08_allbyallfollowups.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfollow up analysis for ABA and ST all by all\\ndata figure 1\\n\\nShaina Lu\\nZador & Gillis Labs\\nApril 2020\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "follow up analysis for ABA and ST all by all\n",
    "data figure 1\n",
    "\n",
    "Shaina Lu\n",
    "Zador & Gillis Labs\n",
    "April 2020\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import stats\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split #stratify train/test split\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_style():\n",
    "    #function for matplotlib formatting\n",
    "    plt.style.use(['seaborn-white','seaborn-notebook'])\n",
    "    plt.rcParams['figure.figsize'] = [6,4]\n",
    "    plt.rcParams['axes.spines.top'] = False       #remove top line\n",
    "    plt.rcParams['axes.spines.right'] = False     #remove right line\n",
    "    plt.rcParams['axes.linewidth'] = 2.0          #set weight of axes\n",
    "    plt.rcParams['axes.titlesize'] = 20           #set font size of title\n",
    "    plt.rcParams['axes.labelsize'] = 18           #set font size of x,y labels\n",
    "    plt.rcParams['axes.labelpad'] = 14            #space between labels and axes\n",
    "    plt.rcParams['xtick.labelsize'] = 14          #set x label size\n",
    "    plt.rcParams['ytick.labelsize'] = 14          #set y label size\n",
    "    plt.rcParams['legend.fontsize'] = 16          #set legend font size\n",
    "    \n",
    "set_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#infiles\n",
    "ST_CANTIN_FILT_PATH = \"/home/slu/spatial/data/cantin_ST_filt_v2.h5\"\n",
    "ALLEN_FILT_PATH = \"/home/slu/spatial/data/ABAISH_filt_v6_avgdup.h5\" #v6 averaged duplicates\n",
    "ONTOLOGY_PATH = \"/data/slu/allen_adult_mouse_ISH/ontologyABA.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ontology\n",
    "ontology = pd.read_csv(ONTOLOGY_PATH)\n",
    "ontology = ontology.drop([ontology.columns[5], ontology.columns[6]], axis=1)\n",
    "ontology = ontology.fillna(-1)  #make root's parent -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ST\n",
    "STspotsmeta = pd.read_hdf(ST_CANTIN_FILT_PATH, key='STspotsmeta', mode='r')\n",
    "STspots = pd.read_hdf(ST_CANTIN_FILT_PATH, key='STspots', mode='r')\n",
    "STpropont = pd.read_hdf(ST_CANTIN_FILT_PATH, key='STpropont', mode='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ABA\n",
    "ABAvoxmeta = pd.read_hdf(ALLEN_FILT_PATH, key='metabrain', mode='r')\n",
    "ABAvox = pd.read_hdf(ALLEN_FILT_PATH, key='avgvoxbrain', mode='r')\n",
    "ABApropont = pd.read_hdf(ALLEN_FILT_PATH, key='propontology', mode='r')\n",
    "geneIDName = pd.read_hdf(ALLEN_FILT_PATH, key='geneIDName', mode='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__pre-filter ST and ABA datasets as is done for all by all in each case__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "copied and pasted pre-processing functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zscore(voxbrain):\n",
    "    \"\"\"zscore voxbrain or subsets of voxbrain (rows: voxels, cols: genes)\"\"\"\n",
    "    #z-score \n",
    "    scaler = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "    scaler.fit(voxbrain)\n",
    "    z_voxbrain = scaler.transform(voxbrain, copy=True)\n",
    "    \n",
    "    #store z-scored voxbrain as pandas dataframe\n",
    "    z_voxbrain = pd.DataFrame(z_voxbrain)\n",
    "    z_voxbrain.columns = voxbrain.columns\n",
    "    z_voxbrain.index = voxbrain.index\n",
    "    \n",
    "    return z_voxbrain\n",
    "\n",
    "def splitdata(data, testratio):\n",
    "    \"\"\"generic fcn to split data in train/test folds\"\"\"\n",
    "    #set seed so train and test will always split the same in diff run so ML algorithm doesn't see whole dataset (BAD)\n",
    "    np.random.seed(42)\n",
    "    shuffindices = np.random.permutation(len(data))\n",
    "    testsize = int(len(data) * testratio)\n",
    "    testindices = shuffindices[:testsize]\n",
    "    trainindices = shuffindices[testsize:]\n",
    "    return data.iloc[trainindices], data.iloc[testindices]\n",
    "\n",
    "def filterproponto(sampleonto):\n",
    "    \"\"\"pre-processing for propogated ontology for ST data\"\"\"\n",
    "    #remove brain areas that don't have any samples\n",
    "    sampleonto_sums = sampleonto.apply(lambda col: col.sum(), axis=0)\n",
    "    sampleonto = sampleonto.loc[:,sampleonto_sums > 5] #greater than 5 becuase less is not enough for train/test split to have non-zero areas\n",
    "    \n",
    "    return sampleonto\n",
    "\n",
    "def getleaves(propontvox, ontology):\n",
    "    #leaves are brain areas in the ontology that never show up in the parent column\n",
    "    allareas = list(propontvox)\n",
    "    parents = list(ontology.parent)\n",
    "    for i in range(len(parents)): #convert parents from float to int, ids are ints\n",
    "        parents[i] = int(parents[i])\n",
    "    \n",
    "    #remove parents from all areas\n",
    "    leaves = []\n",
    "    for area in allareas:\n",
    "        if int(area) not in parents:\n",
    "            leaves.append(area)\n",
    "    \n",
    "    return leaves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ST pre-processing\n",
    "STpropont = filterproponto(STpropont)\n",
    "STspots = STspots.astype('float64') #convert int to float for z-scoring\n",
    "#get leaf areas\n",
    "leaves = getleaves(STpropont, ontology)\n",
    "leafSTpropont = STpropont.loc[STspotsmeta.id.isin(leaves),leaves] #subset prop onto for leaf areas\n",
    "leafSTspots = STspots.loc[STspotsmeta.id.isin(leaves),:] #subset data for samples from leaves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ABA pre-processing\n",
    "ABApropont = filterproponto(ABApropont)\n",
    "#get leaf ares only\n",
    "leaves = getleaves(ABApropont,ontology)\n",
    "leafABApropont = ABApropont.loc[ABAvoxmeta.ids.isin(leaves),leaves] #subset propontvox for leaf areas\n",
    "leafABAvox = ABAvox.loc[ABAvoxmeta.ids.isin(leaves),:] #subset voxbrain for voxels from leaves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Examples for learnability of ARA brain areas in both ABA ISH and ST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explain what an AUROC means via example: in an average performing brain area in each dataset, how many voxels are misclassified?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__read in AUROC files from ABA ISH and ST w/ lasso alpha = 0.1__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load in test AUROC's from ABA\n",
    "#TRAIN1_ALLBYALL_PATH = \"allbyallABA0p1_train0420.csv\"\n",
    "TEST1_ALLBYALL_PATH = \"/home/slu/spatial/data/allbyallABA0p1_test0420.csv\"\n",
    "#TRAIN2_ALLBYALL_PATH = \"allbyallABA0p1_f2_train0420.csv\"\n",
    "TEST2_ALLBYALL_PATH = \"/home/slu/spatial/data/allbyallABA0p1_f2_test0420.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fold1\n",
    "#train_allbyall_f1 = pd.read_csv(TRAIN1_ALLBYALL_PATH, sep=',')\n",
    "#train_allbyall_f1 = train_allbyall_f1.set_index(train_allbyall_f1.columns)\n",
    "\n",
    "ABA_test_f1 = pd.read_csv(TEST1_ALLBYALL_PATH, sep=',')\n",
    "ABA_test_f1 = ABA_test_f1.set_index(ABA_test_f1.columns)\n",
    "\n",
    "#fold2\n",
    "#train_allbyall_f2 = pd.read_csv(TRAIN2_ALLBYALL_PATH, sep=',')\n",
    "#train_allbyall_f2 = train_allbyall_f2.set_index(train_allbyall_f2.columns)\n",
    "\n",
    "ABA_test_f2 = pd.read_csv(TEST2_ALLBYALL_PATH, sep=',')\n",
    "ABA_test_f2 = ABA_test_f2.set_index(ABA_test_f2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#average the test performance across the two folds\n",
    "ABA_test_allbyall_avg = (ABA_test_f1 + ABA_test_f2)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ST\n",
