run_slc_ophys_mice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mouseBehavior import mouseBehaviorData as mbd\n",
    "import numpy as np\n",
    "import os, glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([431018, 431023, 431245, 431246, 431249, 431252, 432375, 433245,\n",
       "       433248, 435838, 436662, 437204, 440298, 442709, 445002, 445270,\n",
       "       445271, 447663, 448897, 448900, 449441, 450471, 450705, 450710,\n",
       "       451787, 451790, 453909, 453911, 453913, 456564, 456912, 456915,\n",
       "       456916, 456917, 457766, 458151, 458155, 459768, 459773, 459777,\n",
       "       460746, 461946, 462544, 462885, 462887, 464204, 464206, 464207,\n",
       "       467190, 472271, 472873, 472874, 476067, 476631, 477052, 479458,\n",
       "       479461, 479835, 479839, 481295, 483803, 484408, 484627, 484631,\n",
       "       485152, 488458, 491060], dtype=int64)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mouselistfile =  r\"\\\\allen\\programs\\braintv\\workgroups\\nc-ophys\\corbettb\\NP_behavior_pipeline\\behavior_sessions\\ophys_pipeline\\good_slc_mouse_IDs.npy\"\n",
    "mouseInfo = np.load(mouselistfile)\n",
    "mouseInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def checkforpickle(directory, mouseID):\n",
    "    pkllist = [f for f in os.listdir(directory) if '.pkl' in f]\n",
    "    mouseID = str(mouseID)\n",
    "    pklpath = None\n",
    "    for pkl in pkllist:\n",
    "        if pkl[:6]==mouseID:\n",
    "            pklpath = os.path.join(directory, pkl)\n",
    "    return pklpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "431018\n",
      "431023\n",
      "431245\n",
      "431246\n",
      "431249\n",
      "431252\n",
      "432375\n",
      "433245\n",
      "433248\n",
      "435838\n",
      "436662\n",
      "437204\n",
      "440298\n",
      "442709\n",
      "445002\n",
      "445270\n",
      "445271\n",
      "447663\n",
      "448897\n",
      "448900\n",
      "449441\n",
      "450471\n",
      "450705\n",
      "450710\n",
      "451787\n",
      "451790\n",
      "453909\n",
      "453911\n",
      "453913\n",
      "456564\n",
      "456912\n",
      "456915\n",
      "456916\n",
      "456917\n",
      "457766\n",
      "458151\n",
      "458155\n",
      "459768\n",
      "459773\n",
      "459777\n",
      "460746\n",
      "461946\n",
      "462544\n",
      "462885\n",
      "462887\n",
      "464204\n",
      "464206\n",
      "464207\n",
      "467190\n",
      "472271\n",
      "472873\n",
      "472874\n",
      "476067\n",
      "476631\n",
      "477052\n",
      "479458\n",
      "479461\n",
      "479835\n",
      "479839\n",
      "481295\n",
      "483803\n",
      "484408\n",
      "484627\n",
      "484631\n",
      "485152\n",
      "488458\n",
      "491060\n"
     ]
    }
   ],
   "source": [
    "pkldir = r\"\\\\allen\\programs\\braintv\\workgroups\\nc-ophys\\corbettb\\NP_behavior_pipeline\\behavior_sessions\\ophys_pipeline\"\n",
    "dataframe_save_dir = r\"\\\\allen\\programs\\braintv\\workgroups\\nc-ophys\\corbettb\\NP_behavior_pipeline\\behavior_sessions\\ophys_pipeline\\dataframes\"\n",
    "for mouse in mouseInfo:\n",
    "    if isinstance(mouseInfo[0], tuple):\n",
    "        mouse = mouse[0]\n",
    "    print(mouse)\n",
    "    \n",
    "    #First look to see if there's already a pkl file with this mouse' data\n",
    "    pklpath = checkforpickle(pkldir, mouse)\n",
    "    try:\n",
    "        if pklpath is not None:\n",
    "            m = mbd()\n",
    "            m.loadFromPickle(pklpath)\n",
    "#             m.add_weight_and_water_history()\n",
    "#             m.calculate_behavior_metrics()\n",
    "#             m.add_metadata_to_dataframe()\n",
    "            m.beh_df.drop(columns=['cumulative_rewards'], inplace=True)\n",
    "            m.save_dataframe_separately(dataframe_save_dir)\n",
    "    \n",
    "        else:\n",
    "            m = mbd(mouse_id=mouse, daysBeforeHandoff=100)\n",
    "            m.buildBehaviorDataframe(all_sessions=True)\n",
    "            m.get_mouse_metadata()\n",
    "            m.add_weight_and_water_history()\n",
    "            \n",
    "        m.saveDirectory = pkldir\n",
    "        m.saveToPickle()\n",
    "    except:\n",
    "        print('failed to load data for mouse ' + mouse)\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stage</th>\n",
       "      <th>WE_ml</th>\n",
       "      <th>Wt_g</th>\n",
       "      <th>session_datetime_local</th>\n",
       "      <th>earned_rewards</th>\n",
       "      <th>total_rewards</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OPHYS_5_images_A_passive</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2019-12-17 09:29:14.254000-08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OPHYS_6_images_A</td>\n",
       "      <td>0.29</td>\n",
       "      <td>19.39</td>\n",
       "      <td>2019-12-16 09:16:05.363000-08:00</td>\n",
       "      <td>38</td>\n",
       "      <td>43</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OPHYS_4_images_A</td>\n",
       "      <td>0.59</td>\n",
       "      <td>20.30</td>\n",
       "      <td>2019-12-13 09:10:21.396000-08:00</td>\n",
       "      <td>80</td>\n",
       "      <td>85</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OPHYS_3_images_B</td>\n",
       "      <td>0.98</td>\n",
       "      <td>20.35</td>\n",
       "      <td>2019-12-12 09:10:58.122000-08:00</td>\n",
       "      <td>137</td>\n",
       "      <td>142</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OPHYS_2_images_B_passive</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2019-12-11 09:11:27.745000-08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OPHYS_1_images_B</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2019-12-10 12:53:17.355000-08:00</td>\n",
       "      <td>118</td>\n",
       "      <td>123</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OPHYS_3_images_B</td>\n",
       "      <td>0.04</td>\n",
       "      <td>19.76</td>\n",
       "      <td>2019-12-09 09:04:18.208000-08:00</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OPHYS_3_images_B</td>\n",
       "      <td>0.03</td>\n",
       "      <td>19.62</td>\n",
       "      <td>2019-12-06 08:52:52.903000-08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>OPHYS_1_images_B</td>\n",
       "      <td>0.27</td>\n",
       "      <td>19.7</td>\n",
       "      <td>2019-12-05 09:15:09.313000-08:00</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>OPHYS_0_images_B_habituation</td>\n",
       "      <td>0.32</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2019-12-04 09:16:57.305000-08:00</td>\n",
       "      <td>42</td>\n",
       "      <td>47</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OPHYS_0_images_B_habituation</td>\n",
       "      <td>0.61</td>\n",
       "      <td>20.38</td>\n",
       "      <td>2019-12-03 09:02:05.686000-08:00</td>\n",
       "      <td>83</td>\n",
       "      <td>88</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OPHYS_0_images_B_habituation</td>\n",
