different plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "faa298ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import scipy.io as sio\n",
    "import scipy\n",
    "from statsmodels.stats.anova import AnovaRM\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "be59ab6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bbc6fc0c",
   "metadata": {},
   "source": [
    "### load DDM datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "be9f34a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_DDM = pd.read_csv(('DDMdifResultDutilh2019.csv'))\n",
    "df1_DDM[\"Dataset\"] = [\"Dutilh (2019)\"]*len(df1_DDM)\n",
    "df1_DDM[\"Model\"] = [\"DDM\"]*len(df1_DDM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "70b0d200",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_DDM = pd.read_csv(('DDMdifResultDutilh2013.csv'))\n",
    "df2_DDM[\"Dataset\"] = [\"Dutilh (2013)\"]*len(df2_DDM)\n",
    "df2_DDM[\"Model\"] = [\"DDM\"]*len(df2_DDM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bbb581ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_DDM = pd.read_csv(('DDMdifResultEvans2017.csv'))\n",
    "df3_DDM[\"Dataset\"] = [\"Evans (2017)\"]*len(df3_DDM)\n",
    "df3_DDM[\"Model\"] = [\"DDM\"]*len(df3_DDM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bc273c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_DDM = pd.read_csv(('DDMdifResultEJ2008.csv'))\n",
    "df4_DDM[\"Dataset\"] = [\"Wagenmakers (2008)\"]*len(df4_DDM)\n",
    "df4_DDM[\"Model\"] = [\"DDM\"]*len(df4_DDM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0a8641e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5_DDM = pd.read_csv(('DDMdifResultRae2014.csv'))\n",
    "df5_DDM[\"Dataset\"] = [\"Rae (2014)\"]*len(df5_DDM)\n",
    "df5_DDM[\"Model\"] = [\"DDM\"]*len(df5_DDM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5a63c4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df6_DDM = pd.read_csv(('DDMdifResultLerche2017.csv'))\n",
    "df6_DDM[\"Dataset\"] = [\"Lerche (2017)\"]*len(df6_DDM)\n",
    "df6_DDM[\"Model\"] = [\"DDM\"]*len(df6_DDM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9a86609",
   "metadata": {},
   "source": [
    "### load DDM-OUM datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "c8190241",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_DDM_OUM = pd.read_csv(('DDMOUMResultDutilh2019.csv'))\n",
    "#df1_DDM_OUM[\"Dataset\"] = [\"Dutilh (2019)\"]*len(df1_DDM_OUM)\n",
    "#df1_DDM_OUM[\"Model\"] = [\"DDM-OUM\"]*len(df1_DDM_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "21740a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_DDM_OUM = pd.read_csv(('DDMOUMResultDutilh2013.csv'))\n",
    "df2_DDM_OUM[\"Dataset\"] = [\"Dutilh (2013)\"]*len(df2_DDM_OUM)\n",
    "df2_DDM_OUM[\"Model\"] = [\"DDM-OUM\"]*len(df2_DDM_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "bb569c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_DDM_OUM = pd.read_csv(('DDMOUMResultEvans2017.csv'))\n",
    "df3_DDM_OUM[\"Dataset\"] = [\"Evans (2017)\"]*len(df3_DDM_OUM)\n",
    "df3_DDM_OUM[\"Model\"] = [\"DDM-OUM\"]*len(df3_DDM_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "c0fdb0f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_DDM_OUM = pd.read_csv(('DDMOUMResultEJ2008.csv'))\n",
    "df4_DDM_OUM[\"Dataset\"] = [\"Wagenmakers (2008)\"]*len(df4_DDM_OUM)\n",
    "df4_DDM_OUM[\"Model\"] = [\"DDM-OUM\"]*len(df4_DDM_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "164d86f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5_DDM_OUM = pd.read_csv(('DDMOUMResultRae2014.csv'))\n",
    "df5_DDM_OUM[\"Dataset\"] = [\"Rae (2014)\"]*len(df5_DDM_OUM)\n",
    "df5_DDM_OUM[\"Model\"] = [\"DDM-OUM\"]*len(df5_DDM_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "07b72a8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df6_DDM_OUM = pd.read_csv(('DDMOUMResultLerche2017.csv'))\n",
    "df6_DDM_OUM[\"Dataset\"] = [\"Lerche (2017)\"]*len(df6_DDM_OUM)\n",
    "df6_DDM_OUM[\"Model\"] = [\"DDM-OUM\"]*len(df6_DDM_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "e4248345",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Participant</th>\n",
       "      <th>Drift Rate</th>\n",
       "      <th>Boundary Separation</th>\n",
       "      <th>Non-decision Time</th>\n",
       "      <th>Effective Inhibition</th>\n",
       "      <th>SAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.769518</td>\n",
       "      <td>0.638003</td>\n",
       "      <td>0.344789</td>\n",
       "      <td>-0.841342</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.576003</td>\n",
       "      <td>0.650436</td>\n",
       "      <td>0.332237</td>\n",
       "      <td>1.922181</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5.855983</td>\n",
       "      <td>0.688884</td>\n",
       "      <td>0.262646</td>\n",
       "      <td>-8.451214</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1.058509</td>\n",
       "      <td>0.507199</td>\n",
       "      <td>0.264839</td>\n",
       "      <td>0.677033</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2.598080</td>\n",
       "      <td>0.511279</td>\n",
       "      <td>0.235455</td>\n",
       "      <td>-2.564643</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1.574646</td>\n",
       "      <td>0.583464</td>\n",
       "      <td>0.250692</td>\n",
       "      <td>0.930172</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.954225</td>\n",
       "      <td>0.599292</td>\n",
       "      <td>0.250365</td>\n",
       "      <td>-0.203623</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1.771463</td>\n",
       "      <td>0.674713</td>\n",
       "      <td>0.213251</td>\n",
       "      <td>-0.530863</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1.879478</td>\n",
       "      <td>0.753220</td>\n",
       "      <td>0.213078</td>\n",
       "      <td>2.135479</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0.554763</td>\n",
       "      <td>0.504026</td>\n",
       "      <td>0.203571</td>\n",
       "      <td>4.452669</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1.464583</td>\n",
       "      <td>0.613658</td>\n",
       "      <td>0.223301</td>\n",
       "      <td>0.775976</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>0.729418</td>\n",
       "      <td>0.500958</td>\n",
       "      <td>0.236435</td>\n",
       "      <td>-2.326942</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>1.364771</td>\n",
       "      <td>0.636792</td>\n",
       "      <td>0.213740</td>\n",
       "      <td>1.461429</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>2.160817</td>\n",
       "      <td>0.583768</td>\n",
       "      <td>0.333234</td>\n",
       "      <td>-1.723724</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>1.956831</td>\n",
       "      <td>0.994972</td>\n",
       "      <td>0.169215</td>\n",
       "      <td>1.338573</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>2.622702</td>\n",
       "      <td>0.622845</td>\n",
       "      <td>0.295931</td>\n",
       "      <td>-1.705464</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>3.136986</td>\n",
       "      <td>0.624338</td>\n",
       "      <td>0.288555</td>\n",
       "      <td>2.710601</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>1.024311</td>\n",
       "      <td>0.500586</td>\n",
       "      <td>0.378476</td>\n",
       "      <td>-0.864269</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>2.996162</td>\n",
       "      <td>0.678625</td>\n",
       "      <td>0.246937</td>\n",
       "      <td>0.371504</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>2.164622</td>\n",
       "      <td>0.606578</td>\n",
       "      <td>0.248247</td>\n",
       "      <td>-2.094068</td>\n",
       "      <td>Speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.605289</td>\n",
       "      <td>0.667493</td>\n",
       "      <td>0.359528</td>\n",
       "      <td>-1.047510</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>1.195104</td>\n",
       "      <td>0.710657</td>\n",
       "      <td>0.408460</td>\n",
       "      <td>-0.230010</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>1.381798</td>\n",
       "      <td>0.786911</td>\n",
       "      <td>0.280714</td>\n",
       "      <td>0.778327</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>0.588091</td>\n",
       "      <td>0.721462</td>\n",
       "      <td>0.316445</td>\n",
       "      <td>1.139924</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>2.365260</td>\n",
       "      <td>0.833396</td>\n",
       "      <td>0.312735</td>\n",
       "      <td>-0.341015</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>0.895448</td>\n",
       "      <td>0.633773</td>\n",
       "      <td>0.342791</td>\n",
       "      <td>0.221104</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>1.530998</td>\n",
       "      <td>0.599781</td>\n",
       "      <td>0.268212</td>\n",
       "      <td>-1.646195</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>1.546899</td>\n",
       "      <td>0.915445</td>\n",
       "      <td>0.304567</td>\n",
       "      <td>-1.089575</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>1.270959</td>\n",
       "      <td>0.780713</td>\n",
       "      <td>0.327420</td>\n",
       "      <td>-0.476789</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>1.136395</td>\n",
       "      <td>0.999223</td>\n",
       "      <td>0.202372</td>\n",
       "      <td>0.145153</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>1.096768</td>\n",
       "      <td>0.614085</td>\n",
       "      <td>0.260286</td>\n",
       "      <td>-0.099769</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>1.118136</td>\n",
       "      <td>0.937549</td>\n",
