WALiSuite_GenerateSummaryPlots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import scipy as sp\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "# matplotlib.use('Agg')\n",
    "# %matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "from itertools import groupby\n",
    "from operator import itemgetter\n",
    "import dabest\n",
    "from nptdms import *\n",
    "import math\n",
    "from collections import Counter\n",
    "import shutil\n",
    "import progressbar\n",
    "from svgutils.compose import *\n",
    "from matplotlib.lines import Line2D\n",
    "import random\n",
    "import dabest\n",
    "plt.rcParams['pdf.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get all the metric ES and CIs to a DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def collectEffectSizes(rootDirectory):\n",
    "#     temp = {'ORNs':[],'Sex-Satiety-Air':[],'LightIntensity':[], 'Metric':[], 'ES':[], 'CIs':[], 'pVal':[]}\n",
    "    temp = {'ORNs':[],'Sex-Satiety-Air':[],'ES':[], 'CIs':[], 'pVal':[],'LightIntensity':[], 'parent_n':[],'parent_std':[],\n",
    "           'offspring_n':[],'offspring_std':[]}\n",
    "    \n",
    "    ornList = os.listdir(rootDirectory)\n",
    "    bar = progressbar.ProgressBar()\n",
    "    ## go thru the ORNs \n",
    "    for ORN in bar(ornList):\n",
    "#         rootDir = os.path.join(rootDirectory,ORN)\n",
    "#         metricList = next(os.walk(rootDir))[1]\n",
    "        rootDir = os.path.join(rootDirectory,ORN,\"weighted_TSALE\",\"P10\")\n",
    "        #print(ORN)\n",
    "        ## go thru the metrics for each ORN folder\n",
    "#         for metric in metricList:\n",
    "#             rootDir_metric_P10 = os.path.join(rootDir,metric,'P10')\n",
    "#             files_in_metric_dir = os.listdir(rootDir_metric_P10)\n",
    "        files_in_metric_dir = os.listdir(rootDir)\n",
    "\n",
    "        os.chdir(rootDir)\n",
    "            ## go thru each csv file for each metric\n",
    "        for afile in files_in_metric_dir:\n",
    "            if afile[-4:] == '.csv':\n",
    "                conditions = afile[:-4].strip().split('_')\n",
    "                sex_satiety_air = conditions[-3] + '_' + conditions[-2] + '_' + conditions[-1] \n",
    "                tempDF = pd.read_csv(afile)\n",
    "                ## go thru each row in the csv files\n",
    "                for row in range(len(tempDF)):\n",
    "                    lightInt = tempDF['reference_group'][row].split('_')[-2]\n",
    "                    ES = tempDF['stat_summary'][row]\n",
    "                    CI_low = tempDF['bca_ci_low'][row]\n",
    "                    CI_up = tempDF['bca_ci_high'][row]\n",
    "                    \n",
    "                    parent_N = tempDF['Parent_N'][row]\n",
    "                    parent_STD = tempDF['Parent_SD'][row]\n",
    "                    offspring_N = tempDF['Offspring_N'][row]\n",
    "                    offspring_STD = tempDF['Offspring_SD'][row]\n",
    "                    try:\n",
    "                        p_val = tempDF['pvalue_2samp_ind_ttest'][row]\n",
    "                    except:\n",
    "                        p_val = np.nan                    \n",
    "                    temp['ORNs'].append(ORN)\n",
    "                    temp['Sex-Satiety-Air'].append(sex_satiety_air)\n",
    "                    temp['LightIntensity'].append(lightInt)\n",
    "#                     temp['Metric'].append(metric)\n",
    "                    temp['ES'].append(ES)\n",
    "                    temp['CIs'].append([CI_low,CI_up])\n",
    "                    temp['pVal'].append(p_val)\n",
    "                    temp['parent_n'].append(parent_N)\n",
    "                    temp['parent_std'].append(parent_STD)\n",
    "                    temp['offspring_n'].append(offspring_N)\n",
    "                    temp['offspring_std'].append(offspring_STD)\n",
    "                #print(afile)\n",
    "#     All_ORN_EffectSizes_df = pd.DataFrame(temp,columns=['ORNs','Sex-Satiety-Air','LightIntensity', 'Metric', 'ES', 'CIs', 'pVal'])\n",
    "    All_ORN_EffectSizes_df = pd.DataFrame(temp,columns=['ORNs','LightIntensity','Sex-Satiety-Air', 'ES', 'CIs', 'pVal','parent_n',\n",
    "                                                       'parent_std','offspring_n','offspring_std'])\n",
    "\n",
    "    return All_ORN_EffectSizes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% |########################################################################|\n"
     ]
    }
   ],
   "source": [
    "## Read the .csv files, not .pkl\n",
    "All_ORN_EffectSizes_df = collectEffectSizes(rootDirectory = \"C:/Users/tumkayat/Desktop/WALISAR_Paper/Figure2/Data/All_merged_intensity_wTSALE/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>ORNs</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>ES</th>\n",
       "      <th>CIs</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
       "      <th>offspring_n</th>\n",
       "      <th>offspring_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>Or49a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>-0.059049</td>\n",
       "      <td>[-0.225626930562, 0.113771492849]</td>\n",
       "      <td>0.503232</td>\n",
       "      <td>52</td>\n",
       "      <td>0.447253</td>\n",
       "      <td>52</td>\n",
       "      <td>0.417327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Or49a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>0.219587</td>\n",
       "      <td>[0.09652762321219999, 0.34228582025399995]</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>99</td>\n",
       "      <td>0.416904</td>\n",
       "      <td>52</td>\n",
       "      <td>0.334003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Or49a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.086580</td>\n",
       "      <td>[-0.0362762003651, 0.212884275455]</td>\n",
       "      <td>0.190583</td>\n",
       "      <td>52</td>\n",
       "      <td>0.337188</td>\n",
       "      <td>52</td>\n",
       "      <td>0.322132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Or49a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.177558</td>\n",
       "      <td>[0.06739299158289999, 0.290724739063]</td>\n",
       "      <td>0.002847</td>\n",
       "      <td>52</td>\n",
       "      <td>0.271149</td>\n",
       "      <td>52</td>\n",
       "      <td>0.310243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Or49a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>-0.086761</td>\n",
       "      <td>[-0.23388802619600002, 0.051731168937499995]</td>\n",
       "      <td>0.316406</td>\n",
       "      <td>104</td>\n",
       "      <td>0.564220</td>\n",
       "      <td>52</td>\n",
       "      <td>0.340187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Or49a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>0.038274</td>\n",
       "      <td>[-0.0803027247644, 0.15688427602]</td>\n",
       "      <td>0.564349</td>\n",
       "      <td>104</td>\n",
       "      <td>0.421249</td>\n",
       "      <td>52</td>\n",
       "      <td>0.297697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>Or49a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>-0.025430</td>\n",
       "      <td>[-0.13814987565, 0.0854474675034]</td>\n",
       "      <td>0.662848</td>\n",
       "      <td>104</td>\n",
       "      <td>0.340163</td>\n",
       "      <td>52</td>\n",
       "      <td>0.332628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>Or49a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>-0.049171</td>\n",
       "      <td>[-0.14551367009000002, 0.045872394187599995]</td>\n",
       "      <td>0.351428</td>\n",
       "      <td>104</td>\n",
       "      <td>0.323534</td>\n",
       "      <td>52</td>\n",
       "      <td>0.261676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORNs LightIntensity     Sex-Satiety-Air        ES  \\\n",
       "276  Or49a           42uW        male_fed_Air -0.059049   \n",
       "277  Or49a           70uW        male_fed_Air  0.219587   \n",
       "278  Or49a           42uW      male_fed_NoAir  0.086580   \n",
       "279  Or49a           70uW      male_fed_NoAir  0.177558   \n",
       "280  Or49a           42uW    male_starved_Air -0.086761   \n",
       "281  Or49a           70uW    male_starved_Air  0.038274   \n",
       "282  Or49a           42uW  male_starved_NoAir -0.025430   \n",
       "283  Or49a           70uW  male_starved_NoAir -0.049171   \n",
       "\n",
       "                                              CIs      pVal  parent_n  \\\n",
       "276             [-0.225626930562, 0.113771492849]  0.503232        52   \n",
       "277    [0.09652762321219999, 0.34228582025399995]  0.001406        99   \n",
       "278            [-0.0362762003651, 0.212884275455]  0.190583        52   \n",
       "279         [0.06739299158289999, 0.290724739063]  0.002847        52   \n",
       "280  [-0.23388802619600002, 0.051731168937499995]  0.316406       104   \n",
       "281             [-0.0803027247644, 0.15688427602]  0.564349       104   \n",
       "282             [-0.13814987565, 0.0854474675034]  0.662848       104   \n",
       "283  [-0.14551367009000002, 0.045872394187599995]  0.351428       104   \n",
       "\n",
       "     parent_std  offspring_n  offspring_std  \n",
       "276    0.447253           52       0.417327  \n",
       "277    0.416904           52       0.334003  \n",
       "278    0.337188           52       0.322132  \n",
       "279    0.271149           52       0.310243  \n",
       "280    0.564220           52       0.340187  \n",
       "281    0.421249           52       0.297697  \n",
       "282    0.340163           52       0.332628  \n",
       "283    0.323534           52       0.261676  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_ORN_EffectSizes_df[All_ORN_EffectSizes_df[\"ORNs\"] == 'Or49a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Insert cohen's d in to the table\n",
    "def cohensD(row):\n",
    "    n1, n2, s1, s2 = row['parent_n'], row['offspring_n'], row['parent_std'], row['offspring_std']\n",
    "    \n",
    "    pooled_sd = np.sqrt(((n1 - 1)*(s1**2) + (n2 - 1)*(s2**2)) / (n1 + n2 - 2))\n",
    "    d = row['ES'] / pooled_sd\n",
    "    \n",
    "    return d\n",
    "\n",
    "All_ORN_EffectSizes_df['Cohens_d'] = All_ORN_EffectSizes_df.apply(cohensD, axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## used this function for combo prediction analyses\n",
    "def sigmoid(x):\n",
    "\n",
    "    if type(x) == list:\n",
    "        return 1 / (1 + math.exp(-float(x[0]))), 1 / (1 + math.exp(-float(x[1])))\n",
    "    \n",
    "    else:\n",
    "        return 1 / (1 + math.exp(-x))\n",
    "\n",
    "All_ORN_EffectSizes_df['ES_sig'] = All_ORN_EffectSizes_df.apply(lambda x: sigmoid(x['ES']), axis=1)\n",
    "All_ORN_EffectSizes_df['CIs_sig'] = All_ORN_EffectSizes_df.apply(lambda x: sigmoid(x['CIs']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 11)\n",
      "(24, 11)\n"
     ]
    }
   ],
   "source": [
    "All_ORN_EffectSizes_df['Tag'] = All_ORN_EffectSizes_df['ORNs'] + '_'+ All_ORN_EffectSizes_df['Sex-Satiety-Air'] \n",
    "\n",
    "a = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoWind']\n",
    "b = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Wind']\n",
    "# c = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir']\n",
    "# d = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air']\n",
    "\n",
    "\n",
    "print a.shape\n",
    "print b.shape\n",
    "# print c.shape\n",
    "# print d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "All_adult_male_fed_noair_df = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df[\"Sex-Satiety-Air\"] == 'male_fed_NoAir') | (All_ORN_EffectSizes_df[\"Sex-Satiety-Air\"] == 'male_fed_NoWind')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "All_adult_male_fed_noair_df.to_csv(\"C:/Users/tumkayat/Desktop/All_adult_male_fed_noair_df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "All_ORN_EffectSizes_df.to_csv(\"C:/Users/tumkayat/Desktop/allData.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## To find what is in one list but not the other\n",
    "set(a['Tag']).symmetric_difference(set(b['Tag']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "All_ORN_EffectSizes_df['ORN-Intensity-Sex-Satiety-Air'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delta-delta plotting function for any given two groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import *\n",
    "def ddplot(df,colname,idx,fsize=(10,5),color='k',s=60):\n",
    "    \n",
    "    ## Calculate Pooled CIs\n",
    "    \n",
    "    def calculateSTD(mean,CI_ub,n1,n2):\n",
    "        moe = abs(CI_ub - mean)\n",
    "        std_and_n = moe / 1.96\n",
    "        std = std_and_n / sqrt((1./n1)+(1./n2))\n",
    "        return std\n",
    "\n",
    "    ## Calculate CIs for delta-deltas\n",
    "    def calculatePooledMOE(std1,std2,n1=52,n2=52):\n",
    "        pooledSD = sqrt(((n1-1)*(std1**2)+(n2-1)*(std2**2))/(n1+n2-2))\n",
    "        moe = 1.96 * pooledSD * sqrt((1./n1)+(1./n2))\n",
    "        return moe, pooledSD\n",
    "\n",
    "    temp = {'reference group':[], 'test group':[], 'ES':[], 'MOE':[]}\n",
    "\n",
    "    for i in idx:\n",
    "        ## get the group names to compare\n",
    "        group1 = i[0]\n",
    "        group2 = i[1]\n",
    "\n",
    "        ## get Mean and calculate pooled SD of the first group\n",
    "        group1_ES = df[df[col_name] == group1]['ES'].values[0]\n",
    "        group1_CI_ub = df[df[col_name] == group1]['CIs'].values[0][1]\n",
    "        group1_parent_n = df[df[col_name] == group1]['parent_n'].values\n",
    "        group1_offspring_n = df[df[col_name] == group1]['offspring_n'].values\n",
    "\n",
    "        group1_STD = calculateSTD(group1_ES, group1_CI_ub, group1_parent_n, group1_offspring_n)\n",
    "        group1_N = (group1_parent_n + group1_offspring_n)/2.\n",
    "\n",
    "        ## get Mean and calculate pooled SD of the second group\n",
    "        group2_ES = df[df[col_name] == group2]['ES'].values[0]\n",
    "        group2_CI_ub = df[df[col_name] == group2]['CIs'].values[0][1]\n",
    "        group2_parent_n = df[df[col_name] == group2]['parent_n'].values\n",
    "        group2_offspring_n = df[df[col_name] == group2]['offspring_n'].values\n",
    "\n",
    "        group2_STD = calculateSTD(group2_ES, group2_CI_ub, group2_parent_n, group2_offspring_n)\n",
    "        group2_N = (group2_parent_n + group2_offspring_n)/2.\n",
    "\n",
    "        ## calculate delta-delta effect size and MOE\n",
    "        deltadelta_ES = group2_ES - group1_ES\n",
    "\n",
    "        deltadelta_MOE, deltadelta_STD = calculatePooledMOE(group1_STD, group2_STD, group1_N, group2_N)\n",
    "\n",
    "        temp['reference group'].append(group1)\n",
    "        temp['test group'].append(group2)\n",
    "        temp['ES'].append(deltadelta_ES)\n",
    "        temp['MOE'].append(deltadelta_MOE)\n",
    "    \n",
    "    fig = plt.figure(figsize=fsize)\n",
    "    ax1 = fig.add_subplot(111)\n",
    "\n",
    "    style='ticks'\n",
    "    context='notebook'\n",
    "    font='Arial'\n",
    "    colorPalette = 'muted'\n",
    "    sns.set(style=style,context =context,font=font,font_scale=1.5)\n",
    "    color = color\n",
    "    s = s\n",
    "\n",
    "    x = 2\n",
    "\n",
    "    for i in range(len(temp['ES'])):\n",
    "\n",
    "        y = temp['ES'][i]\n",
    "        ax1.scatter(x, y, color = color, s = s)\n",
    "        ax1.plot([x, x], [y-temp['MOE'][0],y+temp['MOE'][0]],color=color, lw=1)\n",
    "        x = x + 2\n",
    "\n",
    "    sns.despine(ax=ax1)\n",
    "    ax1.tick_params(left=True,top=False, bottom=False, right=False)    \n",
    "    ax1.set_ylim(-.5,.5)\n",
    "    ax1.set_xticks(range(1,2*(len(temp['ES'])),2)) ## sets the y ticks\n",
    "    ax1.set_xticklabels([(i + ' V ' + j) for i in temp['reference group'] for j in temp['test group']],rotation=30, fontsize=8)  ## sets the x ticks' labels\n",
    "    plt.axhline(0, color='k', lw='.5', ls='-')\n",
    "    ax1.set_ylabel('delta-delta ES')\n",
    "    \n",
    "    stats = pd.DataFrame(temp)\n",
    "    return fig, stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ORN = 'Or92a'\n",
    "f, b = ddplot(df, colname = 'ORN-Intensity-Sex-Satiety-Air',\n",
    "      idx = [[ORN + '_7mV_fileName_NoAir_Constant', ORN + '_14mV_fileName_NoAir_Pulse'],\n",
    "             [ORN + '_14mV_fileName_NoAir_Constant', ORN + '_21mV_fileName_NoAir_Pulse'],\n",
    "             [ORN + '_21mV_fileName_NoAir_Constant', ORN + '_28mV_fileName_NoAir_Pulse']])\n",
    "\n",
    "f.savefig('/Users/tumkayat/Desktop/ORScreening/TransferToSOD/PulseVConstantLED_ORexperiments/' + ORN + '/Pulsed_v_Constant.pdf',dpi=1000,bbox_inches='tight')\n",
    "b.to_csv('/Users/tumkayat/Desktop/ORScreening/TransferToSOD/PulseVConstantLED_ORexperiments/' + ORN + '/Pulsed_v_Constant.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End of delta-delta function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Mean across 3-intensities and Pooling the SDs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import *\n",
    "def calculatePooledMOE(data):\n",
    "    sd_list = []\n",
    "    for CI_pair in data:\n",
    "        sd = calculateSD(CI_pair[0], CI_pair[1])\n",
    "        sd_list.append(sd)\n",
    "    \n",
    "    pooled_moe = poolSDs(sd_list[0], sd_list[1],sd_list[2])\n",
    "    return pooled_moe\n",
    "\n",
    "def calculateSD(CI_lb ,CI_ub):\n",
    "    moe = abs(CI_ub - CI_lb)\n",
    "    std_and_n = moe / 1.96\n",
    "    std = std_and_n / sqrt((1./104)+(1./52))\n",
    "    return std\n",
    "\n",
    "def poolSDs(std1,std2,std3,n1=52,n2=52,n3=52):\n",
    "    pooledSD = sqrt(((n1-1)*(std1**2)+(n2-1)*(std2**2)+(n3-1)*(std3**2))/(n1+n2+n3-3))\n",
    "    moe = 1.96 * pooledSD * sqrt((1./n1)+(1./n2)+(1./n3))\n",
    "    return moe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "temp = {'ORNs':[], 'Sex-Satiety-Air':[], 'ES_Mean':[], 'MOE_Pooled':[]}\n",
    "\n",
    "for g,d in All_ORN_EffectSizes_df.groupby(['ORNs','Sex-Satiety-Air']):\n",
    "    ORN = g[0]\n",
    "    ES_Mean = d['ES'].mean()\n",
    "    pooled_moe = calculatePooledMOE(d['CIs'])\n",
    "    conditions = d['Sex-Satiety-Air'].iloc[0]\n",
    "    \n",
    "    temp['ORNs'].append(ORN)\n",
    "    temp['ES_Mean'].append(ES_Mean)\n",
    "    temp['MOE_Pooled'].append(pooled_moe)\n",
    "    temp['Sex-Satiety-Air'].append(conditions)\n",
    "    \n",
    "allIntensities_PooledAverage_df = pd.DataFrame(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "allIntensities_PooledAverage_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Mean across 3-intensities and Pooling the SDs: END"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing the Empirical Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"C:/Users/tumkayat/Desktop/ORScreening/All_merged_intensity_wTSALE/Gr21a/weighted_TSALE/weighted_TSALE_values.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df.assign(Sex_Satiety_Wind = pd.Series(df['Sex'] + '_' + df['Satiety'] + '_' + df['Wind status'], index = df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Z_scores = {'Di':[], 'Si':[], 'ORNs':[], 'Sex_Satiety_Wind':[], 'LightInt':[]}\n",
    "\n",
    "rootDirectory = \"C:/Users/tumkayat/Desktop/ORScreening/All_merged_intensity_wTSALE/\"\n",
    "ornList = os.listdir(rootDirectory)\n",
    "bar = progressbar.ProgressBar()\n",
    "\n",
    "## go thru the ORNs\n",
    "for ORN in bar(ornList):\n",
    "    rootDir = os.path.join(rootDirectory, ORN, \"weighted_TSALE\", \"weighted_TSALE_values.pkl\")\n",
    "    \n",
    "    df = pd.read_pickle(rootDir)\n",
    "    df = df.assign(Sex_Satiety_Wind = pd.Series(df['Sex'] + '_' + df['Satiety'] + '_' + df['Wind status'], index = df.index))\n",
    "    \n",
    "    for condition in df['Sex_Satiety_Wind'].unique():\n",
    "        for intensity in df['Light Intensity(uW/mm2)'].unique():\n",
    "            dfOI = df[(df['Sex_Satiety_Wind'] == condition) & (df['Light Intensity(uW/mm2)'] == intensity)] \n",
    "            ## calculate the mean difference as Offspring - Parent, since having 2 or 3 independent groups does not affect the mean\n",
    "            ctrl_wTSALE = dfOI[dfOI['Status'] == 'Parent']['weighted_TSALE_P10']\n",
    "            exp_wTSALE = dfOI[dfOI['Status'] == 'Offspring']['weighted_TSALE_P10']\n",
    "\n",
    "            Di = exp_wTSALE.mean() - ctrl_wTSALE.mean()\n",
    "            \n",
    "            ## calculate Si for three genotypes and then get the average - different than combininb the controls\n",
    "            genotypes = df['Genotype'].unique()\n",
    "            \n",
    "            g0_data = dfOI[dfOI['Genotype'] == genotypes[0]]['weighted_TSALE_P10']\n",
    "            g1_data = dfOI[dfOI['Genotype'] == genotypes[1]]['weighted_TSALE_P10']\n",
    "            g2_data = dfOI[dfOI['Genotype'] == genotypes[2]]['weighted_TSALE_P10']\n",
    "            \n",
    "            Si = (g0_data.std() + g1_data.std() + g2_data.std()) / 3.\n",
    "            \n",
    "            Z_scores['ORNs'].append(ORN)\n",
    "            Z_scores['Sex_Satiety_Wind'].append(condition)\n",
    "            Z_scores['LightInt'].append(intensity)\n",
    "            Z_scores['Di'].append(Di)\n",
    "            Z_scores['Si'].append(Si)\n",
    "                     \n",
    "Z_scores_df = pd.DataFrame(Z_scores)\n",
    "Z_scores_df_dropna = Z_scores_df.dropna()\n",
    "\n",
    "a0 = np.percentile(Z_scores_df_dropna['Si'], 10)\n",
    "Z_scores_df_dropna['Zi'] = Z_scores_df_dropna['Di'] / (a0 + Z_scores_df_dropna['Si'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Z_scores_df_dropna['Tag'] = Z_scores_df_dropna['ORNs'] + '_' + Z_scores_df_dropna['Sex_Satiety_Wind']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "male_fed_noair = Z_scores_df_dropna[Z_scores_df_dropna['Sex_Satiety_Wind'] == 'male_fed_NoAir']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a0 = np.percentile(male_fed_noair['Si'], 90)\n",
    "male_fed_noair['Zi'] = male_fed_noair['Di'] / (a0 + male_fed_noair['Si'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "##Drop Gr66a, EBprot blows up otherwise\n",
    "a = Z_scores_df_dropna[Z_scores_df_dropna['ORNs'] != 'Gr66a']\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a.to_csv('C:/Users/tumkayat/Desktop/allData_Z_Gr66aREMOVED_CombosADDED.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##!!!! Calculate Z scores for stand-alone dataframes\n",
    "\n",
    "def calculate_Z(Di, ):\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing the Empirical Bayes: END"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PlotMe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def generateSummaryPlot(df, condition1, condition2, metric, sort_by=None, sort_by_order=None, fsize= (5,25), gap_thru_yaxis = 5, offset = 1, s =30):\n",
    "    \n",
    "    df_condition = df[((df['Sex-Satiety-Air'] == condition1) | (df['Sex-Satiety-Air'] == condition2)) & (df['Metric'] == metric)]\n",
    "\n",
    "    ORs_with_both_conditions = []\n",
    "    for OR in df_condition['ORNs'].unique():\n",
    "        list_of_conditions_per_ORN = df_condition[df_condition['ORNs'] == OR]['Sex-Satiety-Air']\n",
    "\n",
    "        ## if an ORN has both conditions drop one\n",
    "        if (condition1 in list_of_conditions_per_ORN.values) & (condition2 in list_of_conditions_per_ORN.values):\n",
    "                ORs_with_both_conditions.append(OR)\n",
    "\n",
    "#     print 'Before dropping', df_condition.shape\n",
    "    for i in ORs_with_both_conditions:\n",
    "        drop_df = df_condition[(df_condition['ORNs'] == i) & (df_condition['Sex-Satiety-Air'] == condition2)]\n",
    "        ind = list(drop_df.index.values)\n",
    "#         print ind\n",
    "        df_condition = df_condition.drop(ind)\n",
    "\n",
    "    df_condition = df_condition.reset_index(drop=True)\n",
    "#     print 'After dropping',df_condition.shape\n",
    "\n",
    "    ## If none, will use the first df's order\n",
    "    fig = plt.figure(figsize=fsize)\n",
    "    ax1 = fig.add_subplot(111)\n",
    "    plt.title(condition1,fontsize = 15)\n",
    "\n",
    "    style='ticks'\n",
    "    context='notebook'\n",
    "    font='Arial'\n",
    "    colorPalette = 'muted'\n",
    "    sns.set(style=style,context =context,font=font)\n",
    "    s = s\n",
    "    gap_thru_yaxis = gap_thru_yaxis\n",
    "    offset = offset\n",
    "    i = 0\n",
    "\n",
    "    if sort_by is None:\n",
    "        ORNList_sorted = sort_by_order\n",
    "        keep_the_ORN_Order = sort_by_order\n",
    "        \n",
    "    else:\n",
    "        df_condition_sorted = df_condition[df_condition['LightIntensity'] == '70uW'].sort_values(by=['ES'],ascending=False)\n",
    "        ORNList_sorted = df_condition_sorted['ORNs'].unique()\n",
    "        keep_the_ORN_Order = ORNList_sorted\n",
    "\n",
    "    y_val = np.arange(1, len(ORNList_sorted)*gap_thru_yaxis, gap_thru_yaxis)\n",
    "\n",
    "    for OR in ORNList_sorted:\n",
    "        OR_df = df_condition[df_condition['ORNs'] == OR]\n",
    "        y = y_val[i] + offset\n",
    "\n",
    "        i = i+1\n",
    "    #     print OR, len(OR_df)\n",
    "        for row in range(len(OR_df)):\n",
    "            ES = OR_df.iloc[row]['ES']\n",
    "            CIs= OR_df.iloc[row]['CIs']\n",
    "            intensity = OR_df.iloc[row]['LightIntensity']\n",
    "\n",
    "            if intensity == '14uW':\n",
    "                color = sns.dark_palette(\"red\",n_colors=3)[2]\n",
    "\n",
    "            elif intensity == '42uW':\n",
    "                color = sns.dark_palette(\"red\",n_colors=3)[1]\n",
    "\n",
    "            elif intensity == '70uW':\n",
    "                color = sns.dark_palette(\"red\",n_colors=3)[0]\n",
    "\n",
    "            ax1.scatter(ES, y, color = color, s = s)\n",
    "            ax1.plot([CIs[0], CIs[1]], [y,y], color = color, lw=.5)\n",
    "            y = y - offset\n",
    "\n",
    "    sns.despine(ax=ax1)\n",
    "    ax1.tick_params(left=True,top=False, bottom=False, right=False)    \n",
    "\n",
    "    ax1.set_xlim(-1.0,1.0)\n",
    "    ax1.set_ylim(-1, y_val[-1]+gap_thru_yaxis)\n",
    "    ax1.set_yticks(y_val)  ## sets the y ticks\n",
    "    ax1.set_yticklabels(ORNList_sorted, fontsize=8)  ## sets the x ticks' labels\n",
    "    plt.axvline(0,color='k',lw='0.4',ls='-')\n",
    "    # ax1.set_ylabel('ORNs')\n",
    "    ax1.set_xlabel('weighted TSALE',fontsize=10)\n",
    "    plt.xticks(fontsize=8)\n",
    "\n",
    "    from matplotlib.lines import Line2D\n",
    "    custom_lines = [Line2D([0], [0], color = sns.dark_palette(\"red\",n_colors=3)[2], marker = 'o', lw=1),\n",
    "                    Line2D([0], [0], color = sns.dark_palette(\"red\",n_colors=3)[1], marker = 'o', lw=1),\n",
    "                    Line2D([0], [0], color = sns.dark_palette(\"red\",n_colors=3)[0], marker = 'o', lw=1)]\n",
    "\n",
    "    ax1.legend(custom_lines, ['14uW', '42uW', '70uW'])\n",
    "\n",
    "    return fig, keep_the_ORN_Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "All_ORN_EffectSizes_df_70_uw = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['LightIntensity'] == '70uW']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male_fed_Air', 'male_fed_NoAir', 'male_starved_Air',\n",
       "       'male_starved_NoAir', 'male_fed_NoWind', 'male_fed_Wind',\n",
       "       'male_starved_NoWind', 'male_starved_Wind', 'female_fed_Air',\n",
       "       'female_fed_NoAir', 'female_starved_Air', 'female_starved_NoAir'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_ORN_EffectSizes_df['Sex-Satiety-Air'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Metric'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2601\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2602\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2603\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Metric'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-a3f87effd190>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m f, ORNorder = generateSummaryPlot(All_ORN_EffectSizes_df,condition1=condition1,condition2 = condition2,metric = metric,\n\u001b[1;32m----> 6\u001b[1;33m                         sort_by= False, sort_by_order = False, fsize=(4,6), gap_thru_yaxis = 2, offset = 0.5, s =30)\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;31m#order = ORNorder\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-4-86cd4329c45b>\u001b[0m in \u001b[0;36mgenerateSummaryPlot\u001b[1;34m(df, condition1, condition2, metric, sort_by, sort_by_order, fsize, gap_thru_yaxis, offset, s)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgenerateSummaryPlot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcondition1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcondition2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetric\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort_by\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort_by_order\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfsize\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m25\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgap_thru_yaxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moffset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mdf_condition\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Sex-Satiety-Air'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcondition1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Sex-Satiety-Air'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mcondition2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Metric'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mmetric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mORs_with_both_conditions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2915\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2916\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2917\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2918\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2919\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2602\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2603\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2604\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2605\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2606\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Metric'"
     ]
    }
   ],
   "source": [
    "condition1 = 'female_fed_Air'\n",
    "condition2 = 'female_fed_Air'\n",
    "metric = 'ES'\n",
    "\n",
    "f, ORNorder = generateSummaryPlot(All_ORN_EffectSizes_df,condition1=condition1,condition2 = condition2,metric = metric,\n",
    "                        sort_by= False, sort_by_order = False, fsize=(4,6), gap_thru_yaxis = 2, offset = 0.5, s =30)\n",
    "#order = ORNorder\n",
    "\n",
    "# f.savefig(\"C:/Users/tumkayat/Desktop/ORScreening/ORNs_Combo/\" + metric + \"_\" + condition + \".pdf\",dpi=1000,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the ES dataframe into positive, negative, neutral valence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Drop the GRs for other sections\n",
    "drop_list = ['Gr21a','Gr66a','Gr63a']\n",
    "All_ORN_EffectSizes_df = All_ORN_EffectSizes_df[~All_ORN_EffectSizes_df['ORNs'].isin(drop_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "positive_valence = []\n",
    "negative_valence = []\n",
    "neutral_valence = []\n",
    "\n",
    "temp_df = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir') & (All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE')]\n",
    " \n",
    "for row in range(len(temp_df)):\n",
    "    ORN = temp_df.iloc[row]['ORNs']\n",
    "    p = temp_df.iloc[row]['pVal']\n",
    "    ES = temp_df.iloc[row]['ES']\n",
    "\n",
    "    if p <= 0.05:\n",
    "        if ES > 0:\n",
    "            if not ORN in positive_valence:\n",
    "                positive_valence.append(ORN)\n",
    "        \n",
    "        elif ES < 0:\n",
    "            if not ORN in negative_valence:\n",
    "                negative_valence.append(ORN)\n",
    "                \n",
    "for OR in temp_df['ORNs'].unique():\n",
    "    if not OR in positive_valence:\n",
    "        if not OR in negative_valence:\n",
    "            neutral_valence.append(OR)\n",
    "                \n",
    "print 'Total number of ORNs:', len(temp_df['ORNs'].unique())    \n",
    "print 'Total number of positive ORNs:', len(positive_valence)  \n",
    "print 'Total number of negative ORNs:', len(negative_valence)\n",
    "print 'Total number of neutral ORNs:', len(neutral_valence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "positive_ORN_df = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['ORNs'].isin(positive_valence)]\n",
    "negative_ORN_df = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['ORNs'].isin(negative_valence)]\n",
    "neutral_ORN_df = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['ORNs'].isin(neutral_valence)]\n",
    "print All_ORN_EffectSizes_df.shape\n",
    "print positive_ORN_df.shape\n",
    "print negative_ORN_df.shape\n",
    "print neutral_ORN_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "condition1='male_fed_NoAir'\n",
    "# condition2 = 'male_starved-asghar_NoAir'\n",
    "metric = 'weighted_TSALE'\n",
    "\n",
    "f, ORNorder = generateSummaryPlot(All_ORN_EffectSizes_df,condition1=condition1, condition2 = None,metric = metric,\n",
    "                        sort_by = None, fsize = (5,12), gap_thru_yaxis = 3, offset = .5, s =30)\n",
    "\n",
    "theOrder = ORNorder\n",
    "\n",
    "f.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/Delete_\" + metric + \"_\" + condition1 + \".pdf\",dpi=1000,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plots for pairs of conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Or49a 14uW missing in fed_air\n",
    "## Or24a 14uW missing in fed_noAir\n",
    "drop_list = ['Or49a','Or24a']\n",
    "\n",
    "for i in drop_list:\n",
    "    drop_df = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['ORNs'] == i) & (All_ORN_EffectSizes_df['LightIntensity'] == \"14uW\")]\n",
    "    ind = list(drop_df.index.values)\n",
    "    print len(ind)\n",
    "    All_ORN_EffectSizes_df = All_ORN_EffectSizes_df.drop(ind)\n",
    "    \n",
    "All_ORN_EffectSizes_df = All_ORN_EffectSizes_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "All_ORN_EffectSizes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Drop the GRs for other sections\n",
    "drop_list = ['Gr21a','Gr66a','Gr63a']\n",
    "All_ORN_EffectSizes_df = All_ORN_EffectSizes_df[~All_ORN_EffectSizes_df['ORNs'].isin(drop_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## To make the lists equal drop Or49a-Or24a\n",
    "\n",
    "## Or49a 14uW missing in fed_air\n",
    "## Or24a 14uW missing in fed_noAir\n",
    "drop_list = ['Or49a','Or24a']\n",
    "\n",
    "for i in drop_list:\n",
    "    drop_df = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['ORNs'] == i)]\n",
    "    ind = list(drop_df.index.values)\n",
    "    print len(ind)\n",
    "    All_ORN_EffectSizes_df = All_ORN_EffectSizes_df.drop(ind)\n",
    "    \n",
    "All_ORN_EffectSizes_df = All_ORN_EffectSizes_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['ORNs'] == \"Or24a\") & (All_ORN_EffectSizes_df['LightIntensity'] == \"14uW\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# male_fed_NoAir_ES = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]['ES']\n",
    "# male_fed_Air_ES = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')]['ES']\n",
    "# male_starved_NoAir_ES = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir')]['ES']\n",
    "# male_starved_Air_ES = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air')]['ES']\n",
    "\n",
    "# male_fed_NoAir_labels = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]['ORNs']\n",
    "# male_fed_Air_labels = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')]['ORNs']\n",
    "# male_starved_NoAir_labels = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir')]['ORNs']\n",
    "# male_starved_Air_labels = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air')]['ORNs']\n",
    "\n",
    "# male_fed_NoAir_int = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]['LightIntensity']\n",
    "# male_fed_Air_int = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')]['LightIntensity']\n",
    "# male_starved_NoAir_int = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir')]['LightIntensity']\n",
    "# male_starved_Air_int = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air')]['LightIntensity']\n",
    "\n",
    "male_fed_NoAir_ES = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]['ES']\n",
    "male_fed_Air_ES = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')]['ES']\n",
    "male_starved_NoAir_ES = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir')]['ES']\n",
    "male_starved_Air_ES = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air')]['ES']\n",
    "\n",
    "male_fed_NoAir_labels = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]['ORNs']\n",
    "male_fed_Air_labels = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')]['ORNs']\n",
    "male_starved_NoAir_labels = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir')]['ORNs']\n",
    "male_starved_Air_labels = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air')]['ORNs']\n",
    "\n",
    "male_fed_NoAir_int = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]['LightIntensity']\n",
    "male_fed_Air_int = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')]['LightIntensity']\n",
    "male_starved_NoAir_int = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir')]['LightIntensity']\n",
    "male_starved_Air_int = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air')]['LightIntensity']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "All_ORN_EffectSizes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import scipy\n",
    "c1_ES = male_fed_NoAir_ES\n",
    "c2_ES = male_fed_Air_ES\n",
    "c3_ES = male_starved_NoAir_ES\n",
    "c4_ES = male_starved_Air_ES\n",
    "\n",
    "c1_labels = male_fed_NoAir_labels\n",
    "c2_labels = male_fed_Air_labels\n",
    "c3_labels = male_starved_NoAir_labels\n",
    "c4_labels = male_starved_Air_labels\n",
    "\n",
    "c1_int = male_fed_NoAir_int\n",
    "c2_int = male_fed_Air_int\n",
    "c3_int = male_starved_NoAir_int\n",
    "c4_int = male_starved_Air_int\n",
    "\n",
    "x = c1_ES\n",
    "y = c3_ES\n",
    "labels = c1_labels\n",
    "lightInt = c1_int\n",
    "\n",
    "temp = {'x':x.values, 'y':y.values, 'labels':labels.values, 'lightInt':lightInt.values}\n",
    "data = pd.DataFrame.from_dict(temp)\n",
    "\n",
    "# fig = plt.figure(figsize=(6,6))\n",
    "# ax = fig.add_subplot(111)\n",
    "\n",
    "p = sns.lmplot(x = 'x', y = 'y',legend_out=False, data = data,hue='lightInt', truncate=True,fit_reg=True, scatter_kws={\"alpha\" : 0.5})\n",
    "\n",
    "# sns.regplot(x = 'x', y = 'y', data = data,truncate=True, line_kws={\"color\" : 'black'},scatter_kws={\"alpha\" : 0})\n",
    "\n",
    "p.set(ylim = (-0.7, .7), xlim = (-0.7, .7), xlabel = 'male_fed_NoAir', ylabel = 'male_starved_NoAir')\n",
    "\n",
    "sns.set(style=\"ticks\")\n",
    "sns.despine(trim=True)\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(x=x, y=y)\n",
    "annotation = \"$\\it{R-squared = %.2f}$\" % (r_value**2) + \"\\n\" + \"$\\it{slope = %.2f}$\" % (slope) + \"\\n\" + \"$\\it{p = %.2f}$\" % (p_value)\n",
    "\n",
    "# plt.text(0.7, 0.3, annotation, fontsize=15)\n",
    "\n",
    "def label_point(df, ax):\n",
    "    for i, point in df.iterrows():\n",
    "        ax.text(point['x']+.02, point['y'], str(point['labels']))\n",
    "\n",
    "label_point(data, plt.gca())\n",
    "# p.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/RegressionPlot_\" + \"5_seperate_lb\" + \".pdf\",dpi=1000,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Paired plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE']\n",
    "df['abs_ES'] = abs(df['ES'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "condition = 'male_starved_NoAir'\n",
    "df_fed_noair = df[df['Sex-Satiety-Air'] == condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig,b = bs.contrastplot(df_fed_noair, x = 'LightIntensity', y = 'abs_ES',\n",
    "                                color_col= 'LightIntensity', paired = False, fig_size=(8,10),                    \n",
    "                              idx = ('14uW','42uW','70uW'))\n",
    "\n",
    "plt.title(condition)\n",
    "fname = condition + '_singleIntensity'\n",
    "\n",
    "plt.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/PairedPlots/SeperateIntensities/\" +  fname + '.pdf',dpi=1000,bbox_inches='tight')                                    \n",
    "b.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/PairedPlots/SeperateIntensities/\" + fname + '.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "0.25*0.25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delta delta pandas df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last version of d-d preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:82: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:99: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:117: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:134: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:173: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:178: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:183: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:188: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:23: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:37: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:51: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:66: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:152: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:157: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:162: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:167: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n"
     ]
    }
   ],
   "source": [
    "## Get the tags to match different conditions across tags\n",
    "All_ORN_EffectSizes_df['Tag'] = All_ORN_EffectSizes_df['ORNs'] + '_' + All_ORN_EffectSizes_df['LightIntensity']\n",
    "\n",
    "## Fill up a single df with all the data using TAG as a reference\n",
    "data = {'ORNs':[], 'LightInt':[],'male_fed_noair_ES':[], 'male_fed_air_ES':[], 'male_starved_noair_ES':[], 'male_starved_air_ES':[],\n",
    "       'male_fed_noair_CIs':[], 'male_fed_air_CIs':[], 'male_starved_noair_CIs':[], 'male_starved_air_CIs':[],\n",
    "       'male_fed_noair_parent_n':[],'male_fed_noair_parent_std':[],'male_fed_noair_offspring_n':[], 'male_fed_noair_offspring_std':[],\n",
    "       'male_fed_air_parent_n':[],'male_fed_air_parent_std':[],'male_fed_air_offspring_n':[], 'male_fed_air_offspring_std':[],\n",
    "       'male_starved_noair_parent_n':[],'male_starved_noair_parent_std':[],'male_starved_noair_offspring_n':[], 'male_starved_noair_offspring_std':[],\n",
    "       'male_starved_air_parent_n':[],'male_starved_air_parent_std':[],'male_starved_air_offspring_n':[], 'male_starved_air_offspring_std':[],\n",
    "        ##female\n",
    "       'female_fed_noair_ES':[], 'female_fed_air_ES':[], 'female_starved_noair_ES':[], 'female_starved_air_ES':[],\n",
    "       'female_fed_noair_CIs':[], 'female_fed_air_CIs':[], 'female_starved_noair_CIs':[], 'female_starved_air_CIs':[],\n",
    "       'female_fed_noair_parent_n':[],'female_fed_noair_parent_std':[],'female_fed_noair_offspring_n':[], 'female_fed_noair_offspring_std':[],\n",
    "       'female_fed_air_parent_n':[],'female_fed_air_parent_std':[],'female_fed_air_offspring_n':[], 'female_fed_air_offspring_std':[],\n",
    "       'female_starved_noair_parent_n':[],'female_starved_noair_parent_std':[],'female_starved_noair_offspring_n':[], 'female_starved_noair_offspring_std':[],\n",
    "       'female_starved_air_parent_n':[],'female_starved_air_parent_std':[],'female_starved_air_offspring_n':[], 'female_starved_air_offspring_std':[]}\n",
    "        \n",
    "for tag in All_ORN_EffectSizes_df['Tag'].unique():\n",
    "    dfOI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Tag'] == tag)]\n",
    "    \n",
    "    ## Get the ES\n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_NoAir']['ES'].values:\n",
    "        data['male_fed_noair_ES'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_NoAir']['ES'].values[0])\n",
    "        data['male_fed_noair_parent_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_NoAir']['parent_n'].values[0])\n",
    "        data['male_fed_noair_parent_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_NoAir']['parent_std'].values[0])\n",
    "        data['male_fed_noair_offspring_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_NoAir']['offspring_n'].values[0])\n",
    "        data['male_fed_noair_offspring_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_NoAir']['offspring_std'].values[0])\n",
    "       \n",
    "    else:\n",
    "        data['male_fed_noair_ES'].append(np.nan)\n",
    "        data['male_fed_noair_parent_n'].append(np.nan)\n",
    "        data['male_fed_noair_parent_std'].append(np.nan)\n",
    "        data['male_fed_noair_offspring_n'].append(np.nan)\n",
    "        data['male_fed_noair_offspring_std'].append(np.nan)\n",
    "        \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_Air']['ES'].values:\n",
    "        data['male_fed_air_ES'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_Air']['ES'].values[0])\n",
    "        data['male_fed_air_parent_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_Air']['parent_n'].values[0])\n",
    "        data['male_fed_air_parent_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_Air']['parent_std'].values[0])\n",
    "        data['male_fed_air_offspring_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_Air']['offspring_n'].values[0])\n",
    "        data['male_fed_air_offspring_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_Air']['offspring_std'].values[0])\n",
    "                                                          \n",
    "    else:\n",
    "        data['male_fed_air_ES'].append(np.nan)\n",
    "        data['male_fed_air_parent_n'].append(np.nan)\n",
    "        data['male_fed_air_parent_std'].append(np.nan)\n",
    "        data['male_fed_air_offspring_n'].append(np.nan)\n",
    "        data['male_fed_air_offspring_std'].append(np.nan)\n",
    "                                                      \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_NoAir']['ES'].values:\n",
    "        data['male_starved_noair_ES'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_NoAir']['ES'].values[0])\n",
    "        data['male_starved_noair_parent_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_NoAir']['parent_n'].values[0])\n",
    "        data['male_starved_noair_parent_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_NoAir']['parent_std'].values[0])\n",
    "        data['male_starved_noair_offspring_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_NoAir']['offspring_n'].values[0])\n",
    "        data['male_starved_noair_offspring_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_NoAir']['offspring_std'].values[0])\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        data['male_starved_noair_ES'].append(np.nan)\n",
    "        data['male_starved_noair_parent_n'].append(np.nan)\n",
    "        data['male_starved_noair_parent_std'].append(np.nan)\n",
    "        data['male_starved_noair_offspring_n'].append(np.nan)\n",
    "        data['male_starved_noair_offspring_std'].append(np.nan)\n",
    "        \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_Air']['ES'].values:\n",
    "        data['male_starved_air_ES'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_Air']['ES'].values[0])\n",
    "        data['male_starved_air_parent_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_Air']['parent_n'].values[0])\n",
    "        data['male_starved_air_parent_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_Air']['parent_std'].values[0])\n",
    "        data['male_starved_air_offspring_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_Air']['offspring_n'].values[0])\n",
    "        data['male_starved_air_offspring_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_Air']['offspring_std'].values[0])\n",
    "        \n",
    "        \n",
    "    else:\n",
    "        data['male_starved_air_ES'].append(np.nan)\n",
    "        data['male_starved_air_parent_n'].append(np.nan)\n",
    "        data['male_starved_air_parent_std'].append(np.nan)\n",
    "        data['male_starved_air_offspring_n'].append(np.nan)\n",
    "        data['male_starved_air_offspring_std'].append(np.nan)\n",
    "                \n",
    "    ## Get the ES FEMALE\n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_NoAir']['ES'].values:\n",
    "               \n",
    "        ## female\n",
    "        data['female_fed_noair_ES'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_NoAir']['ES'].values[0])\n",
    "        data['female_fed_noair_parent_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_NoAir']['parent_n'].values[0])\n",
    "        data['female_fed_noair_parent_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_NoAir']['parent_std'].values[0])\n",
    "        data['female_fed_noair_offspring_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_NoAir']['offspring_n'].values[0])\n",
    "        data['female_fed_noair_offspring_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_NoAir']['offspring_std'].values[0])\n",
    "    else:\n",
    "                  \n",
    "        ## female                                              \n",
    "        data['female_fed_noair_ES'].append(np.nan)\n",
    "        data['female_fed_noair_parent_n'].append(np.nan)\n",
    "        data['female_fed_noair_parent_std'].append(np.nan)\n",
    "        data['female_fed_noair_offspring_n'].append(np.nan)\n",
    "        data['female_fed_noair_offspring_std'].append(np.nan)\n",
    "        \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_Air']['ES'].values:\n",
    "                \n",
    "        ## female\n",
    "        data['female_fed_air_ES'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_Air']['ES'].values[0])\n",
    "        data['female_fed_air_parent_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_Air']['parent_n'].values[0])\n",
    "        data['female_fed_air_parent_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_Air']['parent_std'].values[0])\n",
