loftus_masson_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c7fff57-a84b-4c2f-8e09-f79004af7712",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('/home1/djhalp/pybeh_pd')\n",
    "import pybeh_pd as pb\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec023e39-798d-41d6-a536-16ab6120177e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset from Loftus & Masson (1994) https://link.springer.com/article/10.3758/BF03210951\n",
    "mat = np.array([[10, 13, 13],\n",
    "          [6, 8, 8], \n",
    "          [11, 14, 14],\n",
    "          [22, 23, 25], \n",
    "          [16, 18, 20], \n",
    "          [15, 17, 17], \n",
    "          [1, 1, 4],\n",
    "          [12, 15, 17],\n",
    "          [9, 12, 12],\n",
    "          [8, 9, 12]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2759318-c5f4-47a5-b1ea-c89d897c142c",
   "metadata": {},
   "source": [
    "# Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbaafdb5-7227-410e-8846-645b9f8cb018",
   "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>cond_1</th>\n",
       "      <th>cond_2</th>\n",
       "      <th>cond_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cond_1  cond_2  cond_3\n",
       "sub                        \n",
       "1        10      13      13\n",
       "2         6       8       8\n",
       "3        11      14      14\n",
       "4        22      23      25\n",
       "5        16      18      20\n",
       "6        15      17      17\n",
       "7         1       1       4\n",
       "8        12      15      17\n",
       "9         9      12      12\n",
       "10        8       9      12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat_df = pd.DataFrame(mat)\n",
    "mat_df.columns = ['cond_1', 'cond_2', 'cond_3']\n",
    "df_table_1 = mat_df.reset_index().rename(columns={'index': 'sub'})\n",
    "df_table_1['sub'] = df_table_1['sub'] + 1\n",
    "df_table_1.set_index('sub')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a3e84e00-3f6d-461d-a154-01c86d39daaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cond_1    11.0\n",
       "cond_2    13.0\n",
       "cond_3    14.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_table_1[['cond_1', 'cond_2', 'cond_3']].mean(axis=0) #table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b077fcf-16fb-469d-bb1f-239d7c6c5164",
   "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>sub</th>\n",
       "      <th>cond</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>cond_1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10</td>\n",
       "      <td>cond_2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10</td>\n",
       "      <td>cond_3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sub    cond  value\n",
       "0     1  cond_1     10\n",
       "1     2  cond_1      6\n",
       "2     3  cond_1     11\n",
       "3     4  cond_1     22\n",
       "4     5  cond_1     16\n",
       "5     6  cond_1     15\n",
       "6     7  cond_1      1\n",
       "7     8  cond_1     12\n",
       "8     9  cond_1      9\n",
       "9    10  cond_1      8\n",
       "10    1  cond_2     13\n",
       "11    2  cond_2      8\n",
       "12    3  cond_2     14\n",
       "13    4  cond_2     23\n",
       "14    5  cond_2     18\n",
       "15    6  cond_2     17\n",
       "16    7  cond_2      1\n",
       "17    8  cond_2     15\n",
       "18    9  cond_2     12\n",
       "19   10  cond_2      9\n",
       "20    1  cond_3     13\n",
       "21    2  cond_3      8\n",
       "22    3  cond_3     14\n",
       "23    4  cond_3     25\n",
       "24    5  cond_3     20\n",
       "25    6  cond_3     17\n",
       "26    7  cond_3      4\n",
       "27    8  cond_3     17\n",
       "28    9  cond_3     12\n",
       "29   10  cond_3     12"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#turn into long pandas dataframe so it's more like our traditional datasets\n",
    "df_long = pd.melt(df_table_1, id_vars='sub', var_name='cond')\n",
    "df_long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9a310333-e57a-4729-a360-1d87f08045a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_subs = df_long['sub'].nunique()\n",
    "n_conds = df_long['cond'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31de0913-ae00-4609-99b0-cf017d5f8d73",
   "metadata": {},
   "source": [
    "# Table 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f34987b-7166-48bc-a337-94557c817aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.733333333333333"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = df_long['value'].mean()\n",
    "M_S = df_long.groupby(['sub'], as_index=False).agg({'value': 'mean'})\n",
    "M_C = df_long.groupby(['cond'], as_index=False).agg({'value': 'mean'})\n",
    "M #grand mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9dc7de77-f4fb-43fa-9043-1319107f7be0",
   "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>sub</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>7.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>23.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>16.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>14.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>9.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sub      value\n",
       "0    1  12.000000\n",
       "1    2   7.333333\n",
       "2    3  13.000000\n",
       "3    4  23.333333\n",
       "4    5  18.000000\n",
       "5    6  16.333333\n",
       "6    7   2.000000\n",
       "7    8  14.666667\n",
       "8    9  11.000000\n",
       "9   10   9.666667"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#subject means\n",
    "M_S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a75a790-c1ca-4b8b-b455-0f8a8c66bc34",
   "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>cond</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cond_1</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cond_2</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cond_3</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cond  value\n",
       "0  cond_1   11.0\n",
       "1  cond_2   13.0\n",
       "2  cond_3   14.2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#condition means\n",
    "M_C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434e1782-0417-4432-a23f-fd3a93398d20",
   "metadata": {},
   "source": [
    "## Table 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66770a83-3e28-45c9-b520-ac3356d15088",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Table 3\n",
    "df_table_3 = df_long.copy()\n",
    "df_table_3['M'] = df_long['value'].mean()\n",
    "df_table_3['M_S'] = df_long.groupby(['sub'])['value'].transform('mean')\n",
    "df_table_3['value'] = df_table_3['value'] - df_table_3['M_S'] + df_table_3['M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3391d537-57d0-47c7-89a7-372fcd4e8889",
   "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>cond</th>\n",
       "      <th>cond_1</th>\n",
       "      <th>cond_2</th>\n",