       "      <td>0.483412</td>\n",
       "      <td>0.261632</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>13</td>\n",
       "      <td>1.228659</td>\n",
       "      <td>0.616839</td>\n",
       "      <td>0.327878</td>\n",
       "      <td>-1.538488</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>1.466993</td>\n",
       "      <td>0.825309</td>\n",
       "      <td>0.525682</td>\n",
       "      <td>1.004546</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "      <td>1.656705</td>\n",
       "      <td>0.651692</td>\n",
       "      <td>0.302732</td>\n",
       "      <td>-1.387223</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>1.899377</td>\n",
       "      <td>0.586360</td>\n",
       "      <td>0.382893</td>\n",
       "      <td>-1.158936</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>17</td>\n",
       "      <td>1.749662</td>\n",
       "      <td>0.665366</td>\n",
       "      <td>0.349912</td>\n",
       "      <td>0.639511</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>18</td>\n",
       "      <td>0.527001</td>\n",
       "      <td>0.770160</td>\n",
       "      <td>0.440236</td>\n",
       "      <td>-0.859464</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>1.700021</td>\n",
       "      <td>0.626400</td>\n",
       "      <td>0.387083</td>\n",
       "      <td>-2.240315</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>20</td>\n",
       "      <td>1.213359</td>\n",
       "      <td>0.818424</td>\n",
       "      <td>0.330753</td>\n",
       "      <td>-0.208504</td>\n",
       "      <td>Accuracy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Participant  Drift Rate  Boundary Separation  \\\n",
       "0            0            1    2.769518             0.638003   \n",
       "1            1            2    2.576003             0.650436   \n",
       "2            2            3    5.855983             0.688884   \n",
       "3            3            4    1.058509             0.507199   \n",
       "4            4            5    2.598080             0.511279   \n",
       "5            5            6    1.574646             0.583464   \n",
       "6            6            7    1.954225             0.599292   \n",
       "7            7            8    1.771463             0.674713   \n",
       "8            8            9    1.879478             0.753220   \n",
       "9            9           10    0.554763             0.504026   \n",
       "10          10           11    1.464583             0.613658   \n",
       "11          11           12    0.729418             0.500958   \n",
       "12          12           13    1.364771             0.636792   \n",
       "13          13           14    2.160817             0.583768   \n",
       "14          14           15    1.956831             0.994972   \n",
       "15          15           16    2.622702             0.622845   \n",
       "16          16           17    3.136986             0.624338   \n",
       "17          17           18    1.024311             0.500586   \n",
       "18          18           19    2.996162             0.678625   \n",
       "19          19           20    2.164622             0.606578   \n",
       "20          20            1    1.605289             0.667493   \n",
       "21          21            2    1.195104             0.710657   \n",
       "22          22            3    1.381798             0.786911   \n",
       "23          23            4    0.588091             0.721462   \n",
       "24          24            5    2.365260             0.833396   \n",
       "25          25            6    0.895448             0.633773   \n",
       "26          26            7    1.530998             0.599781   \n",
       "27          27            8    1.546899             0.915445   \n",
       "28          28            9    1.270959             0.780713   \n",
       "29          29           10    1.136395             0.999223   \n",
       "30          30           11    1.096768             0.614085   \n",
       "31          31           12    1.118136             0.937549   \n",
       "32          32           13    1.228659             0.616839   \n",
       "33          33           14    1.466993             0.825309   \n",
       "34          34           15    1.656705             0.651692   \n",
       "35          35           16    1.899377             0.586360   \n",
       "36          36           17    1.749662             0.665366   \n",
       "37          37           18    0.527001             0.770160   \n",
       "38          38           19    1.700021             0.626400   \n",
       "39          39           20    1.213359             0.818424   \n",
       "\n",
       "    Non-decision Time  Effective Inhibition       SAT  \n",
       "0            0.344789             -0.841342     Speed  \n",
       "1            0.332237              1.922181     Speed  \n",
       "2            0.262646             -8.451214     Speed  \n",
       "3            0.264839              0.677033     Speed  \n",
       "4            0.235455             -2.564643     Speed  \n",
       "5            0.250692              0.930172     Speed  \n",
       "6            0.250365             -0.203623     Speed  \n",
       "7            0.213251             -0.530863     Speed  \n",
       "8            0.213078              2.135479     Speed  \n",
       "9            0.203571              4.452669     Speed  \n",
       "10           0.223301              0.775976     Speed  \n",
       "11           0.236435             -2.326942     Speed  \n",
       "12           0.213740              1.461429     Speed  \n",
       "13           0.333234             -1.723724     Speed  \n",
       "14           0.169215              1.338573     Speed  \n",
       "15           0.295931             -1.705464     Speed  \n",
       "16           0.288555              2.710601     Speed  \n",
       "17           0.378476             -0.864269     Speed  \n",
       "18           0.246937              0.371504     Speed  \n",
       "19           0.248247             -2.094068     Speed  \n",
       "20           0.359528             -1.047510  Accuracy  \n",
       "21           0.408460             -0.230010  Accuracy  \n",
       "22           0.280714              0.778327  Accuracy  \n",
       "23           0.316445              1.139924  Accuracy  \n",
       "24           0.312735             -0.341015  Accuracy  \n",
       "25           0.342791              0.221104  Accuracy  \n",
       "26           0.268212             -1.646195  Accuracy  \n",
       "27           0.304567             -1.089575  Accuracy  \n",
       "28           0.327420             -0.476789  Accuracy  \n",
       "29           0.202372              0.145153  Accuracy  \n",
       "30           0.260286             -0.099769  Accuracy  \n",
       "31           0.483412              0.261632  Accuracy  \n",
       "32           0.327878             -1.538488  Accuracy  \n",
       "33           0.525682              1.004546  Accuracy  \n",
       "34           0.302732             -1.387223  Accuracy  \n",
       "35           0.382893             -1.158936  Accuracy  \n",
       "36           0.349912              0.639511  Accuracy  \n",
       "37           0.440236             -0.859464  Accuracy  \n",
       "38           0.387083             -2.240315  Accuracy  \n",
       "39           0.330753             -0.208504  Accuracy  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_DDM_OUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "da168175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Participant</th>\n",
       "      <th>SAT</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Speed</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>2.769518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Speed</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>2.576003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Speed</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>5.855983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Speed</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>1.058509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Speed</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>2.598080</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>155</th>\n",
       "      <td>16</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-1.158936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>17</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.639511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>18</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.859464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>19</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-2.240315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>20</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.208504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Participant       SAT             Parameter  Difference\n",
       "0              1     Speed            Drift Rate    2.769518\n",
       "1              2     Speed            Drift Rate    2.576003\n",
       "2              3     Speed            Drift Rate    5.855983\n",
       "3              4     Speed            Drift Rate    1.058509\n",
       "4              5     Speed            Drift Rate    2.598080\n",
       "..           ...       ...                   ...         ...\n",
       "155           16  Accuracy  Effective Inhibition   -1.158936\n",
       "156           17  Accuracy  Effective Inhibition    0.639511\n",
       "157           18  Accuracy  Effective Inhibition   -0.859464\n",
       "158           19  Accuracy  Effective Inhibition   -2.240315\n",