    "ST_TEST1_ALLBYALL_PATH = \"/home/slu/spatial/data/allbyallST0p1_f1_test0508.csv\"\n",
    "ST_TEST2_ALLBYALL_PATH = \"/home/slu/spatial/data/allbyallST0p1_f2_test0508.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ST_test_f1 = pd.read_csv(ST_TEST1_ALLBYALL_PATH, sep=',')\n",
    "ST_test_f1 = ST_test_f1.set_index(ST_test_f1.columns)\n",
    "\n",
    "ST_test_f2 = pd.read_csv(ST_TEST2_ALLBYALL_PATH, sep=',')\n",
    "ST_test_f2 = ST_test_f2.set_index(ST_test_f2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ST_test_allbyall_avg = (ST_test_f1 + ST_test_f2)/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__summary statistics to define an average performing area__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABA_test_avg_upper = ABA_test_allbyall_avg.values[np.triu_indices_from(ABA_test_allbyall_avg, k=1)]\n",
    "ABA_test_avg_upper = ABA_test_avg_upper[ABA_test_avg_upper != 0] #remove 0's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=156519, minmax=(0.18333333333333338, 1.0000000000000018), mean=0.9958435754281836, variance=0.0011406135866141062, skewness=-11.586730164203207, kurtosis=151.2754293908355)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.stats.describe(ABA_test_avg_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(ABA_test_avg_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "#what's the median for either of the folds?\n",
    "print(np.median(ABA_test_f1.values[np.triu_indices_from(ABA_test_f1, k=1)]))\n",
    "print(np.median(ABA_test_f2.values[np.triu_indices_from(ABA_test_f2, k=1)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ST_test_avg_upper = ST_test_allbyall_avg.values[np.triu_indices_from(ST_test_allbyall_avg, k=1)]\n",
    "#no 0's here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DescribeResult(nobs=106030, minmax=(0.04999999999999999, 1.0000000000000009), mean=0.8829401965771095, variance=0.023905302593669842, skewness=-1.487979543773035, kurtosis=1.4227267641611476)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.stats.describe(ST_test_avg_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9591836734693878"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(ST_test_avg_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9691119691119692\n",
      "0.9727272727272728\n"
     ]
    }
   ],
   "source": [
    "#what's the median for either of the folds?\n",
    "print(np.median(ST_test_f1.values[np.triu_indices_from(ST_test_f1, k=1)]))\n",
    "print(np.median(ST_test_f2.values[np.triu_indices_from(ST_test_f2, k=1)]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: the median did not change for either fold between the first version of this with the wrong filtering and the correct filtering. So did not re-run the rest of this sub-section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 14,  14,  46,  94,  94, 105, 105, 115, 166, 232, 342, 359, 362]), array([227, 362, 111, 111, 409, 306, 406, 166, 430, 289, 439, 430, 378]))\n"
     ]
    }
   ],
   "source": [
    "#which brain areas are the median performing area for the average?\n",
    "STmedareas = np.where(ST_test_allbyall_avg == 0.9591836734693878)\n",
    "print(STmedareas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([ 38,  78,  97, 105, 139, 149, 178, 287]), array([460, 279, 431, 279, 390, 154, 223, 350]))\n",
      "(array([  2,  10,  23,  30,  50,  52,  91,  91,  96, 104, 104, 112, 116,\n",
      "       143, 185, 189, 225, 253, 260, 260, 286, 318, 371, 384, 386, 411]), array([101,  88, 414, 174, 451, 454, 196, 198, 386, 205, 373, 236, 422,\n",
      "       303, 260, 301, 451, 410, 361, 418, 360, 414, 418, 454, 457, 415]))\n"
     ]
    }
   ],
   "source": [
    "#what about the median of the two folds?\n",
    "fold1medareas = np.where(ST_test_f1 == 0.9691119691119692)\n",
    "print(fold1medareas)\n",
    "fold2medareas = np.where(ST_test_f2 == 0.9727272727272728)\n",
    "print(fold2medareas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9591836734693878"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ST_test_allbyall_avg.iloc[14,362]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ST_test_f1.iloc[14,362]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For ABA, the median performing brain areas pairs have an AUROC=1, which means that they classify all voxels perfectly. For ST, there are 13 brain area pairs that have the median AUROC=0.9591836734693878 when the two test folds are averaged. Let's see how many spots are incorrectly classified for these brain area pairs in the following section. \\\n",
    "\\\n",
    "In the end we looked at the medians performing brain areas from each of the two folds separately because the median pairs from the averaged test fold may have a non-representative AUROC in one of the folds (i.e. AUROC=1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__How many incorrectly classified spots in ST median AUROC brain area pairs?__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we look at precision in the top half of the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "#AUROC\n",
    "def analytical_auroc(featurevector, binarylabels):\n",
    "    \"\"\"analytical calculation of auroc\n",
    "       inputs: feature (mean rank of expression level), binary label\n",
    "       returns: auroc\n",
    "    \"\"\"\n",
    "    #sort ctxnotctx binary labels by mean rank, aescending\n",
    "    s = sorted(zip(featurevector, binarylabels))\n",
    "    feature_sort, binarylabels_sort = map(list, zip(*s))\n",
    "    #print(feature_sort)\n",
    "    print(binarylabels_sort)\n",
    "    \n",
    "    #how many 0's in the top half of the list?\n",
    "    numneg = 0\n",
    "    for i in range(len(binarylabels_sort)-1,int(len(binarylabels_sort)/2),-1): #include 1 extra in top half for odd length lists\n",
    "        if binarylabels_sort[i] == 1:\n",
    "            numneg += 1\n",
    "    #this is precision for the top half of the list\n",
    "    numneg = numneg/int(len(binarylabels_sort)/2)\n",
    "\n",
    "    #get the sum of the ranks in feature vector corresponding to 1's in binary vector\n",
    "    sumranks = 0\n",
    "    for i in range(len(binarylabels_sort)):\n",
    "        if binarylabels_sort[i] == 1:\n",
    "            sumranks = sumranks + feature_sort[i]\n",
    "    \n",
    "    poslabels = binarylabels.sum()\n",
    "    neglabels = (len(binarylabels) - poslabels) #- (len(binarylabels) - binarylabels.count())  #trying to subtract out \n",
    "    \n",
    "    auroc = ((sumranks/(neglabels*poslabels)) - ((poslabels+1)/(2*neglabels)))\n",
    "    \n",
    "    return auroc, numneg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "#probe specific brain areas we're interested in\n",
    "def probemedianareas(data,propont,medareas): \n",
    "    areas = list(propont)\n",
    "    precision1 = []\n",
    "    for i in range(len(medareas[0])):\n",
    "        area1 = areas[medareas[0][i]]\n",
    "        area2 = areas[medareas[1][i]]\n",
    "        #print(area1)\n",
    "        #print(area2)\n",
    "        #get binary label vectors\n",
    "        ylabels = propont.loc[propont[area1]+propont[area2] != 0, area1]\n",
    "        #subset train and test sets for only samples in the two areas\n",
    "        Xcurr = data.loc[propont[area1]+propont[area2] != 0, :]\n",
    "        #split train test for X data and y labels\n",
    "        #split data function is seeded so all will split the same wa\n",
    "        Xtrain, Xtest, ytrain, ytest = train_test_split(Xcurr, ylabels, test_size=0.5,\\\n",
    "                                                        random_state=42, shuffle=True,\\\n",
    "                                                        stratify=ylabels)\n",
    "\n",
    "        #z-score current X data\n",
    "        zXtrain = zscore(Xtrain)\n",
    "        zXtest = zscore(Xtest)\n",