    "        data['female_fed_air_offspring_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_Air']['offspring_n'].values[0])\n",
    "        data['female_fed_air_offspring_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_Air']['offspring_std'].values[0])                                              \n",
    "                                                      \n",
    "    else:\n",
    "        \n",
    "        ## female                                           \n",
    "        data['female_fed_air_ES'].append(np.nan)\n",
    "        data['female_fed_air_parent_n'].append(np.nan)\n",
    "        data['female_fed_air_parent_std'].append(np.nan)\n",
    "        data['female_fed_air_offspring_n'].append(np.nan)\n",
    "        data['female_fed_air_offspring_std'].append(np.nan)\n",
    "        \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_NoAir']['ES'].values:\n",
    "        \n",
    "        ## female\n",
    "        data['female_starved_noair_ES'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_NoAir']['ES'].values[0])\n",
    "        data['female_starved_noair_parent_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_NoAir']['parent_n'].values[0])\n",
    "        data['female_starved_noair_parent_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_NoAir']['parent_std'].values[0])\n",
    "        data['female_starved_noair_offspring_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_NoAir']['offspring_n'].values[0])\n",
    "        data['female_starved_noair_offspring_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_NoAir']['offspring_std'].values[0])                                            \n",
    "    else:\n",
    "        \n",
    "        ## female                                            \n",
    "        data['female_starved_noair_ES'].append(np.nan)\n",
    "        data['female_starved_noair_parent_n'].append(np.nan)\n",
    "        data['female_starved_noair_parent_std'].append(np.nan)\n",
    "        data['female_starved_noair_offspring_n'].append(np.nan)\n",
    "        data['female_starved_noair_offspring_std'].append(np.nan)                                            \n",
    "\n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_Air']['ES'].values:\n",
    "        \n",
    "        ## female                                             \n",
    "        data['female_starved_air_ES'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_Air']['ES'].values[0])\n",
    "        data['female_starved_air_parent_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_Air']['parent_n'].values[0])\n",
    "        data['female_starved_air_parent_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_Air']['parent_std'].values[0])\n",
    "        data['female_starved_air_offspring_n'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_Air']['offspring_n'].values[0])\n",
    "        data['female_starved_air_offspring_std'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_Air']['offspring_std'].values[0])                                             \n",
    "    else:\n",
    "        \n",
    "        ## female                                            \n",
    "        data['female_starved_air_ES'].append(np.nan)\n",
    "        data['female_starved_air_parent_n'].append(np.nan)\n",
    "        data['female_starved_air_parent_std'].append(np.nan)\n",
    "        data['female_starved_air_offspring_n'].append(np.nan)\n",
    "        data['female_starved_air_offspring_std'].append(np.nan)        \n",
    "                \n",
    "    ## Get the CIs   \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_NoAir']['CIs'].values:\n",
    "        data['male_fed_noair_CIs'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_NoAir']['CIs'].values[0])\n",
    "    else:\n",
    "        data['male_fed_noair_CIs'].append(np.nan)\n",
    "        \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_Air']['CIs'].values:\n",
    "        data['male_fed_air_CIs'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_fed_Air']['CIs'].values[0])\n",
    "    else:\n",
    "        data['male_fed_air_CIs'].append(np.nan)\n",
    "        \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_NoAir']['CIs'].values:\n",
    "        data['male_starved_noair_CIs'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_NoAir']['CIs'].values[0])\n",
    "    else:\n",
    "        data['male_starved_noair_CIs'].append(np.nan)\n",
    "\n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_Air']['CIs'].values:\n",
    "        data['male_starved_air_CIs'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'male_starved_Air']['CIs'].values[0])\n",
    "    else:\n",
    "        data['male_starved_air_CIs'].append(np.nan)\n",
    "                                                    \n",
    "    ## Get the CIs FEMALE                                                \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_NoAir']['CIs'].values:\n",
    "        data['female_fed_noair_CIs'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_NoAir']['CIs'].values[0])\n",
    "    else:\n",
    "        data['female_fed_noair_CIs'].append(np.nan)\n",
    "        \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_Air']['CIs'].values:\n",
    "        data['female_fed_air_CIs'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_fed_Air']['CIs'].values[0])\n",
    "    else:\n",
    "        data['female_fed_air_CIs'].append(np.nan)\n",
    "        \n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_NoAir']['CIs'].values:\n",
    "        data['female_starved_noair_CIs'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_NoAir']['CIs'].values[0])\n",
    "    else:\n",
    "        data['female_starved_noair_CIs'].append(np.nan)\n",
    "\n",
    "    if dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_Air']['CIs'].values:\n",
    "        data['female_starved_air_CIs'].append(dfOI[dfOI['Sex-Satiety-Air'] == 'female_starved_Air']['CIs'].values[0])\n",
    "    else:\n",
    "        data['female_starved_air_CIs'].append(np.nan) \n",
    "                                                                                                   \n",
    "    data['ORNs'].append(tag.split('_')[0])\n",
    "    data['LightInt'].append(tag.split('_')[1])\n",
    "#     data['parent_n'].append(dfOI)\n",
    "    \n",
    "\n",
    "deltadelta_df = pd.DataFrame(data)    \n",
    "### Calculate delta-deltas\n",
    "\n",
    "# Wind\n",
    "deltadelta_df['male_fed_air_V_male_fed_noair_ES'] = deltadelta_df['male_fed_air_ES'] - deltadelta_df['male_fed_noair_ES']\n",
    "deltadelta_df['male_starved_air_V_male_starved_noair_ES'] = deltadelta_df['male_starved_air_ES'] - deltadelta_df['male_starved_noair_ES']\n",
    "\n",
    "# Starvation\n",
    "deltadelta_df['male_fed_noair_V_male_starved_noair_ES'] = deltadelta_df['male_starved_noair_ES'] - deltadelta_df['male_fed_noair_ES']\n",
    "deltadelta_df['male_fed_air_V_male_starved_air_ES'] = deltadelta_df['male_starved_air_ES'] - deltadelta_df['male_fed_air_ES']\n",
    "\n",
    "# Sex\n",
    "deltadelta_df['female_fed_noair_V_male_fed_noair_ES'] = deltadelta_df['female_fed_noair_ES'] - deltadelta_df['male_fed_noair_ES']\n",
    "deltadelta_df['female_fed_air_V_male_fed_air_ES'] = deltadelta_df['female_fed_air_ES'] - deltadelta_df['male_fed_air_ES']\n",
    "deltadelta_df['female_starved_noair_V_male_starved_noair_ES'] = deltadelta_df['female_starved_noair_ES'] - deltadelta_df['male_starved_noair_ES']\n",
    "deltadelta_df['female_starved_air_V_male_starved_air_ES'] = deltadelta_df['female_starved_air_ES'] - deltadelta_df['male_starved_air_ES']                                                   \n",
    "                                                    \n",
    "## Calculate Pooled CIs\n",
    "from math import *\n",
    "def calculateSTD(mean,CI_ub,n1,n2):\n",
    "    moe = abs(CI_ub - mean)\n",
    "    std_and_n = moe / 1.96\n",
    "    std = std_and_n / sqrt((1./n1)+(1./n2))\n",
    "    return std\n",
    "\n",
    "## Calculate CIs for delta-deltas\n",
    "def calculatePooledMOE(std1,std2,n1=52,n2=52):\n",
    "    pooledSD = sqrt(((n1-1)*(std1**2)+(n2-1)*(std2**2))/(n1+n2-2))\n",
    "    moe = 1.96 * pooledSD * sqrt((1./n1)+(1./n2))\n",
    "    return moe, pooledSD\n",
    "\n",
    "male_fed_air_V_male_fed_noair_CIs = []\n",
    "male_starved_air_V_male_starved_noair_CIs = []\n",
    "male_fed_noair_V_male_starved_noair_CIs = []\n",
    "male_fed_air_V_male_starved_air_CIs = []\n",
    "##female\n",
    "female_fed_noair_V_male_fed_noair_CIs = []                                                  \n",
    "female_fed_air_V_male_fed_air_CIs = []                                                   \n",
    "female_starved_noair_V_male_starved_noair_CIs = []\n",
    "female_starved_air_V_male_starved_air_CIs = []                                                   \n",
    "                                                    \n",
    "male_fed_air_V_male_fed_noair_pooledSD_list = []\n",
    "male_starved_air_V_male_starved_noair_pooledSD_list = []\n",
    "male_fed_noair_V_male_starved_noair_pooledSD_list = []\n",
    "male_fed_air_V_male_starved_air_pooledSD_list = []\n",
    "##female\n",
    "female_fed_noair_V_male_fed_noair_pooledSD_list = []                                                  \n",
    "female_fed_air_V_male_fed_air_pooledSD_list = []                                                   \n",
    "female_starved_noair_V_male_starved_noair_pooledSD_list = []\n",
    "female_starved_air_V_male_starved_air_pooledSD_list = []                                                     \n",
    "\n",
    "for row in range(deltadelta_df.shape[0]):\n",
    "    ## calculate pooled CIs if ES is not nan\n",
    "    if not np.isnan(deltadelta_df['male_fed_noair_ES'].iloc[row]):\n",
    "        # Wind\n",
    "        ## Calculate the STDs for each delta-effect size\n",
    "        male_fed_noair_std = calculateSTD(deltadelta_df['male_fed_noair_ES'].iloc[row], deltadelta_df['male_fed_noair_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['male_fed_noair_parent_n'].iloc[row],deltadelta_df['male_fed_noair_offspring_n'].iloc[row])\n",
    "\n",
    "        male_fed_air_std = calculateSTD(deltadelta_df['male_fed_air_ES'].iloc[row], deltadelta_df['male_fed_air_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['male_fed_air_parent_n'].iloc[row],deltadelta_df['male_fed_air_offspring_n'].iloc[row])\n",
    "\n",
    "        ## Get the pooled sample size for delta-effect size\n",
    "        male_fed_noair_pooled_N = (deltadelta_df['male_fed_noair_parent_n'].iloc[row] + deltadelta_df['male_fed_noair_offspring_n'].iloc[row])/2.\n",
    "        male_fed_air_pooled_N = (deltadelta_df['male_fed_air_parent_n'].iloc[row] + deltadelta_df['male_fed_air_offspring_n'].iloc[row])/2.\n",
    "\n",
    "        ## Calculate the pooled CIs\n",
    "\n",
    "        male_fed_air_V_male_fed_noair_moe, male_fed_air_V_male_fed_noair_pooledSD = calculatePooledMOE(male_fed_noair_std, male_fed_air_std, male_fed_noair_pooled_N, male_fed_air_pooled_N)\n",
    "        male_fed_air_V_male_fed_noair_CIs.append([deltadelta_df['male_fed_air_V_male_fed_noair_ES'].iloc[row] - male_fed_air_V_male_fed_noair_moe,\n",
    "                                                 deltadelta_df['male_fed_air_V_male_fed_noair_ES'].iloc[row] + male_fed_air_V_male_fed_noair_moe])\n",
    "\n",
    "        male_fed_air_V_male_fed_noair_pooledSD_list.append(male_fed_air_V_male_fed_noair_pooledSD)\n",
    "        \n",
    "        ## Calculate the STDs for each delta-effect size\n",
    "        male_starved_noair_std = calculateSTD(deltadelta_df['male_starved_noair_ES'][row], deltadelta_df['male_starved_noair_CIs'][row][1],\n",
    "                                         deltadelta_df['male_starved_noair_parent_n'][row],deltadelta_df['male_starved_noair_offspring_n'][row])\n",
    "\n",
    "        male_starved_air_std = calculateSTD(deltadelta_df['male_starved_air_ES'][row], deltadelta_df['male_starved_air_CIs'][row][1],\n",
    "                                         deltadelta_df['male_starved_air_parent_n'][row],deltadelta_df['male_starved_air_offspring_n'][row])\n",
    "\n",
    "        ## Get the pooled sample size for delta-effect size\n",
    "        male_starved_noair_pooled_N = (deltadelta_df['male_starved_noair_parent_n'][row] + deltadelta_df['male_starved_noair_offspring_n'][row])/2.\n",
    "        male_starved_air_pooled_N = (deltadelta_df['male_starved_air_parent_n'][row] + deltadelta_df['male_starved_air_offspring_n'][row])/2.\n",
    "\n",
    "        ## Calculate the pooled CIs\n",
    "        male_starved_air_V_male_starved_noair_moe, male_starved_air_V_male_starved_noair_pooledSD = calculatePooledMOE(male_starved_noair_std, male_starved_air_std, male_starved_noair_pooled_N, male_starved_air_pooled_N)\n",
    "        male_starved_air_V_male_starved_noair_CIs.append([deltadelta_df['male_starved_air_V_male_starved_noair_ES'][row] - male_starved_air_V_male_starved_noair_moe,\n",
    "                                                 deltadelta_df['male_starved_air_V_male_starved_noair_ES'][row] + male_starved_air_V_male_starved_noair_moe])\n",
    "        \n",
    "        male_starved_air_V_male_starved_noair_pooledSD_list.append(male_starved_air_V_male_starved_noair_pooledSD)\n",
    "        # Starvation\n",
    "        ## Calculate the STDs for each delta-effect size\n",
    "        male_fed_noair_std = calculateSTD(deltadelta_df['male_fed_noair_ES'][row], deltadelta_df['male_fed_noair_CIs'][row][1],\n",
    "                                         deltadelta_df['male_fed_noair_parent_n'][row],deltadelta_df['male_fed_noair_offspring_n'][row])\n",
    "\n",
    "        male_starved_noair_std = calculateSTD(deltadelta_df['male_starved_noair_ES'][row], deltadelta_df['male_starved_noair_CIs'][row][1],\n",
    "                                         deltadelta_df['male_starved_noair_parent_n'][row],deltadelta_df['male_starved_noair_offspring_n'][row])\n",
    "\n",
    "        ## Get the pooled sample size for delta-effect size\n",
    "        male_fed_noair_pooled_N = (deltadelta_df['male_fed_noair_parent_n'][row] + deltadelta_df['male_fed_noair_offspring_n'][row])/2.\n",
    "        male_starved_noair_pooled_N = (deltadelta_df['male_starved_noair_parent_n'][row] + deltadelta_df['male_starved_noair_offspring_n'][row])/2.\n",
    "\n",
    "        ## Calculate the pooled CIs\n",
    "        male_fed_noair_V_male_starved_noair_moe, male_fed_noair_V_male_starved_noair_pooledSD = calculatePooledMOE(male_fed_noair_std, male_starved_noair_std, male_fed_noair_pooled_N, male_starved_noair_pooled_N)\n",
    "        male_fed_noair_V_male_starved_noair_CIs.append([deltadelta_df['male_fed_noair_V_male_starved_noair_ES'][row] - male_fed_noair_V_male_starved_noair_moe,\n",
    "                                                 deltadelta_df['male_fed_noair_V_male_starved_noair_ES'][row] + male_fed_noair_V_male_starved_noair_moe])\n",
    "\n",
    "        male_fed_noair_V_male_starved_noair_pooledSD_list.append(male_fed_noair_V_male_starved_noair_pooledSD)\n",
    "        \n",
    "        ## Calculate the STDs for each delta-effect size\n",
    "        male_fed_air_std = calculateSTD(deltadelta_df['male_fed_air_ES'][row], deltadelta_df['male_fed_air_CIs'][row][1],\n",
    "                                         deltadelta_df['male_fed_air_parent_n'][row],deltadelta_df['male_fed_air_offspring_n'][row])\n",
    "\n",
    "        male_starved_air_std = calculateSTD(deltadelta_df['male_starved_air_ES'][row], deltadelta_df['male_starved_air_CIs'][row][1],\n",
    "                                         deltadelta_df['male_starved_air_parent_n'][row],deltadelta_df['male_starved_air_offspring_n'][row])\n",
    "\n",
    "        ## Get the pooled sample size for delta-effect size\n",
    "        male_fed_air_pooled_N = (deltadelta_df['male_fed_air_parent_n'][row] + deltadelta_df['male_fed_air_offspring_n'][row])/2.\n",
    "        male_starved_air_pooled_N = (deltadelta_df['male_starved_air_parent_n'][row] + deltadelta_df['male_starved_air_offspring_n'][row])/2.\n",
    "\n",
    "        ## Calculate the pooled CIs\n",
    "        male_fed_air_V_male_starved_air_moe, male_fed_air_V_male_starved_air_pooledSD = calculatePooledMOE(male_fed_air_std, male_starved_air_std, male_fed_air_pooled_N, male_starved_air_pooled_N)\n",
    "        male_fed_air_V_male_starved_air_CIs.append([deltadelta_df['male_fed_air_V_male_starved_air_ES'][row] - male_fed_air_V_male_starved_air_moe,\n",
    "                                                 deltadelta_df['male_fed_air_V_male_starved_air_ES'][row] + male_fed_air_V_male_starved_air_moe])\n",
    "        \n",
    "        male_fed_air_V_male_starved_air_pooledSD_list.append(male_fed_air_V_male_starved_air_pooledSD)\n",
    "    else:\n",
    "        male_fed_air_V_male_fed_noair_CIs.append(np.nan)\n",
    "        male_starved_air_V_male_starved_noair_CIs.append(np.nan)\n",
    "        male_fed_noair_V_male_starved_noair_CIs.append(np.nan)\n",
    "        male_fed_air_V_male_starved_air_CIs.append(np.nan) \n",
    "        \n",
    "        male_fed_air_V_male_fed_noair_pooledSD_list.append(np.nan)\n",
    "        male_starved_air_V_male_starved_noair_pooledSD_list.append(np.nan)\n",
    "        male_fed_noair_V_male_starved_noair_pooledSD_list.append(np.nan)\n",
    "        male_fed_air_V_male_starved_air_pooledSD_list.append(np.nan)\n",
    "    \n",
    "    ## female                                                \n",
    "    if not np.isnan(deltadelta_df['female_fed_noair_ES'].iloc[row]):\n",
    "        # Sex\n",
    "        ## Calculate the STDs for each delta-effect size\n",
    "        female_fed_noair_std = calculateSTD(deltadelta_df['female_fed_noair_ES'].iloc[row], deltadelta_df['female_fed_noair_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['female_fed_noair_parent_n'].iloc[row],deltadelta_df['female_fed_noair_offspring_n'].iloc[row])\n",
    "\n",
    "        male_fed_noair_std = calculateSTD(deltadelta_df['male_fed_noair_ES'].iloc[row], deltadelta_df['male_fed_noair_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['male_fed_noair_parent_n'].iloc[row],deltadelta_df['male_fed_noair_offspring_n'].iloc[row])\n",
    "\n",
    "        ## Get the pooled sample size for delta-effect size\n",
    "        female_fed_noair_pooled_N = (deltadelta_df['female_fed_noair_parent_n'].iloc[row] + deltadelta_df['female_fed_noair_offspring_n'].iloc[row])/2.\n",
    "        male_fed_noair_pooled_N = (deltadelta_df['male_fed_noair_parent_n'].iloc[row] + deltadelta_df['male_fed_noair_offspring_n'].iloc[row])/2.\n",
    "\n",
    "        ## Calculate the pooled CIs\n",
    "\n",
    "        female_fed_noair_V_male_fed_noair_moe, female_fed_noair_V_male_fed_noair_pooledSD = calculatePooledMOE(female_fed_noair_std, male_fed_noair_std, female_fed_noair_pooled_N, male_fed_noair_pooled_N)\n",
    "        female_fed_noair_V_male_fed_noair_CIs.append([deltadelta_df['female_fed_noair_V_male_fed_noair_ES'].iloc[row] - female_fed_noair_V_male_fed_noair_moe,\n",
    "                                                 deltadelta_df['female_fed_noair_V_male_fed_noair_ES'].iloc[row] + female_fed_noair_V_male_fed_noair_moe])\n",
    "\n",
    "        female_fed_noair_V_male_fed_noair_pooledSD_list.append(female_fed_noair_V_male_fed_noair_pooledSD)\n",
    "                                                    \n",
    "                                                    \n",
    "        ## Calculate the STDs for each delta-effect size\n",
    "        female_fed_air_std = calculateSTD(deltadelta_df['female_fed_air_ES'].iloc[row], deltadelta_df['female_fed_air_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['female_fed_air_parent_n'].iloc[row],deltadelta_df['female_fed_air_offspring_n'].iloc[row])\n",
    "\n",
    "        male_fed_air_std = calculateSTD(deltadelta_df['male_fed_air_ES'].iloc[row], deltadelta_df['male_fed_air_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['male_fed_air_parent_n'].iloc[row],deltadelta_df['male_fed_air_offspring_n'].iloc[row])\n",
    "\n",
    "        ## Get the pooled sample size for delta-effect size\n",
    "        female_fed_air_pooled_N = (deltadelta_df['female_fed_air_parent_n'].iloc[row] + deltadelta_df['female_fed_air_offspring_n'].iloc[row])/2.\n",
    "        male_fed_air_pooled_N = (deltadelta_df['male_fed_air_parent_n'].iloc[row] + deltadelta_df['male_fed_air_offspring_n'].iloc[row])/2.\n",
    "\n",
    "        ## Calculate the pooled CIs\n",
    "\n",
    "        female_fed_air_V_male_fed_air_moe, female_fed_air_V_male_fed_air_pooledSD = calculatePooledMOE(female_fed_air_std, male_fed_air_std, female_fed_air_pooled_N, male_fed_air_pooled_N)\n",
    "        female_fed_air_V_male_fed_air_CIs.append([deltadelta_df['female_fed_air_V_male_fed_air_ES'].iloc[row] - female_fed_air_V_male_fed_air_moe,\n",
    "                                                 deltadelta_df['female_fed_air_V_male_fed_air_ES'].iloc[row] + female_fed_air_V_male_fed_air_moe])\n",
    "\n",
    "        female_fed_air_V_male_fed_air_pooledSD_list.append(female_fed_air_V_male_fed_air_pooledSD)                                            \n",
    "                                                    \n",
    "        \n",
    "        ## Calculate the STDs for each delta-effect size\n",
    "        female_starved_noair_std = calculateSTD(deltadelta_df['female_starved_noair_ES'].iloc[row], deltadelta_df['female_starved_noair_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['female_starved_noair_parent_n'].iloc[row],deltadelta_df['female_starved_noair_offspring_n'].iloc[row])\n",
    "\n",
    "        male_starved_noair_std = calculateSTD(deltadelta_df['male_starved_noair_ES'].iloc[row], deltadelta_df['male_starved_noair_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['male_starved_noair_parent_n'].iloc[row],deltadelta_df['male_starved_noair_offspring_n'].iloc[row])\n",
    "\n",
    "        ## Get the pooled sample size for delta-effect size\n",
    "        female_starved_noair_pooled_N = (deltadelta_df['female_starved_noair_parent_n'].iloc[row] + deltadelta_df['female_starved_noair_offspring_n'].iloc[row])/2.\n",
    "        male_starved_noair_pooled_N = (deltadelta_df['male_starved_noair_parent_n'].iloc[row] + deltadelta_df['male_starved_noair_offspring_n'].iloc[row])/2.\n",
    "\n",
    "        ## Calculate the pooled CIs\n",
    "\n",
    "        female_starved_noair_V_male_starved_noair_moe, female_starved_noair_V_male_starved_noair_pooledSD = calculatePooledMOE(female_starved_noair_std, male_starved_noair_std, female_starved_noair_pooled_N, male_starved_noair_pooled_N)\n",
    "        female_starved_noair_V_male_starved_noair_CIs.append([deltadelta_df['female_starved_noair_V_male_starved_noair_ES'].iloc[row] - female_starved_noair_V_male_starved_noair_moe,\n",
    "                                                 deltadelta_df['female_starved_noair_V_male_starved_noair_ES'].iloc[row] + female_starved_noair_V_male_starved_noair_moe])\n",
    "\n",
    "        female_starved_noair_V_male_starved_noair_pooledSD_list.append(female_starved_noair_V_male_starved_noair_pooledSD)\n",
    "                                                    \n",
    "                                                    \n",
    "        ## Calculate the STDs for each delta-effect size\n",
    "        female_starved_air_std = calculateSTD(deltadelta_df['female_starved_air_ES'].iloc[row], deltadelta_df['female_starved_air_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['female_starved_air_parent_n'].iloc[row],deltadelta_df['female_starved_air_offspring_n'].iloc[row])\n",
    "\n",
    "        male_starved_air_std = calculateSTD(deltadelta_df['male_starved_air_ES'].iloc[row], deltadelta_df['male_starved_air_CIs'].iloc[row][1],\n",
    "                                         deltadelta_df['male_starved_air_parent_n'].iloc[row],deltadelta_df['male_starved_air_offspring_n'].iloc[row])\n",
    "\n",
    "        ## Get the pooled sample size for delta-effect size\n",
    "        female_starved_air_pooled_N = (deltadelta_df['female_starved_air_parent_n'].iloc[row] + deltadelta_df['female_starved_air_offspring_n'].iloc[row])/2.\n",
    "        male_starved_air_pooled_N = (deltadelta_df['male_starved_air_parent_n'].iloc[row] + deltadelta_df['male_starved_air_offspring_n'].iloc[row])/2.\n",
    "\n",
    "        ## Calculate the pooled CIs\n",
    "\n",
    "        female_starved_air_V_male_starved_air_moe, female_starved_air_V_male_starved_air_pooledSD = calculatePooledMOE(female_starved_air_std, male_starved_air_std, female_starved_air_pooled_N, male_starved_air_pooled_N)\n",
    "        female_starved_air_V_male_starved_air_CIs.append([deltadelta_df['female_starved_air_V_male_starved_air_ES'].iloc[row] - female_starved_air_V_male_starved_air_moe,\n",
    "                                                 deltadelta_df['female_starved_air_V_male_starved_air_ES'].iloc[row] + female_starved_air_V_male_starved_air_moe])\n",
    "\n",
    "        female_starved_air_V_male_starved_air_pooledSD_list.append(female_starved_air_V_male_starved_air_pooledSD)                                            \n",
    "                                                    \n",
    "    else:\n",
    "        female_fed_noair_V_male_fed_noair_CIs.append(np.nan)\n",
    "        female_starved_noair_V_male_starved_noair_CIs.append(np.nan)\n",
    "        female_starved_air_V_male_starved_air_CIs.append(np.nan)\n",
    "        female_fed_air_V_male_fed_air_CIs.append(np.nan)\n",
    "        \n",
    "        female_fed_noair_V_male_fed_noair_pooledSD_list.append(np.nan)\n",
    "        female_starved_noair_V_male_starved_noair_pooledSD_list.append(np.nan)\n",
    "        female_starved_air_V_male_starved_air_pooledSD_list.append(np.nan)\n",
    "        female_fed_air_V_male_fed_air_pooledSD_list.append(np.nan)\n",
    "         \n",
    "deltadelta_df['male_fed_air_V_male_fed_noair_CIs'] = male_fed_air_V_male_fed_noair_CIs\n",
    "deltadelta_df['male_starved_air_V_male_starved_noair_CIs'] = male_starved_air_V_male_starved_noair_CIs\n",
    "deltadelta_df['male_fed_noair_V_male_starved_noair_CIs'] = male_fed_noair_V_male_starved_noair_CIs\n",
    "deltadelta_df['male_fed_air_V_male_starved_air_CIs'] = male_fed_air_V_male_starved_air_CIs    \n",
    "\n",
    "##female\n",
    "deltadelta_df['female_fed_noair_V_male_fed_noair_CIs'] = female_fed_noair_V_male_fed_noair_CIs\n",
    "deltadelta_df['female_starved_noair_V_male_starved_noair_CIs'] = female_starved_noair_V_male_starved_noair_CIs\n",
    "deltadelta_df['female_starved_air_V_male_starved_air_CIs'] = female_starved_air_V_male_starved_air_CIs\n",
    "deltadelta_df['female_fed_air_V_male_fed_air_CIs'] = female_fed_air_V_male_fed_air_CIs                                                    \n",
    "                                                    \n",
    "deltadelta_df['male_fed_air_V_male_fed_noair_pooledSD'] = male_fed_air_V_male_fed_noair_pooledSD_list\n",
    "deltadelta_df['male_starved_air_V_male_starved_noair_pooledSD'] = male_starved_air_V_male_starved_noair_pooledSD_list\n",
    "deltadelta_df['male_fed_noair_V_male_starved_noair_pooledSD'] = male_fed_noair_V_male_starved_noair_pooledSD_list\n",
    "deltadelta_df['male_fed_air_V_male_starved_air_pooledSD'] = male_fed_air_V_male_starved_air_pooledSD_list\n",
    "                                                    \n",
    "##female\n",
    "deltadelta_df['female_fed_noair_V_male_fed_noair_pooledSD'] = female_fed_noair_V_male_fed_noair_pooledSD_list\n",
    "deltadelta_df['female_starved_noair_V_male_starved_noair_pooledSD'] = female_starved_noair_V_male_starved_noair_pooledSD_list\n",
    "deltadelta_df['female_starved_air_V_male_starved_air_pooledSD'] = female_starved_air_V_male_starved_air_pooledSD_list\n",
    "deltadelta_df['female_fed_air_V_male_fed_air_pooledSD'] = female_fed_air_V_male_fed_air_pooledSD_list                                                     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "deltadelta_df.to_csv('C:/Users/tumkayat/Desktop/WALISAR_Paper/Figure3/AllDeltaDeltadeltadelta_ES_ALL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [-0.19681566134726183, 0.10486238066866185]\n",
       "1       [-0.011026503561477843, 0.23367513589547784]\n",
       "2        [-0.19770577477590703, 0.07148832593190704]\n",
       "3         [-0.2163248515918906, 0.16828656115509058]\n",
       "4        [-0.4057190036658481, -0.03661366217575193]\n",
       "5           [-0.459851115399711, -0.098631234306649]\n",
       "6         [-0.5471444995470578, -0.1924194311689422]\n",
       "7      [-0.25733118022407464, -0.032448249857925604]\n",
       "8        [-0.05862096932224131, 0.16524575784224133]\n",
       "9        [0.016897478378994746, 0.37278959460260525]\n",
       "10       [-0.09899361647645682, 0.21601534792245686]\n",
       "11         [-0.3450234558298718, 0.0237526388522718]\n",
       "12       [-0.3586353863045341, 0.002393016344934107]\n",
       "13       [-0.18199762434528347, 0.18935961201588344]\n",
       "14        [-0.09217903247768451, 0.2613750354356845]\n",
       "15       [-0.3576820357391254, 0.011043548335325365]\n",
       "16        [-0.2014814161763125, 0.06379560642985249]\n",
       "17       [-0.2948812886362966, -0.02627399536990338]\n",
       "18       [-0.024578000792010127, 0.2069103860434101]\n",
       "19       [-0.16271195190275026, 0.06191278617455026]\n",
       "20       [-0.11437647803793982, 0.06211321622633983]\n",
       "21     [-0.23108494405494834, -0.013080726377451654]\n",
       "22       [-0.02789319325323554, 0.18938260481495556]\n",
       "23      [-0.16028148691452682, 0.037476540937926836]\n",
       "24        [-0.18121284509118388, 0.0948929571951839]\n",
       "25       [-0.19072574856956104, 0.03161677857356103]\n",
       "26       [-0.02440605308574574, 0.21020982583772574]\n",
       "27         [0.08184173502141029, 0.3511963987205897]\n",
       "28         [-0.0946796795445535, 0.1457882033093335]\n",
       "29       [-0.3365088905002977, -0.10528119769970228]\n",
       "                           ...                      \n",
       "134      [-0.009456156425718037, 0.3213728394031581]\n",
       "135        [-0.136533232607427, 0.13238514561342696]\n",
       "136      [-0.18164525450131772, 0.09381294167291773]\n",
       "137      [-0.21917007872411776, 0.09344743651163777]\n",
       "138      [-0.05217075067619488, 0.18545278162335488]\n",
       "139      [-0.07479083232208925, 0.14809168488708924]\n",
       "140     [-0.020814338575845248, 0.20567693629984526]\n",
       "141       [-0.13453906880973115, 0.1092315240997312]\n",
       "142       [-0.335749411258193, -0.04340608134120699]\n",
       "143      [-0.40310095277748514, -0.1067297776925149]\n",
       "144       [-0.2055919615668047, 0.03702357788280469]\n",
       "145      [-0.15914205484399435, 0.11297583282899437]\n",
       "146      [-0.12263196593116031, 0.09707659711178031]\n",
       "147      [-0.01095410172878171, 0.23103820930978164]\n",
       "148      [0.004718544064311048, 0.21747184024868893]\n",
       "149       [-0.03277299140098722, 0.2909173752591072]\n",
       "150      [-0.047238757285086364, 0.2650990452584864]\n",
       "151       [-0.4652390198749956, -0.2194797384070044]\n",
       "152      [-0.11970101466094112, 0.18784089218394112]\n",
       "153     [-0.041629857305201856, 0.21569854514860187]\n",
       "154      [-0.3167233657586256, -0.05294670222077444]\n",
       "155      [-0.05442738618896743, 0.22964599307596745]\n",
       "156     [-0.008855020738014183, 0.25424680018641416]\n",
       "157      [-0.12081162613692197, 0.13643382502312196]\n",
       "158      [-0.11862547949424658, 0.26336809587704657]\n",
       "159      [-0.24897520463420597, 0.16363097965148599]\n",
       "160       [-0.18067011228729232, 0.1591614834192923]\n",
       "161      [-0.04552859710667906, 0.23644612911887908]\n",
       "162        [0.05315125319390779, 0.2942910440960923]\n",
       "163         [0.0868172126576189, 0.3127643767243812]\n",
       "Name: male_fed_air_V_male_fed_noair_CIs, Length: 164, dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltadelta_df['male_fed_air_V_male_fed_noair_CIs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "deltadelta_df.to_pickle('C:/Users/tumkayat/Desktop/deltadelta_ES_ALL.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Last version: END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Add CIs for delta-delta CI calcuations\n",
    "# male_fed_noair_CI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]['CIs']\n",
    "# male_fed_air_CI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')]['CIs']\n",
    "# male_starved_noair_CI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir')]['CIs']\n",
    "# male_starved_air_CI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air')]['CIs']\n",
    "\n",
    "male_fed_noair_CI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')]['CIs']\n",
    "male_fed_air_CI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')]['CIs']\n",
    "male_starved_noair_CI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir')]['CIs']\n",
    "male_starved_air_CI = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_Air')]['CIs']\n",
    "\n",
    "temp = {'ORNs':male_fed_NoAir_labels.values, 'LightInt':male_fed_NoAir_int.values, 'male_fed_noair':male_fed_NoAir_ES.values,'male_fed_noair_CI':male_fed_noair_CI.values,\n",
    "       'male_fed_air':male_fed_Air_ES.values,'male_fed_air_CI':male_fed_air_CI.values,'male_starved_noair':male_starved_NoAir_ES.values,'male_starved_noair_CI':male_starved_noair_CI.values,\n",
    "        'male_starved_air':male_starved_Air_ES.values,'male_starved_air_CI':male_starved_air_CI.values}\n",
    "data = pd.DataFrame.from_dict(temp)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Starvation effect\n",
    "data['delta_male_fed_Noair_V_male_starved_Noair'] = data['male_starved_noair'] - data['male_fed_noair']\n",
    "data['delta_male_fed_air_V_male_starved_air'] = data['male_starved_air'] - data['male_fed_air']\n",
    "\n",
    "## Wind effect\n",
    "data['delta_male_fed_Noair_V_male_fed_air'] = data['male_fed_air'] - data['male_fed_noair']\n",
    "data['delta_male_starved_Noair_V_male_starved_air'] = data['male_starved_air'] - data['male_starved_noair']\n",
    "\n",
    "## Both parameters changed\n",
    "data['delta_male_fed_Noair_V_male_starved_air'] = data['male_fed_noair'] - data['male_starved_air']\n",
    "data['delta_male_fed_air_V_male_starved_Noair'] = data['male_fed_air'] - data['male_starved_noair']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def bootstrapDiffDist(x,y,n=10):\n",
    "    diff_matrix = []\n",
    "\n",
    "    for i in range(n):\n",
    "        scrambled_x = []\n",
    "        scrambled_y = []\n",
    "        temp = []\n",
    "        for j in range(len(x)):\n",
    "            rand_id_x = random.randint(0,len(x)-1)\n",
    "            rand_id_y = random.randint(0,len(y)-1)\n",
    "\n",
    "            rand_item_x = x[rand_id_x]\n",
    "            rand_item_y = y[rand_id_y]\n",
    "\n",
    "            scrambled_x.append(rand_item_x)\n",
    "            scrambled_y.append(rand_item_y)\n",
    "\n",
    "        for k in range(len(scrambled_x)):\n",
    "            d = scrambled_x[k] - scrambled_y[k]\n",
    "            temp.append(d)\n",
    "\n",
    "        diff_matrix.append(temp)\n",
    "\n",
    "    diff_dist = np.mean(np.asarray(diff_matrix),axis=0)\n",
    "#     print diff_dist\n",
    "    return diff_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "condition = 'delta_male_fed_air_V_male_starved_air'\n",
    "x = data[condition]          \n",
    " \n",
    "bootstrapped_diff = bootstrapDiffDist(data['male_starved_air'], data['male_fed_air'])\n",
    "controlDist = bootstrapped_diff\n",
    "\n",
    "# bootstrapped_diff = dabest.bootstrap_tools.bootstrap(data['male_fed_air'] , data['male_starved_noair'])\n",
    "# controlDist = bootstrapped_diff.stat_array\n",
    "lowerTail_df = data[data[condition] < np.sort(bootstrapped_diff)[0]]\n",
    "upperTail_df = data[data[condition] > np.sort(bootstrapped_diff)[-1]]\n",
    "\n",
    "fname = condition\n",
    "\n",
    "p = sns.distplot(controlDist, rug=False, hist=False, color='black')\n",
    "p = sns.distplot(x, rug=True,label='Real data')\n",
    "plt.axvline(np.sort(bootstrapped_diff)[0],ymax=1,color = 'red')\n",
    "plt.axvline(np.sort(bootstrapped_diff)[-1],ymax=1,color = 'red')\n",
    "from matplotlib.lines import Line2D\n",
    "custom_lines = [Line2D([0], [0], color = sns.dark_palette(\"black\",n_colors=2)[1], marker = ' ', lw=2),\n",
    "                Line2D([0], [0], color = sns.dark_palette(\"blue\",n_colors=2)[1], marker = ' ', lw=2),\n",
    "                Line2D([0], [0], color = sns.dark_palette(\"red\",n_colors=2)[1], marker = ' ', lw=2)]\n",
    "\n",
    "plt.legend(custom_lines, ['Bootstrap dist.', 'Real data', 'Min-Max \\nBootstrapped values'])\n",
    "\n",
    "# p.set_ylim(0,30)\n",
    "sns.set(style=\"ticks\")\n",
    "sns.despine(trim=True)\n",
    "plt.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/Histograms/DiffBootstrap/DistComparisons/\" +  fname + '_withVLines.pdf',dpi=1000,bbox_inches='tight')                                    \n",
    "lowerTail_df.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/Histograms/DiffBootstrap/DistComparisons/\" + fname + '_lowerTail.csv')\n",
    "upperTail_df.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/Histograms/DiffBootstrap/DistComparisons/\"  + fname + '_upperTail.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data[data['delta_male_fed_air_V_male_starved_Noair']<-0.17]['delta_male_fed_air_V_male_starved_Noair']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data['delta_male_fed_Noair_V_male_starved_Noair']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting delta-delta forest plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reverse-engineer the STDs of delta ES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from math import *\n",
    "def calculateSTD(mean,CI_ub):\n",
    "    moe = abs(CI_ub - mean)\n",
    "    std_and_n = moe / 1.96\n",
    "    std = std_and_n / sqrt((1./104)+(1./52))\n",
    "    return std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "calculateSTD(0.089400077, 0.222700163)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Calculate std for the df\n",
    "\n",
    "male_fed_air_std = []\n",
    "male_fed_noair_std = []\n",
    "male_starved_air_std = []\n",
    "male_starved_noair_std = []\n",
    "\n",
    "for row in range(data.shape[0]):\n",
    "    \n",
    "    CI_ub = data.iloc[row]['male_fed_air_CI'][1]\n",
    "    mean = data.iloc[row]['male_fed_air']\n",
    "    male_fed_air_std.append(calculateSTD(mean,CI_ub))\n",
    "    \n",
    "    CI_ub = data.iloc[row]['male_fed_noair_CI'][1]\n",
    "    mean = data.iloc[row]['male_fed_noair']\n",
    "    male_fed_noair_std.append(calculateSTD(mean,CI_ub))\n",
    "    \n",
    "    CI_ub = data.iloc[row]['male_starved_air_CI'][1]\n",
    "    mean = data.iloc[row]['male_starved_air']\n",
    "    male_starved_air_std.append(calculateSTD(mean,CI_ub))\n",
    "    \n",
    "    CI_ub = data.iloc[row]['male_starved_noair_CI'][1]\n",
    "    mean = data.iloc[row]['male_starved_noair']\n",
    "    male_starved_noair_std.append(calculateSTD(mean,CI_ub))\n",
    "\n",
    "data['male_fed_air_std'] = male_fed_air_std\n",
    "data['male_fed_noair_std'] = male_fed_noair_std\n",
    "data['male_starved_air_std'] = male_starved_air_std\n",
    "data['male_starved_noair_std'] = male_starved_noair_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Calculate CIs for delta-deltas\n",
    "def calculatePooledMOE(std1,std2,n1=52,n2=52):\n",
    "    pooledSD = np.sqrt(((n1-1)*(std1**2)+(n2-1)*(std2**2))/(n1+n2-2))\n",
    "    moe = 1.96 * pooledSD * np.sqrt((1./n1)+(1./n2))\n",
    "    return moe, pooledSD "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "delta_male_fed_Noair_V_male_fed_air_CI = []\n",
    "delta_male_starved_Noair_V_male_starved_air_CI = []\n",
    "\n",
    "delta_male_fed_Noair_V_male_starved_Noair_CI = []\n",
    "delta_male_fed_air_V_male_starved_air_CI = []\n",
    "\n",
    "for row in range(data.shape[0]):\n",
    "    moe = calculatePooledMOE(data['male_fed_noair_std'][row],data['male_fed_air_std'][row])\n",
    "    dd_mean = data['delta_male_fed_Noair_V_male_fed_air'][row]\n",
    "    delta_male_fed_Noair_V_male_fed_air_CI.append([dd_mean-moe,dd_mean+moe])\n",
    "    \n",
    "    moe = calculatePooledMOE(data['male_starved_noair_std'][row],data['male_starved_air_std'][row])\n",
    "    dd_mean = data['delta_male_starved_Noair_V_male_starved_air'][row]\n",
    "    delta_male_starved_Noair_V_male_starved_air_CI.append([dd_mean-moe,dd_mean+moe])\n",
    "    \n",
    "    moe = calculatePooledMOE(data['male_fed_noair_std'][row],data['male_starved_noair_std'][row])\n",
    "    dd_mean = data['delta_male_fed_Noair_V_male_starved_Noair'][row]\n",
    "    delta_male_fed_Noair_V_male_starved_Noair_CI.append([dd_mean-moe,dd_mean+moe])\n",
    "    \n",
    "    moe = calculatePooledMOE(data['male_fed_air_std'][row],data['male_starved_air_std'][row])\n",
    "    dd_mean = data['delta_male_fed_air_V_male_starved_air'][row]\n",
    "    delta_male_fed_air_V_male_starved_air_CI.append([dd_mean-moe,dd_mean+moe])\n",
    "\n",
    "data['delta_male_fed_Noair_V_male_fed_air_CI'] = delta_male_fed_Noair_V_male_fed_air_CI\n",
    "data['delta_male_starved_Noair_V_male_starved_air_CI'] = delta_male_starved_Noair_V_male_starved_air_CI\n",
    "data['delta_male_fed_Noair_V_male_starved_Noair_CI'] = delta_male_fed_Noair_V_male_starved_Noair_CI\n",
    "data['delta_male_fed_air_V_male_starved_air_CI'] = delta_male_fed_air_V_male_starved_air_CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def WALiForest(df, column, sort = None, sorting_order = None, fsize= (5,25), gap_thru_yaxis = 5, offset = .8, s =30, c ='red', xlim = (-0.8, 0.8)):\n",
    "   \n",
    "    ## If none, will use the first df's order\n",
    "    fig = plt.figure(figsize=fsize)\n",
    "    ax1 = fig.add_subplot(111)\n",
    "#     plt.title(column,fontsize = 15)\n",
    "\n",
    "    style='ticks'\n",
    "    context='notebook'\n",
    "    font='Arial'\n",
    "    colorPalette = 'muted'\n",
    "    sns.set(style=style,context =context,font=font)\n",
    "    s = s\n",
    "    gap_thru_yaxis = gap_thru_yaxis\n",
    "    offset = offset\n",
    "    i = 0\n",
    "    ## Drop the nans\n",
    "    df = df.dropna(subset=[column])\n",
    "    \n",
    "    if sort == True:\n",
    "        \n",
    "        if not sorting_order == None:\n",
    "            df_OI = df[df['ORNs'].apply(lambda x: x in sorting_order) & \\\n",
    "                     (((df['Sex-Satiety-Air']) == \"male_fed_NoAir\") | (df['Sex-Satiety-Air'] == \"male_fed_NoWind\"))]\n",
    "            \n",
    "            df_sorted = df_OI[df_OI['LightIntensity'] == '70uW'].sort_values(by=[column], ascending=False)\n",
    "            \n",
    "#         df_sorted = df.sort_values(by=[column],ascending=False)\n",
    "\n",
    "            ORNList_sorted = df_sorted['ORNs']\n",
    "            keep_the_ORN_Order = ORNList_sorted\n",
    "        \n",
    "        else:\n",
    "            df_sorted = df[df['LightIntensity'] == '70uW'].sort_values(by=[column], ascending=False)\n",
    "            \n",
    "            ORNList_sorted = df_sorted['ORNs']\n",
    "            keep_the_ORN_Order = ORNList_sorted\n",
    "    \n",
    "    else:\n",
    "        ORNList_sorted = sorting_order\n",
    "        keep_the_ORN_Order = ORNList_sorted\n",
    "\n",
    "    y_val = np.arange(1, len(ORNList_sorted)*gap_thru_yaxis, gap_thru_yaxis)\n",
    "\n",
    "    for OR in ORNList_sorted:\n",
    "        ## This version is for combos\n",
    "#         OR_df = df[(df['ORNs'] == OR) & ((df['Sex-Satiety-Air'] == \"male_fed_NoAir\") | (df['Sex-Satiety-Air'] == \"male_fed_NoWind\"))]\n",
    "        \n",
    "        ## updated here\n",
    "        OR_df = df[(df['ORNs'] == OR)]\n",
    "        \n",
    "        y = y_val[i] + offset\n",
    "\n",
    "        i = i+1\n",
    "\n",
    "        for row in range(len(OR_df)):\n",
    "            ES = OR_df.iloc[row][column]\n",
    "#             CIs = OR_df.iloc[row][column[:-3] + '_CIs']\n",
    "            CIs = OR_df.iloc[row]['CIs']\n",
    "#             MOE = OR_df.iloc[row]['Pooled MOE']\n",
    "#             CIs = [ES-MOE, ES+MOE]\n",
    "            intensity = OR_df.iloc[row]['LightIntensity']\n",
    "            \n",
    "            if intensity == '14uW':\n",
    "                color = sns.dark_palette(c,n_colors=3)[2]\n",
    "\n",
    "            elif intensity == '42uW':\n",
    "                color = sns.dark_palette(c,n_colors=3)[1]\n",
    "\n",
    "            elif intensity == '70uW':\n",
    "                color = sns.dark_palette(c,n_colors=3)[0]\n",
    "            \n",
    "            else:\n",
    "                color = sns.dark_palette(c,n_colors=3)[1]\n",
    "                \n",
    "            ax1.scatter(ES, y, color = color, s = s)\n",
    "\n",
    "            ax1.plot([float(CIs[0]), float(CIs[1])], [y,y], color = color, lw=.5)\n",
    "            y = y - offset\n",
    "\n",
    "    sns.despine(ax=ax1)\n",
    "    ax1.tick_params(left=True,top=False, bottom=False, right=False)    \n",
    "\n",
    "    ax1.set_xlim(xlim[0], xlim[1])\n",
    "    ax1.set_ylim(-1, y_val[-1] + gap_thru_yaxis)\n",
    "    ax1.set_yticks(y_val)  ## sets the y ticks\n",
    "    ax1.set_yticklabels(ORNList_sorted, fontsize=12)  ## sets the x ticks' labels\n",
    "    plt.axvline(0,color='k',lw='0.4',ls='-')\n",
    "    # ax1.set_ylabel('ORNs')\n",
    "    ax1.set_xlabel('wTSALE [95%CI]',fontsize=12)\n",
    "#     plt.xticks([-0.8, -0.4, 0, 0.4, 0.8], ('-0.8', '-0.4', '0', '0.4', '0.8'), fontsize=12)\n",
    "\n",
    "    from matplotlib.lines import Line2D\n",
    "    custom_lines = [Line2D([0], [0], color = sns.dark_palette(c,n_colors=3)[2], marker = 'o', lw=0),\n",
    "                    Line2D([0], [0], color = sns.dark_palette(c,n_colors=3)[1], marker = 'o', lw=0),\n",
    "                    Line2D([0], [0], color = sns.dark_palette(c,n_colors=3)[0], marker = 'o', lw=0)]\n",
    "\n",
    "    ax1.legend(custom_lines, ['14uW', '42uW', '70uW'])\n",
    "#     ax1.legend(custom_lines, ['Merged intensities'])\n",
    "\n",
    "    return fig, keep_the_ORN_Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mtry_read\u001b[1;34m(path, encoding)\u001b[0m\n\u001b[0;32m    165\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mread_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpkl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# noqa: E722\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mread_wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    145\u001b[0m                             \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                             is_text=False)\n\u001b[0m\u001b[0;32m    147\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m# Python 3 and binary mode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mtry_read\u001b[1;34m(path, encoding)\u001b[0m\n\u001b[0;32m    172\u001b[0m                 return read_wrapper(\n\u001b[1;32m--> 173\u001b[1;33m                     lambda f: pc.load(f, encoding=encoding, compat=False))\n\u001b[0m\u001b[0;32m    174\u001b[0m             \u001b[1;31m# compat pickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mread_wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    145\u001b[0m                             \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                             is_text=False)\n\u001b[0m\u001b[0;32m    147\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m# Python 3 and binary mode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mread_pickle\u001b[1;34m(path, compression)\u001b[0m\n\u001b[0;32m    178\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 179\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mtry_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    180\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# noqa: E722\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mtry_read\u001b[1;34m(path, encoding)\u001b[0m\n\u001b[0;32m    176\u001b[0m                 return read_wrapper(\n\u001b[1;32m--> 177\u001b[1;33m                     lambda f: pc.load(f, encoding=encoding, compat=True))\n\u001b[0m\u001b[0;32m    178\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mread_wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    145\u001b[0m                             \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                             is_text=False)\n\u001b[0m\u001b[0;32m    147\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m# Python 3 and binary mode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mtry_read\u001b[1;34m(path, encoding)\u001b[0m\n\u001b[0;32m    165\u001b[0m                 \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"ignore\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mread_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpkl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    167\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# noqa: E722\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mread_wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    145\u001b[0m                             \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                             is_text=False)\n\u001b[0m\u001b[0;32m    147\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m# Python 3 and binary mode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mtry_read\u001b[1;34m(path, encoding)\u001b[0m\n\u001b[0;32m    172\u001b[0m                 return read_wrapper(\n\u001b[1;32m--> 173\u001b[1;33m                     lambda f: pc.load(f, encoding=encoding, compat=False))\n\u001b[0m\u001b[0;32m    174\u001b[0m             \u001b[1;31m# compat pickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mread_wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    145\u001b[0m                             \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                             is_text=False)\n\u001b[0m\u001b[0;32m    147\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m# Python 3 and binary mode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-38d5a24bfe22>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# All_ORN_EffectSizes_df = pd.read_csv(\"C:/Users/tumkayat/Desktop/All_wTSALE_values.csv\",dtype={'CIs': object})\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mAll_ORN_EffectSizes_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_pickle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mread_pickle\u001b[1;34m(path, compression)\u001b[0m\n\u001b[0;32m    180\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# noqa: E722\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    181\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mPY3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 182\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mtry_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'latin1'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    183\u001b[0m         \u001b[1;32mraise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mtry_read\u001b[1;34m(path, encoding)\u001b[0m\n\u001b[0;32m    175\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# noqa: E722\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m                 return read_wrapper(\n\u001b[1;32m--> 177\u001b[1;33m                     lambda f: pc.load(f, encoding=encoding, compat=True))\n\u001b[0m\u001b[0;32m    178\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    179\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mtry_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\pickle.py\u001b[0m in \u001b[0;36mread_wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    144\u001b[0m         f, fh = _get_handle(path, 'rb',\n\u001b[0;32m    145\u001b[0m                             \u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m                             is_text=False)\n\u001b[0m\u001b[0;32m    147\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    148\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text)\u001b[0m\n\u001b[0;32m    428\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    429\u001b[0m             \u001b[1;31m# Python 3 and binary mode\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    431\u001b[0m         \u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl'"
