need to analyse all the columns in the flydata_AS_df with the same arena, exp, maze, starving_cond, habituation_cond and find the total time the fly spent in each area\n",
    "for index, row in flydata_AS_resume_df.iterrows():\n",
    "    arena = row['arena']\n",
    "    exp = row['exp']\n",
    "    maze = row['maze']\n",
    "    starving_cond = row['starving_cond']\n",
    "    habituation_cond = row['habituation_cond']\n",
    "    \n",
    "    # Create a mask that selects rows where 'arena', 'exp', 'maze', 'starving_cond', and 'habituation_cond' match the current row\n",
    "    \n",
    "    mask = (flydata_AS_df['arena'] == arena) & (flydata_AS_df['exp'] == exp) & (flydata_AS_df['maze'] == maze) & (flydata_AS_df['starving_cond'] == starving_cond) & (flydata_AS_df['habituation_cond'] == habituation_cond)\n",
    "\n",
    "    #now within these rows we want to count how many consecutive columns with the same area orange or blue we have before the fly moves to another area and pick the maximum value of this count\n",
    "    #we save it in the new df\n",
    "    max_consec_time_blue = 0\n",
    "    max_consec_time_orang = 0\n",
    "    consec_time_blue = 0\n",
    "    consec_time_orang = 0\n",
    "    for i in range(len(flydata_AS_df.loc[mask])):\n",
    "        if flydata_AS_df.loc[mask, 'area'].iloc[i] == 'blue':\n",
    "            consec_time_blue += 1\n",
    "            consec_time_orang = 0\n",
    "        elif flydata_AS_df.loc[mask, 'area'].iloc[i] == 'orange':\n",
    "            consec_time_orang += 1\n",
    "            consec_time_blue = 0\n",
    "        else:\n",
    "            consec_time_orang = 0\n",
    "            consec_time_blue = 0\n",
    "        max_consec_time_blue = max(max_consec_time_blue, consec_time_blue)\n",
    "        max_consec_time_orang = max(max_consec_time_orang, consec_time_orang)\n",
    "    \n",
    "    flydata_AS_resume_df.at[index, 'max_consec_time_blue'] = 0.03448 * max_consec_time_blue\n",
    "    flydata_AS_resume_df.at[index, 'max_consec_time_oran'] = 0.03448 * max_consec_time_orang   \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#total time with reinforced object \n",
    "#if habituation condition is blue then put in this column the value of tot_time_blue, if habituation condition is orange then put in this column the value of tot_time_oran\n",
    "for index, row in flydata_AS_resume_df.iterrows():\n",
    "    if row['habituation_cond'] == 'blue':\n",
    "        flydata_AS_resume_df.at[index, 'tot_time_reinforc_obj'] = row['tot_time_blue']\n",
    "    elif row['habituation_cond'] == 'orange':\n",
    "        flydata_AS_resume_df.at[index, 'tot_time_reinforc_obj'] = row['tot_time_oran']\n",
    "    else:\n",
    "        flydata_AS_resume_df.at[index, 'tot_time_reinforc_obj'] = np.nan\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
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       "      <td>0.509434</td>\n",
       "      <td>24.20496</td>\n",
       "      <td>37.06600</td>\n",
       "      <td>567.29944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13885466</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>fed</td>\n",
       "      <td>orange</td>\n",
       "      <td>orange</td>\n",
       "      <td>864.89632</td>\n",
       "      <td>1687.72704</td>\n",
       "      <td>1047.15760</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>54.71976</td>\n",
       "      <td>30.68720</td>\n",
       "      <td>1687.72704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13989868</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>fed</td>\n",
       "      <td>orange</td>\n",
       "      <td>blue</td>\n",
       "      <td>47.27208</td>\n",
       "      <td>1974.04896</td>\n",
       "      <td>1578.45992</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>0.987500</td>\n",
       "      <td>21.99824</td>\n",
       "      <td>357.86792</td>\n",
       "      <td>1974.04896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>133 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          arena  exp  maze starving_cond habituation_cond first_choice  \\\n",
       "0             2    5     0       starved           unused         blue   \n",
       "104402        2    5     1       starved           unused       orange   \n",
       "208804        2    5     2       starved           unused       orange   \n",
       "313206        7    5     0       starved           orange       orange   \n",
       "417608        7    5     1       starved           orange       orange   \n",
       "...         ...  ...   ...           ...              ...          ...   \n",
       "13572260      6    3     1           fed           orange         blue   \n",
       "13676662      6    3     2           fed           orange         blue   \n",
       "13781064      8    3     0           fed           orange       orange   \n",