       "      <td>0.93</td>\n",
       "      <td>20.36</td>\n",
       "      <td>2019-12-02 09:12:33.560000-08:00</td>\n",
       "      <td>129</td>\n",
       "      <td>134</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TRAINING_5_images_B_handoff_ready</td>\n",
       "      <td>0</td>\n",
       "      <td>20.43</td>\n",
       "      <td>2019-11-27 08:20:44.096000-08:00</td>\n",
       "      <td>160</td>\n",
       "      <td>165</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TRAINING_5_images_B_handoff_ready</td>\n",
       "      <td>1.1</td>\n",
       "      <td>20.90</td>\n",
       "      <td>2019-11-26 08:36:34.181000-08:00</td>\n",
       "      <td>153</td>\n",
       "      <td>158</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TRAINING_5_images_B_handoff_ready</td>\n",
       "      <td>0.85</td>\n",
       "      <td>20.60</td>\n",
       "      <td>2019-11-25 08:48:13.255000-08:00</td>\n",
       "      <td>118</td>\n",
       "      <td>123</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TRAINING_5_images_B_epilogue</td>\n",
       "      <td>1.07</td>\n",
       "      <td>20.70</td>\n",
       "      <td>2019-11-22 10:07:52.436000-08:00</td>\n",
       "      <td>149</td>\n",
       "      <td>154</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TRAINING_4_images_B_training</td>\n",
       "      <td>1.04</td>\n",
       "      <td>20.50</td>\n",
       "      <td>2019-11-21 10:08:26.612000-08:00</td>\n",
       "      <td>145</td>\n",
       "      <td>150</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TRAINING_3_images_B_10uL_reward</td>\n",
       "      <td>1.44</td>\n",
       "      <td>20.80</td>\n",
       "      <td>2019-11-20 10:06:16.801000-08:00</td>\n",
       "      <td>139</td>\n",
       "      <td>148</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>TRAINING_3_images_B_10uL_reward</td>\n",
       "      <td>1.46</td>\n",
       "      <td>21.30</td>\n",
       "      <td>2019-11-19 10:05:15.058000-08:00</td>\n",
       "      <td>140</td>\n",
       "      <td>152</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TRAINING_3_images_B_10uL_reward</td>\n",
       "      <td>0</td>\n",
       "      <td>20.50</td>\n",
       "      <td>2019-11-18 09:59:44.900000-08:00</td>\n",
       "      <td>115</td>\n",
       "      <td>128</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TRAINING_2_gratings_flashed</td>\n",
       "      <td>1.33</td>\n",
       "      <td>20.70</td>\n",
       "      <td>2019-11-15 10:42:28.323000-08:00</td>\n",
       "      <td>128</td>\n",
       "      <td>138</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TRAINING_1_gratings</td>\n",
       "      <td>1.1</td>\n",
       "      <td>20.30</td>\n",
       "      <td>2019-11-14 10:27:00.819000-08:00</td>\n",
       "      <td>100</td>\n",
       "      <td>120</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TRAINING_1_gratings</td>\n",
       "      <td>0.75</td>\n",
       "      <td>19.70</td>\n",
       "      <td>2019-11-13 10:28:54.700000-08:00</td>\n",
       "      <td>64</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>TRAINING_1_gratings</td>\n",
       "      <td>0.91</td>\n",
       "      <td>20.30</td>\n",
       "      <td>2019-11-11 10:40:47.250000-08:00</td>\n",
       "      <td>84</td>\n",
       "      <td>98</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TRAINING_0_gratings_autorewards_15min</td>\n",
       "      <td>0.6</td>\n",
       "      <td>20.00</td>\n",
       "      <td>2019-11-08 10:16:59.126000-08:00</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    stage  WE_ml    Wt_g  \\\n",
       "2                OPHYS_5_images_A_passive                  \n",
       "3                        OPHYS_6_images_A   0.29   19.39   \n",
       "4                        OPHYS_4_images_A   0.59   20.30   \n",
       "5                        OPHYS_3_images_B   0.98   20.35   \n",
       "6                OPHYS_2_images_B_passive                  \n",
       "7                        OPHYS_1_images_B                  \n",
       "8                        OPHYS_3_images_B   0.04   19.76   \n",
       "9                        OPHYS_3_images_B   0.03   19.62   \n",
       "10                       OPHYS_1_images_B   0.27    19.7   \n",
       "11           OPHYS_0_images_B_habituation   0.32    20.2   \n",
       "12           OPHYS_0_images_B_habituation   0.61   20.38   \n",
       "13           OPHYS_0_images_B_habituation   0.93   20.36   \n",
       "14      TRAINING_5_images_B_handoff_ready      0   20.43   \n",
       "15      TRAINING_5_images_B_handoff_ready    1.1   20.90   \n",
       "16      TRAINING_5_images_B_handoff_ready   0.85   20.60   \n",
       "17           TRAINING_5_images_B_epilogue   1.07   20.70   \n",
       "18           TRAINING_4_images_B_training   1.04   20.50   \n",
       "19        TRAINING_3_images_B_10uL_reward   1.44   20.80   \n",
       "20        TRAINING_3_images_B_10uL_reward   1.46   21.30   \n",
       "21        TRAINING_3_images_B_10uL_reward      0   20.50   \n",
       "22            TRAINING_2_gratings_flashed   1.33   20.70   \n",
       "23                    TRAINING_1_gratings    1.1   20.30   \n",
       "24                    TRAINING_1_gratings   0.75   19.70   \n",
       "25                    TRAINING_1_gratings   0.91   20.30   \n",
       "26  TRAINING_0_gratings_autorewards_15min    0.6   20.00   \n",
       "\n",
       "             session_datetime_local  earned_rewards  total_rewards  age  \n",
       "2  2019-12-17 09:29:14.254000-08:00               0              0  124  \n",
       "3  2019-12-16 09:16:05.363000-08:00              38             43  123  \n",
       "4  2019-12-13 09:10:21.396000-08:00              80             85  120  \n",
       "5  2019-12-12 09:10:58.122000-08:00             137            142  119  \n",
       "6  2019-12-11 09:11:27.745000-08:00               0              0  118  \n",
       "7  2019-12-10 12:53:17.355000-08:00             118            123  117  \n",
       "8  2019-12-09 09:04:18.208000-08:00               2              7  116  \n",
       "9  2019-12-06 08:52:52.903000-08:00               0              5  113  \n",
       "10 2019-12-05 09:15:09.313000-08:00              35             40  112  \n",
       "11 2019-12-04 09:16:57.305000-08:00              42             47  111  \n",
       "12 2019-12-03 09:02:05.686000-08:00              83             88  110  \n",
       "13 2019-12-02 09:12:33.560000-08:00             129            134  109  \n",
       "14 2019-11-27 08:20:44.096000-08:00             160            165  104  \n",
       "15 2019-11-26 08:36:34.181000-08:00             153            158  103  \n",
       "16 2019-11-25 08:48:13.255000-08:00             118            123  102  \n",
       "17 2019-11-22 10:07:52.436000-08:00             149            154   99  \n",
       "18 2019-11-21 10:08:26.612000-08:00             145            150   98  \n",