     ]
    }
   ],
   "source": [
    "# All_ORN_EffectSizes_df = pd.read_csv(\"C:/Users/tumkayat/Desktop/All_wTSALE_values.csv\",dtype={'CIs': object})\n",
    "All_ORN_EffectSizes_df = pd.read_pickle(\"C:/_BackUp_20190829/Desktop/ORScreening/All_merged_intensity_wTSALE/All_wTSALE_values.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male_fed_Air', 'male_fed_NoAir', 'male_starved_Air',\n",
       "       'male_starved_NoAir', 'male_fed_NoWind', 'male_fed_Wind',\n",
       "       'male_starved_NoWind', 'male_starved_Wind', 'female_fed_Air',\n",
       "       'female_fed_NoAir', 'female_starved_Air', 'female_starved_NoAir'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_ORN_EffectSizes_df['Sex-Satiety-Air'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "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>ORNs</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>ES</th>\n",
       "      <th>CIs</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
       "      <th>offspring_n</th>\n",
       "      <th>offspring_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.096322</td>\n",
       "      <td>[-0.271783601028, 0.09682792519719999]</td>\n",
       "      <td>0.268726</td>\n",
       "      <td>104</td>\n",
       "      <td>0.435779</td>\n",
       "      <td>52</td>\n",
       "      <td>0.565826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.124929</td>\n",
       "      <td>[-0.256574689051, 0.0126161731179]</td>\n",
       "      <td>0.060864</td>\n",
       "      <td>104</td>\n",
       "      <td>0.364928</td>\n",
       "      <td>52</td>\n",
       "      <td>0.411518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.158710</td>\n",
       "      <td>[0.0415470553644, 0.27722062437]</td>\n",
       "      <td>0.007930</td>\n",
       "      <td>104</td>\n",
       "      <td>0.328501</td>\n",
       "      <td>52</td>\n",
       "      <td>0.369246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.035585</td>\n",
       "      <td>[-0.13198194316099998, 0.20892616016499999]</td>\n",
       "      <td>0.701844</td>\n",
       "      <td>104</td>\n",
       "      <td>0.529201</td>\n",
       "      <td>52</td>\n",
       "      <td>0.478036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.096420</td>\n",
       "      <td>[-0.256201938573, 0.06208356356769999]</td>\n",
       "      <td>0.254923</td>\n",
       "      <td>104</td>\n",
       "      <td>0.498711</td>\n",
       "      <td>52</td>\n",
       "      <td>0.427993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.344887</td>\n",
       "      <td>[0.202494592022, 0.485609587263]</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>104</td>\n",
       "      <td>0.454192</td>\n",
       "      <td>52</td>\n",
       "      <td>0.408579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.089420</td>\n",
       "      <td>[-0.06017828182440001, 0.232062442357]</td>\n",
       "      <td>0.200331</td>\n",
       "      <td>142</td>\n",
       "      <td>0.413527</td>\n",
       "      <td>104</td>\n",
       "      <td>0.586734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.215146</td>\n",
       "      <td>[0.088887320757, 0.34375344746799996]</td>\n",
       "      <td>0.001494</td>\n",
       "      <td>142</td>\n",
       "      <td>0.474608</td>\n",
       "      <td>104</td>\n",
       "      <td>0.436235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.186897</td>\n",
       "      <td>[0.07217431420960001, 0.303050983258]</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>142</td>\n",
       "      <td>0.424406</td>\n",
       "      <td>104</td>\n",
       "      <td>0.431311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.226712</td>\n",
       "      <td>[0.043806233414199994, 0.411971434572]</td>\n",
       "      <td>0.013032</td>\n",
       "      <td>102</td>\n",
       "      <td>0.474317</td>\n",
       "      <td>52</td>\n",
       "      <td>0.556290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.000818</td>\n",
       "      <td>[-0.173114978482, 0.16581644655]</td>\n",
       "      <td>0.992489</td>\n",
       "      <td>102</td>\n",
       "      <td>0.460662</td>\n",
       "      <td>52</td>\n",
       "      <td>0.505056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.097697</td>\n",
       "      <td>[-0.0503067634203, 0.240789145147]</td>\n",
       "      <td>0.182080</td>\n",
       "      <td>102</td>\n",
       "      <td>0.403493</td>\n",
       "      <td>52</td>\n",
       "      <td>0.448870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>Orco</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.113971</td>\n",
       "      <td>[-0.296534335059, 0.07022502350910001]</td>\n",
       "      <td>0.238610</td>\n",
       "      <td>99</td>\n",
       "      <td>0.486033</td>\n",
       "      <td>52</td>\n",
       "      <td>0.513117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Orco</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.050173</td>\n",
       "      <td>[-0.263759181265, 0.16309462044]</td>\n",
       "      <td>0.625365</td>\n",
       "      <td>99</td>\n",
       "      <td>0.514771</td>\n",
       "      <td>52</td>\n",
       "      <td>0.655804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>Orco</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.039523</td>\n",
       "      <td>[-0.217875799759, 0.141737093061]</td>\n",
       "      <td>0.656992</td>\n",
       "      <td>99</td>\n",
       "      <td>0.454869</td>\n",
       "      <td>52</td>\n",
       "      <td>0.507689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORNs LightIntensity   Sex-Satiety-Air        ES  \\\n",
       "255  Or47b           14uW  female_fed_NoAir -0.096322   \n",
       "256  Or47b           42uW  female_fed_NoAir -0.124929   \n",
       "257  Or47b           70uW  female_fed_NoAir  0.158710   \n",
       "335  Or65a           14uW  female_fed_NoAir  0.035585   \n",
       "336  Or65a           42uW  female_fed_NoAir -0.096420   \n",
       "337  Or65a           70uW  female_fed_NoAir  0.344887   \n",
       "419  Or67d           14uW  female_fed_NoAir  0.089420   \n",
       "420  Or67d           42uW  female_fed_NoAir  0.215146   \n",
       "421  Or67d           70uW  female_fed_NoAir  0.186897   \n",
       "587  Or88a           14uW  female_fed_NoAir  0.226712   \n",
       "588  Or88a           42uW  female_fed_NoAir -0.000818   \n",
       "589  Or88a           70uW  female_fed_NoAir  0.097697   \n",
       "647   Orco           14uW  female_fed_NoAir -0.113971   \n",
       "648   Orco           42uW  female_fed_NoAir -0.050173   \n",
       "649   Orco           70uW  female_fed_NoAir -0.039523   \n",
       "\n",
       "                                             CIs      pVal  parent_n  \\\n",
       "255       [-0.271783601028, 0.09682792519719999]  0.268726       104   \n",
       "256           [-0.256574689051, 0.0126161731179]  0.060864       104   \n",
       "257             [0.0415470553644, 0.27722062437]  0.007930       104   \n",
       "335  [-0.13198194316099998, 0.20892616016499999]  0.701844       104   \n",
       "336       [-0.256201938573, 0.06208356356769999]  0.254923       104   \n",
       "337             [0.202494592022, 0.485609587263]  0.000016       104   \n",
       "419       [-0.06017828182440001, 0.232062442357]  0.200331       142   \n",
       "420        [0.088887320757, 0.34375344746799996]  0.001494       142   \n",
       "421        [0.07217431420960001, 0.303050983258]  0.001633       142   \n",
       "587       [0.043806233414199994, 0.411971434572]  0.013032       102   \n",
       "588             [-0.173114978482, 0.16581644655]  0.992489       102   \n",
       "589           [-0.0503067634203, 0.240789145147]  0.182080       102   \n",
       "647       [-0.296534335059, 0.07022502350910001]  0.238610        99   \n",
       "648             [-0.263759181265, 0.16309462044]  0.625365        99   \n",
       "649            [-0.217875799759, 0.141737093061]  0.656992        99   \n",
       "\n",
       "     parent_std  offspring_n  offspring_std  \n",
       "255    0.435779           52       0.565826  \n",
       "256    0.364928           52       0.411518  \n",
       "257    0.328501           52       0.369246  \n",
       "335    0.529201           52       0.478036  \n",
       "336    0.498711           52       0.427993  \n",
       "337    0.454192           52       0.408579  \n",
       "419    0.413527          104       0.586734  \n",
       "420    0.474608          104       0.436235  \n",
       "421    0.424406          104       0.431311  \n",
       "587    0.474317           52       0.556290  \n",
       "588    0.460662           52       0.505056  \n",
       "589    0.403493           52       0.448870  \n",
       "647    0.486033           52       0.513117  \n",
       "648    0.514771           52       0.655804  \n",
       "649    0.454869           52       0.507689  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combos = ['Or33b-Or47a','Or33b-Or85a','Or42b-Or92a']\n",
    "# l = ['Or22c','Or42b','Or22c-Or42b']\n",
    "# a = All_ORN_EffectSizes_df[((All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoWind') | (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')) & (All_ORN_EffectSizes_df['ORNs'].apply (lambda x: x in l))]\n",
    "\n",
    "a = All_ORN_EffectSizes_df[((All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'female_fed_NoAir'))]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Combos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:88: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# combos = ['Or33b-Or47a','Or33b-Or85a','Or42b-Or92a']\n",
    "ORNs = ['Or67d','Or56a']\n",
    "\n",
    "l = ORNs + [str(ORNs[0]) + '-' + str(ORNs[1])]\n",
    "a = All_ORN_EffectSizes_df[((All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoWind') | (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_NoAir')) & (All_ORN_EffectSizes_df['ORNs'].apply (lambda x: x in l))]\n",
    "\n",
    "lightintensities = ['14uW','42uW','70uW']\n",
    "\n",
    "## Calculate CIs for delta-deltas\n",
    "def calculatePooledMOE(std1,std2,n1=52,n2=52):\n",
    "    pooledSD = np.sqrt(((n1-1)*(std1**2)+(n2-1)*(std2**2))/(n1+n2-2))\n",
    "    moe = 1.96 * pooledSD * np.sqrt((1./n1)+(1./n2))\n",
    "    return moe, pooledSD \n",
    "\n",
    "for intensity in lightintensities:\n",
    "    ## Select the DF for a given ORN and intensity\n",
    "    a_ORN1 = a[(a['ORNs'] == ORNs[0]) & (a['LightIntensity'] == intensity)]\n",
    "    a_ORN2 = a[(a['ORNs'] == ORNs[1]) & (a['LightIntensity'] == intensity)]\n",
    "\n",
    "    ## Get ES, SD, N info for the first ORN\n",
    "    ORN1_ES = a_ORN1['ES'].values[0]\n",
    "    \n",
    "    ORN1_parent_n = a_ORN1['parent_n'].values[0]\n",
    "    ORN1_offspring_n = a_ORN1['offspring_n'].values[0]\n",
    "    ORN1_N = (ORN1_parent_n + ORN1_offspring_n) / 2.\n",
    "    \n",
    "    ORN1_parent_sd = a_ORN1['parent_std'].values[0]\n",
    "    ORN1_offspring_sd = a_ORN1['offspring_std'].values[0]\n",
    "    \n",
    "    ## Calculate pooled SD for ORN1\n",
    "    m, ORN1_sd = calculatePooledMOE(ORN1_parent_sd, ORN1_offspring_sd, n1=ORN1_parent_n, n2=ORN1_offspring_n)\n",
    "    \n",
    "    ## Get ES, SD, N info for the second ORN\n",
    "    ORN2_ES = a_ORN2['ES'].values[0]\n",
    "    \n",
    "    ORN2_parent_n = a_ORN2['parent_n'].values[0]\n",
    "    ORN2_offspring_n = a_ORN2['offspring_n'].values[0]\n",
    "    ORN2_N = (ORN2_parent_n + ORN2_offspring_n) / 2.\n",
    "    \n",
    "    ORN2_parent_sd = a_ORN2['parent_std'].values[0]\n",
    "    ORN2_offspring_sd = a_ORN2['offspring_std'].values[0]\n",
    "    \n",
    "    ## Calculate pooled SD for ORN2\n",
    "    m, ORN2_sd = calculatePooledMOE(ORN2_parent_sd, ORN2_offspring_sd, n1=ORN2_parent_n, n2=ORN2_offspring_n)\n",
    "    \n",
    "    ## Calculate pooled MOE for ORN1 and ORN2 COMBO\n",
    "    ORN1_2_pooled_moe, ORN1_2_pooled_SD = calculatePooledMOE(ORN1_sd, ORN2_sd, n1=ORN1_N, n2=ORN2_N)\n",
    "    \n",
    "    ## Summation model\n",
    "    ORN1_2_summation_ES = ORN1_ES + ORN2_ES\n",
    "    ORN1_2_summation_CIs = [ORN1_2_summation_ES-ORN1_2_pooled_moe, ORN1_2_summation_ES+ORN1_2_pooled_moe]\n",
    "    \n",
    "    ## Maxpooling model\n",
    "    ORN1_2_maxPooling_ES = max(abs(ORN1_ES), abs(ORN2_ES))\n",
    "    ORN1_2_maxPooling_CIs = [ORN1_2_maxPooling_ES-ORN1_2_pooled_moe, ORN1_2_maxPooling_ES+ORN1_2_pooled_moe]\n",
    "    \n",
    "#     ## Minimization model\n",
    "#     ORN1_2_minimization_ES = min(ORN1_ES, ORN2_ES)\n",
    "#     ORN1_2_minimization_CIs = [ORN1_2_minimization_ES-ORN1_2_pooled_moe, ORN1_2_minimization_ES+ORN1_2_pooled_moe]\n",
    "    \n",
    "    ## DELTA-DELTA: Check similarities between observed and predcitions\n",
    "    a_ORN_combo = a[(a['ORNs'] == ORNs[0] + '-' + ORNs[1]) & (a['LightIntensity'] == intensity)]\n",
    "    a_ORN_combo_ES = a_ORN_combo['ES'].values[0]\n",
    "    \n",
    "    ORN_combo_parent_n = a_ORN_combo['parent_n'].values[0]\n",
    "    ORN_combo_offspring_n = a_ORN_combo['offspring_n'].values[0]\n",
    "    ORN_combo_N = (ORN_combo_parent_n + ORN_combo_offspring_n) / 2.\n",
    "    \n",
    "    ORN_combo_parent_sd = a_ORN_combo['parent_std'].values[0]\n",
    "    ORN_combo_offspring_sd = a_ORN_combo['offspring_std'].values[0]\n",
    "    \n",
    "    ## Calculate pooled SD for ORN_combo\n",
    "    m, ORN_combo_sd = calculatePooledMOE(ORN_combo_parent_sd, ORN_combo_offspring_sd, n1=ORN_combo_parent_n, n2=ORN_combo_offspring_n)\n",
    "    \n",
    "    dd_summation_v_observed_moe, dd_summation_v_observed_sd = calculatePooledMOE(ORN1_2_pooled_SD, ORN1_2_pooled_SD, n1=ORN_combo_N, n2=ORN_combo_N)\n",
    "\n",
    "    ### delta-delta summation\n",
    "    dd_summation_v_observed_ES = ORN1_2_summation_ES - a_ORN_combo_ES\n",
    "    dd_summation_v_observed_CIs = [dd_summation_v_observed_ES-dd_summation_v_observed_moe, dd_summation_v_observed_ES+dd_summation_v_observed_moe]\n",
    "   \n",
    "    ### delta-delta max-pooling\n",
    "    dd_maxpool_v_observed_ES = ORN1_2_maxPooling_ES - a_ORN_combo_ES\n",
    "    dd_maxpool_v_observed_CIs = [dd_maxpool_v_observed_ES-dd_summation_v_observed_moe, dd_maxpool_v_observed_ES+dd_summation_v_observed_moe]\n",
    "        \n",
    "    ## Add the new values to the df\n",
    "    row = len(a)\n",
    "    \n",
    "    a.loc[row, 'ORNs'] = ORNs[0] + '_' + ORNs[1] + '-summation'\n",
    "    a.loc[row, 'LightIntensity'] = intensity\n",
    "    a.loc[row, 'Sex-Satiety-Air'] = a_ORN1['Sex-Satiety-Air'].values[0]\n",
    "    a.loc[row, 'ES'] = ORN1_2_summation_ES\n",
    "    a.at[row, 'CIs'] = ORN1_2_summation_CIs\n",
    "    \n",
    "    a.loc[row+1, 'ORNs'] = ORNs[0] + '_' + ORNs[1] + '-maxPooling'\n",
    "    a.loc[row+1, 'LightIntensity'] = intensity\n",
    "    a.loc[row+1, 'Sex-Satiety-Air'] = a_ORN1['Sex-Satiety-Air'].values[0]\n",
    "    a.loc[row+1, 'ES'] = ORN1_2_maxPooling_ES\n",
    "    a.at[row+1, 'CIs'] = ORN1_2_maxPooling_CIs\n",
    "    \n",
    "    a.loc[row+2, 'ORNs'] = ORNs[0] + '_' + ORNs[1] + '-dd-summation'\n",
    "    a.loc[row+2, 'LightIntensity'] = intensity\n",
    "    a.loc[row+2, 'Sex-Satiety-Air'] = a_ORN1['Sex-Satiety-Air'].values[0]\n",
    "    a.loc[row+2, 'ES'] = dd_summation_v_observed_ES\n",
    "    a.at[row+2, 'CIs'] = dd_summation_v_observed_CIs\n",
    "    \n",
    "    a.loc[row+3, 'ORNs'] = ORNs[0] + '_' + ORNs[1] + '-dd-maxPooling'\n",
    "    a.loc[row+3, 'LightIntensity'] = intensity\n",
    "    a.loc[row+3, 'Sex-Satiety-Air'] = a_ORN1['Sex-Satiety-Air'].values[0]\n",
    "    a.loc[row+3, 'ES'] = dd_maxpool_v_observed_ES\n",
    "    a.at[row+3, 'CIs'] = dd_maxpool_v_observed_CIs\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORNs</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>ES</th>\n",
       "      <th>CIs</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
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       "      <th>offspring_std</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Or56a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>-0.125251</td>\n",
       "      <td>[-0.29731707255199996, 0.0478460048635]</td>\n",
       "      <td>1.520109e-01</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.469907</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.501852</td>\n",
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       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>Or56a</td>\n",
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       "      <td>male_fed_NoAir</td>\n",
       "      <td>-0.187432</td>\n",
       "      <td>[-0.31721647046, -0.05006416482469999]</td>\n",
       "      <td>1.026717e-02</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.413241</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.361450</td>\n",
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       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>Or56a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>-0.087345</td>\n",
       "      <td>[-0.259920895305, 0.0803377442403]</td>\n",
       "      <td>2.881384e-01</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.420461</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.517448</td>\n",
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       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.100424</td>\n",
       "      <td>[-0.00945099671192, 0.208993650482]</td>\n",
       "      <td>5.317194e-02</td>\n",
       "      <td>208.0</td>\n",
       "      <td>0.381288</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.482972</td>\n",
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       "      <th>444</th>\n",
       "      <td>Or67d</td>\n",
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       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.161257</td>\n",
       "      <td>[0.0664158679858, 0.254006790144]</td>\n",
       "      <td>5.308794e-04</td>\n",
       "      <td>208.0</td>\n",
       "      <td>0.359597</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.401270</td>\n",
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       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.242343</td>\n",
       "      <td>[0.152944876623, 0.332504427733]</td>\n",
       "      <td>2.631603e-07</td>\n",
       "      <td>208.0</td>\n",
       "      <td>0.370388</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.376660</td>\n",
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       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.112171</td>\n",
       "      <td>[-0.007289399062140001, 0.23525383638500003]</td>\n",
       "      <td>6.580340e-02</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.337057</td>\n",
       "      <td>46.0</td>\n",
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       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.023478</td>\n",
       "      <td>[-0.1430741474, 0.08276318729399999]</td>\n",
       "      <td>6.705269e-01</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.299150</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.328256</td>\n",
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       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.049944</td>\n",
       "      <td>[-0.0401722485997, 0.14245096213400002]</td>\n",
       "      <td>2.847450e-01</td>\n",
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       "      <td>0.259890</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.260009</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Or67d_Or56a-summation</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>-0.024827</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Or67d_Or56a-maxPooling</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
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       "      <th>11</th>\n",
       "      <td>Or67d_Or56a-dd-summation</td>\n",
       "      <td>14uW</td>\n",
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       "      <td>-0.136998</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>12</th>\n",
       "      <td>Or67d_Or56a-dd-maxPooling</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>[-0.1276533418044834, 0.15381354439648337]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Or67d_Or56a-summation</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>-0.026175</td>\n",
       "      <td>[-0.1299231426361096, 0.07757382835810961]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Or67d_Or56a-maxPooling</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Or67d_Or56a-dd-summation</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>-0.002697</td>\n",
       "      <td>[-0.1248678606321873, 0.11947395647558731]</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Or67d_Or56a-dd-maxPooling</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Or67d_Or56a-summation</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.154998</td>\n",
       "      <td>[0.04581300482878804, 0.264183693346412]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Or67d_Or56a-maxPooling</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.242343</td>\n",
       "      <td>[0.13315807627818804, 0.35152876479581197]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Or67d_Or56a-dd-summation</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.105055</td>\n",
       "      <td>[-0.023518661507101246, 0.2336276986533013]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Or67d_Or56a-dd-maxPooling</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>0.192400</td>\n",
       "      <td>[0.06382640994229874, 0.3209727701027013]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ORNs LightIntensity  Sex-Satiety-Air        ES  \\\n",
       "311                      Or56a           14uW   male_fed_NoAir -0.125251   \n",
       "312                      Or56a           42uW   male_fed_NoAir -0.187432   \n",
       "313                      Or56a           70uW   male_fed_NoAir -0.087345   \n",
       "443                      Or67d           14uW   male_fed_NoAir  0.100424   \n",
       "444                      Or67d           42uW   male_fed_NoAir  0.161257   \n",
       "445                      Or67d           70uW   male_fed_NoAir  0.242343   \n",
       "464                Or67d-Or56a           14uW  male_fed_NoWind  0.112171   \n",
       "465                Or67d-Or56a           42uW  male_fed_NoWind -0.023478   \n",
       "466                Or67d-Or56a           70uW  male_fed_NoWind  0.049944   \n",
       "9        Or67d_Or56a-summation           14uW   male_fed_NoAir -0.024827   \n",
       "10      Or67d_Or56a-maxPooling           14uW   male_fed_NoAir  0.125251   \n",
       "11    Or67d_Or56a-dd-summation           14uW   male_fed_NoAir -0.136998   \n",
       "12   Or67d_Or56a-dd-maxPooling           14uW   male_fed_NoAir  0.013080   \n",
       "13       Or67d_Or56a-summation           42uW   male_fed_NoAir -0.026175   \n",
       "14      Or67d_Or56a-maxPooling           42uW   male_fed_NoAir  0.187432   \n",
       "15    Or67d_Or56a-dd-summation           42uW   male_fed_NoAir -0.002697   \n",
       "16   Or67d_Or56a-dd-maxPooling           42uW   male_fed_NoAir  0.210909   \n",
       "17       Or67d_Or56a-summation           70uW   male_fed_NoAir  0.154998   \n",
       "18      Or67d_Or56a-maxPooling           70uW   male_fed_NoAir  0.242343   \n",
       "19    Or67d_Or56a-dd-summation           70uW   male_fed_NoAir  0.105055   \n",
       "20   Or67d_Or56a-dd-maxPooling           70uW   male_fed_NoAir  0.192400   \n",
       "\n",
       "                                              CIs          pVal  parent_n  \\\n",
       "311       [-0.29731707255199996, 0.0478460048635]  1.520109e-01     104.0   \n",
       "312        [-0.31721647046, -0.05006416482469999]  1.026717e-02     104.0   \n",
       "313            [-0.259920895305, 0.0803377442403]  2.881384e-01     104.0   \n",
       "443           [-0.00945099671192, 0.208993650482]  5.317194e-02     208.0   \n",
       "444             [0.0664158679858, 0.254006790144]  5.308794e-04     208.0   \n",
       "445              [0.152944876623, 0.332504427733]  2.631603e-07     208.0   \n",
       "464  [-0.007289399062140001, 0.23525383638500003]  6.580340e-02     104.0   \n",
       "465          [-0.1430741474, 0.08276318729399999]  6.705269e-01     104.0   \n",
       "466       [-0.0401722485997, 0.14245096213400002]  2.847450e-01     104.0   \n",
       "9      [-0.1443387630840794, 0.09468510525407942]           NaN       NaN   \n",
       "10     [0.005739046909920581, 0.2447629152480794]           NaN       NaN   \n",
       "11   [-0.2777311517984834, 0.0037357344024833983]           NaN       NaN   \n",
       "12     [-0.1276533418044834, 0.15381354439648337]           NaN       NaN   \n",
       "13     [-0.1299231426361096, 0.07757382835810961]           NaN       NaN   \n",
       "14      [0.08368310257689038, 0.2911800735711096]           NaN       NaN   \n",
       "15     [-0.1248678606321873, 0.11947395647558731]           NaN       NaN   \n",
       "16     [0.08873838458081267, 0.33308020168858726]           NaN       NaN   \n",
       "17       [0.04581300482878804, 0.264183693346412]           NaN       NaN   \n",
       "18     [0.13315807627818804, 0.35152876479581197]           NaN       NaN   \n",
       "19    [-0.023518661507101246, 0.2336276986533013]           NaN       NaN   \n",
       "20      [0.06382640994229874, 0.3209727701027013]           NaN       NaN   \n",
       "\n",
       "     parent_std  offspring_n  offspring_std  \n",
       "311    0.469907         52.0       0.501852  \n",
       "312    0.413241         52.0       0.361450  \n",
       "313    0.420461         52.0       0.517448  \n",
       "443    0.381288        104.0       0.482972  \n",
       "444    0.359597        104.0       0.401270  \n",
       "445    0.370388        104.0       0.376660  \n",
       "464    0.337057         46.0       0.347009  \n",
       "465    0.299150         46.0       0.328256  \n",
       "466    0.259890         46.0       0.260009  \n",
       "9           NaN          NaN            NaN  \n",
       "10          NaN          NaN            NaN  \n",
       "11          NaN          NaN            NaN  \n",
       "12          NaN          NaN            NaN  \n",
       "13          NaN          NaN            NaN  \n",
       "14          NaN          NaN            NaN  \n",
       "15          NaN          NaN            NaN  \n",
       "16          NaN          NaN            NaN  \n",
       "17          NaN          NaN            NaN  \n",
       "18          NaN          NaN            NaN  \n",
       "19          NaN          NaN            NaN  \n",
       "20          NaN          NaN            NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Combos: End"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_order = pd.read_csv(\"C:/Users/tumkayat/Desktop/WALISAR_Paper/Figure2/EBprot/EBprot_analysis_of_all_adult_ORNs_ALL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_order['Condition'] = df_order['ZI.Protein'].apply(lambda x: x.split('_')[1] + '_' + x.split('_')[2] +'_' + x.split('_')[3])\n",
    "df_order['ORNs'] = df_order['ZI.Protein'].apply(lambda x: x.split('_')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ZI.Protein</th>\n",
       "      <th>ZI.MedianRatio</th>\n",
       "      <th>ZI.PepNum</th>\n",
       "      <th>ZI.PepNumRemoved</th>\n",
       "      <th>ZI.Probability</th>\n",
       "      <th>ZI.FLAG</th>\n",
       "      <th>ZI.PosteriorOdds</th>\n",
       "      <th>ZI.BFDR</th>\n",
       "      <th>Condition</th>\n",
       "      <th>ORNs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>Gr63a_male_starved_Air</td>\n",
       "      <td>-0.236</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.737</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Gr63a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54</td>\n",
       "      <td>Or35a_male_fed_Air</td>\n",
       "      <td>0.255</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.408</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or35a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>Or42b_male_fed_NoAir</td>\n",
       "      <td>0.188</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.296</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or42b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62</td>\n",
       "      <td>Or42b_male_fed_Air</td>\n",
       "      <td>0.121</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.286</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or42b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82</td>\n",
       "      <td>Or47a_male_fed_Air</td>\n",
       "      <td>0.231</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.480</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or47a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>106</td>\n",
       "      <td>Or59b_male_fed_Air</td>\n",
       "      <td>-0.165</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.511</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or59b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110</td>\n",
       "      <td>Or59c_male_fed_Air</td>\n",
       "      <td>-0.162</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.482</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or59c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>143</td>\n",
       "      <td>Or67d_male_fed_NoAir</td>\n",
       "      <td>0.202</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.605</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or67d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>174</td>\n",
       "      <td>Or83c_male_fed_Air</td>\n",
       "      <td>0.274</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.286</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or83c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>212</td>\n",
       "      <td>Or98a_male_starved_Air</td>\n",
       "      <td>-0.201</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.603</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or98a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>218</td>\n",
       "      <td>Orco_male_starved_Air</td>\n",
       "      <td>0.381</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Orco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>220</td>\n",
       "      <td>Orco_male_fed_Air</td>\n",
       "      <td>0.405</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.112</td>\n",
       "      <td>0.000</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Orco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>42</td>\n",
       "      <td>Or33b_male_fed_Air</td>\n",
       "      <td>-0.199</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.722</td>\n",
       "      <td>0.001</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or33b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>81</td>\n",
       "      <td>Or47a_male_fed_NoAir</td>\n",
       "      <td>0.195</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.567</td>\n",
       "      <td>0.001</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or47a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>223</td>\n",
       "      <td>Orco_female_starved_NoAir</td>\n",
       "      <td>0.215</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.567</td>\n",
       "      <td>0.001</td>\n",
       "      <td>female_starved_NoAir</td>\n",
       "      <td>Orco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8</td>\n",
       "      <td>Gr63a_male_fed_Air</td>\n",
       "      <td>-0.271</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.164</td>\n",
       "      <td>0.002</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Gr63a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>112</td>\n",
       "      <td>Or59c_male_starved_Air</td>\n",
       "      <td>-0.228</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.746</td>\n",
       "      <td>0.002</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or59c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>48</td>\n",
       "      <td>Or33b-Or85a_male_fed_Wind</td>\n",
       "      <td>-0.203</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.941</td>\n",
       "      <td>0.003</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>Or33b-Or85a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10</td>\n",
       "      <td>Or10a_male_starved_Air</td>\n",
       "      <td>0.192</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.813</td>\n",
       "      <td>0.004</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or10a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>186</td>\n",
       "      <td>Or85d_male_starved_Air</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.936</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.675</td>\n",
       "      <td>0.007</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or85d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>142</td>\n",
       "      <td>Or67d_female_fed_Air</td>\n",
       "      <td>0.242</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.579</td>\n",
       "      <td>0.010</td>\n",
       "      <td>female_fed_Air</td>\n",
       "      <td>Or67d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>56</td>\n",
       "      <td>Or35a_male_starved_Air</td>\n",
       "      <td>0.180</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.542</td>\n",
       "      <td>0.013</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or35a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>219</td>\n",
       "      <td>Orco_male_fed_NoAir</td>\n",
       "      <td>0.253</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.449</td>\n",
       "      <td>0.016</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Orco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>44</td>\n",
       "      <td>Or33b_male_starved_Air</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.917</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.405</td>\n",
       "      <td>0.019</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or33b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>155</td>\n",
       "      <td>Or67d-Or85d_male_fed_NoWind</td>\n",
       "      <td>0.189</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.683</td>\n",
       "      <td>0.024</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>Or67d-Or85d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19</td>\n",
       "      <td>Or19a_male_starved_NoAir</td>\n",
       "      <td>0.164</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.619</td>\n",
       "      <td>0.030</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>Or19a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>135</td>\n",
       "      <td>Or67b_male_starved_NoAir</td>\n",
       "      <td>0.203</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.830</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.586</td>\n",
       "      <td>0.035</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>Or67b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>195</td>\n",
       "      <td>Or85f_male_starved_NoAir</td>\n",
       "      <td>-0.162</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.386</td>\n",
       "      <td>0.041</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>Or85f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>148</td>\n",
       "      <td>Or67d_female_starved_Air</td>\n",
       "      <td>0.173</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.292</td>\n",
       "      <td>0.047</td>\n",
       "      <td>female_starved_Air</td>\n",
       "      <td>Or67d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>65</td>\n",
       "      <td>Or42b-Or92a_male_fed_NoWind</td>\n",
       "      <td>0.186</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.777</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.249</td>\n",
       "      <td>0.053</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>Or42b-Or92a</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>176</td>\n",
       "      <td>Or83c_male_starved_Air</td>\n",
       "      <td>0.048</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.370</td>\n",
       "      <td>0.775</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or83c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>202</td>\n",
       "      <td>Or88a_male_starved_Air</td>\n",
       "      <td>0.047</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.224</td>\n",
       "      <td>0.776</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or88a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>193</td>\n",
       "      <td>Or85f_male_fed_NoAir</td>\n",
       "      <td>0.023</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.311</td>\n",
       "      <td>0.777</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or85f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>13</td>\n",
       "      <td>Or13a_male_fed_NoAir</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.624</td>\n",
       "      <td>0.778</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or13a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>92</td>\n",
       "      <td>Or47b_female_starved_Air</td>\n",
       "      <td>0.019</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.244</td>\n",
       "      <td>0.779</td>\n",
       "      <td>female_starved_Air</td>\n",
       "      <td>Or47b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>131</td>\n",
       "      <td>Or67a_male_fed_NoAir</td>\n",
       "      <td>0.059</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.745</td>\n",
       "      <td>0.780</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or67a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>169</td>\n",
       "      <td>Or82a_male_starved_NoAir</td>\n",
       "      <td>0.063</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.820</td>\n",
       "      <td>0.782</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>Or82a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>66</td>\n",
       "      <td>Or42b-Or92a_male_fed_Wind</td>\n",
       "      <td>0.041</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.907</td>\n",
       "      <td>0.783</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>Or42b-Or92a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>222</td>\n",
       "      <td>Orco_female_fed_Air</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.745</td>\n",
       "      <td>0.784</td>\n",
       "      <td>female_fed_Air</td>\n",
       "      <td>Orco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>105</td>\n",
       "      <td>Or59b_male_fed_NoAir</td>\n",
       "      <td>0.046</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.618</td>\n",
       "      <td>0.785</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or59b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>26</td>\n",
       "      <td>Or22a_male_fed_Air</td>\n",
       "      <td>0.049</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.736</td>\n",
       "      <td>0.786</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or22a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>77</td>\n",
       "      <td>Or46a_male_fed_NoAir</td>\n",
       "      <td>0.025</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.700</td>\n",
       "      <td>0.787</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or46a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>70</td>\n",
       "      <td>Or43a_male_fed_Air</td>\n",
       "      <td>0.068</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-8.560</td>\n",
       "      <td>0.788</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or43a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>196</td>\n",
       "      <td>Or85f_male_starved_Air</td>\n",
       "      <td>0.051</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.145</td>\n",
       "      <td>0.789</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or85f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>14</td>\n",
       "      <td>Or13a_male_fed_Air</td>\n",
       "      <td>0.023</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.680</td>\n",
       "      <td>0.790</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or13a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>213</td>\n",
       "      <td>Or9a_male_starved_NoAir</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.598</td>\n",
       "      <td>0.791</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>Or9a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>201</td>\n",
       "      <td>Or88a_male_starved_NoAir</td>\n",
       "      <td>0.020</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.554</td>\n",
       "      <td>0.792</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>Or88a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>91</td>\n",
       "      <td>Or47b_female_starved_NoAir</td>\n",
       "      <td>0.040</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.945</td>\n",
       "      <td>0.793</td>\n",
       "      <td>female_starved_NoAir</td>\n",
       "      <td>Or47b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>85</td>\n",
       "      <td>Or47b_male_starved_NoAir</td>\n",
       "      <td>0.039</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.064</td>\n",
       "      <td>0.794</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>Or47b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>126</td>\n",
       "      <td>Or65c_male_fed_Air</td>\n",
       "      <td>0.006</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.007</td>\n",
       "      <td>0.795</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or65c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>49</td>\n",
       "      <td>Or33c_male_fed_NoAir</td>\n",
       "      <td>0.013</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.267</td>\n",
       "      <td>0.796</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or33c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>73</td>\n",
       "      <td>Or43b_male_starved_NoAir</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.003</td>\n",
       "      <td>0.797</td>\n",
       "      <td>male_starved_NoAir</td>\n",
       "      <td>Or43b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>165</td>\n",
       "      <td>Or7a_male_fed_NoAir</td>\n",
       "      <td>0.019</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.876</td>\n",
       "      <td>0.798</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or7a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>210</td>\n",
       "      <td>Or98a_male_fed_Air</td>\n",
       "      <td>0.010</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.820</td>\n",
       "      <td>0.799</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or98a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>78</td>\n",
       "      <td>Or46a_male_fed_Air</td>\n",
       "      <td>0.026</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.608</td>\n",
       "      <td>0.800</td>\n",
       "      <td>male_fed_Air</td>\n",
       "      <td>Or46a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>159</td>\n",
       "      <td>Or69a_male_fed_NoAir</td>\n",
       "      <td>0.012</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.409</td>\n",
       "      <td>0.800</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or69a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>190</td>\n",
       "      <td>Or85e_male_starved_Air</td>\n",
       "      <td>0.054</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.543</td>\n",
       "      <td>0.801</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or85e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>108</td>\n",
       "      <td>Or59b_male_starved_Air</td>\n",
       "      <td>0.006</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.608</td>\n",
       "      <td>0.802</td>\n",
       "      <td>male_starved_Air</td>\n",
       "      <td>Or59b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>197</td>\n",
       "      <td>Or88a_female_starved_NoAir</td>\n",
       "      <td>0.026</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.890</td>\n",
       "      <td>0.803</td>\n",
       "      <td>female_starved_NoAir</td>\n",
       "      <td>Or88a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>29</td>\n",
       "      <td>Or22b_male_fed_NoAir</td>\n",
       "      <td>0.046</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-7.795</td>\n",
       "      <td>0.804</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>Or22b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>224 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                   ZI.Protein  ZI.MedianRatio  ZI.PepNum  \\\n",
       "0             6       Gr63a_male_starved_Air          -0.236          3   \n",
       "1            54           Or35a_male_fed_Air           0.255          3   \n",
       "2            61         Or42b_male_fed_NoAir           0.188          5   \n",
       "3            62           Or42b_male_fed_Air           0.121          5   \n",
       "4            82           Or47a_male_fed_Air           0.231          5   \n",
       "5           106           Or59b_male_fed_Air          -0.165          3   \n",
       "6           110           Or59c_male_fed_Air          -0.162          5   \n",
       "7           143         Or67d_male_fed_NoAir           0.202          5   \n",
       "8           174           Or83c_male_fed_Air           0.274          3   \n",
       "9           212       Or98a_male_starved_Air          -0.201          3   \n",
       "10          218        Orco_male_starved_Air           0.381          3   \n",
       "11          220            Orco_male_fed_Air           0.405          3   \n",
       "12           42           Or33b_male_fed_Air          -0.199          3   \n",
       "13           81         Or47a_male_fed_NoAir           0.195          5   \n",
       "14          223    Orco_female_starved_NoAir           0.215          3   \n",
       "15            8           Gr63a_male_fed_Air          -0.271          3   \n",
       "16          112       Or59c_male_starved_Air          -0.228          3   \n",
       "17           48    Or33b-Or85a_male_fed_Wind          -0.203          3   \n",
       "18           10       Or10a_male_starved_Air           0.192          3   \n",
       "19          186       Or85d_male_starved_Air          -0.220          3   \n",
       "20          142         Or67d_female_fed_Air           0.242          3   \n",
       "21           56       Or35a_male_starved_Air           0.180          3   \n",
       "22          219          Orco_male_fed_NoAir           0.253          3   \n",
       "23           44       Or33b_male_starved_Air          -0.195          3   \n",
       "24          155  Or67d-Or85d_male_fed_NoWind           0.189          3   \n",
       "25           19     Or19a_male_starved_NoAir           0.164          3   \n",
       "26          135     Or67b_male_starved_NoAir           0.203          3   \n",
       "27          195     Or85f_male_starved_NoAir          -0.162          3   \n",
       "28          148     Or67d_female_starved_Air           0.173          3   \n",
       "29           65  Or42b-Or92a_male_fed_NoWind           0.186          3   \n",
       "..          ...                          ...             ...        ...   \n",
       "194         176       Or83c_male_starved_Air           0.048          3   \n",
       "195         202       Or88a_male_starved_Air           0.047          3   \n",
       "196         193         Or85f_male_fed_NoAir           0.023          3   \n",
       "197          13         Or13a_male_fed_NoAir          -0.025          3   \n",
       "198          92     Or47b_female_starved_Air           0.019          3   \n",
       "199         131         Or67a_male_fed_NoAir           0.059          3   \n",
       "200         169     Or82a_male_starved_NoAir           0.063          3   \n",
       "201          66    Or42b-Or92a_male_fed_Wind           0.041          3   \n",
       "202         222          Orco_female_fed_Air           0.000          3   \n",
       "203         105         Or59b_male_fed_NoAir           0.046          3   \n",
       "204          26           Or22a_male_fed_Air           0.049          3   \n",
       "205          77         Or46a_male_fed_NoAir           0.025          3   \n",