       "13885466      8    3     1           fed           orange       orange   \n",
       "13989868      8    3     2           fed           orange         blue   \n",
       "\n",
       "          tot_time_blue  tot_time_oran  tot_time_neutral  \\\n",
       "0            1964.70488        0.00000        1635.07608   \n",
       "104402        251.60056     3101.51048         246.66992   \n",
       "208804        517.82064      673.56680        2408.39352   \n",
       "313206        325.52568      242.15304        3032.10224   \n",
       "417608          0.03448     2505.55816        1094.18832   \n",
       "...                 ...            ...               ...   \n",
       "13572260      506.71808     2178.17056         914.89232   \n",
       "13676662      600.71056     2101.17672         897.89368   \n",
       "13781064      487.09896      567.29944        2545.38256   \n",
       "13885466      864.89632     1687.72704        1047.15760   \n",
       "13989868       47.27208     1974.04896        1578.45992   \n",
       "\n",
       "          rate_interactions_blue  rate_interactions_oran  \\\n",
       "0                       1.000000                0.000000   \n",
       "104402                  0.276316                0.723684   \n",
       "208804                  0.368421                0.631579   \n",
       "313206                  0.709677                0.290323   \n",
       "417608                  0.000000                1.000000   \n",
       "...                          ...                     ...   \n",
       "13572260                0.344086                0.655914   \n",
       "13676662                0.253521                0.746479   \n",
       "13781064                0.490566                0.509434   \n",
       "13885466                0.375000                0.625000   \n",
       "13989868                0.012500                0.987500   \n",
       "\n",
       "          max_consec_time_blue  max_consec_time_oran  tot_time_reinforc_obj  \n",
       "0                    958.26816               0.00000                    NaN  \n",
       "104402                63.23632             440.65440                    NaN  \n",
       "208804               139.26472              81.89000                    NaN  \n",
       "313206                53.65088              50.16840              242.15304  \n",
       "417608                 0.03448            2371.43096             2505.55816  \n",
       "...                        ...                   ...                    ...  \n",
       "13572260              76.71800             623.22600             2178.17056  \n",
       "13676662             164.29720             827.07176             2101.17672  \n",
       "13781064              24.20496              37.06600              567.29944  \n",
       "13885466              54.71976              30.68720             1687.72704  \n",
       "13989868              21.99824             357.86792             1974.04896  \n",
       "\n",
       "[133 rows x 14 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flydata_AS_resume_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>head_x</th>\n",
       "      <th>head_y</th>\n",
       "      <th>thorax_x</th>\n",
       "      <th>thorax_y</th>\n",
       "      <th>abdo_x</th>\n",
       "      <th>abdo_y</th>\n",
       "      <th>time</th>\n",
       "      <th>arena</th>\n",
       "      <th>maze</th>\n",
       "      <th>exp</th>\n",
       "      <th>starving_cond</th>\n",
       "      <th>habituation_cond</th>\n",
       "      <th>pos_blue_obj</th>\n",
       "      <th>area</th>\n",
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       "      <th>1</th>\n",
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       "      <td>134.386978</td>\n",
       "      <td>443.216705</td>\n",
       "      <td>150.719879</td>\n",
       "      <td>443.798920</td>\n",
       "      <td>172.404236</td>\n",
       "      <td>446.566772</td>\n",
       "      <td>0.103448</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>starved</td>\n",
       "      <td>unused</td>\n",
       "      <td>left</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>134.329590</td>\n",
       "      <td>440.829559</td>\n",
       "      <td>150.600082</td>\n",
       "      <td>443.740784</td>\n",
       "      <td>172.276642</td>\n",
       "      <td>446.632080</td>\n",
       "      <td>0.137931</td>\n",
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       "      <td>starved</td>\n",
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       "      <td>104397</td>\n",
       "      <td>105.827072</td>\n",
       "      <td>472.403839</td>\n",
       "      <td>112.086449</td>\n",
       "      <td>456.528503</td>\n",
       "      <td>118.310509</td>\n",
       "      <td>440.118469</td>\n",