       "19 2019-11-20 10:06:16.801000-08:00             139            148   97  \n",
       "20 2019-11-19 10:05:15.058000-08:00             140            152   96  \n",
       "21 2019-11-18 09:59:44.900000-08:00             115            128   95  \n",
       "22 2019-11-15 10:42:28.323000-08:00             128            138   92  \n",
       "23 2019-11-14 10:27:00.819000-08:00             100            120   91  \n",
       "24 2019-11-13 10:28:54.700000-08:00              64             86   90  \n",
       "25 2019-11-11 10:40:47.250000-08:00              84             98   88  \n",
       "26 2019-11-08 10:16:59.126000-08:00               0            121   85  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.beh_df[['stage', 'WE_ml', 'Wt_g', 'session_datetime_local', 'earned_rewards', 'total_rewards', 'age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d7896c8>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x59ef6688>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(m.beh_df['WS_ml'], m.beh_df['Wt_g'], 'ko')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                                                   993541036\n",
       "behavior_training_id                                                      None\n",
       "created_at                                          2019-12-10 22:11:01.193171\n",
       "updated_at                                          2019-12-11 05:01:15.157702\n",
       "storage_directory            /allen/programs/braintv/production/visualbehav...\n",
       "temp_output_file_location    /allen/programs/braintv/production/incoming/vi...\n",
       "foraging_id                               c3444858-3483-41fb-9ec1-47a1093e4c17\n",
       "donor_id                                                             962177495\n",
       "ophys_session_id                                                   9.93507e+08\n",
       "equipment_id                                                         506540632\n",
       "user_id                                                              877318103\n",
       "rig                                                                        2P3\n",
       "pklfile                      //allen/programs/braintv/production/visualbeha...\n",
       "trials                            index auto_rewarded change_contrast  chan...\n",
       "stage                                                         OPHYS_1_images_B\n",
       "running                      [[0.0, 0.0166164083339, 0.0332629511877, 0.050...\n",
       "session_datetime                              2019-12-10 12:53:17.355000-08:00\n",
       "session_datetime_local                        2019-12-10 12:53:17.355000-08:00\n",
       "session_datetime_utc                          2019-12-10 20:53:17.355000+00:00\n",
       "cumulative_rewards                                                         108\n",
       "timeFromLastSession                                                    27.8164\n",
       "engaged_dprime                                                         1.81044\n",
       "session_day_of_week                                                          1\n",
       "WE_ml                                                                         \n",
       "WS_ml                                                                         \n",
       "weight_datetime                                                               \n",
       "Wt_g                                                                          \n",
       "Name: 7, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.beh_df.iloc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found non-behavior pickle file: //allen/programs/braintv/production/visualbehavior/prod0/specimen_788982914/behavior_session_819904061\\819721037.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mouseBehavior.py:134: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return z[0] - z[1]\n",
      "mouseBehavior.py:130: RuntimeWarning: invalid value encountered in divide\n",
      "  engagedTrialFARate = fas/float(fas+crs)\n",
      "c:\\users\\svc_ccg\\appdata\\local\\continuum\\anaconda3\\envs\\lims\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1914: RuntimeWarning: invalid value encountered in greater\n",
      "  cond1 = (0 < q) & (q < 1)\n",
      "c:\\users\\svc_ccg\\appdata\\local\\continuum\\anaconda3\\envs\\lims\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1914: RuntimeWarning: invalid value encountered in less\n",
      "  cond1 = (0 < q) & (q < 1)\n"
     ]
    }
   ],
   "source": [
    "m = mbd(mouse_id=mouseInfo[0], daysBeforeHandoff=100)\n",
    "m.buildBehaviorDataframe(all_sessions=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'id', u'behavior_training_id', u'created_at', u'updated_at',\n",
       "       u'storage_directory', u'temp_output_file_location', u'foraging_id',\n",
       "       u'donor_id', u'ophys_session_id', u'equipment_id', u'user_id', u'rig',\n",
       "       u'pklfile', u'trials', u'stage', u'session_datetime',\n",
       "       u'session_datetime_local', u'session_datetime_utc',\n",
       "       u'cumulative_rewards', u'timeFromLastSession', u'engaged_dprime',\n",
       "       u'session_day_of_week'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.beh_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = m.beh_df['trials'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'index', u'auto_rewarded', u'change_contrast', u'change_frame',\n",
       "       u'change_image_category', u'change_image_name', u'change_ori',\n",
       "       u'change_time', u'cumulative_reward_number', u'cumulative_volume',\n",
       "       u'delta_ori', u'endframe', u'endtime', u'initial_contrast',\n",
       "       u'initial_image_category', u'initial_image_name', u'initial_ori',\n",
       "       u'lick_times', u'optogenetics', u'response_latency', u'response_time',\n",
       "       u'response_type', u'reward_frames', u'reward_times', u'reward_volume',\n",
       "       u'rewarded', u'scheduled_change_time', u'startframe', u'starttime',\n",
       "       u'trial_length', u'stimulus_distribution', u'task', u'user_id',\n",
       "       u'stim_duration', u'LDT_mode', u'distribution_mean', u'stimulus',\n",
       "       u'mouse_id', u'prechange_minimum', u'computer_name',\n",
       "       u'blank_duration_range', u'response_window', u'behavior_session_uuid',\n",
       "       u'blank_screen_timeout', u'session_duration', u'stage',\n",
       "       u'startdatetime', u'date', u'year', u'month', u'day', u'hour',\n",
       "       u'dayofweek', u'number_of_rewards', u'rig_id', u'trial_type',\n",
       "       u'lick_frames', u'reward_licks', u'reward_lick_count',\n",
       "       u'reward_lick_latency', u'reward_rate', u'response', u'color'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mouseBehavior.py:134: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return z[0] - z[1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1         NP3\n",
       "2         NP3\n",