    "\n",
    "        #LASSO\n",
    "        #currauroc_train, currauroc_test = applyLASSO(zXtrain, zXtest, ytrain, ytest)\n",
    "        lasso_reg = Lasso(alpha=0.1, max_iter=10000)\n",
    "        #lasso_reg = LinearRegression()\n",
    "        lasso_reg.fit(zXtrain, ytrain)\n",
    "\n",
    "        #train\n",
    "        predictions_train = lasso_reg.predict(zXtrain)\n",
    "        #auroc_train = analytical_auroc(sp.stats.mstats.rankdata(predictions_train), ytrain)\n",
    "        #test\n",
    "        predictions_test = lasso_reg.predict(zXtest)\n",
    "        auroc_test, currtopnegcount = analytical_auroc(sp.stats.mstats.rankdata(predictions_test), ytest)\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_true = ytest, y_score = predictions_test, pos_label = 1, drop_intermediate=False)\n",
    "        precision1.append(currtopnegcount)\n",
    "    \n",
    "    return precision1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "STprecision1 = probemedianareas(leafSTspots, leafSTpropont, fold1medareas)\n",
    "STprecision2 = probemedianareas(leafSTspots, leafSTpropont, fold2medareas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7399265227526097\n",
      "0.7471516927741624\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(STprecision1))\n",
    "print(np.mean(STprecision2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, what ended up happening basically is that I calculated precision for the top half of any given list of ranked samples for the median performing brain area pairs. Saved as variables precision1 and precision2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at precision when I get all the 1's - i.e. all the instances of one class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "#AUROC\n",
    "def analytical_auroc(featurevector, binarylabels):\n",
    "    \"\"\"analytical calculation of auroc\n",
    "       inputs: feature (mean rank of expression level), binary label\n",
    "       returns: auroc\n",
    "    \"\"\"\n",
    "    #sort ctxnotctx binary labels by mean rank, aescending\n",
    "    s = sorted(zip(featurevector, binarylabels))\n",
    "    feature_sort, binarylabels_sort = map(list, zip(*s))\n",
    "    #print(feature_sort)\n",
    "    #print(binarylabels_sort)\n",
    "    \n",
    "    #how many 0's until i reach my last 1?\n",
    "    truepos = 0\n",
    "    for i in range(len(binarylabels_sort)-1,0,-1): #backwards cause sorted aescending\n",
    "        if binarylabels_sort[i] == 1:\n",
    "            truepos += 1\n",
    "            lastposindex = i\n",
    "    \n",
    "    falsepos = 0\n",
    "    for i in range(len(binarylabels_sort)-1,lastposindex,-1):\n",
    "        if binarylabels_sort[i] == 0:\n",
    "            falsepos += 1\n",
    "            \n",
    "    #this is precision for the top half of the list\n",
    "    precision = truepos/(truepos+falsepos)\n",
    "\n",
    "    #get the sum of the ranks in feature vector corresponding to 1's in binary vector\n",
    "    sumranks = 0\n",
    "    for i in range(len(binarylabels_sort)):\n",
    "        if binarylabels_sort[i] == 1:\n",
    "            sumranks = sumranks + feature_sort[i]\n",
    "    \n",
    "    poslabels = binarylabels.sum()\n",
    "    neglabels = (len(binarylabels) - poslabels) #- (len(binarylabels) - binarylabels.count())  #trying to subtract out \n",
    "    \n",
    "    auroc = ((sumranks/(neglabels*poslabels)) - ((poslabels+1)/(2*neglabels)))\n",
    "    \n",
    "    return auroc, precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "STprecision1 = probemedianareas(leafSTspots, leafSTpropont, fold1medareas)\n",
    "STprecision2 = probemedianareas(leafSTspots, leafSTpropont, fold2medareas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8367102342422437\n",
      "0.848750084932345\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(STprecision1))\n",
    "print(np.mean(STprecision2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8459171788876152"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean([*STprecision1, *STprecision2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the median performing pair of brain areas in ABA ISH, there is a threshold in classification that can be drawn where I can correctly predict all instances of one class without any false positives (precision = 1). In contrast, in the ST, no such threshold can be found for the median performing brain areas (average precision = 0.846)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Caudoputamen: relationship between size and performance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the ABA ISH, the CP is the lowest performing brain area (it stands out on the heat map) and it happens to be the largest leaf brain area by sample size (and volume by definition). So, we ask: \n",
    "1. Is there a correlation between size and performance in general?\n",
    "2. Does performance rise as you downsample in a particular brain region?\n",
    "3. Is the CP more transcriptionally diverse in general? (average correlation of voxel profiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__What is the lowest performing area in ST? (Is it also the Caudoputamen?)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def copyoverdiag(allbyall):\n",
    "    #copy uppertriangular to lower triangular\n",
    "    allbyall2 = allbyall\n",
    "    allbyall2 = allbyall.fillna(0, inplace=False)\n",
    "    temp = allbyall2.T\n",
    "    allbyall2 = allbyall2.values  #convert to numpy\n",
    "    temp = temp.values\n",
    "    allbyall2[np.tril_indices_from(allbyall)] = temp[np.tril_indices_from(allbyall)]\n",
    "    \n",
    "    return allbyall2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "STavgfull = copyoverdiag(ST_test_allbyall_avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5889431987780576"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(np.mean(STavgfull, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([450]),)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(np.mean(STavgfull, axis=0) == 0.5889431987780576)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'421'"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ST_test_allbyall_avg)[450]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>id</th>\n",
       "      <th>acronym</th>\n",
       "      <th>name</th>\n",
       "      <th>parent</th>\n",
       "      <th>allencolor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>421</td>\n",
       "      <td>VISl1</td>\n",
       "      <td>Lateral visual area, layer 1</td>\n",
       "      <td>409.0</td>\n",
       "      <td>#08858C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id acronym                          name  parent allencolor\n",
       "1258  421   VISl1  Lateral visual area, layer 1   409.0    #08858C"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ontology.loc[ontology.id == 421,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No, the lowest performing area in the ST is not the CP, but VISI1 or lateral visual area, layer 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(list(ST_test_allbyall_avg))):\n",
    "    if list(ST_test_allbyall_avg)[i] == '672': #CP\n",
    "        index = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6193299545540688"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(STavgfull, axis=0)[index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Is there a correlation between size and performance in general?__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: all by all AUROC data already read in above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get number of samples per brain area\n",
    "ABAsize = leafABApropont.sum(axis=0)\n",
    "STsize = leafSTpropont.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3012.0\n",