       "      <th>cond_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.733333</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>13.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.400000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>13.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.733333</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>13.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.400000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>14.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.733333</td>\n",
       "      <td>12.733333</td>\n",
       "      <td>14.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.400000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>13.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.733333</td>\n",
       "      <td>11.733333</td>\n",
       "      <td>14.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.066667</td>\n",
       "      <td>13.066667</td>\n",
       "      <td>15.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.733333</td>\n",
       "      <td>13.733333</td>\n",
       "      <td>13.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.066667</td>\n",
       "      <td>12.066667</td>\n",
       "      <td>15.066667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cond     cond_1     cond_2     cond_3\n",
       "sub                                  \n",
       "1     10.733333  13.733333  13.733333\n",
       "2     11.400000  13.400000  13.400000\n",
       "3     10.733333  13.733333  13.733333\n",
       "4     11.400000  12.400000  14.400000\n",
       "5     10.733333  12.733333  14.733333\n",
       "6     11.400000  13.400000  13.400000\n",
       "7     11.733333  11.733333  14.733333\n",
       "8     10.066667  13.066667  15.066667\n",
       "9     10.733333  13.733333  13.733333\n",
       "10    11.066667  12.066667  15.066667"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_table_3.pivot_table(values='value', index='sub', columns='cond')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "93b26db1-35d8-4136-a545-2d515e0070d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.73333333333333"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = df_table_3['value'].mean()\n",
    "M_S = df_table_3.groupby(['sub']).agg({'value': 'mean'})\n",
    "M_C = df_table_3.groupby(['cond']).agg({'value': 'mean'})\n",
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9277087-e34a-47bc-a435-8c5e8ada1964",
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.733333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         value\n",
       "sub           \n",
       "1    12.733333\n",
       "2    12.733333\n",
       "3    12.733333\n",
       "4    12.733333\n",
       "5    12.733333\n",
       "6    12.733333\n",
       "7    12.733333\n",
       "8    12.733333\n",
       "9    12.733333\n",
       "10   12.733333"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M_S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9f87b5ab-4b81-4650-b1b6-0dc423708c05",
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cond</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cond_1</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cond_2</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cond_3</th>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        value\n",
       "cond         \n",
       "cond_1   11.0\n",
       "cond_2   13.0\n",
       "cond_3   14.2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M_C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33af8ec0-646b-473e-b94f-bf719f55c1e4",
   "metadata": {},
   "source": [
    "# Full computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77faf463-6997-4857-a8c0-5d98af3df587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5209332748849111"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_a = df_long.copy()\n",
    "df_a['M'] = df_a['value'].mean()\n",
    "df_a['M_S'] = df_a.groupby(['sub'])['value'].transform('mean')\n",
    "df_a['M_C'] = df_a.groupby(['cond'])['value'].transform('mean')\n",
    "M_C = df_a.groupby(['cond']).agg({'value': 'mean'})['value'].values\n",
    "\n",
    "#appendix A\n",
    "# df_a['y_ij'] = ((df_a['value'] + df_a['M'] - df_a['M_S'])\n",
    "df_a['S_W'] = ((df_a['value'] + df_a['M'] - df_a['M_S'] - df_a['M_C']) ** 2) \n",
    "\n",
    "SS_W = df_a['S_W'].sum() #Figure 3\n",
    "\n",
    "# MS_SxC = df_a['S_W'].mean() #biased estimate\n",
    "df = (n_subs - 1) * (n_conds - 1) #18, pg.482\n",
    "MS_SxC = SS_W / df #same as using n - 1 in standard deviation computation\n",
    "\n",
    "SE = np.sqrt(MS_SxC / n_subs)\n",
    "tval = sp.stats.t.ppf(0.975, df)\n",
    "CI = SE * tval # Eq. 2, pg. 482\n",
    "CI"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e141828-7357-4852-951e-52033dbcb803",
   "metadata": {},
   "source": [
    "### Equal Variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "81b641e7-9e50-445d-9ada-859bf3765e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Figure 5\n",
    "plt.errorbar(x=[1, 2, 5], y=M_C, yerr=CI)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6b55f85-e0f7-4cac-b79c-9eea8d6571f2",
   "metadata": {},
   "source": [
    "### Unequal Variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bea74404-139b-4454-8c71-3dc4eb18fc4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_MS = df_a.groupby(['cond', 'M_C'], as_index=False).agg({'S_W': 'sum'})\n",
    "df_MS['MS_W'] = df_MS['S_W'] / (n_subs - 1)\n",
    "df_MS['estimator'] = (n_conds / (n_conds - 1)) * (df_MS['MS_W'] - (MS_SxC / (n_conds)))\n",
    "df_MS['CI'] = np.sqrt(df_MS['estimator'] / n_subs) * sp.stats.t.ppf(0.975, (n_subs-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac4ef73f-f9d5-4a0d-9d02-835c4ca916d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAh70lEQVR4nO3de3TU9Z3/8ec7N+73GREIIUAmWERBjFyEUG1dFtHVbasVtturZ2n7k1V/vdmu28uue6/drVt7fsppPW73p6BdS3UrtdrtzxOxiAKCICCEmwaQJATCNQnJvH9/zJCm6YRMwiTf5JvX4xwPM9/5fGde+TTz7jfv+c7na+6OiIiEV1bQAUREpGup0IuIhJwKvYhIyKnQi4iEnAq9iEjI5QQdIJVIJOKFhYVBxxAR6TU2btxY7e7RVI/1yEJfWFjIhg0bgo4hItJrmNmBth5T60ZEJORU6EVEQk6FXkQk5FToRURCToVeRCTkVOhFREJOhV5EJORU6EVEQk6FXkSkB7jj0XXc8ei6LnluFXoRkZBToRcRCTkVehGRkGu30JvZY2ZWaWbbUjz2FTNzM4u0se9+M9tqZpvNTKuUiYgEIJ0j+seBRa03mtl44I+Ad9vZ/3p3n+HuJR2PJyIiF6vdQu/uZUBNiof+Dfga4JkOJSIimdOpHr2Z3QIcdPct7Qx14EUz22hmy9p5zmVmtsHMNlRVVXUmloiIpNDhC4+Y2UDgfmBhGsPnufshM7sEeMnMdib/QvgD7r4CWAFQUlKivxJERDKkM0f0k4GJwBYz2w/kA5vM7NLWA939UPLfSmA1MKvzUUVEpDM6XOjdfau7X+Luhe5eCFQAM939/ZbjzGyQmQ05f5vEXwB/cOaOiIh0rXROr1wJrAOmmFmFmd15gbFjzWxN8u5oYK2ZbQFeB5539xcyEVpERNLXbo/e3Ze283hhi9uHgMXJ23uB6ReZT0RELpK+GSsiEnIq9CIiIadCLyIScir0IiIhp0IvIhJyKvQiIiGnQi8iEnIq9CIiIadCLyLSQzTFu2Y9xw6vXikiIplxpqGR9XtrKNtdxZaK43gXrdurQi8i0k3icWf74ROU7a7ilV3VbDxwjIamOP1ysuifm8WwAbk0NsXJyc5ss0WFXkSkC71fW8cru6t4ZXc1a8urqTndAMBllw7hM/MKKY1FuKZwJJ9+7HWAjBd5UKEXEcmosw1NrN93lFd2V/PK7ip2HTkFQGRwPz5YHKU0FmF+UYRLhvbvtkwq9CIiFyEed3a8f6K5sL+xL9GOycvJYvbEkdx2dT7zi6JcdukQsrIskIwq9CIiHVR5oq65sK