       "3         NP3\n",
       "4         NP3\n",
       "5         NP3\n",
       "6         NP3\n",
       "7         NP3\n",
       "8         NP3\n",
       "9         NP3\n",
       "10        NP3\n",
       "11        NP3\n",
       "12        NP3\n",
       "13        NP3\n",
       "14        NP3\n",
       "15        NP3\n",
       "16        NP3\n",
       "17        NP3\n",
       "18        NP3\n",
       "19        NP3\n",
       "20        NP3\n",
       "21        NP3\n",
       "22        NP3\n",
       "23        NP3\n",
       "24        NP3\n",
       "25        NP3\n",
       "26        NP3\n",
       "27        NP3\n",
       "28        NP3\n",
       "29        NP3\n",
       "30        NP3\n",
       "       ...   \n",
       "41         F3\n",
       "42         F3\n",
       "43         F3\n",
       "44         F3\n",
       "45         F3\n",
       "46         F3\n",
       "47         F3\n",
       "48         F3\n",
       "49         F3\n",
       "50         F3\n",
       "51         F3\n",
       "52         F3\n",
       "53         F3\n",
       "54         F3\n",
       "55         F3\n",
       "56         F3\n",
       "57         F3\n",
       "58         F3\n",
       "59         F3\n",
       "60         F3\n",
       "61         F3\n",
       "62         F3\n",
       "63         F3\n",
       "64         F3\n",
       "65         F6\n",
       "66    unknown\n",
       "67    unknown\n",
       "68         F2\n",
       "69         F2\n",
       "70         F5\n",
       "Name: rig, Length: 70, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.buildBehaviorDataframe(all_sessions=True)\n",
    "m.beh_df['rig']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                                                   981740006\n",
       "behavior_training_id                                                      None\n",
       "created_at                                          2019-11-13 19:31:00.813789\n",
       "updated_at                                          2019-11-13 19:31:02.230230\n",
       "storage_directory            /allen/programs/braintv/production/visualbehav...\n",
       "temp_output_file_location    /allen/programs/braintv/production/incoming/vi...\n",
       "foraging_id                               406c3154-c629-44d7-9eef-abf1da8c2087\n",
       "donor_id                                                             962177495\n",
       "ophys_session_id                                                           NaN\n",
       "equipment_id                                                         791748416\n",
       "user_id                                                              648047749\n",
       "rig                                                                         F1\n",
       "pklfile                      //allen/programs/braintv/production/visualbeha...\n",
       "trials                            index auto_rewarded change_contrast  chan...\n",
       "stage                                                      TRAINING_1_gratings\n",
       "running                      [[0.0, 0.0264092960279, 0.0430421017809, 0.059...\n",
       "session_datetime                              2019-11-13 10:28:54.700000-08:00\n",
       "session_datetime_local                        2019-11-13 10:28:54.700000-08:00\n",
       "session_datetime_utc                          2019-11-13 18:28:54.700000+00:00\n",
       "cumulative_rewards                                                         -19\n",
       "timeFromLastSession                                                    47.8019\n",
       "engaged_dprime                                                         1.78243\n",
       "session_day_of_week                                                          2\n",
       "WE_ml                                                                     0.75\n",
       "WS_ml                                                                     1.52\n",
       "weight_datetime                                            2019-11-13 11:33:13\n",
       "Wt_g                                                                     19.70\n",
       "hit_rate_engaged                                                      0.911765\n",
       "FA_rate_engaged                                                       0.333333\n",
       "abort_rate_engaged                                                    0.845691\n",
       "earned_rewards                                                              64\n",
       "total_rewards                                                               86\n",
       "Maternal_Index                                                          473628\n",
       "Paternal_Index                                                          474375\n",
       "age                                                                         90\n",
       "baseline_weight                                                          24.97\n",
       "Name: 24, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.beh_df.iloc[-3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "trials = m.beh_df.iloc[-3]['trials']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'index', u'auto_rewarded', u'change_contrast', u'change_frame',\n",
       "       u'change_image_category', u'change_image_name', u'change_ori',\n",
       "       u'change_time', u'cumulative_reward_number', u'cumulative_volume',\n",
       "       u'delta_ori', u'endframe', u'endtime', u'initial_contrast',\n",
       "       u'initial_image_category', u'initial_image_name', u'initial_ori',\n",
       "       u'lick_times', u'optogenetics', u'response_latency', u'response_time',\n",
       "       u'response_type', u'reward_frames', u'reward_times', u'reward_volume',\n",
       "       u'rewarded', u'scheduled_change_time', u'startframe', u'starttime',\n",
       "       u'trial_length', u'stimulus_distribution', u'task', u'user_id',\n",
       "       u'stim_duration', u'LDT_mode', u'distribution_mean', u'stimulus',\n",
       "       u'mouse_id', u'prechange_minimum', u'computer_name',\n",
       "       u'blank_duration_range', u'response_window', u'behavior_session_uuid',\n",
       "       u'blank_screen_timeout', u'session_duration', u'stage',\n",
       "       u'startdatetime', u'date', u'year', u'month', u'day', u'hour',\n",
       "       u'dayofweek', u'number_of_rewards', u'rig_id', u'trial_type',\n",
       "       u'lick_frames', u'reward_licks', u'reward_lick_count',\n",
       "       u'reward_lick_latency', u'reward_rate', u'response', u'color'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n",
      "22\n"
     ]
    }
   ],
   "source": [
    "print(trials['cumulative_reward_number'].max())\n",
    "print(trials[trials['response_type']!='EARLY_RESPONSE']['auto_rewarded'].sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>response_type</th>\n",
       "      <th>auto_rewarded</th>\n",
       "      <th>cumulative_reward_number</th>\n",