      "2051.0\n"
     ]
    }
   ],
   "source": [
    "#number of samples in CP in each dataset\n",
    "print(ABAsize['672'])\n",
    "print(STsize['672'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85.62075134168157\n",
      "57.02608695652174\n"
     ]
    }
   ],
   "source": [
    "#average number of samples not including CP\n",
    "print(ABAsize.drop('672').mean())\n",
    "print(STsize.drop('672').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getminsize(aurocdata,size):\n",
    "    maxsize = pd.DataFrame(index=list(aurocdata),columns=list(aurocdata))\n",
    "    minsize = pd.DataFrame(index=list(aurocdata),columns=list(aurocdata))\n",
    "    areas = list(aurocdata)\n",
    "    for i in range(aurocdata.shape[0]):\n",
    "        size1 = size[areas[i]]\n",
    "        print(i)\n",
    "        for j in range(i+1,aurocdata.shape[0]):\n",
    "            size2 = size[areas[j]]\n",
    "            minsize.iloc[i,j] = min(size1,size2)\n",
    "            maxsize.iloc[i,j] = max(size1,size2)\n",
    "    \n",
    "    return minsize, maxsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "50\n",
      "51\n",
      "52\n",
      "53\n",
      "54\n",
      "55\n",
      "56\n",
      "57\n",
      "58\n",
      "59\n",
      "60\n",
      "61\n",
      "62\n",
      "63\n",
      "64\n",
      "65\n",
      "66\n",
      "67\n",
      "68\n",
      "69\n",
      "70\n",
      "71\n",
      "72\n",
      "73\n",
      "74\n",
      "75\n",
      "76\n",
      "77\n",
      "78\n",
      "79\n",
      "80\n",
      "81\n",
      "82\n",
      "83\n",
      "84\n",
      "85\n",
      "86\n",
      "87\n",
      "88\n",
      "89\n",
      "90\n",
      "91\n",
      "92\n",
      "93\n",
      "94\n",
      "95\n",
      "96\n",
      "97\n",
      "98\n",
      "99\n",
      "100\n",
      "101\n",
      "102\n",
      "103\n",
      "104\n",
      "105\n",
      "106\n",
      "107\n",
      "108\n",
      "109\n",
      "110\n",
      "111\n",
      "112\n",
      "113\n",
      "114\n",
      "115\n",
      "116\n",
      "117\n",
      "118\n",
      "119\n",
      "120\n",
      "121\n",
      "122\n",
      "123\n",
      "124\n",
      "125\n",
      "126\n",
      "127\n",
      "128\n",
      "129\n",
      "130\n",
      "131\n",
      "132\n",
      "133\n",
      "134\n",
      "135\n",
      "136\n",
      "137\n",
      "138\n",
      "139\n",
      "140\n",
      "141\n",
      "142\n",
      "143\n",
      "144\n",
      "145\n",
      "146\n",
      "147\n",
      "148\n",
      "149\n",
      "150\n",
      "151\n",
      "152\n",
      "153\n",
      "154\n",
      "155\n",
      "156\n",
      "157\n",
      "158\n",
      "159\n",
      "160\n",
      "161\n",
      "162\n",
      "163\n",
      "164\n",
      "165\n",
      "166\n",
      "167\n",
      "168\n",
      "169\n",
      "170\n",
      "171\n",
      "172\n",
      "173\n",
      "174\n",
      "175\n",
      "176\n",
      "177\n",
      "178\n",
      "179\n",
      "180\n",
      "181\n",
      "182\n",
      "183\n",
      "184\n",
      "185\n",
      "186\n",
      "187\n",
      "188\n",
      "189\n",
      "190\n",
      "191\n",
      "192\n",
      "193\n",
      "194\n",
      "195\n",
      "196\n",
      "197\n",
      "198\n",
      "199\n",
      "200\n",
      "201\n",
      "202\n",
      "203\n",
      "204\n",
      "205\n",
      "206\n",
      "207\n",
      "208\n",
      "209\n",
      "210\n",
      "211\n",
      "212\n",
      "213\n",
      "214\n",
      "215\n",
      "216\n",
      "217\n",
      "218\n",
      "219\n",
      "220\n",
      "221\n",
      "222\n",
      "223\n",
      "224\n",
      "225\n",
      "226\n",
      "227\n",
      "228\n",
      "229\n",
      "230\n",
      "231\n",
      "232\n",
      "233\n",
      "234\n",
      "235\n",
      "236\n",
      "237\n",
      "238\n",
      "239\n",
      "240\n",
      "241\n",
      "242\n",
      "243\n",
      "244\n",
      "245\n",
      "246\n",
      "247\n",
      "248\n",
      "249\n",
      "250\n",
      "251\n",
      "252\n",
      "253\n",
      "254\n",
      "255\n",
      "256\n",
      "257\n",
      "258\n",
      "259\n",
      "260\n",
      "261\n",
      "262\n",
      "263\n",
      "264\n",
      "265\n",
      "266\n",
      "267\n",
      "268\n",
      "269\n",
      "270\n",
      "271\n",
      "272\n",
      "273\n",
      "274\n",
      "275\n",
      "276\n",
      "277\n",
      "278\n",
      "279\n",
      "280\n",
      "281\n",
      "282\n",
      "283\n",
      "284\n"
     ]
    }
   ],
   "source": [
    "ABAminsize, ABAmaxsize = getminsize(ABA_test_allbyall_avg, ABAsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "50\n",
      "51\n",
      "52\n",
      "53\n",
      "54\n",
      "55\n",
      "56\n",
      "57\n",
      "58\n",
      "59\n",
      "60\n",
      "61\n",
      "62\n",
      "63\n",
      "64\n",
      "65\n",
      "66\n",
      "67\n",
      "68\n",
      "69\n",
      "70\n",
      "71\n",
      "72\n",
      "73\n",
      "74\n",
      "75\n",
      "76\n",
      "77\n",
      "78\n",
      "79\n",
      "80\n",
      "81\n",
      "82\n",
      "83\n",
      "84\n",
      "85\n",
      "86\n",
      "87\n",
      "88\n",
      "89\n",
      "90\n",
      "91\n",
      "92\n",
      "93\n",
      "94\n",
      "95\n",
      "96\n",
      "97\n",
      "98\n",
      "99\n",
      "100\n",
      "101\n",
      "102\n",
      "103\n",
      "104\n",
      "105\n",
      "106\n",
      "107\n",
      "108\n",
      "109\n",
      "110\n",
      "111\n",
      "112\n",
      "113\n",
      "114\n",
      "115\n",
      "116\n",
      "117\n",
      "118\n",
      "119\n",
      "120\n",
      "121\n",
      "122\n",
      "123\n",
      "124\n",
      "125\n",
      "126\n",
      "127\n",
      "128\n",
      "129\n",
      "130\n",
      "131\n",
      "132\n",
      "133\n",
      "134\n",
      "135\n",
      "136\n",
      "137\n",
      "138\n",
      "139\n",
      "140\n",
      "141\n",
      "142\n",
      "143\n",
      "144\n",
      "145\n",
      "146\n",
      "147\n",
      "148\n",
      "149\n",
      "150\n",
      "151\n",
      "152\n",
      "153\n",
      "154\n",
      "155\n",
      "156\n",
      "157\n",
      "158\n",
      "159\n",
      "160\n",
      "161\n",
      "162\n",
      "163\n",
      "164\n",
      "165\n",
      "166\n",
      "167\n",
      "168\n",
      "169\n",
      "170\n",
      "171\n",
      "172\n",
      "173\n",
      "174\n",
      "175\n",
      "176\n",
      "177\n",
      "178\n",
      "179\n",
      "180\n",
      "181\n",
      "182\n",
      "183\n",
      "184\n",
      "185\n",
      "186\n",
      "187\n",
      "188\n",
      "189\n",
      "190\n",
      "191\n",
      "192\n",
      "193\n",
      "194\n",
      "195\n",
      "196\n",
      "197\n",
      "198\n",
      "199\n",
      "200\n",
      "201\n",
      "202\n",
      "203\n",
      "204\n",
      "205\n",
      "206\n",
      "207\n",
      "208\n",
      "209\n",
      "210\n",
      "211\n",
      "212\n",
      "213\n",
      "214\n",
      "215\n",
      "216\n",
      "217\n",
      "218\n",
      "219\n",
      "220\n",
      "221\n",
      "222\n",
      "223\n",
      "224\n",
      "225\n",
      "226\n",
      "227\n",
      "228\n",
      "229\n",
      "230\n",
      "231\n",
      "232\n",
      "233\n",
      "234\n",
      "235\n",
      "236\n",
      "237\n",
      "238\n",
      "239\n",
      "240\n",
      "241\n",
      "242\n",
      "243\n",
      "244\n",
      "245\n",
      "246\n",
      "247\n",
      "248\n",
      "249\n",
      "250\n",
      "251\n",
      "252\n",
      "253\n",
      "254\n",
      "255\n",
      "256\n",
      "257\n",
      "258\n",
      "259\n",
      "260\n",