       "159           20  Accuracy  Effective Inhibition   -0.208504\n",
       "\n",
       "[160 rows x 4 columns]"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.melt(df1_DDM_OUM, \n",
    "                 id_vars=['Participant', 'SAT'], \n",
    "                 var_name = \"Parameter\", \n",
    "                 value_name = \"Difference\",\n",
    "                 value_vars=['Drift Rate','Boundary Separation','Non-decision Time','Effective Inhibition'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00a1732",
   "metadata": {},
   "source": [
    "### load OUM datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "e1f8ae83",
   "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>Unnamed: 0</th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Difference</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>0.910059</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>1.123171</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>1.849794</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>0.210795</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>0.068762</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.873316</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>76</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>5.222158</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>6.104587</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.273064</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>7.361161</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0             Parameter  Difference        Dataset Model\n",
       "0            0            Drift Rate    0.910059  Dutilh (2019)   OUM\n",
       "1            1            Drift Rate    1.123171  Dutilh (2019)   OUM\n",
       "2            2            Drift Rate    1.849794  Dutilh (2019)   OUM\n",
       "3            3            Drift Rate    0.210795  Dutilh (2019)   OUM\n",
       "4            4            Drift Rate    0.068762  Dutilh (2019)   OUM\n",
       "..         ...                   ...         ...            ...   ...\n",
       "75          75  Effective Inhibition   -0.873316  Dutilh (2019)   OUM\n",
       "76          76  Effective Inhibition    5.222158  Dutilh (2019)   OUM\n",
       "77          77  Effective Inhibition    6.104587  Dutilh (2019)   OUM\n",
       "78          78  Effective Inhibition   -0.273064  Dutilh (2019)   OUM\n",
       "79          79  Effective Inhibition    7.361161  Dutilh (2019)   OUM\n",
       "\n",
       "[80 rows x 5 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_OUM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "486405f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "be3ecb23",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_OUM = pd.read_csv(('OUMdifResultDutilh2019.csv'))\n",
    "df1_OUM[\"Dataset\"] = [\"Dutilh (2019)\"]*len(df1_OUM)\n",
    "df1_OUM[\"Model\"] = [\"OUM\"]*len(df1_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f6633c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_OUM = pd.read_csv(('OUMdifResultDutilh2013.csv'))\n",
    "df2_OUM[\"Dataset\"] = [\"Dutilh (2013)\"]*len(df2_OUM)\n",
    "df2_OUM[\"Model\"] = [\"OUM\"]*len(df2_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "59921a40",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3_OUM = pd.read_csv(('OUMdifResultEvans2017.csv'))\n",
    "df3_OUM[\"Dataset\"] = [\"Evans (2017)\"]*len(df3_OUM)\n",
    "df3_OUM[\"Model\"] = [\"OUM\"]*len(df3_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "99518998",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4_OUM = pd.read_csv(('OUMdifResultEJ2008.csv'))\n",
    "df4_OUM[\"Dataset\"] = [\"Wagenmakers (2008)\"]*len(df4_OUM)\n",
    "df4_OUM[\"Model\"] = [\"OUM\"]*len(df4_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "740664fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5_OUM = pd.read_csv(('OUMdifResultRae2014.csv'))\n",
    "df5_OUM[\"Dataset\"] = [\"Rae (2014)\"]*len(df5_OUM)\n",
    "df5_OUM[\"Model\"] = [\"OUM\"]*len(df5_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "190c5e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "df6_OUM = pd.read_csv(('OUMdifResultLerche2017.csv'))\n",
    "df6_OUM[\"Dataset\"] = [\"Lerche (2017)\"]*len(df6_OUM)\n",
    "df6_OUM[\"Model\"] = [\"OUM\"]*len(df6_OUM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "98b612f8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>Parameter</th>\n",
       "      <th>Difference</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>1.126387</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>DDM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>1.579487</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>DDM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>2.290597</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>DDM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>0.391332</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>DDM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Drift Rate</td>\n",
       "      <td>0.113059</td>\n",
       "      <td>Dutilh (2019)</td>\n",
       "      <td>DDM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>147</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.408172</td>\n",
       "      <td>Lerche (2017)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>148</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.577311</td>\n",
       "      <td>Lerche (2017)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>149</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.310762</td>\n",
       "      <td>Lerche (2017)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>150</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.335869</td>\n",
       "      <td>Lerche (2017)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>151</td>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-2.417830</td>\n",
       "      <td>Lerche (2017)</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1869 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0             Parameter  Difference        Dataset Model\n",
       "0             0            Drift Rate    1.126387  Dutilh (2019)   DDM\n",
       "1             1            Drift Rate    1.579487  Dutilh (2019)   DDM\n",
       "2             2            Drift Rate    2.290597  Dutilh (2019)   DDM\n",
       "3             3            Drift Rate    0.391332  Dutilh (2019)   DDM\n",
       "4             4            Drift Rate    0.113059  Dutilh (2019)   DDM\n",
       "..          ...                   ...         ...            ...   ...\n",
       "147         147  Effective Inhibition    0.408172  Lerche (2017)   OUM\n",
       "148         148  Effective Inhibition    0.577311  Lerche (2017)   OUM\n",
       "149         149  Effective Inhibition   -0.310762  Lerche (2017)   OUM\n",
       "150         150  Effective Inhibition   -0.335869  Lerche (2017)   OUM\n",
       "151         151  Effective Inhibition   -2.417830  Lerche (2017)   OUM\n",
       "\n",
       "[1869 rows x 5 columns]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [df1_DDM, df2_DDM, df3_DDM, df4_DDM, df5_DDM, df6_DDM,\n",
    "          df1_OUM, df2_OUM, df3_OUM, df4_OUM, df5_OUM, df6_OUM]#,\n",
    "          #df1_DDM_OUM, df2_DDM_OUM, df3_DDM_OUM, df4_DDM_OUM, df5_DDM_OUM,df6_DDM_OUM]\n",
    "\n",
    "df = pd.concat(frames)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "896a36ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = df[df[\"Parameter\"]==\"Drift Rate\"]\n",
    "df0 = df0[df0[\"Dataset\"]==\"Dutilh (2013)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "250f1657",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "95797e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0.loc[df0[\"Model\"]==\"DDM\",\"Difference\"] = (df0[df0[\"Model\"]==\"DDM\"][\"Difference\"]-np.mean(df0[df0[\"Model\"]==\"DDM\"][\"Difference\"]))/np.std(df0[df0[\"Model\"]==\"DDM\"][\"Difference\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2ced40ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df0.loc[df0[\"Model\"]==\"OUM\",\"Difference\"] = (df0[df0[\"Model\"]==\"OUM\"][\"Difference\"]-np.mean(df0[df0[\"Model\"]==\"OUM\"][\"Difference\"]))/np.std(df0[df0[\"Model\"]==\"OUM\"][\"Difference\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63a1494e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "43bd3895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df[df[\"Parameter\"]==\"Drift Rate\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "2ccf7c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df[df[\"Parameter\"]==\"Boundary Separation\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "#ax.set_ylim(-6, 4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "89c2e0e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df[df[\"Parameter\"]==\"Non-decision Time\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5a3998b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "de7d0bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df[df[\"Parameter\"]==\"Effective Inhibition\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78812b0d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13177618",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b5ae9285",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=1.1004033989067845, pvalue=0.2782642901220877)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ttest_1samp(np.array(dff[dff[\"Dataset\"]==\"Lerche (2017)\"][\"Difference\"]), 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e0fbcc2",
   "metadata": {},
   "source": [
    "## Standardize parameters for each model across SAT conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "2f87f24f",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets_labels = [\"Dutilh (2019)\", \"Dutilh (2013)\", \"Evans (2017)\",\n",