       "      <th>cumulative_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HIT</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HIT</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>HIT</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>HIT</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>HIT</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>FA</td>\n",
       "      <td>None</td>\n",
       "      <td>5</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>CR</td>\n",
       "      <td>None</td>\n",
       "      <td>6</td>\n",
       "      <td>0.035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>0.065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>CR</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>0.075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>11</td>\n",
       "      <td>0.085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>0.095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>0.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>14</td>\n",
       "      <td>0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>15</td>\n",
       "      <td>0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>16</td>\n",
       "      <td>0.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>17</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>FA</td>\n",
       "      <td>None</td>\n",
       "      <td>17</td>\n",
       "      <td>0.145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>18</td>\n",
       "      <td>0.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>19</td>\n",
       "      <td>0.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>21</td>\n",
       "      <td>0.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>22</td>\n",
       "      <td>0.195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>23</td>\n",
       "      <td>0.205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>24</td>\n",
       "      <td>0.215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>25</td>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>HIT</td>\n",
       "      <td>False</td>\n",
       "      <td>26</td>\n",
       "      <td>0.235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>84</td>\n",
       "      <td>0.740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>MISS</td>\n",
       "      <td>True</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>CR</td>\n",
       "      <td>None</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>85</td>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>MISS</td>\n",
       "      <td>True</td>\n",
       "      <td>86</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>86</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>CR</td>\n",
       "      <td>None</td>\n",
       "      <td>86</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>86</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>CR</td>\n",
       "      <td>None</td>\n",
       "      <td>86</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>86</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>CR</td>\n",
       "      <td>None</td>\n",
       "      <td>86</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>MISS</td>\n",
       "      <td>False</td>\n",
       "      <td>86</td>\n",
       "      <td>0.750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    response_type auto_rewarded  cumulative_reward_number  cumulative_volume\n",
       "3             HIT          True                         1              0.005\n",
       "4             HIT          True                         2              0.010\n",
       "26            HIT          True                         3              0.015\n",
       "55            HIT          True                         4              0.020\n",
       "87            HIT          True                         5              0.025\n",
       "97             FA          None                         5              0.025\n",
       "104           HIT         False                         6              0.035\n",
       "151            CR          None                         6              0.035\n",
       "161           HIT         False                         7              0.045\n",
       "175           HIT         False                         8              0.055\n",
       "209           HIT         False                         9              0.065\n",
       "218           HIT         False                        10              0.075\n",
       "227            CR          None                        10              0.075\n",
       "238           HIT         False                        11              0.085\n",
       "248           HIT         False                        12              0.095\n",
       "256           HIT         False                        13              0.105\n",
       "259           HIT         False                        14              0.115\n",
       "266           HIT         False                        15              0.125\n",
       "283           HIT         False                        16              0.135\n",
       "293           HIT         False                        17              0.145\n",
       "300            FA          None                        17              0.145\n",
       "301           HIT         False                        18              0.155\n",
       "307           HIT         False                        19              0.165\n",
       "309           HIT         False                        20              0.175\n",
       "312           HIT         False                        21              0.185\n",
       "317           HIT         False                        22              0.195\n",
       "322           HIT         False                        23              0.205\n",
       "332           HIT         False                        24              0.215\n",
       "337           HIT         False                        25              0.225\n",
       "338           HIT         False                        26              0.235\n",
       "..            ...           ...                       ...                ...\n",
       "857          MISS         False                        84              0.740\n",
       "861          MISS         False                        84              0.740\n",
       "862          MISS         False                        84              0.740\n",
       "863          MISS         False                        84              0.740\n",
       "864          MISS         False                        84              0.740\n",
       "865          MISS         False                        84              0.740\n",
       "866          MISS         False                        84              0.740\n",
       "867          MISS         False                        84              0.740\n",
       "868          MISS         False                        84              0.740\n",
       "869          MISS         False                        84              0.740\n",
       "870          MISS          True                        85              0.745\n",