      "261\n",
      "262\n",
      "263\n",
      "264\n",
      "265\n",
      "266\n",
      "267\n",
      "268\n",
      "269\n",
      "270\n",
      "271\n",
      "272\n",
      "273\n",
      "274\n",
      "275\n",
      "276\n",
      "277\n",
      "278\n",
      "279\n",
      "280\n",
      "281\n",
      "282\n",
      "283\n",
      "284\n",
      "285\n",
      "286\n",
      "287\n",
      "288\n",
      "289\n",
      "290\n",
      "291\n",
      "292\n",
      "293\n",
      "294\n",
      "295\n",
      "296\n",
      "297\n",
      "298\n",
      "299\n",
      "300\n",
      "301\n",
      "302\n",
      "303\n",
      "304\n",
      "305\n",
      "306\n",
      "307\n",
      "308\n",
      "309\n",
      "310\n",
      "311\n",
      "312\n",
      "313\n",
      "314\n",
      "315\n",
      "316\n",
      "317\n",
      "318\n",
      "319\n",
      "320\n",
      "321\n",
      "322\n",
      "323\n",
      "324\n",
      "325\n",
      "326\n",
      "327\n",
      "328\n",
      "329\n",
      "330\n",
      "331\n",
      "332\n",
      "333\n",
      "334\n",
      "335\n",
      "336\n",
      "337\n",
      "338\n",
      "339\n",
      "340\n",
      "341\n",
      "342\n",
      "343\n",
      "344\n",
      "345\n",
      "346\n",
      "347\n",
      "348\n",
      "349\n",
      "350\n",
      "351\n",
      "352\n",
      "353\n",
      "354\n",
      "355\n",
      "356\n",
      "357\n",
      "358\n",
      "359\n",
      "360\n",
      "361\n",
      "362\n",
      "363\n",
      "364\n",
      "365\n",
      "366\n",
      "367\n",
      "368\n",
      "369\n",
      "370\n",
      "371\n",
      "372\n",
      "373\n",
      "374\n",
      "375\n",
      "376\n",
      "377\n",
      "378\n",
      "379\n",
      "380\n",
      "381\n",
      "382\n",
      "383\n",
      "384\n",
      "385\n",
      "386\n",
      "387\n",
      "388\n",
      "389\n",
      "390\n",
      "391\n",
      "392\n",
      "393\n",
      "394\n",
      "395\n",
      "396\n",
      "397\n",
      "398\n",
      "399\n",
      "400\n",
      "401\n",
      "402\n",
      "403\n",
      "404\n",
      "405\n",
      "406\n",
      "407\n",
      "408\n",
      "409\n",
      "410\n",
      "411\n",
      "412\n",
      "413\n",
      "414\n",
      "415\n",
      "416\n",
      "417\n",
      "418\n",
      "419\n",
      "420\n",
      "421\n",
      "422\n",
      "423\n",
      "424\n",
      "425\n",
      "426\n",
      "427\n",
      "428\n",
      "429\n",
      "430\n",
      "431\n",
      "432\n",
      "433\n",
      "434\n",
      "435\n",
      "436\n",
      "437\n",
      "438\n",
      "439\n",
      "440\n",
      "441\n",
      "442\n",
      "443\n",
      "444\n",
      "445\n",
      "446\n",
      "447\n",
      "448\n",
      "449\n",
      "450\n",
      "451\n",
      "452\n",
      "453\n",
      "454\n",
      "455\n",
      "456\n",
      "457\n",
      "458\n",
      "459\n",
      "460\n"
     ]
    }
   ],
   "source": [
    "STminsize, STmaxsize = getminsize(ST_test_allbyall_avg, STsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get upper triangulars\n",
    "ABA_test_avg_upper = ABA_test_allbyall_avg.values[np.triu_indices_from(ABA_test_allbyall_avg, k=1)]\n",
    "ST_test_avg_upper = ST_test_allbyall_avg.values[np.triu_indices_from(ST_test_allbyall_avg, k=1)]\n",
    "ABAminsize_upper = ABAminsize.values[np.triu_indices_from(ABAminsize, k=1)]\n",
    "STminsize_upper = STminsize.values[np.triu_indices_from(STminsize, k=1)]\n",
    "ABAmaxsize_upper = ABAmaxsize.values[np.triu_indices_from(ABAmaxsize, k=1)]\n",
    "STmaxsize_upper = STmaxsize.values[np.triu_indices_from(STmaxsize, k=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance as a function of minimum size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ABAminsize_upper, ABA_test_avg_upper)\n",
    "plt.xlabel(\"minimum size of the two brain areas\\nABA ISH\")\n",
    "plt.ylabel(\"AUROC\")\n",
    "plt.savefig(\"051220_5.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(ABAminsize_upper, ABA_test_avg_upper, kind='hex') \\\n",
    "   .set_axis_labels(\"minimum size of the two brain areas\\nABA ISH\", \"AUROC\")\n",
    "plt.savefig(\"051220_6.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(STminsize_upper, ST_test_avg_upper)\n",
    "plt.xlabel(\"minimum size of the two brain areas\\nST\")\n",
    "plt.ylabel(\"AUROC\")\n",
    "plt.savefig(\"051220_7.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(STminsize_upper, ST_test_avg_upper, kind='hex') \\\n",
    "   .set_axis_labels(\"minimum size of the two brain areas\\nST ISH\", \"AUROC\")\n",
    "plt.savefig(\"051220_8.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance as a function of maximum size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAExCAYAAACqHw9wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOzdeVxU5f4H8M8wMjBssiOCiVozyA5u6GAihiRqrimmZWImZbd+pqJlZZpLejVNLa92y8qILC3SxFxTUzTDJRMQl3JBlmFfBwZmzu8P7kwMc2aBmcOi3/fr1St5zjPnPGcOzHeenccwDANCCCGEQxbtXQBCCCEPPgo2hBBCOEfBhhBCCOco2BBCCOEcBRtCCCGco2DThFgshlgsbu9iEELIA4eCDSGEEM5RsCGEEMI5CjaEEEI4R8GGEEII5yjYEEII4RwFG0IIIZyjYEMIIYRzFGwIIYRwjoINIYQQznVp7wIY4/fff8enn36KjIwMSKVSrFmzBhMnTtT7muzsbLz33nu4cuUKunbtiqlTp2LevHng8XicldNnyQHOzt0ebr8/Wv1vXfemytP0eNPXNWVMHkJI++D677NTBJuamhqIRCKMHz8eixcvNpi/qqoK8fHx6N+/P/bs2YO///4bS5YsgY2NDeLj481evgctyKgYc19seVRpbIFIVx5CSPtoq7/PThFshg0bhmHDhgEA3njjDYP59+3bB5lMhrVr18La2hoikQi3bt3Czp07MWvWLE5rN4QQQrQ9kH02ly9fRv/+/WFtba1Oi4iIgFQqRU5Ojlmv9aDWaszBZ8kBg+8PvX+EtJ+2/Pt8IINNUVERXFxcNNJcXV3VxwghhLStBzLYANBqKmMYhjWdEEII9x7IYOPq6qpVgykuLgYArRoP4c6bo8QI93HUm8fQcUIIdz55Lsyk4y3xQAabkJAQpKeno66uTp2WlpYGd3d3eHt7t2PJHi6+ng6YENZDbx5Dxwkh3Il41N2k4y3RKYJNdXU1srKykJWVBaVSidzcXGRlZSE3NxcAsGHDBsycOVOdf+zYsRAKhViyZAmuX7+Ow4cPY8eOHTQSrY3VyBUAGAO5DB0nhHBFKOAjbgD7F/C4Ad4QCvhmu1anCDZXr17F+PHjMX78eNTW1mLLli0YP348Nm/eDAAoLCzEvXv31Pnt7e3x2WefQSqVYtKkSVixYgXi4+Mxa9as9rqFh5JnV2sEeOlvJjN0nBDCrXfH+sPP014jzc/THu+O9TfrdTrFPJtBgwYhOztb5/H3339fK00sFiMpKYnLYhEDRB4OOHldqjfPvdIa+Ht1baMSEUKaW3soG5l5lRppmXmVWHsoG8vMGHA6Rc2GdGbUjEZIRyWTK3Aks4D12JHMAsjkCrNdi4IN4cyfOWUI9HLSm8fQcUIId6SVtcgtk7EeyyuTQVpZa7ZrUbAhnPm7qApVdfV68xg6Tgjhjru9NTy7ClmPeToK4W5vzXqsNSjYEM4EeDmivEZ/MDF0nBDCHaGAj65C9q57B+suD99oNNI5lcvqUW2gzdfQcUIId2RyBcpl7F/4ymX1Zu2z6RSj0UjnwwPg281B/W+2YQA8AMHeNPSZkPYiraxFXjl7v0x+eS2klbXo6WJrlmtRzcZEgvYuQAfV19MeznYCONsJ4NtsDL+K7//yEELah7u9Nbo7Up9NpyBv7wJ0AF2tu4D/v98kvkXjhLDvXxqiPv7DS0NYJ4390CQPIaTtCQV8RPt5sB6L9vMwa58NNaMRk3SxAE4vjkK9gsG1/Ar4dnPQWVux4AFKpvH/hJCOYWlsXwCN82ryymTwdBQi2s9DnW4uPEa19j6BWCwGAL2rFTT3oG/+pau/RcVWYIH0t0bq/QYU++EprRnKQGPtJvW1x00vJCHEZDK5AtLKWrjbW5u1RqNCzWgmGtDzwV5qxdA3EZlcCWllLWRyBe4UV2uNXimpkuNavnagAYDsgkqUVFFDJCEdgVDAR08XW04CDUDNaCb78055exfBZAI+0NoRjt0crfHfX//CL9mFyC2ToXuTKngXvgWu5VdAqSNiKZTAtfwKDHnUtfWFJ4R0ClSzMZH5FnNoPyP6dmv1L4Kj0BK7zt1FTqkMSgbIKZVh55nbWJWaBQAGh02aa1glIaRjo2BDcPFOKbo5tnyIYy8XG50TwlSL+DUolXrPYeg4IeTBQMHGRFYd5B3sat36ghRW1iFQxzL/NgLd562RNyC3jL1up1rEz93eGt27WrHm6d7Vyqzj+AkhHVcH+ajsvOo6yBfz8loTCsIDDmUUoItF4+gz4J/5MiN1jMEHgKIqOTwc2AOJakKYUMBHTIAna56YAE/OOiMJIR0LBRui7sBvUP4z+kyhbNxAyc7KEnZW7AFBKOBjRF/2PcqbTghbGtsXsyQ+8HYSgs8DvJ2EmCXxMfs4fkJIx0XzbJpozTybgCUHUMVVgToAL0dr2Ft3wbV89rt8fkhP8Hg81glhXfia32W4HsdPCOm4KNg00ZpgM/U/Z/Db7TKuitTheTsJcWT+MACgQEII0Yma0Uxk18YfrCL3jjVUWDUQgOsJYYSQzo2CjYmuS9lnx3Ohh5M1wvu4tNn1jGHulWEJIQ8mCjYmUrRhI2TSC+E4llXYdhc0grlXhiWEPJgo2Jgo2Ltt1kbjAZDVK3G/TNYm1zPGs+E9WzSiTNf6aYSQBx+tjWai09fbpqbBACio6DiBBgBmhD+iNeKMTYNCiVWpWTiSWcC6fhoh5MFHwcZEleyrtXDi1eSLbXcxI9TWGzeRdFVqFnaeua3+WbV+GgAsG+vPQckIIR0Nfa00kYdN212rTMZ985O7vQAWALo7WuPZ8Edgq2e5mpeTLmD5/gw0KHQHHZlcgSOZBazHVOunEUIefBRsTDRrqLi9i2A2dlZ88P+3jSYPQBe+BXo4646m98tqNVZ4ZiOtrEWujn4m1bBpQsiDr9MEm6SkJERFRSEwMBATJ05Eenq63vz79+/HuHHjEBwcDIlEgoULF6Kw0Pz9K1V1bdiOZiY+LkLW9Ko6BfLK66DEP4Ekp9RwP5G+Goq7vTW6O7Jfj4ZNE/Lw6BTBJjU1FatXr0ZCQgJSUlIQGhqKOXPmIDc3lzX/hQsXkJiYiAkTJuCnn37CRx99hFu3bmHhwoVmL9sNabXZz8m1wso6PBv+iHqtMs+u1jpXd66qM9zMlVcmw92SGtaRZkIBH9E6FvOkYdOEPDw6RbDZuXMnJkyYgClTpqBPnz54++234ebmhuTkZNb8ly9fRrdu3fD888+jR48eCAkJwYwZM3DlyhWzl21CaHezn5Nr1XIlZoT3xMFXh2JCqBeUDIMaeetXjRYK+Jj1+XkMX38C0RtPavXj0EKchJAOPxpNLpcjIyMD8fHxGukSiQSXLl1ifU1YWBg2btyI48ePY/jw4SgtLUVqaioef/xxs5dv+8lbZj+nLrYCPqrN1qHOwwdHr2PPxft6c9lZ8Q3WbqrqFOo8bCPNuvAtsGysPxJjfGn9NEIeUh2+ZlNaWgqFQgFXV8196l1cXHT2wYSGhmLDhg1YuHAhAgICMHjwYDAMg7Vr15q9fJdyKsx+Tl26Ci3R29X04W92VnzYWXXBwT/zDOad1M9bXSuxUL+WDz4P8HIU6tx+gK0fh9ZPI+Th1eFrNio8Hs+oNAC4efMmVq5ciZdffhkREREoLCzEunXr8M4772DdunVcF5UzueXsI7d8u9mjpKoW0irjBiv0cLbBpG1nkF9RpzNPNwcrjAr0VE+8bForARpHmdXWKzDqw19ZX68aadbTpWMtHEoIaR8dPtg4OTmBz+dr1WKKi4u1ajsq27dvR1BQEF544QUAgK+vL4RCIaZPn4758+fD05N958jWeKafJ76+YLiGYA4WvH82Omvqbkm1UX0uHg5WcLEVIDNP/+Kh3RyskPrq43C2E6jTVLUSlZ4utpDJFejuKGQdsUYjzQghTXX4ZjSBQAB/f3+kpaVppKelpSE0NJT1NbW1teDzNZtqVD+be/ueNphnqcYWaACgRq6E0FL/o/Swt8L3L0lQUdtg8DqjAj01Ao0uNNKMEGKsDl+zAYBZs2YhMTERQUFBCAsLQ3JyMqRSKeLi4gAAiYmJAKBuIhs+fDjefvttfP311xg6dCikUilWr14Nf39/dO9u3tFjtwo6xj6ddQaWjokN8kSDUqlzgiWg2XRmLFVetp06CSFEpVMEm9jYWJSWlmLbtm2QSqUQiUTYsWMHvLy8AAB5eZrNWBMnTkR1dTWSkpKwdu1a2NvbY9CgQVi0aJHZy1ZTb7im0BaUAMYFe+JoVgGqmzSpCS0tMCnMG0tj+6JewaBbV2vklmn3/bjaCbD3JQmElnyc/7sEvt0cNGo3K368gl3n7qGBafylsbTkYV7ko5g3QoRlY/3xmKstPjvzN+IlvfDMYB/WMq7+6Sq+Pn8PzwzsgTfHBJj5HSCEmOKXrHx8de4uZoQ/guF9u5n9/LQtdBOt2RZ67q7zOJTRMfaY+XHeEIg8HJCVW46tv9zAn/crUFglRzcHK4zo6w6GYfD1+Rydr+fzNPfn8fO0x6tRfZCQdLnFZUl9bQj8PJ0AACez8zFz5wWtPF/M6odhYvP/UhNCjHe7uAKR/9Ye6HNi0VD4uDiY7ToUbJpoTbDZcuw6Nhy5wVWRWiT1XxH47mIOvku/Z9TMf67dfn80AMBnyQGDeQgh7aOt/j47/ACBjm7/xTvtXQQAjc1lyb/fxc4ztztEoAGAPb/fxeqfrurNY+g4IYQ7v2Tlm3S8JSjYmOh6sby9iwAAGBvUHb9kd4zmPJX/nv4LSb/d1ZvH0HFCCHd2nvnbpOMtQcHGRCGedu1dBABAvYGRZu1hfIgXIkVuevMYOk4I4U54bxeTjrcEBRsTrZ3KPteHK110PLHf/i6BZ9eONYlypqQ33psQrDePoeOEEO7EDfQx6XhLULAxUa0JqyW3hK0VH5PDvKBrU8yC8loM6u3cJmUxxrgQTwgFfDjbCWDFvqoQrHgwavIoIYQbznYC9HFj32+qj5vQrH+fFGxMxeN+MN+T/h44t2QE3hsfCC8n3RuRWXVp2xn73bvycW1FDGZJfOBkYwkAcLKxxCyJDzY8HQKgcVtoFwf2GpdrV2vaFpqQdnbgX4/Dz9Mequ+EPDROezjwL/Oukt8pJnV2ZBU13O7U6W4vwMapoeqlX6L9PNRL+Dc1XOyGHy7p3y7A3HbNjoC1oIve7QOklbXIr2BfQDS/vJYW6ySknVkLuiD1tcdRUiXHtfwKrQnd5kI1GxPdLqnh9PyD+2h20OnaiCxuQA+9Q56tOPha0YX/T/uYru0DaFtoQjoHZzsBhjzqylnTNtVsTFSoY9l/c+AB2P9HHi7cKVOvN6ZrI7LsfP0rOde1cFUdD3srjPTvhqOZecir0B7e3b2rlVGBQijgY0Rfd3yRpj0faURfd1qsk5CHBAUbE528LuXs3AwAhmHf/bL5kv+PONvo3FVTaGkBmYGFOptqusVAFz6PtdkuJsDT6EChY3yAznRCyIOHmtFM1JYjwNh2v1QRCviY3M+b9dikMG/Y6thRk03TLQYWx4jh52kP/v9+U/gWjZ2Hi2PERp1LJlfgaBZ7QD6aJaUBAoQ8JCjYmOh6G24xoNr9Upe3RvthlsQHXo5CWPxv2+ZZEh+8+5Q/YnTsO6Ni0aT/p+n2AGsPZSMzr1I95FqhBDLzKrH2kHHrx0kra3VONjV0P4SQBwc1o5mor6cdTtwobpNrGepQb9qfc7ekBgCDR5xt0YVvgeVPBeBwZgFrM1t3R2vsfH4gHnG2AQDcL5Opr3Mks4D1WkcyC5AY42uwKU01QIB28yTk4UbBxkTSyrZbG82Y3S8bFEqsO3QNRzILkFsmQ/cmm5k93b8He/+Lfzf0cbPFqtQsjdeF93LGfZYgAfxTKzE0bFm1myfbdWk3T0IeHhRsTBTVtxv2XsoznLGVLAB0dzJ+98tVqVkaH+xNBxc031XTo6s1Bvd2wetPiFhft6f0PmwFfFSz9Kt062ptdK2EdvMkhNB+Nk20Zj8bmVyBvu/8zEl5vByF+Oz5/njEWXv+CptKWT0Gv3+MtanMy1GIo68Pg1DAR6WsHsv3Z+DsX8XIK69Ft67WqJDVs76uiwUPDUrtX5G+nvY4+FrLZhjL5ArWiZ+EkPbH9d8n1WxMtCvNfEtwNze4t7PRgQYAlu/P0Dmx836ZDG+l/Im1k4LwwdHr2HPxn9UG2LaJVmELNABwr6QGMrlCXTZjflGbD9cmhLS/BoVSqwm96bw+c6FgY6JPf/3LpNe72gkwJsgTDIBjWVLklcnUH9Z7L97Hub9LjHrwMrkCaX/pH6iw9+J9WFta4OT1IpPKDABVdQrcLalh7evh4heVEMINfU3vqnl95kCfBiYa5utq0us/nhaGd58KwPKnAnBk/jCMD/VCVZ0CVXUKMPjnwS/fn6H3PNLKWuQbsZrBD5fum23fm9r6BvUvak6pDMomE1BXpWaZ5RqEEO7I5Aq9I07NOQ+Ogo2Jpof3avVreQC8/jfcGABKquU4kc0+ATLpt7t4O+UqGnTsMaBvDbKmauRKuNlb6TxuK+Cr5+iMCfLUe67aeiUOZ7BvG3s4I58mbBLSwbXlPDi9waa8vBxTpkzBxo0b9Z5k48aNiIuLQ1VV201w7CjsrCxb/VoGwOP/Po4nN53EyI0nELH2OIqr2VeRVjLArnN3dNYYVEOMjRHo1VXnsWq5AkoGUDBKnRu1qdTIG3BfR3/P/bJamrBJSAfXlgvl6v042b17N65fv45nn31W70lmzJiB7Oxs7N6922wF6yzSb5eY9HqFEriWX4XrBdUwZligvqrt0ti+eH5IT9gaGFCga/mYpvLL65ByOQ98HQuY2Qr4EHk4QFe3DN8CsDchEBNCuKfvS6q558HpDTbHjx/HE088AVdX/f0Sbm5uiI6OxrFjx8xWsM6iu6PuJikuGKranv+7hHVeTGvxdASbyf290aBU6tw5VKEEKuu43euHEGI6XduWmHsenN7RaLdu3cKYMWOMOlFQUBBOnDhhjjJ1Lkzbrl2sr2q7fH8GMvP0bzXQUg3Kxi2ef79divyyWnRztEaMfzcsje2LegUDL0ch7rO0+Xo70VI0hHQGurYtMft19B2UyWSwsbHRl0XNxsYGNTXcbiTWEe06x908Gza6qrb6RpWY6uXIx/CIs43WL2IXPjDSn5aiIeRBwPU8OL3BxtHREbm5uUadKDc3F46OjmYpVGeSfqesza7l7Wits2orraxFQUWd2a9pZ8XHI842On8RaSkaQogx9PbZBAQE4NChQ0ad6NChQwgICDBLodgkJSUhKioKgYGBmDhxItLT0/Xml8vl+PDDDxEVFYWAgABERkbiyy+/NHu5ZktaP/S5pWT1CtQr2IcRuNtbw8vJ8NDnlprcz1tvDUVVBT8yfxiOL4zEkfnDsGysP03oJIRo0PuJMHHiRNy4cQPr16/Xe5INGzbg5s2bmDRpklkLp5KamorVq1cjISEBKSkpCA0NxZw5c/TWuhYsWIBff/0V7733Hn7++Wd8+OGH6rXPzGneCJHZz6lLaU29zsEBhoY+21nx0dfTHt276u5HsbPiw9PBunHxz67WmCXxwVuj/Ywqm6rmQ01nhBA2epvRRo4cicjISHz66ae4cOECpkyZAl9fX9jZ2aG6uhqZmZnYs2cPLl26hMjISERHR3NSyJ07d2LChAmYMmUKAODtt9/Gr7/+iuTkZCxYsEAr/+nTp5GWloYjR47A2blxJ01vb/ZdLE0lkytgxeehTkeNw5zc7a30dro3bdLKLZXBzd4KA3o54eVhj6KXmx2EAj5kcgXeSvkTe5usjabyiLMNymT1jUOwm417oEU0CSGmMLg22qZNm/DOO+9g3759uHz5stZxhmEwbtw4LF++nJMCyuVyZGRkID4+XiNdIpHg0qVLrK85evQoAgMD8fnnnyMlJQXW1tZ4/PHHMX/+fNjamrcD7G5JTZsEGgCI8tXf6W7MqBKhgI+1k4LgILTU6GdxsO6iMZItt6wWO8/chpJhYMHj0dpnhBCTGAw21tbWWLduHWbPno3Dhw/jxo0bqKqqgp2dHR577DGMHDmSk+YpldLSUigUCq25Pi4uLkhLS2N9zb1793DhwgUIBAJs2bIFFRUVWLlyJaRSKTZv3mzW8tXWt92SLDPCexqVz9CokuZByd7KEk99dJo1794LORorSXO1SB8h5MFm9KrPYrGY06BiCI9ldiFbGtBY2+LxeNiwYQPs7e0BNDa9zZ49G0VFRQYnqbZEXRuu/2VMRaIlzV2qoHSnuFrn+ki6tiwwdltoQggBWrjFQH19Pe7cuYPKykrY29ujZ8+esLTkdkkSJycn8Pl8FBYWaqQXFxfrDBpubm7w8PBQBxoA6NOnD4DGIdrmDDY3C807iVIfNzvd/TWm7EmhWh8pR8cW0GyM3RaaEEIAI1d9vnXrFl577TUMGDAAY8eOxTPPPIOxY8eif//+eO2113Dz5k3OCigQCODv76/VZJaWlobQ0FDW14SFhUEqlaK6ulqddvv2bQCAl5eXWctXWCU36/n00bf8iylL/esbyWZnxV5zMfcifYSQB5vBYHP8+HFMnjwZhw4dgqOjI4YNG4YxY8YgMjISTk5OOHToECZPnozjx49zVshZs2bhhx9+wHfffYdbt26p+1/i4uIAAImJiUhMTFTnHzNmDBwdHfHGG2/gxo0buHDhAlatWoWYmBi4uLiYtWxjgrqb9Xy6eDlaa3y4y+QK3CmuhkyuMMueFLrWR5rcj30UH60QQAhpCb3NaPfu3cPrr78OR0dHbNq0CcOGDdPKc+rUKSxbtgwLFizAvn370KNHD7MXMjY2FqWlpdi2bRukUilEIhF27NihrqXk5eVp5Le1tcXOnTuxcuVKTJ48GQ4ODnjiiSdYh0mbqo+7HSx4jVsAcGmkfzcIBXzW5rLwXs64r6MJzNjmLl0j2RoUSvD+NxqNVggghLQWj2EYnR+T7777Lvbv348ff/xR7zyVnJwcjBs3Dk899RSWLVvGSUHbgmoARHZ2ttGvkckVGL7+OPIruGlO695k4csufAss35/BuhaZnRWftTPf20mII/OHGV0L0TXAgObZEEJMobdmk5aWhgkTJhicEOnt7Y0JEybg5MmTZi1cZyCtrIWUo0BjI7DAT68MhbOdAEDrFts0trnL0AADrhfpI4Q82PT22eTn58PX19eoE/n6+kIqNbwp14PG3d4aOrZ0MVmtXKkxKEDfFq4yuQKTwrxavSeFKQMMCCHEEL01GysrK6O3DZDJZBAIBGYpVGei68PfHLo32xNG3xBlT0chVo4PBACjm7tUTWP2VpZ6BxjQfBpCiKn0Bps+ffrg5MmTeO655wye6OTJk+q5LA+TQ1fzDGdqpeZNYKohyob2jzHU3NW8yczd3gr5OrYnoPk0hBBz0NuMFhsbi7S0NHz33Xd6T7J3716cOXMGsbGxZi1cZ/BYNzuznMfOio/ujtYaTWCvPyFSD29WMccWrs2bzHQFGoDm0xBCzEPvaDS5XI6pU6fi2rVriI2NxeTJk+Hv7w97e3tUVlYiIyMDe/bsQWpqKnx9fbF79+5O3ZTWmtFo2fmViNl0yuRrz5L4qIcdO9sI8MHR63pXA2jt6DCZXIHojSeNXi1glsSH1kAjhJhMb7ABGpeFefXVV3HhwgXWtcgYhkFYWBg+/PBDuLm5cVbQttDaoc/9Vh5BDcvESR4AQ9Nv7Kz4eLp/D41Aomt4c2s++JsHpTvF1Ri+/oTOeUHdHKxQWFmnMZ+GVncmhJjK4NpoLi4uSEpKwvHjx3H48GFcv34d1dXVsLW1hUgkQnR0NEaMGNEWZe2QhAI+HnEW4lp+ldYxY+Z57n4xHP5e/2ynbWg1AFVnvaGaja6hzK8/IdI5yMDbSYh98yJQWVdP82kIIWZl9EKcUVFRiIqK4rIsnZJMrsDd4mrDGVl4OwnR281eI03f8Oa8Mhlyy2rw1W93DS64qeqXUVENZW5QKBHi3ZU12AwXuyGvXIYDV3IxOqg7/L26qo/9+2Am/nv6b3RRACIvO1TJlZj7eB9MHvAIAOCjY9fxxdnbmDnYR+fupdO3n8GZv8sg6eWIpLkSY98mQkgb8FlyQP3v2++PNvv5DTajGYthGPz4448YP368OU7XLtq6z4atWaxSVo/B7x/TuRrAcLEbdp27q/dc90tlGPfRaRSZuEioBQ/YEheMecl/tPi1384diIG9GptVPz15E+8d1H5P3x4lxuxhj5pURkKIaZoGmebMGXRMboxnGAb79+/HqFGj8MYbb5ijTJ1My2N1d0drnSPIPjh6XeceMsPF7vglu5D12JHMApRW1yH2w1MYuu64yYEGaFzvrTWBBgCmbD+v/jdboNGXTgh58BhsRktPT8enn36KO3fuoGvXrhg3bpx6teVff/0V77//Pv766y/Y2Nhgzpw5nBe4oymu1D1smM2jbrb44WUJ7IXa+wDp66+xs+Jj2sAeSPrtDuvxvDIZnv7PWdwsbF2THhc+OnYdaTfZg6PK9O1nqEmNkHair1ajOm6u2o3eYHPhwgU8//zzaGhoUKddvnwZMpkMdXV12LRpExwcHPDyyy9j5syZcHBwMEuhOpMDf7ZsUufNwmqsO5SN98YHaB0ztBwN38JCZ+e+u4M1/irqOIEGAL44exvSKt178ADAmb/L2qYwhJB2pbcZ7ZNPPoFAIMDHH3+MS5cuISUlBSKRCNu2bcOWLVswdepUHD16FP/6178eykADABPD9C9Syubr83fwdsqfaFBorqqmWo6GjaejEI842+jc5CzYuyvn2xy01MzBPpD0ctSbx9BxQsiDQW+wuXLlCqZOnYqoqCgIhUL4+vpi8eLFqKiowFNPPYXly5c/tEFGpZ+Pc4tfo1ACu87d1VrkUt+OmarlaHStILBiXAA62nSYeSNEBpvIqAmNkPZjqImszQYIlJWV4bHHHtNIe