       "206          70           Or43a_male_fed_Air           0.068          5   \n",
       "207         196       Or85f_male_starved_Air           0.051          3   \n",
       "208          14           Or13a_male_fed_Air           0.023          3   \n",
       "209         213      Or9a_male_starved_NoAir           0.005          3   \n",
       "210         201     Or88a_male_starved_NoAir           0.020          3   \n",
       "211          91   Or47b_female_starved_NoAir           0.040          3   \n",
       "212          85     Or47b_male_starved_NoAir           0.039          3   \n",
       "213         126           Or65c_male_fed_Air           0.006          3   \n",
       "214          49         Or33c_male_fed_NoAir           0.013          3   \n",
       "215          73     Or43b_male_starved_NoAir           0.005          3   \n",
       "216         165          Or7a_male_fed_NoAir           0.019          3   \n",
       "217         210           Or98a_male_fed_Air           0.010          3   \n",
       "218          78           Or46a_male_fed_Air           0.026          3   \n",
       "219         159         Or69a_male_fed_NoAir           0.012          3   \n",
       "220         190       Or85e_male_starved_Air           0.054          3   \n",
       "221         108       Or59b_male_starved_Air           0.006          3   \n",
       "222         197   Or88a_female_starved_NoAir           0.026          3   \n",
       "223          29         Or22b_male_fed_NoAir           0.046          5   \n",
       "\n",
       "     ZI.PepNumRemoved  ZI.Probability  ZI.FLAG  ZI.PosteriorOdds  ZI.BFDR  \\\n",
       "0                   0          -1.000      NaN             8.737    0.000   \n",
       "1                   0           0.999      NaN             7.408    0.000   \n",
       "2                   0           1.000      NaN            11.296    0.000   \n",
       "3                   0           1.000      NaN            11.286    0.000   \n",
       "4                   0           1.000      NaN            15.480    0.000   \n",
       "5                   0          -1.000      NaN            10.511    0.000   \n",
       "6                   0          -0.999      NaN             7.482    0.000   \n",
       "7                   0           1.000      NaN            10.605    0.000   \n",
       "8                   0           0.998      NaN             6.286    0.000   \n",
       "9                   0          -0.999      NaN             6.603    0.000   \n",
       "10                  0           1.000      NaN            13.032    0.000   \n",
       "11                  0           1.000      NaN            14.112    0.000   \n",
       "12                  0          -0.997      NaN             5.722    0.001   \n",
       "13                  0           0.996      NaN             5.567    0.001   \n",
       "14                  0           0.996      NaN             5.567    0.001   \n",
       "15                  0          -0.985      NaN             4.164    0.002   \n",
       "16                  0          -0.991      NaN             4.746    0.002   \n",
       "17                  0          -0.981      NaN             3.941    0.003   \n",
       "18                  0           0.978      NaN             3.813    0.004   \n",
       "19                  0          -0.936      NaN             2.675    0.007   \n",
       "20                  0           0.929      NaN             2.579    0.010   \n",
       "21                  0           0.927      NaN             2.542    0.013   \n",
       "22                  0           0.920      NaN             2.449    0.016   \n",
       "23                  0          -0.917      NaN             2.405    0.019   \n",
       "24                  0           0.843      NaN             1.683    0.024   \n",
       "25                  0           0.835      NaN             1.619    0.030   \n",
       "26                  0           0.830      NaN             1.586    0.035   \n",
       "27                  0          -0.800      NaN             1.386    0.041   \n",
       "28                  0           0.784      NaN             1.292    0.047   \n",
       "29                  0           0.777      NaN             1.249    0.053   \n",
       "..                ...             ...      ...               ...      ...   \n",
       "194                 0           0.000      NaN            -7.370    0.775   \n",
       "195                 0           0.000      NaN            -7.224    0.776   \n",
       "196                 0           0.000      NaN            -7.311    0.777   \n",
       "197                 0           0.000      NaN            -6.624    0.778   \n",
       "198                 0           0.000      NaN            -7.244    0.779   \n",
       "199                 0           0.000      NaN            -6.745    0.780   \n",
       "200                 0           0.000      NaN            -6.820    0.782   \n",
       "201                 0           0.000      NaN            -6.907    0.783   \n",
       "202                 0           0.000      NaN            -6.745    0.784   \n",
       "203                 0           0.000      NaN            -6.618    0.785   \n",
       "204                 0           0.000      NaN            -6.736    0.786   \n",
       "205                 0           0.000      NaN            -6.700    0.787   \n",
       "206                 0           0.000      NaN            -8.560    0.788   \n",
       "207                 0           0.000      NaN            -6.145    0.789   \n",
       "208                 0           0.000      NaN            -6.680    0.790   \n",
       "209                 0           0.000      NaN            -6.598    0.791   \n",
       "210                 0           0.000      NaN            -6.554    0.792   \n",
       "211                 0           0.000      NaN            -5.945    0.793   \n",
       "212                 0           0.000      NaN            -6.064    0.794   \n",
       "213                 0           0.000      NaN            -6.007    0.795   \n",
       "214                 0           0.000      NaN            -5.267    0.796   \n",
       "215                 0           0.000      NaN            -6.003    0.797   \n",
       "216                 0           0.000      NaN            -5.876    0.798   \n",
       "217                 0           0.000      NaN            -5.820    0.799   \n",
       "218                 0           0.000      NaN            -5.608    0.800   \n",
       "219                 0           0.000      NaN            -5.409    0.800   \n",
       "220                 0           0.000      NaN            -4.543    0.801   \n",
       "221                 0           0.000      NaN            -4.608    0.802   \n",
       "222                 0           0.000      NaN            -3.890    0.803   \n",
       "223                 0           0.000      NaN            -7.795    0.804   \n",
       "\n",
       "                Condition         ORNs  \n",
       "0        male_starved_Air        Gr63a  \n",
       "1            male_fed_Air        Or35a  \n",
       "2          male_fed_NoAir        Or42b  \n",
       "3            male_fed_Air        Or42b  \n",
       "4            male_fed_Air        Or47a  \n",
       "5            male_fed_Air        Or59b  \n",
       "6            male_fed_Air        Or59c  \n",
       "7          male_fed_NoAir        Or67d  \n",
       "8            male_fed_Air        Or83c  \n",
       "9        male_starved_Air        Or98a  \n",
       "10       male_starved_Air         Orco  \n",
       "11           male_fed_Air         Orco  \n",
       "12           male_fed_Air        Or33b  \n",
       "13         male_fed_NoAir        Or47a  \n",
       "14   female_starved_NoAir         Orco  \n",
       "15           male_fed_Air        Gr63a  \n",
       "16       male_starved_Air        Or59c  \n",
       "17          male_fed_Wind  Or33b-Or85a  \n",
       "18       male_starved_Air        Or10a  \n",
       "19       male_starved_Air        Or85d  \n",
       "20         female_fed_Air        Or67d  \n",
       "21       male_starved_Air        Or35a  \n",
       "22         male_fed_NoAir         Orco  \n",
       "23       male_starved_Air        Or33b  \n",
       "24        male_fed_NoWind  Or67d-Or85d  \n",
       "25     male_starved_NoAir        Or19a  \n",
       "26     male_starved_NoAir        Or67b  \n",
       "27     male_starved_NoAir        Or85f  \n",
       "28     female_starved_Air        Or67d  \n",
       "29        male_fed_NoWind  Or42b-Or92a  \n",
       "..                    ...          ...  \n",
       "194      male_starved_Air        Or83c  \n",
       "195      male_starved_Air        Or88a  \n",
       "196        male_fed_NoAir        Or85f  \n",
       "197        male_fed_NoAir        Or13a  \n",
       "198    female_starved_Air        Or47b  \n",
       "199        male_fed_NoAir        Or67a  \n",
       "200    male_starved_NoAir        Or82a  \n",
       "201         male_fed_Wind  Or42b-Or92a  \n",
       "202        female_fed_Air         Orco  \n",
       "203        male_fed_NoAir        Or59b  \n",
       "204          male_fed_Air        Or22a  \n",
       "205        male_fed_NoAir        Or46a  \n",
       "206          male_fed_Air        Or43a  \n",
       "207      male_starved_Air        Or85f  \n",
       "208          male_fed_Air        Or13a  \n",
       "209    male_starved_NoAir         Or9a  \n",
       "210    male_starved_NoAir        Or88a  \n",
       "211  female_starved_NoAir        Or47b  \n",
       "212    male_starved_NoAir        Or47b  \n",
       "213          male_fed_Air        Or65c  \n",
       "214        male_fed_NoAir        Or33c  \n",
       "215    male_starved_NoAir        Or43b  \n",
       "216        male_fed_NoAir         Or7a  \n",
       "217          male_fed_Air        Or98a  \n",
       "218          male_fed_Air        Or46a  \n",
       "219        male_fed_NoAir        Or69a  \n",
       "220      male_starved_Air        Or85e  \n",
       "221      male_starved_Air        Or59b  \n",
       "222  female_starved_NoAir        Or88a  \n",
       "223        male_fed_NoAir        Or22b  \n",
       "\n",
       "[224 rows x 11 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "medianRatio_order = df_order[(df_order['Condition'] == 'male_fed_NoAir') | (df_order['Condition'] == 'male_fed_NoWind')].sort_values(by=['ZI.Probability'],ascending=True)['ORNs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39     Or85d\n",
       "40     Or56a\n",
       "48     Or59c\n",
       "49     Gr21a\n",
       "58     Or33b\n",
       "62     Or98a\n",
       "82     Or71a\n",
       "97     Or42a\n",
       "101    Or19b\n",
       "102    Or49b\n",
       "105    Or33a\n",
       "110    Or88a\n",
       "128    Or85b\n",
       "151    Or67c\n",
       "160    Or65b\n",
       "181    Or82a\n",
       "196    Or85f\n",
       "197    Or13a\n",
       "199    Or67a\n",
       "203    Or59b\n",
       "205    Or46a\n",
       "214    Or33c\n",
       "219    Or69a\n",
       "216     Or7a\n",
       "223    Or22b\n",
       "174    Or23a\n",
       "178    Or43a\n",
       "184    Or85e\n",
       "161    Or65a\n",
       "168    Or65c\n",
       "167    Or22a\n",
       "145    Or19a\n",
       "124    Or43b\n",
       "119    Or85a\n",
       "106     Or9a\n",
       "88     Or67b\n",
       "79     Or92a\n",
       "56     Or10a\n",
       "55     Or49a\n",
       "44     Or83c\n",
       "37     Or47b\n",
       "30     Or35a\n",
       "22      Orco\n",
       "13     Or47a\n",
       "7      Or67d\n",
       "2      Or42b\n",
       "Name: ORNs, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medianRatio_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "exclude = ['Or33b-Or47a', 'Or33b-Or85a', 'Or42b-Or92a','Or67d-Or42b', 'Or67d-Or47a', 'Or67d-Or56a', 'Or67d-Or85d', 'Gr66a','Gr63a']\n",
    "medianRatio_order = medianRatio_order[~medianRatio_order.isin(exclude)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['male_fed_Air', 'male_fed_NoAir', 'male_starved_Air',\n",
       "       'male_starved_NoAir', 'male_fed_NoWind', 'male_fed_Wind',\n",
       "       'male_starved_NoWind', 'male_starved_Wind', 'female_fed_Air',\n",
       "       'female_fed_NoAir', 'female_starved_Air', 'female_starved_NoAir'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_ORN_EffectSizes_df['Sex-Satiety-Air'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoAir') | (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_starved_NoWind')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "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>ORNs</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>ES</th>\n",
       "      <th>CIs</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
       "      <th>offspring_n</th>\n",
       "      <th>offspring_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.096322</td>\n",
       "      <td>[-0.271783601028, 0.09682792519719999]</td>\n",
       "      <td>0.268726</td>\n",
       "      <td>104</td>\n",
       "      <td>0.435779</td>\n",
       "      <td>52</td>\n",
       "      <td>0.565826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.124929</td>\n",
       "      <td>[-0.256574689051, 0.0126161731179]</td>\n",
       "      <td>0.060864</td>\n",
       "      <td>104</td>\n",
       "      <td>0.364928</td>\n",
       "      <td>52</td>\n",
       "      <td>0.411518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.158710</td>\n",
       "      <td>[0.0415470553644, 0.27722062437]</td>\n",
       "      <td>0.007930</td>\n",
       "      <td>104</td>\n",
       "      <td>0.328501</td>\n",
       "      <td>52</td>\n",
       "      <td>0.369246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.035585</td>\n",
       "      <td>[-0.13198194316099998, 0.20892616016499999]</td>\n",
       "      <td>0.701844</td>\n",
       "      <td>104</td>\n",
       "      <td>0.529201</td>\n",
       "      <td>52</td>\n",
       "      <td>0.478036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.096420</td>\n",
       "      <td>[-0.256201938573, 0.06208356356769999]</td>\n",
       "      <td>0.254923</td>\n",
       "      <td>104</td>\n",
       "      <td>0.498711</td>\n",
       "      <td>52</td>\n",
       "      <td>0.427993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.344887</td>\n",
       "      <td>[0.202494592022, 0.485609587263]</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>104</td>\n",
       "      <td>0.454192</td>\n",
       "      <td>52</td>\n",
       "      <td>0.408579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.089420</td>\n",
       "      <td>[-0.06017828182440001, 0.232062442357]</td>\n",
       "      <td>0.200331</td>\n",
       "      <td>142</td>\n",
       "      <td>0.413527</td>\n",
       "      <td>104</td>\n",
       "      <td>0.586734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.215146</td>\n",
       "      <td>[0.088887320757, 0.34375344746799996]</td>\n",
       "      <td>0.001494</td>\n",
       "      <td>142</td>\n",
       "      <td>0.474608</td>\n",
       "      <td>104</td>\n",
       "      <td>0.436235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.186897</td>\n",
       "      <td>[0.07217431420960001, 0.303050983258]</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>142</td>\n",
       "      <td>0.424406</td>\n",
       "      <td>104</td>\n",
       "      <td>0.431311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.226712</td>\n",
       "      <td>[0.043806233414199994, 0.411971434572]</td>\n",
       "      <td>0.013032</td>\n",
       "      <td>102</td>\n",
       "      <td>0.474317</td>\n",
       "      <td>52</td>\n",
       "      <td>0.556290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.000818</td>\n",
       "      <td>[-0.173114978482, 0.16581644655]</td>\n",
       "      <td>0.992489</td>\n",
       "      <td>102</td>\n",
       "      <td>0.460662</td>\n",
       "      <td>52</td>\n",
       "      <td>0.505056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.097697</td>\n",
       "      <td>[-0.0503067634203, 0.240789145147]</td>\n",
       "      <td>0.182080</td>\n",
       "      <td>102</td>\n",
       "      <td>0.403493</td>\n",
       "      <td>52</td>\n",
       "      <td>0.448870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>Orco</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.113971</td>\n",
       "      <td>[-0.296534335059, 0.07022502350910001]</td>\n",
       "      <td>0.238610</td>\n",
       "      <td>99</td>\n",
       "      <td>0.486033</td>\n",
       "      <td>52</td>\n",
       "      <td>0.513117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>Orco</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.050173</td>\n",
       "      <td>[-0.263759181265, 0.16309462044]</td>\n",
       "      <td>0.625365</td>\n",
       "      <td>99</td>\n",
       "      <td>0.514771</td>\n",
       "      <td>52</td>\n",
       "      <td>0.655804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>Orco</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.039523</td>\n",
       "      <td>[-0.217875799759, 0.141737093061]</td>\n",
       "      <td>0.656992</td>\n",
       "      <td>99</td>\n",
       "      <td>0.454869</td>\n",
       "      <td>52</td>\n",
       "      <td>0.507689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORNs LightIntensity   Sex-Satiety-Air        ES  \\\n",
       "267  Or47b           14uW  female_fed_NoAir -0.096322   \n",
       "268  Or47b           42uW  female_fed_NoAir -0.124929   \n",
       "269  Or47b           70uW  female_fed_NoAir  0.158710   \n",
       "347  Or65a           14uW  female_fed_NoAir  0.035585   \n",
       "348  Or65a           42uW  female_fed_NoAir -0.096420   \n",
       "349  Or65a           70uW  female_fed_NoAir  0.344887   \n",
       "431  Or67d           14uW  female_fed_NoAir  0.089420   \n",
       "432  Or67d           42uW  female_fed_NoAir  0.215146   \n",
       "433  Or67d           70uW  female_fed_NoAir  0.186897   \n",
       "599  Or88a           14uW  female_fed_NoAir  0.226712   \n",
       "600  Or88a           42uW  female_fed_NoAir -0.000818   \n",
       "601  Or88a           70uW  female_fed_NoAir  0.097697   \n",
       "659   Orco           14uW  female_fed_NoAir -0.113971   \n",
       "660   Orco           42uW  female_fed_NoAir -0.050173   \n",
       "661   Orco           70uW  female_fed_NoAir -0.039523   \n",
       "\n",
       "                                             CIs      pVal  parent_n  \\\n",
       "267       [-0.271783601028, 0.09682792519719999]  0.268726       104   \n",
       "268           [-0.256574689051, 0.0126161731179]  0.060864       104   \n",
       "269             [0.0415470553644, 0.27722062437]  0.007930       104   \n",
       "347  [-0.13198194316099998, 0.20892616016499999]  0.701844       104   \n",
       "348       [-0.256201938573, 0.06208356356769999]  0.254923       104   \n",
       "349             [0.202494592022, 0.485609587263]  0.000016       104   \n",
       "431       [-0.06017828182440001, 0.232062442357]  0.200331       142   \n",
       "432        [0.088887320757, 0.34375344746799996]  0.001494       142   \n",
       "433        [0.07217431420960001, 0.303050983258]  0.001633       142   \n",
       "599       [0.043806233414199994, 0.411971434572]  0.013032       102   \n",
       "600             [-0.173114978482, 0.16581644655]  0.992489       102   \n",
       "601           [-0.0503067634203, 0.240789145147]  0.182080       102   \n",
       "659       [-0.296534335059, 0.07022502350910001]  0.238610        99   \n",
       "660             [-0.263759181265, 0.16309462044]  0.625365        99   \n",
       "661            [-0.217875799759, 0.141737093061]  0.656992        99   \n",
       "\n",
       "     parent_std  offspring_n  offspring_std  \n",
       "267    0.435779           52       0.565826  \n",
       "268    0.364928           52       0.411518  \n",
       "269    0.328501           52       0.369246  \n",
       "347    0.529201           52       0.478036  \n",
       "348    0.498711           52       0.427993  \n",
       "349    0.454192           52       0.408579  \n",
       "431    0.413527          104       0.586734  \n",
       "432    0.474608          104       0.436235  \n",
       "433    0.424406          104       0.431311  \n",
       "599    0.474317           52       0.556290  \n",
       "600    0.460662           52       0.505056  \n",
       "601    0.403493           52       0.448870  \n",
       "659    0.486033           52       0.513117  \n",
       "660    0.514771           52       0.655804  \n",
       "661    0.454869           52       0.507689  "
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select_ORNs = ['Orco','Or47b','Or67d','Or88a','Or65a']\n",
    "a = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'female_fed_NoAir') & (All_ORN_EffectSizes_df['ORNs'].apply(lambda x: x in select_ORNs))]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ORNs', 'LightInt', 'male_fed_noair_ES', 'male_fed_air_ES',\n",
       "       'male_starved_noair_ES', 'male_starved_air_ES', 'male_fed_noair_CIs',\n",
       "       'male_fed_air_CIs', 'male_starved_noair_CIs', 'male_starved_air_CIs',\n",
       "       'male_fed_noair_parent_n', 'male_fed_noair_parent_std',\n",
       "       'male_fed_noair_offspring_n', 'male_fed_noair_offspring_std',\n",
       "       'male_fed_air_parent_n', 'male_fed_air_parent_std',\n",
       "       'male_fed_air_offspring_n', 'male_fed_air_offspring_std',\n",
       "       'male_starved_noair_parent_n', 'male_starved_noair_parent_std',\n",
       "       'male_starved_noair_offspring_n', 'male_starved_noair_offspring_std',\n",
       "       'male_starved_air_parent_n', 'male_starved_air_parent_std',\n",
       "       'male_starved_air_offspring_n', 'male_starved_air_offspring_std',\n",
       "       'female_fed_noair_ES', 'female_fed_air_ES', 'female_starved_noair_ES',\n",
       "       'female_starved_air_ES', 'female_fed_noair_CIs', 'female_fed_air_CIs',\n",
       "       'female_starved_noair_CIs', 'female_starved_air_CIs',\n",
       "       'female_fed_noair_parent_n', 'female_fed_noair_parent_std',\n",
       "       'female_fed_noair_offspring_n', 'female_fed_noair_offspring_std',\n",
       "       'female_fed_air_parent_n', 'female_fed_air_parent_std',\n",
       "       'female_fed_air_offspring_n', 'female_fed_air_offspring_std',\n",
       "       'female_starved_noair_parent_n', 'female_starved_noair_parent_std',\n",
       "       'female_starved_noair_offspring_n',\n",
       "       'female_starved_noair_offspring_std', 'female_starved_air_parent_n',\n",
       "       'female_starved_air_parent_std', 'female_starved_air_offspring_n',\n",
       "       'female_starved_air_offspring_std', 'male_fed_air_V_male_fed_noair_ES',\n",
       "       'male_starved_air_V_male_starved_noair_ES',\n",
       "       'male_fed_noair_V_male_starved_noair_ES',\n",
       "       'male_fed_air_V_male_starved_air_ES',\n",
       "       'female_fed_noair_V_male_fed_noair_ES',\n",
       "       'female_fed_air_V_male_fed_air_ES',\n",
       "       'female_starved_noair_V_male_starved_noair_ES',\n",
       "       'female_starved_air_V_male_starved_air_ES',\n",
       "       'male_fed_air_V_male_fed_noair_CIs',\n",
       "       'male_starved_air_V_male_starved_noair_CIs',\n",
       "       'male_fed_noair_V_male_starved_noair_CIs',\n",
       "       'male_fed_air_V_male_starved_air_CIs',\n",
       "       'female_fed_noair_V_male_fed_noair_CIs',\n",
       "       'female_starved_noair_V_male_starved_noair_CIs',\n",
       "       'female_starved_air_V_male_starved_air_CIs',\n",
       "       'female_fed_air_V_male_fed_air_CIs',\n",
       "       'male_fed_air_V_male_fed_noair_pooledSD',\n",
       "       'male_starved_air_V_male_starved_noair_pooledSD',\n",
       "       'male_fed_noair_V_male_starved_noair_pooledSD',\n",
       "       'male_fed_air_V_male_starved_air_pooledSD',\n",
       "       'female_fed_noair_V_male_fed_noair_pooledSD',\n",
       "       'female_starved_noair_V_male_starved_noair_pooledSD',\n",
       "       'female_starved_air_V_male_starved_air_pooledSD',\n",
       "       'female_fed_air_V_male_fed_air_pooledSD'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deltadelta_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "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>ORNs</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>ES</th>\n",
       "      <th>CIs</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
       "      <th>offspring_n</th>\n",
       "      <th>offspring_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.096322</td>\n",
       "      <td>[-0.271783601028, 0.09682792519719999]</td>\n",
       "      <td>0.268726</td>\n",
       "      <td>104</td>\n",
       "      <td>0.435779</td>\n",
       "      <td>52</td>\n",
       "      <td>0.565826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.124929</td>\n",
       "      <td>[-0.256574689051, 0.0126161731179]</td>\n",
       "      <td>0.060864</td>\n",
       "      <td>104</td>\n",
       "      <td>0.364928</td>\n",
       "      <td>52</td>\n",
       "      <td>0.411518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Or47b</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.158710</td>\n",
       "      <td>[0.0415470553644, 0.27722062437]</td>\n",
       "      <td>0.007930</td>\n",
       "      <td>104</td>\n",
       "      <td>0.328501</td>\n",
       "      <td>52</td>\n",
       "      <td>0.369246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.035585</td>\n",
       "      <td>[-0.13198194316099998, 0.20892616016499999]</td>\n",
       "      <td>0.701844</td>\n",
       "      <td>104</td>\n",
       "      <td>0.529201</td>\n",
       "      <td>52</td>\n",
       "      <td>0.478036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.096420</td>\n",
       "      <td>[-0.256201938573, 0.06208356356769999]</td>\n",
       "      <td>0.254923</td>\n",
       "      <td>104</td>\n",
       "      <td>0.498711</td>\n",
       "      <td>52</td>\n",
       "      <td>0.427993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>Or65a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.344887</td>\n",
       "      <td>[0.202494592022, 0.485609587263]</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>104</td>\n",
       "      <td>0.454192</td>\n",
       "      <td>52</td>\n",
       "      <td>0.408579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.089420</td>\n",
       "      <td>[-0.06017828182440001, 0.232062442357]</td>\n",
       "      <td>0.200331</td>\n",
       "      <td>142</td>\n",
       "      <td>0.413527</td>\n",
       "      <td>104</td>\n",
       "      <td>0.586734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.215146</td>\n",
       "      <td>[0.088887320757, 0.34375344746799996]</td>\n",
       "      <td>0.001494</td>\n",
       "      <td>142</td>\n",
       "      <td>0.474608</td>\n",
       "      <td>104</td>\n",
       "      <td>0.436235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Or67d</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.186897</td>\n",
       "      <td>[0.07217431420960001, 0.303050983258]</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>142</td>\n",
       "      <td>0.424406</td>\n",
       "      <td>104</td>\n",
       "      <td>0.431311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.226712</td>\n",
       "      <td>[0.043806233414199994, 0.411971434572]</td>\n",
       "      <td>0.013032</td>\n",
       "      <td>102</td>\n",
       "      <td>0.474317</td>\n",
       "      <td>52</td>\n",
       "      <td>0.556290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.000818</td>\n",
       "      <td>[-0.173114978482, 0.16581644655]</td>\n",
       "      <td>0.992489</td>\n",
       "      <td>102</td>\n",
       "      <td>0.460662</td>\n",
       "      <td>52</td>\n",
       "      <td>0.505056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Or88a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>0.097697</td>\n",
       "      <td>[-0.0503067634203, 0.240789145147]</td>\n",
       "      <td>0.182080</td>\n",
       "      <td>102</td>\n",
       "      <td>0.403493</td>\n",
       "      <td>52</td>\n",
       "      <td>0.448870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>Orco</td>\n",
       "      <td>14uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.113971</td>\n",
       "      <td>[-0.296534335059, 0.07022502350910001]</td>\n",
       "      <td>0.238610</td>\n",
       "      <td>99</td>\n",
       "      <td>0.486033</td>\n",
       "      <td>52</td>\n",
       "      <td>0.513117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>Orco</td>\n",
       "      <td>42uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.050173</td>\n",
       "      <td>[-0.263759181265, 0.16309462044]</td>\n",
       "      <td>0.625365</td>\n",
       "      <td>99</td>\n",
       "      <td>0.514771</td>\n",
       "      <td>52</td>\n",
       "      <td>0.655804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>Orco</td>\n",
       "      <td>70uW</td>\n",
       "      <td>female_fed_NoAir</td>\n",
       "      <td>-0.039523</td>\n",
       "      <td>[-0.217875799759, 0.141737093061]</td>\n",
       "      <td>0.656992</td>\n",
       "      <td>99</td>\n",
       "      <td>0.454869</td>\n",
       "      <td>52</td>\n",
       "      <td>0.507689</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORNs LightIntensity   Sex-Satiety-Air        ES  \\\n",
       "255  Or47b           14uW  female_fed_NoAir -0.096322   \n",
       "256  Or47b           42uW  female_fed_NoAir -0.124929   \n",
       "257  Or47b           70uW  female_fed_NoAir  0.158710   \n",
       "335  Or65a           14uW  female_fed_NoAir  0.035585   \n",
       "336  Or65a           42uW  female_fed_NoAir -0.096420   \n",
       "337  Or65a           70uW  female_fed_NoAir  0.344887   \n",
       "419  Or67d           14uW  female_fed_NoAir  0.089420   \n",
       "420  Or67d           42uW  female_fed_NoAir  0.215146   \n",
       "421  Or67d           70uW  female_fed_NoAir  0.186897   \n",
       "587  Or88a           14uW  female_fed_NoAir  0.226712   \n",
       "588  Or88a           42uW  female_fed_NoAir -0.000818   \n",
       "589  Or88a           70uW  female_fed_NoAir  0.097697   \n",
       "647   Orco           14uW  female_fed_NoAir -0.113971   \n",
       "648   Orco           42uW  female_fed_NoAir -0.050173   \n",
       "649   Orco           70uW  female_fed_NoAir -0.039523   \n",
       "\n",
       "                                             CIs      pVal  parent_n  \\\n",
       "255       [-0.271783601028, 0.09682792519719999]  0.268726       104   \n",
       "256           [-0.256574689051, 0.0126161731179]  0.060864       104   \n",
       "257             [0.0415470553644, 0.27722062437]  0.007930       104   \n",
       "335  [-0.13198194316099998, 0.20892616016499999]  0.701844       104   \n",
       "336       [-0.256201938573, 0.06208356356769999]  0.254923       104   \n",
       "337             [0.202494592022, 0.485609587263]  0.000016       104   \n",
       "419       [-0.06017828182440001, 0.232062442357]  0.200331       142   \n",
       "420        [0.088887320757, 0.34375344746799996]  0.001494       142   \n",
       "421        [0.07217431420960001, 0.303050983258]  0.001633       142   \n",
       "587       [0.043806233414199994, 0.411971434572]  0.013032       102   \n",
       "588             [-0.173114978482, 0.16581644655]  0.992489       102   \n",
       "589           [-0.0503067634203, 0.240789145147]  0.182080       102   \n",
       "647       [-0.296534335059, 0.07022502350910001]  0.238610        99   \n",
       "648             [-0.263759181265, 0.16309462044]  0.625365        99   \n",
       "649            [-0.217875799759, 0.141737093061]  0.656992        99   \n",
       "\n",
       "     parent_std  offspring_n  offspring_std  \n",
       "255    0.435779           52       0.565826  \n",
       "256    0.364928           52       0.411518  \n",
       "257    0.328501           52       0.369246  \n",
       "335    0.529201           52       0.478036  \n",
       "336    0.498711           52       0.427993  \n",
       "337    0.454192           52       0.408579  \n",
       "419    0.413527          104       0.586734  \n",
       "420    0.474608          104       0.436235  \n",
       "421    0.424406          104       0.431311  \n",
       "587    0.474317           52       0.556290  \n",
       "588    0.460662           52       0.505056  \n",
       "589    0.403493           52       0.448870  \n",
       "647    0.486033           52       0.513117  \n",
       "648    0.514771           52       0.655804  \n",
       "649    0.454869           52       0.507689  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting all the effect sizes\n",
    "# f, o = WALiForest(a, column = 'ES', sort = False, sorting_order = list(medianRatio_order.iloc[::-1]), xlim=(-0.7, 0.7),\n",
    "#                  gap_thru_yaxis = 5, offset = 1.1)\n",
    "\n",
    "## Plotting effect sizes of a given dataframe (a)\n",
    "f, o = WALiForest(a, column = 'ES', sort = False, sorting_order=['Or65a', 'Or88a', 'Or67d', 'Or47b', 'Orco'], xlim=(-0.7, 0.7),\n",
    "                 gap_thru_yaxis = 5, offset = 1.1, fsize=(3,6))\n",
    "f.savefig(\"C:/Users/tumkayat/Desktop/female_starved_airOFF.pdf\",dpi=1000,bbox_inches='tight')\n",
    "\n",
    "\n",
    "## Plotting delta-delta effect sizes\n",
    "# f, o = WALiForest(deltadelta_df, column = 'male_fed_air_V_male_fed_noair_ES', \n",
    "#                   sort = None, sorting_order = list(medianRatio_order.iloc[::-1]), xlim=(-0.7, 0.7),\n",
    "#                  gap_thru_yaxis = 5, offset = 1.1)\n",
    "\n",
    "## 'red','steelblue','seagreen' used for combo plots\n",
    "# f, o = WALiForest(a, column='ES', sort_by=None, sort_by_order = list(reversed(['Or22c','Or42b','Or22c-Or42b'])),fsize=(5,4),c='red')\n",
    "# f1, o1 = WALiForest(a, column='ES', sort_by=None, sort_by_order = list(reversed(l)),fsize=(5,4),c='red')\n",
    "# f2, o2 = WALiForest(a, column='ES', sort_by=None, sort_by_order = list(reversed([str(ORNs[0] + '_' + ORNs[1] + '-summation'), str(ORNs[0] + '_' + ORNs[1] + '-maxPooling')])),fsize=(5,3),c='steelblue')\n",
    "# f3, o3 = WALiForest(a, column='ES', sort_by=None, sort_by_order = list(reversed([str(ORNs[0] + '_' + ORNs[1] + '-dd-summation'), str(ORNs[0] + '_' + ORNs[1] + '-dd-maxPooling')])),fsize=(5,3),c='seagreen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname = str(l[2])\n",
    "fname1 = str(l[2]) + '_combo_noair_observed'\n",
    "fname2 = str(l[2]) + '_combo_noair_predicted'\n",
    "fname3 = str(l[2]) + '_combo_noair_dd'\n",
    "\n",
    "f1.savefig(\"C:/Users/tumkayat/Desktop/ORScreening/ORNs_Combo/All_Results_MaxPoolFixed/\" + fname1 + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "f2.savefig(\"C:/Users/tumkayat/Desktop/ORScreening/ORNs_Combo/All_Results_MaxPoolFixed/\" + fname2 + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "f3.savefig(\"C:/Users/tumkayat/Desktop/ORScreening/ORNs_Combo/All_Results_MaxPoolFixed/\" + fname3 + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "a.to_csv(\"C:/Users/tumkayat/Desktop/ORScreening/ORNs_Combo/All_Results_MaxPoolFixed/\" + fname + \".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "f.savefig(\"C:/Users/tumkayat/Desktop/WALIforest_of_'male_fed_air_V_male_fed_noair_ES'.pdf\",dpi=1000,bbox_inches='tight')\n",
    "a.to_csv(\"C:/Users/tumkayat/Desktop/WALIforest_of_'male_fed_air_V_male_fed_noair_ES'.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stitching up combo .csv results into a one df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def collect_combo_ES(rootDirectory):\n",
    "       \n",
    "    file_list = os.listdir(rootDirectory)\n",
    "    bar = progressbar.ProgressBar()\n",
    "    \n",
    "    df = pd.DataFrame()\n",
    "    \n",
    "    ## go thru the files \n",
    "    for f in bar(file_list):\n",
    "        if f[-4:] == '.csv':\n",
    "            print(f)\n",
    "            \n",
    "            rootDir = os.path.join(rootDirectory, f)\n",
    "            new_frame = pd.read_csv(rootDir, index_col= 0, dtype={'ES':float, 'pVal':float,\n",
    "               'parent_n':float, 'parent_std':float, 'offspring_n':float, 'offspring_std':float},\n",
    "                                   converters={\"CIs\": lambda x: x.strip(\"[]\").replace(\"'\",\"\").split(\", \")})\n",
    "            \n",
    "            df = df.append(new_frame, ignore_index=True)\n",
    "    cols = [u'ORNs', u'LightIntensity', u'Sex-Satiety-Air', u'ES', u'CIs', u'pVal',\n",
    "       u'parent_n', u'parent_std', u'offspring_n', u'offspring_std']        \n",
    "    \n",
    "    df = df[cols]\n",
    "    return df\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100% |########################################################################|\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Or22c-Or42b.csv\n",
      "Or22c-Or47a.csv\n",
      "Or22c-Or56a.csv\n",
      "Or22c-Or85d.csv\n",
      "Or33b-Or47a.csv\n",
      "Or33b-Or85a.csv\n",
      "Or42b-Or92a.csv\n",
      "Or67d-Or42b.csv\n",
      "Or67d-Or47a.csv\n",
      "Or67d-Or56a.csv\n",
      "Or67d-Or85d.csv\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "r = 'C:/Users/tumkayat/Desktop/WALISAR_Paper/Figure5/ORNs_Combo/All_Results_MaxPoolFixed/'\n",
    "d = collect_combo_ES(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the combo figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "combo_list = ['Or22c', 'Or42b', 'Or22c-Or42b', 'Or22c', 'Or47a', 'Or22c-Or47a', 'Or22c', 'Or56a', 'Or22c-Or56a', \n",
    "              'Or22c', 'Or85d', 'Or22c-Or85d', 'Or33b', 'Or47a', 'Or33b-Or47a', 'Or33b', 'Or85a', 'Or33b-Or85a', \n",
    "              'Or42b', 'Or92a', 'Or42b-Or92a', 'Or67d', 'Or42b', 'Or67d-Or42b', 'Or67d', 'Or47a', 'Or67d-Or47a',\n",
    "              'Or67d', 'Or56a', 'Or67d-Or56a', 'Or67d', 'Or85d', 'Or67d-Or85d']\n",
    "\n",
    "only_combo_list = [ 'Or33b-Or47a', 'Or33b-Or85a', \n",
    "              'Or42b-Or92a', 'Or67d-Or42b', 'Or67d-Or47a',\n",
    "              'Or67d-Or56a', 'Or67d-Or85d']\n",
    "\n",
    "singletons = ['Or42b', 'Or47a', 'Or56a', 'Or85d', 'Or33b', 'Or85a', 'Or92a', 'Or67d']\n",
    "\n",
    "# d = d.drop_duplicates('ES')\n",
    "\n",
    "# summation_pred = d[((d['ORNs'].str.contains(\"-summation\"))) & (d['ORNs'].str.contains(\"-dd\") == False)]\n",
    "# maxPool_pred = d[((d['ORNs'].str.contains(\"-max\"))) & (d['ORNs'].str.contains(\"-dd\") == False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# d = d.drop_duplicates('ES')\n",
    "# f1, o1 = WALiForest(d, column='ES_sig', sort_by=True, sort_by_order = singletons, fsize=(3,10), c='red')\n",
    "\n",
    "f1, o1 = WALiForest(d, column='ES', sort=True, sorting_order = list(reversed(only_combo_list)), fsize=(3,10), c='red', gap_thru_yaxis=5, offset=0.9, xlim=(-0.8,0.8))\n",
    "# f1, o1 = WALiForest(d, column='ES', sort_by=True, sort_by_order = list(reversed(singletons)), fsize=(3,10), c='red')\n",
    "\n",
    "# f2, o2 = WALiForest(a, column='ES', sort_by=None, sort_by_order = list(reversed([str(ORNs[0] + '_' + ORNs[1] + '-summation'), str(ORNs[0] + '_' + ORNs[1] + '-maxPooling')])),fsize=(5,3),c='steelblue')\n",
    "# f3, o3 = WALiForest(a, column='ES', sort_by=None, sort_by_order = list(reversed([str(ORNs[0] + '_' + ORNs[1] + '-dd-summation'), str(ORNs[0] + '_' + ORNs[1] + '-dd-maxPooling')])),fsize=(5,3),c='seagreen')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "## generate a list of the summation and maxpooling predictions in the ORDER of the combo plot!\n",
    "combos_summation = [i.split('-')[0] + '_' + i.split('-')[1] + '-summation' for i in o1]\n",
    "\n",
    "combos_maxPooling = [i.split('-')[0] + '_' + i.split('-')[1] + '-maxPooling' for i in o1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'combos_summation' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-dc374b95464e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcombos_summation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'combos_summation' is not defined"
     ]
    }
   ],
   "source": [
    "combos_summation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'd' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-352133518b55>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# f1, o1 = WALiForest(d, column='ES_sig', sort_by=None, sort_by_order = combos_summation, fsize=(3,10), c='red')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mf1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mWALiForest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ES_sig'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort_by\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msort_by_order\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcombos_maxPooling\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'red'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'd' is not defined"
     ]
    }
   ],
   "source": [
    "# f1, o1 = WALiForest(d, column='ES_sig', sort_by=None, sort_by_order = combos_summation, fsize=(3,10), c='red')\n",
    "f1, o1 = WALiForest(d, column='ES_sig', sort_by=None, sort_by_order = combos_maxPooling, fsize=(3,10), c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1.savefig(\"C:/Users/tumkayat/Desktop/Combinations.pdf\", dpi=1000, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "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>ORNs</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>ES</th>\n",
       "      <th>CIs</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
       "      <th>offspring_n</th>\n",
       "      <th>offspring_std</th>\n",
       "      <th>ExpConditions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>Or22c-Or42b</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.149051</td>\n",
       "      <td>[-0.291677932994, -0.0146655087397]</td>\n",
       "      <td>0.035927</td>\n",
       "      <td>104</td>\n",
       "      <td>0.396583</td>\n",
       "      <td>48</td>\n",
       "      <td>0.396878</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>Or22c-Or42b</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.248114</td>\n",
       "      <td>[-0.384141807901, -0.105588591293]</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>104</td>\n",
       "      <td>0.407816</td>\n",
       "      <td>48</td>\n",
       "      <td>0.391766</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Or22c-Or42b</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.186152</td>\n",
       "      <td>[-0.336326058505, -0.0503269183438]</td>\n",
       "      <td>0.015697</td>\n",
       "      <td>104</td>\n",
       "      <td>0.447922</td>\n",
       "      <td>48</td>\n",
       "      <td>0.402038</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Or22c-Or42b</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.205025</td>\n",
       "      <td>[-0.419687685966, 0.0137311669682]</td>\n",
       "      <td>0.070189</td>\n",
       "      <td>104</td>\n",
       "      <td>0.594120</td>\n",
       "      <td>48</td>\n",
       "      <td>0.607626</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Or22c-Or42b</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.318921</td>\n",
       "      <td>[-0.485529672452, -0.152204569966]</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>104</td>\n",
       "      <td>0.504756</td>\n",
       "      <td>48</td>\n",
       "      <td>0.427415</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Or22c-Or42b</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.294836</td>\n",
       "      <td>[-0.462123635491, -0.116335738182]</td>\n",
       "      <td>0.001553</td>\n",
       "      <td>104</td>\n",
       "      <td>0.493323</td>\n",
       "      <td>48</td>\n",
       "      <td>0.473330</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Or22c-Or47a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.121534</td>\n",
       "      <td>[-0.300674820306, 0.0661994773669]</td>\n",
       "      <td>0.180536</td>\n",
       "      <td>103</td>\n",
       "      <td>0.491656</td>\n",
       "      <td>52</td>\n",
       "      <td>0.536330</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Or22c-Or47a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.257088</td>\n",
       "      <td>[-0.424381516672, -0.0876791382995]</td>\n",
       "      <td>0.002618</td>\n",
       "      <td>103</td>\n",
       "      <td>0.439950</td>\n",
       "      <td>52</td>\n",
       "      <td>0.521558</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Or22c-Or47a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.145989</td>\n",
       "      <td>[-0.330865646837, 0.0528962597423]</td>\n",
       "      <td>0.102006</td>\n",
       "      <td>103</td>\n",
       "      <td>0.411058</td>\n",
       "      <td>52</td>\n",
       "      <td>0.629073</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Or22c-Or47a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.187436</td>\n",
       "      <td>[-0.0598644042188, 0.412603137586]</td>\n",
       "      <td>0.120160</td>\n",
       "      <td>103</td>\n",
       "      <td>0.560434</td>\n",
       "      <td>52</td>\n",
       "      <td>0.600147</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Or22c-Or47a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.030399</td>\n",
       "      <td>[-0.214624245171, 0.269244599645]</td>\n",
       "      <td>0.803470</td>\n",
       "      <td>103</td>\n",
       "      <td>0.620585</td>\n",
       "      <td>52</td>\n",
       "      <td>0.679089</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Or22c-Or47a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.321416</td>\n",
       "      <td>[-0.555969702105, -0.0724142028174]</td>\n",
       "      <td>0.006787</td>\n",
       "      <td>103</td>\n",
       "      <td>0.559684</td>\n",
       "      <td>52</td>\n",
       "      <td>0.646382</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Or22c-Or56a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.041696</td>\n",
       "      <td>[-0.149052970832, 0.0698224129509]</td>\n",
       "      <td>0.444311</td>\n",
       "      <td>104</td>\n",
       "      <td>0.298332</td>\n",
       "      <td>52</td>\n",
       "      <td>0.335408</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Or22c-Or56a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.093359</td>\n",
       "      <td>[-0.20753087712, 0.0255699821177]</td>\n",
       "      <td>0.128486</td>\n",
       "      <td>104</td>\n",
       "      <td>0.355162</td>\n",
       "      <td>52</td>\n",
       "      <td>0.336266</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Or22c-Or56a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.107674</td>\n",
       "      <td>[-0.229496507183, 0.0180003178812]</td>\n",
       "      <td>0.113630</td>\n",
       "      <td>104</td>\n",
       "      <td>0.406309</td>\n",
       "      <td>52</td>\n",
       "      <td>0.360467</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Or22c-Or56a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.145342</td>\n",
       "      <td>[-0.370704382188, 0.0869378396362]</td>\n",
       "      <td>0.222218</td>\n",
       "      <td>104</td>\n",
       "      <td>0.647713</td>\n",
       "      <td>52</td>\n",
       "      <td>0.626936</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>Or22c-Or56a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.229550</td>\n",
       "      <td>[-0.404486692257, -0.0579179506308]</td>\n",
       "      <td>0.015323</td>\n",
       "      <td>104</td>\n",
       "      <td>0.548341</td>\n",
       "      <td>52</td>\n",
       "      <td>0.457174</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Or22c-Or56a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.178031</td>\n",
       "      <td>[-0.340620442573, -0.00916655547989]</td>\n",
       "      <td>0.044902</td>\n",
       "      <td>104</td>\n",
       "      <td>0.469978</td>\n",
       "      <td>52</td>\n",
       "      <td>0.431853</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Or22c-Or85d</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.036555</td>\n",
       "      <td>[-0.17031630868, 0.0996627969785]</td>\n",
       "      <td>0.634348</td>\n",
       "      <td>104</td>\n",
       "      <td>0.460379</td>\n",
       "      <td>47</td>\n",
       "      <td>0.356019</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Or22c-Or85d</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.197186</td>\n",
       "      <td>[-0.327852237949, -0.0540818733326]</td>\n",
       "      <td>0.007395</td>\n",
       "      <td>104</td>\n",
       "      <td>0.404228</td>\n",
       "      <td>47</td>\n",
       "      <td>0.395549</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Or22c-Or85d</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.246199</td>\n",
       "      <td>[-0.429631900653, -0.0606110576221]</td>\n",
       "      <td>0.006286</td>\n",
       "      <td>104</td>\n",
       "      <td>0.454717</td>\n",
       "      <td>47</td>\n",
       "      <td>0.551799</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>Or22c-Or85d</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.323838</td>\n",
       "      <td>[-0.571635103188, -0.071658841656]</td>\n",
       "      <td>0.012609</td>\n",
       "      <td>104</td>\n",
       "      <td>0.644176</td>\n",
       "      <td>47</td>\n",
       "      <td>0.657002</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Or22c-Or85d</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.406606</td>\n",
       "      <td>[-0.595687637923, -0.193640555368]</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>104</td>\n",
       "      <td>0.584112</td>\n",
       "      <td>47</td>\n",
       "      <td>0.513312</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Or22c-Or85d</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.530935</td>\n",
       "      <td>[-0.712631195319, -0.327263916125]</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>104</td>\n",
       "      <td>0.578454</td>\n",
       "      <td>47</td>\n",
       "      <td>0.470097</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.031003</td>\n",