       "871          MISS         False                        85              0.745\n",
       "872          MISS         False                        85              0.745\n",
       "873            CR          None                        85              0.745\n",
       "874          MISS         False                        85              0.745\n",
       "875          MISS         False                        85              0.745\n",
       "876          MISS         False                        85              0.745\n",
       "877          MISS         False                        85              0.745\n",
       "878          MISS         False                        85              0.745\n",
       "879          MISS         False                        85              0.745\n",
       "881          MISS         False                        85              0.745\n",
       "882          MISS         False                        85              0.745\n",
       "883          MISS          True                        86              0.750\n",
       "888          MISS         False                        86              0.750\n",
       "889            CR          None                        86              0.750\n",
       "890          MISS         False                        86              0.750\n",
       "891            CR          None                        86              0.750\n",
       "892          MISS         False                        86              0.750\n",
       "893            CR          None                        86              0.750\n",
       "894          MISS         False                        86              0.750\n",
       "\n",
       "[311 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials[trials['response_type'] != 'EARLY_RESPONSE'][['response_type','auto_rewarded', 'cumulative_reward_number', 'cumulative_volume']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>behavior_training_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>storage_directory</th>\n",
       "      <th>temp_output_file_location</th>\n",
       "      <th>foraging_id</th>\n",
       "      <th>donor_id</th>\n",
       "      <th>ophys_session_id</th>\n",
       "      <th>equipment_id</th>\n",
       "      <th>...</th>\n",
       "      <th>session_datetime_utc</th>\n",
       "      <th>cumulative_rewards</th>\n",
       "      <th>timeFromLastSession</th>\n",
       "      <th>engaged_dprime</th>\n",
       "      <th>session_day_of_week</th>\n",
       "      <th>WE_ml</th>\n",
       "      <th>WS_ml</th>\n",
       "      <th>weight_datetime</th>\n",
       "      <th>Wt_g</th>\n",
       "      <th>Maternal_Index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>994880774</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-12-17 18:45:58.920609</td>\n",
       "      <td>2019-12-18 05:11:40.830527</td>\n",
       "      <td>/allen/programs/braintv/production/visualbehav...</td>\n",
       "      <td>/allen/programs/braintv/production/incoming/vi...</td>\n",
       "      <td>80e3879f-873a-4d0e-9c50-3c076daaee35</td>\n",
       "      <td>962177495</td>\n",
       "      <td>994869518.0</td>\n",
       "      <td>506540632</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-12-17 17:29:14.254000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>24.218889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>473628.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>994705194</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-12-16 18:36:00.208328</td>\n",
       "      <td>2019-12-17 05:06:51.049206</td>\n",
       "      <td>/allen/programs/braintv/production/visualbehav...</td>\n",
       "      <td>/allen/programs/braintv/production/incoming/vi...</td>\n",
       "      <td>1e137a99-937a-4818-ba47-7c7a4ef0a7e0</td>\n",
       "      <td>962177495</td>\n",
       "      <td>994689000.0</td>\n",
       "      <td>506540632</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-12-16 17:16:05.363000+00:00</td>\n",
       "      <td>35</td>\n",
       "      <td>72.095278</td>\n",
       "      <td>0.960346</td>\n",
       "      <td>0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2019-12-16 10:36:02</td>\n",
       "      <td>19.39</td>\n",
       "      <td>473628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>994174745</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-12-13 18:26:00.845921</td>\n",
       "      <td>2019-12-14 08:58:28.807791</td>\n",
       "      <td>/allen/programs/braintv/production/visualbehav...</td>\n",
       "      <td>/allen/programs/braintv/production/incoming/vi...</td>\n",
       "      <td>190791ad-21ee-4d0b-9663-733c029537cc</td>\n",
       "      <td>962177495</td>\n",
       "      <td>994137503.0</td>\n",
       "      <td>506540632</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-12-13 17:10:21.396000+00:00</td>\n",
       "      <td>75</td>\n",
       "      <td>23.989722</td>\n",
       "      <td>inf</td>\n",
       "      <td>4</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.92</td>\n",
       "      <td>2019-12-13 10:30:11</td>\n",
       "      <td>20.30</td>\n",
       "      <td>473628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>993957378</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-12-12 18:31:02.457549</td>\n",
       "      <td>2019-12-13 05:01:41.094178</td>\n",
       "      <td>/allen/programs/braintv/production/visualbehav...</td>\n",
       "      <td>/allen/programs/braintv/production/incoming/vi...</td>\n",
       "      <td>1cd63fae-67b2-4856-963b-3dceae77ec62</td>\n",
       "      <td>962177495</td>\n",
       "      <td>993944055.0</td>\n",
       "      <td>506540632</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-12-12 17:10:58.122000+00:00</td>\n",
       "      <td>132</td>\n",
       "      <td>23.991667</td>\n",
       "      <td>1.547587</td>\n",
       "      <td>3</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2019-12-12 10:30:31</td>\n",
       "      <td>20.35</td>\n",
       "      <td>473628.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>993706715</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-12-11 18:30:55.499705</td>\n",
       "      <td>2019-12-12 04:51:38.172886</td>\n",
       "      <td>/allen/programs/braintv/production/visualbehav...</td>\n",
       "      <td>/allen/programs/braintv/production/incoming/vi...</td>\n",
       "      <td>cd5ce955-d6ed-4778-a697-b3604ecb507f</td>\n",
       "      <td>962177495</td>\n",
       "      <td>993669512.0</td>\n",
       "      <td>506540632</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-12-11 17:11:27.745000+00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>20.302778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
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       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>473628.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id behavior_training_id                 created_at  \\\n",