    "                   \"EJ (2008)\", \"Rae (2014)\", \"Lerche (2017)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "b61af990",
   "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>Unnamed: 0</th>\n",
       "      <th>Boundary Separation</th>\n",
       "      <th>Drift Rate</th>\n",
       "      <th>Effective Inhibition</th>\n",
       "      <th>Non-decision Time</th>\n",
       "      <th>SAT</th>\n",
       "      <th>v_mean</th>\n",
       "      <th>v_sd</th>\n",
       "      <th>a_mean</th>\n",
       "      <th>a_sd</th>\n",
       "      <th>ter_mean</th>\n",
       "      <th>ter_sd</th>\n",
       "      <th>k_mean</th>\n",
       "      <th>k_sd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.199078</td>\n",
       "      <td>-0.542636</td>\n",
       "      <td>0.000813</td>\n",
       "      <td>2.377332</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.697628</td>\n",
       "      <td>0.810793</td>\n",
       "      <td>0.275445</td>\n",
       "      <td>0.965031</td>\n",
       "      <td>Speed</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.444736</td>\n",
       "      <td>0.285488</td>\n",
       "      <td>0.011444</td>\n",
       "      <td>-0.718475</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.604919</td>\n",
       "      <td>1.459526</td>\n",
       "      <td>0.053250</td>\n",
       "      <td>-0.742589</td>\n",
       "      <td>Speed</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-0.954579</td>\n",
       "      <td>-1.333295</td>\n",
       "      <td>0.298327</td>\n",
       "      <td>0.052547</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>-0.679786</td>\n",
       "      <td>1.842950</td>\n",
       "      <td>-0.258163</td>\n",
       "      <td>0.777521</td>\n",
       "      <td>Speed</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>1.358109</td>\n",
       "      <td>-0.459704</td>\n",
       "      <td>0.450931</td>\n",
       "      <td>0.642139</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>73</td>\n",
       "      <td>0.176554</td>\n",
       "      <td>-0.472532</td>\n",
       "      <td>0.275314</td>\n",
       "      <td>-0.267872</td>\n",
       "      <td>Speed</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>-0.962590</td>\n",
       "      <td>0.321155</td>\n",
       "      <td>-1.423221</td>\n",
       "      <td>1.686505</td>\n",
       "      <td>Accuracy</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>-1.148284</td>\n",
       "      <td>1.859567</td>\n",
       "      <td>-2.687444</td>\n",
       "      <td>-0.824720</td>\n",
       "      <td>Speed</td>\n",
       "      <td>0.190131</td>\n",
       "      <td>0.11495</td>\n",
       "      <td>3.890886</td>\n",
       "      <td>2.707955</td>\n",
       "      <td>0.324609</td>\n",
       "      <td>0.270596</td>\n",
       "      <td>0.237881</td>\n",
       "      <td>1.912503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Boundary Separation  Drift Rate  Effective Inhibition  \\\n",
       "0            0             2.199078   -0.542636              0.000813   \n",
       "1            1            -0.697628    0.810793              0.275445   \n",
       "2            2             1.444736    0.285488              0.011444   \n",
       "3            3             0.604919    1.459526              0.053250   \n",
       "4            4            -0.954579   -1.333295              0.298327   \n",
       "..         ...                  ...         ...                   ...   \n",
       "71          71            -0.679786    1.842950             -0.258163   \n",
       "72          72             1.358109   -0.459704              0.450931   \n",
       "73          73             0.176554   -0.472532              0.275314   \n",
       "74          74            -0.962590    0.321155             -1.423221   \n",
       "75          75            -1.148284    1.859567             -2.687444   \n",
       "\n",
       "    Non-decision Time       SAT    v_mean     v_sd    a_mean      a_sd  \\\n",
       "0            2.377332  Accuracy  0.190131  0.11495  3.890886  2.707955   \n",
       "1            0.965031     Speed  0.190131  0.11495  3.890886  2.707955   \n",
       "2           -0.718475  Accuracy  0.190131  0.11495  3.890886  2.707955   \n",
       "3           -0.742589     Speed  0.190131  0.11495  3.890886  2.707955   \n",
       "4            0.052547  Accuracy  0.190131  0.11495  3.890886  2.707955   \n",
       "..                ...       ...       ...      ...       ...       ...   \n",
       "71           0.777521     Speed  0.190131  0.11495  3.890886  2.707955   \n",
       "72           0.642139  Accuracy  0.190131  0.11495  3.890886  2.707955   \n",
       "73          -0.267872     Speed  0.190131  0.11495  3.890886  2.707955   \n",
       "74           1.686505  Accuracy  0.190131  0.11495  3.890886  2.707955   \n",
       "75          -0.824720     Speed  0.190131  0.11495  3.890886  2.707955   \n",
       "\n",
       "    ter_mean    ter_sd    k_mean      k_sd  \n",
       "0   0.324609  0.270596  0.237881  1.912503  \n",
       "1   0.324609  0.270596  0.237881  1.912503  \n",
       "2   0.324609  0.270596  0.237881  1.912503  \n",
       "3   0.324609  0.270596  0.237881  1.912503  \n",
       "4   0.324609  0.270596  0.237881  1.912503  \n",
       "..       ...       ...       ...       ...  \n",
       "71  0.324609  0.270596  0.237881  1.912503  \n",
       "72  0.324609  0.270596  0.237881  1.912503  \n",
       "73  0.324609  0.270596  0.237881  1.912503  \n",
       "74  0.324609  0.270596  0.237881  1.912503  \n",
       "75  0.324609  0.270596  0.237881  1.912503  \n",
       "\n",
       "[76 rows x 14 columns]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_SAT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f9d6821",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "string indices must be integers",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/5g/vqmn12yd48z6l6jf2s2yy62r0000gn/T/ipykernel_66679/1029691044.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m             \u001b[0mdf_z\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_z\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_SAT_dif\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m             \u001b[0mdf_SAT\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstandardize_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m             \u001b[0mdf_SAT_dif\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_difference_OUM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_SAT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0mdf_z\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_z\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_SAT_dif\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/var/folders/5g/vqmn12yd48z6l6jf2s2yy62r0000gn/T/ipykernel_66679/2356637108.py\u001b[0m in \u001b[0;36mstandardize_result\u001b[0;34m(df100_DDM, model)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;31m## calculate mean and std of each param for one dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mdf100_DDM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"v_mean\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf100_DDM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Drift Rate\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf100_DDM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mdf100_DDM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"v_sd\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf100_DDM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Drift Rate\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf100_DDM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
     ]
    }
   ],
   "source": [
    "datasets_list = [\"Dutilh2019\", \"Dutilh2013\", \"Evans2017\", \"EJ2008\", \"Rae2014\", \"Lerche2017\"]\n",
    "models_list = [\"DDMOUM\", \"DDM\", \"OUM\"]\n",
    "df_z = pd.DataFrame(columns = [\"Parameter\", \"Difference\", \"Dataset\", \"Model\"])\n",
    "\n",
    "for model in models_list:\n",
    "    for dataset in datasets_list:\n",
    "        if model == \"DDM\":\n",
    "            df_SAT = standardize_result(dataset, model)\n",
    "            df_SAT_dif = get_difference_DDM(dataset, df_SAT, model)\n",
    "            df_z = df_z.append(df_SAT_dif)\n",
    "        else:\n",
    "            df_SAT = standardize_result(dataset, model)\n",
    "            df_SAT_dif = get_difference_OUM(dataset, df_SAT, model)\n",
    "            df_z = df_z.append(df_SAT_dif)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "f9f7478d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.barplot(data=df_z[df_z[\"Parameter\"]==\"Drift Rate\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "3d24bd51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Drift Rate\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "651d6715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.barplot(data=df_z[df_z[\"Parameter\"]==\"Boundary Separation\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "6e537cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Boundary Separation\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "ca74d4a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.barplot(data=df_z[df_z[\"Parameter\"]==\"Non-decision Time\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "a31c50ec",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Non-decision Time\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "ac37f717",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.barplot(data=df_z[df_z[\"Parameter\"]==\"Effective Inhibition\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "203f0dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Effective Inhibition\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e68a832",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "344a3a72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3c05def",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8fedaa23",