       "      <td>3599.896552</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>fed</td>\n",
       "      <td>orange</td>\n",
       "      <td>left</td>\n",
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       "      <th>14094266</th>\n",
       "      <td>104398</td>\n",
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       "      <td>472.236908</td>\n",
       "      <td>111.745148</td>\n",
       "      <td>456.459320</td>\n",
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       "      <td>440.161835</td>\n",
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       "      <td>neutral</td>\n",
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       "      <td>104400</td>\n",
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       "      <td>112.073944</td>\n",
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       "      <td>118.488075</td>\n",
       "      <td>437.254028</td>\n",
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       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>fed</td>\n",
       "      <td>orange</td>\n",
       "      <td>left</td>\n",
       "      <td>neutral</td>\n",
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       "    <tr>\n",
       "      <th>14094269</th>\n",
       "      <td>104401</td>\n",
       "      <td>108.612175</td>\n",
       "      <td>469.340302</td>\n",
       "      <td>112.304985</td>\n",
       "      <td>453.512299</td>\n",
       "      <td>118.425552</td>\n",
       "      <td>437.072662</td>\n",
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       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>fed</td>\n",
       "      <td>orange</td>\n",
       "      <td>left</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14094270 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index      head_x      head_y    thorax_x    thorax_y      abdo_x  \\\n",
       "0              0  134.416687  443.169678  150.718246  443.773621  172.266769   \n",
       "1              1  134.481506  443.224609  153.064728  443.615417  172.361008   \n",
       "2              2  134.433365  443.181183  150.749207  443.730011  172.447113   \n",
       "3              3  134.386978  443.216705  150.719879  443.798920  172.404236   \n",
       "4              4  134.329590  440.829559  150.600082  443.740784  172.276642   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "14094265  104397  105.827072  472.403839  112.086449  456.528503  118.310509   \n",
       "14094266  104398  105.399048  472.236908  111.745148  456.459320  118.223831   \n",
       "14094267  104399  105.769691  471.988434  111.970802  456.144928  118.426575   \n",
       "14094268  104400  105.867027  469.476105  112.073944  455.944672  118.488075   \n",
       "14094269  104401  108.612175  469.340302  112.304985  453.512299  118.425552   \n",
       "\n",
       "              abdo_y         time  arena  maze  exp starving_cond  \\\n",
       "0         446.496918     0.000000      2     0    5       starved   \n",
       "1         446.615692     0.034483      2     0    5       starved   \n",
       "2         446.680237     0.068966      2     0    5       starved   \n",
       "3         446.566772     0.103448      2     0    5       starved   \n",
       "4         446.632080     0.137931      2     0    5       starved   \n",
       "...              ...          ...    ...   ...  ...           ...   \n",
       "14094265  440.118469  3599.896552      8     2    3           fed   \n",
       "14094266  440.161835  3599.931034      8     2    3           fed   \n",
       "14094267  439.839447  3599.965517      8     2    3           fed   \n",
       "14094268  437.254028  3600.000000      8     2    3           fed   \n",
       "14094269  437.072662  3600.034483      8     2    3           fed   \n",
       "\n",
       "         habituation_cond pos_blue_obj     area  \n",
       "0                  unused         left  neutral  \n",
       "1                  unused         left  neutral  \n",
       "2                  unused         left  neutral  \n",
       "3                  unused         left  neutral  \n",
       "4                  unused         left  neutral  \n",
       "...                   ...          ...      ...  \n",
       "14094265           orange         left  neutral  \n",
       "14094266           orange         left  neutral  \n",
       "14094267           orange         left  neutral  \n",
       "14094268           orange         left  neutral  \n",
       "14094269           orange         left  neutral  \n",
       "\n",
       "[14094270 rows x 15 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flydata_AS_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sqve the resume df to a CSV file\n",
    "flydata_AS_resume_df.to_csv('flydata_AS_resume.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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