8tr6b61O/aMZ++dgKlsSizJo6kf252wEkTVOhFRNpRd66J1/fVNPfad75/EoBRg/IojUUojUWZH4swuhvbMR2hQi8i0oq7s/P9k82Fff2+Ghoa4+RlZ3HNxBF8/cbLKI1F+MClQwNrx3REu4XezB4DbgYq3X1aq8e+AnwXiLp7dYp9FwEPAdnAj9z9nzKSWkQkwypP1rF2d3WyJVNN9al6AIpHD+aTcyZQGoswe+IoBuT1jHZMR6RzRP848DDwk5YbzWw88EfAu6l2MrNs4IfJMRXAG2b2nLtvv5jAIiKZUHeuiTf21/DK7mrKdlU1t2NGDspjflGkuSVz6bCe2Y7piHSuGVtmZoUpHvo34GvAs23sOgsoT147FjNbBdwKqNCLSLdzd945cpJXdlVTtruK1/fVUN8YJzfbKJkwkq8tmsKCWJSpY3pHO6YjOtWjN7NbgIPuvsWszQkZB7zX4n4FMPsCz7kMWAZQUFDQmVgiIr+n6mQ9r5YnCvva3dVUnky0Y4ouGcyfzS5gQSzK7EkjGZgX7o8rO/zTmdlA4H5gYXtDU2xrcyUHd18BrAAoKSnpohUfRCTM6s41sfHAseYlBrYfPgHAiIG5zCuKsCB5dszY4QMCTtq9OvN/Y5OBicD5o/l8YJOZzXL391uMqwDGt7ifDxzqbFARkdbcnd2Vpyjbdf7smKPUnUu0Y2YWjOCrf5xox1w+NnztmI7ocKF3963AJefvm9l+oCTFWTdvADEzmwgcBJYAf9b5qCIicPRUPWvLq5u/sHTkRKIdMzk6iCXXFLCgOHF2zKB+4W7HdEQ6p1euBK4DImZWAXzb3X/cxtixJE6jXOzujWa2HPgVidMrH3P3tzMXXUT6gvrGRDvmfGHfdjDRjhne3I6JMD8WZVwfa8d0RDpn3Sxt5/HCFrcPAYtb3F8DrLmIfCLSx7g7e6pOUbYrUdhf21vD2XNN5GQZMyeM4CsLiymNRZk2bhjZfbgd0xH620ZEAldzuoFXy6ubv4l6uLYOgEmRQXy8JJ/SWJQ5k0cxWO2YTtGsiUi3a2iMJ9sxicK+7VAt7jC0fw7zYxHujkWZXxRh/MiBQUcNBRV6EelyiXbM6ebC/treo5xpaCI7y5hZMJz/fUMxpbEIV+YPVzumC6jQi0iXOHa6gVf3VPNKstd+KNmOKRw1kI/NzKc0FmHu5FEM6Z8bcNLwU6EXkYxoaIzz5ru/OzvmrYOJdsyQ/jnMmxzhrg9FKC2KUjBK7ZjupkIvIp3i7uyrPt1c2NftOcrpZDtmxvjh3PPhGKWxKNPzh3XJdVAlfSr0IpK22jPnEu2Y3VWU7arm4PGzAEwYNZCPzBxHaSzK3MmjGKp2TIc99fm5XfbcKvQi0qZzTXE2v3ecV3ZVUba7mrcqjhN3GNIvh2uLRvHF6yZTGoswYdSgoKPKBajQi0gzd+fA0TOJI/bd1azbc5RT9Y1kGcwYP5y//FCMBcURpucPVzumF1GhF+njas+eY92easqSvfb3ahLtmPwRA7hlxlgWxCLMnRxh2AC1Y3orFXqRPqaxKc6WiuPNSwxsfi/RjhncL4e5k0exrHQSpbEoE0YN5ALXm5BeRIVepA949+iZxBrtu6v4bflRTibbMVfmD2f59UWUFkeZMX44uWrHhJIKvUgInag7x2/Lj/LK7irWlldz4OgZAMYNH8DN08dQGoty7eRRDB+YF3BS6Q4q9CIhkGjH1DYvMbD5veM0xZ1BednMnTyKz82bSGkswsTIILVj+iAVepFe6r2aM82XzHt1TzUn6xqxZDvmf103mflFEa4qGEFejtoxfZ0KvUgvcbLuHOv2HG3+Jur+ZDtm7LD+3HTF79oxIwapHSO/T4VepIdqijtvVRxvLuyb3k20YwbmZTN30ig+c20hpcVRJqkdI+1I51KCjwE3A5XuPi257QHgViAOVAKfSV5dqvW++4GTQBPQ6O4lmYsuEj4Vx840F/ZXy49Se/YcZnDFuGF84YOJ0x5nqh0jHZTOEf3jwMPAT1ps+667fxPAzO4GvgV8oY39r09x4XARAU7VN/LanqPNH6LurT4NwJhh/fnjy0dTGosyryjCSLVj5CKkc83YMjMrbLXtRIu7gwDPcC6RUGqKO9sO1jYvMbDpwDEa486A3GzmTBrJn8+ZwILiCJOjg9WOkYzpdI/ezP4e+BRQC1zfxjAHXjQzBx519xUXeL5lwDKAgoKCzsaSDrjj0XVA166aJ3Dw+FnWJgv7q+XVHD9zDoBp44byFwsmURqLcPWEEfTLyQ44qYRVpwu9u98P3G9m3wCWA99OMWyeux8ys0uAl8xsp7uXtfF8K4AVACUlJfoLQXqt0/WNrN93tHmJgT1ViXbM6KH9uOEDoymNRZhfFGHU4H4BJ5W+IhNn3TwJPE+KQn/+A1p3rzSz1cAsIGWhF+mt4nHn7UMnmpcY2HjgGOeanP65WcyeOIqlswpYUBwldonaMRKMThV6M4u5++7k3VuAnSnGDAKy3P1k8vZC4G87nVSkBzlcezZxLdTyatburuJYsh1z+dih3Dl/EgtiEWZOGEH/XLVjJHjpnF65ErgOiJhZBYkj98VmNoXE6ZUHSJ5xY2ZjgR+5+2JgNLA6eQSTAzzp7i90xQ8h0tXONDSyfm9N8qi9mvLKUwBcMqQfH7psNAuKI8wrihBRO0Z6oHTOulmaYvOP2xh7CFicvL0XmH5R6UQCEo872w+faF5iYOOBYzQ0xemXk8XsSaNYcs14SmNRikerHSM9n74ZK5L0fm1d8/nsa8urqTndAMAHxgzls/MKKY1FKSlUO0Z6HxV66bPONjSxft/v1o7ZdSTRjokM7sd1xVFKk+2YS4b0DzipyMVRoZc+Ix53drx/ormwv7Ev0Y7Jy8li9sSR3HZ1PqWxKJddOkTtGAkVFXoJtcoTdc2FfW15NdWnEu2Yyy4dwqevnUBpLMqsiSPVjpFQU6GXUKk718Tr+2qae+073z8JQGRwHvOLIpTGosyPRRg9VO0Y6TtU6KVXc3d2vn+yubCv31dDQ2OiHTOrcCRfv3EcpbEIH7h0KFlZasdI36RCL71O5ck6Xi2vbv7CUtXJegCmjB7Cp+ZMoLQ4yqzCkQzIUztGBFTopReoO9fEG/trkr32anYcTiyeOmpQHvNjyXZMUYRLh6kdI5KKCr30OO7OO0dO8squasp2V/H6vhrqG+PkZWdRUjiC+xZdRmkswtQxaseIpEOFXnqEqpP1vFqeKOxrd1dTmWzHxC4ZzCdmT6C0OMLsiSMZmKdfWZGO0rtGAlF3romNB441LzGwPdmOGTEwl/mxKKWxCKWxCGOGDQg4qUjvp0Iv3cLd2V15irJd58+OOUrduTi52cbVE0bw1T+ewoJYlMvHqh0jkmkq9NJljp6qZ215dfMXlo6cSLRjJkcHseSaAhYUR5g9cRSD+unXUKQr6R0mGVPfmGjHnC/s2w4m2jHDB+YyryjCgliE+bEo44arHSPSnVTopdPcnT1Vp5ovmffa3hrOnmsiJ8uYOWEEX1lYTGksyrRxw8hWO0YkMCr00iE1pxsSX1ZKfhP1cG0dAJOig/h4SWJRsDmTRzFY7RiRHkPvRrmghsY4m9491lzYtx6sxR2GDchNrh0TYX4sQv6IgUFHFZE2pHMpwceAm4FKd5+W3PYAcCuJSwlWAp85fyHwVvsuAh4CsklcYvCfMphduoC7s7f6NK8kz45Zt/coZxqS7ZiCEXzphmJKi6NcoXaMSK+RzhH948DDwE9abPuuu38TwMzuBr5F8rqx55lZNvBD4I+ACuANM3vO3bdnILdk0PEzDbxafrT5qP3g8bMATIwMal6jfc6kkQzpnxtwUhHpjHSuGVtmZoWttp1ocXcQ4Cl2nQWUJ68di5mtIvFXgAp9D1F5oo7Kk/Vc9cBLuMOQ/jnML4pw1/VFlMYijB+pdoxIGHS6R29mfw98CqgFrk8xZBzwXov7FcDsCzzfMmAZQEFBQWdjSZp+vf0I+46eYWBeNvd+uJjS4ghXjhtGTnZW0NFEJMM6/a529/vdfTzwBLA8xZBUDdxUR/7nn2+Fu5e4e0k0Gu1sLEnD4dqzfPW/tjAwL5vLxw7lnhtizCwYoSIvElKZeGc/CXwsxfYKYHyL+/nAH3xgK92rKe7cu2oz9Y1xii4ZTJaujSoSep0q9GYWa3H3FmBnimFvADEzm2hmecAS4LnOvJ5kzsO/KWf9vhoeuHUaA3SdVJE+od1Cb2YrgXXAFDOrMLM7gX8ys21m9hawELgnOXasma0BcPdGEi2dXwE7gKfd/e0u+jkkDa/vq+Gh/9nFR64ax8euzg86joh0k3TOulmaYvOP2xh7CFjc4v4aYE2n00nGHDvdwD2r3qRg5EAe+NNpQccRkW6kb8b2Ae7O1555i+pT9fzsi/O0PIFIH6PTLPqA/3ztAC9tP8J9iy7jivxhQccRkW6mQh9y2w+d4O+e38H1U6LcOX9i0HFEJAAq9CF2pqGR5Ss3MXxALg/ePh3TqZQifZKatSH2nefeZl/1aZ64czajBvcLOo6IBERH9CH17OaDPL2hgruuK+LaokjQcUQkQCr0IXTg6GnuX72NqyeM4N4bYu3vICKhpkIfMg2Nce5e+SZZBg8tmaH1a0REPfqwefDFd9hSUcsjfz5TV30SEUBH9KHy8juVrCjbyydmF7Bo2pig44hID6FCHxKVJ+r48tNbmDJ6CN+8eWrQcUSkB1HrJgTicedLT2/hdEMjq/5sDv21KqWItKAj+hB4pGwPa8ur+fafXE5s9JCg44hID6NC38ttevcY33txFzddOYYl14xvfwcR6XNU6Hux2rPnuHvlm4wZ1p9//OgVWuJARFJSj76Xcnf+6mdbOVxbx0+/MJeh/XODjiQiPZSO6HupVW+8x/NbD/OVhVOYWTAi6Dgi0oOlcynBx8ys0sy2tdj2XTPbaWZvmdlqMxvexr77zWyrmW02sw0ZzN2n7Tpykr/577cpjUX4/IJJQccRkR4unSP6x4FFrba9BExz9yuBXcA3LrD/9e4+w91LOhdRWqo718TyJzcxuF8O3/v4dLKy1JcXkQtrt9C7exlQ02rbi8mLfwO8BuhK093kgV9sZ9eRU3zv4zO4ZEj/oOOISC+QiQ9jPwc81cZjDrxoZg486u4r2noSM1sGLAMoKCjIQKzw+eXWwzyx/l0+v2ASHyyOXvTzPfX5uRlIJSI93UV9GGtm9wONwBNtDJnn7jOBG4G7zGxBW8/l7ivcvcTdS6LRiy9iYVNx7Az3PfMW0/OH8eWFU4KOIyK9SKcLvZl9GrgZ+IS7e6ox7n4o+W8lsBqY1dnX68sam+Lcs2ozcYcfLJ1JXo5OlhKR9HWqYpjZIuA+4BZ3P9PGmEFmNuT8bWAhsC3VWLmw7/96NxsPHOMfPnoFBaO09LCIdEw6p1euBNYBU8yswszuBB4GhgAvJU+dfCQ5dqyZrUnuOhpYa2ZbgNeB5939hS75KULst+XV/PDlcj5eks8t08cGHUdEeqF2P4x196UpNv+4jbGHgMXJ23uB6ReVro87eqqee5/azKTIIL5zy+VBxxGRXkpLIPRQ8bjz5Z9u4fjZczz+2VkMzNP/VCLSOfpUr4d67NV9vPxOFX990weYOnZo0HFEpBdToe+BtlbU8s8v7GTh1NF8cs6EoOOISC+nQt/DnKw7x/KVm4gO7se/3Hallh4WkYumxm8P4u588+fbeK/mDKuWzWX4wLygI4lICOiIvgd5ZtNBfr75EPd8uJhZE0cGHUdEQkKFvofYW3WKbz27jdkTR7L8Q0VBxxGREFGh7wHqG5tY/uSb9MvJ4qElV5GtpYdFJIPUo+8B/nHNTrYfPsGPPlXCpcO09LCIZJaO6AP26+1HePy3+/nMtYXcMHV00HFEJIRU6AN0uPYsX/2vLVw+dijfWHxZ0HFEJKRU6APSFHfuXbWZ+sY4P1h6Ff1ysoOOJCIhpR59QB7+TTnr99Xw4O3TmRQdHHQcEQkxHdEH4PV9NTz0P7v4yFXj+NjMcUHHEZGQU6HvZsdON3DPqjcpGDmQB/50mpY4EJEup9ZNN3J3vvbMW1SfqudnX5zH4H6afhHpejqi70b/+doBXtp+hPsWXcYV+cOCjiMifUQ6lxJ8zMwqzWxbi23fNbOdZvaWma02s+Ft7LvIzN4xs3Iz+3oGc/c62w+d4O+e38H1U6LcOX9i0HFEpA9J54j+cWBRq20vAdPc/UpgF/CN1juZWTbwQ+BGYCqw1MymXlTaXupMQyPLV25i+IBcHrx9uvryItKt2i307l4G1LTa9qK7Nybvvgbkp9h1FlDu7nvdvQFYBdx6kXl7pe889zb7qk/z/TtmMGpwv6DjiEgfk4ke/eeAX6bYPg54r8X9iuS2lMxsmZltMLMNVVVVGYjVMzy7+SBPb6jgruuKuLYoEnQcEemDLqrQm9n9QCPwRKqHU2zztp7L3Ve4e4m7l0Sj0YuJ1WMcOHqa+1dv4+oJI7j3hljQcUSkj+r0+X1m9mngZuDD7p6qgFcA41vczwcOdfb1epuGxjh3r3yTLIOHlswgJ1snOIlIMDpVfcxsEXAfcIu7n2lj2BtAzMwmmlkesAR4rnMxe58HX3yHLRW1/MttV5I/YmDQcUSkD0vn9MqVwDpgiplVmNmdwMPAEOAlM9tsZo8kx441szUAyQ9rlwO/AnYAT7v72130c/QoL79TyYqyvXxidgGLpo0JOo6I9HGWuusSrJKSEt+wYUPQMTql8mQdix96hVGD+vHs8nn0z9WqlCLS9cxso7uXpHpM38HPoHjc+dJTWzhV38iTfzFHRV5EegR9QphBj5TtYW15Nd/+k8spHj0k6DgiIoAKfcZsevcY33txFzddMYYl14xvfwcRkW6iQp8BtWfPcffKNxkzrD//8NErtMSBiPQo6tFfJHfnr362lcO1dfz0C3MZNiA36EgiIr9HR/QXadUb7/H81sN8eWExMwtGBB1HROQPqNBfhF1HTvI3//0284sifGHB5KDjiIikpELfSXXnmlj+5CYG98vhX++YTlaW+vIi0jOpR99JD/xiO7uOnOI/PjeLS4b0DzqOiEibdETfCb/cepgn1r/L5xdM4oPF4VhpU0TCS4W+gyqOneG+Z95iev4wvrxwStBxRETapULfAY1Nce5ZtZm4ww+WziQvR9MnIj2fevQd8P1f72bjgWP8+9KrKBilpYdFpHfQIWmafltezQ9fLufjJfncMn1s0HFERNKmQp+Go6fqufepzUyKDOI7t1wedBwRkQ5R66Yd8bjz5Z9u4fjZczz+2VkMzNOUiUjvoiP6djz26j5efqeKv77pA0wdOzToOCIiHZbOpQQfM7NKM9vWYtvtZva2mcXNLOUVTZLj9pvZ1uTlBnvdJaO2VtTyzy/sZOHU0XxyzoSg44iIdEo6R/SPA4tabdsGfBQoS2P/6919RluXuOqpTtU38pcrNxEZ3I9/ue1KLT0sIr1Wuw1ndy8zs8JW23YAoS1+7s5fr97KuzVnWLVsLsMH5gUdSUSk07q6R+/Ai2a20cyWXWigmS0zsw1mtqGqqqqLY13YM5sO8vPNh7jnw8XMmjgy0CwiIherqwv9PHefCdwI3GVmC9oa6O4r3L3E3Uui0eDWj9lbdYpvPbuN2RNHsvxDRYHlEBHJlC4t9O5+KPlvJbAamNWVr3ex6hubWP7km/TLyeL7S2aQraWHRSQEuqzQm9kgMxty/jawkMSHuD3WP67ZyfbDJ/jubdMZM2xA0HFERDIindMrVwLrgClmVmFmd5rZR8ysApgLPG9mv0qOHWtma5K7jgbWmtkW4HXgeXd/oWt+jIv36+1HePy3+/nMtYXcMHV00HFERDImnbNulrbx0OoUYw8Bi5O39wLTLypdNzlce5av/tcWLh87lG8svizoOCIiGdXnvxnbFHfuXbWZ+sY4P1h6Ff1ysoOOJCKSUX1+4ZaHf1PO+n01PHj7dCZFBwcdR0Qk4/r0Ef3r+2p46H928ZGrxvGxmeOCjiMi0iX6bKE/drqBe1a9ScHIgTzwp9NC+y1fEZE+2bpxd772zFtUn6rnZ1+cx+B+fXIaRKSP6JNH9P/52gFe2n6E+xZdxhX5w4KOIyLSpfpcod9+6AR/9/wOrp8S5XPzJgYdR0Sky/WpQn+moZHlKzcxfEAuD94+nSwtcSAifUCfak5/57m32Vd9mifunM2owf2CjiMi0i36zBH9s5sP8vSGCu66rohriyJBxxER6TZ9otAfOHqa+1dv4+oJI7j3hljQcUREulWoCv0dj67jjkfX/d62hsY4d698kyyDh5bMICc7VD+yiEi7Qt+j/96L77Clopb/84mZ5I8YGHQcEZFuF+rD25ffqeTRsr18YnYBN14xJug4IiKBCG2hrzxZx1d+uoUpo4fwzZunBh1HRCQwoWzdxOPOl57awqn6Rp78izn0z9XSwyLSd4XyiP6Rsj2sLa/m239yOcWjhwQdR0QkUOlcSvAxM6s0s20ttt1uZm+bWdzMSi6w7yIze8fMys3s65kKfSEn6xr53ou7uOmKMSy5Znx3vKSISI+WzhH948CiVtu2AR8FytraycyygR8CNwJTgaVm1qXN8samOHuqTjFmWH/+4aNXaOlhERHSKPTuXgbUtNq2w93faWfXWUC5u+919wZgFXBrp5O2w93Zd/QM9Y1x/n3pVQwbkNtVLyUi0qt0ZY9+HPBei/sVyW0pmdkyM9tgZhuqqqo6/GK1Z89xtqGJ8SMGMLNgRMfTioiEVFcW+lR9E29rsLuvcPcSdy+JRqMdfrHhA/OYNnYoY4b17/C+IiJh1pWnV1YALT8NzQcOdeHradlhEZEUuvKI/g0gZmYTzSwPWAI814WvJyIiKaRzeuVKYB0wxcwqzOxOM/uImVUAc4HnzexXybFjzWwNgLs3AsuBXwE7gKfd/e2u+kFERCS1dls37r60jYdWpxh7CFjc4v4aYE2n04mIyEUL5TdjRUTkd1ToRURCToVeRCTkQrV65VOfnxt0BBGRHkdH9CIiIadCLyIScir0IiIhp0IvIhJyKvQiIiGnQi8iEnIq9CIiIadCLyIScir0IiIhZ+5tXvQpMGZWBRzo5O4RoDqDcTJFuTpGuTpGuTomjLkmuHvKy/P1yEJ/Mcxsg7uXBJ2jNeXqGOXqGOXqmL6WS60bEZGQU6EXEQm5MBb6FUEHaINydYxydYxydUyfyhW6Hr2IiPy+MB7Ri4hICyr0IiIh1ysLvZk9ZmaVZratjcfNzP7dzMrN7C0zm9lDcl1nZrVmtjn537e6Kdd4M/t/ZrbDzN42s3tSjOn2OUszV7fPmZn1N7PXzWxLMtffpBgTxHylkyuQ37Hka2eb2Ztm9osUjwXynkwjV1Dvyf1mtjX5mhtSPJ7Z+XL3XvcfsACYCWxr4/HFwC8BA+YA63tIruuAXwQwX2OAmcnbQ4BdwNSg5yzNXN0+Z8k5GJy8nQusB+b0gPlKJ1cgv2PJ1/4S8GSq1w/qPZlGrqDek/uByAUez+h89cojencvA2ouMORW4Cee8Bow3MzG9IBcgXD3w+6+KXn7JLADGNdqWLfPWZq5ul1yDk4l7+Ym/2t91kIQ85VOrkCYWT5wE/CjNoYE8p5MI1dPldH56pWFPg3jgPda3K+gBxSQpLnJP71/aWaXd/eLm1khcBWJo8GWAp2zC+SCAOYs+ef+ZqASeMnde8R8pZELgvkd+z7wNSDexuNB/X59nwvngmDmy4EXzWyjmS1L8XhG5yushd5SbOsJRz6bSKxHMR34AfDz7nxxMxsMPAPc6+4nWj+cYpdumbN2cgUyZ+7e5O4zgHxglplNazUkkPlKI1e3z5eZ3QxUuvvGCw1Lsa1L5yvNXEG9J+e5+0zgRuAuM1vQ6vGMzldYC30FML7F/XzgUEBZmrn7ifN/erv7GiDXzCLd8dpmlkuimD7h7j9LMSSQOWsvV5BzlnzN48DLwKJWDwX6O9ZWroDmax5wi5ntB1YBHzKz/9tqTBDz1W6uoH6/3P1Q8t9KYDUwq9WQjM5XWAv9c8Cnkp9czwFq3f1w0KHM7FIzs+TtWSTm/2g3vK4BPwZ2uPu/tjGs2+csnVxBzJmZRc1sePL2AOAGYGerYUHMV7u5gpgvd/+Gu+e7eyGwBPiNu/95q2HdPl/p5Aro92uQmQ05fxtYCLQ+Uy+j85XT6bQBMrOVJD4tj5hZBfBtEh9M4e6PAGtIfGpdDpwBPttDct0GfNHMGoGzwBJPfsTexeYBnwS2Jvu7AH8FFLTIFsScpZMriDkbA/yHmWWTeOM/7e6/MLMvtMgVxHylkyuo37E/0APmK51cQczXaGB18v9fcoAn3f2FrpwvLYEgIhJyYW3diIhIkgq9iEjIqdCLiIScCr2ISMip0IuIhJwKvYhIyKnQi4iE3P8HNKkU7zIDAX8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x=[1, 2, 5], y='M_C', yerr='CI', data=df_MS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4149d795-50b5-47d2-9c45-fed734f68ab3",
   "metadata": {},
   "source": [
    "# Using analytic pybeh function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5962d33-cb84-46a1-a187-919adbdf923b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#function requires a column for \"subject\", \"condition\" and the \"value\" for each subject in each condition\n",
    "df_loftus_masson = pb.loftus_masson_analytic(df_long, 'sub', 'cond', 'value')\n",
    "plt.errorbar(x=[1, 2, 5], y='M_C', yerr='CI_equal', data=df_loftus_masson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1d5c6b8-6a25-4ce4-9828-8c66bf45d1ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x=[1, 2, 5], y='M_C', yerr='CI_unequal', data=df_loftus_masson)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9978950-767c-4998-971a-a2736c82b587",
   "metadata": {},
   "source": [
    "## FR1 SPC example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8266cc02-5435-4d5c-8f38-76e0d626f561",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cmlreaders as cml\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "92d01ab6-5a66-4d8c-bfa7-c53627b132c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_fr1_events = cml.CMLReader.load_events(experiments=[\"FR1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c3ddceee-5709-495f-8e61-5aeaabdaa36a",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_evs = all_fr1_events.query('type == \"WORD\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e3502cb-bfc9-4da2-9c2f-a969e83b4790",
   "metadata": {},
   "outputs": [],
   "source": [
    "spc_sess_df = word_evs.groupby(['subject', 'session', 'serialpos'], as_index=False).agg({'recalled': 'mean'})\n",
    "spc_df = spc_sess_df.groupby(['subject', 'serialpos'], as_index=False).agg({'recalled': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aca53f5f-46e5-4aed-ba7f-9b9a8f5cca95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b55bebfba90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#between-subject errorbars\n",
    "g = sns.relplot(x='serialpos', y='recalled', data=spc_df, kind='line', err_style=\"bars\")\n",
    "g.set(xlabel='Serial Position', ylabel='P(recalled)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4d4e9603-7647-4411-b4ae-89871584cd7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#within-subject errorbars -- notice they are smaller than above\n",
    "spc_df_loftus_masson = pb.loftus_masson_analytic(spc_df, 'subject', 'serialpos', 'recalled')\n",
    "plt.errorbar(x='serialpos', y='M_C', yerr='CI_equal', data=spc_df_loftus_masson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "61b6e54d-3fbf-4848-b545-eb478b15dd8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.errorbar(x='serialpos', y='M_C', yerr='CI_unequal', data=spc_df_loftus_masson)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13cf3763-689d-4d96-9800-873ac64efa4f",
   "metadata": {},
   "source": [
    "# Cousineau Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a71df926-e5f1-45d5-ac59-74fac6ac471b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for ease of plotting\n",
    "df_long['time'] = 1\n",
    "df_long.loc[df_long['cond'] == 'cond_2', 'time'] = 2\n",
    "df_long.loc[df_long['cond'] == 'cond_3', 'time'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5fac5734-dd0a-4582-b5ca-5469d4cf2e4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cousineau = df_long.copy()\n",