   "metadata": {},
   "source": [
    "## Not Standardize parameters for each model across SAT conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "2d1cb38f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "datasets_labels = [\"Dutilh (2019)\", \"Dutilh (2013)\", \"Evans (2017)\",\n",
    "                   \"EJ (2008)\", \"Rae (2014)\", \"Lerche (2017)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "47ceb7a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets_list = [\"Dutilh2019\", \"Dutilh2013\", \"Evans2017\", \"EJ2008\", \"Rae2014\", \"Lerche2017\"]\n",
    "models_list = [\"DDMOUM\", \"DDM\", \"OUM\"]\n",
    "df_z = pd.DataFrame(columns = [\"Parameter\", \"Difference\", \"Dataset\", \"Model\"])\n",
    "\n",
    "for model in models_list:\n",
    "    for dataset in datasets_list:\n",
    "        if model == \"DDM\":\n",
    "            df_SAT = pd.read_csv(model + \"Result\" + dataset + \".csv\")\n",
    "\n",
    "            df_SAT_dif = get_difference_DDM(dataset, df_SAT, model)\n",
    "            df_z = df_z.append(df_SAT_dif)\n",
    "        else:\n",
    "            df_SAT = pd.read_csv(model + \"Result\" + dataset + \".csv\")\n",
    "            df_SAT_dif = get_difference_OUM(dataset, df_SAT, model)\n",
    "            df_z = df_z.append(df_SAT_dif)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "2350d265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Drift Rate\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "84c3ecba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Drift Rate\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "7e453189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Boundary Separation\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "b9468d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.barplot(data=df_z[df_z[\"Parameter\"]==\"Non-decision Time\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "fc5d6286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAACZF6AcAAAAAwKQI/QAAAAAAmBShHwAAAAAAk+Ir+wAAAMoJf3//XH8GACAvhH4AAIByYsGCBY4uAQBQznB5PwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTYiI/mMquZ3fZpZ8BewYo6c8kSVKAd4Cin422S78AAAAAUJo40w8AAAAAgElxph8FEjz1kN36chkwQEq6fhbdxTdQwVO3261vAAAAAMD/x5l+AAAAAABMitAPAAAAAIBJEfoBAAAAADApQj8AAAAAACbFRH4AALuLnTPIbn0NGBCjpKQ0SVKNypW0zU59J8yYY5d+ANjy9/fP9WcAgGMQ+gEAAGA3CxYscHQJAICbcHk/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgU9/SXAeVhwisAAAAAQPlD6AcAB2KWawAAAJQkQj8AOBCzXAMAAJRtbjvcZPnTIkkyPA1l3JPh4IoKh9APAAAAAEAeLH9arKG/PCL0AwBuS8FTD9mtL5cBA6SkpOs/+wYqeOp2u/UNAABQHMzeDwAAAACASRH6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATMrF0QUAKJ/8/f1z/RkAAABA2UHoB1AkCxYscHQJAAAAAG6By/sBAAAAADApQj8AAAAAACZF6AcAAAAAwKQI/QAAAAAAmFS5Cf3JyckKDw+Xt7e3fH19NWzYMKWmpua7zdKlS3XvvffK29tbFotFFy9etEu/AAAAAACUB+Um9IeHh+vw4cPavHmzNmzYoB07dmjEiBH5bnPlyhWFhYVpypQpdu0XAAAAAIDyoFx8ZV9cXJxiYmK0b98+tW3bVpK0aNEi9ejRQ3PnzlVgYGCu240dO1aStH37drv2CwAAAABAeVAuzvTv2bNHvr6+1mAuSd26dZOTk5P27t1b6v2mp6crJSXF5gEAAAAAQFlTLkJ/YmKiqlWrZrPMxcVFfn5+SkxMLPV+Z8+eLR8fH+sjKCioyDUAAAAAAFBSHBr6J02aJIvFku/jp59+cmSJuZo8ebIuXbpkfZw6dcrRJQEAAAAAkIND7+mfMGGCIiIi8m1Tp04dBQQE6Ny5czbLr127puTkZAUEBBR5/0Xt193dXe7u7kXeLwAAKHv8/f1z/RkAgPLMoaHf39+/QH9UO3bsqIsXLyo2NlZt2rSRJG3dulXZ2dlq3759kfdfUv0CAFDWEGhvbcGCBY4uAQAAuysXs/c3btxYYWFhGj58uJYsWaLMzEyNGjVK/fr1s86wf/r0aXXt2lUrVqxQu3btJF2/Zz8xMVHHjh2TJB06dEheXl4KDg6Wn59fgfoFAMAMCLQAANyeysVEfpK0cuVKNWrUSF27dlWPHj101113aenSpdb1mZmZOnr0qK5cuWJdtmTJErVq1UrDhw+XJN1zzz1q1aqV1q9fX+B+AQAAAAAor8rFmX5J8vPzU3R0dJ7rQ0JCZBiGzbLp06dr+vTpxeoXAAAAAIDyqtyc6QcAAAAAAIVD6AcAAAAAwKTKzeX9MA9mkAYAAACA0kHoR6ljBmmUFj5gAgAAwO2O0A/AtPiACQAAALc77ukHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrZ+wEAZRpfvQgAAFB0hH4AQJnGVy8CAAAUHZf3AwAAAABgUpzpNxkugwUAAAAA3EDoNxkugwUAAAAA3EDoBwCgmLjKCgAAlFWEfgAAiomrrAAAQFnFRH4AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQDwAAAACASRH6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFIuji4AKIv8/f1z/RkAAAAAyhNCP5CLBQsWOLoEAAAAACg2Lu8HAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQDwAAAACASRH6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQDwAAAACASRH6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQDwAAAACASRH6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJNycXQBAAAAAAD7q3R0o5wy0yRJ2a4VldrwAQdXBEcg9AMAAACACTllpskpI83RZcDBuLwfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyq3IT+5ORkhYeHy9vbW76+vho2bJhSU1Pz3Wbp0qW699575e3tLYvFoosXL+ZoExISIovFYvN47bXXSugoAAAAAAAoPeUm9IeHh+vw4cPavHmzNmzYoB07dmjEiBH5bnPlyhWFhYVpypQp+babMWOGzp49a308++yz9iwdAAAAAACHcHF0AQURFxenmJgY7du3T23btpUkLVq0SD169NDcuXMVGBiY63Zjx46VJG3fvj3f/r28vBQQEGDPkgEAAAAAcLhycaZ/z5498vX1tQZ+SerWrZucnJy0d+/eYvf/2muvqUqVKmrVqpXmzJmja9eu5ds+PT1dKSkpNg8AAAAAAMqacnGmPzExUdWqVbNZ5uLiIj8/PyUmJhar79GjR6t169by8/PT7t27NXnyZJ09e1ZvvvlmntvMnj1br7zySrH2CwAAAABASXPomf5JkyblmETvr4+ffvqpRGsYP3687r33XjVv3lxPP/205s2bp0WLFik9PT3PbSZPnqxLly5ZH6dOnSrRGgEAAAAAKAqHnumfMGGCIiIi8m1Tp04dBQQE6Ny5czbLr127puTkZLvfi9++fXtdu3ZN8fHxatiwYa5t3N3d5e7ubtf9AgAAAABgbw4N/f7+/vL3979lu44dO+rixYuKjY1VmzZtJElbt25Vdna22rdvb9ea9u/fLycnpxy3EwAAAAAAUN6Ui3v6GzdurLCwMA0fPlxLlixRZmamRo0apX79+lln7j99+rS6du2qFStWqF27dpKuzwWQmJioY8eOSZIOHTokLy8vBQcHy8/PT3v27NHevXvVpUsXeXl5ac+ePRo3bpwGDhyoypUrO+x4AQAAAACwh3Ixe78krVy5Uo0aNVLXrl3Vo0cP3XXXXVq6dKl1fWZmpo4ePaorV65Yly1ZskStWrXS8OHDJUn33HOPWrVqpfXr10u6fpn+qlWr1LlzZ91555169dVXNW7cOJt+AQAAAAAor8rFmX5J8vPzU3R0dJ7rQ0JCZBiGzbLp06dr+vTpeW7TunVrffPNN/YqEQAAAACAMqXcnOkHAAAAAACFQ+gHAAAAAMCkys3l/QAAAAAAc5n1g6+S050lSX7uWZrS6qJjCzIhQj8AAAAAwCGS0531x/+FfpQMLu8HAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQDwAAAACASRH6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQDwAAAACASRH6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQDwAAAACASRUr9B87dkybNm3Sn3/+KUkyDMMuRQEAAAAAgOIrUuj/448/1K1bNzVo0EA9evTQ2bNnJUnDhg3ThAkT7FogAAAAAAAomiKF/nHjxsnFxUUJCQmqUKGCdfnjjz+umJgYuxUHAAAAAACKzqUoG33xxRfatGmT7rjjDpvl9evX18mTJ+1SGAAAAAAAKJ4inelPS0uzOcN/Q3Jystzd3YtdFAAAAAAAKL4ihf67775bK1assD63WCzKzs7WG2+8oS5dutitOAAAAAAAUHRFurz/jTfeUNeuXfXdd98pIyNDzz//vA4fPqzk5GTt2rXL3jUCAAAAAIAiKNKZ/qZNm+rnn3/WXXfdpV69eiktLU2PPvqofvjhB9WtW9feNQIAAAAAgCIo0pl+SfLx8dGLL75oz1oAAAAAAIAdFelMf2RkpNasWZNj+Zo1a7R8+fJiFwUAAAAAAIqvSKF/9uzZqlq1ao7l1apV06xZs4pdFAAAAAAAKL4ihf6EhATVrl07x/JatWopISGh2EUBAAAAAIDiK1Lor1atmg4ePJhj+YEDB1SlSpViFwUAAAAAAIqvSKG/f//+Gj16tLZt26asrCxlZWVp69atGjNmjPr162fvGgEAAAAAQBEUafb+mTNnKj4+Xl27dpWLy/UusrOzNWjQIO7pBwAAAACgjChS6Hdzc9Pq1as1c+ZMHThwQJ6enmrWrJlq1apl7/oAAAAAAEARFSn039CgQQM1aNDAXrUAAAAAAAA7KlLoz8rKUlRUlLZs2aJz584pOzvbZv3WrVvtUhwAAAAAACi6IoX+MWPGKCoqSj179lTTpk1lsVjsXRcAAAAAACimIoX+VatW6eOPP1aPHj3sXQ8AAAAAALCTIn1ln5ubm+rVq2fvWgAAAAAAgB0VKfRPmDBBCxYskGEY9q4HAAAAAADYSZEu79+5c6e2bdumjRs36s4775Srq6vN+rVr19qlOAAAAAAAUHRFCv2+vr565JFH7F0LAAAAAACwoyKF/sjISHvXAQAAAACA3ex6dpdd+hmwZ4CS/kySJAV4Byj62Wi79FtainRPvyRdu3ZNX375pd577z1dvnxZknTmzBmlpqbarTgAAAAAAFB0RTrTf/LkSYWFhSkhIUHp6em6//775eXlpddff13p6elasmSJvesEAAAAAACFVKQz/WPGjFHbtm114cIFeXp6Wpc/8sgj2rJli92KAwAAAAAARVekM/1ff/21du/eLTc3N5vlISEhOn36tF0KAwAAAAAAxVOkM/3Z2dnKysrKsfy3336Tl5dXsYsCAAAAAADFV6TQ3717d82fP9/63GKxKDU1VdOmTVOPHj3sVRsAAAAAACiGIl3eP3fuXIWFhalJkya6evWqBgwYoF9++UVVq1bVRx99ZO8aAQAAAABAERQp9AcFBenAgQNavXq1Dhw4oNTUVA0bNkzh4eE2E/sBAAAAAADHKXToz8zMVKNGjbRhwwaFh4crPDy8JOoCAAAAAADFVOh7+l1dXXX16tWSqAUAAAAAANhRkSbyGzlypF5//XVdu3bN3vUAAAAAAAA7KdI9/fv27dOWLVv0xRdfqFmzZqpYsaLN+rVr19qlOAAAAAAAUHRFCv2+vr7q06ePvWsBAAAAAAB2VKTQHxkZae86AAAAAACAnRXpnn5Junbtmr788ku99957unz5siTpzJkzSk1NtVtxAAAAAACg6Ip0pv/kyZMKCwtTQkKC0tPTdf/998vLy0uvv/660tPTtWTJEnvXCQAAAAAACqlIZ/rHjBmjtm3b6sKFC/L09LQuf+SRR7Rlyxa7FQcAAAAAAIquSGf6v/76a+3evVtubm42y0NCQnT69Gm7FAYAAAAAAIqnSGf6s7OzlZWVlWP5b7/9Ji8vr2IXBQAAAAAAiq9Iob979+6aP3++9bnFYlFqaqqmTZumHj162Ks2G8nJyQoPD5e3t7d8fX01bNiwfCcNTE5O1rPPPquGDRvK09NTwcHBGj16tC5dumTTLiEhQT179lSFChVUrVo1Pffcc7p27VqJHAMAAAAAAKWpSJf3z5s3T6GhoWrSpImuXr2qAQMG6JdfflHVqlX10Ucf2btGSVJ4eLjOnj2rzZs3KzMzU0OGDNGIESMUHR2da/szZ87ozJkzmjt3rpo0aaKTJ0/q6aef1pkzZ/Tvf/9bkpSVlaWePXsqICBAu3fv1tmzZzVo0CC5urpq1qxZJXIcAAAAAACUliKF/jvuuEMHDhzQ6tWrdeDAAaWmpmrYsGEKDw+3mdjPXuLi4hQTE6N9+/apbdu2kqRFixapR48emjt3rgIDA3Ns07RpU33yySfW53Xr1tWrr76qgQMH6tq1a3JxcdEXX3yhI0eO6Msvv1T16tXVsmVLzZw5Uy+88IKmT5+eY84CAAAAAADKkwJf3t+6dWtduHBBkjRjxgxlZGQoPDxcb7zxht555x09+eSTJRL4JWnPnj3y9fW1Bn5J6tatm5ycnLR3794C93Pp0iV5e3vLxcXF2m+zZs1UvXp1a5vQ0FClpKTo8OHDefaTnp6ulJQUmwcAAAAAAGVNgUN/XFyc0tLSJEmvvPJKvvfT21tiYqKqVatms8zFxUV+fn5KTEwsUB/nz5/XzJkzNWLECJt+bw78kqzP8+t39uzZ8vHxsT6CgoIKeigAAAAAAJSaAl/e37JlSw0ZMkR33XWXDMPQ3LlzValSpVzbTp06tUB9Tpo0Sa+//nq+beLi4gpaYp5SUlLUs2dPNWnSRNOnTy92f5MnT9b48eNt+if4AwAAAADKmgKH/qioKE2bNk0bNmyQxWLRxo0brZfJ38xisRQ49E+YMEERERH5tqlTp44CAgJ07tw5m+XXrl1TcnKyAgIC8t3+8uXLCgsLk5eXlz799FO5urpa1wUEBOjbb7+1aZ+UlGRdlxd3d3e5u7vnu18AAAAAABytwKG/YcOGWrVqlSTJyclJW7ZsyXHJfWH5+/vL39//lu06duyoixcvKjY2Vm3atJEkbd26VdnZ2Wrfvn2e26WkpCg0NFTu7u5av369PDw8cvT76quv6ty5c9Zj2bx5s7y9vdWkSZNiHBkAAAAAAI5XpIn8pk2bluel/SWhcePGCgsL0/Dhw/Xtt99q165dGjVqlPr162eduf/06dNq1KiR9cx9SkqKunfvrrS0NH3wwQdKSUlRYmKiEhMTlZWVJUnq3r27mjRpoieeeEIHDhzQpk2b9NJLL2nkyJGcyQcAAAAAlHtFmshvxowZpTqRnyStXLlSjRo1UteuXdWjRw/dddddWrp0qXV9Zmamjh49qitXrkiSvv/+e+3du1eHDh1SvXr1VKNGDevj1KlTkiRnZ2dt2LBBzs7O6tixowYOHKhBgwZpxowZpXpsAAAAAACUBIdO5FcYfn5+io6OznN9SEiIDMOwPr/33nttnuelVq1a+vzzz+1SIwAAAAAAZYlDJ/IDAAAAAAAlx6ET+QEAAAAAgJJT4NB/s+zsbHvXAQAAAAAA7KzAoX/9+vV64IEH5OrqqvXr1+fb9uGHHy52YQAAAAAAoHgKHPp79+6txMREVatWTb17986zncVisX4lHgAAAAAAcJwCh/6bL+nn8n4AAAAAAMq+Qt/Tn52draioKK1du1bx8fGyWCyqU6eO+vTpoyeeeEIWi6Uk6gQAAAAAAIXkVJjGhmHo4Ycf1pNPPqnTp0+rWbNmuvPOOxUfH6+IiAg98sgjJVUnAAAAAAAopEKd6Y+KitKOHTu0ZcsWdenSxWbd1q1b1bt3b61YsUKDBg2ya5EAAAAAAKDwCnWm/6OPPtKUKVNyBH5Juu+++zRp0iStXLnSbsUBAAAAAICiK1ToP3jwoMLCwvJc/8ADD+jAgQPFLgoAAAAAABRfoUJ/cnKyqlevnuf66tWr68KFC8UuCgAAAAAAFF+hQn9WVpZcXPKeBsDZ2VnXrl0rdlEAAAAAAKD4CjWRn2EYioiIkLu7e67r09PT7VIUAAAAAAAovkKF/sGDB9+yDTP3AwAAAABQNhQq9EdGRpZUHQAAAAAAwM4KdU8/AAAAAAAoPwj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEmVm9CfnJys8PBweXt7y9fXV8OGDVNqamq+7Z999lk1bNhQnp6eCg4O1ujRo3Xp0iWbdhaLJcdj1apVJX04AAAAAACUOBdHF1BQ4eHhOnv2rDZv3qzMzEwNGTJEI0aMUHR0dK7tz5w5ozNnzmju3Llq0qSJTp48qaefflpnzpzRv//9b5u2kZGRCgsLsz739fUtyUMBAAAAAKBUlIvQHxcXp5iYGO3bt09t27aVJC1atEg9evTQ3LlzFRgYmGObpk2b6pNPPrE+r1u3rl599VUNHDhQ165dk4vL/z90X19fBQQEFLie9PR0paenW5+npKQU5bAAAAAAAChR5eLy/j179sjX19ca+CWpW7ducnJy0t69ewvcz6VLl+Tt7W0T+CVp5MiRqlq1qtq1a6dly5bJMIx8+5k9e7Z8fHysj6CgoMIdEAAAAAAApaBchP7ExERVq1bNZpmLi4v8/PyUmJhYoD7Onz+vmTNnasSIETbLZ8yYoY8//libN29Wnz599Mwzz2jRokX59jV58mRdunTJ+jh16lThDggAAAAAgFLg0Mv7J02apNdffz3fNnFxccXeT0pKinr27KkmTZpo+vTpNutefvll68+tWrVSWlqa5syZo9GjR+fZn7u7u9zd3YtdFwAAAAAAJcmhoX/ChAmKiIjIt02dOnUUEBCgc+fO2Sy/du2akpOTb3kv/uXLlxUWFiYvLy99+umncnV1zbd9+/btNXPmTKWnpxPsAQAAAADlmkNDv7+/v/z9/W/ZrmPHjrp48aJiY2PVpk0bSdLWrVuVnZ2t9u3b57ldSkqKQkND5e7urvXr18vDw+OW+9q/f78qV65M4AcAAABQ6mLnDLJbXwMGxCgpKU2SVKNyJW2zU98JM+bYpR+UjnIxe3/jxo0VFham4cOHa8mSJcrMzNSoUaPUr18/68z9p0+fVteuXbVixQq1a9dOKSkp6t69u65cuaIPP/xQKSkp1ln2/f395ezsrP/85z9KSkpShw4d5OHhoc2bN2vWrFmaOHGiIw8XAAAAAAC7KBehX5JWrlypUaNGqWvXrnJyclKfPn20cOFC6/rMzEwdPXpUV65ckSR9//331pn969WrZ9PXiRMnFBISIldXVy1evFjjxo2TYRiqV6+e3nzzTQ0fPrz0DgwAAAAAgBJSbkK/n5+foqOj81wfEhJi81V799577y2/ei8sLExhYWF2qxEAAAAAgLKkXHxlHwAAAAAAKDxCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBSLo4uAAAAAABQfgRPPWS3vlwGDJCSkq7/7Buo4Knb7dY3ruNMPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJuXi6AIAAAAAACir/P39c/25vCD0AwAAAACQhwULFji6hGLh8n4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMKlyE/qTk5MVHh4ub29v+fr6atiwYUpNTc13m6eeekp169aVp6en/P391atXL/300082bRISEtSzZ09VqFBB1apV03PPPadr166V5KEAAAAAAFAqyk3oDw8P1+HDh7V582Zt2LBBO3bs0IgRI/Ldpk2bNoqMjFRcXJw2bdokwzDUvXt3ZWVlSZKysrLUs2dPZWRkaPfu3Vq+fLmioqI0derU0jgkAAAAAABKVLmYvT8uLk4xMTHat2+f2rZtK0latGiRevTooblz5yowMDDX7W7+UCAkJET/+Mc/1KJFC8XHx6tu3br64osvdOTIEX355ZeqXr26WrZsqZkzZ+qFF17Q9OnT5ebmVirHBwAAAABASSgXZ/r37NkjX19fa+CXpG7dusnJyUl79+4tUB9paWmKjIxU7dq1FRQUZO23WbNmql69urVdaGioUlJSdPjw4Tz7Sk9PV0pKis0DAAAAAICyplyE/sTERFWrVs1mmYuLi/z8/JSYmJjvtu+8844qVaqkSpUqaePGjdq8ebP1DH5iYqJN4JdkfZ5fv7Nnz5aPj4/1ceNDBAAAAAAAyhKHhv5JkybJYrHk+/jrxHuFFR4erh9++EFfffWVGjRooL59++rq1avF6nPy5Mm6dOmS9XHq1Kli9QcAAAAAQElw6D39EyZMUERERL5t6tSpo4CAAJ07d85m+bVr15ScnKyAgIB8t79xNr5+/frq0KGDKleurE8//VT9+/dXQECAvv32W5v2SUlJkpRvv+7u7nJ3d893vwAAAAAAOJpDQ7+/v7/8/f1v2a5jx466ePGiYmNj1aZNG0nS1q1blZ2drfbt2xd4f4ZhyDAMpaenW/t99dVXde7cOevtA5s3b5a3t7eaNGlShCMCAAAAAKDsKBf39Ddu3FhhYWEaPny4vv32W+3atUujRo1Sv379rDP3nz59Wo0aNbKeuf/11181e/ZsxcbGKiEhQbt379Zjjz0mT09P9ejRQ5LUvXt3NWnSRE888YQOHDigTZs26aWXXtLIkSM5kw8AAAAAKPfKReiXpJUrV6pRo0bq2rWrevToobvuuktLly61rs/MzNTRo0d15coVSZKHh4e+/vpr9ejRQ/Xq1dPjjz8uLy8v7d6923pW39nZWRs2bJCzs7M6duyogQMHatCgQZoxY4ZDjhEAAAAAAHty6OX9heHn56fo6Og814eEhMgwDOvzwMBAff7557fst1atWgVqBwAAAABAeVNuzvQDAAAAAIDCIfQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMCkXRxdwu8jOzlZGRoajy0AxuLq6ytnZ2dFlAAAAAECBEfpLQUZGhk6cOKHs7GxHl4Ji8vX1VUBAgCwWi6NLAQAAAIBbIvSXMMMwdPbsWTk7OysoKEhOTtxRUR4ZhqErV67o3LlzkqQaNWo4uCIAAAAAuDVCfwm7du2arly5osDAQFWoUMHR5aAYPD09JUnnzp1TtWrVuNQfAAAAQJnHaecSlpWVJUlyc3NzcCWwhxsf3GRmZjq4EgAAAAC4NUJ/KeEecHPg/yMAAACA8oTQDwAAAACASRH6UWK2b98ui8WiixcvFnibkJAQzZ8/v8RqAgAAAIDbCaH/NhYRESGLxaKnn346x7qRI0fKYrEoIiKi9AsDAAAAANgFof82FxQUpFWrVunPP/+0Lrt69aqio6MVHBzswMoAAAAAAMVF6L/NtW7dWkFBQVq7dq112dq1axUcHKxWrVpZl6Wnp2v06NGqVq2aPDw8dNddd2nfvn02fX3++edq0KCBPD091aVLF8XHx+fY386dO3X33XfL09NTQUFBGj16tNLS0krs+AAAAADgdkboh4YOHarIyEjr82XLlmnIkCE2bZ5//nl98sknWr58ub7//nvVq1dPoaGhSk5OliSdOnVKjz76qB566CHt379fTz75pCZNmmTTx/HjxxUWFqY+ffro4MGDWr16tXbu3KlRo0aV/EECAAAAwG2I0A8NHDhQO3fu1MmTJ3Xy5Ent2rVLAwcOtK5PS0vTu+++qzlz5uiBBx5QkyZN9P7778vT01MffPCBJOndd99V3bp1NW/ePDVs2FDh4eE55gOYPXu2wsPDNXbsWNWvX1+dOnXSwoULtWLFCl29erU0DxkAAAAAbgsuji4Ajufv76+ePXsqKipKhmGoZ8+eqlq1qnX98ePHlZmZqb/97W/WZa6urmrXrp3i4uIkSXFxcWrfvr1Nvx07drR5fuDAAR08eFArV660LjMMQ9nZ2Tpx4oQaN25cEocHAAAAALctQj8kXb/E/8Zl9osXLy6RfaSmpuqpp57S6NGjc6xj0kAAAAAAsD9CPyRJYWFhysjIkMViUWhoqM26unXrys3NTbt27VKtWrUkSZmZmdq3b5/Gjh0rSWrcuLHWr19vs90333xj87x169Y6cuSI6tWrV3IHAgAAAACw4p5+SJKcnZ0VFxenI0eOyNnZ2WZdxYoV9fe//13PPfecYmJidOTIEQ0fPlxXrlzRsGHDJElPP/20fvnlFz333HM6evSooqOjFRUVZdPPCy+8oN27d2vUqFHav3+/fvnlF61bt46J/AAAAACghBD6YeXt7S1vb+9c17322mvq06ePnnjiCbVu3VrHjh3Tpk2bVLlyZUnXL8//5JNP9Nlnn6lFixZasmSJZs2aZdNH8+bN9dVXX+nnn3/W3XffrVatWmnq1KkKDAws8WMDAAAAgNuRxTAMw9FFlHcpKSny8fHRpUuXcoTmq1ev6sSJE6pdu7Y8PDwcVCHshf+fAAAAKC8GDBigpKQkSVL16tUVHR3t4IpyKg81llX55dCbcaYfAAAAAACTIvQDAAAAAGBShH4AAAAAAEyK0A8AAAAAgEkR+gEAAAAAMClCPwAAAAAAJkXoBwAAAADApAj9AAAAAACYFKEfAAAAAACTIvQDAAAAAGBSLo4u4HbV5rkVpbq/2DmDCtU+IiJCy5cvlyS5uLjIz89PzZs3V//+/RURESEnp+ufF4WEhOjkyZOSJA8PD1WvXl3t2rXT008/rfvuu8/aX3x8vGrXri0nJyclJCSoZs2a1nVnz55VUFCQsrKydOLECYWEhFjXLV++XG+//bYOHz4sZ2dntW7dWs8995wefPBBa5uoqCiNHTtWFy9ezHEcFotFn376qXr37l3kGgAAAACgvOJMP/IUFhams2fPKj4+Xhs3blSXLl00ZswYPfjgg7p27Zq13YwZM3T27FkdPXpUK1askK+vr7p166ZXX301R581a9bUihW2H3gsX77cJoDfMHHiRD311FN6/PHHdfDgQX377be666671KtXL7399ttFPq7C1AAAAAAA5RmhH3lyd3dXQECAatasqdatW2vKlClat26dNm7cqKioKGs7Ly8vBQQEKDg4WPfcc4+WLl2ql19+WVOnTtXRo0dt+hw8eLAiIyNtlkVGRmrw4ME2y7755hvNmzdPc+bM0cSJE1WvXj01btxYr776qsaOHavx48fr1KlTRTqugtYAAAAAAOUdoR+Fct9996lFixZau3Ztvu3GjBkjwzC0bt06m+UPP/ywLly4oJ07d0qSdu7cqQsXLuihhx6yaffRRx+pUqVKeuqpp3L0PWHCBGVmZuqTTz4p0jEUtAYAAAAAKO8I/Si0Ro0aKT4+Pt82fn5+qlatWo52rq6uGjhwoJYtWyZJWrZsmQYOHChXV1ebdj///LPq1q0rNze3HH0HBgbK29tbP//8c5HqL2gNAAAAAFDeEfpRaIZhyGKxFLnd0KFDtWbNGiUmJmrNmjUaOnRontuXlILWAAAAAADlGaEfhRYXF6fatWvn2+aPP/7Q77//nmu7Zs2aqVGjRurfv78aN26spk2b5mjToEED/frrr8rIyMix7syZM0pJSVGDBg0kSd7e3kpLS1N2drZNuxuz+fv4+BSpBgAAAAAo7wj9KJStW7fq0KFD6tOnT77tFixYICcnJ/Xu3TvX9UOHDtX27dvzPMPer18/paam6r333suxbu7cuXJ1dbXW0LBhQ127dk379++3aff9999LkvXDgcLWAAAAAADlnYujC0DZlZ6ersTERGVlZSkpKUkxMTGaPXu2HnzwQQ0aNMja7vLly0pMTFRmZqZOnDihDz/8UP/85z81e/Zs1atXL9e+hw8frscee0y+vr65ru/YsaPGjBmj5557ThkZGerdu7cyMzP14YcfasGCBZo/f76CgoIkSXfeeae6d++uoUOHat68eapTp46OHj2qsWPH6vHHH8/zq/huVQMAAAAAlHeEfgeJnTPo1o0cLCYmRjVq1JCLi4sqV66sFi1aaOHChRo8eLCcnP7/RSJTp07V1KlT5ebmpoCAAHXo0EFbtmxRly5d8uzbxcVFVatWzXf/8+fPV/PmzfXOO+/opZdekrOzs1q3bq3PPvssx0z7q1ev1rRp0/TUU0/pzJkzuuOOO/TII4/o5ZdfLlYNAAAAAFCeWYySnC3tNpGSkiIfHx9dunRJ3t7eNuuuXr2qEydOqHbt2vLw8HBQhbAX/n8CAACgvBgwYICSkpIkSdWrV1d0dLSDK8qpPNRYVuWXQ2/GPf0AAAAAAJhUuQn9ycnJCg8Pl7e3t3x9fTVs2DClpqbmu81TTz2lunXrytPTU/7+/urVq5d++uknmzYWiyXHY9WqVSV5KAAAAAAAlIpyE/rDw8N1+PBhbd68WRs2bNCOHTs0YsSIfLdp06aNIiMjFRcXp02bNskwDHXv3l1ZWVk27SIjI3X27FnrI68Z5wEAAAAAKE/KxUR+cXFxiomJ0b59+9S2bVtJ0qJFi9SjRw/NnTtXgYGBuW5384cCISEh+sc//qEWLVooPj5edevWta7z9fVVQEBAyR4EAAAAAAClrFyc6d+zZ498fX2tgV+SunXrJicnJ+3du7dAfaSlpSkyMlK1a9e2ftXbDSNHjlTVqlXVrl07LVu2TLea2zA9PV0pKSk2DwAAAAAAyppycaY/MTFR1apVs1nm4uIiPz8/JSYm5rvtO++8o+eff15paWlq2LChNm/eLDc3N+v6GTNm6L777lOFChX0xRdf6JlnnlFqaqpGjx6dZ5+zZ8/WK6+8UryDAgAAAIAS5O/vn+vPuL04NPRPmjRJr7/+er5t4uLiirWP8PBw3X///Tp79qzmzp2rvn37ateuXdavW7v5e9xbtWqltLQ0zZkzJ9/QP3nyZI0fP976PCUlJcfVAwAAAADgSAsWLHB0CSgDHBr6J0yYoIiIiHzb1KlTRwEBATp37pzN8mvXrik5OfmW9+L7+PjIx8dH9evXV4cOHVS5cmV9+umn6t+/f67t27dvr5kzZyo9PV3u7u65tnF3d89zHQAAAAAAZYVDQ7+/v3+BLjPp2LGjLl68qNjYWLVp00aStHXrVmVnZ6t9+/YF3p9hGDIMQ+np6Xm22b9/vypXrkyoBwAAAACUe+Xinv7GjRsrLCxMw4cP15IlS5SZmalRo0apX79+1pn7T58+ra5du2rFihVq166dfv31V61evVrdu3eXv7+/fvvtN7322mvy9PRUjx49JEn/+c9/lJSUpA4dOsjDw0ObN2/WrFmzNHHiREceLgAAAAAAdlEuQr8krVy5UqNGjVLXrl3l5OSkPn36aOHChdb1mZmZOnr0qK5cuSJJ8vDw0Ndff6358+frwoULql69uu655x7t3r3bOimgq6urFi9erHHjxskwDNWrV09vvvmmhg8fXuLHkzCjWYnv42bBUw8Vqn1ERISWL18u6f9Pmti8eXP1799fERERcnK6/sUPISEhOnnypKTrv/Pq1aurXbt2evrpp3XfffdZ+4uPj1ft2rXl5OSkhIQE1axZ07ru7NmzCgoKUlZWlk6cOKGQkJBiHi0AAAAAQCpHod/Pz0/R0dF5rg8JCbH5qr3AwEB9/vnn+fYZFhamsLAwu9VoNmFhYYqMjFRWVpaSkpIUExOjMWPG6N///rfWr18vF5frw2fGjBkaPny4MjIyFB8frw8//FDdunXTzJkz9eKLL9r0WbNmTa1YsUKTJ0+2Llu+fLlq1qyphISEUj0+AAAAADA7J0cXgLLL3d1dAQEBqlmzplq3bq0pU6Zo3bp12rhxo6KioqztvLy8FBAQoODgYN1zzz1aunSpXn75ZU2dOlVHjx616XPw4MGKjIy0WRYZGanBgweXxiEBAAAAwG2F0I9Cue+++9SiRQutXbs233ZjxoyRYRhat26dzfKHH35YFy5c0M6dOyVJO3fu1IULF/TQQw+VWM0AAAAAcLsi9KPQGjVqpPj4+Hzb+Pn5qVq1ajnaubq6auDAgVq2bJkkadmyZRo4cKBcXV1LqFoAAAAAuH0R+lFohmHIYrEUud3QoUO1Zs0aJSYmas2aNRo6dGhJlAkAAAAAtz1CPwotLi5OtWvXzrfNH3/8od9//z3Xds2aNVOjRo3Uv39/NW7cWE2bNi2pUgEAAADgtkboR6Fs3bpVhw4dUp8+ffJtt2DBAjk5Oal37965rh86dKi2b9/OWX4AAAAAKEHl5iv7UPrS09OVmJho85V9s2fP1oMPPqhBgwZZ212+fFmJiYnKzMzUiRMn9OGHH+qf//ynZs+erXr16uXa9/Dhw/XYY4/J19e3lI4GAAAAAG4/hH4HCZ56yNEl3FJMTIxq1KghFxcXVa5cWS1atNDChQs1ePBgOTn9/4tEpk6dqqlTp8rNzU0BAQHq0KGDtmzZoi5duuTZt4uLi6pWrVoahwEAAAAAty1CP3IVFRWlqKioW7a71Sz+N4SEhMgwjDzXt2zZMt/1AAAAAIDC455+AAAAAABMitAPAAAAAIBJEfoBAAAAADApQj8AAAAAACZF6AcAAAAAwKQI/QAAAAAAmBRf2QcAAAAAcAh/f/9cf4b9EPoBAAAAAA6xYMECR5dgelzeDwAAAACASRH6AQAAAAAwKS7vd5C/Lfpbqe5v17O7irTdqVOnNG3aNMXExOj8+fOqUaOGevfuralTp6pKlSqSpJCQEI0dO1Zjx4612Xb69On67LPPtH//fklSRESEli9frqeeekpLliyxaTty5Ei98847Gjx4sKKioopUKwAAAADAFmf6kadff/1Vbdu21S+//KKPPvpIx44d05IlS7RlyxZ17NhRycnJhe4zKChIq1at0p9//mlddvXqVUVHRys4ONie5QMAAADAbY/QjzyNHDlSbm5u+uKLL9S5c2cFBwfrgQce0JdffqnTp0/rxRdfLHSfrVu3VlBQkNauXWtdtnbtWgUHB6tVq1b2LB8AAAAAbnuEfuQqOTlZmzZt0jPPPCNPT0+bdQEBAQoPD9fq1atlGEah+x46dKgiIyOtz5ctW6YhQ4YUu2YAAAAAgC1CP3L1yy+/yDAMNW7cONf1jRs31oULF/T7778Xuu+BAwdq586dOnnypE6ePKldu3Zp4MCBxS0ZAAAAAPAXTOSHfBXlTP6t+Pv7q2fPnoqKipJhGOrZs6eqVq1q9/0AAAAAwO2OM/3IVb169WSxWBQXF5fr+ri4OFWuXFn+/v7y9vbWpUuXcrS5ePGifHx8ct1+6NChioqK0vLlyzV06FC71g4AAAAAuI7Qj1xVqVJF999/v9555x2bmfYlKTExUStXrtTjjz8ui8Wihg0bKjY2Nkcf33//vRo0aJBr/2FhYcrIyFBmZqZCQ0NL5BgAAAAA4HZH6Eee3n77baWnpys0NFQ7duzQqVOnFBMTo/vvv181a9bUq6++KkkaN26c/vvf/+rVV19VXFycfvzxR7344ovas2ePxowZk2vfzs7OiouL05EjR+Ts7FyahwUAAAAAtw3u6XeQXc/ucnQJt1S/fn199913mjZtmvr27avk5GQFBASod+/emjZtmvz8/CRJnTp10saNGzVjxgzNmzdPTk5OatasmbZs2aKmTZvm2b+3t3dpHQoAAAAA3JYsRknM1HabSUlJkY+Pjy5dupQjyF69elUnTpxQ7dq15eHh4aAKYS/8/wQAAABQFuSXQ2/G5f0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQX0qYL9Ec+P8