       "      <td>[-0.13514388386, 0.198818675115]</td>\n",
       "      <td>0.714225</td>\n",
       "      <td>104</td>\n",
       "      <td>0.475575</td>\n",
       "      <td>52</td>\n",
       "      <td>0.459306</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.045552</td>\n",
       "      <td>[-0.201701078871, 0.113518267797]</td>\n",
       "      <td>0.540245</td>\n",
       "      <td>104</td>\n",
       "      <td>0.387782</td>\n",
       "      <td>52</td>\n",
       "      <td>0.490303</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.115707</td>\n",
       "      <td>[-0.0801880010065, 0.306288867321]</td>\n",
       "      <td>0.184012</td>\n",
       "      <td>104</td>\n",
       "      <td>0.414585</td>\n",
       "      <td>52</td>\n",
       "      <td>0.605970</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.175575</td>\n",
       "      <td>[-0.0652803464595, 0.424908431005]</td>\n",
       "      <td>0.158012</td>\n",
       "      <td>156</td>\n",
       "      <td>0.623276</td>\n",
       "      <td>52</td>\n",
       "      <td>0.659647</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.062354</td>\n",
       "      <td>[-0.313566514932, 0.197513861469]</td>\n",
       "      <td>0.646824</td>\n",
       "      <td>104</td>\n",
       "      <td>0.625153</td>\n",
       "      <td>52</td>\n",
       "      <td>0.595190</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.243238</td>\n",
       "      <td>[0.0119616345403, 0.471488434999]</td>\n",
       "      <td>0.043100</td>\n",
       "      <td>104</td>\n",
       "      <td>0.583113</td>\n",
       "      <td>52</td>\n",
       "      <td>0.592478</td>\n",
       "      <td>70uW_male_fed_Wind</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>Or42b-Or92a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_starved_NoWind</td>\n",
       "      <td>0.046449</td>\n",
       "      <td>[-0.119788396423, 0.219946285604]</td>\n",
       "      <td>0.585332</td>\n",
       "      <td>81</td>\n",
       "      <td>0.396665</td>\n",
       "      <td>34</td>\n",
       "      <td>0.408547</td>\n",
       "      <td>14uW_male_starved_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>Or42b-Or92a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_starved_NoWind</td>\n",
       "      <td>0.118420</td>\n",
       "      <td>[-0.0400023161909, 0.278997300493]</td>\n",
       "      <td>0.122024</td>\n",
       "      <td>81</td>\n",
       "      <td>0.325682</td>\n",
       "      <td>34</td>\n",
       "      <td>0.396837</td>\n",
       "      <td>42uW_male_starved_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>Or42b-Or92a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_starved_NoWind</td>\n",
       "      <td>0.089857</td>\n",
       "      <td>[-0.0776773545973, 0.248990003797]</td>\n",
       "      <td>0.203563</td>\n",
       "      <td>80</td>\n",
       "      <td>0.276623</td>\n",
       "      <td>34</td>\n",
       "      <td>0.440401</td>\n",
       "      <td>70uW_male_starved_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Or42b-Or92a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_starved_Wind</td>\n",
       "      <td>0.116081</td>\n",
       "      <td>[-0.185813979088, 0.416948363249]</td>\n",
       "      <td>0.481649</td>\n",
       "      <td>81</td>\n",
       "      <td>0.667441</td>\n",
       "      <td>34</td>\n",
       "      <td>0.599732</td>\n",
       "      <td>14uW_male_starved_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>Or42b-Or92a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_starved_Wind</td>\n",
       "      <td>0.036940</td>\n",
       "      <td>[-0.234441500614, 0.310325575155]</td>\n",
       "      <td>0.781590</td>\n",
       "      <td>81</td>\n",
       "      <td>0.542336</td>\n",
       "      <td>34</td>\n",
       "      <td>0.663243</td>\n",
       "      <td>42uW_male_starved_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>Or42b-Or92a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_starved_Wind</td>\n",
       "      <td>0.230282</td>\n",
       "      <td>[0.0193901428363, 0.43689810896]</td>\n",
       "      <td>0.035210</td>\n",
       "      <td>80</td>\n",
       "      <td>0.440618</td>\n",
       "      <td>34</td>\n",
       "      <td>0.451220</td>\n",
       "      <td>70uW_male_starved_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>Or67d-Or42b</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.153430</td>\n",
       "      <td>[0.0281289351218, 0.275649955027]</td>\n",
       "      <td>0.014893</td>\n",
       "      <td>93</td>\n",
       "      <td>0.341416</td>\n",
       "      <td>51</td>\n",
       "      <td>0.374637</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>Or67d-Or42b</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.146243</td>\n",
       "      <td>[0.0258961050842, 0.269367455944]</td>\n",
       "      <td>0.022276</td>\n",
       "      <td>93</td>\n",
       "      <td>0.374771</td>\n",
       "      <td>51</td>\n",
       "      <td>0.331207</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>Or67d-Or42b</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.050129</td>\n",
       "      <td>[-0.180077202647, 0.0736655889723]</td>\n",
       "      <td>0.463449</td>\n",
       "      <td>93</td>\n",
       "      <td>0.409179</td>\n",
       "      <td>51</td>\n",
       "      <td>0.335815</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>Or67d-Or42b</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.106305</td>\n",
       "      <td>[-0.0940439604525, 0.293445774012]</td>\n",
       "      <td>0.290950</td>\n",
       "      <td>93</td>\n",
       "      <td>0.549479</td>\n",
       "      <td>51</td>\n",
       "      <td>0.509813</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>Or67d-Or42b</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.114467</td>\n",
       "      <td>[-0.0459627126021, 0.27816699939]</td>\n",
       "      <td>0.206023</td>\n",
       "      <td>93</td>\n",
       "      <td>0.537809</td>\n",
       "      <td>51</td>\n",
       "      <td>0.406997</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>Or67d-Or42b</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.036860</td>\n",
       "      <td>[-0.129595590635, 0.189521143873]</td>\n",
       "      <td>0.678788</td>\n",
       "      <td>93</td>\n",
       "      <td>0.526516</td>\n",
       "      <td>51</td>\n",
       "      <td>0.415179</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>Or67d-Or47a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.101741</td>\n",
       "      <td>[-0.0515653515614, 0.247915662897]</td>\n",
       "      <td>0.214019</td>\n",
       "      <td>89</td>\n",
       "      <td>0.467249</td>\n",
       "      <td>52</td>\n",
       "      <td>0.428758</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>Or67d-Or47a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.088904</td>\n",
       "      <td>[-0.0556219078205, 0.228643780922]</td>\n",
       "      <td>0.219856</td>\n",
       "      <td>89</td>\n",
       "      <td>0.400450</td>\n",
       "      <td>52</td>\n",
       "      <td>0.404277</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>Or67d-Or47a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.204553</td>\n",
       "      <td>[0.0748953317982, 0.337151978229]</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>89</td>\n",
       "      <td>0.382262</td>\n",
       "      <td>52</td>\n",
       "      <td>0.373977</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>Or67d-Or47a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.021457</td>\n",
       "      <td>[-0.242286280885, 0.185819641873]</td>\n",
       "      <td>0.855042</td>\n",
       "      <td>89</td>\n",
       "      <td>0.644387</td>\n",
       "      <td>52</td>\n",
       "      <td>0.548820</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>Or67d-Or47a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.124415</td>\n",
       "      <td>[-0.0719988972984, 0.327422810887]</td>\n",
       "      <td>0.247899</td>\n",
       "      <td>89</td>\n",
       "      <td>0.574743</td>\n",
       "      <td>52</td>\n",
       "      <td>0.476767</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>Or67d-Or47a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.269796</td>\n",
       "      <td>[0.075618547921, 0.462386163395]</td>\n",
       "      <td>0.008494</td>\n",
       "      <td>89</td>\n",
       "      <td>0.531571</td>\n",
       "      <td>52</td>\n",
       "      <td>0.540418</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.112171</td>\n",
       "      <td>[-0.00728939906214, 0.235253836385]</td>\n",
       "      <td>0.065803</td>\n",
       "      <td>104</td>\n",
       "      <td>0.337057</td>\n",
       "      <td>46</td>\n",
       "      <td>0.347009</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>-0.023478</td>\n",
       "      <td>[-0.1430741474, 0.082763187294]</td>\n",
       "      <td>0.670527</td>\n",
       "      <td>104</td>\n",
       "      <td>0.299150</td>\n",
       "      <td>46</td>\n",
       "      <td>0.328256</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.049944</td>\n",
       "      <td>[-0.0401722485997, 0.142450962134]</td>\n",
       "      <td>0.284745</td>\n",
       "      <td>104</td>\n",
       "      <td>0.259890</td>\n",
       "      <td>46</td>\n",
       "      <td>0.260009</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.120080</td>\n",
       "      <td>[-0.299674644143, 0.0637510711943]</td>\n",
       "      <td>0.250313</td>\n",
       "      <td>104</td>\n",
       "      <td>0.562903</td>\n",
       "      <td>46</td>\n",
       "      <td>0.448112</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.123595</td>\n",
       "      <td>[-0.281351847175, 0.0267985372807]</td>\n",
       "      <td>0.151054</td>\n",
       "      <td>104</td>\n",
       "      <td>0.488487</td>\n",
       "      <td>46</td>\n",
       "      <td>0.418684</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>Or67d-Or56a</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>-0.018397</td>\n",
       "      <td>[-0.155077970542, 0.118774095492]</td>\n",
       "      <td>0.800693</td>\n",
       "      <td>104</td>\n",
       "      <td>0.415257</td>\n",
       "      <td>46</td>\n",
       "      <td>0.373782</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>Or67d-Or85d</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.139425</td>\n",
       "      <td>[-0.0128001564427, 0.283134828153]</td>\n",
       "      <td>0.061523</td>\n",
       "      <td>99</td>\n",
       "      <td>0.412823</td>\n",
       "      <td>52</td>\n",
       "      <td>0.428312</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>Or67d-Or85d</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.180886</td>\n",
       "      <td>[0.0590672934191, 0.302662788056]</td>\n",
       "      <td>0.012542</td>\n",
       "      <td>99</td>\n",
       "      <td>0.441862</td>\n",
       "      <td>52</td>\n",
       "      <td>0.284853</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Or67d-Or85d</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>0.212938</td>\n",
       "      <td>[0.0628485147734, 0.360192104074]</td>\n",
       "      <td>0.004648</td>\n",
       "      <td>99</td>\n",
       "      <td>0.416135</td>\n",
       "      <td>52</td>\n",
       "      <td>0.438092</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Or67d-Or85d</td>\n",
       "      <td>14uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.024093</td>\n",
       "      <td>[-0.207684745258, 0.245947222962]</td>\n",
       "      <td>0.835984</td>\n",
       "      <td>99</td>\n",
       "      <td>0.579474</td>\n",
       "      <td>52</td>\n",
       "      <td>0.596281</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Or67d-Or85d</td>\n",
       "      <td>42uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.156768</td>\n",
       "      <td>[-0.0619979181664, 0.371454377089]</td>\n",
       "      <td>0.209925</td>\n",
       "      <td>99</td>\n",
       "      <td>0.686416</td>\n",
       "      <td>52</td>\n",
       "      <td>0.521744</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>Or67d-Or85d</td>\n",
       "      <td>70uW</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>0.236576</td>\n",
       "      <td>[0.0290282399788, 0.421127983052]</td>\n",
       "      <td>0.032009</td>\n",
       "      <td>99</td>\n",
       "      <td>0.595770</td>\n",
       "      <td>52</td>\n",
       "      <td>0.479912</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ORNs LightIntensity      Sex-Satiety-Air        ES  \\\n",
       "132  Or22c-Or42b           14uW      male_fed_NoWind -0.149051   \n",
       "133  Or22c-Or42b           42uW      male_fed_NoWind -0.248114   \n",
       "134  Or22c-Or42b           70uW      male_fed_NoWind -0.186152   \n",
       "135  Or22c-Or42b           14uW        male_fed_Wind -0.205025   \n",
       "136  Or22c-Or42b           42uW        male_fed_Wind -0.318921   \n",
       "137  Or22c-Or42b           70uW        male_fed_Wind -0.294836   \n",
       "138  Or22c-Or47a           14uW      male_fed_NoWind -0.121534   \n",
       "139  Or22c-Or47a           42uW      male_fed_NoWind -0.257088   \n",
       "140  Or22c-Or47a           70uW      male_fed_NoWind -0.145989   \n",
       "141  Or22c-Or47a           14uW        male_fed_Wind  0.187436   \n",
       "142  Or22c-Or47a           42uW        male_fed_Wind  0.030399   \n",
       "143  Or22c-Or47a           70uW        male_fed_Wind -0.321416   \n",
       "144  Or22c-Or56a           14uW      male_fed_NoWind -0.041696   \n",
       "145  Or22c-Or56a           42uW      male_fed_NoWind -0.093359   \n",
       "146  Or22c-Or56a           70uW      male_fed_NoWind -0.107674   \n",
       "147  Or22c-Or56a           14uW        male_fed_Wind -0.145342   \n",
       "148  Or22c-Or56a           42uW        male_fed_Wind -0.229550   \n",
       "149  Or22c-Or56a           70uW        male_fed_Wind -0.178031   \n",
       "150  Or22c-Or85d           14uW      male_fed_NoWind -0.036555   \n",
       "151  Or22c-Or85d           42uW      male_fed_NoWind -0.197186   \n",
       "152  Or22c-Or85d           70uW      male_fed_NoWind -0.246199   \n",
       "153  Or22c-Or85d           14uW        male_fed_Wind -0.323838   \n",
       "154  Or22c-Or85d           42uW        male_fed_Wind -0.406606   \n",
       "155  Or22c-Or85d           70uW        male_fed_Wind -0.530935   \n",
       "212  Or33b-Or47a           14uW      male_fed_NoWind  0.031003   \n",
       "213  Or33b-Or47a           42uW      male_fed_NoWind -0.045552   \n",
       "214  Or33b-Or47a           70uW      male_fed_NoWind  0.115707   \n",
       "215  Or33b-Or47a           14uW        male_fed_Wind  0.175575   \n",
       "216  Or33b-Or47a           42uW        male_fed_Wind -0.062354   \n",
       "217  Or33b-Or47a           70uW        male_fed_Wind  0.243238   \n",
       "..           ...            ...                  ...       ...   \n",
       "278  Or42b-Or92a           14uW  male_starved_NoWind  0.046449   \n",
       "279  Or42b-Or92a           42uW  male_starved_NoWind  0.118420   \n",
       "280  Or42b-Or92a           70uW  male_starved_NoWind  0.089857   \n",
       "281  Or42b-Or92a           14uW    male_starved_Wind  0.116081   \n",
       "282  Or42b-Or92a           42uW    male_starved_Wind  0.036940   \n",
       "283  Or42b-Or92a           70uW    male_starved_Wind  0.230282   \n",
       "568  Or67d-Or42b           14uW      male_fed_NoWind  0.153430   \n",
       "569  Or67d-Or42b           42uW      male_fed_NoWind  0.146243   \n",
       "570  Or67d-Or42b           70uW      male_fed_NoWind -0.050129   \n",
       "571  Or67d-Or42b           14uW        male_fed_Wind  0.106305   \n",
       "572  Or67d-Or42b           42uW        male_fed_Wind  0.114467   \n",
       "573  Or67d-Or42b           70uW        male_fed_Wind  0.036860   \n",
       "574  Or67d-Or47a           14uW      male_fed_NoWind  0.101741   \n",
       "575  Or67d-Or47a           42uW      male_fed_NoWind  0.088904   \n",
       "576  Or67d-Or47a           70uW      male_fed_NoWind  0.204553   \n",
       "577  Or67d-Or47a           14uW        male_fed_Wind -0.021457   \n",
       "578  Or67d-Or47a           42uW        male_fed_Wind  0.124415   \n",
       "579  Or67d-Or47a           70uW        male_fed_Wind  0.269796   \n",
       "580  Or67d-Or56a           14uW      male_fed_NoWind  0.112171   \n",
       "581  Or67d-Or56a           42uW      male_fed_NoWind -0.023478   \n",
       "582  Or67d-Or56a           70uW      male_fed_NoWind  0.049944   \n",
       "583  Or67d-Or56a           14uW        male_fed_Wind -0.120080   \n",
       "584  Or67d-Or56a           42uW        male_fed_Wind -0.123595   \n",
       "585  Or67d-Or56a           70uW        male_fed_Wind -0.018397   \n",
       "586  Or67d-Or85d           14uW      male_fed_NoWind  0.139425   \n",
       "587  Or67d-Or85d           42uW      male_fed_NoWind  0.180886   \n",
       "588  Or67d-Or85d           70uW      male_fed_NoWind  0.212938   \n",
       "589  Or67d-Or85d           14uW        male_fed_Wind  0.024093   \n",
       "590  Or67d-Or85d           42uW        male_fed_Wind  0.156768   \n",
       "591  Or67d-Or85d           70uW        male_fed_Wind  0.236576   \n",
       "\n",
       "                                      CIs      pVal  parent_n  parent_std  \\\n",
       "132   [-0.291677932994, -0.0146655087397]  0.035927       104    0.396583   \n",
       "133    [-0.384141807901, -0.105588591293]  0.000988       104    0.407816   \n",
       "134   [-0.336326058505, -0.0503269183438]  0.015697       104    0.447922   \n",
       "135    [-0.419687685966, 0.0137311669682]  0.070189       104    0.594120   \n",
       "136    [-0.485529672452, -0.152204569966]  0.000652       104    0.504756   \n",
       "137    [-0.462123635491, -0.116335738182]  0.001553       104    0.493323   \n",
       "138    [-0.300674820306, 0.0661994773669]  0.180536       103    0.491656   \n",
       "139   [-0.424381516672, -0.0876791382995]  0.002618       103    0.439950   \n",
       "140    [-0.330865646837, 0.0528962597423]  0.102006       103    0.411058   \n",
       "141    [-0.0598644042188, 0.412603137586]  0.120160       103    0.560434   \n",
       "142     [-0.214624245171, 0.269244599645]  0.803470       103    0.620585   \n",
       "143   [-0.555969702105, -0.0724142028174]  0.006787       103    0.559684   \n",
       "144    [-0.149052970832, 0.0698224129509]  0.444311       104    0.298332   \n",
       "145     [-0.20753087712, 0.0255699821177]  0.128486       104    0.355162   \n",
       "146    [-0.229496507183, 0.0180003178812]  0.113630       104    0.406309   \n",
       "147    [-0.370704382188, 0.0869378396362]  0.222218       104    0.647713   \n",
       "148   [-0.404486692257, -0.0579179506308]  0.015323       104    0.548341   \n",
       "149  [-0.340620442573, -0.00916655547989]  0.044902       104    0.469978   \n",
       "150     [-0.17031630868, 0.0996627969785]  0.634348       104    0.460379   \n",
       "151   [-0.327852237949, -0.0540818733326]  0.007395       104    0.404228   \n",
       "152   [-0.429631900653, -0.0606110576221]  0.006286       104    0.454717   \n",
       "153    [-0.571635103188, -0.071658841656]  0.012609       104    0.644176   \n",
       "154    [-0.595687637923, -0.193640555368]  0.000281       104    0.584112   \n",
       "155    [-0.712631195319, -0.327263916125]  0.000002       104    0.578454   \n",
       "212      [-0.13514388386, 0.198818675115]  0.714225       104    0.475575   \n",
       "213     [-0.201701078871, 0.113518267797]  0.540245       104    0.387782   \n",
       "214    [-0.0801880010065, 0.306288867321]  0.184012       104    0.414585   \n",
       "215    [-0.0652803464595, 0.424908431005]  0.158012       156    0.623276   \n",
       "216     [-0.313566514932, 0.197513861469]  0.646824       104    0.625153   \n",
       "217     [0.0119616345403, 0.471488434999]  0.043100       104    0.583113   \n",
       "..                                    ...       ...       ...         ...   \n",
       "278     [-0.119788396423, 0.219946285604]  0.585332        81    0.396665   \n",
       "279    [-0.0400023161909, 0.278997300493]  0.122024        81    0.325682   \n",
       "280    [-0.0776773545973, 0.248990003797]  0.203563        80    0.276623   \n",
       "281     [-0.185813979088, 0.416948363249]  0.481649        81    0.667441   \n",
       "282     [-0.234441500614, 0.310325575155]  0.781590        81    0.542336   \n",
       "283      [0.0193901428363, 0.43689810896]  0.035210        80    0.440618   \n",
       "568     [0.0281289351218, 0.275649955027]  0.014893        93    0.341416   \n",
       "569     [0.0258961050842, 0.269367455944]  0.022276        93    0.374771   \n",
       "570    [-0.180077202647, 0.0736655889723]  0.463449        93    0.409179   \n",
       "571    [-0.0940439604525, 0.293445774012]  0.290950        93    0.549479   \n",
       "572     [-0.0459627126021, 0.27816699939]  0.206023        93    0.537809   \n",
       "573     [-0.129595590635, 0.189521143873]  0.678788        93    0.526516   \n",
       "574    [-0.0515653515614, 0.247915662897]  0.214019        89    0.467249   \n",
       "575    [-0.0556219078205, 0.228643780922]  0.219856        89    0.400450   \n",
       "576     [0.0748953317982, 0.337151978229]  0.003365        89    0.382262   \n",
       "577     [-0.242286280885, 0.185819641873]  0.855042        89    0.644387   \n",
       "578    [-0.0719988972984, 0.327422810887]  0.247899        89    0.574743   \n",
       "579      [0.075618547921, 0.462386163395]  0.008494        89    0.531571   \n",
       "580   [-0.00728939906214, 0.235253836385]  0.065803       104    0.337057   \n",
       "581       [-0.1430741474, 0.082763187294]  0.670527       104    0.299150   \n",
       "582    [-0.0401722485997, 0.142450962134]  0.284745       104    0.259890   \n",
       "583    [-0.299674644143, 0.0637510711943]  0.250313       104    0.562903   \n",
       "584    [-0.281351847175, 0.0267985372807]  0.151054       104    0.488487   \n",
       "585     [-0.155077970542, 0.118774095492]  0.800693       104    0.415257   \n",
       "586    [-0.0128001564427, 0.283134828153]  0.061523        99    0.412823   \n",
       "587     [0.0590672934191, 0.302662788056]  0.012542        99    0.441862   \n",
       "588     [0.0628485147734, 0.360192104074]  0.004648        99    0.416135   \n",
       "589     [-0.207684745258, 0.245947222962]  0.835984        99    0.579474   \n",
       "590    [-0.0619979181664, 0.371454377089]  0.209925        99    0.686416   \n",
       "591     [0.0290282399788, 0.421127983052]  0.032009        99    0.595770   \n",
       "\n",
       "     offspring_n  offspring_std             ExpConditions  \n",
       "132           48       0.396878      14uW_male_fed_NoWind  \n",
       "133           48       0.391766      42uW_male_fed_NoWind  \n",
       "134           48       0.402038      70uW_male_fed_NoWind  \n",
       "135           48       0.607626        14uW_male_fed_Wind  \n",
       "136           48       0.427415        42uW_male_fed_Wind  \n",
       "137           48       0.473330        70uW_male_fed_Wind  \n",
       "138           52       0.536330      14uW_male_fed_NoWind  \n",
       "139           52       0.521558      42uW_male_fed_NoWind  \n",
       "140           52       0.629073      70uW_male_fed_NoWind  \n",
       "141           52       0.600147        14uW_male_fed_Wind  \n",
       "142           52       0.679089        42uW_male_fed_Wind  \n",
       "143           52       0.646382        70uW_male_fed_Wind  \n",
       "144           52       0.335408      14uW_male_fed_NoWind  \n",
       "145           52       0.336266      42uW_male_fed_NoWind  \n",
       "146           52       0.360467      70uW_male_fed_NoWind  \n",
       "147           52       0.626936        14uW_male_fed_Wind  \n",
       "148           52       0.457174        42uW_male_fed_Wind  \n",
       "149           52       0.431853        70uW_male_fed_Wind  \n",
       "150           47       0.356019      14uW_male_fed_NoWind  \n",
       "151           47       0.395549      42uW_male_fed_NoWind  \n",
       "152           47       0.551799      70uW_male_fed_NoWind  \n",
       "153           47       0.657002        14uW_male_fed_Wind  \n",
       "154           47       0.513312        42uW_male_fed_Wind  \n",
       "155           47       0.470097        70uW_male_fed_Wind  \n",
       "212           52       0.459306      14uW_male_fed_NoWind  \n",
       "213           52       0.490303      42uW_male_fed_NoWind  \n",
       "214           52       0.605970      70uW_male_fed_NoWind  \n",
       "215           52       0.659647        14uW_male_fed_Wind  \n",
       "216           52       0.595190        42uW_male_fed_Wind  \n",
       "217           52       0.592478        70uW_male_fed_Wind  \n",
       "..           ...            ...                       ...  \n",
       "278           34       0.408547  14uW_male_starved_NoWind  \n",
       "279           34       0.396837  42uW_male_starved_NoWind  \n",
       "280           34       0.440401  70uW_male_starved_NoWind  \n",
       "281           34       0.599732    14uW_male_starved_Wind  \n",
       "282           34       0.663243    42uW_male_starved_Wind  \n",
       "283           34       0.451220    70uW_male_starved_Wind  \n",
       "568           51       0.374637      14uW_male_fed_NoWind  \n",
       "569           51       0.331207      42uW_male_fed_NoWind  \n",
       "570           51       0.335815      70uW_male_fed_NoWind  \n",
       "571           51       0.509813        14uW_male_fed_Wind  \n",
       "572           51       0.406997        42uW_male_fed_Wind  \n",
       "573           51       0.415179        70uW_male_fed_Wind  \n",
       "574           52       0.428758      14uW_male_fed_NoWind  \n",
       "575           52       0.404277      42uW_male_fed_NoWind  \n",
       "576           52       0.373977      70uW_male_fed_NoWind  \n",
       "577           52       0.548820        14uW_male_fed_Wind  \n",
       "578           52       0.476767        42uW_male_fed_Wind  \n",
       "579           52       0.540418        70uW_male_fed_Wind  \n",
       "580           46       0.347009      14uW_male_fed_NoWind  \n",
       "581           46       0.328256      42uW_male_fed_NoWind  \n",
       "582           46       0.260009      70uW_male_fed_NoWind  \n",
       "583           46       0.448112        14uW_male_fed_Wind  \n",
       "584           46       0.418684        42uW_male_fed_Wind  \n",
       "585           46       0.373782        70uW_male_fed_Wind  \n",
       "586           52       0.428312      14uW_male_fed_NoWind  \n",
       "587           52       0.284853      42uW_male_fed_NoWind  \n",
       "588           52       0.438092      70uW_male_fed_NoWind  \n",
       "589           52       0.596281        14uW_male_fed_Wind  \n",
       "590           52       0.521744        42uW_male_fed_Wind  \n",
       "591           52       0.479912        70uW_male_fed_Wind  \n",
       "\n",
       "[72 rows x 11 columns]"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combinations_df = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['ORNs'].str.contains('-')]\n",
    "combinations_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model the predictions for all combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "\n",
    "    if type(x) == list:\n",
    "        return 1 / (1 + math.exp(-float(x[0]))), 1 / (1 + math.exp(-float(x[1])))\n",
    "    \n",
    "    else:\n",
    "        return 1 / (1 + math.exp(-x))\n",
    "    \n",
    "def calculatePooledMOE(std1,std2,n1=52,n2=52):\n",
    "    \n",
    "    pooledSD = np.sqrt(((n1-1)*(std1**2)+(n2-1)*(std2**2))/(n1+n2-2))\n",
    "    moe = 1.96 * pooledSD * np.sqrt((1./n1)+(1./n2))\n",
    "    \n",
    "    return moe, pooledSD \n",
    "\n",
    "All_ORN_EffectSizes_df['ExpConditions'] = All_ORN_EffectSizes_df['LightIntensity'] + '_' + All_ORN_EffectSizes_df['Sex-Satiety-Air']\n",
    "combinations_df = All_ORN_EffectSizes_df[All_ORN_EffectSizes_df['ORNs'].str.contains('-')]\n",
    "\n",
    "temp = {'ORNs':[], 'LightIntensity':[], 'Sex-Satiety-Air':[], 'ES':[], 'CIs':[], 'pVal':[], 'parent_n':[], 'parent_std':[],\n",
    "            'offspring_n':[], 'offspring_std':[], 'ExpConditions':[]}\n",
    "\n",
    "for i in range(len(combinations_df['ORNs'])):\n",
    "    \n",
    "    ORNs = combinations_df['ORNs'].values[i].split('-')\n",
    "    conditions = combinations_df['ExpConditions'].values[i].replace(\"Wind\", \"Air\")\n",
    "    \n",
    "    ## In case \"Air\" used in the name instead of \"Wind\"\n",
    "    alt_conditions = conditions.replace(\"Wind\", \"Air\")\n",
    "    \n",
    "    ORN1 = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['ORNs'] == ORNs[0]) & ((All_ORN_EffectSizes_df['ExpConditions'] == conditions) | (All_ORN_EffectSizes_df['ExpConditions'] == conditions))]\n",
    "    ORN2 = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['ORNs'] == ORNs[1]) & ((All_ORN_EffectSizes_df['ExpConditions'] == conditions) | (All_ORN_EffectSizes_df['ExpConditions'] == conditions))]\n",
    "    \n",
    "    ## Get ES, SD, N info for the first ORN\n",
    "    ORN1_ES = ORN1['ES'].values[0]\n",
    "    \n",
    "    ORN1_parent_n = ORN1['parent_n'].values[0]\n",
    "    ORN1_offspring_n = ORN1['offspring_n'].values[0]\n",
    "    ORN1_N = (ORN1_parent_n + ORN1_offspring_n) / 2.\n",
    "    \n",
    "    ORN1_parent_sd = ORN1['parent_std'].values[0]\n",
    "    ORN1_offspring_sd = ORN1['offspring_std'].values[0]\n",
    "    \n",
    "    ## Calculate pooled SD for ORN1\n",
    "    m, ORN1_sd = calculatePooledMOE(ORN1_parent_sd, ORN1_offspring_sd, n1=ORN1_parent_n, n2=ORN1_offspring_n)\n",
    "    \n",
    "    ## Get ES, SD, N info for the second ORN\n",
    "    ORN2_ES = ORN2['ES'].values[0]\n",
    "    \n",
    "    ORN2_parent_n = ORN2['parent_n'].values[0]\n",
    "    ORN2_offspring_n = ORN2['offspring_n'].values[0]\n",
    "    ORN2_N = (ORN2_parent_n + ORN2_offspring_n) / 2.\n",
    "    \n",
    "    ORN2_parent_sd = ORN2['parent_std'].values[0]\n",
    "    ORN2_offspring_sd = ORN2['offspring_std'].values[0]\n",
    "    \n",
    "    ## Calculate pooled SD for ORN2\n",
    "    m, ORN2_sd = calculatePooledMOE(ORN2_parent_sd, ORN2_offspring_sd, n1=ORN2_parent_n, n2=ORN2_offspring_n)\n",
    "    \n",
    "    ## Calculate pooled MOE for ORN1 and ORN2 COMBO\n",
    "    ORN1_2_pooled_moe, ORN1_2_pooled_SD = calculatePooledMOE(ORN1_sd, ORN2_sd, n1=ORN1_N, n2=ORN2_N)\n",
    "    \n",
    "    ## Summation model\n",
    "    ORN1_2_summation_ES = ORN1_ES + ORN2_ES\n",
    "    ORN1_2_summation_CIs = [ORN1_2_summation_ES-ORN1_2_pooled_moe, ORN1_2_summation_ES+ORN1_2_pooled_moe]\n",
    "    \n",
    "    ## Maxpooling model -- based on the original values\n",
    "    ORN1_2_maxPooling_ES = max((ORN1_ES), (ORN2_ES))\n",
    "    ORN1_2_maxPooling_CIs = [ORN1_2_maxPooling_ES-ORN1_2_pooled_moe, ORN1_2_maxPooling_ES+ORN1_2_pooled_moe]\n",
    "    \n",
    "    ## Maxpooling model -- based on the absolute values, keep the sign\n",
    "    ORN1_2_maxPooling_abs_signed_ES = max(ORN1_ES, ORN2_ES, key = abs)\n",
    "    ORN1_2_maxPooling_abs_signed_CIs = [ORN1_2_maxPooling_abs_signed_ES - ORN1_2_pooled_moe, ORN1_2_maxPooling_abs_signed_ES + ORN1_2_pooled_moe]\n",
    "   \n",
    "    ## Minpooling model -- based on the original values\n",
    "    ORN1_2_minPooling_ES = min(ORN1_ES, ORN2_ES)\n",
    "    ORN1_2_minPooling_CIs = [ORN1_2_minPooling_ES - ORN1_2_pooled_moe, ORN1_2_minPooling_ES + ORN1_2_pooled_moe]\n",
    "    \n",
    "    ## Minpooling model -- based on the absolute values, keep the sign\n",
    "    ORN1_2_minPooling_abs_signed_ES = min(ORN1_ES, ORN2_ES, key=abs)\n",
    "    ORN1_2_minPooling_abs_signed_CIs = [ORN1_2_minPooling_abs_signed_ES - ORN1_2_pooled_moe, ORN1_2_minPooling_abs_signed_ES + ORN1_2_pooled_moe]\n",
    "    \n",
    "    #### Transformed data points\n",
    "    ORN1_ES_transformed = sigmoid(ORN1_ES)\n",
    "    ORN2_ES_transformed = sigmoid(ORN2_ES)\n",
    "    \n",
    "    ORN1_CIs_transformed = sigmoid(ORN1['CIs'].values[0])\n",
    "    ORN2_CIs_transformed = sigmoid(ORN2['CIs'].values[0])\n",
    "    \n",
    "    combo_ES_transformed = sigmoid(combinations_df['ES'].values[i])\n",
    "    combo_CIs_transformed = sigmoid(combinations_df['CIs'].values[i])\n",
    "    \n",
    "    ORN1_2_pooled_moe_transformed, ORN1_2_pooled_SD = calculatePooledMOE(sigmoid(ORN1_sd), sigmoid(ORN2_sd), n1=ORN1_N, n2=ORN2_N)\n",
    "#     ORN1_2_pooled_moe_transformed = [-.3,.3]\n",
    "    \n",
    "    ## Transformed Summation model\n",
    "    ORN1_2_summation_ES_tra = ORN1_ES_transformed + ORN2_ES_transformed\n",
    "#     ORN1_2_summation_CIs_tra = sigmoid([ORN1_2_summation_ES_tra - ORN1_2_pooled_moe, ORN1_2_summation_ES_tra + ORN1_2_pooled_moe])\n",
    "    ORN1_2_summation_CIs_tra = [ORN1_2_summation_ES_tra - ORN1_2_pooled_moe_transformed, ORN1_2_summation_ES_tra + ORN1_2_pooled_moe_transformed]\n",
    "\n",
    "    ## Transformed  Maxpooling model -- based on the original values\n",
    "    ORN1_2_maxPooling_ES_tra = max((ORN1_ES_transformed), (ORN2_ES_transformed))\n",
    "    ORN1_2_maxPooling_CIs_tra = [ORN1_2_maxPooling_ES_tra - ORN1_2_pooled_moe_transformed, ORN1_2_maxPooling_ES_tra + ORN1_2_pooled_moe_transformed]\n",
    "      \n",
    "    ## Transformed Minpooling model -- based on the original values\n",
    "    ORN1_2_minPooling_ES_tra = min(ORN1_ES_transformed, ORN2_ES_transformed)\n",
    "    ORN1_2_minPooling_CIs_tra = [ORN1_2_minPooling_ES_tra - ORN1_2_pooled_moe_transformed, ORN1_2_minPooling_ES_tra + ORN1_2_pooled_moe_transformed]\n",
    "    \n",
    "    ## Add the new values to the df\n",
    "    \n",
    "    combo_name = combinations_df['ORNs'].values[i]\n",
    "    \n",
    "    temp['ORNs'].extend([ORNs[0] + '-transformed', ORNs[1] + '-transformed', combo_name + '-transformed', \\\n",
    "                         combo_name + '-summation', combo_name + '-maxPooling', combo_name + '-maxPooling_abs_sign',\\\n",
    "                         combo_name + '-minPooling', combo_name + '-minPooling_abs_signed', combo_name + '-summation_trans', \\\n",
    "                         combo_name + '-maxPooling_trans', combo_name + '-minPooling_trans'])\n",
    "    \n",
    "    temp['ES'].extend([ORN1_ES_transformed, ORN2_ES_transformed, combo_ES_transformed, \\\n",
    "                       ORN1_2_summation_ES, ORN1_2_maxPooling_ES, ORN1_2_maxPooling_abs_signed_ES, ORN1_2_minPooling_ES, \\\n",
    "                       ORN1_2_minPooling_abs_signed_ES, ORN1_2_summation_ES_tra, ORN1_2_maxPooling_ES_tra, ORN1_2_minPooling_ES_tra])\n",
    "    \n",
    "    temp['CIs'].extend([ORN1_CIs_transformed, ORN2_CIs_transformed, combo_CIs_transformed, \\\n",
    "                        ORN1_2_summation_CIs, ORN1_2_maxPooling_CIs, ORN1_2_maxPooling_abs_signed_CIs, ORN1_2_minPooling_CIs, \\\n",
    "                        ORN1_2_minPooling_abs_signed_CIs, ORN1_2_summation_CIs_tra, ORN1_2_maxPooling_CIs_tra, ORN1_2_minPooling_CIs_tra])\n",
    "                         \n",
    "    temp['LightIntensity'].extend(11 * [combinations_df['LightIntensity'].values[i]])                      \n",
    "    temp['Sex-Satiety-Air'].extend([ORN1['Sex-Satiety-Air'].values[0], ORN2['Sex-Satiety-Air'].values[0]] + \\\n",
    "                                    9 * [combinations_df['Sex-Satiety-Air'].values[i]])  \n",
    "    \n",
    "    temp['pVal'].extend(11 * [np.nan])\n",
    "    temp['parent_n'].extend(11 * [combinations_df['parent_n'].values[i]])\n",
    "    temp['parent_std'].extend(11 * [combinations_df['parent_std'].values[i]])\n",
    "    temp['offspring_n'].extend(11 * [combinations_df['offspring_n'].values[i]])\n",
    "    temp['offspring_std'].extend(11 * [combinations_df['offspring_std'].values[i]])\n",
    "    temp['ExpConditions'].extend([ORN1['ExpConditions'].values[0], ORN2['ExpConditions'].values[0]] + \\\n",
    "                                 9 * [combinations_df['ExpConditions'].values[i]])  \n",
    "\n",
    "temp_df = pd.DataFrame(temp, columns= ['ORNs', 'LightIntensity', 'Sex-Satiety-Air', 'ES', 'CIs', 'pVal',\n",
    "       'parent_n', 'parent_std', 'offspring_n', 'offspring_std', 'ExpConditions', 'ES_sig', 'CIs_sig'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "dfs = [temp_df, All_ORN_EffectSizes_df]\n",
    "\n",
    "## Concatanated the predictions df for combos and observed effect sizes\n",
    "All_ORN_ES_latest_df = pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>CIs</th>\n",
       "      <th>CIs_sig</th>\n",
       "      <th>ES</th>\n",
       "      <th>ES_sig</th>\n",
       "      <th>ExpConditions</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>ORNs</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>offspring_n</th>\n",
       "      <th>offspring_std</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>[-0.13514388386, 0.198818675115]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.031003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.459306</td>\n",
       "      <td>0.714225</td>\n",
       "      <td>104</td>\n",
       "      <td>0.475575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>[-0.201701078871, 0.113518267797]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.045552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.490303</td>\n",
       "      <td>0.540245</td>\n",
       "      <td>104</td>\n",
       "      <td>0.387782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>[-0.0801880010065, 0.30628886732099997]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.115707</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.605970</td>\n",
       "      <td>0.184012</td>\n",
       "      <td>104</td>\n",
       "      <td>0.414585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>[-0.0652803464595, 0.42490843100500003]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.175575</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_Wind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.659647</td>\n",
       "      <td>0.158012</td>\n",
       "      <td>156</td>\n",
       "      <td>0.623276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>[-0.313566514932, 0.197513861469]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.062354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_Wind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.595190</td>\n",
       "      <td>0.646824</td>\n",
       "      <td>104</td>\n",
       "      <td>0.625153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>[0.0119616345403, 0.471488434999]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.243238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_Wind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or33b-Or47a</td>\n",
       "      <td>male_fed_Wind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.592478</td>\n",
       "      <td>0.043100</td>\n",
       "      <td>104</td>\n",
       "      <td>0.583113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         CIs CIs_sig        ES ES_sig  \\\n",
       "144         [-0.13514388386, 0.198818675115]     NaN  0.031003    NaN   \n",
       "145        [-0.201701078871, 0.113518267797]     NaN -0.045552    NaN   \n",
       "146  [-0.0801880010065, 0.30628886732099997]     NaN  0.115707    NaN   \n",
       "147  [-0.0652803464595, 0.42490843100500003]     NaN  0.175575    NaN   \n",
       "148        [-0.313566514932, 0.197513861469]     NaN -0.062354    NaN   \n",
       "149        [0.0119616345403, 0.471488434999]     NaN  0.243238    NaN   \n",
       "\n",
       "            ExpConditions LightIntensity         ORNs  Sex-Satiety-Air  \\\n",
       "144  14uW_male_fed_NoWind           14uW  Or33b-Or47a  male_fed_NoWind   \n",
       "145  42uW_male_fed_NoWind           42uW  Or33b-Or47a  male_fed_NoWind   \n",
       "146  70uW_male_fed_NoWind           70uW  Or33b-Or47a  male_fed_NoWind   \n",
       "147    14uW_male_fed_Wind           14uW  Or33b-Or47a    male_fed_Wind   \n",
       "148    42uW_male_fed_Wind           42uW  Or33b-Or47a    male_fed_Wind   \n",
       "149    70uW_male_fed_Wind           70uW  Or33b-Or47a    male_fed_Wind   \n",
       "\n",
       "     offspring_n  offspring_std      pVal  parent_n  parent_std  \n",
       "144           52       0.459306  0.714225       104    0.475575  \n",
       "145           52       0.490303  0.540245       104    0.387782  \n",
       "146           52       0.605970  0.184012       104    0.414585  \n",
       "147           52       0.659647  0.158012       156    0.623276  \n",
       "148           52       0.595190  0.646824       104    0.625153  \n",
       "149           52       0.592478  0.043100       104    0.583113  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "All_ORN_ES_latest_df[All_ORN_ES_latest_df['ORNs'] == 'Or33b-Or47a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "singletons = ['Or42b', 'Or47a', 'Or56a', 'Or85d', 'Or33b', 'Or85a', 'Or92a', 'Or67d']\n",
    "\n",
    "combos = [ 'Or33b-Or47a', 'Or33b-Or85a', \n",
    "           'Or42b-Or92a', 'Or67d-Or42b', 'Or67d-Or47a',\n",
    "           'Or67d-Or56a', 'Or67d-Or85d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 216x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Switch the sort function to FALSE while plotting the predictions, use already sorted list \n",
    "## order of singletons/combos instead. To use this, first plot the real singletons, and generate a\n",
    "## list of singletons_trans using the order stored in o1 variable.\n",
    "\n",
    "f1, o1 = WALiForest(All_ORN_ES_latest_df, column='ES', sort = True, sorting_order = list((combos)), fsize=(3,8), c='red', xlim=(0, 0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1.savefig(\"C:/Users/tumkayat/Desktop/Singletons_transformed.pdf\", dpi=1000, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "## use the ordered ORN list from combos to plot the predictions\n",
    "# singletons_trans = [i + '-transformed' for i in o1]\n",
    "combos_trans = [i.split('-')[0] + '-' + i.split('-')[1] + '-transformed' for i in o1]\n",
    "# # All_ORN_ES_latest_df_dropped_duplicate_ES_for_singletons = All_ORN_ES_latest_df.drop_duplicates('ES')\n",
    "\n",
    "# # combos_summation = [i.split('-')[0] + '-' + i.split('-')[1] + '-summation' for i in o1]\n",
    "# # combos_maxPooling = [i.split('-')[0] + '-' + i.split('-')[1] + '-maxPooling' for i in o1]\n",
    "# # combos_maxPooling_abs_sign = [i.split('-')[0] + '-' + i.split('-')[1] + '-maxPooling_abs_sign' for i in o1]\n",
    "# # combos_minPooling = [i.split('-')[0] + '-' + i.split('-')[1] + '-minPooling' for i in o1]\n",
    "# # combos_minPooling_abs_signed = [i.split('-')[0] + '-' + i.split('-')[1] + '-minPooling_abs_signed' for i in o1]\n",
    "# # combos_summation_trans = [i.split('-')[0] + '-' + i.split('-')[1] + '-summation_trans' for i in o1]\n",
    "# # combos_minPooling_trans = [i.split('-')[0] + '-' + i.split('-')[1] + '-minPooling_trans' for i in o1]\n",
    "# # combos_maxPooling_trans = [i.split('-')[0] + '-' + i.split('-')[1] + '-maxPooling_trans' for i in o1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Or67d-Or85d-transformed',\n",
       " 'Or67d-Or47a-transformed',\n",
       " 'Or42b-Or92a-transformed',\n",
       " 'Or33b-Or47a-transformed',\n",
       " 'Or33b-Or85a-transformed',\n",
       " 'Or67d-Or56a-transformed',\n",
       " 'Or67d-Or42b-transformed']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos_trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA for combo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fed_nowind_data = All_ORN_ES_latest_df[(All_ORN_ES_latest_df['Sex-Satiety-Air'] == \"male_fed_NoAir\") \\\n",
    "                                       | (All_ORN_ES_latest_df['Sex-Satiety-Air'] == \"male_fed_NoWind\")]\n",
    "\n",
    "singletons_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in singletons))]\n",
    "# singletons_df = singletons_df.drop_duplicates('ES')\n",
    "\n",
    "combos_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos))]\n",
    "# combos_df = combos_df.drop_duplicates('ES')\n",
    "\n",
    "## Transformed data\n",
    "singletons_trans_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in singletons_trans))]\n",
    "combos_trans_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_trans))]\n",
    "\n",
    "## Predictions\n",
    "combos_summation_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_summation))]\n",
    "combos_maxPooling_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_maxPooling))]\n",
    "combos_maxPooling_abs_sign_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_maxPooling_abs_sign))]\n",
    "combos_minPooling_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_minPooling))]\n",
    "combos_minPooling_abs_signed_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_minPooling_abs_signed))] \n",
    "combos_summation_trans_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_summation_trans))]  \n",
    "combos_minPooling_trans_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_minPooling_trans))] \n",
    "combos_maxPooling_trans_df = fed_nowind_data[(fed_nowind_data['ORNs'].apply(lambda x: x in combos_maxPooling_trans))] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "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>CIs</th>\n",
       "      <th>CIs_sig</th>\n",
       "      <th>ES</th>\n",
       "      <th>ES_sig</th>\n",
       "      <th>ExpConditions</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>ORNs</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>offspring_n</th>\n",
       "      <th>offspring_std</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.09190213559066446, 0.35546212375235553]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.223682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or33b-Or47a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.459306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.475575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[-0.15444230012316223, 0.08577770269516223]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.034332</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or33b-Or47a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.490303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.387782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[-0.013892319061033054, 0.22611155387703305]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.106110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or33b-Or47a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.605970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.414585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>[-0.13853924235969037, 0.15536920277327035]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.008415</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or33b-Or85a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.567649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.384669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>[-0.1841959230674761, 0.07851743010147613]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.052839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or33b-Or85a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.538659</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.420897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>[-0.1232637745207481, 0.13582981015074808]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or33b-Or85a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.634576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.432511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>[0.11834935244103947, 0.42691182754596047]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.272631</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or42b-Or92a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.348482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.354937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>[0.4231945197036909, 0.7068899528909092]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.565042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or42b-Or92a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.421563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.349426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>[-0.01235808330034524, 0.29830888224894525]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.142975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or42b-Or92a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.406607</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.327068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>[0.15539021043187431, 0.41478163307812577]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.285086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or67d-Or42b-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>51</td>\n",
       "      <td>0.374637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>0.341416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>[0.5618185027294333, 0.8045127579765667]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.683166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or67d-Or42b-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>51</td>\n",
       "      <td>0.331207</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>0.374771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>[0.09576698681757395, 0.35158123244902606]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.223674</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or67d-Or42b-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>51</td>\n",
       "      <td>0.335815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93</td>\n",
       "      <td>0.409179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>[0.20459848335928893, 0.4345019389927111]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.319550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or67d-Or47a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.428758</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>0.467249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>[0.19990712221566576, 0.40140651746233413]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.300657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or67d-Or47a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.404277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>0.400450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>[0.36005374871087614, 0.5602644006691239]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.460159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or67d-Or47a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.373977</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89</td>\n",