    "df_cousineau['M'] = df_long['value'].mean()\n",
    "df_cousineau['M_S'] = df_long.groupby('sub')['value'].transform('mean')\n",
    "df_cousineau['adj_value'] = df_cousineau['value'] + df_cousineau['M'] - df_cousineau['M_S']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "390102f1-a491-4594-b1e0-a85c4334b713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b55d381f510>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='time', y='adj_value', data=df_cousineau, kind='line', err_style=\"bars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7feee365-503b-49d6-a254-d8eec20e977b",
   "metadata": {},
   "source": [
    "## Morey/Cousineau/O'Brien adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9ec93518-c6d7-4031-8faf-7a042cb7cb39",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cousineau['cmo_adj_value'] = (np.sqrt(n_conds / (n_conds - 1)) * (df_cousineau['value'] - df_cousineau['M_S'])) + df_cousineau['M']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "403c55fe-38e7-4dd6-b35a-18e6e7c330bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b55c2570a50>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x='time', y='cmo_adj_value', data=df_cousineau, kind='line', err_style=\"bars\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "74779039-7ca3-4ae8-9a1e-174f48642a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using units with catplot should solve the same problem by adjusting the bootstrap procedure\n",
    "# https://seaborn.pydata.org/generated/seaborn.catplot.html\n",
    "# https://github.com/mwaskom/seaborn/issues/2769\n",
    "# ideally will be on correct axis but currently only implemented for scatter plots (see below)\n",
    "g = sns.catplot(x='time', y='adj_value', data=df_cousineau, units='sub', kind='point')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9e4fb21a-9a52-4813-8b5d-e68f4679e769",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='time', y='adj_value', data=df_cousineau, units='sub', native_scale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f1c06a9b-1e25-443e-9a04-92b7262c373a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using pybeh_pd\n",
    "df_cousineau = pb.cousineau(df_long, 'sub', 'cond', 'value')\n",
    "g = sns.catplot(x='cond', y='adj_value', data=df_cousineau, units='sub', kind='point')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "316de68c-12d3-4600-b730-4363bda03631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='cond', y='cmo_adj_value', data=df_cousineau, units='sub', kind='point')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dda4f57-fa4c-4d3b-a1f2-caf3ac9b43a4",
   "metadata": {},
   "source": [
    "## FR1 example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e055f824-ce3d-4c79-a8ce-89e6521c9d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cousineau = pb.cousineau(spc_df, 'subject', 'serialpos', 'recalled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2fb90946-e140-4d29-b89a-589dfebcfbd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b55c4a8ebd0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='serialpos', y='recalled', data=df_cousineau, kind='point', units='subject')\n",
    "g.set(xlabel='Serial Position', ylabel='P(recalled)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b7394867-8ea7-4028-b789-e4fd0a97d736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b55c4b4fed0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#notice error bars are slightly smaller when not doing the correction (and runs faster)\n",
    "g = sns.catplot(x='serialpos', y='recalled', data=df_cousineau, kind='point')\n",
    "g.set(xlabel='Serial Position', ylabel='P(recalled)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d90d1185-0a39-450e-acc4-747ccb73d166",
   "metadata": {},
   "source": [
    "# Within-list analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "113c19eb-716a-452f-b148-b00536e3b67d",
   "metadata": {},
   "source": [
    "Sometimes we are actually interested in visualizing functions within *list* and not just within subject. In this case, we can apply the same logic from Cousineau to normalize within list. We then average these to get the average within-list function for each subject (because we assume most of the variance is within subject). The same procedure can then be used as if these were the subject scores. This will make less of a difference for binary measures (like recalled or not) but potentially relevant for continuous measures like RTs or neural indices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc747263-d7dd-4cfd-b5ff-cb346ea1cfb0",
   "metadata": {},
   "source": [
    "First we need to preprocess the FR1 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9d7fcfd1-771c-4d2d-a774-17f5ffca2b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "events = all_fr1_events.query('type == [\"WORD\", \"REC_WORD\"]')\n",
    "events = events.assign(row_order=range(len(events)))\n",
    "events = events[events['list'] > 0] # removing practice lists\n",
    "\n",
    "#removing subjects who were presented with the same word multiple times (probably repeated data)\n",
    "word_evs = events.query('type == \"WORD\"')\n",
    "n_word_pres = word_evs.groupby(['subject', 'session', 'list', 'item_num'], as_index=False).size()\n",
    "bad_lists = n_word_pres.query('size >= 2')\n",
    "bad_subs = bad_lists.subject.unique().tolist()\n",
    "\n",
    "#drop subjects without 12 items per list\n",
    "n_list_pres = word_evs.groupby(['subject', 'session', 'list'], as_index=False).size()\n",
    "bad_lists = n_list_pres.query('size != 12')\n",
    "bad_subs += bad_lists.subject.unique().tolist()\n",
    "\n",
    "#drop subjects with no recalls\n",
    "n_types = events.groupby('subject', as_index=False).agg({'type': 'nunique'})\n",
    "no_recall_subs = n_types.query('type != 2')\n",
    "bad_subs += no_recall_subs.subject.unique().tolist()\n",
    "\n",
    "#drop subs with messed up recalls\n",
    "rec_evs = events.query('type == \"REC_WORD\"')\n",
    "joint_evs = word_evs.merge(rec_evs[['subject', 'session', 'list', 'item_name']], how='left', indicator=True)\n",
    "bad_subs += joint_evs.query('(recalled == 1 and _merge != \"both\") or (recalled == 0 and _merge == \"both\")').subject.unique().tolist()\n",
    "\n",
    "events.query('subject not in @bad_subs', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "11b0f4b4-f10c-4c89-83e7-b65c8350ec05",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fixing serial position in REC_WORD events\n",
    "events.loc[events['type'] == \"REC_WORD\", \"serialpos\"] = np.nan\n",
    "events.sort_values('type', inplace=True)\n",
    "#bfill because WORD will come after REC_WORD after sorting\n",
    "events['serialpos'] = events.groupby(['subject', 'session', 'list', 'item_name'])['serialpos'].bfill()\n",
    "events.sort_values('row_order', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ece83676-2b6a-4ef2-87f7-ffe7a9305f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_evs = events.query('type == \"WORD\"')\n",