IAAAAoDwh9JewG99Bn5GR4eBKYA9XrlyRJLm6ujq4EgAAAAC4NRdHF2B2Li4uqlChgn7//Xe5urrKyYnPWcojwzB05coVnTt3Tr6+vtYPcwAAAACgLCP0lzCLxaIaNWroxIkTOnnypKPLQTH5+voqICDA0WUAAAAAQIEQ+kuBm5ub6tevzyX+5Zyrqytn+AEAAACUK4T+UuLk5CQPDw9HlwEAAAAAuI1wgzkAAAAAACZF6AcAAAAAwKQI/QAAAAAAmBT39NuBYRiSpJSUFAdXAgAAAAC4HdzInzfyaF4I/XZw+fJlSVJQUJCDKwEAAAAA3E4uX74sHx+fPNdbjFt9LIBbys7O1pkzZ+Tl5SWLxeLocsqFlJQUBQUF6dSpU/L29nZ0OTAxxhpKC2MNpYWxhtLCWENpYawVjWEYunz5sgIDA+XklPed+5zptwMnJyfdcccdji6jXPL29uYfNkoFYw2lhbGG0sJYQ2lhrKG0MNYKL78z/DcwkR8AAAAAACZF6AcAAAAAwKQI/XAId3d3TZs2Te7u7o4uBSbHWENpYayhtDDWUFoYaygtjLWSxUR+AAAAAACYFGf6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAOlpGRoXr16mn37t2OLiWHjIwMhYSE6LvvvnN0KQCAIiD0o8DuvfdejR071vo8JCRE8+fPtz63WCz67LPPCt3v0aNHFRAQoMuXLxe/SDs7f/68qlWrpt9++83RpdxWbsexduTIEd1xxx1KS0tzdCmwA8ba7SMiIkIWiyXHIywszNrmr69huVmyZIlq166tTp06SZLi4+M1bNgw1a5dW56enqpbt66mTZumjIwMm+0OHjyou+++Wx4eHgoKCtIbb7yRo+81a9aoUaNG8vDwULNmzfT555/brE9NTdWoUaN0xx13yNPTU02aNNGSJUus693c3DRx4kS98MILhf31oJzhtQtFERERod69e5dI31u2bFHjxo2VlZVVIv0XR0xMjFq2bKns7GxHl3JLhH6TuPlNh6urq6pXr677779fy5YtK/RA3L59uywWiy5evGizfO3atZo5c6Ydq75u8uTJevbZZ+Xl5WXdf69evVSjRg1VrFhRLVu21MqVK3Nsd6s3MWvXrlX37t1VpUoVWSwW7d+/P0cfx48f1yOPPCJ/f395e3urb9++SkpKsq6vWrWqBg0apGnTptn3oMsxxlrRxtpTTz2lunXrytPTU/7+/urVq5d++ukn6/omTZqoQ4cOevPNN+170OVYQcJUWeWosRYfH5/r78xisWjNmjWSGGslISwsTGfPnrV5fPTRRwXe3jAMvf322xo2bJh12U8//aTs7Gy99957Onz4sN566y0tWbJEU6ZMsbZJSUlR9+7dVatWLcXGxmrOnDmaPn26li5dam2ze/du9e/fX8OGDdMPP/yg3r17q3fv3vrxxx+tbcaPH6+YmBh9+OGHiouL09ixYzVq1CitX7/e2iY8PFw7d+7U4cOHi/prQgH89W9s7dq19fzzz+vq1aulsn9H/p28wTAMPfDAAzk+4Oe1q2BKMoA7wvPPP6+XXnpJzs7Okq6Ppfvvv9/63r1jx47atGlTju0WL16skJAQeXh4qH379vr2229t1i9dulT33nuvvL29c30veuM9am6Pffv2Sbr+2u/q6prrv4myhtBvIjfedMTHx2vjxo3q0qWLxowZowcffFDXrl0rdv9+fn7WPwL2kpCQoA0bNigiIsK6bPfu3WrevLk++eQTHTx4UEOGDNGgQYO0YcMGmza3ehOTlpamu+66S6+//nqu+05LS1P37t1lsVi0detW7dq1SxkZGXrooYdswuuQIUO0cuVKJScn2/XYyzPGWuHGmiS1adNGkZGRiouL06ZNm2QYhrp3727zyfWQIUP07rvv2uV3aBbFDVOO4MixFhQUlOP39corr6hSpUp64IEHrO0Ya/bl7u6ugIAAm0flypULvH1sbKyOHz+unj17WpeFhYUpMjJS3bt3V506dfTwww9r4sSJWrt2rbXNypUrlZGRoWXLlunOO+9Uv379NHr0aJtQtGDBAoWFhem5555T48aNNXPmTLVu3Vpvv/22tc3u3bs1ePBg3XvvvQoJCdGIESPUokULmzfJlStX1t/+9jetWrWqqL8mFNCN171ff/1Vb731lt57771SOfng6L+TN8yfP18WiyXXdbx2lb6/Xl1Umnbu3Knjx4+rT58+1mU7duzQ/fffr88//1yxsbHq0qWLHnroIf3www/WNqtXr9b48eM1bdo0ff/992rRooVCQ0N17tw5a5srV64oLCzM5oPUm3Xq1CnH39Mnn3xStWvXVtu2ba3tIiIitHDhwhI4ejszYAqDBw82evXqlWP5li1bDEnG+++/bxiGYZw4ccKQZPzwww/WNhcuXDAkGdu2bbOuv/kxePBgwzAMo3PnzsaYMWOs29WqVct46623rM9v7Kd3796Gp6enUa9ePWPdunX51j1nzhyjbdu2tzy+Hj16GEOGDLE+79u3r9GzZ0+bNu3btzeeeuqpHNvmdsyGYRibNm0ynJycjEuXLlmXXbx40bBYLMbmzZtt2tauXdv45z//ecs6bweMtcKPtdwcOHDAkGQcO3bMuiw9Pd1wd3c3vvzyy1tufzvIa6zd0L9/f6Nv3742yzIyMowqVaoYy5cvNwzDMDZu3Gj87W9/M3x8fAw/Pz+jZ8+eNr/zG//PPvnkE+Pee+81PD09jebNmxu7d++2tomPjzcefPBBw9fX16hQoYLRpEkT47///W+edZW1sdayZUtj6NChNssYa/Zzq3FqGDlfw/7qzTffNBo1anTLfb344otGmzZtrM+feOKJHPveunWrIclITk42DMMwgoKCcux76tSpRvPmza3Phw8fbrRt29b47bffjOzsbGPr1q1GpUqVjK+++spmuxdeeMHo3LnzLetE0eU2nh599FGjVatW1ufnz583+vXrZwQGBhqenp5G06ZNjejoaJttsrKyjFmzZhkhISGGh4eH0bx5c2PNmjX57rssvHb98MMPRs2aNY2zZ88akoxPP/3UZj2vXbd2q9ekQ4cOGWFhYUbFihWNatWqGQMHDjR+//136/rOnTsbI0eONMaMGWNUqVLFuPfeew3DMIwff/zR6Nmzp+Hl5WVUqlTJuOuuu6x/T2/sc86cOUZAQIDh5+dnPPPMM0ZGRoa136tXrxoTJkwwAgMDjQoVKhjt2rUztm3blu+xjBw50vjf//3fWx5zkyZNjFdeecX6vF27dsbIkSOtz7OysozAwEBj9uzZObbdtm2bIcm4cOFCvvvIyMgw/P39jRkzZtgsP3nyZI73c2URZ/pN7r777lOLFi1szgzkJygoSJ988omk6/d1nT17VgsWLCjw/l555RX17dtXBw8eVI8ePRQeHp7vGfKvv/7a5tOyvFy6dEl+fn7W53v27FG3bt1s2oSGhmrPnj0FrjU9PV0Wi0Xu7u7WZR4eHnJyctLOnTtt2rZr105ff/11gfu+HTHWCi4tLU2RkZGqXbu2goKCrMvd3NzUsmVLxloBhYeH6z//+Y9SU1OtyzZt2qQrV67okUcekXT9dz1+/Hh999132rJli5ycnPTII4/kuBXlxRdf1MSJE7V//341aNBA/fv3t55JGjlypNLT07Vjxw4dOnRIr7/+uipVqpRnXWVprMXGxmr//v02l41LjDV727BhgypVqmTzmDVrVoG3P3nypAIDA/Ntc+zYMS1atEhPPfWUdVliYqKqV69u0+7G88TExHzb3FgvSYsWLVKTJk10xx13yM3NTWFhYVq8eLHuuecem+0CAwN18uTJAh8Xiu/HH3/U7t275ebmZl129epVtWnTRv/973/1448/asSIEXriiSdsrsyYPXu2VqxYoSVLlujw4cMaN26cBg4cqK+++irPfTn6tevKlSsaMGCAFi9erICAgFzb8NpVPBcvXtR9992nVq1a6bvvvlNMTIySkpLUt29fm3bLly+Xm5ubdu3apSVLluj06dO655575O7urq1btyo2NlZDhw61ueJi27ZtOn78uLZt26bly5crKipKUVFR1vWjRo3Snj17tGrVKh08eFCPPfaYwsLC9Msvv+RZb0HGZHZ2ti5fvmwdkxkZGYqNjbUZk05OTurWrVux/p6uX79ef/zxh4YMGWKzPDg4WNWrVy/zY9LF0QWg5DVq1EgHDx4sUFtnZ2frP5pq1arJ19e3UPuKiIhQ//79JUmzZs3SwoUL9e233+Z5D+7Jkydv+Y/5448/1r59+/Tee+9ZlxXkTcytdOjQQRUrVtQLL7ygWbNmyTAMTZo0SVlZWTp79qxN28DAQJvLhpA7xlr+3nnnHT3//PNKS0tTw4YNtXnzZps3chJvqv/qRpi62ZQpUzRlyhSFhoaqYsWK+vTTT/XEE09IkqKjo/Xwww9bbw+5+ZJASVq2bJn8/f115MgRNW3a1Lp84sSJ1kurX3nlFd155506duyYGjVqpISEBPXp00fNmjWTJNWpUyffmsvCWLvhgw8+UOPGja2Tw92MsWY/Xbp00bvvvmuz7OZQdCt//vmnPDw88lx/+vRphYWF6bHHHtPw4cOLXGdeFi1apG+++Ubr169XrVq1tGPHDo0cOVKBgYE2b5w9PT115coVu+8ftm687l27dk3p6elycnKyuR2jZs2amjhxovX5s88+q02bNunjjz9Wu3btlJ6erlmzZunLL79Ux44dJV1/3dq5c6fee+89de7cOdf9Ovq1a9y4cerUqZN69eqVbzteu4ru7bffVqtWrWw+lFy2bJmCgoL0888/q0GDBpKk+vXr20wKOmXKFPn4+GjVqlVydXWVJGvbGypXrqy3335bzs7OatSokXr27KktW7Zo+PDhSkhIUGRkpBISEqwfcE6cOFExMTGKjIzM80PSgnwgOnfuXKWmplo/uDh//ryysrJyHZM3z6VUWB988IFCQ0N1xx135FhXHsYkof82YBhGnvdG2Vvz5s2tP1esWFHe3t4298/81a3e6Gzbtk1DhgzR+++/rzvvvNOutfr7+2vNmjX6+9//roULF8rJyUn9+/dX69at5eRkexEMb3QKhrGWv/DwcN1///06e/as5s6dq759+2rXrl02dTHWbOUXplxcXNS3b1+tXLlSTzzxhNLS0rRu3Tqbe45/+eUXTZ06VXv37tX58+etZ/gTEhJsQv/N46lGjRqSpHPnzqlRo0YaPXq0/v73v+uLL75Qt27d1KdPH5v2f1UWxtqNOqKjo/Xyyy/nup6xZj8VK1ZUvXr1irx91apVdejQoVzXnTlzRl26dFGnTp1sJuiTpICAAJvJZyVZn984U5pXmxvr//zzT02ZMkWffvqp9YOv5s2ba//+/Zo7d65N6E9OTpa/v3+RjxMFc+N1Ly0tTW+99ZZcXFxsPsDMysrSrFmz9PHHH+v06dPKyMhQenq6KlSoIOn6VSFXrlzR/fffb9NvRkaGWrVqled+HfnatX79em3durVAJ1h47Sq6AwcOaNu2bblerXb8+HFrkG/Tpo3Nuv379+vuu++2Bv7c3HnnndbJ9qTrf0tvvK4dOnRIWVlZOT4oSE9PV5UqVfLs81ZjMjo6Wq+88orWrVunatWq5dmuuH777TfrB2u5KQ9jktB/G4iLi1Pt2rUlyRpmDcOwrs/MzLTbvv76YmCxWPKd0b1q1aq6cOFCruu++uorPfTQQ3rrrbc0aNAgm3W3ehNTUN27d9fx48d1/vx5ubi4yNfXVwEBATnO5PFGp2AYa/nz8fGRj4+P6tevrw4dOqhy5cr69NNPrVcsSNfHWt26dQvdt1ndKkyFh4erc+fOOnfunDZv3ixPT0+bqz0eeugh1apVS++//74CAwOVnZ2tpk2b5piY6ObxdOODqxvj6cknn1RoaKj++9//6osvvtDs2bM1b948Pfvss7nWVBbGmiT9+9//1pUrV3Ls5wbGWtnRqlUrvfvuuzk+OD19+rS6dOlinQj0rx9Id+zYUS+++KIyMzOtY3jz5s1q2LChdSLBjh07asuWLTZfg7p582brGeDMzExlZmbm6NvZ2TnHa+qPP/6Yb2iEfdz8urds2TK1aNFCH3zwgfU2nTlz5mjBggWaP3++mjVrpooVK2rs2LHW17Ubtzz997//Vc2aNW36vvmWxr9y5GvX1q1bdfz48RxX/fXp00d33323tm/fbl3Ga1fRpaam6qGHHsp1QsUbH3hL18fgzTw9PW/Zd37vy1JTU+Xs7KzY2FibDwYk5Xu7XH5jctWqVXryySe1Zs0amw8nq1atKmdnZ7v+PY2MjFSVKlX08MMP57q+POQE7uk3ua1bt+rQoUPWT4hvDMibL1//69em3LjcuDS+D7NVq1Y6cuRIjuXbt29Xz5499frrr2vEiBE51t94E3Ozm9/EFFbVqlXl6+urrVu36ty5czn+UfNG59YYa4VjGIYMw1B6errNcsZa4XTq1ElBQUFavXq1Vq5cqccee8z6xuOPP/7Q0aNH9dJLL6lr165q3Lhxnm8ebiUoKEhPP/201q5dqwkTJuj999/Ps21ZGWsffPCBHn744TzfiDDW7Cc9PV2JiYk2j/Pnzxd4+y5duig1NdXm6/BOnz6te++9V8HBwZo7d65+//13a983DBgwQG5ubho2bJgOHz6s1atXa8GCBRo/fry1zZgxYxQTE6N58+bpp59+0vTp0/Xdd99p1KhRkiRvb2917txZzz33nLZv364TJ04oKipKK1assM6NccPXX3+t7t27F/XXhCJwcnLSlClT9NJLL+nPP/+UJO3atUu9evXSwIED1aJFC9WpU0c///yzdZsmTZrI3d1dCQkJqlevns3j5nlk/sqRr12TJk3SwYMHtX//futDkt566y1FRkbatOW1q+hat26tw4cPKyQkJMfY+GvQv1nz5s319ddfF/nkTatWrZSVlaVz587l2G9+QTyvMfnRRx9pyJAh+uijj2y+9US6/t6yTZs2NmMyOztbW7ZsKdLfU8MwFBkZqUGDBuV6pcPVq1d1/PjxMj8mOdNvIjfedGRlZSkpKUkxMTGaPXu2HnzwQeunsp6enurQoYNee+011a5dW+fOndNLL71k00+tWrVksVi0YcMG9ejRQ56envl+ClccoaGhevLJJ5WVlWX95G/btm168MEHNWbMGPXp08f6BsfNzc16We+YMWPUuXNnzZs3Tz179tSqVav03Xff2Vz6mJycrISEBJ05c0bS9cniJFm/Tkm6/sld48aN5e/vrz179mjMmDEaN26cGjZsaO3nypUrio2NLdSkTGbHWCvcWPv111+1evVqde/eXf7+/vrtt9/02muvydPTUz169LD2Ex8fr9OnT+eYEOl2dmOs3czFxUVVq1a1Ph8wYICWLFmin3/+Wdu2bbMur1y5sqpUqaKlS5eqRo0aSkhI0KRJkwpdw9ixY/XAAw+oQYMGunDhgrZt26bGjRvn2d7Rr2vS9ct7d+zYkeO7sm9grNlXTEyMzVkySWrYsKH1/tHs7Gy5uOT9lqtKlSp65JFHtHLlSs2ePVvS9dB07NgxHTt2LMc9pDeuoPLx8dEXX3yhkSNHqk2bNqpataqmTp1qE8w6deqk6OhovfTSS5oyZYrq16+vzz77zOb2llWrVmny5MnWCVFr1aqlV199VU8//bS1zZ49e3Tp0iX97//+bxF/Syiqxx57TM8995wWL16siRMnqn79+vr3v/+t3bt3q3LlynrzzTeVlJSkJk2aSJK8vLw0ceJEjRs3TtnZ2brrrrt06dIl7dq1S97e3ho8eHCu+3Hka9dfX8NuCA4Otl5BKPHaVVCXLl3KcaKlSpUqGjlypN5//331799fzz//vPz8/HTs2DGtWrVK//znP3Ochb9h1KhRWrRokfr166fJkyfLx8dH33zzjdq1a2fznjkvDRo0UHh4uAYNGqR58+apVatW+v3337VlyxY1b948R3C/ITQ0VMuXL7dZFh0drcGDB2vBggVq3769dUx6enrKx8dHkjR+/HgNHjxYbdu2Vbt27TR//nylpaXZTMJ340PUY8eOSbp+C4KXl5eCg4Nt5mTZunWrTpw4oSeffDLXGr/55hu5u7sX+2RQiXPU1wbAvgYPHmz92jMXFxfD39/f6Natm7Fs2TIjKyvLpu2RI0eMjh07Gp6enkbLli2NL774wvo1ajfMmDHDCAgIMCwWS6G+Ru2vX63i4+NjREZG5ll3ZmamERgYaMTExOR6LDc//vo1QR9//LHRoEEDw83NzbjzzjtzfIVWZGRkrv1MmzbN2uaFF14wqlevbri6uhr169c35s2bZ2RnZ9v0Ex0dbTRs2DDPY7jdMNYKP9ZOnz5tPPDAA0a1atUMV1dX44477jAGDBhg/PTTTzb9zJo1ywgNDc3zGG43ef3/+eu/xyNHjhiSjFq1auX497t582ajcePGhru7u9G8eXNj+/btNuPnVl8taRiGMWrUKKNu3bqGu7u74e/vbzzxxBPG+fPn86zb0a9rhmEYkydPNoKCgnL8m7yBsVZ6rl27ZlSoUOGWX5d24MABo1q1asbly5dLqbLC6du3r/Hqq686ugzTy+vr1mbPnm34+/sbqampxh9//GH06tXLqFSpklGtWjXjpZdeMgYNGmSzXXZ2tjF//nyjYcOGhqurq+Hv72+Ehobm+BrGm5WF166b5fa3nteuW8vr/9mwYcMMwzCMn3/+2XjkkUcMX19fw9PT02jUqJExduxY69/Pv74Hu+HAgQNG9+7djQoVKhheXl7G3XffbRw/fty6z7+O2zFjxtiMk4yMDGPq1KlGSEiI4erqatSoUcN45JFHjIMHD+Z5LH/88Yfh4eFh836pc+fOuR7fjfeQNyxatMgIDg423NzcjHbt2hnffPONzfpp06bl2s9f30v279/f6NSpU541jhgxItevpyxrLIZx0w23gAMsXrxY69ev16ZNmxxdSq46dOig0aNHa8CAAY4uBcVUlsdaRkaG6tevr+joaP3tb39zdDkoJsYapOuTP61YsUIzZsxQfHz8Le8njYqKUps2bazfFFFWZGRk6I033tCECRMKdG8vyi9eu1DWPPfcc0pJSbH5xoiy4vz582rYsKG+++47mytSyiIu74fDPfXUU7p48aIuX75s/ZqtsuL8+fN69NFHbSZaQ/lVlsdaQkKCpkyZwhsZk2CsQZJatmypKlWq6F//+leBJpCKiIgo+aKKwM3NLcftWTAnXrtQ1rz44ot65513lJ2dnWPCUUeLj4/XO++8U+YDvyRxph8AAAAAAJMqWx+XAAAAAAAAuyH0AwAAAABgUoR+AAAAAABMitAPAAAAAIBJEfoBAAAAADApQj8AAAAAACZF6AcAALmKiIiQxWKRxWKRq6urqlevrvvvv1/Lli1TdnZ2gfuJioqSr69vyRWah4iICPXu3bvU9wsAQFlC6AcAAHkKCwvT2bNnFR8fr40bN6pLly4aM2aMHnzwQV27ds3R5QEAgFsg9AMAgDy5u7srICBANWvWVOvWrTVlyhStW7dOGzduVFRUlCTpzTffVLNmzVSxYkUFBQXpmWeeUWpqqiRp+/btGjJkiC5dumS9amD69OmSpH/9619q27atvLy8FBAQoAEDBujcuXPWfV+4cEHh4eHy9/eXp6en6tevr8jISOv6U6dOqW/fvvL19ZWfn5969eql+Ph4SdL06dO1fPlyrVu3zrrf7du3l8avDACAMoXQDwAACuW+++5TixYttHbtWkmSk5OTFi5cqMOHD2v58uXaunWrnn/+eUlSp06dNH/+fHl7e+vs