       "      <td>0.382262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>[-0.1443387630840794, 0.09468510525407942]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or67d-Or56a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>46</td>\n",
       "      <td>0.347009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.337057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>[-0.1299231426361096, 0.07757382835810961]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.026175</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or67d-Or56a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>46</td>\n",
       "      <td>0.328256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.299150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>[0.04581300482878804, 0.264183693346412]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.154998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or67d-Or56a-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>46</td>\n",
       "      <td>0.260009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>104</td>\n",
       "      <td>0.259890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>[-0.21667055742767724, -0.004872462726322774]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.110772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14uW_male_fed_NoWind</td>\n",
       "      <td>14uW</td>\n",
       "      <td>Or67d-Or85d-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.428312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>0.412823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>[-0.08882808745314029, 0.10602458815514025]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.008598</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42uW_male_fed_NoWind</td>\n",
       "      <td>42uW</td>\n",
       "      <td>Or67d-Or85d-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.284853</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>0.441862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>[0.16049265308492056, 0.3730426308404795]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.266768</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70uW_male_fed_NoWind</td>\n",
       "      <td>70uW</td>\n",
       "      <td>Or67d-Or85d-summation</td>\n",
       "      <td>male_fed_NoWind</td>\n",
       "      <td>52</td>\n",
       "      <td>0.438092</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99</td>\n",
       "      <td>0.416135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               CIs CIs_sig        ES ES_sig  \\\n",
       "3       [0.09190213559066446, 0.35546212375235553]     NaN  0.223682    NaN   \n",
       "14     [-0.15444230012316223, 0.08577770269516223]     NaN -0.034332    NaN   \n",
       "25    [-0.013892319061033054, 0.22611155387703305]     NaN  0.106110    NaN   \n",
       "69     [-0.13853924235969037, 0.15536920277327035]     NaN  0.008415    NaN   \n",
       "80      [-0.1841959230674761, 0.07851743010147613]     NaN -0.052839    NaN   \n",
       "91      [-0.1232637745207481, 0.13582981015074808]     NaN  0.006283    NaN   \n",
       "135     [0.11834935244103947, 0.42691182754596047]     NaN  0.272631    NaN   \n",
       "146       [0.4231945197036909, 0.7068899528909092]     NaN  0.565042    NaN   \n",
       "157    [-0.01235808330034524, 0.29830888224894525]     NaN  0.142975    NaN   \n",
       "267     [0.15539021043187431, 0.41478163307812577]     NaN  0.285086    NaN   \n",
       "278       [0.5618185027294333, 0.8045127579765667]     NaN  0.683166    NaN   \n",
       "289     [0.09576698681757395, 0.35158123244902606]     NaN  0.223674    NaN   \n",
       "333      [0.20459848335928893, 0.4345019389927111]     NaN  0.319550    NaN   \n",
       "344     [0.19990712221566576, 0.40140651746233413]     NaN  0.300657    NaN   \n",
       "355      [0.36005374871087614, 0.5602644006691239]     NaN  0.460159    NaN   \n",
       "399     [-0.1443387630840794, 0.09468510525407942]     NaN -0.024827    NaN   \n",
       "410     [-0.1299231426361096, 0.07757382835810961]     NaN -0.026175    NaN   \n",
       "421       [0.04581300482878804, 0.264183693346412]     NaN  0.154998    NaN   \n",
       "465  [-0.21667055742767724, -0.004872462726322774]     NaN -0.110772    NaN   \n",
       "476    [-0.08882808745314029, 0.10602458815514025]     NaN  0.008598    NaN   \n",
       "487      [0.16049265308492056, 0.3730426308404795]     NaN  0.266768    NaN   \n",
       "\n",
       "            ExpConditions LightIntensity                   ORNs  \\\n",
       "3    14uW_male_fed_NoWind           14uW  Or33b-Or47a-summation   \n",
       "14   42uW_male_fed_NoWind           42uW  Or33b-Or47a-summation   \n",
       "25   70uW_male_fed_NoWind           70uW  Or33b-Or47a-summation   \n",
       "69   14uW_male_fed_NoWind           14uW  Or33b-Or85a-summation   \n",
       "80   42uW_male_fed_NoWind           42uW  Or33b-Or85a-summation   \n",
       "91   70uW_male_fed_NoWind           70uW  Or33b-Or85a-summation   \n",
       "135  14uW_male_fed_NoWind           14uW  Or42b-Or92a-summation   \n",
       "146  42uW_male_fed_NoWind           42uW  Or42b-Or92a-summation   \n",
       "157  70uW_male_fed_NoWind           70uW  Or42b-Or92a-summation   \n",
       "267  14uW_male_fed_NoWind           14uW  Or67d-Or42b-summation   \n",
       "278  42uW_male_fed_NoWind           42uW  Or67d-Or42b-summation   \n",
       "289  70uW_male_fed_NoWind           70uW  Or67d-Or42b-summation   \n",
       "333  14uW_male_fed_NoWind           14uW  Or67d-Or47a-summation   \n",
       "344  42uW_male_fed_NoWind           42uW  Or67d-Or47a-summation   \n",
       "355  70uW_male_fed_NoWind           70uW  Or67d-Or47a-summation   \n",
       "399  14uW_male_fed_NoWind           14uW  Or67d-Or56a-summation   \n",
       "410  42uW_male_fed_NoWind           42uW  Or67d-Or56a-summation   \n",
       "421  70uW_male_fed_NoWind           70uW  Or67d-Or56a-summation   \n",
       "465  14uW_male_fed_NoWind           14uW  Or67d-Or85d-summation   \n",
       "476  42uW_male_fed_NoWind           42uW  Or67d-Or85d-summation   \n",
       "487  70uW_male_fed_NoWind           70uW  Or67d-Or85d-summation   \n",
       "\n",
       "     Sex-Satiety-Air  offspring_n  offspring_std  pVal  parent_n  parent_std  \n",
       "3    male_fed_NoWind           52       0.459306   NaN       104    0.475575  \n",
       "14   male_fed_NoWind           52       0.490303   NaN       104    0.387782  \n",
       "25   male_fed_NoWind           52       0.605970   NaN       104    0.414585  \n",
       "69   male_fed_NoWind           52       0.567649   NaN       104    0.384669  \n",
       "80   male_fed_NoWind           52       0.538659   NaN       104    0.420897  \n",
       "91   male_fed_NoWind           52       0.634576   NaN       104    0.432511  \n",
       "135  male_fed_NoWind           52       0.348482   NaN       104    0.354937  \n",
       "146  male_fed_NoWind           52       0.421563   NaN       104    0.349426  \n",
       "157  male_fed_NoWind           52       0.406607   NaN       104    0.327068  \n",
       "267  male_fed_NoWind           51       0.374637   NaN        93    0.341416  \n",
       "278  male_fed_NoWind           51       0.331207   NaN        93    0.374771  \n",
       "289  male_fed_NoWind           51       0.335815   NaN        93    0.409179  \n",
       "333  male_fed_NoWind           52       0.428758   NaN        89    0.467249  \n",
       "344  male_fed_NoWind           52       0.404277   NaN        89    0.400450  \n",
       "355  male_fed_NoWind           52       0.373977   NaN        89    0.382262  \n",
       "399  male_fed_NoWind           46       0.347009   NaN       104    0.337057  \n",
       "410  male_fed_NoWind           46       0.328256   NaN       104    0.299150  \n",
       "421  male_fed_NoWind           46       0.260009   NaN       104    0.259890  \n",
       "465  male_fed_NoWind           52       0.428312   NaN        99    0.412823  \n",
       "476  male_fed_NoWind           52       0.284853   NaN        99    0.441862  \n",
       "487  male_fed_NoWind           52       0.438092   NaN        99    0.416135  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combos_summation_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikits.bootstrap as skb\n",
    "import scipy as sp\n",
    "\n",
    "# import matplotlib.ticker as ticker\n",
    "## http://scipy-lectures.org/packages/statistics/index.html#linear-models-multiple-factors-and-analysis-of-variance\n",
    "\n",
    "x = combos_summation_df['ES']\n",
    "y = combos_df['ES']\n",
    "# z = combos_trans_df['ES']\n",
    "\n",
    "dff = pd.DataFrame({'x':x.values, 'y':y.values})\n",
    "# dff = pd.DataFrame({'x':x.values, 'y':y.values, 'z':z.values})\n",
    "model = sp.stats.linregress(x,y)\n",
    "\n",
    "def r2(x,y):\n",
    "    reg = sp.stats.linregress(x,y)\n",
    "    return reg.rvalue**2\n",
    "\n",
    "def B(x,y):\n",
    "    reg = sp.stats.linregress(x,y)\n",
    "    return reg.slope\n",
    "\n",
    "rsquared_CIs = skb.ci((x, y), \n",
    "              statfunction=r2,  # Our custom function.\n",
    "              n_samples=2000 # Default of 10000 samples is overkill.\n",
    "                     )\n",
    "\n",
    "B_CIs = skb.ci((x, y), \n",
    "              statfunction=B,  # Our custom function.\n",
    "              n_samples=2000 # Default of 10000 samples is overkill.\n",
    "                     )\n",
    "\n",
    "r2 = model.rvalue**2\n",
    "B = model.slope\n",
    "\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "independent = 'min-pooling_transformed'\n",
    "\n",
    "sns.regplot(x, y, ax=ax1, line_kws={'linewidth':1})\n",
    "ax1.set_xlabel(independent, fontsize = 14)\n",
    "ax1.set_ylabel('Combinations', fontsize = 14)\n",
    "ax1.annotate(u'r\\u00b2 = {} [95CI {}, {}] \\n\\u03B2 = {} [95CI {}, {}]  \\np-val = {}'.format(np.round(r2, 2), \\\n",
    "            np.round(rsquared_CIs[0], 2), np.round(rsquared_CIs[1], 2), np.round(B, 2), \\\n",
    "            np.round(B_CIs[0], 2), np.round(B_CIs[1], 2), np.round(model.pvalue, 2)), \n",
    "                xy=(0.4, 0.9),\n",
    "                    horizontalalignment='left',\n",
    "                    fontsize=14,\n",
    "                    xycoords='axes fraction')\n",
    "\n",
    "## https://kite.com/python/docs/matplotlib.ticker.IndexLocator\n",
    "# ax1.xaxis.set_major_locator(ticker.MultipleLocator(0.05))\n",
    "# ax1.yaxis.set_major_locator(ticker.MultipleLocator(0.05))\n",
    "\n",
    "sns.despine()\n",
    "# plt.savefig(\"C:/Users/tumkayat/Desktop/Combinations_v_\" + independent + \".pdf\", dpi=1000, bbox_inches='tight')                                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.14905057, -0.24811418, -0.1861519 , -0.12153447, -0.25708774,\n",
       "       -0.14598908, -0.0416961 , -0.09335905, -0.10767438, -0.03655483,\n",
       "       -0.19718646, -0.24619883,  0.03100262, -0.04555234,  0.11570689,\n",
       "        0.04229588,  0.04764406,  0.05917788,  0.12509855,  0.19162603,\n",
       "        0.17018805,  0.15342967,  0.1462426 , -0.05012936,  0.1017405 ,\n",
       "        0.08890374,  0.20455307,  0.11217088, -0.02347771,  0.04994383,\n",
       "        0.13942509,  0.18088584,  0.21293817,  0.10917297,  0.07649974,\n",
       "       -0.01031827])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Write the multiple linear regression summary into a csv file\n",
    "model_df = (model.summary2().tables[0])\n",
    "model_df.to_csv(\"C:\\\\Users\\\\tumkayat\\\\Desktop\\\\All_combo_ORNs_analyzed/Regressions/Transformed_Summation_and_MinPooling__predict_combos.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bland_altman_plot(m1, m2,\n",
    "                      sd_limit=1.96,\n",
    "                      ax=None,\n",
    "                      scatter_kwds=None,\n",
    "                      mean_line_kwds=None,\n",
    "                      limit_lines_kwds=None):\n",
    "    \"\"\"\n",
    "    Bland-Altman Plot.\n",
    "\n",
    "    A Bland-Altman plot is a graphical method to analyze the differences\n",
    "    between two methods of measurement. The mean of the measures is plotted\n",
    "    against their difference.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    m1, m2: pandas Series or array-like\n",
    "\n",
    "    sd_limit : float, default 1.96\n",
    "        The limit of agreements expressed in terms of the standard deviation of\n",
    "        the differences. If `md` is the mean of the differences, and `sd` is\n",
    "        the standard deviation of those differences, then the limits of\n",
    "        agreement that will be plotted will be\n",
    "                       md - sd_limit * sd, md + sd_limit * sd\n",
    "        The default of 1.96 will produce 95% confidence intervals for the means\n",
    "        of the differences.\n",
    "        If sd_limit = 0, no limits will be plotted, and the ylimit of the plot\n",
    "        defaults to 3 standard deviatons on either side of the mean.\n",
    "\n",
    "    ax: matplotlib.axis, optional\n",
    "        matplotlib axis object to plot on.\n",
    "\n",
    "    scatter_kwargs: keywords\n",
    "        Options to to style the scatter plot. Accepts any keywords for the\n",
    "        matplotlib Axes.scatter plotting method\n",
    "\n",
    "    mean_line_kwds: keywords\n",
    "        Options to to style the scatter plot. Accepts any keywords for the\n",
    "        matplotlib Axes.axhline plotting method\n",
    "\n",
    "    limit_lines_kwds: keywords\n",
    "        Options to to style the scatter plot. Accepts any keywords for the\n",
    "        matplotlib Axes.axhline plotting method\n",
    "\n",
    "   Returns\n",
    "    -------\n",
    "    ax: matplotlib Axis object\n",
    "    \"\"\"\n",
    "\n",
    "    import numpy as np\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    if len(m1) != len(m2):\n",
    "        raise ValueError('m1 does not have the same length as m2.')\n",
    "    if sd_limit < 0:\n",
    "        raise ValueError('sd_limit ({}) is less than 0.'.format(sd_limit))\n",
    "\n",
    "    means = np.mean([m1, m2], axis=0)\n",
    "    diffs = m1 - m2\n",
    "    mean_diff = np.mean(diffs)\n",
    "    std_diff = np.std(diffs, axis=0)\n",
    "\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "\n",
    "    scatter_kwds = scatter_kwds or {}\n",
    "    if 's' not in scatter_kwds:\n",
    "        scatter_kwds['s'] = 20\n",
    "    mean_line_kwds = mean_line_kwds or {}\n",
    "    limit_lines_kwds = limit_lines_kwds or {}\n",
    "    for kwds in [mean_line_kwds, limit_lines_kwds]:\n",
    "        if 'color' not in kwds:\n",
    "            kwds['color'] = 'gray'\n",
    "        if 'linewidth' not in kwds:\n",
    "            kwds['linewidth'] = 1\n",
    "    if 'linestyle' not in mean_line_kwds:\n",
    "        kwds['linestyle'] = '--'\n",
    "    if 'linestyle' not in limit_lines_kwds:\n",
    "        kwds['linestyle'] = ':'\n",
    "\n",
    "    ax.scatter(means, diffs, **scatter_kwds)\n",
    "    ax.axhline(mean_diff, **mean_line_kwds)  # draw mean line.\n",
    "\n",
    "    # Annotate mean line with mean difference.\n",
    "    ax.annotate('mean diff:\\n{}'.format(np.round(mean_diff, 2)),\n",
    "                xy=(0.99, 0.5),\n",
    "                horizontalalignment='right',\n",
    "                verticalalignment='center',\n",
    "                fontsize=14,\n",
    "                xycoords='axes fraction')\n",
    "\n",
    "    if sd_limit > 0:\n",
    "        half_ylim = (1.5 * sd_limit) * std_diff\n",
    "        ax.set_ylim(mean_diff - half_ylim,\n",
    "                    mean_diff + half_ylim)\n",
    "\n",
    "        limit_of_agreement = sd_limit * std_diff\n",
    "        lower = mean_diff - limit_of_agreement\n",
    "        upper = mean_diff + limit_of_agreement\n",
    "        for j, lim in enumerate([lower, upper]):\n",
    "            ax.axhline(lim, **limit_lines_kwds)\n",
    "        ax.annotate('-SD{}: {}'.format(sd_limit, np.round(lower, 2)),\n",
    "                    xy=(0.99, 0.07),\n",
    "                    horizontalalignment='right',\n",
    "                    verticalalignment='bottom',\n",
    "                    fontsize=14,\n",
    "                    xycoords='axes fraction')\n",
    "        ax.annotate('+SD{}: {}'.format(sd_limit, np.round(upper, 2)),\n",
    "                    xy=(0.99, 0.92),\n",
    "                    horizontalalignment='right',\n",
    "                    fontsize=14,\n",
    "                    xycoords='axes fraction')\n",
    "\n",
    "    elif sd_limit == 0:\n",
    "        half_ylim = 3 * std_diff\n",
    "        ax.set_ylim(mean_diff - half_ylim,\n",
    "                    mean_diff + half_ylim)\n",
    "\n",
    "    ax.set_ylabel('Difference', fontsize=15)\n",
    "    ax.set_xlabel('Means', fontsize=15)\n",
    "    ax.tick_params(labelsize=13)\n",
    "    plt.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_bland_altman_plot = bland_altman_plot(combos_df['ES'].values, combos_minPooling_df['ES'].values)\n",
    "\n",
    "title = \"Combinations v. Min-pooling\"\n",
    "my_bland_altman_plot.set_title(title, fontsize=14)\n",
    "plt.savefig(\"C:/Users/tumkayat/Desktop/\" + title + \".pdf\", dpi=1000, bbox_inches='tight')                                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summation and Maxpooling cannot predict combo (non-sense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x0 = np.zeros(33)\n",
    "x1 = combos_summation_df['ES']\n",
    "x2 = combos_maxPooling_df['ES']\n",
    "X = np.column_stack((x0, x1, x2))\n",
    "y = combos_df['ES'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = svm.SVR()\n",
    "clf.fit(X, y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 575,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-20.995045369604064"
      ]
     },
     "execution_count": 575,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "cv = cross_val_score(svm.SVR(), X, y, cv=10)\n",
    "cv.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict combo wTSALE score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x0 = np.ones(len(y))\n",
    "x1 = []\n",
    "x2 = []\n",
    "\n",
    "for i in range(combos_df.shape[0]):\n",
    "    combo = combos_df.iloc[i]['ORNs']\n",
    "    lightInt = combos_df.iloc[i]['LightIntensity']\n",
    "    \n",
    "    singleton_1, singleton_2 = combo.split('-')\n",
    "    \n",
    "    x1.append(singletons_df[(singletons_df['ORNs'] == singleton_1) & (singletons_df['LightIntensity'] == lightInt)]['ES'].values[0])\n",
    "    x2.append(singletons_df[(singletons_df['ORNs'] == singleton_2) & (singletons_df['LightIntensity'] == lightInt)]['ES'].values[0])\n",
    "#     print  singleton_1, singleton_2, lightInt\n",
    "X = np.column_stack((x0, x1, x2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14218519779090794"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "cv = cross_val_score(svm.SVR(), X, y, cv=10, scoring = 'r2')\n",
    "cv.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anaylsis of the Bell paper data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\Anaconda3\\envs\\ipykernel_py2\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_bell = pd.DataFrame.from_csv(\"C:/Users/tumkayat/Desktop/BellnWilson_Digitize.csv\", index_col=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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>Figure</th>\n",
       "      <th>Panel</th>\n",
       "      <th>LightIntensity</th>\n",
       "      <th>Control</th>\n",
       "      <th>Experiment</th>\n",
       "      <th>ORNs</th>\n",
       "      <th>y-axis (mm)</th>\n",
       "      <th>y-axis (max-tick)</th>\n",
       "      <th>Ctrl ES (mm)</th>\n",
       "      <th>Ctrl 95CI (mm)</th>\n",
       "      <th>...</th>\n",
       "      <th>Exp N</th>\n",
       "      <th>Conversion Factor</th>\n",
       "      <th>Ctrl ES</th>\n",
       "      <th>Ctrl 95CI</th>\n",
       "      <th>Exp ES</th>\n",
       "      <th>Exp 95CI</th>\n",
       "      <th>Ctrl SD</th>\n",
       "      <th>Exp SD</th>\n",
       "      <th>Pooled MOE</th>\n",
       "      <th>Delta ES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "      <td>No-Gal4 ctrl</td>\n",
       "      <td>Orco-Gal4</td>\n",
       "      <td>Orco</td>\n",
       "      <td>20.80</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>1.59</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.045865</td>\n",
       "      <td>-0.015288</td>\n",
       "      <td>0.044423</td>\n",
       "      <td>0.093603</td>\n",
       "      <td>0.096159</td>\n",
       "      <td>0.031977</td>\n",
       "      <td>-0.008365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>2</td>\n",
       "      <td>No-Gal4 ctrl</td>\n",
       "      <td>Orco-Gal4</td>\n",
       "      <td>Orco</td>\n",
       "      <td>20.80</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>1.98</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.057115</td>\n",
       "      <td>0.004038</td>\n",
       "      <td>0.026827</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>0.058070</td>\n",
       "      <td>0.030444</td>\n",
       "      <td>0.010962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>3</td>\n",
       "      <td>No-Gal4 ctrl</td>\n",
       "      <td>Orco-Gal4</td>\n",
       "      <td>Orco</td>\n",
       "      <td>20.80</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.49</td>\n",
       "      <td>2.07</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.059712</td>\n",
       "      <td>0.314423</td>\n",
       "      <td>0.100385</td>\n",
       "      <td>0.121860</td>\n",
       "      <td>0.217294</td>\n",
       "      <td>0.060234</td>\n",
       "      <td>0.300288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>4</td>\n",
       "      <td>No-Gal4 ctrl</td>\n",
       "      <td>Orco-Gal4</td>\n",
       "      <td>Orco</td>\n",
       "      <td>20.80</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.78</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>0.483462</td>\n",
       "      <td>0.091442</td>\n",
       "      <td>0.163658</td>\n",
       "      <td>0.197937</td>\n",
       "      <td>0.061493</td>\n",
       "      <td>0.465577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>5</td>\n",
       "      <td>No-Gal4 ctrl</td>\n",
       "      <td>Orco-Gal4</td>\n",
       "      <td>Orco</td>\n",
       "      <td>20.80</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.50</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>0.043269</td>\n",
       "      <td>0.479712</td>\n",
       "      <td>0.094038</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.203557</td>\n",
       "      <td>0.053897</td>\n",
       "      <td>0.471346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>6</td>\n",
       "      <td>No-Gal4 ctrl</td>\n",
       "      <td>Orco-Gal4</td>\n",
       "      <td>Orco</td>\n",
       "      <td>20.80</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.76</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.535673</td>\n",
       "      <td>0.094038</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>0.203557</td>\n",
       "      <td>0.055331</td>\n",
       "      <td>0.531058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>7</td>\n",
       "      <td>No-Gal4 ctrl</td>\n",
       "      <td>Orco-Gal4</td>\n",
       "      <td>Orco</td>\n",
       "      <td>20.80</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.37</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>0.558462</td>\n",
       "      <td>0.092885</td>\n",
       "      <td>0.080651</td>\n",
       "      <td>0.201059</td>\n",
       "      <td>0.052681</td>\n",
       "      <td>0.552115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>8</td>\n",
       "      <td>No-Gal4 ctrl</td>\n",
       "      <td>Orco-Gal4</td>\n",
       "      <td>Orco</td>\n",
       "      <td>20.80</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.23</td>\n",
       "      <td>...</td>\n",
       "      <td>72</td>\n",
       "      <td>0.028846</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.035481</td>\n",
       "      <td>0.505096</td>\n",
       "      <td>0.096635</td>\n",
       "      <td>0.072410</td>\n",
       "      <td>0.209177</td>\n",
       "      <td>0.053926</td>\n",
       "      <td>0.494712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Or42b</td>\n",
       "      <td>15.57</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.045865</td>\n",
       "      <td>-0.003982</td>\n",
       "      <td>0.045344</td>\n",
       "      <td>0.093603</td>\n",
       "      <td>0.080140</td>\n",
       "      <td>0.032972</td>\n",
       "      <td>0.002941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Or42b</td>\n",
       "      <td>15.57</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.057115</td>\n",
       "      <td>-0.009634</td>\n",
       "      <td>0.068080</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>0.120324</td>\n",
       "      <td>0.044230</td>\n",
       "      <td>-0.002711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Or42b</td>\n",
       "      <td>15.57</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.059712</td>\n",
       "      <td>0.122286</td>\n",
       "      <td>0.075273</td>\n",
       "      <td>0.121860</td>\n",
       "      <td>0.133037</td>\n",
       "      <td>0.047438</td>\n",
       "      <td>0.108152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Or42b</td>\n",
       "      <td>15.57</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>0.154014</td>\n",
       "      <td>0.079255</td>\n",
       "      <td>0.163658</td>\n",
       "      <td>0.140075</td>\n",
       "      <td>0.057643</td>\n",
       "      <td>0.136130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Or42b</td>\n",
       "      <td>15.57</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>0.043269</td>\n",
       "      <td>0.176108</td>\n",
       "      <td>0.076044</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.134399</td>\n",
       "      <td>0.041309</td>\n",
       "      <td>0.167743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>6</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Or42b</td>\n",
       "      <td>15.57</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.153500</td>\n",
       "      <td>0.077714</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>0.137351</td>\n",
       "      <td>0.044610</td>\n",
       "      <td>0.148885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>7</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Or42b</td>\n",
       "      <td>15.57</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>0.198202</td>\n",
       "      <td>0.065639</td>\n",
       "      <td>0.080651</td>\n",
       "      <td>0.116010</td>\n",
       "      <td>0.036430</td>\n",
       "      <td>0.191856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1</td>\n",
       "      <td>Or42b</td>\n",
       "      <td>15.57</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012845</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.035481</td>\n",
       "      <td>0.212974</td>\n",
       "      <td>0.062428</td>\n",
       "      <td>0.072410</td>\n",
       "      <td>0.110335</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>0.202589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM2</td>\n",
       "      <td>Or22a</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.045865</td>\n",
       "      <td>0.007752</td>\n",
       "      <td>0.049096</td>\n",
       "      <td>0.093603</td>\n",
       "      <td>0.093724</td>\n",
       "      <td>0.033590</td>\n",
       "      <td>0.014675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM2</td>\n",
       "      <td>Or22a</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.057115</td>\n",
       "      <td>-0.003359</td>\n",
       "      <td>0.070930</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>0.135406</td>\n",
       "      <td>0.045081</td>\n",
       "      <td>0.003564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM2</td>\n",
       "      <td>Or22a</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.059712</td>\n",
       "      <td>0.015375</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.121860</td>\n",
       "      <td>0.106056</td>\n",
       "      <td>0.041160</td>\n",
       "      <td>0.001240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM2</td>\n",
       "      <td>Or22a</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>0.063178</td>\n",
       "      <td>0.059173</td>\n",
       "      <td>0.163658</td>\n",
       "      <td>0.112962</td>\n",
       "      <td>0.051033</td>\n",
       "      <td>0.045294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM2</td>\n",
       "      <td>Or22a</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>0.043269</td>\n",
       "      <td>0.062403</td>\n",
       "      <td>0.060982</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.116415</td>\n",
       "      <td>0.036715</td>\n",
       "      <td>0.054038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>6</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM2</td>\n",
       "      <td>Or22a</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.089793</td>\n",
       "      <td>0.080362</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>0.153411</td>\n",
       "      <td>0.046349</td>\n",
       "      <td>0.085178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>7</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM2</td>\n",
       "      <td>Or22a</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>0.092248</td>\n",
       "      <td>0.080749</td>\n",
       "      <td>0.080651</td>\n",
       "      <td>0.154151</td>\n",
       "      <td>0.043259</td>\n",
       "      <td>0.085902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM2</td>\n",
       "      <td>Or22a</td>\n",
       "      <td>15.48</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012920</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.035481</td>\n",
       "      <td>0.074289</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.072410</td>\n",
       "      <td>0.127267</td>\n",
       "      <td>0.036484</td>\n",
       "      <td>0.063905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA2</td>\n",
       "      <td>Or92a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.045865</td>\n",
       "      <td>0.015247</td>\n",
       "      <td>0.052530</td>\n",
       "      <td>0.093603</td>\n",
       "      <td>0.092842</td>\n",
       "      <td>0.034909</td>\n",
       "      <td>0.022170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA2</td>\n",
       "      <td>Or92a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.057115</td>\n",
       "      <td>-0.052146</td>\n",
       "      <td>0.100064</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>0.176853</td>\n",
       "      <td>0.054420</td>\n",
       "      <td>-0.045223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA2</td>\n",
       "      <td>Or92a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.059712</td>\n",
       "      <td>0.080077</td>\n",
       "      <td>0.022165</td>\n",
       "      <td>0.121860</td>\n",
       "      <td>0.039175</td>\n",
       "      <td>0.035819</td>\n",
       "      <td>0.065942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA2</td>\n",
       "      <td>Or92a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>0.123254</td>\n",
       "      <td>0.084305</td>\n",
       "      <td>0.163658</td>\n",
       "      <td>0.149000</td>\n",
       "      <td>0.058967</td>\n",
       "      <td>0.105370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA2</td>\n",
       "      <td>Or92a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>0.043269</td>\n",
       "      <td>0.091992</td>\n",
       "      <td>0.071236</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.125903</td>\n",
       "      <td>0.039675</td>\n",
       "      <td>0.083627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>6</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA2</td>\n",
       "      <td>Or92a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.085971</td>\n",
       "      <td>0.066496</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>0.117525</td>\n",
       "      <td>0.041081</td>\n",
       "      <td>0.081355</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",
       "      <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>50</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM5</td>\n",
       "      <td>Or85a</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.059712</td>\n",
       "      <td>0.035414</td>\n",
       "      <td>0.062293</td>\n",
       "      <td>0.121860</td>\n",
       "      <td>0.118918</td>\n",
       "      <td>0.043216</td>\n",
       "      <td>0.021279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM5</td>\n",
       "      <td>Or85a</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>0.019618</td>\n",
       "      <td>0.062930</td>\n",
       "      <td>0.163658</td>\n",
       "      <td>0.120134</td>\n",
       "      <td>0.052005</td>\n",
       "      <td>0.001733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM5</td>\n",
       "      <td>Or85a</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>0.043269</td>\n",
       "      <td>0.053885</td>\n",
       "      <td>0.066879</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.127673</td>\n",
       "      <td>0.038894</td>\n",
       "      <td>0.045520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>6</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM5</td>\n",
       "      <td>Or85a</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.053376</td>\n",
       "      <td>0.065223</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>0.124511</td>\n",
       "      <td>0.040825</td>\n",
       "      <td>0.048760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>7</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM5</td>\n",
       "      <td>Or85a</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>0.025223</td>\n",
       "      <td>0.062293</td>\n",
       "      <td>0.080651</td>\n",
       "      <td>0.118918</td>\n",
       "      <td>0.035979</td>\n",
       "      <td>0.018877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM5</td>\n",
       "      <td>Or85a</td>\n",
       "      <td>15.70</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>0.012739</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.035481</td>\n",
       "      <td>0.055032</td>\n",
       "      <td>0.049427</td>\n",
       "      <td>0.072410</td>\n",
       "      <td>0.094356</td>\n",
       "      <td>0.029897</td>\n",
       "      <td>0.044647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DC4 + DP1m</td>\n",
       "      <td>Ir64a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.045865</td>\n",
       "      <td>-0.045740</td>\n",
       "      <td>0.087636</td>\n",
       "      <td>0.093603</td>\n",
       "      <td>0.154888</td>\n",
       "      <td>0.046243</td>\n",
       "      <td>-0.038817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DC4 + DP1m</td>\n",
       "      <td>Ir64a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.057115</td>\n",
       "      <td>-0.010122</td>\n",
       "      <td>0.058808</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>0.103938</td>\n",
       "      <td>0.041670</td>\n",
       "      <td>-0.003199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DC4 + DP1m</td>\n",
       "      <td>Ir64a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.059712</td>\n",
       "      <td>0.046381</td>\n",
       "      <td>0.080205</td>\n",
       "      <td>0.121860</td>\n",
       "      <td>0.141754</td>\n",
       "      <td>0.048925</td>\n",
       "      <td>0.032246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DC4 + DP1m</td>\n",
       "      <td>Ir64a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>0.084177</td>\n",
       "      <td>0.092120</td>\n",
       "      <td>0.163658</td>\n",
       "      <td>0.162814</td>\n",
       "      <td>0.061113</td>\n",
       "      <td>0.066292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DC4 + DP1m</td>\n",
       "      <td>Ir64a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>0.043269</td>\n",
       "      <td>0.086996</td>\n",
       "      <td>0.076233</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.134734</td>\n",
       "      <td>0.041374</td>\n",
       "      <td>0.078630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DC4 + DP1m</td>\n",
       "      <td>Ir64a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.079693</td>\n",
       "      <td>0.142345</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>0.251580</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.075077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>7</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DC4 + DP1m</td>\n",
       "      <td>Ir64a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>0.085330</td>\n",
       "      <td>0.078539</td>\n",
       "      <td>0.080651</td>\n",
       "      <td>0.138810</td>\n",
       "      <td>0.040925</td>\n",
       "      <td>0.078984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>8</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DC4 + DP1m</td>\n",
       "      <td>Ir64a</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.035481</td>\n",
       "      <td>0.093786</td>\n",
       "      <td>0.095452</td>\n",
       "      <td>0.072410</td>\n",
       "      <td>0.168701</td>\n",
       "      <td>0.046084</td>\n",
       "      <td>0.083401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA3</td>\n",
       "      <td>Or67b</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.045865</td>\n",
       "      <td>-0.016400</td>\n",
       "      <td>0.059833</td>\n",
       "      <td>0.093603</td>\n",
       "      <td>0.105750</td>\n",
       "      <td>0.037041</td>\n",
       "      <td>-0.009477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA3</td>\n",
       "      <td>Or67b</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.057115</td>\n",
       "      <td>-0.008456</td>\n",
       "      <td>0.064958</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>0.114807</td>\n",
       "      <td>0.043343</td>\n",
       "      <td>-0.001533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA3</td>\n",
       "      <td>Or67b</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.059712</td>\n",
       "      <td>-0.021525</td>\n",
       "      <td>0.072261</td>\n",
       "      <td>0.121860</td>\n",
       "      <td>0.127715</td>\n",
       "      <td>0.046554</td>\n",
       "      <td>-0.035659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA3</td>\n",
       "      <td>Or67b</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>0.066624</td>\n",
       "      <td>0.087636</td>\n",
       "      <td>0.163658</td>\n",
       "      <td>0.154888</td>\n",
       "      <td>0.059868</td>\n",
       "      <td>0.048739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA3</td>\n",
       "      <td>Or67b</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>0.043269</td>\n",
       "      <td>0.036131</td>\n",
       "      <td>0.072261</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.127715</td>\n",
       "      <td>0.040020</td>\n",
       "      <td>0.027765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>6</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA3</td>\n",
       "      <td>Or67b</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.046381</td>\n",
       "      <td>0.067265</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>0.118883</td>\n",
       "      <td>0.041315</td>\n",
       "      <td>0.041765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>7</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA3</td>\n",
       "      <td>Or67b</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>0.050865</td>\n",
       "      <td>0.066624</td>\n",
       "      <td>0.080651</td>\n",
       "      <td>0.117751</td>\n",
       "      <td>0.036763</td>\n",
       "      <td>0.044519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>4</td>\n",
       "      <td>B</td>\n",
       "      <td>8</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>VA3</td>\n",
       "      <td>Or67b</td>\n",
       "      <td>15.61</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>0.012812</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.035481</td>\n",
       "      <td>0.085330</td>\n",
       "      <td>0.058808</td>\n",
       "      <td>0.072410</td>\n",
       "      <td>0.103938</td>\n",
       "      <td>0.032666</td>\n",
       "      <td>0.074945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1 + VA2</td>\n",
       "      <td>Or42b + Or92a</td>\n",
       "      <td>20.11</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.045865</td>\n",
       "      <td>0.030234</td>\n",
       "      <td>0.105221</td>\n",
       "      <td>0.093603</td>\n",
       "      <td>0.151842</td>\n",
       "      <td>0.049261</td>\n",
       "      <td>0.037157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1 + VA2</td>\n",
       "      <td>Or42b + Or92a</td>\n",
       "      <td>20.11</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>-0.006923</td>\n",
       "      <td>0.057115</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>0.165689</td>\n",
       "      <td>0.116562</td>\n",
       "      <td>0.239101</td>\n",
       "      <td>0.070957</td>\n",
       "      <td>0.012095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1 + VA2</td>\n",
       "      <td>Or42b + Or92a</td>\n",
       "      <td>20.11</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>0.014135</td>\n",
       "      <td>0.059712</td>\n",
       "      <td>0.217802</td>\n",
       "      <td>0.155346</td>\n",
       "      <td>0.121860</td>\n",
       "      <td>0.224175</td>\n",
       "      <td>0.069113</td>\n",
       "      <td>0.203667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1 + VA2</td>\n",
       "      <td>Or42b + Or92a</td>\n",
       "      <td>20.11</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>0.277673</td>\n",
       "      <td>0.140825</td>\n",
       "      <td>0.163658</td>\n",
       "      <td>0.203222</td>\n",
       "      <td>0.075400</td>\n",
       "      <td>0.259788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1 + VA2</td>\n",
       "      <td>Or42b + Or92a</td>\n",
       "      <td>20.11</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>0.008365</td>\n",
       "      <td>0.043269</td>\n",
       "      <td>0.330980</td>\n",
       "      <td>0.137444</td>\n",
       "      <td>0.088305</td>\n",
       "      <td>0.198342</td>\n",
       "      <td>0.057248</td>\n",
       "      <td>0.322614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>6</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1 + VA2</td>\n",
       "      <td>Or42b + Or92a</td>\n",
       "      <td>20.11</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.050769</td>\n",
       "      <td>0.339533</td>\n",
       "      <td>0.156539</td>\n",
       "      <td>0.103611</td>\n",
       "      <td>0.225898</td>\n",
       "      <td>0.065773</td>\n",
       "      <td>0.334917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>7</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1 + VA2</td>\n",
       "      <td>Or42b + Or92a</td>\n",
       "      <td>20.11</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>0.006346</td>\n",
       "      <td>0.039519</td>\n",
       "      <td>0.332770</td>\n",
       "      <td>0.167081</td>\n",
       "      <td>0.080651</td>\n",
       "      <td>0.241111</td>\n",
       "      <td>0.065096</td>\n",
       "      <td>0.326424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>8</td>\n",
       "      <td>From Fig2E</td>\n",
       "      <td>DM1 + VA2</td>\n",
       "      <td>Or42b + Or92a</td>\n",
       "      <td>20.11</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.019891</td>\n",
       "      <td>0.010385</td>\n",
       "      <td>0.035481</td>\n",
       "      <td>0.315067</td>\n",
       "      <td>0.232919</td>\n",
       "      <td>0.072410</td>\n",
       "      <td>0.336120</td>\n",
       "      <td>0.085686</td>\n",
       "      <td>0.304683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>80 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Figure Panel  LightIntensity       Control  Experiment           ORNs  \\\n",
       "0        2     E               1  No-Gal4 ctrl   Orco-Gal4           Orco   \n",
       "1        2     E               2  No-Gal4 ctrl   Orco-Gal4           Orco   \n",
       "2        2     E               3  No-Gal4 ctrl   Orco-Gal4           Orco   \n",
       "3        2     E               4  No-Gal4 ctrl   Orco-Gal4           Orco   \n",
       "4        2     E               5  No-Gal4 ctrl   Orco-Gal4           Orco   \n",
       "5        2     E               6  No-Gal4 ctrl   Orco-Gal4           Orco   \n",
       "6        2     E               7  No-Gal4 ctrl   Orco-Gal4           Orco   \n",
       "7        2     E               8  No-Gal4 ctrl   Orco-Gal4           Orco   \n",
       "8        4     A               1    From Fig2E         DM1          Or42b   \n",
       "9        4     A               2    From Fig2E         DM1          Or42b   \n",
       "10       4     A               3    From Fig2E         DM1          Or42b   \n",
       "11       4     A               4    From Fig2E         DM1          Or42b   \n",
       "12       4     A               5    From Fig2E         DM1          Or42b   \n",
       "13       4     A               6    From Fig2E         DM1          Or42b   \n",
       "14       4     A               7    From Fig2E         DM1          Or42b   \n",
       "15       4     A               8    From Fig2E         DM1          Or42b   \n",
       "16       4     A               1    From Fig2E         DM2          Or22a   \n",
       "17       4     A               2    From Fig2E         DM2          Or22a   \n",
       "18       4     A               3    From Fig2E         DM2          Or22a   \n",
       "19       4     A               4    From Fig2E         DM2          Or22a   \n",
       "20       4     A               5    From Fig2E         DM2          Or22a   \n",
       "21       4     A               6    From Fig2E         DM2          Or22a   \n",
       "22       4     A               7    From Fig2E         DM2          Or22a   \n",
       "23       4     A               8    From Fig2E         DM2          Or22a   \n",
       "24       4     A               1    From Fig2E         VA2          Or92a   \n",
       "25       4     A               2    From Fig2E         VA2          Or92a   \n",
       "26       4     A               3    From Fig2E         VA2          Or92a   \n",
       "27       4     A               4    From Fig2E         VA2          Or92a   \n",
       "28       4     A               5    From Fig2E         VA2          Or92a   \n",
       "29       4     A               6    From Fig2E         VA2          Or92a   \n",
       "..     ...   ...             ...           ...         ...            ...   \n",
       "50       4     A               3    From Fig2E         DM5          Or85a   \n",
       "51       4     A               4    From Fig2E         DM5          Or85a   \n",
       "52       4     A               5    From Fig2E         DM5          Or85a   \n",
       "53       4     A               6    From Fig2E         DM5          Or85a   \n",
       "54       4     A               7    From Fig2E         DM5          Or85a   \n",
       "55       4     A               8    From Fig2E         DM5          Or85a   \n",
       "56       4     B               1    From Fig2E  DC4 + DP1m          Ir64a   \n",
       "57       4     B               2    From Fig2E  DC4 + DP1m          Ir64a   \n",
       "58       4     B               3    From Fig2E  DC4 + DP1m          Ir64a   \n",
       "59       4     B               4    From Fig2E  DC4 + DP1m          Ir64a   \n",
       "60       4     B               5    From Fig2E  DC4 + DP1m          Ir64a   \n",
       "61       4     B               6    From Fig2E  DC4 + DP1m          Ir64a   \n",
       "62       4     B               7    From Fig2E  DC4 + DP1m          Ir64a   \n",
       "63       4     B               8    From Fig2E  DC4 + DP1m          Ir64a   \n",
       "64       4     B               1    From Fig2E         VA3          Or67b   \n",
       "65       4     B               2    From Fig2E         VA3          Or67b   \n",
       "66       4     B               3    From Fig2E         VA3          Or67b   \n",
       "67       4     B               4    From Fig2E         VA3          Or67b   \n",
       "68       4     B               5    From Fig2E         VA3          Or67b   \n",
       "69       4     B               6    From Fig2E         VA3          Or67b   \n",
       "70       4     B               7    From Fig2E         VA3          Or67b   \n",
       "71       4     B               8    From Fig2E         VA3          Or67b   \n",
       "72       5     A               1    From Fig2E   DM1 + VA2  Or42b + Or92a   \n",
       "73       5     A               2    From Fig2E   DM1 + VA2  Or42b + Or92a   \n",
       "74       5     A               3    From Fig2E   DM1 + VA2  Or42b + Or92a   \n",
       "75       5     A               4    From Fig2E   DM1 + VA2  Or42b + Or92a   \n",
       "76       5     A               5    From Fig2E   DM1 + VA2  Or42b + Or92a   \n",
       "77       5     A               6    From Fig2E   DM1 + VA2  Or42b + Or92a   \n",
       "78       5     A               7    From Fig2E   DM1 + VA2  Or42b + Or92a   \n",
       "79       5     A               8    From Fig2E   DM1 + VA2  Or42b + Or92a   \n",
       "\n",