    "rec_evs = events.query('type == \"REC_WORD\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "81cb0b78-4935-40ae-b8fa-e8cc7238dbca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/pandas/core/indexing.py:1817: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n",
      "/usr/global/miniconda/py310_23.1.0-1/envs/workshop/lib/python3.7/site-packages/ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "# loading rec evs\n",
    "rec_evs = events.query('type == \"REC_WORD\"')\n",
    "rec_evs['repeat'] = rec_evs.duplicated(subset=['subject', 'session', 'list', 'item_name'])\n",
    "rec_evs['correct'] = (rec_evs['repeat'] == False) & (rec_evs['intrusion'] == 0)\n",
    "rec_evs['next_rectime'] = rec_evs.groupby(['subject', 'session', 'list']).rectime.shift(-1)\n",
    "rec_evs['next_correct'] = rec_evs.groupby(['subject', 'session', 'list']).correct.shift(-1)\n",
    "rec_evs['irt'] = rec_evs['next_rectime'] - rec_evs['rectime']\n",
    "rec_evs.loc[(rec_evs['correct'] == False) | (rec_evs['next_correct'] == False), 'irt'] = np.nan\n",
    "rec_evs['correct_outpos'] = rec_evs.groupby(['subject', 'session', 'list'])['correct'].cumsum()\n",
    "rec_evs['n_correct'] = rec_evs.groupby(['subject', 'session', 'list'])['correct_outpos'].transform('max')\n",
    "rec_evs['irt_s'] = rec_evs['irt'] / 1000\n",
    "rec_evs['next_serialpos'] = rec_evs.groupby(['subject', 'session', 'list']).serialpos.shift(-1)\n",
    "rec_evs['transition'] = rec_evs['next_serialpos'] - rec_evs['serialpos']\n",
    "rec_evs.loc[(rec_evs['correct'] == False) | (rec_evs['next_correct'] == False), 'transition'] = np.nan\n",
    "rec_evs['next_item_name'] = rec_evs.groupby(['subject', 'session', 'list']).item_name.shift(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "093404da-98d7-4333-89c1-c5791fbf7750",
   "metadata": {},
   "source": [
    "Standard between-subjects error bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "624c9561-d21c-440d-8eb1-8709bf42890d",
   "metadata": {},
   "outputs": [],
   "source": [
    "crl_list_df = rec_evs.groupby(['subject', 'session', 'list', 'transition'], as_index=False).agg({'irt_s': 'mean'})\n",
    "crl_sess_df = crl_list_df.groupby(['subject', 'session', 'transition'], as_index=False).agg({'irt_s': 'mean'})\n",
    "crl_df = crl_sess_df.groupby(['subject', 'transition'], as_index=False).agg({'irt_s': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "697d240d-fbaf-4bcd-9655-577617dad79e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b55c4f1a290>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='transition', y='irt_s', data=crl_df.query('abs(transition) < 6'),\n",
    "                order=np.arange(-5, 6),\n",
    "                kind='point')\n",
    "g.set(xlabel='Lag', ylabel='IRT (s)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5abfa7a7-514a-436b-9f61-da2c7a31a77e",
   "metadata": {},
   "source": [
    "Cousineau within-subject error bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bca56f97-264b-4fad-aef8-f7885a726ef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b55c82744d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV8AAAFgCAYAAAAcmXr5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAruElEQVR4nO3deXhV5bXH8e/KHOYpjAFBQEFQERAErAPSFqvV1mrVtmhbFaf22sHa6Xa8vZ2ut9daWymKrUPV2ta5aqvijKABmUFlEgLIGBICmbPuH+ckBsxEkn32GX6f58nDydk7510h4cc+e+/1vubuiIhIbKWFXYCISCpS+IqIhEDhKyISAoWviEgIFL4iIiHICLuAIzVz5kx/5plnwi5DRKS1rLEnE+7Id/fu3WGXICLSbgkXviIiyUDhKyISAoWviEgIFL4iIiFQ+IqIhEDhKyISAoWviEgIFL4iIiFQ+IqIhEDhKyISAoWviEgIEm5inbaYNW8RhUVl5PfM5d4rJoddjohIaoRvYVEZG3cfCLsMEZF6Ou0gIhICha+ISAgUviIiIVD4ioiEQOErIhICha+ISAgUviIiIVD4ioiEQOErIhICha+ISAgUviIiIVD4ioiEQOErIhKClJjVLJY0faWItIbCt4Np+koRaQ2ddhARCYHCV0QkBApfEZEQKHxFREKg8BURCYHCV0QkBApfEZEQBB6+ZpZuZm+Z2ZONbDvDzIrNbGn044dB1yMiEg9i0WRxA7AG6NbE9lfc/dwY1CEiEjcCPfI1s3zgHODOIMcREUk0QZ92uAW4CahtZp8pZrbMzJ42szGN7WBms82swMwKdu3aFUSdIiIxFVj4mtm5wE53X9zMbkuAo9z9ROB3wKON7eTuc919ortPzMvL6/hiRURiLMgj32nAeWa2CXgQmG5m9zXcwd1L3L00+vgpINPM+gRYk4hIXAgsfN39u+6e7+5DgUuA+e7+hYb7mFl/M7Po40nRevYEVZOISLyI+ZSSZnYNgLvPAS4ErjWzaqAMuMTdPdY1iYjEWkzC191fBF6MPp7T4PnbgNtiUYOISDxRh5uISAgUviIiIdAyQglM68WJJC6FbwLTenEiiUunHUREQqDwFREJgcJXRCQECl8RkRAofEVEQqDwFREJgcJXRCQECl8RkRAofEVEQpDUHW7FZVU8vKSQnSXlAByoqKa6ppaMdP2fIyLhStoUWrm1mDNvfpGfPLGaA5U1AOzcX8HFcxeyv7wq5OpEJNUlZfhWVNdw5d0F7D1Q+aFti98r4kePrwqhKhGRDyRl+P571Q7ej55qaMwTy7axp7QihhWJiBwqKcN3zfaSZrdX1Tjrd2k2MBEJT1KGb7fczBb3ufOVDazbuT8G1YiIfFhShu85xw/AWtjn36t3MOM3L3Pl3QUsfm9vTOoSEamTlOE7uFcnrjljeKPb0gx6d86q//y5NTv4zO2vc9GcBTy/Zge1tVo8WUSCl7T3+d708WMZ2COXO17ewOa9BwHIyUjj7i9PYvxRPXli2Tb++NIG3t4ROfXw5qYi3txUwDH9unD1acM5b9xAMnU/sIgEJGnTxcyYdcpRvPStMxjcKxeAAT1ymXx0bzLT07hgfD7PfO0j3PXFiUwa1qv+697ZUco3/7aM03/9AvNe3ciBiuqwvgURSWJJG751zIyMtMa/TTNj+qh+PHT1FB6+biofO65f/bZtxeX815OrmfrL+fzvv99mt25NE5EOlPTh21rjh/Rk7mUTee4bp/PZiflkpkcu2RWXVfG7+euY9sv5/ODRlWzeczDkSkUkGSh8DzOibxd+feGJvPrt6Vx92tF0yY6cFq+oruXehe9xxs0v8NUH3mLl1uKQKxWRRKbwbUK/bjl89xOjee0707lp5rH06ZINQK1HOuTO/d2rzJq3iNfW7cbd2bT7ADf+bRmb9kSaN7btK+Nfq94P81sQkTiWtHc7dJTuuZlcd8YIvjxtGA8v2crcl9ezKXrq4ZV3d/PKu7sZ2bcLhUUHKauqrf+6iuparr53Md85exTXnN74bW8ikrp05NtKOZnpfG7yEJ7/5hnc/vnxnJjfvX7buztLDwnehn79zFq27iuLVZkikiAUvkcoPc04+/gBPHr9NO6/avIht6k1ptbhsaVbY1SdiCQKhW8bmRlTh/fh+58Y3eK+O0t0m5qIHErnfNspv2cu6WlGTTNtyU+t2M60EX2YMbovZi3NOhG/Zs1bRGFRGfk9c7n3islhlyOS0HTk2069u2Rz9tj+ze6zc38FV91TwGduX8CiDXtiVFnHKywqY+PuAxQW6Ry2SHspfDvAj88bwzH9unzo+Yx0Y9zgHvWfL9m8j4vnLuSLf3qDVdt0n7BIKlP4doA+XbJ59Ppp/PT8MeRkRv5Ku+Vk8OzXT+fR66fxj2unMGnoBxfmXnx7F+fc+ir/8cBbbNqtSd1FUpHCt4N0ysrgsilDGdA9MolP7y7ZDOvTGYAJR/Xir1efwp++dDKjB3Sr/5rHl21jxm9e4j8fXVG/wrKIpAaFb4yYGWce25d/fvVUfnvJOIb06gRAda1z38LNnPY/L/DrZ9ZSXKaVlUVSgcI3xtLSjPPHDeK5b5zOf31qLHldI23L5VW1/OHF9Zz26xeY89J6yqLL3YtIclL4hiQrI61+vuFvffxYuuZE7vorLqvil0+v5fT/eYG/LHqPqprGO+dEJLEpfEPWKSuD688cwSs3ncnVpx9NdkbkR7JzfwXff2QlH/3NSzyxbJuWNxJJMgrfONGjUxbfPXs0L33rTD43eQjpaZFmjE17DvLVB97ik7e9ykvv7MI9EsIHKqrZXx45P1xaXsXBSq24IZJIFL5xpn/3HH7+6eN59uunce4JA+qfX7WthMvveoNL71jI7S+uY8ovnmd3aSUAu0orOeXnz/Ps6h1hlS0iR0jhG6eOzuvCbZ8bz5NfPZXTjsmrf37hhr386pm3KSk/9Ei3pLyaa+9bzJrtJbEuVUTaQOEb58YO6s49X57E/VdNPqRbrjHVtc6dr2yMTWEi0i6Bh6+ZpZvZW2b2ZCPbzMxuNbN1ZrbczMYHXU+imjq8D49cN5VenbKa3e/NTXtjVJGItEcsjnxvANY0se1sYGT0YzZwewzqSVhmRrfc5ieiy0hP3FnTRFJJoOFrZvnAOcCdTexyPnCPRywEepjZgCb2FWD6qH7Nbs/rkl1/R4SIxK+gj3xvAW4CmuoUGARsafB5YfS5Q5jZbDMrMLOCXbt2dXiRieSKjwyjZ6fMJrcv2riX7z68Qs0ZInEusPA1s3OBne6+uLndGnnuQ4dt7j7X3Se6+8S8vLxGviR1DOqRywOzTzlkDTmAwdFJ3QEefHMLl9/1BsUHNU+ESLwK8sh3GnCemW0CHgSmm9l9h+1TCAxu8Hk+sC3AmpLCqP7deOwrpzKoR2QGtUE9cnnl29O558uT6tuUF6zfwwW3v8Z7ezRlpUg8Cix83f277p7v7kOBS4D57v6Fw3Z7HLgsetfDKUCxu28PqqZkkxVtRa77c9qIPjxy3bT6GdPW7zrAp/+wgALdASESd2J+n6+ZXWNm10Q/fQrYAKwD7gCui3U9yWZE3y48ev00Jh7VE4C9Byr53B2LtIKySJyJSfi6+4vufm708Rx3nxN97O5+vbsPd/fj3b0gFvUku16ds7jvysmcP24gAJU1tdzw4FJuee4d3QkhEifU4ZakcjLTueXicXx9xjH1z93y3Lt87a9LKa/SXMEiYVP4JjEz44YZI/ntJePISo/8qB9buo0v3LmIPaUVIVcnktoUving/HGDuP+qyfTqHGlNLniviE//YQHrdpaGXJlI6lL4poiJQ3vx6HXTGJ4XWdRz896DfPoPr/Haut0hVyaSmhS+KWRI7048fN00po3oDcD+8mouv+sNHnxjc8iViaQehW+K6Z6