2bM6e/asJk6cKEnKzMzUzJkzdeDAAX322WeKj49XRESEdT8vv/yyjhw5oo0bNyouLk7vvvuuqlatat02NDRUXl5e+vrrr7Vr1y5VqlRJYWFhysjI0MSJE9W3b1/rlQpnz55Vp06dSvcXBQBAGeDi6AIAAED506hRIx08eFCSNHbsWOvykJAQ/eMf/9DTTz+td955R25ubvLx8ZHFYlFAQIBNH0OHDrX+XKdOHS1cuFD/8z//o9TUVFWqVEkJCQlq1aqV2rZta+37htWrVys7O1v//Oc/ZbFYJEmRkZHy9fXV9u3b1b17d3l6eio9PT3HfgEAuJ1wph8AABSaYRjWsP3ll1+qa9euqlmzpry8vPTEE0/ojz/+0JUrV/LtIzY2Vg899JCCg4Pl5eWlzp07S5ISEhIkSX//+9+1atUqtWzZUs8//7x2795t3fbAgQM6duyYvLy8VKlSJVWqVEl+fn66evWqjh8/XkJHDQBA+UPoBwAAhRYXF6fatWsrPj5eDz74oJo3b65PPvlEsbGxWrx4sSQpIyMjz+3T0tIUGhoqb29vrVy5Uvv27dOnn35qs90DDzygkydPaty4cTpz5oy6du1qvTUgNTVVbdq00f79+20eP//8swYMGFDCRw8AQPnB5f0AAKBQtm7dqkOHDmncuHGKjY1Vdna25s2bJyen6+cSPv74Y5v2bm5uysrKsln2008/6Y8//tBrr72moKAgSdJ3332XY1/+/v4aPHiwBg8erLvvvlvPPfec5s6dq9atW2v16tWqVq2avL29c60zt/0CAHC74Uw/AADIU3p6uhITE3X69Gl9//33mjVrlnr16qUHH3xQgwYNUr169ZSZmalFixbp119/1b/+9S8tWbLEpo+QkBClpqZqy5YtOn/+vK5cuaLg4GC5ublZt1u/fr1mzpxps93UqVO1bt06HTt2TIcPH9aGDRvUuHFjSVJ4eLiqVq2qXr166euvv9aJEye0fft2jR49Wr/99pt1vwcPHtTRo0d1/vx5ZWZmls4vDQCAMoTQDwAA8hQTE6MaNWooJCREYWFh2rZtmxYuXKh169bJ2dlZLVq00JtvvqnXX39dTZs21cqVKzV79mybPjp16qSnn35ajz/+uPz9/fXGG2/I399fUVFRWrNmjZo0aaLXXntNc+fOtdnOzc1NkydPVvPmzXXPPffI2dlZq1atkiRVqFBBO3bsUHBwsB599FE1btxYw4YN09WrV61n/ocPH66GDRuqbdu28vf3165du0rnlwYAQBliMQzDcHQRAAAAAADA/jjTDwAAAACASRH6AQAAAAAwKUI/AAAAAAAmRegHAAAAAMCkCP0AAAAAAJgUoR8AAAAAAJMi9AMAAAAAYFKEfgAAAAAATIrQDwAAAACASRH6AQAAAAAwKUI/AAAAAAAm9f8A4MtSHNjYknwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.barplot(data=df_z[df_z[\"Parameter\"]==\"Non-decision Time\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "6f4760b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Non-decision Time\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "427d9739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.barplot(data=df_z[df_z[\"Parameter\"]==\"Effective Inhibition\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "e771f073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.boxplot(data=df_z[df_z[\"Parameter\"]==\"Effective Inhibition\"], x=\"Dataset\", y=\"Difference\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b92ae633",
   "metadata": {},
   "source": [
    "## Cohen's d: first calculate difference then standardize\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c1f5fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run SAT_help_functions.ipynb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee525d05",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b370b99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74ae5150",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f912158",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df = pd.read_csv(\"DDMresultDutilh2019.csv\")\n",
    "#df_dif = get_difference(\"Dutilh2019\", df, \"DDM\")\n",
    "#get_cohen_d(df_dif, \"DDM\")\n",
    "#df_dif_z = standardize_result(df_dif,\"DDM\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22de744c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb9bf5f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "61ea95f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets_list = [\"Dutilh2019\", \"Dutilh2013\", \"Evans2017\", \"EJ2008\", \"Rae2014\", \"Lerche2017\"]\n",
    "models_list = [\"DDMOUM\", \"DDM\", \"OUM\"]\n",
    "df_all = pd.DataFrame(columns = [\"Parameter\", \"Cohen's d\", \"Dataset\", \"Model\"])\n",
    "\n",
    "for model in models_list:\n",
    "    for dataset in datasets_list:\n",
    "        df = pd.read_csv(model + \"result\" + dataset + \".csv\")\n",
    "        df_dif = get_difference(dataset, df, model)\n",
    "        df_cohen_d = get_cohen_d(df_dif,model)\n",
    "        df_all = df_all.append(df_cohen_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc856baa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4292ff9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f43d51e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets_labels = [\"Dutilh (2019)\", \"Dutilh (2013)\", \"Evans (2017)\",\n",
    "                   \"Wagenmakers (2008)\", \"Rae (2014)\", \"Lerche (2017)\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6546e22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_all[df_all[\"Parameter\"]==\"Drift Rate\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fabc2a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "df = df_all.loc[(df_all['Parameter'] == 'Drift Rate') & (df_all['Model'] != \"DDMOUM\")]\n",
    "sns.set_palette(\"Spectral\")\n",
    "\n",
    "sns.barplot(data=df, x=\"Dataset\", y=\"Cohen's d\", hue=\"Model\", ax=ax)\n",
    "ax.axhline(y=0)\n",
    "\n",
    "ax.legend(loc='upper right', title = \"Model\", bbox_to_anchor=(-0.06, 1))\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "078d1ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "df = df_all.loc[(df_all['Parameter'] == 'Boundary Separation') & (df_all['Model'] != \"DDMOUM\")]\n",
    "\n",
    "sns.barplot(data=df, x=\"Dataset\", y=\"Cohen's d\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.legend(loc='upper left', title = \"Model\", bbox_to_anchor=(1.06, 1))\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "70b86fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "df = df_all.loc[(df_all['Parameter'] == 'Non-decision Time') & (df_all['Model'] != \"DDMOUM\")]\n",
    "\n",
    "sns.barplot(data=df, x=\"Dataset\", y=\"Cohen's d\", hue=\"Model\",ax=ax)\n",
    "ax.axhline(y=0)\n",
    "ax.legend(loc='upper right', title = \"Model\", bbox_to_anchor=(-0.06, 1))\n",
    "#ax.legend().remove()\n",
    "\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6ffe971d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.12156862745098039, 0.4666666666666667, 0.7058823529411765),\n",
       " (1.0, 0.4980392156862745, 0.054901960784313725)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.color_palette()[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79b32747",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3cd0a4dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "df = df_all.loc[df_all['Parameter'] == 'Effective Inhibition']\n",
    "\n",
    "sns.barplot(data=df, x=\"Dataset\", y=\"Cohen's d\", hue=\"Model\", hue_order = [\"OUM\", \"DDMOUM\"],\n",
    "            ax=ax, palette=sns.color_palette()[1:3])\n",
    "ax.axhline(y=0)\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "ax.legend(#h, labels = ['OUM', 'OUM on DDM Bootstrapped Data'],\n",
    "          title = \"Model\", loc='upper left', bbox_to_anchor=(1.06, 1))\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2053655",
   "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>Parameter</th>\n",
       "      <th>Cohen's d</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.331155</td>\n",
       "      <td>Dutilh2019</td>\n",
       "      <td>DDMOUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.462007</td>\n",
       "      <td>Dutilh2013</td>\n",
       "      <td>DDMOUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.27309</td>\n",
       "      <td>Evans2017</td>\n",
       "      <td>DDMOUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.210696</td>\n",
       "      <td>EJ2008</td>\n",
       "      <td>DDMOUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.807487</td>\n",
       "      <td>Rae2014</td>\n",
       "      <td>DDMOUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>-0.665705</td>\n",
       "      <td>Lerche2017</td>\n",
       "      <td>DDMOUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>2.037032</td>\n",
       "      <td>Dutilh2019</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.797497</td>\n",
       "      <td>Dutilh2013</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>1.042998</td>\n",
       "      <td>Evans2017</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>1.10772</td>\n",
       "      <td>EJ2008</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.831223</td>\n",
       "      <td>Rae2014</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Effective Inhibition</td>\n",
       "      <td>0.178509</td>\n",
       "      <td>Lerche2017</td>\n",
       "      <td>OUM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Parameter Cohen's d     Dataset   Model\n",
       "3  Effective Inhibition  0.331155  Dutilh2019  DDMOUM\n",
       "3  Effective Inhibition -0.462007  Dutilh2013  DDMOUM\n",
       "3  Effective Inhibition  -0.27309   Evans2017  DDMOUM\n",
       "3  Effective Inhibition  0.210696      EJ2008  DDMOUM\n",
       "3  Effective Inhibition  0.807487     Rae2014  DDMOUM\n",
       "3  Effective Inhibition -0.665705  Lerche2017  DDMOUM\n",
       "3  Effective Inhibition  2.037032  Dutilh2019     OUM\n",
       "3  Effective Inhibition  0.797497  Dutilh2013     OUM\n",
       "3  Effective Inhibition  1.042998   Evans2017     OUM\n",
       "3  Effective Inhibition   1.10772      EJ2008     OUM\n",
       "3  Effective Inhibition  0.831223     Rae2014     OUM\n",
       "3  Effective Inhibition  0.178509  Lerche2017     OUM"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f4aba850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "df = df_all.loc[df_all['Parameter'] == 'Effective Inhibition']\n",
    "df = df.loc[df['Model'] == 'OUM']\n",
    "\n",
    "\n",
    "sns.barplot(data=df, x=\"Dataset\", y=\"Cohen's d\", hue=\"Model\",\n",
    "            ax=ax, palette=sns.color_palette()[1:3])\n",
    "ax.axhline(y=0)\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "ax.legend(#h, labels = ['OUM', 'OUM on DDM Bootstrapped Data'],\n",
    "          title = \"Model\", loc='upper left', bbox_to_anchor=(1.06, 1))\n",
    "ax.set_xticks(range(len(datasets_labels)), labels=datasets_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99314a0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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