       "    y-axis (mm)  y-axis (max-tick)  Ctrl ES (mm)  Ctrl 95CI (mm)    ...     \\\n",
       "0         20.80                0.6         -0.24            1.59    ...      \n",
       "1         20.80                0.6         -0.24            1.98    ...      \n",
       "2         20.80                0.6          0.49            2.07    ...      \n",
       "3         20.80                0.6          0.62            2.78    ...      \n",
       "4         20.80                0.6          0.29            1.50    ...      \n",
       "5         20.80                0.6          0.16            1.76    ...      \n",
       "6         20.80                0.6          0.22            1.37    ...      \n",
       "7         20.80                0.6          0.36            1.23    ...      \n",
       "8         15.57                0.2           NaN             NaN    ...      \n",
       "9         15.57                0.2           NaN             NaN    ...      \n",
       "10        15.57                0.2           NaN             NaN    ...      \n",
       "11        15.57                0.2           NaN             NaN    ...      \n",
       "12        15.57                0.2           NaN             NaN    ...      \n",
       "13        15.57                0.2           NaN             NaN    ...      \n",
       "14        15.57                0.2           NaN             NaN    ...      \n",
       "15        15.57                0.2           NaN             NaN    ...      \n",
       "16        15.48                0.2           NaN             NaN    ...      \n",
       "17        15.48                0.2           NaN             NaN    ...      \n",
       "18        15.48                0.2           NaN             NaN    ...      \n",
       "19        15.48                0.2           NaN             NaN    ...      \n",
       "20        15.48                0.2           NaN             NaN    ...      \n",
       "21        15.48                0.2           NaN             NaN    ...      \n",
       "22        15.48                0.2           NaN             NaN    ...      \n",
       "23        15.48                0.2           NaN             NaN    ...      \n",
       "24        15.61                0.2           NaN             NaN    ...      \n",
       "25        15.61                0.2           NaN             NaN    ...      \n",
       "26        15.61                0.2           NaN             NaN    ...      \n",
       "27        15.61                0.2           NaN             NaN    ...      \n",
       "28        15.61                0.2           NaN             NaN    ...      \n",
       "29        15.61                0.2           NaN             NaN    ...      \n",
       "..          ...                ...           ...             ...    ...      \n",
       "50        15.70                0.2           NaN             NaN    ...      \n",
       "51        15.70                0.2           NaN             NaN    ...      \n",
       "52        15.70                0.2           NaN             NaN    ...      \n",
       "53        15.70                0.2           NaN             NaN    ...      \n",
       "54        15.70                0.2           NaN             NaN    ...      \n",
       "55        15.70                0.2           NaN             NaN    ...      \n",
       "56        15.61                0.2           NaN             NaN    ...      \n",
       "57        15.61                0.2           NaN             NaN    ...      \n",
       "58        15.61                0.2           NaN             NaN    ...      \n",
       "59        15.61                0.2           NaN             NaN    ...      \n",
       "60        15.61                0.2           NaN             NaN    ...      \n",
       "61        15.61                0.2           NaN             NaN    ...      \n",
       "62        15.61                0.2           NaN             NaN    ...      \n",
       "63        15.61                0.2           NaN             NaN    ...      \n",
       "64        15.61                0.2           NaN             NaN    ...      \n",
       "65        15.61                0.2           NaN             NaN    ...      \n",
       "66        15.61                0.2           NaN             NaN    ...      \n",
       "67        15.61                0.2           NaN             NaN    ...      \n",
       "68        15.61                0.2           NaN             NaN    ...      \n",
       "69        15.61                0.2           NaN             NaN    ...      \n",
       "70        15.61                0.2           NaN             NaN    ...      \n",
       "71        15.61                0.2           NaN             NaN    ...      \n",
       "72        20.11                0.4           NaN             NaN    ...      \n",
       "73        20.11                0.4           NaN             NaN    ...      \n",
       "74        20.11                0.4           NaN             NaN    ...      \n",
       "75        20.11                0.4           NaN             NaN    ...      \n",
       "76        20.11                0.4           NaN             NaN    ...      \n",
       "77        20.11                0.4           NaN             NaN    ...      \n",
       "78        20.11                0.4           NaN             NaN    ...      \n",
       "79        20.11                0.4           NaN             NaN    ...      \n",
       "\n",
       "    Exp N  Conversion Factor   Ctrl ES  Ctrl 95CI    Exp ES  Exp 95CI  \\\n",
       "0      72           0.028846 -0.006923   0.045865 -0.015288  0.044423   \n",
       "1      72           0.028846 -0.006923   0.057115  0.004038  0.026827   \n",
       "2      72           0.028846  0.014135   0.059712  0.314423  0.100385   \n",
       "3      72           0.028846  0.017885   0.080192  0.483462  0.091442   \n",
       "4      72           0.028846  0.008365   0.043269  0.479712  0.094038   \n",
       "5      72           0.028846  0.004615   0.050769  0.535673  0.094038   \n",
       "6      72           0.028846  0.006346   0.039519  0.558462  0.092885   \n",
       "7      72           0.028846  0.010385   0.035481  0.505096  0.096635   \n",
       "8      48           0.012845 -0.006923   0.045865 -0.003982  0.045344   \n",
       "9      48           0.012845 -0.006923   0.057115 -0.009634  0.068080   \n",
       "10     48           0.012845  0.014135   0.059712  0.122286  0.075273   \n",
       "11     48           0.012845  0.017885   0.080192  0.154014  0.079255   \n",
       "12     48           0.012845  0.008365   0.043269  0.176108  0.076044   \n",
       "13     48           0.012845  0.004615   0.050769  0.153500  0.077714   \n",
       "14     48           0.012845  0.006346   0.039519  0.198202  0.065639   \n",
       "15     48           0.012845  0.010385   0.035481  0.212974  0.062428   \n",
       "16     56           0.012920 -0.006923   0.045865  0.007752  0.049096   \n",
       "17     56           0.012920 -0.006923   0.057115 -0.003359  0.070930   \n",
       "18     56           0.012920  0.014135   0.059712  0.015375  0.055556   \n",
       "19     56           0.012920  0.017885   0.080192  0.063178  0.059173   \n",
       "20     56           0.012920  0.008365   0.043269  0.062403  0.060982   \n",
       "21     56           0.012920  0.004615   0.050769  0.089793  0.080362   \n",
       "22     56           0.012920  0.006346   0.039519  0.092248  0.080749   \n",
       "23     56           0.012920  0.010385   0.035481  0.074289  0.066667   \n",
       "24     48           0.012812 -0.006923   0.045865  0.015247  0.052530   \n",
       "25     48           0.012812 -0.006923   0.057115 -0.052146  0.100064   \n",
       "26     48           0.012812  0.014135   0.059712  0.080077  0.022165   \n",
       "27     48           0.012812  0.017885   0.080192  0.123254  0.084305   \n",
       "28     48           0.012812  0.008365   0.043269  0.091992  0.071236   \n",
       "29     48           0.012812  0.004615   0.050769  0.085971  0.066496   \n",
       "..    ...                ...       ...        ...       ...       ...   \n",
       "50     56           0.012739  0.014135   0.059712  0.035414  0.062293   \n",
       "51     56           0.012739  0.017885   0.080192  0.019618  0.062930   \n",
       "52     56           0.012739  0.008365   0.043269  0.053885  0.066879   \n",
       "53     56           0.012739  0.004615   0.050769  0.053376  0.065223   \n",
       "54     56           0.012739  0.006346   0.039519  0.025223  0.062293   \n",
       "55     56           0.012739  0.010385   0.035481  0.055032  0.049427   \n",
       "56     48           0.012812 -0.006923   0.045865 -0.045740  0.087636   \n",
       "57     48           0.012812 -0.006923   0.057115 -0.010122  0.058808   \n",
       "58     48           0.012812  0.014135   0.059712  0.046381  0.080205   \n",
       "59     48           0.012812  0.017885   0.080192  0.084177  0.092120   \n",
       "60     48           0.012812  0.008365   0.043269  0.086996  0.076233   \n",
       "61     48           0.012812  0.004615   0.050769  0.079693  0.142345   \n",
       "62     48           0.012812  0.006346   0.039519  0.085330  0.078539   \n",
       "63     48           0.012812  0.010385   0.035481  0.093786  0.095452   \n",
       "64     48           0.012812 -0.006923   0.045865 -0.016400  0.059833   \n",
       "65     48           0.012812 -0.006923   0.057115 -0.008456  0.064958   \n",
       "66     48           0.012812  0.014135   0.059712 -0.021525  0.072261   \n",
       "67     48           0.012812  0.017885   0.080192  0.066624  0.087636   \n",
       "68     48           0.012812  0.008365   0.043269  0.036131  0.072261   \n",
       "69     48           0.012812  0.004615   0.050769  0.046381  0.067265   \n",
       "70     48           0.012812  0.006346   0.039519  0.050865  0.066624   \n",
       "71     48           0.012812  0.010385   0.035481  0.085330  0.058808   \n",
       "72     32           0.019891 -0.006923   0.045865  0.030234  0.105221   \n",
       "73     32           0.019891 -0.006923   0.057115  0.005172  0.165689   \n",
       "74     32           0.019891  0.014135   0.059712  0.217802  0.155346   \n",
       "75     32           0.019891  0.017885   0.080192  0.277673  0.140825   \n",
       "76     32           0.019891  0.008365   0.043269  0.330980  0.137444   \n",
       "77     32           0.019891  0.004615   0.050769  0.339533  0.156539   \n",
       "78     32           0.019891  0.006346   0.039519  0.332770  0.167081   \n",
       "79     32           0.019891  0.010385   0.035481  0.315067  0.232919   \n",
       "\n",
       "     Ctrl SD    Exp SD  Pooled MOE  Delta ES  \n",
       "0   0.093603  0.096159    0.031977 -0.008365  \n",
       "1   0.116562  0.058070    0.030444  0.010962  \n",
       "2   0.121860  0.217294    0.060234  0.300288  \n",
       "3   0.163658  0.197937    0.061493  0.465577  \n",
       "4   0.088305  0.203557    0.053897  0.471346  \n",
       "5   0.103611  0.203557    0.055331  0.531058  \n",
       "6   0.080651  0.201059    0.052681  0.552115  \n",
       "7   0.072410  0.209177    0.053926  0.494712  \n",
       "8   0.093603  0.080140    0.032972  0.002941  \n",
       "9   0.116562  0.120324    0.044230 -0.002711  \n",
       "10  0.121860  0.133037    0.047438  0.108152  \n",
       "11  0.163658  0.140075    0.057643  0.136130  \n",
       "12  0.088305  0.134399    0.041309  0.167743  \n",
       "13  0.103611  0.137351    0.044610  0.148885  \n",
       "14  0.080651  0.116010    0.036430  0.191856  \n",
       "15  0.072410  0.110335    0.033898  0.202589  \n",
       "16  0.093603  0.093724    0.033590  0.014675  \n",
       "17  0.116562  0.135406    0.045081  0.003564  \n",
       "18  0.121860  0.106056    0.041160  0.001240  \n",
       "19  0.163658  0.112962    0.051033  0.045294  \n",
       "20  0.088305  0.116415    0.036715  0.054038  \n",
       "21  0.103611  0.153411    0.046349  0.085178  \n",
       "22  0.080651  0.154151    0.043259  0.085902  \n",
       "23  0.072410  0.127267    0.036484  0.063905  \n",
       "24  0.093603  0.092842    0.034909  0.022170  \n",
       "25  0.116562  0.176853    0.054420 -0.045223  \n",
       "26  0.121860  0.039175    0.035819  0.065942  \n",
       "27  0.163658  0.149000    0.058967  0.105370  \n",
       "28  0.088305  0.125903    0.039675  0.083627  \n",
       "29  0.103611  0.117525    0.041081  0.081355  \n",
       "..       ...       ...         ...       ...  \n",
       "50  0.121860  0.118918    0.043216  0.021279  \n",
       "51  0.163658  0.120134    0.052005  0.001733  \n",
       "52  0.088305  0.127673    0.038894  0.045520  \n",
       "53  0.103611  0.124511    0.040825  0.048760  \n",
       "54  0.080651  0.118918    0.035979  0.018877  \n",
       "55  0.072410  0.094356    0.029897  0.044647  \n",
       "56  0.093603  0.154888    0.046243 -0.038817  \n",
       "57  0.116562  0.103938    0.041670 -0.003199  \n",
       "58  0.121860  0.141754    0.048925  0.032246  \n",
       "59  0.163658  0.162814    0.061113  0.066292  \n",
       "60  0.088305  0.134734    0.041374  0.078630  \n",
       "61  0.103611  0.251580    0.068182  0.075077  \n",
       "62  0.080651  0.138810    0.040925  0.078984  \n",
       "63  0.072410  0.168701    0.046084  0.083401  \n",
       "64  0.093603  0.105750    0.037041 -0.009477  \n",
       "65  0.116562  0.114807    0.043343 -0.001533  \n",
       "66  0.121860  0.127715    0.046554 -0.035659  \n",
       "67  0.163658  0.154888    0.059868  0.048739  \n",
       "68  0.088305  0.127715    0.040020  0.027765  \n",
       "69  0.103611  0.118883    0.041315  0.041765  \n",
       "70  0.080651  0.117751    0.036763  0.044519  \n",
       "71  0.072410  0.103938    0.032666  0.074945  \n",
       "72  0.093603  0.151842    0.049261  0.037157  \n",
       "73  0.116562  0.239101    0.070957  0.012095  \n",
       "74  0.121860  0.224175    0.069113  0.203667  \n",
       "75  0.163658  0.203222    0.075400  0.259788  \n",
       "76  0.088305  0.198342    0.057248  0.322614  \n",
       "77  0.103611  0.225898    0.065773  0.334917  \n",
       "78  0.080651  0.241111    0.065096  0.326424  \n",
       "79  0.072410  0.336120    0.085686  0.304683  \n",
       "\n",
       "[80 rows x 23 columns]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Revert the 95CIs to Standart Deviation(SD)\n",
    "from math import *\n",
    "\n",
    "def calculateSTD(moe, n):\n",
    "    # moe = z * (SD / N**1/2)\n",
    "    # SD = (moe * N**1/2) / z\n",
    "    std = ((moe / 1.) * sqrt(n)) / 1.96\n",
    "    return std\n",
    "\n",
    "df_bell['Ctrl SD'] = df_bell.apply(lambda x: calculateSTD(x['Ctrl 95CI'], x['Ctrl N']), axis=1)\n",
    "df_bell['Exp SD'] = df_bell.apply(lambda x: calculateSTD(x['Exp 95CI'], x['Exp N']), axis=1)\n",
    "\n",
    "## Using the SD for Ctrl and Exp, calculate pooled SD (and 95CIs) for each Mean Difference\n",
    "\n",
    "def calculatePooledMOE(std1, std2, n1 = 52, n2 = 52):\n",
    "    \n",
    "    pooledSD = np.sqrt(((n1-1)*(std1**2) + (n2-1)*(std2**2)) / (n1+n2-2))\n",
    "    moe = 1.96 * pooledSD / np.sqrt(n1 + n2)\n",
    "    \n",
    "    return moe\n",
    "\n",
    "df_bell['Pooled MOE'] = df_bell.apply(lambda x: calculatePooledMOE(x['Ctrl SD'], x['Exp SD'], \\\n",
    "                                                                    x['Ctrl N'], x['Exp N']), axis=1)\n",
    "\n",
    "df_bell['Delta ES'] = df_bell['Exp ES'] - df_bell['Ctrl ES']\n",
    "df_bell = df_bell.rename(columns={'Light Int': 'LightIntensity'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4uDjs3r0b/fr1w+jRo9GpUyf7z2tqarB69WqUl5dj586dAIAVK1bg+PHj2LFj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\nkq23AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fc2fda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ORN_order = df_bell['ORNs'].unique()\n",
    "f1, o1 = WALiForest(df_bell, column='Delta ES', sort_by=None, sort_by_order = ORN_order[::-1], fsize=(3,15), gap_thru_yaxis= 3, offset = .3, c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f1.savefig(\"C:/Users/tumkayat/Desktop/BellWilson_plot.pdf\", dpi=1000, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the diff between observed and predicted vaues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = pd.DataFrame.from_csv(\"C:/Users/tumkayat/Desktop/Or33b-Or47a_combo_air.csv\")\n",
    "combo = ['Or33b','Or47a']\n",
    "intensities = ['14uW','42uW','70uW']\n",
    "\n",
    "temp = {'ORNs':[], 'Int':[], 'summation_diff':[], 'maxpool_diff': [], 'minimization_diff': []} #,'Sum':[]}\n",
    "\n",
    "for i in intensities:\n",
    "    observed_ES = a[(a['ORNs'] == combo[0] + '-' + combo[1]) & (a['LightIntensity'] == i)]['ES'].values[0]\n",
    "    summation_ES = a[(a['ORNs'] == combo[0] + '_' + combo[1] + '-summation') & (a['LightIntensity'] == i)]['ES'].values[0]\n",
    "    maxpool_ES = a[(a['ORNs'] == combo[0] + '_' + combo[1] + '-maxPooling') & (a['LightIntensity'] == i)]['ES'].values[0]\n",
    "    minimization_ES = a[(a['ORNs'] == combo[0] + '_' + combo[1] + '-minimization') & (a['LightIntensity'] == i)]['ES'].values[0]\n",
    "    \n",
    "    temp['ORNs'].append(combo[0] + '-' + combo[1])\n",
    "    temp['Int'].append(i)\n",
    "    temp['summation_diff'].append(abs(observed_ES - summation_ES))\n",
    "    temp['maxpool_diff'].append(abs(observed_ES - maxpool_ES))\n",
    "    temp['minimization_diff'].append(abs(observed_ES - minimization_ES))\n",
    "#     temp['Sum'].append('NA')\n",
    "    \n",
    "temp['ORNs'].append('TotalDiff')\n",
    "temp['Int'].append('NA')\n",
    "temp['summation_diff'].append(np.sum(temp['summation_diff']))\n",
    "temp['maxpool_diff'].append(np.sum(temp['maxpool_diff']))\n",
    "temp['minimization_diff'].append(np.sum(temp['minimization_diff']))\n",
    "\n",
    "combo_df = pd.DataFrame(temp)\n",
    "combo_df.to_csv(\"C:/Users/tumkayat/Desktop/\" + combo[0] + '-' + combo[1] + \".csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.sum(temp['maxpool_diff'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replicated ORN data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Select data if Sex-Satiety-Air column contains 'asghar' or '2' in it.\n",
    "df_replication = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric']=='weighted_TSALE') & ((All_ORN_EffectSizes_df['Sex-Satiety-Air'].str.contains(\"-asghar\"))\n",
    "                            | (All_ORN_EffectSizes_df['Sex-Satiety-Air'].str.contains(\"2\")))]\n",
    "df_replication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_replication_male_fed_NoAir = df_replication[((df_replication['Sex-Satiety-Air'] == (\"male_fed-asghar_NoAir\"))\n",
    "                            | (df_replication['Sex-Satiety-Air'] == (\"male_fed2_NoAir\")))]\n",
    "\n",
    "df_replication_male_fed_Air = df_replication[((df_replication['Sex-Satiety-Air'] == (\"male_fed-asghar_Air\"))\n",
    "                            | (df_replication['Sex-Satiety-Air'] == (\"male_fed2_Air\")))]\n",
    "\n",
    "df_replication_male_starved_NoAir = df_replication[((df_replication['Sex-Satiety-Air'] == (\"male_starved-asghar_NoAir\"))\n",
    "                            | (df_replication['Sex-Satiety-Air'] == (\"male_starved2_NoAir\")))]\n",
    "\n",
    "df_replication_male_starved_Air = df_replication[((df_replication['Sex-Satiety-Air'] == (\"male_starved-asghar_Air\"))\n",
    "                            | (df_replication['Sex-Satiety-Air'] == (\"male_starved2_Air\")))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Replication female Or67d\n",
    "df_replication_female = df_replication[df_replication['Sex-Satiety-Air'].str.contains('female')]\n",
    "\n",
    "df_replication_female_fed_NoAir = df_replication_female[df_replication_female['Sex-Satiety-Air'] == ('female_fed-asghar_NoAir')]\n",
    "df_replication_female_fed_Air = df_replication_female[df_replication_female['Sex-Satiety-Air'] == ('female_fed-asghar_Air')]\n",
    "df_replication_female_starved_NoAir = df_replication_female[df_replication_female['Sex-Satiety-Air'] == ('female_starved-asghar_NoAir')]\n",
    "df_replication_female_starved_Air = df_replication_female[df_replication_female['Sex-Satiety-Air'] == ('female_starved-asghar_Air')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Original female Or67d\n",
    "df_female_Or67d_fed_NoAir = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric']=='weighted_TSALE') & (All_ORN_EffectSizes_df['ORNs'] == (\"Or67d\"))\n",
    "                            & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == (\"female_fed_NoAir\"))]\n",
    "\n",
    "df_female_Or67d_fed_Air = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric']=='weighted_TSALE') & (All_ORN_EffectSizes_df['ORNs'] == (\"Or67d\"))\n",
    "                            & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == (\"female_fed_Air\"))]\n",
    "\n",
    "df_female_Or67d_starved_NoAir = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric']=='weighted_TSALE') & (All_ORN_EffectSizes_df['ORNs'] == (\"Or67d\"))\n",
    "                            & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == (\"female_starved_NoAir\"))]\n",
    "\n",
    "df_female_Or67d_starved_Air = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric']=='weighted_TSALE') & (All_ORN_EffectSizes_df['ORNs'] == (\"Or67d\"))\n",
    "                            & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == (\"female_starved_Air\"))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len(df_replication_male_fed_NoAir) + len(df_replication_male_fed_Air) + len(df_replication_male_starved_NoAir) + len(df_replication_male_starved_Air)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "column = 'ES'\n",
    "f, ORNorder = WALiForest(df_replication_male_starved_Air,column,sort_by=False, sort_by_order=order)\n",
    "order = ORNorder\n",
    "plt.title('male_starved_Air')\n",
    "plt.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/ForestPlots/deltaPlots/ORNsReplications/\" + 'male_starved_Air' + '.pdf',dpi=1000,bbox_inches='tight')                                    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = data[(data['ORNs'] == 'Or88a') & (data['LightInt'] == '70uW')]['male_fed_noair']\n",
    "y = data[(data['ORNs'] == 'Or88a') & (data['LightInt'] == '70uW')]['male_fed_air']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[(data['ORNs'] == 'Or88a') & (data['LightInt'] == '70uW')]['delta_male_fed_Noair_V_male_fed_air']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "All_ORN_EffectSizes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_df = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['Metric'] == 'weighted_TSALE') & (All_ORN_EffectSizes_df['ORNs'] == 'Or42b')\n",
    "                      & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == 'male_fed_Air')\n",
    "                      \n",
    "                      ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = [14,42,70]\n",
    "y = y_df['ES'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = interp1d(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xnew = np.linspace(14, 70, num=100, endpoint=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.plot(x, y, 'o', xnew, f(xnew), '-')\n",
    "plt.legend(['data', 'linear'], loc='best')\n",
    "plt.ylim(ymin=0)\n",
    "plt.xlabel('Light Intensity (uW/mm2)')\n",
    "plt.ylabel('weighted_TSALE')\n",
    "sns.set(style=\"ticks\")\n",
    "sns.despine(trim=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f(xnew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "t = pd.read_pickle('K:/ACC/Tayfuntumkaya/DATA/CompleteWALiSARORNData_Analyzed/Gr63a/weighted_TSALE/weighted_TSALE_values.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t['Sex-Satiety-Wind'] = t['Sex'] + '-' + t['Satiety'] +'-' +  t['Wind status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k = t[(t['Sex-Satiety-Wind'] == 'male-fed-Air') & (t['Genotype'] == 'w1118-UAS-CsChrimson') & (t['Light Intensity(uW/mm2)'] == '14uW')]['weighted_TSALE_P10']\n",
    "l = t[(t['Sex-Satiety-Wind'] == 'male-fed-Air') & (t['Genotype'] == 'w1118-Gr63a-Gal4') & (t['Light Intensity(uW/mm2)'] == '14uW')]['weighted_TSALE_P10']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = t['Genotype'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(a)):\n",
    "    if (('w1118' in a[i]) or ('W1118' in a[i])) and ('UAS' in a[i]):\n",
    "        responder_ctrl =  a[i]\n",
    "        \n",
    "    if (('w1118' in a[i]) or ('W1118' in a[i])) and (('Gal4' in a[i]) or ('gal4' in a[i])) :\n",
    "        driver_ctrl =  a[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "driver_ctrl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dabest.bootstrap_tools.bootstrap(k.dropna().values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyses of the control groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def GetGenotypeMeans(temp, rootDirectory):\n",
    "\n",
    "    ornList = os.listdir(rootDirectory)\n",
    "    bar = progressbar.ProgressBar()\n",
    "\n",
    "    for i in bar(range(len(ornList))):\n",
    "        ## read the data in a df\n",
    "        ORN = ornList[i]\n",
    "        \n",
    "        print '%s is in progress...' %(ORN)\n",
    "        \n",
    "        rootDir = os.path.join(rootDirectory,ORN)\n",
    "        df = pd.read_pickle(rootDir + '/weighted_TSALE_values.pkl')\n",
    "\n",
    "        ## combined these variables so can choose groups of data\n",
    "        df['Sex-Satiety-Wind-Intensity'] = df['Sex'] + '-' + df['Satiety'] + '-' + df['Wind status'] + '-' + df['Light Intensity(uW/mm2)']\n",
    "\n",
    "        ## detect responder and driver control genetoypes\n",
    "        genotypes = df['Genotype'].unique()\n",
    "        for i in range(len(genotypes)):\n",
    "            if (('w1118' in genotypes[i]) or ('W1118' in genotypes[i])) and ('UAS' in genotypes[i]):\n",
    "                responder_ctrl =  genotypes[i]\n",
    "\n",
    "            elif (('w1118' in genotypes[i]) or ('W1118' in genotypes[i])) and (('Gal4' in genotypes[i]) or ('gal4' in genotypes[i])):\n",
    "                driver_ctrl =  genotypes[i]\n",
    "\n",
    "            elif (('UAS' in genotypes[i]) or ('uas' in genotypes[i])) and (('Gal4' in genotypes[i]) or ('gal4' in genotypes[i])):\n",
    "                experiment = genotypes[i]\n",
    "\n",
    "        conditions = df['Sex-Satiety-Wind-Intensity'].unique()\n",
    "\n",
    "        if len(conditions) == 12:\n",
    "\n",
    "            for c in conditions:\n",
    "                responder_ctrl_values_P10 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == responder_ctrl)]['weighted_TSALE_P10']\n",
    "                responder_ctrl_values_P01 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == responder_ctrl)]['weighted_TSALE_P01']\n",
    "\n",
    "                driver_ctrl_values_P10 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == driver_ctrl)]['weighted_TSALE_P10']\n",
    "                driver_ctrl_values_P01 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == driver_ctrl)]['weighted_TSALE_P01']\n",
    "\n",
    "                exp_values_P10 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == experiment)]['weighted_TSALE_P10']\n",
    "                exp_values_P01 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == experiment)]['weighted_TSALE_P01']\n",
    "                try:\n",
    "                    ResponderCtrl_means_P01 = dabest.bootstrap_tools.bootstrap(responder_ctrl_values_P01.dropna().values).summary\n",
    "                    temp['weighted-TSALE'].append(ResponderCtrl_means_P01)\n",
    "                    temp['Status'].append('Responder')\n",
    "                    temp['Epoch'].append('P01')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(responder_ctrl)\n",
    "                    temp['ORN'].append(ORN)\n",
    "                    \n",
    "                    ResponderCtrl_means_P10 = dabest.bootstrap_tools.bootstrap(responder_ctrl_values_P10.dropna().values).summary\n",
    "                    temp['weighted-TSALE'].append(ResponderCtrl_means_P10)\n",
    "                    temp['Status'].append('Responder')\n",
    "                    temp['Epoch'].append('P10')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(responder_ctrl)\n",
    "                    temp['ORN'].append(ORN)\n",
    "                except:\n",
    "                    temp['weighted-TSALE'].append(np.nan)\n",
    "                    temp['Status'].append('Responder')\n",
    "                    temp['Epoch'].append('P01')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(responder_ctrl)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                    temp['weighted-TSALE'].append(np.nan)\n",
    "                    temp['Status'].append('Responder')\n",
    "                    temp['Epoch'].append('P10')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(responder_ctrl)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                try:   \n",
    "                    DriverCtrl_means_P01 = dabest.bootstrap_tools.bootstrap(driver_ctrl_values_P01.dropna().values).summary\n",
    "                    temp['weighted-TSALE'].append(DriverCtrl_means_P01)\n",
    "                    temp['Status'].append('Driver')\n",
    "                    temp['Epoch'].append('P01')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(driver_ctrl)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                    DriverCtrl_means_P10 = dabest.bootstrap_tools.bootstrap(driver_ctrl_values_P10.dropna().values).summary\n",
    "                    temp['weighted-TSALE'].append(DriverCtrl_means_P10)\n",
    "                    temp['Status'].append('Driver')\n",
    "                    temp['Epoch'].append('P10')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(driver_ctrl)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                except: \n",
    "                    temp['weighted-TSALE'].append(np.nan)\n",
    "                    temp['Status'].append('Driver')\n",
    "                    temp['Epoch'].append('P01')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(driver_ctrl)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                    temp['weighted-TSALE'].append(np.nan)\n",
    "                    temp['Status'].append('Driver')\n",
    "                    temp['Epoch'].append('P10')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(driver_ctrl)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                try:   \n",
    "                    exp_means_P01 = dabest.bootstrap_tools.bootstrap(exp_values_P01.dropna().values).summary\n",
    "                    temp['weighted-TSALE'].append(exp_means_P01)\n",
    "                    temp['Status'].append('Experiment')\n",
    "                    temp['Epoch'].append('P01')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(experiment)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                    exp_means_P10 = dabest.bootstrap_tools.bootstrap(exp_values_P10.dropna().values).summary\n",
    "                    temp['weighted-TSALE'].append(exp_means_P10)\n",
    "                    temp['Status'].append('Experiment')\n",
    "                    temp['Epoch'].append('P10')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(experiment)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                except: \n",
    "                    temp['weighted-TSALE'].append(np.nan)\n",
    "                    temp['Status'].append('Experiment')\n",
    "                    temp['Epoch'].append('P01')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(experiment)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "                    temp['weighted-TSALE'].append(np.nan)\n",
    "                    temp['Status'].append('Experiment')\n",
    "                    temp['Epoch'].append('P10')\n",
    "                    temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                    temp['Genotypes'].append(experiment)\n",
    "                    temp['ORN'].append(ORN)\n",
    "\n",
    "        elif len(conditions) > 12:\n",
    "            shortened_conditions = []\n",
    "\n",
    "            for item in conditions:\n",
    "                if (not 'asghar' in item) & (not 'safwan' in item) & (not 'fed2' in item) & (not 'starved2' in item) \\\n",
    "                   & (not '28uW' in item) & (not '56uW' in item) & (not 'female' in item):\n",
    "                    shortened_conditions.append(item)\n",
    "\n",
    "            if len(shortened_conditions) == 12:\n",
    "                print '%s shortened conditions...' %(ORN)\n",
    "                for c in shortened_conditions:\n",
    "                    responder_ctrl_values_P10 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == responder_ctrl)]['weighted_TSALE_P10']\n",
    "                    responder_ctrl_values_P01 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == responder_ctrl)]['weighted_TSALE_P01']\n",
    "\n",
    "                    driver_ctrl_values_P10 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == driver_ctrl)]['weighted_TSALE_P10']\n",
    "                    driver_ctrl_values_P01 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == driver_ctrl)]['weighted_TSALE_P01']\n",
    "\n",
    "                    exp_values_P10 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == experiment)]['weighted_TSALE_P10']\n",
    "                    exp_values_P01 = df[(df['Sex-Satiety-Wind-Intensity'] == c) & (df['Genotype'] == experiment)]['weighted_TSALE_P01']\n",
    "                    try:\n",
    "                        ResponderCtrl_means_P01 = dabest.bootstrap_tools.bootstrap(responder_ctrl_values_P01.dropna().values).summary\n",
    "                        temp['weighted-TSALE'].append(ResponderCtrl_means_P01)\n",
    "                        temp['Status'].append('Responder')\n",
    "                        temp['Epoch'].append('P01')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(responder_ctrl)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                        ResponderCtrl_means_P10 = dabest.bootstrap_tools.bootstrap(responder_ctrl_values_P10.dropna().values).summary\n",
    "                        temp['weighted-TSALE'].append(ResponderCtrl_means_P10)\n",
    "                        temp['Status'].append('Responder')\n",
    "                        temp['Epoch'].append('P10')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(responder_ctrl)\n",
    "                        temp['ORN'].append(ORN)\n",
    "                    except:\n",
    "                        temp['weighted-TSALE'].append(np.nan)\n",
    "                        temp['Status'].append('Responder')\n",
    "                        temp['Epoch'].append('P01')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(responder_ctrl)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                        temp['weighted-TSALE'].append(np.nan)\n",
    "                        temp['Status'].append('Responder')\n",
    "                        temp['Epoch'].append('P10')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(responder_ctrl)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                    try:   \n",
    "                        DriverCtrl_means_P01 = dabest.bootstrap_tools.bootstrap(driver_ctrl_values_P01.dropna().values).summary\n",
    "                        temp['weighted-TSALE'].append(DriverCtrl_means_P01)\n",
    "                        temp['Status'].append('Driver')\n",
    "                        temp['Epoch'].append('P01')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(driver_ctrl)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                        DriverCtrl_means_P10 = dabest.bootstrap_tools.bootstrap(driver_ctrl_values_P10.dropna().values).summary\n",
    "                        temp['weighted-TSALE'].append(DriverCtrl_means_P10)\n",
    "                        temp['Status'].append('Driver')\n",
    "                        temp['Epoch'].append('P10')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(driver_ctrl)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                    except: \n",
    "                        temp['weighted-TSALE'].append(np.nan)\n",
    "                        temp['Status'].append('Driver')\n",
    "                        temp['Epoch'].append('P01')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(driver_ctrl)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                        temp['weighted-TSALE'].append(np.nan)\n",
    "                        temp['Status'].append('Driver')\n",
    "                        temp['Epoch'].append('P10')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(driver_ctrl)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                    try:   \n",
    "                        exp_means_P01 = dabest.bootstrap_tools.bootstrap(exp_values_P01.dropna().values).summary\n",
    "                        temp['weighted-TSALE'].append(exp_means_P01)\n",
    "                        temp['Status'].append('Experiment')\n",
    "                        temp['Epoch'].append('P01')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(experiment)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                        exp_means_P10 = dabest.bootstrap_tools.bootstrap(exp_values_P10.dropna().values).summary\n",
    "                        temp['weighted-TSALE'].append(exp_means_P10)\n",
    "                        temp['Status'].append('Experiment')\n",
    "                        temp['Epoch'].append('P10')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(experiment)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                    except: \n",
    "                        temp['weighted-TSALE'].append(np.nan)\n",
    "                        temp['Status'].append('Experiment')\n",
    "                        temp['Epoch'].append('P01')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(experiment)\n",
    "                        temp['ORN'].append(ORN)\n",
    "\n",
    "                        temp['weighted-TSALE'].append(np.nan)\n",
    "                        temp['Status'].append('Experiment')\n",
    "                        temp['Epoch'].append('P10')\n",
    "                        temp['Sex-Satiety-Wind-Intensity'].append(c)\n",
    "                        temp['Genotypes'].append(experiment)\n",
    "                        temp['ORN'].append(ORN)\n",
    "            else:\n",
    "                print '%s could not shorten conditions...' %(ORN)\n",
    "                pass\n",
    "\n",
    "        elif len(conditions) < 12:\n",
    "            print '%s conditions too short' %(ORN) \n",
    "            pass\n",
    "                \n",
    "    return temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "temp = {'ORN':[],'Genotypes':[], 'Status':[], 'Sex-Satiety-Wind-Intensity':[], 'Epoch':[], 'weighted-TSALE':[]} \n",
    "\n",
    "t = GetGenotypeMeans(temp, 'C:/Users/tumkayat/Desktop/ORScreening/WALiSAR_all_ORNs/Pickles/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ch = pd.read_pickle('C:/Users/tumkayat/Desktop/ORScreening/WALiSAR_all_ORNs/Pickles/Or42a/weighted_TSALE_values.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ch.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ch[(ch['Sex-Satiety-Wind-Intensity'] == 'male-starved-Air-14uW') & (ch['Genotype'] == 'w1118-UAS-CsChrimson')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ch['Sex-Satiety-Wind-Intensity'] = ch['Sex'] + '-' + ch['Satiety'] + '-' + ch['Wind status'] + '-' + ch['Light Intensity(uW/mm2)']\n",
    "len(ch['Sex-Satiety-Wind-Intensity'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tt = ch['Sex-Satiety-Wind-Intensity'].unique()\n",
    "tt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Remove \"Asghar\" from the orco data, since I dont want to drop it.\n",
    "new_condition_col = []\n",
    "for i in range(len(ch)):\n",
    "    if 'asghar' in ch['Sex'][i]:\n",
    "        new = ch['Sex'][i].replace('-asghar','')\n",
    "        new_condition_col.append(new)\n",
    "    else:\n",
    "        new_condition_col.append(ch['Sex'][i])\n",
    "ch['Sex'] = new_condition_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Save the Orco pkl file as it is\n",
    "ch.to_pickle('C:/Users/tumkayat/Desktop/ORScreening/WALiSAR_all_ORNs/Pickles/Orco/weighted_TSALE_values.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "shortened_conditions = []\n",
    "            \n",
    "for item in shortened_conditions:\n",
    "    if (not 'asghar' in item) & (not 'safwan' in item) & (not 'fed2' in item) & (not 'starved2' in item) \\\n",
    "       & (not '28uW' in item) & (not '56uW' in item) & (not 'female' in item):\n",
    "        shortened_conditions.append(item)\n",
    "shortened_conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# dd = pd.DataFrame.from_dict(temp,orient='index').transpose()\n",
    "dd = pd.DataFrame(t,columns=['ORN','Genotypes','Status','Epoch', 'Sex-Satiety-Wind-Intensity', 'weighted-TSALE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "int_col = []\n",
    "int_col_wind = []\n",
    "\n",
    "for i in range(len(dd)):\n",
    "    int_col.append(dd['Sex-Satiety-Wind-Intensity'].iloc[i].split('-')[-1])\n",
    "    int_col_wind.append(dd['Sex-Satiety-Wind-Intensity'].iloc[i].split('-')[-2])\n",
    "    \n",
    "dd['Intensity'] = int_col\n",
    "\n",
    "dd['Wind'] = int_col_wind\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dd['Status-Epoch'] = dd['Status'] + dd['Epoch']\n",
    "dd['Status-Epoch-Int'] = dd['Status'] + '_' + dd['Epoch'] +'_' + dd['Intensity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# dd[dd['Sex-Satiety-Wind-Intensity'].str.contains('asghar')]['Genotypes'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Only some extra Or67d, remove Safwan\n",
    "# dd = dd[~dd['Sex-Satiety-Wind-Intensity'].str.contains('safwan')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# dd['weighted-TSALE'] = dd['Means']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dd['Abs_weighted-TSALE'] = abs(dd['weighted-TSALE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dd[dd['Sex-Satiety-Wind-Intensity'].str.contains('male') & dd['Sex-Satiety-Wind-Intensity'].str.contains('fed')&\n",
    "   dd['Sex-Satiety-Wind-Intensity'].str.contains('14uW') & dd['Sex-Satiety-Wind-Intensity'].str.contains('NoAir')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot overall Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fig,b = bs.contrastplot(dd, x = 'Status-Epoch', y = 'Abs_weighted-TSALE',\n",
    "                                color_col= 'Epoch',\n",
    "                         paired = False, fig_size=(12,12), \n",
    "                        float_contrast = False,\n",
    "                              idx = (('ResponderP01','ResponderP10'),\n",
    "                                     ('DriverP01','DriverP10'),\n",
    "                                    ('ExperimentP01','ExperimentP10')\n",
    "                                    ),\n",
    "                       )\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/ComparingEpochs_abs.pdf\",dpi=1000,bbox_inches='tight')\n",
    "b.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/ComparingEpochs_abs.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break down the data set into conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "male_fed_noAir = dd[dd['Sex-Satiety-Wind-Intensity'].str.contains('male') & dd['Sex-Satiety-Wind-Intensity'].str.contains('fed')&\n",
    "   dd['Sex-Satiety-Wind-Intensity'].str.contains('NoAir')]\n",
    "\n",
    "male_fed_Air = dd[dd['Sex-Satiety-Wind-Intensity'].str.contains('male') & dd['Sex-Satiety-Wind-Intensity'].str.contains('fed')&\n",
    "   dd['Sex-Satiety-Wind-Intensity'].str.contains('-Air')]\n",
    "\n",
    "male_starved_noAir = dd[dd['Sex-Satiety-Wind-Intensity'].str.contains('male') & dd['Sex-Satiety-Wind-Intensity'].str.contains('starved')&\n",
    "   dd['Sex-Satiety-Wind-Intensity'].str.contains('NoAir')]\n",
    "\n",
    "male_starved_Air = dd[dd['Sex-Satiety-Wind-Intensity'].str.contains('male') & dd['Sex-Satiety-Wind-Intensity'].str.contains('starved')&\n",
    "   dd['Sex-Satiety-Wind-Intensity'].str.contains('-Air')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# int_col = []\n",
    "# for i in range(len(male_starved_noAir)):\n",
    "#     int_col.append(male_starved_noAir['Sex-Satiety-Wind-Intensity'].iloc[i].split('-')[-2])\n",
    "# male_starved_noAir['Wind'] = int_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "male_starved_Air.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "male_fed_noAir['Wind-Int-Status-Epoch'] = male_fed_noAir['Wind'] + '_' + male_fed_noAir['Intensity'] + '_' + male_fed_noAir['Status-Epoch']\n",
    "male_fed_Air['Wind-Int-Status-Epoch'] = male_fed_Air['Wind'] + '_' + male_fed_Air['Intensity'] + '_' + male_fed_Air['Status-Epoch']\n",
    "male_starved_noAir['Wind-Int-Status-Epoch'] = male_starved_noAir['Wind'] + '_' + male_starved_noAir['Intensity'] + '_' + male_starved_noAir['Status-Epoch']\n",
    "male_starved_Air['Wind-Int-Status-Epoch'] = male_starved_Air['Wind'] + '_' + male_starved_Air['Intensity'] + '_' + male_starved_Air['Status-Epoch']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "male_fed_total = pd.concat([male_fed_noAir,male_fed_Air])\n",
    "male_starved_total = pd.concat([male_starved_noAir,male_starved_Air])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "male_fed_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dd['abs_weighted-TSALE'] = abs(dd['weighted-TSALE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "male_starved_total['Wind-Int-Status-Epoch'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Responders in All conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig,b = bs.contrastplot(male_fed_total, x = 'Wind-Int-Status-Epoch', y = 'Abs_weighted-TSALE',\n",
    "                                color_col= 'Epoch',\n",
    "                         paired = False, fig_size=(24,12), \n",
    "                        float_contrast = False,\n",
    "                              idx = (('NoAir_14uW_ResponderP01','NoAir_14uW_DriverP10'),\n",
    "                                     ('Air_14uW_ResponderP01','Air_14uW_DriverP10'),\n",
    "                                     ('NoAir_42uW_ResponderP01','NoAir_42uW_DriverP10'),\n",
    "                                     ('Air_42uW_ResponderP01','Air_42uW_DriverP10'),\n",
    "                                     ('NoAir_70uW_ResponderP01','NoAir_70uW_DriverP10'),\n",
    "                                     ('Air_70uW_ResponderP01','Air_70uW_DriverP10'),\n",
    "                                     \n",
    "                                    ),\n",
    "                       )\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname =  \"male_starved\" + '_' + 'Responder_Entire_Exp_abs'\n",
    "fig.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "b.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Drivers across conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig,b = bs.contrastplot(male_fed_total, x = 'Wind-Int-Status-Epoch', y = 'abs_weighted-TSALE',\n",
    "                                color_col= 'Epoch',\n",
    "                         paired = False, fig_size=(16,12), \n",
    "                        float_contrast = False,\n",
    "                              idx = (('NoAir_14uW_DriverP01','NoAir_14uW_DriverP10'),\n",
    "                                     ('Air_14uW_DriverP01','Air_14uW_DriverP10'),\n",
    "                                     ('NoAir_42uW_DriverP01','NoAir_42uW_DriverP10'),\n",
    "                                     ('Air_42uW_DriverP01','Air_42uW_DriverP10'),\n",
    "                                     ('NoAir_70uW_DriverP01','NoAir_70uW_DriverP10'),\n",
    "                                     ('Air_70uW_DriverP01','Air_70uW_DriverP10')\n",
    "                                    ),\n",
    "                       )\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname =  \"male_fed\" + '_' + 'Driver_Entire_Exp_abs'\n",
    "fig.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "b.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiments across conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig,b = bs.contrastplot(male_fed_total, x = 'Wind-Int-Status-Epoch', y = 'abs_weighted-TSALE',\n",
    "                                color_col= 'Epoch',\n",
    "                         paired = False, fig_size=(16,12), \n",
    "                        float_contrast = False,\n",
    "                              idx = (('NoAir_14uW_ExperimentP01','NoAir_14uW_ExperimentP10'),\n",
    "                                     ('Air_14uW_ExperimentP01','Air_14uW_ExperimentP10'),\n",
    "                                     ('NoAir_42uW_ExperimentP01','NoAir_42uW_ExperimentP10'),\n",
    "                                     ('Air_42uW_ExperimentP01','Air_42uW_ExperimentP10'),\n",
    "                                     ('NoAir_70uW_ExperimentP01','NoAir_70uW_ExperimentP10'),\n",
    "                                     ('Air_70uW_ExperimentP01','Air_70uW_ExperimentP10')\n",
    "                                    ),\n",
    "                       )\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname =  \"male_fed\" + '_' + 'Experiment_Entire_Exp_abs'\n",
    "fig.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "b.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Paired Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "male_starved_total[male_starved_total['Wind-Int-Status-Epoch'] == 'Air_14uW_ResponderP01']['Abs_weighted-TSALE'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "male_starved_total[male_starved_total['Abs_weighted-TSALE'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Drop the NAN weighted-TSALE to pair up\n",
    "male_fed_total_dropnan = male_fed_total[~male_fed_total['Abs_weighted-TSALE'].isnull()]\n",
    "male_starved_total_dropnan = male_starved_total[~male_starved_total['Abs_weighted-TSALE'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "## Drop Or49a and  Or24a\n",
    "## Detect the indices, because responder controls belong to these groups do not contain\n",
    "## Or24a in the genotype names \n",
    "male_fed_total_dropnan[male_fed_total_dropnan['Genotypes'].str.contains('Or24a')]\n",
    "male_fed_total_dropnan.loc[899]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "R = male_fed_total_dropnan[male_fed_total_dropnan['Wind-Int-Status-Epoch'] == 'NoAir_14uW_ResponderP01']\n",