byZ+/NIlLTo70tlTXOt95eAW/eGqNlioSacKseYs48+YXmTVvUYe9psI3BWWmp/GLC47ne58YhUUbvP/48gau/ctiLUck0ojCojI27j5AYVFZh72mwjdFmRmzTxvO7Z+fQE5m5NfgX6t2cPEfF7KjpDzk6kSSn8I3xc0c25+Hrp5C367ZAKzYWsynfv8aq7dpOSKJb0GcCoglha9wQn4PHr1+GqMHdANge3E5F81ZwPy1WpBT4lcQpwJiSeErAAzskcvfrpnC9FF9AThQWcOVdxfwp9cia8KVVlRTEe2MU4uySPspfKVel+wM7rhsIl+aNhSAWoefPLGas3/7Mif/7Fm2FUfOBW8tKtMy9SLtpPCVQ6SnGT/65Bh+ev4YonOzs2b7fsqqPlgZo6rWmX1PAc8pgEXaTOErjbpsylC+94nRTW534FfPrNUpCJE2Sonwze+Zy7A+ncnvmRt2KQlld2lls9vf3VnKht1aKUOkLZpfhzxJ3HvF5LBLSEgV1S1PPanpKUXaJiWOfKVtxg3u0ez2bjkZDM/rEptiRJKMwleaNHNs/2ZP1Vw2ZSg5mekxrEgkeSh8pUnZGen8+UuT6hfkbOj0Y/rwtRkjQ6hKJDkofKVZI/p24flvns7vPzeeTlkfHOWOHdSdjHT9+oi0lf71SIsy09M454QB5EXnfwB4fs3OECsSSXwKX2m1tLr5J4G17++nsOhgiNWIJDaFr7SZjn5F2k7h28FSqaHjuTVqLxZpq5RosoilVGjoyEpPo7KmlkUb9lJaUU2XbP0aiRwpHfnKEau766GyppZX390VcjUiiUnhK0es4S1nz+m8r0ibKHzliGVlpNUvO/TC2p3UaNVjkSOm8JUjZmacNTqy4sWeA5UsK9wXbkEiCUjhK21y1qh+9Y+f110PIkdM4SttMm1EH7IzIr8+ut9X5MgpfKVNcrPSmTaiD6BuN5G2UPgmsLAbOurO+wLMX6ujX5EjobvjE1jYDR1njerH91kJRG45u2zK0FDrEUkkOvKVNuvfPYcxA7sBsHD9HkorqkOuSCRxKHylXc4aHbnrQd1uIkdG4SvtMqPBeV/d9SDSegpfaZexA7t/0O329k5q1e0m0ioKX2mXtLQPut12l1ayVN1uIq2i8JV2m65uN0lipRXVHKyMXEzuyHlMFL7Sbqeq202SUG2tc8tz7zD5v59jR0kFAFv2HuTnT62huqa23a/fqvA1szQzO8nMzjGz6WbWr+WvklShbjdJRv/33Dvc8ty7HKisqX/Ogbkvb+CnT65u9+s3G75mNtzM5gLrgF8ClwLXAc+a2UIz+5KZ6ehZ1O0mSaX4YBV3vLKhye1/WbSZ7cVl7RqjpeD8GXAfMNzdP+7uX3D3C939BOA8oDswq10VSFKYPuqD8NUE66lp1rxFnHnzi8yatyjsUtrF3Xnkra2UVzV9aqGm1nn5nfbd195se7G7X9rMtp3ALU1tN7Mc4GUgOzrO3939R4ftcwbwGLAx+tTD7v7TVtQtcWZA91zGDOzGqm0l9d1uWtsttRQWlbFx94Gwy2iTLXsP8vqGPby+fg8L1u+uP8fbnOp2Xnxr1b8OM7sIeMbd95vZfwLjgZ+5+5JmvqwCmO7upWaWCbxqZk+7+8LD9nvF3c9tU/USV84a3Y9V20qi3W67mTm2f9gliTRqR0k5r6+Phu2G3WzZe+SnECYP69WuGlp7aPIDd/+bmZ0KfBy4GbgdaHJmF3d3oDT6aWb0Q3fgJ7EZo/ty6/PvApFbzhS+EpSt+8rqb/+KRE3zig5UsnDDHhZEj2zX72r6CL1ft2ymDu/Dlr0HKXivqNF9PnpcP0b07dq24qNaG751l/vOAW5398fM7MctfZGZpQOLgRHA7929sZNBU8xsGbANuNHdVzXyOrOB2QBDhgxpZckSa2MHdievaza79lfUd7ulpVnYZUkS2Xugku8+vJx/r9pRfyRXWFTGs6t38NHjPrgJq6S8ijc37o2G7R7WbC9p8jV7dc5iytG9mTK8N1OH92ZYn86YGeVVNdz4t2U8uXz7IftPH9WX33z2xHZ/L60N361m9kdgBvArM8umFbepuXsNMM7MegCPmNlYd1/ZYJclwFHRUxOfAB4FRjbyOnOBuQATJ07U0XOcSkszzhrVlwff3FLf7TZ+SM+wy5IkUVVTy+V3vcGKrcWHPF9d68y+p4CbZo6ipLyKBev3sKJwH02dku2ak8HkYZGgnTqiN8f07droQUJOZjq3fW48N5y1n4vnLmTvgUoG9sjhri+e3CHfT2vD97PATOBmd99nZgOAb7V2kOjXvBh9jZUNni9p8PgpM/uDmfVx992tfW2JL2eN7seDb24BYP6anQpf6TD/WvX+h4K3jgO/emZto9tyM9M5eVivSNgO782Ygd1JP4J3ZCP7daV7biZ7D1SSnZHeltIb1Wz4mlkXdy9194PAw3XPu/t2YHvDfRr52jygKhq8uUSPmg/bpz+ww93dzCYROZre095vSoJRt2JGcytn1HW7VVTX8tyaHdz48WNjVZ4kufmtvIUxKz2N8Uf1YOrwPkwd3psT8nuQlRF/7QgtHfk+ZmZLidwOttjdDwCY2dHAmUSOiO8A/t7I1w4A7o6e900DHnL3J83sGgB3nwNcCFxrZtVAGXCJt+bsuYSiNStn5GalM3V4b154e1d9t1t+z04xqE6SXVUrbu36w+fHM31UX3IyO+4INSgt3ed7VvRc7NXANDPrCVQDbwP/BC539/eb+NrlwEmNPD+nwePbgNvaXr7Eo7NG9+OFtyM3oM9fq+WFpGOMGdCVJ5Y1vf3oPp05e2x/zBLjIm9rLpo95e6fd/eh7t7d3Xu7+1R3/++mgldSW8NWY3W7SUcoLDrIXwsKm93nmjOGJ0zwgmY1kwDUdbtBZG23A1rbTdph7fslfOb2BfXdcxmNXCy78WPHcNGE/FiX1i4KXwlEw7XdXnlXN69I2yzasIeL5rxe3+47bnAPXv/uWdz1xYn07JQJwOBeuXxl+siEOuqFlmc1U3O+tMlZoxqu7aYJ1uXIPbPyfWbd9Qb7yyPvnM48No/7r5pMXtdspo/qR49OWQBkpCXmMWRLVb8Rkyok6Rw/KNLtBlrbTY7cXxa9x3V/WUxldWRmsc+Mz2fuZRPplJU8x4MthW9iHcdL3KjrdoPI2m7LtLabtIJ7ZPWI7z+ysr5D7dozhnPzRSeQmZ6YR7hNaem/kTwz+0ZTG939Nx1cjySRht1uz6/ZyUnqdpNm1NQ6P3hsJfcv2lz/3A/OPY4rTh0WYlXBaem/knSgC9C1iQ+RJk0b0bu+s+g5nfeVZpRX1XD9X5bUB29muvHbS8YlbfBCy0e+2zW5ubRVp6wMpqnbTVpQXFbFVfcU8MbGvQB0zkpnzqwJfGRkXsiVBavN53zNbFoH1yJJqO6WM4AXtLabHGZHSTkX//H1+uDt0yWLB2dPSfrghZbD9+NmdqmZ3WhmYwHM7FwzW4DagqUV1O0mTVm/q5QL/rCAte/vB2BIr078/ZqpHJ/fPeTKYqOl0w6/BgYTueXsVjN7D5gCfMfdHw24NkkCA7rnctyAbqzeXsLr0W63zlrbLeW9tbmIL//5TYoOVgEwZmA3/vSlk+nbNSfkymKnpX8FJwPHu3ttdEHM3cAIzekgR2LG6L6s3l5S3+2m5YVS2wtv7+S6+5ZQVhVZIGfaiN7M+cIEuuZkhlxZbLV02qHC3WsB3L0ceEfBK0eq4Xlfdbultn8sLuTKuwvqg/ecEwZw1xdPTrnghZaPfEeZ2fLoYwOGRz83ImtknhBodZIU6rrdtLZb6nJ35r68gV88/cFqE1+cOpQfnntcyv4utBS+o2NShSS1w9d2W1a4Tw0XKaS21vnvp9Yw79WN9c/dNPNYrj09saaA7GgtTab+XqwKkeQ2PRq+oG63VFJZXcuNf1vG48u2AZCeZvziguP57MTBIVcWvpZmNdtvZiWNfOw3s6bXYhY5zKkj+6jbLcWUVlRzxd1v1gdvTmYac2dNUPBGtXTkqxZi6RCHd7tt3VfGoB5NL8QpiWX9rlL2HqgEYN/BSt5+fz83/m1Z/WrDPTplMu/yk5lwlN7x1EmuaYIkrjW862G+jn6Txm+efYez/vclissi9+wWHaxi5i0v1wfvwO45/P2aKQrewyh8JWamj1K3W7J5Ytk2bn3+3Q89Xzd789DenfjHdVMZ0Vdvog+nViOJmYE9Wt/tNmveIgqLysjvmduqJeslHHe9trHZ7ZOP7sWA7jq91Bgd+UpMzYjO9VBZU8ur65pe262wqIyNuw9QWFQWq9KkDVYUFje7/Z0dpTGqJPEofCWm1O2WXDplpTe7vXMSLfvT0RS+ElMN13abv3aX1nZLcC3N0/GJ4wfEqJLEo/CVmEpLM6YfW7e2W4XWdktg7s6Bipomt48Z2I0Lxg+KYUWJReErMddwjt/5mmA9Yf3ptU38c8V2ADIazM9gwGcn5nP/laeQk9n8aYlUpvCVmDu0203hG6RZ8xZx5s0vMmveog593Zfe2cXP/rkaiLQM//lLkxjSK7JE1JBenfj1hSfSvVPyzFSW3zOXYX06k9+z4+7c0NlwibmG3W5rtpeo2y1AdXeNdKT1u0r5yv1L6pd2/9Enj+PUkX1Ijx79JuMsZUHc7qgjXwnFdHW7JaTig1VceXcB+8urAfj85CHMOuWokKtKTApfCcVZ6nZLONU1tVx//5L6I+lTju7Fj88bk9LTQraHwldCUdftBtR3u0l8+9k/19Q3xgzp1YnbPz+BzHRFSFvpb05C09puNwnf/Ys28+cFmwDokp3BnZdPpGfnrHCLSnAKXwnNdHW7JYSFG/bww8dWAmAGt146jmP6aaKc9lL4SmhOULdb3Nu85yDX3reY6ujP5jszRzF9VL8WvkpaQ+EroTm822351uYnaZHY2l9exZX3vEnRwcg8vReMH8Ts044OuarkofCVUDXsdtOph/hRU+t87cGl9bOSjR/Sg59/+njd2dCBFL4SKnW7xaf/+dfbPB9t/R7YPYc5syaoVbiDKXwlVJ2yMpg6vDdAfbebhOvhJYXMeWk9ALmZ6cy9bCJ9u+aEXNWHBdHyG0tqL5bQnTW6Hy++vQuITLSjjqnwLNlcxHceXlH/+f9+9kTGDuoeYkVNS/QVTnTkK6Fr2O2m877h2bavjNn3LKayuhaAr884RvPxBkjhK6Fr2O22YP0eDlaq2y3WDlZWc9U9BewurQDgnBMG8B9njQi5quSm8JW4UHfXQ2V1La+8q263WHJ3vvW35azaVgLA2EHduPnCE3VnQ8AUvhIXtLZbeG59fl39pOh5XbO547KJ5LawNpu0X2Dha2Y5ZvaGmS0zs1Vm9pNG9jEzu9XM1pnZcjMbH1Q9Et8O73ZzV7dbLDy1Yjv/99w7AGRlpDF31gQt9R4jQR75VgDT3f1EYBww08xOOWyfs4GR0Y/ZwO0B1iNx7PBut7qLPhKclVuL+cZDS+s//9VnjuekIT3DKyjFBBa+HlEa/TQz+nH44cz5wD3RfRcCPcxMl1dT1PQG3W4HK5temFHab+f+cmbfU0B5VeQ/uWvPGM6nT8oPuarUEug5XzNLN7OlwE7gWXc/fCGpQcCWBp8XRp87/HVmm1mBmRXs2rUrsHolXB9p0O2m8A1OeVUNV9+7mG3F5UBkas9vfezYkKtKPYGGr7vXuPs4IB+YZGZjD9ulscupHzrZ5+5z3X2iu0/My8sLoFKJBw273SprIkdkOvfbsdyd7z2ygrc27wPg2H5dueWSk5Jy3bV4F5O7Hdx9H/AiMPOwTYXA4Aaf5wPbYlGTxKehvTsf8vmWojL+/NpGhXAHmfvyBh5eshWAnp0yufPyiXTJVqNrGIK82yHPzHpEH+cCM4C1h+32OHBZ9K6HU4Bid98eVE0S3+av3cHd0dUS6tTUOj9+YjW/fPrwXx05UvPX7uCXz0T+HjPSjDlfmMDg6HLvEntBHvkOAF4ws+XAm0TO+T5pZteY2TXRfZ4CNgDrgDuA6wKsR+JYba3z0ydWf/icU9TclzewZe/BmNaUyNydBet2s+dApGOtuKyKrz7wFnVvIH72qbFMPrp3iBVKYO833H05cFIjz89p8NiB64OqQRLH6u0lbNrTdLg68PTK7cw+bXjsikpQpRXVXH1vAa+t21P/3N4DlfWPvzh1KJdMGhJGadKATvZIXNhf3vJ8DqWt2EfgPx9ZcUjwNtQ5O53vf2JUIOPWTe2YqFM8xprCV+LCyH5dyEiz+rXCGnPcwPic2jCe7Cgp54nlTV82OVBRQ8F7+5gyvONPOST6FI+xprkdJC706ZLNeeMGNrk9v2fuIUsOSeNWbSumpoWFSJcV7otNMdIsha/EjZ+cN4ZJw3p96Pl+3bKZd/nJZKbr17UluZktv5ntpElz4oJ+myVudM3J5IGrTuGuL06ka/Te096ds5j/zTM4tn/XkKtLDBOO6kmfLllNbk9PMz56nJZ+jwcKX4kr6WnG9FH96BOd4axbbiad1QTQalkZadw0s+kLaleeOkyzlsUJha9IkrloQj79umUf8lyaGd89exTfOTuYOx3kyCl8RZLMm5uK2FESaa7IzYz8Ex/SK5erTx+u1SniiMJXJMk88Mbm+sfdczMBFLpxSCfTRJLIvoOV9UsCDe6VS7pCN27pyFckiTzy1tb6VUAuOXmIjnjjmMJXJEm4e/0ph/Q046IJWpkinil8RZLEks37eGdHZOWuGaP70rdbTsgVSXMUviJJouGFNs1aFv8UviJJoLisiieXRxaBGdQjl9NGarmteKfwFUkCjy3dWr8S8cUnDyZda7LFPYWvSIJzd+5fFDnlkGZw0URdaEsECl+RBLessJi17+8HYPqovpq7IUEofEUS3AOLPrjQdqkutCUMha9IAttfXsUT0QttA7rncPoxutCWKBS+Igns8WXbOFhZA8BFEweToQnnE4Z+UiIJrO7eXrPIXQ6SOBS+IglqRWExK7eWAHDGMXkM6qELbYlE4SuSoB54Ux1tiUzhK5KADlRU89hbWwHo2zWb6aO0snOiUfiKJKAnl2/jQPRC22cnDtbKzglIPzGRBHT/G1vqH+tCW2JS+IokmFXbilm2ZR8AHxnZh8G9OoVbkLSJlhESSTAPNjjq/VwLF9rye+Ye8qfED4WvxCWFRuPKKmt4NHqhrU+XbGYc16/Z/e+9YnIsypI2UPhKXFJoNO7J5dvYX1ENwIUT8nWhLYHpJyeSQA5ZrUIX2hKawlckQbz9/n6WbN4HwLQRvRnap3O4BUm7KHxFEsShR73qaEt0Cl+RBFBeVcPDSwoB6NU5i4+Naf5Cm8Q/ha9IAnh65XZKyj+40JadkR5yRdJeCl+RBPDAInW0JRuFr0icW7dzP29s2gvA5GG9GJ7XJeSKpCMofEXi3AMNO9om60JbslD4isSxhhfaenTK5ONj+odckXQUha9IHPvXqvcpOlgFwAUn5ZOTqQttyULhKxLHGt7be+kkXWhLJgpfkTi1YVcpCzdELrSdPLQnI/t1Dbki6UgKX5E49dc3P7jQpo625BNY+JrZYDN7wczWmNkqM7uhkX3OMLNiM1sa/fhhUPWIJJLK6lr+vjhyoa1bTgbnnDAg5IqkowU5pWQ18E13X2JmXYHFZvasu68+bL9X3P3cAOsQSTjPrt7BngOVAFwwXhfaklFgR77uvt3dl0Qf7wfWAIOCGk8kmRwyiY4utCWlmJzzNbOhwEnAokY2TzGzZWb2tJmNaeLrZ5tZgZkV7Nq1K8hSRUL33p4DvLpuNwAnDenBqP7dQq5IghB4+JpZF+AfwNfcveSwzUuAo9z9ROB3wKONvYa7z3X3ie4+MS8vL9B6RcLW8ELbpS2s0SaJK9DwNbNMIsH7F3d/+PDt7l7i7qXRx08BmWbWJ8iaROJZVU0tDxVELrR1zc7gXF1oS1pB3u1gwDxgjbv/pol9+kf3w8wmRevZE1RNIvHu+TU72F1aAcD5Jw2kU5aWWUxWQf5kpwGzgBVmtjT63PeAIQDuPge4ELjWzKqBMuASd/cAaxKJa/e/oVMOqSKw8HX3VwFrYZ/bgNuCqkEkHs2at4jCojLye+Yeskrzlr0HeeXdyAXlE/O7M2Zg97BKlBjQexqRGCssKmPj7gMfev6hgi3Uve+7REe9SU/txSJxoLqmlocKIqccOmel88kTB4ZckQRN4SsSB154exc7SiIX2s4bN4gu2XpTmuwUviJxQFNHph6Fr0jItu0r48W3dwIwZmA3jh+kC22pQOErErKHCrZQG73QdumkIURvfZckp/AVCVFNrde3E+dmpnP+OF1oSxUKX5EQvfTOTrYXlwPwyRMH0DUnM+SKJFYUviIhekAdbSlL4SsSkuqaWuavjVxoG9W/K+MG9wi3IIkpha9ISEorqqmJXmnThbbUo/AVCUlJeTUA2RlpfOokLfKSahS+IiGpO+o994SBdM/VhbZUo/AVCZk62lKTGshFYmTl1mLueX0ThUUH658bnteZCUf1DLEqCYvCVyQG/rG4kG/9fVl9J1ud0opqisuq6NEpK5zCJDQ67SASsO3FZXz74eUfCl6AHSUV/OKptbEvSkKn8BUJ2N8LCqmuaXp1rEeXbuVARXUMK5J4oPAVCdh7ew82u72iupYdJeUxqkbihcJXJGB9u2Y3uz09zejdufl9JPkofEUCdsH4/Ga3f3R0P7p30n2+qUbhKxKwEX278PUZxzS6rV+3bL5/zugYVyTxQOErEgM3zBjJnC9MYNKwXtTN4NAtJ4PHv3Iqg3t1CrU2CYfCVyRGZo7tz0NXT2Fon84A9O6STb9uOSFXJWFR+IqIhEDhKyISAoWviEgIFL4iIiFQ+IqIhEDhKyISAoWviEgIFL4iIiFQ+IqIhEDhKyISAoWviEgIFL4iIiFQ+IqIhECrF4vEWH7P3EP+lNSk8BWJsXuvmBx2CRIHdNpBRCQECl8RkRAofEVEQqDwFREJgcJXRCQEgYWvmQ02sxfMbI2ZrTKzGxrZx8zsVjNbZ2bLzWx8UPWIiMSTIG81qwa+6e5LzKwrsNjMnnX31Q32ORsYGf2YDNwe/VNEJKkFduTr7tvdfUn08X5gDTDosN3OB+7xiIVADzMbEFRNIiLxIibnfM1sKHASsOiwTYOALQ0+L+TDAY2ZzTazAjMr2LVrV2B1iojESuDha2ZdgH8AX3P3ksM3N/Il/qEn3Oe6+0R3n5iXlxdEmSIiMRVo+JpZJpHg/Yu7P9zILoXA4Aaf5wPbgqxJRCQemPuHDjQ75oXNDLgb2OvuX2tin3OArwCfIHKh7VZ3n9TC6+4C3mtDSX2A3W34urZI1rFiPV6yjhXr8ZJ1rFiP19axdrv7zMOfDDJ8TwVeAVYAtdGnvwcMAXD3OdGAvg2YCRwEvuTuBQHVU+DuE4N47VQZK9bjJetYsR4vWceK9XgdPVZgt5q5+6s0fk634T4OXB9UDSIi8UodbiIiIUil8J2rsRJuvGQdK9bjJetYsR6vQ8cK7JyviIg0LZWOfEVE4obCV0QkBEkfvmZ2hpkVm9nS6McPYzTuyWZWY2YXBjjG+dHZ4JZG269PDXCsz0fHWm5mC8zsxKDGio43ysxeN7MKM7sx4LFmmtnb0dn1vhPwWHeZ2U4zWxnkONGxWpxZsAPHyjGzN8xsWXSsnwQ1VoMx083sLTN7MgZjbTKzFXX/1jriNVNlAc1X3P3cWA1mZunAr4B/BTzU88Dj7u5mdgLwEDAqoLE2Aqe7e5GZnU3k4kOQM9DtBf4D+FSAY9T9rH4PfJRIx+WbZvb4YbPvdaQ/E7m3/Z6AXr+h1sws2FEqgOnuXhrtbH3VzJ6OTpgVlBuITNjVLcAxGjrT3TusoSPpj3xD8lUibdU7gxzE3Uv9gyumnWlkXowOHGuBuxdFP11IpBU8MO6+093fBKqCHAeYBKxz9w3uXgk8SGS2vUC4+8tE/mMJXCtnFuyosdzdS6OfZkY/Avt9NLN84BzgzqDGCFqqhO+U6Nuhp81sTJADmdkg4NPAnCDHaTDep81sLfBP4MuxGBO4Ang6RmMFrVUz6yW6ZmYW7Mgx0s1sKZGDjmfdPbCxgFuAm/igezZoDvzbzBab2eyOeMFUCN8lwFHufiLwO+DRgMe7Bfi2u9cEPA4A7v6Iu48i8vb8v4Iez8zOJBK+3w56rBhp1cx6iayFmQU7jLvXuPs4Iu+KJpnZ2CDGMbNzgZ3uvjiI12/CNHcfT2QBiOvN7LT2vmBShq+ZXV93gQ3oUvd2yN2fAjLNrE+A400EHjSzTcCFwB/M7FNBjGVmA+uej76dHd6R39vhY0XPK98JnO/uezpqnKbG6+jXb0JSz6zXipkFO5y77wNeJDJnSxCmAedF/409CEw3s/sCGgsAd98W/XMn8AiR01XtftGk/gD680EzySRgc93nMRj7z8CFAb7+iAbf23hga1DfG5EJkdYBU2P88/sxcGOAr58BbACGAVnAMmBMwN/TUGBlDP7ujMiFvVtiMFYe0CP6OJfIpFrnxmDcM4AnAx6jM9C1weMFwMz2vm4q3O1wIXCtmVUDZcAlHv1bTAKfAS4zsyoi39vFAX5vPwR6EzmSB6j2AGeTMrP+QAGRK9m1ZvY14Djv4LfN7l5tZl8hcmdKOnCXu6/qyDEaMrMHiARGHzMrBH7k7vMCGm4aMAtYEX1XBvA9j7wD7GgDgLujd4+kAQ+5e+C3gMVIP+CR6O99BnC/uz/T3hdVe7GISAiS8pyviEi8U/iKiIRA4SsiEgKFr4hICBS+IiIhUPhKSjCz0pb3Eokdha+ISAgUvpKyzOyTZrYoOifsc2bWL/p8npk9a2ZLzOyPZvZeR7ekiyh8JZW9Cpzi7icRmSPgpujzPwLme2QilUeItFaLdKhUaC8WaUo+8FczG0BkXoeN0edPJTItKO7+jJkVNfH1Im2mI19JZb8DbnP344GrgZzo841NMynSoRS+ksq6E5kJDuDyBs+/CnwWwMw+BvSMcV2SAjSxjqQEM6vl0Hl6fwOsB/6PSAAvBE529zPMrC/wAJHQfQm4GBjm7hWxrVqSmcJX5DBmlg3URKebnALc7pEVGkQ6jC64iXzYEOAhM0sDKoGrQq5HkpCOfEVEQqALbiIiIVD4ioiEQOErIhICha+ISAgUviIiIfh/IcX7Q1el9fcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "crl_df_cousineau = pb.cousineau(crl_df.query('abs(transition) < 6'), 'subject', 'transition', 'irt_s')\n",
    "g = sns.catplot(x='transition', y='irt_s', data=crl_df_cousineau, kind='point', order=np.arange(-5, 6), units='subject')\n",
    "g.set(xlabel='Lag', ylabel='IRT (s)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a1cd3e1-2ea5-4d74-a651-ef4050047761",
   "metadata": {},
   "source": [
    "Cousineau within-list error bars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b8425512-bd0c-4dbd-8a41-66ae757ffde7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first average irts within transition type within each list\n",
    "crl_list_df = rec_evs.groupby(['transition', 'subject', 'session', 'list'], as_index=False).agg({'irt_s': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "033b1c61-36dd-4294-9d45-9bb3c6c04c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalize irts within each list\n",
    "crl_list_df_cosineau = pb.cousineau(crl_list_df.query('abs(transition) < 6'), ['subject', 'session', 'list'], 'transition', 'irt_s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9cdcae70-1214-4fac-8757-a8a9fa573194",
   "metadata": {},
   "outputs": [],
   "source": [
    "# average within session\n",
    "crl_within_list_sess_df_cosineau = crl_list_df_cosineau.groupby(['subject', 'session', 'transition'], as_index=False).agg({'adj_irt_s': 'mean'})\n",
    "# average within subject\n",
    "crl_within_list_df_cosineau = crl_within_list_sess_df_cosineau.groupby(['subject', 'transition'], as_index=False).agg({'adj_irt_s': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "13985d46-6381-4bc4-8e09-b228b6d23844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b55ca3b77d0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x='transition', y='adj_irt_s', data=crl_within_list_df_cosineau, \n",
    "                kind='point', order=np.arange(-5, 6), units='subject')\n",
    "g.set(xlabel='Lag', ylabel='IRT (s)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ffcc430-037c-4abe-9857-48d161a3c073",
   "metadata": {},
   "source": [
    "### Perhaps need to add something on how this is handled by [emmeans](https://cran.r-project.org/web/packages/emmeans/vignettes/xplanations.html#arrows)\n",
    "\n",
    "See also [here](https://github.com/rvlenth/emmeans/issues/93) and the [lighthouse plot](https://easystats.github.io/modelbased/articles/estimate_contrasts.html#complex-model) in the see package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f69efa3f-3f04-4227-b092-6b713c81a7fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aad086e9-c08e-40a6-a5b8-fa5a1c7f164e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "workshop",
   "language": "python",
   "name": "workshop"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}