       "2  994880774                 None 2019-12-17 18:45:58.920609   \n",
       "3  994705194                 None 2019-12-16 18:36:00.208328   \n",
       "4  994174745                 None 2019-12-13 18:26:00.845921   \n",
       "5  993957378                 None 2019-12-12 18:31:02.457549   \n",
       "6  993706715                 None 2019-12-11 18:30:55.499705   \n",
       "\n",
       "                  updated_at  \\\n",
       "2 2019-12-18 05:11:40.830527   \n",
       "3 2019-12-17 05:06:51.049206   \n",
       "4 2019-12-14 08:58:28.807791   \n",
       "5 2019-12-13 05:01:41.094178   \n",
       "6 2019-12-12 04:51:38.172886   \n",
       "\n",
       "                                   storage_directory  \\\n",
       "2  /allen/programs/braintv/production/visualbehav...   \n",
       "3  /allen/programs/braintv/production/visualbehav...   \n",
       "4  /allen/programs/braintv/production/visualbehav...   \n",
       "5  /allen/programs/braintv/production/visualbehav...   \n",
       "6  /allen/programs/braintv/production/visualbehav...   \n",
       "\n",
       "                           temp_output_file_location  \\\n",
       "2  /allen/programs/braintv/production/incoming/vi...   \n",
       "3  /allen/programs/braintv/production/incoming/vi...   \n",
       "4  /allen/programs/braintv/production/incoming/vi...   \n",
       "5  /allen/programs/braintv/production/incoming/vi...   \n",
       "6  /allen/programs/braintv/production/incoming/vi...   \n",
       "\n",
       "                            foraging_id   donor_id  ophys_session_id  \\\n",
       "2  80e3879f-873a-4d0e-9c50-3c076daaee35  962177495       994869518.0   \n",
       "3  1e137a99-937a-4818-ba47-7c7a4ef0a7e0  962177495       994689000.0   \n",
       "4  190791ad-21ee-4d0b-9663-733c029537cc  962177495       994137503.0   \n",
       "5  1cd63fae-67b2-4856-963b-3dceae77ec62  962177495       993944055.0   \n",
       "6  cd5ce955-d6ed-4778-a697-b3604ecb507f  962177495       993669512.0   \n",
       "\n",
       "   equipment_id  ...             session_datetime_utc cumulative_rewards  \\\n",
       "2     506540632  ... 2019-12-17 17:29:14.254000+00:00                  0   \n",
       "3     506540632  ... 2019-12-16 17:16:05.363000+00:00                 35   \n",
       "4     506540632  ... 2019-12-13 17:10:21.396000+00:00                 75   \n",
       "5     506540632  ... 2019-12-12 17:10:58.122000+00:00                132   \n",
       "6     506540632  ... 2019-12-11 17:11:27.745000+00:00                  0   \n",
       "\n",
       "  timeFromLastSession engaged_dprime session_day_of_week  WE_ml  WS_ml  \\\n",
       "2           24.218889            NaN                   1                 \n",
       "3           72.095278       0.960346                   0   0.29   1.83   \n",
       "4           23.989722            inf                   4   0.59   0.92   \n",
       "5           23.991667       1.547587                   3   0.98   0.87   \n",
       "6           20.302778            NaN                   2                 \n",
       "\n",
       "       weight_datetime    Wt_g  Maternal_Index  \n",
       "2                                     473628.0  \n",
       "3  2019-12-16 10:36:02   19.39        473628.0  \n",
       "4  2019-12-13 10:30:11   20.30        473628.0  \n",
       "5  2019-12-12 10:30:31   20.35        473628.0  \n",
       "6                                     473628.0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.Maternal_Index\n",
    "m.beh_df['Maternal_Index'] = m.Maternal_Index\n",
    "m.beh_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     124\n",
       "3     123\n",
       "4     120\n",
       "5     119\n",
       "6     118\n",
       "7     117\n",
       "8     116\n",
       "9     113\n",
       "10    112\n",
       "11    111\n",
       "12    110\n",
       "13    109\n",
       "14    104\n",
       "15    103\n",
       "16    102\n",
       "17     99\n",
       "18     98\n",
       "19     97\n",
       "20     96\n",
       "21     95\n",
       "22     92\n",
       "23     91\n",
       "24     90\n",
       "25     88\n",
       "26     85\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "m.beh_df.apply(lambda row: (row['session_datetime_local'].tz_localize(None) - m.birth_date).days, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "saveDir = r\"\\\\allen\\programs\\braintv\\workgroups\\nc-ophys\\corbettb\\NP_behavior_pipeline\\behavior_sessions\\ophys_pipeline\\dataframes\"\n",
    "m.beh_df.to_pickle(os.path.join(saveDir, 'test'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(os.path.join(saveDir, 'test'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stage</th>\n",
       "      <th>WE_ml</th>\n",
       "      <th>Wt_g</th>\n",
       "      <th>session_datetime_local</th>\n",
       "      <th>cumulative_rewards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OPHYS_5_images_A_passive</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2019-12-17 09:29:14.254000-08:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OPHYS_6_images_A</td>\n",
       "      <td>0.29</td>\n",
       "      <td>19.39</td>\n",
       "      <td>2019-12-16 09:16:05.363000-08:00</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OPHYS_4_images_A</td>\n",
       "      <td>0.59</td>\n",
       "      <td>20.30</td>\n",
       "      <td>2019-12-13 09:10:21.396000-08:00</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OPHYS_3_images_B</td>\n",
       "      <td>0.98</td>\n",
       "      <td>20.35</td>\n",
       "      <td>2019-12-12 09:10:58.122000-08:00</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>OPHYS_2_images_B_passive</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2019-12-11 09:11:27.745000-08:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>OPHYS_1_images_B</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>2019-12-10 12:53:17.355000-08:00</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>OPHYS_3_images_B</td>\n",
       "      <td>0.04</td>\n",
       "      <td>19.76</td>\n",
       "      <td>2019-12-09 09:04:18.208000-08:00</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OPHYS_3_images_B</td>\n",
       "      <td>0.03</td>\n",
       "      <td>19.62</td>\n",
       "      <td>2019-12-06 08:52:52.903000-08:00</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>OPHYS_1_images_B</td>\n",
       "      <td>0.27</td>\n",
       "      <td>19.7</td>\n",
       "      <td>2019-12-05 09:15:09.313000-08:00</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>OPHYS_0_images_B_habituation</td>\n",
       "      <td>0.32</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2019-12-04 09:16:57.305000-08:00</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>OPHYS_0_images_B_habituation</td>\n",
       "      <td>0.61</td>\n",
       "      <td>20.38</td>\n",
       "      <td>2019-12-03 09:02:05.686000-08:00</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OPHYS_0_images_B_habituation</td>\n",
       "      <td>0.93</td>\n",