    "D = male_fed_total_dropnan[male_fed_total_dropnan['Wind-Int-Status-Epoch'] == 'NoAir_14uW_DriverP01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "male_fed_total_dropnan.loc[3360:3364]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D.loc[3362]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "R.index[44]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(R.index)):\n",
    "    check = D.index[i] - R.index[i]\n",
    "    if check != 2:\n",
    "        print i\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Responder vs Driver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig,b = bs.contrastplot(male_starved_total, x = 'Wind-Int-Status-Epoch', y = 'Abs_weighted-TSALE',\n",
    "                                color_col= 'Epoch',\n",
    "                         paired = True, fig_size=(24,12), \n",
    "                        float_contrast = False,\n",
    "                              idx = (('NoAir_14uW_ResponderP01', 'NoAir_14uW_DriverP01'),\n",
    "                                     ('NoAir_14uW_ResponderP10', 'NoAir_14uW_DriverP10'),\n",
    "                                     ('Air_14uW_ResponderP01', 'Air_14uW_DriverP01'),\n",
    "                                     ('Air_14uW_ResponderP10', 'Air_14uW_DriverP10'),\n",
    "                                     ('NoAir_42uW_ResponderP01', 'NoAir_42uW_DriverP01'),\n",
    "                                     ('NoAir_42uW_ResponderP10', 'NoAir_42uW_DriverP10'),\n",
    "                                     ('Air_42uW_ResponderP01', 'Air_42uW_DriverP01'),\n",
    "                                     ('Air_42uW_ResponderP10', 'Air_42uW_DriverP10'),\n",
    "                                     ('NoAir_70uW_ResponderP01', 'NoAir_70uW_DriverP01'),\n",
    "                                     ('NoAir_70uW_ResponderP10', 'NoAir_70uW_DriverP10'),\n",
    "                                     ('Air_70uW_ResponderP01', 'Air_70uW_DriverP01'),\n",
    "                                     ('Air_70uW_ResponderP10', 'Air_70uW_DriverP10'),\n",
    "                                    ),\n",
    "                       )\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname =  \"male_starved\" + '_' + 'Controls_paired'\n",
    "fig.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "b.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Responder vs Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig,b = bs.contrastplot(male_fed_total, x = 'Wind-Int-Status-Epoch', y = 'Abs_weighted-TSALE',\n",
    "                                color_col= 'Epoch',\n",
    "                         paired = True, fig_size=(24,12), \n",
    "                        float_contrast = False,\n",
    "                              idx = (('NoAir_14uW_ResponderP01', 'NoAir_14uW_ExperimentP01'),\n",
    "                                     ('NoAir_14uW_ResponderP10', 'NoAir_14uW_ExperimentP10'),\n",
    "                                     ('Air_14uW_ResponderP01', 'Air_14uW_ExperimentP01'),\n",
    "                                     ('Air_14uW_ResponderP10', 'Air_14uW_ExperimentP10'),\n",
    "                                     ('NoAir_42uW_ResponderP01', 'NoAir_42uW_ExperimentP01'),\n",
    "                                     ('NoAir_42uW_ResponderP10', 'NoAir_42uW_ExperimentP10'),\n",
    "                                     ('Air_42uW_ResponderP01', 'Air_42uW_ExperimentP01'),\n",
    "                                     ('Air_42uW_ResponderP10', 'Air_42uW_ExperimentP10'),\n",
    "                                     ('NoAir_70uW_ResponderP01', 'NoAir_70uW_ExperimentP01'),\n",
    "                                     ('NoAir_70uW_ResponderP10', 'NoAir_70uW_ExperimentP10'),\n",
    "                                     ('Air_70uW_ResponderP01', 'Air_70uW_ExperimentP01'),\n",
    "                                     ('Air_70uW_ResponderP10', 'Air_70uW_ExperimentP10'),\n",
    "                                    ),\n",
    "                       )\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname =  \"male_starved\" + '_' + 'Experiment_v_Control_paired'\n",
    "fig.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "b.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequential Paired"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'bs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-884d5d49e20f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m fig,b = bs.contrastplot(male_starved_total, x = 'Wind-Int-Status-Epoch', y = 'Abs_weighted-TSALE',\n\u001b[0m\u001b[0;32m      2\u001b[0m                                 \u001b[0mcolor_col\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;34m'Epoch'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m                          \u001b[0mpaired\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfig_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m24\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m                         \u001b[0mfloat_contrast\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m                               idx = (('NoAir_14uW_DriverP01', 'NoAir_14uW_DriverP10'),\n",
      "\u001b[1;31mNameError\u001b[0m: name 'bs' is not defined"
     ]
    }
   ],
   "source": [
    "fig,b = bs.contrastplot(male_starved_total, x = 'Wind-Int-Status-Epoch', y = 'Abs_weighted-TSALE',\n",
    "                                color_col= 'Epoch',\n",
    "                         paired = True, fig_size=(24,12), \n",
    "                        float_contrast = False,\n",
    "                              idx = (('NoAir_14uW_DriverP01', 'NoAir_14uW_DriverP10'),\n",
    "                                     ('NoAir_14uW_DriverP10', 'Air_14uW_DriverP01'),\n",
    "                                     ('Air_14uW_DriverP01', 'Air_14uW_DriverP10'),\n",
    "                                     ('Air_14uW_DriverP10', 'NoAir_42uW_DriverP01'),\n",
    "                                     ('NoAir_42uW_DriverP01', 'NoAir_42uW_DriverP10'),\n",
    "                                     ('NoAir_42uW_DriverP10', 'Air_42uW_DriverP01'),\n",
    "                                     ('Air_42uW_DriverP01', 'Air_42uW_DriverP10'),\n",
    "                                     ('Air_42uW_DriverP10', 'NoAir_70uW_DriverP01'),\n",
    "                                     ('NoAir_70uW_DriverP01', 'NoAir_70uW_DriverP10'),\n",
    "                                     ('NoAir_70uW_DriverP10', 'Air_70uW_DriverP01'),\n",
    "                                     ('Air_70uW_DriverP01', 'Air_70uW_DriverP10'),\n",
    "                                     \n",
    "                                    ),\n",
    "                       )\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fname =  \"male_starved\" + '_' + 'Driver_SeqPaired'\n",
    "fig.savefig(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "b.to_csv(\"C:/Users/tumkayat/Desktop/WALi Figures/AnalysesofTheControl/BreakDowns/\" + fname + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A generic delta-delta plot function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Figure 2.9 (Orco v Orco-rut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Orco_EffectSizes_df = pd.read_csv(\"C:/Users/tumkayat/Desktop/WALiAnalyses/OrcoVOrcoRut/Orco/ORCO_weighted_TSALE_CombinedControls_P10_male-combined_fed_NoAir.csv\")\n",
    "#Orco_rut_EffectSizes_df = pd.read_csv(\"C:/Users/tumkayat/Desktop/WALiAnalyses/OrcoVOrcoRut/Orco-rut/weighted_TSALE_CombinedControls_P10_Fed_NoAir_Constant.csv\")\n",
    "\n",
    "orco_fwd = pd.read_csv(\"C:/Users/tumkayat/Desktop/WALISAR_Paper/Supp/S2/Orco_forward/Orco_forward_contrast.csv\")\n",
    "orco_rev = pd.read_csv(\"C:/Users/tumkayat/Desktop/WALISAR_Paper/Supp/S2/Orco_reverse/Orco_reverse_contrast.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>CI_LB</th>\n",
       "      <th>CI_UB</th>\n",
       "      <th>Sample_size</th>\n",
       "      <th>Standart_dev</th>\n",
       "      <th>Steps</th>\n",
       "      <th>SummaryES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.110041</td>\n",
       "      <td>0.116621</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.479099</td>\n",
       "      <td>Male_Fed_14uW_NoAir_P01</td>\n",
       "      <td>0.003290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.075479</td>\n",
       "      <td>0.180185</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.540400</td>\n",
       "      <td>Male_Fed_14uW_NoAir_P10</td>\n",
       "      <td>0.052353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-0.171287</td>\n",
       "      <td>0.086374</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.544622</td>\n",
       "      <td>Male_Fed_14uW_Air_P01</td>\n",
       "      <td>-0.042456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.003709</td>\n",
       "      <td>0.291914</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.609182</td>\n",
       "      <td>Male_Fed_14uW_Air_P10</td>\n",
       "      <td>0.147811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.062321</td>\n",
       "      <td>0.273010</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.445336</td>\n",
       "      <td>Male_Fed_42uW_NoAir_P01</td>\n",
       "      <td>0.167665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.167396</td>\n",
       "      <td>0.412498</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.518073</td>\n",
       "      <td>Male_Fed_42uW_NoAir_P10</td>\n",
       "      <td>0.289947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-0.099624</td>\n",
       "      <td>0.174432</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.579275</td>\n",
       "      <td>Male_Fed_42uW_Air_P01</td>\n",
       "      <td>0.037404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.327488</td>\n",
       "      <td>0.599848</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.575691</td>\n",
       "      <td>Male_Fed_42uW_Air_P10</td>\n",
       "      <td>0.463668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.131823</td>\n",
       "      <td>0.348341</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.457656</td>\n",
       "      <td>Male_Fed_70uW_NoAir_P01</td>\n",
       "      <td>0.240082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.151728</td>\n",
       "      <td>0.393705</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.511470</td>\n",
       "      <td>Male_Fed_70uW_NoAir_P10</td>\n",
       "      <td>0.272716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.102967</td>\n",
       "      <td>0.352548</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.527541</td>\n",
       "      <td>Male_Fed_70uW_Air_P01</td>\n",
       "      <td>0.227758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>0.350221</td>\n",
       "      <td>0.594794</td>\n",
       "      <td>153.0</td>\n",
       "      <td>0.516957</td>\n",
       "      <td>Male_Fed_70uW_Air_P10</td>\n",
       "      <td>0.472507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0     CI_LB     CI_UB  Sample_size  Standart_dev  \\\n",
       "0            0 -0.110041  0.116621        153.0      0.479099   \n",
       "1            1 -0.075479  0.180185        153.0      0.540400   \n",
       "2            2 -0.171287  0.086374        153.0      0.544622   \n",
       "3            3  0.003709  0.291914        153.0      0.609182   \n",
       "4            4  0.062321  0.273010        153.0      0.445336   \n",
       "5            5  0.167396  0.412498        153.0      0.518073   \n",
       "6            6 -0.099624  0.174432        153.0      0.579275   \n",
       "7            7  0.327488  0.599848        153.0      0.575691   \n",
       "8            8  0.131823  0.348341        153.0      0.457656   \n",
       "9            9  0.151728  0.393705        153.0      0.511470   \n",
       "10          10  0.102967  0.352548        153.0      0.527541   \n",
       "11          11  0.350221  0.594794        153.0      0.516957   \n",
       "\n",
       "                      Steps  SummaryES  \n",
       "0   Male_Fed_14uW_NoAir_P01   0.003290  \n",
       "1   Male_Fed_14uW_NoAir_P10   0.052353  \n",
       "2     Male_Fed_14uW_Air_P01  -0.042456  \n",
       "3     Male_Fed_14uW_Air_P10   0.147811  \n",
       "4   Male_Fed_42uW_NoAir_P01   0.167665  \n",
       "5   Male_Fed_42uW_NoAir_P10   0.289947  \n",
       "6     Male_Fed_42uW_Air_P01   0.037404  \n",
       "7     Male_Fed_42uW_Air_P10   0.463668  \n",
       "8   Male_Fed_70uW_NoAir_P01   0.240082  \n",
       "9   Male_Fed_70uW_NoAir_P10   0.272716  \n",
       "10    Male_Fed_70uW_Air_P01   0.227758  \n",
       "11    Male_Fed_70uW_Air_P10   0.472507  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orco_fwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deltadeltaPlot(df_ctrl, df_exp):\n",
    "    contrast_means = []\n",
    "    contrast_CI_LB = []\n",
    "    contrast_CI_UB = []\n",
    "    contrast_samplesize = []\n",
    "    contrast_STD = []\n",
    "\n",
    "    for i in range(len(df_ctrl)):\n",
    "        mean_ctrl = df_ctrl['SummaryES'][i]\n",
    "        mean_exp = df_exp['SummaryES'][i]\n",
    "\n",
    "        N_ctrl = df_ctrl['Sample_size'][i]\n",
    "        N_exp = df_exp['Sample_size'][i]\n",
    "\n",
    "        STD_ctrl = df_ctrl['Standart_dev'][i]\n",
    "        STD_exp = df_exp['Standart_dev'][i]\n",
    "\n",
    "    ## Percent\n",
    "#         mean_diff = (mean_exp - mean_ctrl) / mean_ctrl\n",
    "\n",
    "    # Mean diff\n",
    "        mean_diff = (mean_exp - mean_ctrl)\n",
    "    \n",
    "    ## Average\n",
    "#         mean_diff = (mean_exp + mean_ctrl) / 2\n",
    "        \n",
    "#     ## Cohen's d\n",
    "#         STD_pooled_cohensD = np.sqrt(((STD_ctrl**2) + (STD_exp**2)) / 2)\n",
    "#         mean_diff = (mean_exp - mean_ctrl) / STD_pooled_cohensD\n",
    "                \n",
    "        STD_pooled = np.sqrt(((N_ctrl-1) * (STD_ctrl**2) + (N_exp-1)*(STD_exp**2)) / float((N_ctrl+N_exp-2)))\n",
    "        #http://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_confidence_intervals/bs704_confidence_intervals5.html\n",
    "        \n",
    "        moe = 1.96 * STD_pooled * np.sqrt((1./N_ctrl)+(1./N_exp))\n",
    "        CI_LB = mean_diff - moe\n",
    "        CI_UB = mean_diff + moe\n",
    "\n",
    "        contrast_means.append(mean_diff)\n",
    "        contrast_CI_LB.append(CI_LB)\n",
    "        contrast_CI_UB.append(CI_UB)\n",
    "        contrast_samplesize.append((N_ctrl + N_exp)/2.)\n",
    "        contrast_STD.append(STD_pooled)\n",
    "        \n",
    "        stats_table = {'Sample_size':contrast_samplesize,'Standart_dev':contrast_STD, 'SummaryES':contrast_means, 'CI_LB':contrast_CI_LB, 'CI_UB':contrast_CI_UB}\n",
    "        stats_table_df = pd.DataFrame.from_dict(stats_table)\n",
    "        \n",
    "    ## import the libraries, set some parameters\n",
    "    import itertools\n",
    "    from matplotlib.lines import Line2D\n",
    "    import scipy.stats\n",
    "\n",
    "    style='ticks'\n",
    "    context='notebook'\n",
    "    font='Arial'\n",
    "\n",
    "    sns.set(style=style,context =context,font=font)\n",
    "\n",
    "    fig1 = plt.figure(figsize=(10,4))\n",
    "    ax1 = fig1.add_subplot(111)\n",
    "\n",
    "    ## plotting the mean dots , zorder specifies the layer order - i.e dots are in the front\n",
    "    x1 = 1\n",
    "    for i in range(len(contrast_means)):\n",
    "        ax1.scatter(x1, contrast_means[i], s=90, c='k',zorder=10)\n",
    "        ax1.plot([x1,x1], [contrast_CI_LB[i], contrast_CI_UB[i]], color='k', lw=1)\n",
    "        x1 = x1 + 1\n",
    "\n",
    "    ax1.set_ylim(-1,1)\n",
    "    ax1.set_ylabel('delta-delta wTSALE')\n",
    "    ax1.set_xticks(np.arange(0,len(df_ctrl)+1))\n",
    "    xlabel = []\n",
    "    for c in df_ctrl['Steps']:\n",
    "        xlabel.append(c[9:])\n",
    "        ax1.set_xticklabels(xlabel,rotation=45)\n",
    "        \n",
    "#     ax1.legend(custom_lines, legend_labels,bbox_to_anchor=(1, 1))\n",
    "    plt.axhline(color='k', linewidth = '0.5')\n",
    "    sns.despine()\n",
    "    return fig1, stats_table_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, c = deltadeltaPlot(Orco_EffectSizes_df, Orco_rut_EffectSizes_df)\n",
    "fig, c = deltadeltaPlot(orco_fwd, orco_rev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = \"Orco_fwdVrev_cohenD\"\n",
    "fig.savefig(\"C://Users//tumkayat/Desktop/\" + fname + \".pdf\",dpi=1000,bbox_inches='tight')\n",
    "c.to_csv(\"C://Users//tumkayat/Desktop/\" + fname + \".csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Cohen's d comparison for Orco and Gr66a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "C:\\Users\\tumkayat\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## cohen's d extracted from the literature\n",
    "lit_d = pd.read_csv(\"C:/Users/tumkayat/Desktop/Sensitivity_comparison_Gr66a.csv\", dtype={'LightIntensity': str})\n",
    "\n",
    "## cohen's d of my data of interest\n",
    "neuron_of_interest = \"Gr66a\"\n",
    "d_noair = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['ORNs'] == neuron_of_interest) & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == \"male_fed_NoAir\")]\n",
    "# d_air = All_ORN_EffectSizes_df[(All_ORN_EffectSizes_df['ORNs'] == neuron_of_interest) & (All_ORN_EffectSizes_df['Sex-Satiety-Air'] == \"male_fed_Air\")]\n",
    "\n",
    "# d_air['Air'] = 'On'\n",
    "d_noair['Air'] = 'Off'\n",
    "\n",
    "# d_air['Citation'] = 'This study'\n",
    "d_noair['Citation'] = 'This study'\n",
    "\n",
    "\n",
    "## combined into a single DF\n",
    "# cohensd_df = [lit_d, d_noair, d_air]\n",
    "cohensd_df = [lit_d, d_noair]\n",
    "cohensd_df = pd.concat(cohensd_df)\n",
    "\n",
    "cohensd_df[\"Int-Air-Paper\"] = cohensd_df['LightIntensity'].values + \"_\" + cohensd_df['Air'] + \"_\" + cohensd_df['Citation']\n",
    "\n",
    "## Reorganize the DF in a way that air on and off are together\n",
    "cohensd_df_noair = cohensd_df[cohensd_df['Air'] == 'Off']\n",
    "cohensd_df_air = cohensd_df[cohensd_df['Air'] == 'On']\n",
    "\n",
    "cohensd_df = pd.concat([cohensd_df_noair, cohensd_df_air])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>Air</th>\n",
       "      <th>CIs</th>\n",
       "      <th>Citation</th>\n",
       "      <th>Cohens_d</th>\n",
       "      <th>Control</th>\n",
       "      <th>Ctrl 95CI</th>\n",
       "      <th>Ctrl ES</th>\n",
       "      <th>Ctrl N</th>\n",
       "      <th>Ctrl SD</th>\n",
       "      <th>Ctrl SEM</th>\n",
       "      <th>...</th>\n",
       "      <th>ORNs</th>\n",
       "      <th>Panel</th>\n",
       "      <th>SD Pooled</th>\n",
       "      <th>Sex-Satiety-Air</th>\n",
       "      <th>offspring_n</th>\n",
       "      <th>offspring_std</th>\n",
       "      <th>pVal</th>\n",
       "      <th>parent_n</th>\n",
       "      <th>parent_std</th>\n",
       "      <th>Int-Air-Paper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Off</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shao et al, 2017</td>\n",
       "      <td>-3.634753</td>\n",
       "      <td>Gr66a-Gal4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011429</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.213809</td>\n",
       "      <td>0.057143</td>\n",
       "      <td>...</td>\n",
       "      <td>Gr66a</td>\n",
       "      <td>B</td>\n",
       "      <td>0.213809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1_Off_Shao et al, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Off</td>\n",
       "      <td>[-0.520215215034, -0.16085590107699999]</td>\n",
       "      <td>This study</td>\n",
       "      <td>-0.726634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Gr66a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.522477</td>\n",
       "      <td>1.404187e-04</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.456076</td>\n",
       "      <td>14uW_Off_This study</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Off</td>\n",
       "      <td>[-0.855488148665, -0.634191252603]</td>\n",
       "      <td>This study</td>\n",
       "      <td>-1.983464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Gr66a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.266823</td>\n",
       "      <td>1.156726e-21</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.417561</td>\n",
       "      <td>42uW_Off_This study</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Off</td>\n",
       "      <td>[-1.00612975947, -0.814370387395]</td>\n",
       "      <td>This study</td>\n",
       "      <td>-2.703300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Gr66a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male_fed_NoAir</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.220099</td>\n",
       "      <td>2.323757e-31</td>\n",
       "      <td>104.0</td>\n",
       "      <td>0.381829</td>\n",
       "      <td>70uW_Off_This study</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Air                                      CIs          Citation  Cohens_d  \\\n",
       "0   Off                                      NaN  Shao et al, 2017 -3.634753   \n",
       "15  Off  [-0.520215215034, -0.16085590107699999]        This study -0.726634   \n",
       "16  Off       [-0.855488148665, -0.634191252603]        This study -1.983464   \n",
       "17  Off        [-1.00612975947, -0.814370387395]        This study -2.703300   \n",
       "\n",
       "       Control  Ctrl 95CI   Ctrl ES  Ctrl N   Ctrl SD  Ctrl SEM  ...   ORNs  \\\n",
       "0   Gr66a-Gal4        NaN  0.011429    14.0  0.213809  0.057143  ...  Gr66a   \n",
       "15         NaN        NaN       NaN     NaN       NaN       NaN  ...  Gr66a   \n",
       "16         NaN        NaN       NaN     NaN       NaN       NaN  ...  Gr66a   \n",
       "17         NaN        NaN       NaN     NaN       NaN       NaN  ...  Gr66a   \n",
       "\n",
       "    Panel  SD Pooled  Sex-Satiety-Air  offspring_n  offspring_std  \\\n",
       "0       B   0.213809              NaN          NaN            NaN   \n",
       "15    NaN        NaN   male_fed_NoAir         52.0       0.522477   \n",
       "16    NaN        NaN   male_fed_NoAir         52.0       0.266823   \n",
       "17    NaN        NaN   male_fed_NoAir         52.0       0.220099   \n",
       "\n",
       "            pVal parent_n parent_std           Int-Air-Paper  \n",
       "0            NaN      NaN        NaN  1_Off_Shao et al, 2017  \n",
       "15  1.404187e-04    104.0   0.456076     14uW_Off_This study  \n",
       "16  1.156726e-21    104.0   0.417561     42uW_Off_This study  \n",
       "17  2.323757e-31    104.0   0.381829     70uW_Off_This study  \n",
       "\n",
       "[4 rows x 29 columns]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohensd_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LWFZWVkycOBFnZ+cCt8SLuyQqqV69emFmZkZ6ejrbtm3jjTfeYP/+/bobI4VdGp47d06XsMzMzJgwYQK9evUqcBmm0WhKNENHfvnrISMjo9Rm7XhaeTc/NBoNqampT9Xayl/W3NycJk2alHZ45UbuHj7ByJEjdd9o33333VN9Q5cGExMTXf/FpUuXik06ycnJuj/s/HNivfrqq7pv1qNHjxb7fnmzWDx+jp9//ln3etGiRQwZMqTQMTyPD3h8FlWrVqVPnz4AREREoNVqdXcTraysCu1I3rhxo64lMmPGDHx8fArtN0pISCi2o74wjRo10rWYDh8+/MTjV65cyYYNG3R37kpL/nFreR31hdFoNLi4uDBw4ECWLFkCoLspA7ldFsU5efIkK1asYPv27SQkJDxn1KVPktYTODo6MmjQICC3I3TmzJkFRkMX50m/ICWR17rLysoiODi4yHKbN2/Wvc4/er9GjRq6QZQnTpzg+PHjTzyHkZGR3pxaV65c0b3Oe6i6ML/99pvu9eOj0p9G3t2x5ORkYmJi2Lt3LwD9+/fH0NCwQPmyjM/Y2JiOHTsCucMk8i5TC3PkyBG+/vprvvzyS/7973+X6Pwl9be//U33Ou9Z0sIcP36c+Ph4zpw5o/tdzd83uXbt2mI/+6JFiwgICGDy5MkvZEe8JK0SmDZtmq4DPDQ0FE9PT65evVrsMadOneL9999n6dKlz/3+Hh4eunFigYGBurE6j7/fsmXLgNzb7l27dtXbP2bMGF2LccqUKYXeWNi4caPuOce8UdZ5atWqpXudV+ZxwcHBekn1WYY85OnQoYOupeTn50daWhpQ9K3+ksQXFRWl9//xNPH5+PjoXn/11Vd6LdI8d+/eZfr06bqfS3ID5mm0bduWVq1aAbn9d/v37y9QJjMzk1mzZgHoRr1D7uNbHTp0AHKfwJg3b16hd0/Xr1+vO2/Lli2LHM+lJOnTKoHq1auzatUqxo8fz//+9z+OHDlC79696dixIy4uLtjY2FC7dm1u3brFhQsX2L17t97dO0NDQ0aOHPlUt5rzq1mzJrNnz+aTTz7h/v37eHt7M2zYMLp3706VKlU4cOAAK1eu5P79+6hUKubNm1fgDqGTkxM+Pj6sXLmSuLg4Bg0axKhRo2jXrh0ZGRls3bqVX3/9FcjtfM3/xwfg6uqqS5bTpk3jwoULODk5YWJiwpUrV/jtt984ePCg3jF5ieZZ5A0eXbp0KadOnQJy/4iKunvq6uqqu7u4aNEikpOT6dy5M9WrV+fatWvs3LmT3bt36/2hPk18nTp1YsSIEaxfv56UlBSGDBmCp6cnnTt3xtjYmDNnzrBy5UquX78O5PbLPcuzqk8yd+5chg4dSkZGBh9++CFDhgyhZ8+eVK1alQsXLrBy5Urd0IgPPvgAW1tb3bFz5sxh8ODBpKWlsW7dOk6fPs2IESNo0qQJycnJ7NixQ9eCMzY2Zvbs2c90t7WsyXTLTyEnJ4egoCCWL19e6O3+xxkbG9O7d28++OADHB0dC+zPe+ofYP/+/Xp3+QqzY8cO/vnPf3Lv3r1C91etWpX58+fr+oMKExAQwA8//FBkv0yjRo34/vvv9fpAIPdSavz48URGRhZ5bgMDA0aPHk10dDTHjx+nbt26ukGZz+Lq1at6sxVMnTqV9957r8jyM2bMYOPGjcWe08PDg9TUVCIiIjA2Nubw4cO6/qonTbeck5ODv78/q1evLnaM19tvv82CBQsKjKcr6f/3k8odPnyYCRMmFDuMwcvLi+nTpxdIOmfOnGH8+PG68W6FqVmzJgsXLizQWn9RSEvrKRgaGvLee+8xYsQIwsPDiYqK4vTp09y4cYMHDx5Qo0YNateuTcuWLenYsSPdu3cv1QdO+/Tpg5OTE2vXriUyMhK1Wo1Go8HGxoauXbvy7rvvPnFRin/84x+4urqyfv16Dh06pJsSpXHjxvTr14/BgwcXOpzAyMiI77//nuDgYH777TfOnTvH/fv3qVq1Kg0aNMDJyYnhw4fj6OjI4sWLOX78OElJScTGxuLk5PRMn9fW1hYnJydiY2MxMjJ64pQ5s2fPplOnToSEhHDq1Cnu3buHqakp1tbWtG7dmiFDhtC+fXuCg4OJiIggKyuL3bt36z16VBxDQ0OmTp2Kh4cH69evJzo6moSEBLKysqhduzZt27Zl8ODBODs7P9PnLakOHTqwe/du1q1bR0REBJcvX+bBgwe6BU48PT2LvKxr2bIlYWFhhISEEBERoZuaxsTEhCZNmtCtWzc8PT2f+NiakqSlJYSoUKQjXghRoUjSEkJUKJK0hBAVykudtLKzs1Gr1c81yFEI8WJ5qZNWQkICPXv2fCEfRRBCPJuXOmkJIV4+krSEEBWKJC0hRIUiSUsIUaFI0hJCVCiStIQQFcoLkbS2bNlC3759efvtt1m3bl2B/WfOnGHQoEH07t2b6dOny7grISoxxZNWYmIiixYt4ueff2bz5s1s3LixwARrvr6+zJgxg507d6LVatm0aZNC0QohlKZ40jpw4ACdOnXCwsKCatWq0bt3b73pbK9du8bDhw91i5kOGjSo0OluU1NTUavVev9kUKkQLx/F59NKSkrSm+Ssbt26enOYP77fysqq0PXsVq9eTWBgYKHv8cMPPxS7KrAQomz84x//KPVzKp60NBpNgcUp8//8pP15vL298fDw0NuWkJCAp6cnY8aMKXZFXyFExaF40rK2tiYmJkb3c3Jyst5sn9bW1iQnJ+t+vnnzZqGzgZqbm+stYy+EeDkp3qf1t7/9jYMHD5KSksKDBw/YtWuX3tzUDRs2xNTUlNjYWCB3NZwXde5qIUTZUzxp1atXj8mTJzNq1CgGDhyIm5sbrVu35oMPPuDEiRMALFy4ED8/P/r06cP9+/dLfWkmIUTF8VLPEa9Wq+nZsycRERHSpyXES0LxlpYQQjwNSVpCiApFkpYQokKRpCWEqFAkaQkhKhRJWkKICkWSlhCiQpGkJYSoUCRpCSEqFMUfmBYvr9OnT3PlyhUaN25My5YtC52dQ4inJS0tUSYiIyOZPHkye/bsYezYsVy+fFnpkMRLQpKWKHXXrl1jyZIlLFiwgAULFtC6dWvCwsI4cOAAf/31l9LhiQpOLg9FqatevTq1a9emXr16JCUlERMTg0qlIjY2FkNDQ7744gvs7e2VDlNUUNLSEqUmPDycH3/8kZycHHr37k29evVISEhg6tSpBAYGMm3aNExMTGTufvFcJGmJUnHo0CGmTZtGdHQ0tWvX5p133gGgdevWDBw4EAB7e3tq1KhBWlqakqGKCk6Slnhu+/btw9/fnxEjRpCVlUVmZqZuX0ZGBhcvXuTkyZPs2LGDEydO0KpVKwWjFRWd9GmJ5xITE8OcOXOYN28eTk5OvP/++9y5c0c3j//9+/dZs2YN165dIyMjg2+++YaGDRsqHLWoyCRpiWeWk5PDzZs3CQgIoFWrVmRmZnL79m2Cg4MZP348ALVq1WL8+PHUrFmThw8fyuIj4rkpnrSuX7+Or68vt27domnTpixcuBAzMzO9MteuXcPNzQ1bW1sA6tSpw48//qhEuCIfQ0ND3n77bQwMDMjOzsbExISPPvqI33//nXv37mFmZoZKpaJOnTqoVCpMTEyUDlm8BBTv05o1axbvvvsuO3bsoFWrVixbtqxAmZMnT9K/f39CQ0MJDQ2VhPUCMTDI/RUyMsr9/rOxseH48eMcOXIEAwODItepFOJZKZq0srKyOHz4ML179waKXvL+xIkTnD9/ngEDBjBq1CjOnTtXoExqaipqtVrvn9xaL1uFrYni6OjIyJEjGTduHGfPntUlNSFKi6KXh7dv36Z69eq6b+milrw3NTXF3d2d4cOHs2/fPsaPH8/27dv1LjdWr15NYGBgucVeGf3555/8+eef1KpVi1deeQUnJ6dCy73zzjukpKQUuMwXojSU2xJiYWFh+Pn56W1r3LgxV69eJSoqCoDs7Gxef/113XqHRXF3d8ff3x9HR0fdttTUVFJTU/XKJSQk4OnpKUuIlYKoqCjmzZuHu7s7aWlprF+/ni+//BIPDw80Gg0qlUrvMjAnJwdDQ0MFIxYvq3Jrabm6uuLq6qq3LSsri44dO+p+wZOTkwtd8j4oKAg3Nzdq1aoF5F6W5LXO8pibm8udqTJ04MABpk6dSrdu3QBwcnJiwoQJGBoa4u7uDsDWrVtJSUlh1KhRkrBEmVG0w8HY2Jj27duzfft2ADZv3lzokveHDx8mJCQEgOjoaDQaDXZ2duUaa2V348YN9u3bp/vZxcWF7777jjlz5nDkyBEgd3hD586dlQpRVBKKrzB97do1pkyZwq1bt6hfvz7ffPMNNWvWZP369SQlJTFx4kQSExOZMmUKycnJmJqaMnfuXL1Lw6LICtOl5+jRoyxfvhx3d3e9FvOcOXNwcHBg6NChCkYnKhPFx2k1bNiQoKCgAttHjBihe12vXj1++umn8gxLPKZRo0a0bt2a33//HSMjI3r16qXbl5ycrGBkorKR+9HiibRaLZaWlvj4+KBSqVi+fDmzZs3i559/5uDBgwX6KoUoS5K0xBPl9SBcunSJEydO4OnpCUBKSgqLFi2S/kVRrhS/PBQvFo1GQ2RkJGvXriUhIQFra2tGjhxJw4YNmT59OpMnT6ZHjx54eHgoHaqopCRpCZ2bN28ydOhQ1Go16enpABw7doz9+/djbW3NnDlz6Nq1KxqNRka6C8XIb54AcltYQ4cOJS4uTpew8qSnp3PlyhVmzpwpCUsoTn77BJC7eo5arSY7O7vQ/dnZ2ajVat3TC0IoRZKWAHKfOni8hfW49PT0QoenCFGeJGkJgEIfVC+MzJwhlCZJSwBgbW1dquWEKCuStAQAI0eOfOJUMmZmZnh5eZVTREIUTpKWAKBbt27Y2NgUmD0jj5GRETY2Njg7O5dzZELok6QlgNxpk4ODg7G3ty/Q4jIzM8Pe3p7g4GAZ7iAUJ4NLhY6lpSXh4eFERUURFBSkGxHv5eWFs7OzJCzxQpCkJfQYGBjQvXt3unfvrnQoQhRKvjqFEBWKJC0hRIUiSUsIUaFI0hJCVCgvTNJavHgxS5YsKXRfZmYmvr6+uLq64uHhQVxcXDlHJ4R4USietO7du8e0adOKnQM+KCiIqlWrEhYWxrRp05g6dWo5RiiEeJEoPuQhIiKCJk2a4OPjU2SZyMhIJk6cCECHDh1ISUnh+vXrNGjQQFemqMVahRAvF8WT1sCBAwGKvDQESEpKwsrKSvezlZUVCQkJeklr9erVBAYGll2gQogXQrklrbCwMPz8/PS22dnZsWrVqiceq9Vq9ZZc12q1BUZne3t7F5i3PCEhQbcIgxDi5VBuScvV1fWZl5qqV68eSUlJ2NraArlzmdetW1evjLm5Oebm5s8dpxDixaZ4R3xJODs7ExoaCkBMTAympqZ6l4ZCiMrjhU1a69ev59tvvwXAy8uLzMxM+vXrx9y5c/H391c4OiGEUlTavJU4X0JqtZqePXsSERGBjY2N0uEIIUrBC9vSEkKIwkjSEkJUKJK0hBAViiQtIUSFIklLCFGhSNISQlQokrSEEBXKEx/jiYuLIywsjPPnz5Oenk6NGjV49dVX6dOnD40aNSqPGIUQQqfYlpa/vz/u7u788MMPXLx4kQcPHnDx4kW+++47XF1di52ZQQghykKRLa1169axdu1afH19GTp0KNWqVdPte/jwIZs2bSIgIICmTZvi5uZWLsEKIUSRSSs4OJhJkybx3nvvFdhXpUoVRo0aRVpaGhs2bJCkJYQoN0VeHl6+fJlu3boVe3Dv3r1lvnYhRLkqMmk9fPjwifNTmZubc+fOnVIPSgghilJsR3z+2UKfZb8QQpS2Yoc8nDhxotjW1t27d0s9ICGEKE6xSWvcuHE8abotaW0JIcpTkUkrIiKiPOMQQogSKTJpNWzYsDzjEEKIElF83cM8ixcvxtDQkAkTJhTYd+3aNdzc3HSr8dSpU4cff/yxvEMUQrwAFE9a9+7dw8/Pj23btjFmzJhCy5w8eZL+/fsze/bsco5OCPGiUTxpRURE0KRJE3x8fIosc+LECc6fP8+AAQOoWbMm06dPp0WLFuS5euUAACAASURBVHplUlNTSU1N1duWkJBQJjELIZSjeNIaOHAgQLEPX5uamuLu7s7w4cPZt28f48ePZ/v27ZiYmOjKrF69msDAwDKPVwihrBIlrczMTH744Qf69etH48aNmT17Nr/++itt2rQhICAAS0vLJ54jLCwMPz8/vW12dnasWrXqicfm7+dydnYmICCAixcv4ujoqNvu7e2Nh4eH3nEJCQl4eno+8fxCiIqjREnL39+fsLAwunfvTmRkJJs2bWLSpEns3bsXPz8/Fi5c+MRzuLq64urq+kxBBgUF4ebmRq1atQDQarUYGemHbm5u/sTHjoRQwrFjxzhz5gy2tra8+uqr1KxZU+mQKrQSzVy6Y8cOvvnmG1q2bMnu3bvp2LEjY8aM4Z///Cf79u0r6xg5fPgwISEhAERHR6PRaLCzsyvz9xXiee3du5fPP/+c06dPM3v2bE6ePKl0SBVeiVpa9+7do3HjxgD88ccfeHt7A1C9enUyMzPLJLD169eTlJTExIkTmT59OlOmTCE0NBRTU1MCAgIwMJCZosWLLTk5mZUrV/L111/Ttm1bZs+eTWxsLDVq1KBGjRo0bdpU6RArpBIlLTs7O/bt20fdunVJSEiga9euAISEhNCsWbNSCeTx8VkjRozQva5Xrx4//fRTqbyPEOWlVq1amJubc+rUKerXr8+OHTto1aoVx44dw9DQkM8++wwHBwelw6xwSpS0Jk6cyIQJE8jOzqZv377Y29szf/581q1bx7Jly8o6RiEqJCMjI9q2bcuOHTv46aef8Pb2ZuzYscTFxbFkyRLi4+MlaT2DEiWtbt268fvvv5OQkEDLli0BcHNzY8SIEbrLRiEEHDp0iD179mBsbMwrr7zCBx98gLe3N19++SWtW7cGwN7enmrVqslcdM+oyKS1efNmAOrWrYu9vT316tXT3b0DaNWqVdlHJ0QFEhUVhb+/Px9++CFmZmZMmjSJCxcuMHHiRLKysjh69Ci1atXi0qVLHD9+nHHjxikdcoVUZNKaMmUKAAYGBtjY2LBr165yC0qIiiYzM5OdO3cyadIkevXqBYCPjw+rVq2iQYMGfPzxx0ybNo3jx4+TlpZGQECALMH3jIqdmkalUmFpaYmpqWl5xiREhWNiYkJaWhoxMTG6pGVhYcGUKVMICAhgxYoVrFixAgMDA7KysmSs1nMo8dQ0Dx8+5MKFC2RlZRWYGLBdu3ZlE50QFYBWq0WlUtGnTx927NjBJ598Qq1atfjrr7/4+eefUavVnD59WtenJZ5PiTriw8PDmTp1KmlpaQUSlkql4syZM2USnBAVQd7svZ07d8bKyoqYmBgsLS2ZMWMGkPuFf+/ePSVDfKmUKGkFBgbSvn17Jk6cSI0aNco6JiEqjLxWFkDNmjXp0KEDHTp0ICEhge+//x4rKyuioqJYsWKFwpG+PEqUtC5fvszChQtLbSCpEBXZhQsXuHz5Mi4uLqhUKr3Elf/1pUuXSE9PJzAwkCZNmigY8culxCPiExMTJWmJSk2j0WBgYMDPP/9MXFwcxsbGODs76yWuvIRlbW1NQEAAOTk5GBoaKhz5y6VESevDDz9k5syZjBkzhsaNG+vNYwXSES8qh7znXRMTE0lKSmLv3r1kZWUVaHFt3bqVW7du4e3tLQmrDJQoaX3yyScAfPnllwX2SUe8qEy0Wi22trYMHTqUgwcPEh4ejkqlomfPnqhUKnJycqhVq5buyRFR+kqUtGQ5MSEesbKywtnZmXbt2vHdd98RHh6OgYEB3bt3x9DQkM6dOysd4ktNpX3SaqyPUavVWFtbo9VqMTY2Lqu4SoVaraZnz55ERERgY2OjdDiigsu7/Mvf2Z6SksKKFSu4fv06gwcPxtnZWeEoX34lmpRKq9WydOlS2rZty9tvv82NGzf4/PPPmTZtGllZWWUdoxAvBJVKhUajQaVSER8fT2JiIrVr1+bvf/87TZo0kUvCclKipPXTTz8REhLCV199peuE79u3L5GRkSxevLhMAxTiRaHVajEwMCAmJobp06dz9+5dAGrXrs2kSZOoW7euwhFWDiVKWiEhIcyYMYP+/fvrmsW9evVi3rx5bNu2rUwDFEIJGo2GPXv2MHr0aPr27cvo0aPZu3cv58+fx8/PD29vb725sGQm3fJToo54tVpd6Bitpk2bkpKSUupBCaGkmzdvMnToUNRqNenp6UDu4hT79+/H2tqaOXPm0LVrV72+LVF+SvT10LRpU2JiYgps37lz53PPcx0bG8s777zDgAED8Pb25tq1awXKZGZm4uvri6urKx4eHsTFxT3XewpRFI1Gw9ChQ4mLi9MlrDzp6elcuXKFmTNn6vq2RPkrUUtrwoQJ+Pr6cuHCBXJycvjtt9+4cuUK27Ztw9/f/7kC8PX1ZdmyZTg6OhISEsKcOXP4/vvv9coEBQVRtWpVwsLCOHz4MFOnTmXTpk3P9b5CFCYyMhK1Wk12dnah+7Ozs1Gr1URFRdG9e/dyjk5ACVtaLi4uLF68mCNHjmBoaMjq1atRq9X8+9//pl+/fs/85pmZmUycOFG36GqLFi24ceNGgXKRkZG4u7sD0KFDB1JSUrh+/bpemdTUVNRqtd6/hISEZ45NVE5BQUEFWliPS09PJygoqJwiEo8rUUsLcld2Lu0xKCYmJgwYMADIbZYHBgbi4uJSoFxSUhJWVla6n62srEhISKBBgwa6batXryYwMLBU4xOVT2JiYonKyReickqctGJiYjh58iQZGRkF5tT68MMPn3h8WFgYfn5+etvs7OxYtWoVmZmZTJkyhezsbMaOHVvg2Mc7PPNuPefn7e2Nh4eH3raEhAQ8PT2fGJsQeaytrTl27FiJyglllChpLV++nEWLFukWmcxPpVKVKGm5urri6upaYHt6ejrjxo3DwsKC77//vtBR9vXq1SMpKQlbW1sg9+7O42NizM3NMTc3L8nHEaJII0eOZP/+/cVeIpqZmeHl5VWOUYn8SpS0goKC+PTTT/n73/9e6gH4+vrSuHFjZs2aVeRYF2dnZ0JDQ2nfvj0xMTGYmprqXRoKUVq6deuGjY0NcXFxhXbGGxkZYWNjI4/rKKhEHfFpaWnP1eFelNOnTxMREcH//vc/PDw8GDBgAB988AEA69ev59tvvwXAy8uLzMxM+vXrx9y5c5/7jqUQRTEwMCA4OBh7e3vMzMz09pmZmWFvb09wcLAMJlVQiR6YHjduHN27d2fo0KHlEVOpkQemxbPSaDRERUURFBREQkIC1tbWeHl54ezsLAlLYUVeHv773//Wva5Xrx5fffUVMTExNG7cuMDEZiXp0xKiIsmbakbGYr14ikxajw/ezFtl5PGR8SXtiBdCiNJQZNLas2dPecYhhBAlUuJxWmlpafz222/89ddfGBkZ0bx5c/r27Uv16tXLMj4hhNBToqQVHx+Pl5cXd+/exd7eHo1GQ0hICMuWLWPdunUFVqMWQoiyUqLbIPPnz8fW1pY9e/YQEhLCL7/8QkREBE2aNJHhB0KIclWipHXw4EGmTJlCrVq1dNtq166Nr68vBw8eLLPghBDicSVKWlWqVCl0bIqBgUGRU3gIIURZKFHS6tixIwsWLODevXu6bampqSxcuJCOHTuWWXBCiJfLiRMnCA0NJSYmhvj4+Gc6R4k64j///HOGDx+Os7Mz9vb2AMTFxWFpacmPP/74TG8shKhc9uzZw1dffUWXLl3YuXMnDx484P3336dLly5PdZ4SJa369euzbds2QkNDuXDhAqampgwbNgx3d3fd6jxCCFGUtLQ0Nm/eTEBAAO3atePq1atEREQQEBCAoaEhb775ZonPVeJxWtWrV9fNTXXz5k3q1Knz9JELISodjUZD9erVyczM5Pz587Rr1w5bW1sGDhwI5E7g2ahRoxI/H1xsn1Z0dDT9+/fn/Pnzetv/9a9/0adPH44cOfKMH0MIURmEh4ezYMECILdv/MqVK1y8eBGAWrVq0bVrVzQaDcnJySU+Z5FJ6+TJk3zwwQfUr1+/wBQdPj4+NGzYEB8fH86dO/csn0UI8ZI7ePAgM2fOJDo6GoA33niDuLg4tmzZwqVLlwCwt7enRo0aha7CVZQik9bSpUvp27cvK1asKDDi/Y033uDHH3/krbfeknnZhRAF7Nu3jwULFjBmzBhq1qxJWloar776KuPHj+fUqVNs2LCBNWvWsGXLFo4fP07btm1LfO4ik9bx48fx9vYu9uDRo0dz9OjRkn8SIcRL78iRI8yePZvp06fz3nvvYWRkxIULFwBo06YNvr6+2NracuzYMQ4cOEBgYOBTzXdXZEf8/fv3C1wWPq5OnTqkpaWV+M2EEC83rVaLRqPhu+++o2XLluTk5KDVatm+fbuuNdW8eXOaN2+Op6cnmZmZTz0CociWVpMmTTh+/HixBx8/fpz69es/1RsKIV5eKpUKJycnXcIyNDRk/PjxxMfH6zrgAd2KXs8yZKrIpOXm5sa3337LzZs3C92fnJzM4sWL6d2791O/aX6xsbG88847DBgwAG9v70I75K5du8brr7/OgAEDGDBgAO+///5zvacQouzlzXCct3JWXiNIo9HoLQn4tIqcIz4rK4t3332Xq1ev8s4779CmTRtq1KjB3bt3OXbsGP/973+xsbFh7dq1VKtW7ZkD6NGjB8uWLcPR0ZGQkBAiIiL4/vvv9crs3LmTP/74g9mzZz/VuWWOeCHKnkajQaPRYGRkVGCN0jy7d+9mwoQJbNy4kTZt2jzX+xXZp2VsbExQUBCLFi0iODhY73EdS0tLhg4dykcfffRcCSszM5OJEyfi6OgIQIsWLVi7dm2BcidOnOD8+fMMGDCAmjVrMn36dFq0aPHM7yuEKB379u0jNDSUO3fuMGjQIPr27Vto4urVqxfTp08vlUlDS7QaT1ZWFvHx8aSmplKrVi1sbW2fq3lXGI1Gw7hx43jttdf4+OOP9fYtWbIES0tLhg8fzr59+/jqq6/Yvn273vVwamoqqampesflrTAtLS0hSl9UVBTffPMNY8eOJT09nXnz5rF69Wpat26tV66o1tezKtFjPMbGxtjZ2T3XG4WFheHn56e3zc7OjlWrVpGZmcmUKVPIzs5m7NixBY6dMGGC7rWzszMBAQFcvHhR10KD3EcBZMyYEOUjOzub8PBwpk6dSqdOnQCIiIgoMLJ9+/btxMXF6f0NP68SP3v4vFxdXXF1dS2wPT09nXHjxmFhYcH333+PsbFxgTJBQUG4ubnpJiHUarUYGemH7u3tjYeHh962vJaWEKJ0GRoaEh8fz9mzZ3VJ6+HDhwWmm7G3ty/1rpxyS1pF8fX1pXHjxsyaNavIRTAPHz7Mw4cP+eCDD4iOjkaj0RRo+Zmbm2Nubl4eIQtR6alUKr744guysrLIysrC2NgYQ0NDmjZtCkBoaCi1a9fmrbfeKvX3VjRpnT59moiICJo1a6ZrJdWtW5f//Oc/rF+/nqSkJCZOnMj06dOZMmUKoaGhmJqaEhAQIKv8CqGwZs2aoVKpMDIyIiMjgytXrtCkSRMiIiJYvnw53377bZm8b4k64isqGfIgRNnJ62C/evUqVapUYcaMGTRr1oxDhw7h5+dH8+bNy+R9pbkihHhqeQkrJiaG6dOnc+fOHY4ePcqWLVtYsGBBmSUseAH6tIQQLzaNRkNkZCRr164lISEBa2trRo4ciY2NDX5+fowdOxYHBwdGjRpFnz59dP1aZUWSlhCiSDdv3mTo0KGo1WrS09MBOHbsGPv378fa2po5c+bQtWtXAMaMGVMu06/L5aEQolAajYahQ4cSFxenS1h50tPTuXLlCjNnziQnJwd4toefn4UkLSFEoSIjI1Gr1UWubZqdnY1areb3338v17gkaQkhChUUFFSghfW49PR0goKCyimiXJK0hBCFSkxMLFG5hISEMo5EnyQtIUShrK2tS7VcaZGkJYQo1MiRI5845bqZmRleXl7lFFEuSVpCiEJ169YNGxubApMT5DEyMsLGxgZnZ+dyjUuSlhCiUAYGBgQHB2Nvb1+gxWVmZoa9vT3BwcHl/hywDC4VQhTJ0tKS8PBwoqKiCAoK0o2I9/LywtnZWZGJCyRpCSGKZWBgQPfu3enevbvSoQByeSiEqGAkaQkhKhRJWkKICkWSlhCiQpGkJYSoUBRPWjExMQwaNIj+/fvz4Ycfcvfu3QJlMjMz8fX1xdXVFQ8PD+Li4hSIVAjxIlA8aU2dOhV/f3+2bNlCs2bN9FayzhMUFETVqlUJCwtj2rRpTJ06VYFIhRAvAsWT1vbt22nWrBlZWVkkJiYWugxYZGQk7u7uAHTo0IGUlBSuX79e3qEKIV4Aig8uNTY25ty5c/j4+GBkZMSnn35aoExSUhJWVla6n62srEhISKBBgwa6bampqaSmpuodV95TZgghyl65Ja2wsDD8/Pz0ttnZ2bFq1SpatGjBgQMH2LBhA5MnT2bDhg165fJW/sj/8+OPD6xevZrAwMCy+wBCiBdCuSUtV1dXXF1d9bZlZGQQHh6Oi4sLAO7u7nz99dcFjq1Xrx5JSUnY2toCuZPt161bV6+Mt7e3bsHXPAkJCXh6epbmxxBCKEzRy0MjIyNmzZqFtbU1rVq1IiwsjHbt2hUo5+zsTGhoKO3btycmJgZTU1O9S0MAc3PzQvvDhBAvF0WTlqGhIYsWLWLGjBnk5ORQr1495s6dC8D69etJSkpi4sSJeHl5MWPGDPr164eJiQn+/v5Khi2EUJBKq9VqlQ6irKjVanr27ElERAQ2NjZKhyOEKAWKD3kQQoinIUlLCFGhSNISQlQokrSEEBWK4iPiXzZHjx7l9OnTNGzYkLZt21KzZk2lQxLipSItrVK0d+9epkyZwl9//YW/vz/nzp1TOiQhXjrS0iolN2/eZOXKlfj7+9O6dWumTJnCzp07uXnzJvXq1cPJyUnpEIV4KUhLq5SYm5ujUqkwMTEhJSWFQ4cOkZycTHh4ODNnzuTo0aNKhyjES0FaWs8pKyuLnJwcqlSpwnvvvYejoyNxcXH885//xMXFhXv37uHv78+VK1do27at0uEKUeFJ0noOeQtY3r59m88++4wePXoAYG9vj729PQA1atTA2NiYBw8eKBmqEC8NSVrP6PDhwyxatIjx48fTpEkTvYe1MzMzuXTpEtevX+fBgwccPnwYHx8fBaMV4uUhSesZXblyhf79+9OrVy8SEhJYsWIFd+/exd7enmHDhhEZGUl0dDTGxsYEBATQqFEjpUMW4qUgSesZxcfHc+zYMTw9PZkxYwZt2rTBycmJBQsWYGxszNixYxk7dizp6emYmZkpHa4QLw1JWs/o3Xff5fz583z11Vd07NiR999/H4DatWvzww8/4OXlhampqSQsIUqZDHl4RjVr1qRLly6cOXOGAwcO6LZfunQJU1NTDA0NFYxOiJeXtLSeUt70Y1WqVMHNzQ2tVsvWrVt577336NSpE9u3b2fhwoUYGUnVClEW5C/rCQ4dOsS+ffuoXr06PXr0oEWLFgBoNBpq1qzJoEGD6Nq1K7/88guWlpYsWrRIN9xBCFH6JGkVY+/evSxcuBB3d3dOnTqFqampLmlBbuKqVq0atra2TJo0ScFIhag8FE9aMTExzJs3j6ysLBo2bMjXX39dYGaEa9eu4ebmpluNp06dOoWuRF2aMjIy2Lp1K7Nnz8bJyYklS5YQHR1NWloaDg4O9OnTB4AtW7Zw+/ZtRo0aVabxCCFyKd4RP3XqVPz9/dmyZQvNmjUrNBmdPHmS/v37ExoaSmhoaJknLMhdRFatVnPr1i3S09PZtWsXFhYWaLVaPv30U3bv3g1ArVq16Ny5c5nHI4TIpXhLa/v27RgbG5OVlUViYqLe5VeeEydOcP78eQYMGEDNmjWZPn16oeVKS95isPPnz6dp06bcu3ePefPm8dprrwFQvXp1YmJi6NWrF126dCmzOIQQBSmetIyNjTl37hw+Pj4YGRnx6aefFihjamqKu7s7w4cPZ9++fYwfP57t27djYmKiK5OamkpqaqrecQkJCc8UU95q1k2bNgVynx/MS1iQe+lYpUqVZzq3EOL5lNsSYmFhYfj5+elts7OzY9WqVbqfN2zYwObNm9mwYUOx53J3d8ff3x9HR0fdtiVLlhAYGFho+WddQkyj0WBgYEBcXBwXL17k2rVrWFhYsHr1agICArCzs3vqcwohno+i6x5mZGSwb98+XFxcALh//z6dO3fmyJEjeuWCgoJwc3OjVq1aAPTv359FixbRrFkzXZmiWlqenp7PlLS0Wi0qlYrY2FgWLFhAnz59OHToENbW1rz77rs4ODg8y0cWQjwnRS8PjYyMmDVrFtbW1rRq1YqwsDDatWtXoNzhw4d5+PAhH3zwAdHR0Wg0mgKtHHNzc72ZFp6GRqMhMjKStWvXkpCQgLW1NSNHjsTGxoZ58+bxySef4OzszHvvvUdOTo6MdhdCQYqvMJ035CEnJ4d69eoxe/ZsrK2tWb9+PUlJSUycOJHExESmTJlCcnIypqamzJ07V+/SsCglWWH65s2bDB06FLVaTXp6um67mZkZ1tbWzJkzh65du0qyEuIFoXjSKktPSloajQYXFxfi4uLIzs4usN/IyAh7e3vCw8MxMFB8dIgQghdgnJaSIiMjUavVhSYsgOzsbNRqNVFRUeUcmRCiKJU6aQUFBeldEhYmPT2doKCgcopICPEklTppJSYmlqjcs473EkKUvkqdtKytrUu1nBCi7FXqpDVy5MgnzixqZmaGl5dXOUUkhHiSSp20unXrho2NTZET9hkZGWFjY4Ozs3M5RyaEKEqlTloGBgYEBwdjb29foMVlZmaGvb09wcHBMtxBiBeI4g9MK83S0pLw8HDdwqt5I+K9vLxwdnaWhCXEC6bSJy3IbXF1796d7t27Kx2KEOIJpBkhhKhQJGkJISoUSVpCiArlpe7TysnJAWREuxBKsra2LtV1QF/qpJWcnAyAp6enwpEIUXk968zBRXmpp6Z5+PAhJ0+exMrKqkRzYeXNdLpu3bpK/+iO1EUuqYdHnrUupKX1FKpUqUL79u2f+jhra+tS/WaoyKQuckk9PKJ0XUhHvBCiQpGkJYSoUCRpCSEqFMOZM2fOVDqIF4mpqSkdO3bE1NRU6VAUJ3WRS+rhkRehLl7qu4dCiJePXB4KISoUSVpCiApFkpYQokKRpCV0Zs2axfHjx5UOQ3FSD4/88MMPusfhXhSV+u5h//79admyJfXr11c6lBfC7du3+emnn1i5ciX379+nUaNGT1z442Uk9fDIgQMH+Prrr/n9998xMjKiSZMmJXokrixV6ruHXbt2pWbNmrzxxhuMHz+e2rVrKx3SC+HGjRts3bqVDRs20KxZM4YMGYKLi4vSYZU7qYdHYmJi2Lp1K9HR0XTq1IkhQ4bQsmVLRWKp1JeHlpaWBAcHU6NGDfr378+XX35JdHQ0mZmZSoemmPj4eH755Rd+/fVXGjduTK9evQgLC+Pzzz9XOrRyJfXwyP3791Gr1cTHx2NgYEDNmjWZO3cuAQEBisRTqVtaHh4e/PrrrwCkpaURGhpKREQEp06dwtjYmP379yscYfkaMWIEN2/eZMCAAQwaNIgGDRoAkJ2dTdeuXTlw4IDCEZYPqYdHPvvsMw4ePIizszODBg3STUCQmZlJly5diI6OLveYKnXSGjhwIJs3by50X0pKSqW7XDx48CBvvvmm0mEoTurhkZCQEPr27Uu1atUK7EtOTsbKyqrcY6rUSevs2bM4OjoqHYbipk6dWux+Pz+/copEWVIPjwQGBha7/+OPPy6nSAqq1H1akrByvfHGG7zxxhukp6eTlJREp06d6NKlC6mpqVSm7zSph4KOHz/Orl27MDAwwMTEhKioKC5cuKBoTJW6pXX48OFi93fo0KGcInkxDBkyhI0bN+oWqNVoNAwdOpSQkBCFIytfUg+PDB8+nJ9++omqVasCkJGRwahRo9i4caNiMb3UM5c+ydKlSzl69CitW7cu8E2qUqlYs2aNQpEp4969e9y5c0fXl3fz5k3u37+vcFTlT+rhkdu3b6NSqXQ/Z2VlcefOHQUjquRJ6z//+Q+jRo3C29ubnj17Kh2O4j788EPc3d1p164dWq2Wo0eP8s9//lPpsMqd1MMjQ4YMYfDgwXTt2hWtVsvevXsZNWqUojFV6stDgEuXLhESEoKvr6/SobwQkpKSOHLkCCqVCicnJywtLZUOSRFSD4+cPHmS6OhoVCoVb775puJ9wZU+aYlHirpjpOSdIiVIPTxS1JCggQMHlnMkj1Tqy0OAuLg4du7cSUJCAgYGBtStW5e33nqL1157TenQFJWVlcW+ffto06aN0qEoqrLXw59//ql7nZWVRWxsLO3bt1c0aVXqlta6devYtGkTvXv31g2SS05OZteuXbi7uzN69GiFI1RWZmYmo0ePZu3atUqHoiiph0fu3LnD5MmT+emnnxSLoVK3tNasWcPmzZt1t3Pz+Pj44OHhUemTVnp6OtevX1c6DMVJPTxSrVo1rl27pmgMlTppGRkZkZ2dXWD7w4cPMTY2ViAiZfXo0UN3e1ur1XL37l3ef/99haMqf1IPj3h5eenVhVqtpmvXrorGVKkvD7ds2cLixYt58803sbKyQqVSkZSUxKFDh5g8eTL9+vVTOsRylf8bVKVSYW5uTvXq1RWMSBlSD4/kfyBapVJRq1YtmjVrpmBElfwxnv79+/Pzzz/Tvn17qlatiomJCe3bt2fdunWVLmEBzJ8/n4YNG9KwYUMaNGhA9erV8fb2Vjqscif18MjOnTt1jzd16NCBZs2a8cUXXygaU6W+PITc2RzMzc3p2bMnNWrU0G3fu3cv3bt3VzCy8vPxxx9z5swZkpKS9AbZZmdnV6pZXaUeHpk+fTrx8fGcPHmSv/76S7c9Ozube/fuKRhZJb88+VUJAwAAFANJREFUXL16NZs2baJRo0acOHGChQsX6qYkyT/X1ssuLS2NO3fuMHfuXL2R30ZGRlhaWmJkVDm+26QeHlGr1Vy7dq1AXRgaGmJvb4+FhYVywWkrMTc3N+39+/e1Wq1WGxsbq+3cubP28OHDWq1Wqx0wYICSoSkiIyNDe+bMGa1Wq9X+9ttv2vnz52tv3bqlcFTlT+pBX2Jiolar1WoPHz6sXbt2rfbBgweKxlOp+7QA3XCHdu3asWjRIiZNmsT58+f1HhKtLHx9fdmyZQvHjh1jyZIlVK9e/YlzTL2MpB4e+fLLL1m8eDEXLlzgH//4B6dOnVL8OcxKnbScnJyYPHmybn6gDh06MGPGDHx8fLhx44bC0ZU/tVqNr68vu3bt4p133mH8+PHcvHlT6bDKndTDIydOnGDu3LmEhYXxzjvvMG/ePC5duqRoTJU6ac2YMYO//e1vetOOvP3223z//fe0a9dOwciUkZOTQ0pKCuHh4XTr1o3k5GQyMjKUDqvcST08kpOTg0ajISIigq5du/LgwQMePHigaEyVp2exEAYGBgwZMqTA9tatW7Ns2TIFIlLW+++/z9ChQ+nRowcODg707t2biRMnKh1WuZN6eGTgwIF06dKFdu3a0aZNG/r27cuwYcMUjalS3z0UxcvJyVF8Yc4XQWWvB41Go5vF9UVY8EWSlhCiQqnUfVpCiIpHklYR/v73v/Ovf/2LmJgYpUNRRFpamt5I6MpK6kFfWlqa0iFgOHPmzJlKB/Eiatq0KcOHD8fAwKDSPCwbHBxMUFAQ7dq1o3///kRERJCWlqZbVbiykHp4ZO/evYSEhNC2bVsGDBjA8uXLsbS0pGXLlorFJC2tIrRq1QoAa2trhSMpP+vXr+fTTz9l69at9OzZky1btrBr1y6lwyp3Ug+PBAYG0r9/f7Zv307r1q3Zs2eP4pMhVuohD46OjnpzBeWnUqk4c+aMEmEpqm7dukRFRTFq1CiMjIwq7fgkqYdHHB0dWbJkCe7u7piZmZGVlaVoPJU6aZ09e1bpEF4ozZo1Y+zYsajVat58800mTZpUKefKl3p4pE6dOnz11VecPHmSBQsWMH/+fBo0aKBoTDLkgdyxJ7/99hvp6elotVo0Gg1qtRp/f3+lQytX2dnZHDlyhObNm2NhYcGePXtwdnaudGOUpB4eSUtLIzw8nNdff53GjRuzbt06Bg4ciJmZmWIxSdICRo0aRf369Tl69CguLi5ERkby2muvMX/+fKVDKxcbN25k2LBhlX7pLKmHR/Lmk5MlxF5QSUlJrFmzhq+//pq3336bMWPGVKqZKuV7K5fUwyMnTpyge/fuekuI5SdLiCls2LBhbNy4kU2bNqHVahk2bBju7u789ttvSocmhHiMtLSATp068cknn/DFF18wevRoTp06RZUqVZQOq9wFBwfzzTffcOfOHSC35VEZ76JKPTwSGRnJ0qVLuX37tl5LNCIiQrGYpKX1/65evYqtrS2nTp3i8OHDuLq6Uq9ePaXDKlc9evRg+fLlNG/eXOlQFCX18EivXr2YPn06zZo105sYs2HDhorFJC2t/2drawvAq6++yquvvqpwNMqwtLSUP1SkHvKrUaMG3bp1UzoMPdLSEro7RHv37iUzM5OePXvqLeKgZKdreZJ6eOTw4cNA7qVy3mpV+euiQ4cOSoUmSUvwxPnP/fz8yikSZUk9POLl5VXkPpVKxZo1a8oxmsfeX5IWTJgwgSVLluht8/b2ZvXq1QpFVL7++OMPOnfurHQYipN6eOT8+fM4ODgoHUahKnXSyr84Z926dXXbc3JyqF+/PuvXr1cwuvJTmdZ4LI7UwyMvcl1U6o74+fPnF7s4pxCV1YvclqnULa38YmNjOX/+PIMHD+bYsWOKdjSWt9dff73YB4KV7L8oT1IPj3Ts2JEePXoUuf//2rv3oKjKPg7g33Xi4ir3sCwtKwpnQNQBWVhIUpDAiUsOqFxkxhhjDbURGxCBqNGiC0MDoRWFgJQ1LCRNdJsAsV2QXUAQSQWywbgGSTRyb5fz/sHLHnhZpXfmfc+z+DyfGWeWszvjd37ib59zznOeh+T1PapHWjMKCgpQXl6O/v5++Pn54dVXX0VISAiio6NJRxOEra0tVc/V3QmrA08sFsPV1ZV0DL1Y0wJw7tw5FBUVYefOnbCyskJxcTFCQ0OpaVrLli0z2F9QIbE68CwtLfH888+TjqEXW7kU0/sfGhsb6342MTGhahkSkrObDQmrA8/IyIh0hDtiTQuAq6sr3n77bYyNjaG8vBz79++Hm5sb6ViCudNSLLMZ6rfu/xKrA6+oqGjBz8TExAiQZD7WtADEx8fj0Ucfhb29PUpLS+Hl5YWEhATSsQwKu18zjdWB9/vvvxP5e9k1LUyfHu7evRu7d+8mHcVgzX5YlmasDjxStWAjLYZhFhXWtBiGWVTY6eG/tbW1Qa1WQ6PRQCKREN2MUmijo6MQi8V3/QwN13JYHf47pGrBRlqYXpLkpZdeQmdnJ3p6ehAbG4vi4mLSsQQTEREBALjbZuMvvviiQGnIYXXgzazycOrUqTt+htRSPewxHgBBQUHIz8+HlZUVgOktxaKiolBWVkY4mTD8/Pzg6OgIhUKh99ENWpZkYXXgbdmyBYGBgSgpKdF7g4rkkwPs9BDA1NSUrmEBgLW1NVV3ifLy8qBSqdDQ0ED1jHBWB97Jkydx/vx50jH0Yk0LgL29Pd544w2EhIQAAIqLi7F27VrCqYQTHx+PwsJCdHd3UzN5Uh9WB97p06eRnp4Oc3Pzuy4ISAJrWgBOnDiBrKwsHDt2DBzHQSKRIDU1lXQswXR1deG9995DSUmJ3ourtDxEzOrAU6vVkMvlyMvLg5mZ2bz32WathJmamiI+Pl73M8dx6OrqwvLlywmmEo4hnwoIidWBd/z4cfzwww8YGRnRu2Er26yVsC+++ALvvPMOxsbGdMcefvhhlJeXE0wlvAsXLsDLy4t0DOJYHXhyuRyhoaGkY8zBpjwAyMnJwVdffYXt27fjxx9/RHJyMtavX086lqDq6uqQl5cHFxcXbNq0CXv37kV9fT3pWIJjdeB1dHSgqakJQUFBCA4ORnJyMjo6OkjHYk0LmN7nbvXq1bC3t0dbWxsiIiLQ2tpKOpZgLl68iLi4OPj6+uLzzz9HQUEBfHx8cPjwYb2nBvcqVgfetWvXEBYWBisrKxw+fBixsbEwNzdHWFgYrl+/TjYcx3B79uzhLl68yFVVVXHJyclcf38/5+3tTTqWYCIiIrirV6/OO37lyhUuPDycQCIyWB140dHRXHV19bzjCoWCe+GFFwgk4rGRFoCUlBRUVlbi6aefxtDQEPz8/BAZGUk6lmCGh4f1Prbk6OiIv/76i0AiMlgdeAMDA5BKpfOOe3p6ore3l0AiHrt7CODJJ5/EsWPHAGDe/oc0GB0dhUajmbODMABoNBpoNBpCqYTH6sAbHR2943tarVbAJPOxkRaAqqoqhIaGwsfHB97e3ro/tPD09ER6evqcY1qtFmlpaXjmmWfIhCKA1YG3YcMG5Ofnzzuek5MDZ2dn4QPNwqY8ANi2bRuSkpJgZ2c35/EdWtYMHx0dhUwmQ29vLxwdHaHVatHS0gI7OztkZ2fPWT//XsbqwLt16xaioqJgY2MDJycnaLVaNDY2Ynx8HAUFBbCwsCCWjTUtADt27MCXX35JOgZxarUaV65cgUgkgpOTE1xcXHTvDQwMwNbWlmA64bA6TJuYmMC33347pxb+/v7EmzfVTauurg7A9AQ6c3NzeHt7z7meQdOGrQsx5G3ShcTqwCNVC6ovxGdlZele9/b2zpmbJRKJqNpReCEUf7fNwerAI1ULqptWYWEh6QiLBk1L9dwNqwOPbWxBAMdx+PTTT9HW1gYAOHPmDAICApCQkIDh4WHC6RiG0YfqppWeng6lUgmxWIyGhgZkZmYiMTERdnZ2OH78OOl4DMPoQfXp4U8//YRz587hvvvuQ0FBAZ599llIpVJIpVL4+/uTjmdQ2LWcaawOPFK1oHqktWTJEt3dQrVaDU9PT917U1NTpGIJLjExEWVlZRgcHLzjZ5KTkwVMRAarAy87OxuNjY13/X9AapMPqkdaS5cuRU9PD0ZGRnDjxg3ds1bXr1+nZgFAYHr284ULF5CRkQELCwt4eHjAw8MDzs7Oujk5s+cq3atYHXiTk5N49913cfPmTWzcuFFXi0ceeUT3me3btxPJRvU8LZVKhVdeeQXDw8OQyWSIiYnB2bNncfLkSaSlpWHz5s2kIwqut7cXdXV1aGhoQFNTE+6//37k5uaSjiU4Vodpk5OTuHz5Murr69HQ0ICBgQFs2LABr7/+OrFMVI+0JBIJKioqMD4+DnNzcwCAg4MDPvvsM6xZswYA8PPPP8PBwYFgSuGMjIzg6tWraGlpQWtrK0xMTGBvb086luBYHXjGxsYwMzODWCyGhYUFbt26RXzFC6pHWv8EDTOgP/roIyiVSnR3d8PV1RVSqRQeHh6wsbEhHU1QrA68srIyKJVKqFQqrFq1SleLdevWEZ+rxprWAoKDg1FaWko6xv+Vg4MD3N3dERUVBYlEAhMTE9KRiGB14K1duxaenp54+eWXsW7dOtJx5mBNawE0jLSGh4dRU1MDpVIJtVqNlStXwsPDA56enlTt/8jqwGtvb4dSqYRSqURXVxc2bdoEDw8PSKVSois8AKxpLYiGpvWffv31VygUCsjlcgwNDUGpVJKORASrw7SJiQmo1WrU1NRAoVBg6dKlkMvlxPJQfSGe4Y2MjODy5cu4dOkSGhoa8Ntvv+lOl2jC6jDXzZs3dbVobm6GWCyGq6sr0UxUj7Tq6uoWXH6GhmtawcHB6O7uxsaNG+Hm5gZ3d3e9a6Xf61gdeLGxsWhqaoKVlRUkEgnc3d3h6uqqu8tOEtVNy9/fH9999x1CQkJQXFys9zOdnZ1YvXq1wMmEdenSJTg5Oc1bG502rA68r7/+Gm5ubga54CHVTSs6Ohrt7e0YHBzEAw88oDvOcRxEIhEqKioIphPenj179N7Opm1dMVYHXmJiot7jaWlpAifhUf2VkpKSAmNjY8hkMnzwwQek4xB38OBB3WuNRoOKigqDOB0QGqsDb/b1q5laPP744wQTge7NWp977jmO4zhu//79hJMYrpCQENIRDAKrw7SpqSlu165dRDNQPdIyMjJCWFgYWltbERUVNe992k4Henp6dK85jsMvv/yCoaEhgonIYHW4sxs3bqC/v59oBqqb1pkzZ3Dt2jUkJSXhwIEDpOMQFxkZCZFIpLumZ21tTc1SLLPN3l2c5joA0zPjZ34nAMDa2hpHjhwhmonqC/Ez+vr68Oeff0IkEuGxxx6j+vENhjF0VI+0AODUqVPIzc2FkZERNBoNOI7Dvn37IJPJSEcjKi4uDhkZGaRjCOr27dvIyspCX18ffHx8EBQUpHsvJSWFuiW4FQoFvv/+e/T19WHJkiVYsWIFvLy84OvrSzQX1U3r7NmzUCgUKCoqwhNPPAFg+pmrlJQUWFhYICwsjHBCYei7xd/S0qK7zkfLtb3ExEQ89dRTcHFxQU5ODurr63WNqqWlhXA6YWVmZqK5uRmBgYFYsWIFOI7DwMAA5HI5GhsbkZCQQC4cuXsA5AUHB3ODg4Pzjv/xxx9cQEAAgURkFBYWcps3b+ZKSko4lUrF1dbWctu2beNUKhWnUqlIxxNMUFCQ7vXY2BgXFRXFpaWlzXuPBr6+vpxWq513XKPRcH5+fgQS8aheI16j0cDKymrecRsbG6o2MIiMjERubi5KSkrQ09MDiUSCZcuWwdXVlfhzZkIbGBgAAJiamiI7Oxs1NTX48MMPia8hJTQTExP09fXNO97T06NbepoUqk8PtVotBgcHYW1tPef44OAgdb+kdnZ2yMvLQ0ZGBg4dOoTJyUnSkQR34MAB7NixA6mpqfDx8YGZmRk++eQTyGSyObuP0+Do0aOIiIjAmjVrYGtrC5FIhP7+fnR0dBCdDQ9QfvcwPz8fVVVVSEtLw8qVKwEAHR0dSEpKQmBgIHbt2kU4IRnV1dX45ptv8Oabb5KOIrjh4WFoNBpYWlrqjk1NTaGyshI+Pj4EkwlvYmICzc3N6O/vx9TUFB588EGsX7+e+EiL6qYFAO+//z5Onz4NsVgMjUYDjUYDmUyGffv2kY7GGIC33noLR48eJR1DcCdOnMDBgweJL/inD/VNCwDGx8fR3t4OkUgEOzs7mJqa6t47f/48tmzZQjDd/9/sGeD6PPTQQwIlIUvfw8GVlZXYunUrALIPCQvNxcUFNjY2OHLkCPEpDv+J6mtaM0xNTe+4DnZWVtY937RiYmLQ0dGhu7U9G02rXVhaWqK0tBQymUz3gHRtbS11NyMAYNWqVUhPT8drr72Gjz/+GHv37sXWrVvnfKETQ+7G5eJAw63u27dvcwEBAVx9fT3pKMTV1NRwO3fu5GprazmOo+PfX5/g4GDd6+rqau7QoUOcVCrlwsPDubi4OILJKH9g+p+g4S7i8uXLceLECcjlcjg7O5OOQ9TMaqWpqamoqqqCVqslHYkIbtaIWyqVQiqV4u+//0Zrays6OzsJJmOnh8y/OTk5wcnJiXQMg2BpaYnMzEzI5XLqpjrMiIiImHfMyMgIjo6OcHR0JJCIxy7EL4DG3XgYxpBRPSP+n2A9nWEMCxtpLWBiYoItVcMwBoTqpsXmJzHM4kN10woICGDzkxhmkaG6aQ0PDyM8PBypqanU3+pnmMWC6gvxM/OT7vUdpBnmXkL1SIthmMWH6pEWwzCLD2taDMMsKqxpMQyzqLCmxTDMosKaFsMwi8q/AMsgT4fG1dfyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style='ticks'\n",
    "context='notebook'\n",
    "font='Arial'\n",
    "\n",
    "sns.set(style=style,context =context,font=font)\n",
    "\n",
    "fig1 = plt.figure(figsize=(4,4))\n",
    "ax1 = fig1.add_subplot(111)\n",
    "\n",
    "ax1.scatter(range(len(cohensd_df['Cohens_d'])), cohensd_df['Cohens_d'], s=90, c='k',zorder=10)\n",
    "\n",
    "# x1 = 1\n",
    "# for i in range(len(contrast_means)):\n",
    "#     ax1.scatter(x1, contrast_means[i], s=90, c='k',zorder=10)\n",
    "#     ax1.plot([x1,x1], [contrast_CI_LB[i], contrast_CI_UB[i]], color='k', lw=1)\n",
    "#     x1 = x1 + 1\n",
    "\n",
    "# ax1.set_ylim(-0.1,1.0)\n",
    "ax1.set_ylabel(\"Cohen's D\", size = 15)\n",
    "ax1.set_xticks(np.arange(0, len(cohensd_df)))\n",
    "\n",
    "xlabel = []\n",
    "for c in cohensd_df[\"Int-Air-Paper\"]:\n",
    "    xlabel.append(c)\n",
    "    ax1.set_xticklabels(xlabel, rotation=90)\n",
    "\n",
    "for i, txt in enumerate(cohensd_df['Cohens_d']):\n",
    "    ax1.annotate('{:.2f}'.format(txt), (i, cohensd_df['Cohens_d'].values[i]+0.05), rotation = 45)\n",
    "    \n",
    "    \n",
    "#     ax1.legend(custom_lines, legend_labels,bbox_to_anchor=(1, 1))\n",
    "\n",
    "plt.title(\"Gr66a valence\", size=30)\n",
    "plt.axhline(color='k', linewidth = '0.5')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig1.savefig('/Users/tumkayat/Desktop/Gr66a_valence_COHENSD.pdf',dpi=1000,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>ES</th>\n",
       "      <th>Air</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>0.052353</td>\n",
       "      <td>Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>0.289947</td>\n",
       "      <td>Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>0.272716</td>\n",
       "      <td>Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>0.147811</td>\n",
       "      <td>On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>0.463668</td>\n",
       "      <td>On</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>0.472507</td>\n",
       "      <td>On</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ES  Air\n",
       "659  0.052353  Off\n",
       "660  0.289947  Off\n",
       "661  0.272716  Off\n",
       "656  0.147811   On\n",
       "657  0.463668   On\n",
       "658  0.472507   On"
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     },
     "execution_count": 111,
     "metadata": {},
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    "cohensd_df[cohensd_df['Citation'] == 'This study'][['ES', 'Air']]"
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  {
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       "                  Citation  Air  Cohens_d\n",
       "9    Bell and Wilson, 2016  Off  0.000000\n",
       "10   Bell and Wilson, 2016  Off  0.021135\n",
       "11   Bell and Wilson, 2016  Off  0.000000\n",
       "12   Bell and Wilson, 2016  Off -0.045336\n",
       "13   Bell and Wilson, 2016  Off  0.104412\n",
       "14   Bell and Wilson, 2016  Off  0.041807\n",
       "15   Bell and Wilson, 2016  Off  0.087717\n",
       "16   Bell and Wilson, 2016  Off  0.019952\n",
       "659             This study  Off  0.096471\n",
       "660             This study  Off  0.557673\n",
       "661             This study  Off  0.531156\n",
       "1    Bell and Wilson, 2016   On -0.013475\n",
       "2    Bell and Wilson, 2016   On  0.059317\n",
       "3    Bell and Wilson, 2016   On  0.414359\n",
       "4    Bell and Wilson, 2016   On  0.645994\n",
       "5    Bell and Wilson, 2016   On  0.667295\n",
       "6    Bell and Wilson, 2016   On  0.756132\n",
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       "8    Bell and Wilson, 2016   On  0.712990\n",
       "656             This study   On  0.241712\n",
       "657             This study   On  0.802246\n",
       "658             This study   On  0.910735"
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     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohensd_df[[\"Citation\", \"Air\", \"Cohens_d\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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