       "      <td>20.36</td>\n",
       "      <td>2019-12-02 09:12:33.560000-08:00</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TRAINING_5_images_B_handoff_ready</td>\n",
       "      <td>0</td>\n",
       "      <td>20.43</td>\n",
       "      <td>2019-11-27 08:20:44.096000-08:00</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TRAINING_5_images_B_handoff_ready</td>\n",
       "      <td>1.1</td>\n",
       "      <td>20.90</td>\n",
       "      <td>2019-11-26 08:36:34.181000-08:00</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TRAINING_5_images_B_handoff_ready</td>\n",
       "      <td>0.85</td>\n",
       "      <td>20.60</td>\n",
       "      <td>2019-11-25 08:48:13.255000-08:00</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TRAINING_5_images_B_epilogue</td>\n",
       "      <td>1.07</td>\n",
       "      <td>20.70</td>\n",
       "      <td>2019-11-22 10:07:52.436000-08:00</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TRAINING_4_images_B_training</td>\n",
       "      <td>1.04</td>\n",
       "      <td>20.50</td>\n",
       "      <td>2019-11-21 10:08:26.612000-08:00</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TRAINING_3_images_B_10uL_reward</td>\n",
       "      <td>1.44</td>\n",
       "      <td>20.80</td>\n",
       "      <td>2019-11-20 10:06:16.801000-08:00</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>TRAINING_3_images_B_10uL_reward</td>\n",
       "      <td>1.46</td>\n",
       "      <td>21.30</td>\n",
       "      <td>2019-11-19 10:05:15.058000-08:00</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>TRAINING_3_images_B_10uL_reward</td>\n",
       "      <td>0</td>\n",
       "      <td>20.50</td>\n",
       "      <td>2019-11-18 09:59:44.900000-08:00</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TRAINING_2_gratings_flashed</td>\n",
       "      <td>1.33</td>\n",
       "      <td>20.70</td>\n",
       "      <td>2019-11-15 10:42:28.323000-08:00</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TRAINING_1_gratings</td>\n",
       "      <td>1.1</td>\n",
       "      <td>20.30</td>\n",
       "      <td>2019-11-14 10:27:00.819000-08:00</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TRAINING_1_gratings</td>\n",
       "      <td>0.75</td>\n",
       "      <td>19.70</td>\n",
       "      <td>2019-11-13 10:28:54.700000-08:00</td>\n",
       "      <td>-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>TRAINING_1_gratings</td>\n",
       "      <td>0.91</td>\n",
       "      <td>20.30</td>\n",
       "      <td>2019-11-11 10:40:47.250000-08:00</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>TRAINING_0_gratings_autorewards_15min</td>\n",
       "      <td>0.6</td>\n",
       "      <td>20.00</td>\n",
       "      <td>2019-11-08 10:16:59.126000-08:00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    stage  WE_ml    Wt_g  \\\n",
       "2                OPHYS_5_images_A_passive                  \n",
       "3                        OPHYS_6_images_A   0.29   19.39   \n",
       "4                        OPHYS_4_images_A   0.59   20.30   \n",
       "5                        OPHYS_3_images_B   0.98   20.35   \n",
       "6                OPHYS_2_images_B_passive                  \n",
       "7                        OPHYS_1_images_B                  \n",
       "8                        OPHYS_3_images_B   0.04   19.76   \n",
       "9                        OPHYS_3_images_B   0.03   19.62   \n",
       "10                       OPHYS_1_images_B   0.27    19.7   \n",
       "11           OPHYS_0_images_B_habituation   0.32    20.2   \n",
       "12           OPHYS_0_images_B_habituation   0.61   20.38   \n",
       "13           OPHYS_0_images_B_habituation   0.93   20.36   \n",
       "14      TRAINING_5_images_B_handoff_ready      0   20.43   \n",
       "15      TRAINING_5_images_B_handoff_ready    1.1   20.90   \n",
       "16      TRAINING_5_images_B_handoff_ready   0.85   20.60   \n",
       "17           TRAINING_5_images_B_epilogue   1.07   20.70   \n",
       "18           TRAINING_4_images_B_training   1.04   20.50   \n",
       "19        TRAINING_3_images_B_10uL_reward   1.44   20.80   \n",
       "20        TRAINING_3_images_B_10uL_reward   1.46   21.30   \n",
       "21        TRAINING_3_images_B_10uL_reward      0   20.50   \n",
       "22            TRAINING_2_gratings_flashed   1.33   20.70   \n",
       "23                    TRAINING_1_gratings    1.1   20.30   \n",
       "24                    TRAINING_1_gratings   0.75   19.70   \n",
       "25                    TRAINING_1_gratings   0.91   20.30   \n",
       "26  TRAINING_0_gratings_autorewards_15min    0.6   20.00   \n",
       "\n",
       "             session_datetime_local  cumulative_rewards  \n",
       "2  2019-12-17 09:29:14.254000-08:00                   0  \n",
       "3  2019-12-16 09:16:05.363000-08:00                  35  \n",
       "4  2019-12-13 09:10:21.396000-08:00                  75  \n",
       "5  2019-12-12 09:10:58.122000-08:00                 132  \n",
       "6  2019-12-11 09:11:27.745000-08:00                   0  \n",
       "7  2019-12-10 12:53:17.355000-08:00                 108  \n",
       "8  2019-12-09 09:04:18.208000-08:00                  -3  \n",
       "9  2019-12-06 08:52:52.903000-08:00                  -6  \n",
       "10 2019-12-05 09:15:09.313000-08:00                  29  \n",
       "11 2019-12-04 09:16:57.305000-08:00                  36  \n",
       "12 2019-12-03 09:02:05.686000-08:00                  71  \n",
       "13 2019-12-02 09:12:33.560000-08:00                 124  \n",
       "14 2019-11-27 08:20:44.096000-08:00                 152  \n",
       "15 2019-11-26 08:36:34.181000-08:00                 131  \n",
       "16 2019-11-25 08:48:13.255000-08:00                 106  \n",
       "17 2019-11-22 10:07:52.436000-08:00                 127  \n",
       "18 2019-11-21 10:08:26.612000-08:00                 141  \n",
       "19 2019-11-20 10:06:16.801000-08:00                 131  \n",
       "20 2019-11-19 10:05:15.058000-08:00                 135  \n",
       "21 2019-11-18 09:59:44.900000-08:00                 101  \n",
       "22 2019-11-15 10:42:28.323000-08:00                 115  \n",
       "23 2019-11-14 10:27:00.819000-08:00                  91  \n",
       "24 2019-11-13 10:28:54.700000-08:00                 -19  \n",
       "25 2019-11-11 10:40:47.250000-08:00                  56  \n",
       "26 2019-11-08 10:16:59.126000-08:00                   0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df.head()\n",
    "df[['stage', 'WE_ml', 'Wt_g', 'session_datetime_local', 'cumulative_rewards']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'//allen/programs/braintv/production/visualbehavior/prod4/specimen_962177507/behavior_session_994880774\\\\994869518.pkl'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#m.beh_df.drop(columns=['cumulative_rewards'], inplace=True)\n",
    "#m.beh_df.iloc[0]\n",
    "m.beh_df.iloc[0]['pklfile']"
   ]
  }
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