/TRxtFDD/PcmubOvaG9qRwAeNhp98s0xbaLpqHlaFSbnB2ZPwzHF0biyPxhWDbWH+4O1hB72LNdpl18O3eg+t9vjxKz5tGVTgh58DlyvtkAACAASURBVOjts1EqlbC01PzAVP1sa0sLM6p062qH2++PxoXbJfj+Yg4mhnmjn4+zzpUAVNgWudS1Y2ZzbPvLfP/SEEzclobsgkoolADfAhB72OO/M/vjdlENfLs5QCjg43p+JfIqavB2SiYKq/QPcFBtvrb1WHar59nMHvYoZg97lObZENIBqWovXM+zMTgaTSaToazsn07c8vJyAEB1dbVGuoqj48PbBt/Px1mjWW1pbF80KBh8ff4OFCyb3uhb5LI1m5VZC7og9bXHUVIlx7X8Cvh2c1BvvNbd0UadL/gRRzgWW6K42vBIOlXz3aJRflg0yk9nvnkjRDonc6pQgCGk4+IiwDSld1Knr6+vzvXQ2NJ5PB4yMzPNW8I21JpJnU3pWkLm7ZQ/DU7EbGuGFuRsvh01IYSYQm/NZsKECW1Vjk7N0JbKy8b6owvfAkcyC5BXJtNY5LK96NsbZ3KYF94bH0hroxFCzMZsy9U8CFpbszF2lebWbgvAlaZBsnkQpNoMIcScKNg00dotBnQ1R6k61ztCYNGnowVBQsiDh76+msjQKs3Syto2LlHLqQYjUKAhhHBFb5+NrgECKtbW1vD29sYTTzyBF1544aEcDq2a9c9Ws+noWyqrajT2Vpa0hw0hhFN6g82AAQP0vlgmk+HOnTvYtm0bjh49iuTkZNjZ2Zm1gB2dUMDHiL7u+CJNe4HMEX3dO+SHt6qv5nBGPu6X1YJv0biqgZejECP9qc+GEGJ+eoPNrl27DJ5AoVDgu+++w7vvvotPPvkE8+fPN1vhOguFkmUSjZ709tZ8YzXVHKD7ZTJ1ensNySaEPJhM/vrK5/MRFxeHUaNG4ejRo+YoU6cikyvwzfl7rMe+OX9Pazma9qZvGwMVtmV0CCHEFGZrKwkNDcX9+/fNdbpO43pBBRp0VGAalI3HOxJ9AxpUOsvABkJI59FpGuaTkpIQFRWFwMBATJw4Eenp6Ua9Lj09HX5+fhgzZgwn5Sqp1r9fi6HjbU3fNgYqHX1gAyGk8zFbsLl8+TK8vLzMdToNqampWL16NRISEpCSkoLQ0FDMmTMHubm5el9XXl6OxYsXY/DgwZyUCwCCvfWvBWfoeFvTt42Bimo9NEIIMReTgw3DMPj2229x8OBBPPHEE+Yok5adO3diwoQJmDJlCvr06YO3334bbm5uSE5O1vu6pUuXYsKECQgJCeGkXEDjh3cXHe+iKv1OcXWH6gNRbWPg5dhYe1ENPGu+nQEhhJiL3tFozz33nN4X19bW4s6dO6ioqEDv3r0xZ84csxYOAORyOTIyMhAfH6+RLpFIcOnSJZ2vS0pKQlFREV566SV8/PHHZi+XirSyVm+fTezmU5BW1mmtl9ZW2FaAbr6NAc2zIYRwTW+wOX/+vN4XW1lZwdvbG3FxcZgzZw4nc2xKS0uhUCjg6uqqke7i4oK0tDTW12RnZ+Ojjz7C7t27wedz++Hpbm8Nbyf2SZ0AkF/RuIx/TmnbDiuulTew7m3z/UtDYC1ofOxNtzFQBSJCCOGC3mBz7dq1Fp1MLpdDIODmQ0vXlgZsZXj99deRmJiIHj16cFKWpvStnszmcEYBEmN8IRTwOV2TbOK2NGTmVap/ViiBzLxKTNyWhtTXHjfrtQghxBCDm6cZ4+rVq9izZw8OHjyI3377zRynVHNycgKfz0dhYaFGenFxsVZtBwCkUilu3ryJN998E2+++SaAxh1HGYaBn58fduzYgYiICLOWUdXHcTijAHnlMrjZW6Gggn1jsvtlMixNuQJ7a0scy5KybklgqpIqObILKlmPZRdUoqRKTjUZQkibanWwKSsrw759+7Bnzx7cuHEDDMPAx8fHjEVrJBAI4O/vj7S0NIwaNUqdnpaWhpEjR2rl9/DwwP79+zXSvv76a6SlpWHr1q2cjZgDAAYMGAbg8QA7Kz6q6tgHBXx/UXMUnbmb2K7lV7DuDAo01nCu5VdgyKPagZoQQrjS4mDz66+/Yu/evTh+/Djq6+vh4+ODefPmISYmBo899hgXZcSsWbOQmJiIoKAghIWFITk5GVKpFHFxcQCAxMREAMC6detgaWkJkUhze2IXFxcIBAKtdHNpvvxLfrnh7ZbZHMn8p4nNFL7dHNTrnTXHt2g8TgghbcmoYHPv3j18//33SElJQX5+PpydnRETE4OffvoJ8+fPZ61hmFNsbCxKS0uxbds2SKVSiEQi7NixQ11LycvL4/T6+uhb/sVGYIEaufHro6lm7qs67VtLKOCjl4stbhZWax0Te9hTExohpM3p3Txt//792LNnD37//Xfw+XxERkZi/PjxiIyMRE5ODmJiYrB582bOg01bac3maXeKqzF8/QkoWd5FPg9wtROgoFJu1LlM3Wyt6c6bOaUydPlf7YYB+2g0QghpK3o/dRYtWoQePXrgzTffxJgxY+Do+M9seH373DxMDO1nM1zshl3n7hp1LlNn7jdvzlPN/4nx98CaCUFUoyGEtBu9Q58sLS1x//59HDt2DKdOnUJtLS3O2Jy+5V+i/TywbKw/Zkl84O0kBJ/XWHt5fkhPzBzSUyPN1Jn7+przMnIraLImIaRd6a3ZnDlzBvv27cPevXuRmJiId999F08++SQmTJgAd3f3tipjh6cKEkcyC5BXJoNns6HMTWfrN51Ts+RJ882zMWZ7alP7ggghpLX09tk0lZGRgT179iA1NRUVFRVwdnZGSUkJVq5ciUmTJnFdzjbRmj6bpricpGnMtaM3nmRtzjO1L4gQQkxldLBRkcvlOHToEPbs2aNezkYkEiEmJgbR0dGcDX9uC6YGm/a2fH8G60oGsyQ+tPMmIaRdtTjYNJWTk4O9e/ciJSUFeXl5sLCwQGZmpjnL16Y6e7BpOhqNrTmPEELai0nBRoVhGPVkzw8//NAc5WoXnT3YqLRncx4hhLAxS7B5UDwowYYQQjoaalshhBDCOQo2hBBCOEfBhhBCCOco2BBCCOEcBRtCCCGco2BDCCGEcxRsCCGEcI6CDSGEEM5RsCGEEMI5CjZmJJMrcKe4GjK5or2LQgghHQrtD2wGTRfAzC2ToTstgEkIIRoo2JhB8+2Yc0pl6p9paX9CCKFmNJPp2475SGYBNakRQggo2JhMWlmL+yy7YwJAbmnjdsyEEPKwo2BjInd7a9hase8ZY2PFh7u9dRuXiBBCOh4KNhyqrVegQaFs72IQQki7o2BjImllLWp09Ms0KIHl+zPauESEENLxULAxkbu9Nbp11d1UdvavEhokQAh56FGwMZFQwMfg3i46j+eX0yABQgjpNMEmKSkJUVFRCAwMxMSJE5Genq4z7+HDhxEfH4/w8HCEhobi6aefxrFjxzgr27tj/WGnY5CAp6OQBgkQQh56nSLYpKamYvXq1UhISEBKSgpCQ0MxZ84c5ObmsuY/f/48wsPDsWPHDqSkpGDYsGF45ZVX9AYoU9gLLfF0/x6sx6L9PCAUsAciQgh5WPAYhmHauxCGPP300xCLxVi5cqU6beTIkYiJicGCBQuMOsfkyZPRv39/LFmyRGcesVgMAMjOzm5xGZsuWZNXJoMnLVlDCCFqHX65GrlcjoyMDMTHx2ukSyQSXLp0yejzVFdXw8HBwdzFU+vCt0BijC/iBvQAwMMjzjZUoyGEkP/p8MGmtLQUCoUCrq6uGukuLi5IS0sz6hxJSUnIz8/HuHHjuCgiLcRJCCEGdPhgo8Lj8YxKa+7QoUNYt24dPvjgA3h5eXFRNFqIkxBCDOjwX7udnJzA5/NRWFiokV5cXKxV22nu0KFDSExMxNq1azFixAhOykcLcRJCiGEdPtgIBAL4+/trNZmlpaUhNDRU5+tSU1OxaNEirFmzBk8++SRn5ZNW1iK3jH0hzrwymmNDCCFAJ2lGmzVrFhITExEUFISwsDAkJydDKpUiLi4OAJCYmAgAWLduHQDgwIEDSExMRGJiIgYMGKCuFVlaWsLR0dGsZXO3t0Z3RyFyWFZ+pjk2hBDSqFMEm9jYWJSWlmLbtm2QSqUQiUTYsWOHug8mLy9PI/8333yDhoYGrF69GqtXr1anDxw4ELt27TJr2YQCPqL9PDT6bFRojg0hhDTqFPNs2kpr59nQHBtCCNGPgk0TpkzqBBoHC0gra+Fub001GkIIaaJTNKN1FkIBHz1dbNu7GIQQ0uFQGw8hhBDOUbAhhBDCOQo2hBBCOEfBhhBCCOco2BBCCOEcBRtCCCGco2BDCCGEcxRsCCGEcI6CDSGEEM5RsCGEEMI5CjaEEEI4R8GGEEII5yjYEEII4RwFG0IIIZyjYEMIIYRzFGwIIYRwjoINIYQQzlGwIYQQwjkKNoQQQjhHwYYQQgjnKNgQQgjhHAUbQgghnKNgQwghhHMUbAghhHCOgg0hhBDOdZpgk5SUhKioKAQGBmLixIlIT0/Xm//8+fOYOHEiAgMDMWLECCQnJ3NeRplcgTvF1ZDJFZxfixBCOpNOEWxSU1OxevVqJCQkICUlBaGhoZgzZw5yc3NZ89+7dw8vvvgiQkNDkZKSgrlz52LlypU4dOgQJ+VrUCixfH8GojeexPD1JxC98SSW789Ag0LJyfUIIaSz4TEMw7R3IQx5+umnIRaLsXLlSnXayJEjERMTgwULFmjl//e//40jR47g8OHD6rSlS5fi5s2b2L17t87riMViAEB2dnaLyrd8fwZ2nrmtlT5L4oNlY/1bdC5CCHkQdfiajVwuR0ZGBiQSiUa6RCLBpUuXWF9z+fJlrfwRERG4evUq6uvrzVo+mVyBI5kFrMeOZBZQkxohhKATBJvS0lIoFAq4urpqpLu4uKCwsJD1NUVFRXBxcdFIc3V1RUNDA0pLS81aPmllLXLLZKzH8spkkFbWmvV6hBDSGXX4YKPC4/GMStN1TNVaqO81reFub43ujkLWY56OQrjbW5v1eoQQ0hl1+GDj5OQEPp+vVYspLi7Wqu2ouLq6oqioSCt/ly5d4OjoaNbyCQV8RPt5sB6L9vOAUMA36/UIIaQz6vDBRiAQwN/fH2lpaRrpaWlpCA0NZX1NSEgIa/6AgABYWlqavYxLY/tilsQH3k5C8HmAt5MQsyQ+WBrb1+zXIoSQzqhLexfAGLNmzUJiYiKCgoIQFhaG5ORkSKVSxMXFAQASExMBAOvWrQMAxMXFISkpCatWrUJcXBwuXryIH374ARs2bOCkfF34Flg21h+JMb6QVtbC3d6aajSEENJEpwg2sbGxKC0txbZt2yCVSiESibBjxw54eXkBAPLy8jTy9+jRAzt27MCaNWuQnJwMd3d3LF26FDExMZyWUyjgo6eLLafXIISQzqhTzLNpK62dZ0MIIUS/Dt9nQwghpPOjYEMIIYRzFGwIIYRwjoINIYQQzlGwIYQQwrlOMfS5ralGpRFCCDGevpG8VLMhhBDCOZpnQwghhHNUsyGEEMI5CjaEEEI4R8GGEEII5yjYEEII4RwFG0IIIZyjYEMIIYRzFGwIIYRwjoKNGSQlJSEqKgqBgYGYOHEi0tPT27tIGrZs2QKxWKzxn0QiUR9nGAZbtmxBREQEgoKC8Oyzz+LGjRsa5ygvL8eiRYvQr18/9OvXD4sWLUJFRQXnZf/999+RkJCAoUOHQiwW4/vvv9c4bq6yZ2dnY8aMGQgKCsLQoUOxdetWmHsKmqF7WbJkidZzmjJlikYeuVyO9957D4MGDUJISAgSEhKQn5+vkSc3NxcJCQkICQnBoEGDsHLlSsjlcrPdx/bt2zFp0iSEhYUhPDwcCQkJuH79ukaezvBcjLmPzvJMkpKSMHbsWISFhSEsLAxTp07FiRMn1Mc7xPNgiEkOHDjA+Pn5Mbt372Zu3rzJrFixggkJCWHu37/f3kVT27x5MxMTE8NIpVL1f8XFxerj27dvZ0JCQpiff/6Zyc7OZl599VVGIpEwlZWV6jyzZ89mYmNjmQsXLjAXL15kYmNjmblz53Je9hMnTjAbNmxgDh48yAQFBTF79+7VOG6OsldWVjJDhgxhXn31VSY7O5v5+eefmZCQEObTTz9t03tZvHgx8/zzz2s8p9LSUo0877zzDiORSJjTp08zV69eZWbMmME89dRTTENDA8MwDNPQ0MCMGTOGmTFjBnP16lXm9OnTjEQiYVasWGG2+4iPj2f27NnDZGdnM9euXWNefvllZsiQIRpl7QzPxZj76CzP5MiRI8yJEyeY27dvM3/99RfzwQcfMH5+fkxWVhbDMB3jeVCwMdHkyZOZpUuXaqRFR0cz69evb6cSadu8eTMzevRo1mNKpZKRSCTMxx9/rE6TyWRMSEgIk5yczDAMw9y8eZMRiURMenq6Os/vv//OiEQi5tatW9wWvomQkBCND2hzlT0pKYkJDQ1lZDKZOs9HH33EREREMEqlsk3uhWEaP9hefPFFna+pqKhg/P39mR9//FGdlpuby4jFYubUqVMMwzQGNLFYzOTm5qrzpKSkMAEBARofLOZUVVXF+Pr6MseOHWMYpvM+l+b3wTCd95kwDMMMGDCASU5O7jDPg5rRTCCXy5GRkaHRJAUAEokEly5daqdSsbt37x6GDh2KqKgozJ8/H/fu3QMA5OTkoLCwUOMerK2tMWDAAPU9XLp0CTY2NggLC1Pn6devH2xsbNr1Ps1V9suXL6N///6wtrZW54mIiIBUKkVOTk4b3U2jCxcuYPDgwYiJicFbb72F4uJi9bGrV6+ivr4eERER6jRPT0/06dNH41769OkDT09PdZ6hQ4dCLpfj6tWrnJS5uroaSqUSDg4OADrvc2l+Hyqd7ZkoFAocOHAANTU1CA0N7TDPg1Z9NkFpaSkUCgVcXV010l1cXJCWltZOpdIWFBSENWvWoHfv3igpKcG2bdsQFxeHn376CYWFhQDAeg9SqRQAUFRUBGdnZ/B4PPVxHo8HZ2dnFBUVtd2NNGOushcVFcHDw0PjHKpzFhUVoUePHpzdQ1NDhw5FdHQ0vL29cf/+fWzatAkzZ87E999/D4FAgKKiIvD5fDg5OWm8zsXFReNeXFxcNI47OTmBz+dz9qxWrVqFvn37IjQ0FEDnfS7N7wPoXM8kOzsbcXFxqKurg42NDbZu3QqxWIyLFy8CaP/nQcHGDJo+IH1p7WXYsGEaPwcHB+OJJ55ASkoKgoODARguL9txhmE6xH2ao+zN8zD/6/Rsy/sbPXq0+t9isRj+/v6IiorCiRMnMHLkSJ2vY5p10OoqMxf3smbNGly4cAHJycng8/ktul5Hei667qMzPZNevXohJSUFFRUVOHz4MBYvXoxdu3YZfS2unwc1o5lA9e1E9U1Opbi4WOtbREdia2uLRx99FLdv34abmxsA6L0HV1dXFBcXa/wBMQyD0tJSrW9sbclcZXd1ddX6hqlqKmnP+/Pw8ICHhwdu374NoLGcCoUCpaWlGvlKSko07rf5vahq4Oa+l9WrV+PAgQP44osvNL7Vdrbnous+2HTkZyIQCNCzZ08EBgZiwYIF6Nu3Lz7//PMO8zwo2JhAIBDA399fq8ksLS1Noyre0dTV1eHvv/+Gm5sbvL294ebmpnEPdXV1SE9PV99DaGgoampqNPpnLl26pG4Tbi/mKntISAjS09NRV1enzpOWlgZ3d3d4e3u30d1oKykpgVQqhbu7OwAgICAAlpaWOHPmjDpPfn4+bt26pXEvt27d0hh6e+bMGQgEAgQEBJitbCtXrsRPP/2EL774An369NE41pmei777YNORn0lzSqUScrm8wzwP/rvvvvuume7toWRnZ4ctW7bAzc0N1tbW+Pjjj5Geno7Vq1drdTS2l7Vr10IgEECpVOL27dtYsWIF7ty5gxUrVsDBwQENDQ3Yvn07evXqBYVCgffffx+FhYVYsWIFBAIBnJ2d8ccff+Cnn36Cn58f8vLysGzZMvV4fS5VV1fj1q1bKCoqwnfffQeRSAR7e3vU19ebrew+Pj7YvXs3srKy0Lt3b1y4cAFr167F3LlzNTpMubwXCwsLbNy4Eba2tlAoFMjKysJbb70FhUKBt99+GwKBAFZWVigoKMBXX30FX19fVFZW4p133oG9vT0WLlwICwsL9OjRA0eOHMHp06chFotx48YNLF++HE899RSio6PNch/Lly9HSkoKPvzwQ3h6eqKmpgY1NTUAGr+A8Xi8TvFcDN1HdXV1p3km69evV/+N5+Xl4YsvvsD+/fuxYMEC+Pj4dIzn0cpRdaSJr776ihk+fDjj7+/PTJgwgTl//nx7F0nD//3f/zESiYTx9/dnIiIimFdeeYW5ceOG+rhSqWQ2b97MSCQSJiAggJk+fTqTnZ2tcY7S0lJmwYIFTGhoKBMaGsosWLCAKS8v57zs586dY0QikdZ/ixcvNmvZr127xjzzzDNMQEAAI5FImC1btph9eK2+e5HJZEx8fDwTHh7O+Pv7M5GRkczixYs1hssyDMPU1tYyK1asYAYOHMgEBQUxc+fO1cpz//595sUXX2SCgoKYgQMHMitWrGDq6urMdh9s9yASiZjNmzer83SG52LoPjrTM1m8eDETGRnJ+Pv7M+Hh4czMmTPVQ68ZpmM8D9qpkxBCCOeoz4YQQgjnKNgQQgjhHAUbQgghnKNgQwghhHMUbAghhHCOgg0hhBDOUbAhhBDCOQo2hBBCOEfBhhBCCOco2BBCCOEcBRtCCCGco2BDCCGEcxRsCCGEcI6CDSGEEM5RsCGEEMI5CjaEEEI4R8GmkxGLxViyZEl7F6Pdff/99xCLxfjtt9/auygGyWQyrFy5EpGRkejbty+ioqJadZ6cnByIxWJs2bLFzCXsfNrr+dMzaL0u7V0AQh50n3zyCXbt2oX4+HiIxWLY2dnpzFtRUYEvvvgCAwcOxKBBg9qwlOw+//xzODg4YOLEie1dFNLJUbDpZK5cuQILC6qQjhs3DqNHj4alpWV7F8WgtLQ0iEQiLF682GDeiooKbN26Fa+88kqHCDZffvklvLy8KNj8j5eXF65cuQI+n9/eRel06FOrk7GysuoUH7Bc4/P5sLKy6hSBt7CwEI6Oju1dDNJMVVVVi1/D4/FgZWWFLl063vf01txPW+r4f6ltTNUWfPbsWWzduhXDhw9HUFAQnn76aVy+fBkAcP78eUybNg0hISGIiIjARx99pHWe06dP4//+7/8wYsQIBAUFoX///oiPj8f58+c18mVmZiIwMBCzZs0CwzDqdIVCgenTpyM4OBg3btxQp7P12ajSzp49i6lTpyI4OBiPP/44duzYAQAoLy/Hm2++icGDByM4OBhz585FQUGBxjmWLFkCsVjM+p40v2bTduvU1FSMGzcOQUFBiI6Oxt69ewEAubm5ePXVVzFw4ECEhoZi4cKFRv8xXLx4ES+88AIkEgkCAwMxdOhQzJkzR/3+A+xt9mKxWOd/zd+ztLQ0xMfHo3///ggMDMTYsWORnJxsVPkAoKGhATt27EBsbCwCAwMxaNAgzJs3D9nZ2VplzMnJwfnz59Vl0dXe/9tvv2HEiBEAgK1bt6rzs/Xx/PLLL5g0aRICAwMRERGBtWvXoqGhQSvf7du3sWjRIkRERCAgIABRUVFYu3YtampqDN6jWCzG/fv3Ncquup8lS5YgMDAQdXV16vwXL16EWCzGwIEDoVQq1eknT56EWCxGampqi94/YygUCmzZsgXDhw9HQEAAxo4diwMHDmjli4qKwrPPPovMzEzMnj0b/fr1w1NPPQWg8UN648aNePrppzFo0CAEBAQgOjoa69evh0wm0zgPW59N0zRjnwub1NRUJCQkIDIyEgEBARg0aBBefvllXLt2rUX3AwByuRz/+c9/MHr0aAQGBqJ///5ISEhAZmamxnmUSiW2bduG6dOnQyKRICAgAJGRkVi2bBlKS0u1rpuSkoLJkyejf//+CAkJwYgRI7BgwQKUlJQYvL+OF547iPXr10OpVOK5555DfX09PvvsM8yePRtr167F0qVLMWXKFIwdOxYHDx7E5s2b4e3tjXHjxqlf/8MPP6C8vBzjx49Ht27dUFBQgO+++w7PP/88vvzyS/Tv3x8A4Ofnh0WLFmHVqlXYsWMH5s6dC6DxwyY9PR3Lly/HY489ZrC8mZmZ+OWXXzBlyhSMGzcOBw8exIYNG2BlZYWUlBR4eXnhlVdewd27d7Fr1y4sXrwYn3/+uUnv0S+//IJvvvkG06ZNg6OjI/bs2YM333wTlpaW2LhxI8LDwzF//nz8+eef2Lt3L6ysrLBq1Sq95/zrr78QHx8PV1dXPPfcc3BxcUFRUREuXryIa9euISQkROdr161bp5V28uRJHDhwAC4uLuq03bt3Y9myZQgJCUFCQgKEQiHS0tLw7rvv4u7du0Y1dy1cuBAHDx6ERCLBtGnTUFRUhKSkJMTFxSEpKQl+fn4YMGAA1q1bhzVr1sDJyQkJCQkAoDOo9+nTB2+88QbWrFmD6OhoREdHAwBsbW217unrr79GXFwcJk2ahGPHjuGzzz5D165d1dcAgKtXr2LmzJlwcHDA1KlT4eHhgWvXrmHXrl24dOkSdu3apbeWzFZ2AHB2dkZ4eDh++OEHXLx4EYMHDwYAnDt3DhYWFigvL0dmZiYCAgLU6TweD+Hh4S16/4yxfv161NTUYNq0aQAaA/zrr7+Ouro6raa/3NxczJw5E08++SRGjhypDrgFBQXYs2cPRo4ciTFjxqBLly44f/48/vvf/yIrKwuffvqpUWUx9rno8tVXX6Fr166YMmUK3NzccPfuXXz77beYNm0afvjhB/j4+Bh1P/X19Zg9ezYuXbqEcePGYfr06aiqqlKf66uvvkJgYKA676effoqRI0dixIgREAqF6r/XixcvYu/evRAIBACAH3/8EYsXL0b//v3x6quvwtraGrm5uTh16hSKi4vh7Oys/wYZomHv3r2MSCRixo8fz9TV1anTjx49yohEIqZv377MH3/8oU6vq6tjJBIJM2XKJYQ/4wAAEvhJREFUFI3zVFdXa527sLCQGThwIPPCCy9oHUtISGD8/PyYixcvMr/99hvj6+vL/Otf/9LKJxKJmMWLF2ulicVi5vLly1rlEovFzHvvvaeRf/Xq1YxIJGJu3bqlTlu8eDEjEolY35Pm17x37x4jEomY4OBgJicnR51eXFzMBAQEMGKxmPnss880zjFv3jzG39+fqaqqYr2GyhdffMGIRCKN95iN6jmdO3dOZ54///yTCQ4OZqZMmcLU1tYyDMMwBQUFTEBAAPP6669r5X/vvfcYX19f5s6dO3qvffr0aUYkEjGvvfYao1Qq1elZWVlM3759mWnTpmnkHz58ODNjxgy951RRvbebN2/WeSw4OJi5d++eOl2pVDKjR49mJBKJRv6xY8cyMTExTGVlpUb64cOHGZFIxOzdu9dgeXSVPT8/nxGJRMwHH3ygTnv22WeZl156iQkLC2N27NihTp8wYQIzZswY9c8tff/YqJ5/ZGQkU1FRoU6vqKhgIiMjmQEDBjAymUzjPkQiEfPtt99qnauuro6Ry+Va6Rs3btT6XWR7Pi19LrqwfWbcvHmT8ff3Z5YtW6aRru9+du7cyYhEIubUqVMa6ZWVlcywYcM0nqdSqdR4n1S+/fZbRiQSMQcOHFCnzZs3jwkNDWXq6+uNup/mqBlNh2nTpqkjOgB1TSQ4OBhBQUHqdIFAgMDAQNy+fVvj9TY2Nup/V1dXo7S0FBYWFggODsaVK1e0rrdmzRq4urpiwYIFWLhwITw9PbFy5UqjyxsSEoLg4GCtcjEMg2effVYjr+pe7ty5Y/T52YwYMQJeXl7qn52dndGrVy9YWFhg+vTpWtesr6/H/fv39Z7T3t4eAHDs2DGNJpqWysvLQ0JCApydnfHxxx/DysoKAHDo0CHI5XJMnjwZJSUlGv9FRUVBqVTi7Nmzes995MgRAEBCQgJ4PJ463dfXF5GRkbhw4YJRzQqtNWLECHh7e6t/5vF4GDRoEAoLC1FdXQ0AyM7ORnZ2NsaMGQO5XK5xn/369YONjQ3OnDnT6jJ4eHjAx8cH586dAwDU1dXh8uXLkEgkGDBggDq9oqICWVlZGrUac75/06ZNU//OAI2/P3FxcSgvL9caFu3o6Mg60EEgEKhreA0NDSgvL0dJSQmGDBkCAPjjjz+MKosxz0Uf1WcGwzCoqqpCSUkJnJyc0KtXL9bPDF33s2/fPvTu3Rv+/v4az10ul2PIkCG4cOECamtr1WW0trYG0NgkWVFRgZKSEvXzanpde3t71NbW4sSJExpN/saiZjQdevToofFz165dAUDjl6npsbKyMo20u3fvYuPGjTh9+jQqKio0jjX9A1NxdHTEmjVrMGvWLADA119/DQcHh1aXV1+ZVedtXuaW0nVNNzc3jUDdkmuOHj0a+/btw3/+8x98/vnnCA4ORkREBEaPHq0R2PSpqqrC3LlzIZPJsHPnTo0mtFu3bgEAnn/+eZ2vLyoq0nv+nJwcWFhYoE+fPlrHHnvsMRw7dgw5OTmGmxVaie19Vw1AKCsrg62trfo+t2zZorOPyNB9GjJo0CDs3bsXVVVV+PPPP1FXV4fw8HDU19dj06ZNkMvlOH/+PJRKpUawMef717t3b6001XlzcnI00nv06KFzFFlSUhK++eYb3Lx5U6O/CWjs8zSGMc9Fn8zMTHz44Yc4f/68Vp8a2+eOrvu5desWamtr1c2bbEpLS+Hp6Qmgsa9o586dyMrKQn19vUa+pvc+d+5c/P7775g3bx4cHR0xcOBAPP744xg1apTe4fwqFGx00DXKyZghj9XV1Zg+fTpkMhlmzpwJkUgEW1tbWFhYYPv27epvfc0dP35c/e9r166hX79+RpdXX7l0HWv67YQtAALQ27mp67z6ymLoG5FAIMDOnTtx5coV/Prrr0hPT8fmzZuxdetWbNiwQd2PoYtCocD8+fNx8+ZNbN++Xau/S3X9tWvXwt3dnfUcbB8aLbkHrrXk/Y2Pj8fQoUNZ87bkywyb8PBw7N69G+np6bh06RLc3d3Rp08f1NfXQyaT4Y8//sC5c+fA5/MxcOBAnWU0ha7fWzZCoZA1fefOnXj//fcRERGB5557Du7u7rC0tERBQQGWLFlidHlN+b3Pzc3F9OnTYWdnh5deegm9e/eGUCgEj8fD6tWrWQd06LofhmEgEonwxhtv6LyeKpAfPnwY8+fPR1BQEN588014enrCysoKCoUCL7zwgka5fXx8kJqairNnz+Ls2bM4f/483nrrLWzevBlJSUl45JFH9N4jBRsOnD17FlKpFKtXr8akSZM0jm3atIn1NcePH8euXbswceJE3L17F2vXrkX//v11diabm6oWVFZWpjFM9969e21y/eaCgoLUzZV5eXkYP348Nm3aZDDYrFq1CqdOncKyZctYP2RVnaxOTk7qZpKWeuSRR3D69GncunULvr6+GsdUNQq2b6LGaMmHpz49e/YE0PilqbX3aUh4eDh4PB7Onj2Ly5cvq2svYrEYzs7OOHv2LH777Tf4+flpNHWZ8/27deuWegRfa8/x448/wsvLC5988onGl8xTp04Z9XpzOHLkCGpqarBt2zaNWiDQ+DfZvKVAn549e6K0tBTh4eEGpwb8+OOPsLKywpdffqkRvFTvYXMCgQDDhg3DsGHDADQOinjxxRexc+dOLFu2TO+1qM+GA6pvOM2/zZw+fZq1/begoABvvPEG+vTpg3feeQfr16+HUCjE66+/rm5b5ZrqQzgtLU0jfefOnW1yfRW2tvpu3brB2dnZYHPG559/jqSkJDz33HN45plnWPOMGjUKAoEAW7ZsYX1vKysrIZfL9V7niSeeAADs2LFD4xlfv34dx48fR79+/VrdhKZqtze26UYXPz8/iEQifPPNN6xfGBoaGoxqRrW1tdWZz9nZGY899hhOnDiBq1evqj8keTweBg4ciJ9//hk3btzQ+vA05/uXnJyMyspK9c+VlZX45ptv4ODgoFGb0sfCwgI8Hk+jLA0NDfjkk0+Mer056PrM+Pbbb1FYWNiic40fPx6FhYU6/3abNp/y+XzweDyNpkOGYbBt2zat17H9bapGDRrz+0o1Gw7069cPbm5uWPv/7d1/TFX1H8fxJwbJRVBvRtEuXq+zXSIjQmNQrAJSQQK7N6IfTnPL3PqxxiJnrUidXtGrpG6sH6hpabJY59wrEoV6zQZClsYaztFYYwjDTMi4EkNLuf3BuF/wcuEC3m9bvR8bG7v3nM/5nPvP695zzuf9tlppa2sjIiKChoYGysrKMBqNNDY2urft7e1l5cqV9PT0sHXrVjQaDRqNhg0bNvDKK69QUFDAunXr/D7nzMxMtm3bxurVq2lqakKr1VJVVTXks/b+9MEHH1BTU0NycjKRkZG4XC6OHTtGU1MTL7zwgtf9GhsbsVqthIeHM3v2bMrKyga9r9friYuLIyIigrVr15Kfn09GRgaLFi1Cp9Nx8eJFGhsbcTgcVFRUDPutOCkpiYULF1JRUYHT6SQlJYX29nZKSkqYOHEi+fn5Yz5/rVbLjBkzqKioYPr06dx6661oNJpR11MLCAhg8+bNLFu2jEWLFpGdnc2dd97J5cuXOXv2LEeOHCEvL2/EygCxsbEoisL27duZNWsWEyZMICUlxR2KiYmJ7N271/1/v8TERCorKz1ehxv7+Wm1WnJycsjOzsblcmGz2Th37hwWi8XrZabrpaen8+6777JixQrmz5/PH3/8wRdffPF/Xbj58MMPo9FoWLVqFUuWLGHy5MnU1dVRVVWFXq/n2rVrPo/13HPPUVtby+bNmzlx4gSJiYmEhoZy7tw5Tpw4wc0338y+ffsASEtL49ChQyxbtgyTycTVq1dxOBwe64sAli9fTmhoKPHx8dxxxx1cunQJu91OQEDAoGUf3kjY+MHkyZPZtWsXW7Zs4dNPP+Xq1avcc8897Ny5E0VRBoXN+++/z/fff8+aNWsGXVKYN28eixcvpqSkhKSkJNLS0vw659DQUHbs2MHGjRspLi4mJCSEBQsWsGXLFuLj4/167IHmzZtHe3s7lZWVdHR0EBwczIwZM7BYLDz55JNe9/v999/p7e2lvb19yHUyZrOZuLg4ALKzszEYDOzevZvS0lK6urqYOnUqM2fOJDc3l/Dw8BHnWVhYyN13343dbmfTpk2EhIQQHx9Pbm7uuC99FhYWUlBQwLZt2+jp6UGn042peGd0dDR2u53i4mK+/vprPvvsMyZNmoROp8NsNg97A7nfa6+9htPppKSkhEuXLuFyuTh69Kg7bB544AH27t3L9OnTBz3A0T92UFDQkPceb9Tnt3LlSk6dOsX+/fvp6OjAYDBQWFhIVlaWz2MsX74cl8uFoihs2LCB8PBwFi5cSHZ2NhkZGT6PMx56vZ6dO3eydetWPvzwQ2666SbmzJnDvn37WL9+/YhPcQ4UFBREcXExJSUllJWVuR8Que2224iJicFsNru3feyxx+ju7ubjjz/GarUyZcoUUlJSeP311z3KJT377LN89dVXlJaW4nQ6mTp1KtHR0eTn53t8oRhKgOufvtsphBDiX0/u2QghhPA7CRshhBB+J2EjhBDC7yRshBBC+J2EjRBCCL+TsBFCCOF3EjZC+MjpdBITE0NUVJTHotGBUlNTBzUbu+uuu0hKSmLJkiVDNvYaaP/+/URFRTF37twhF9YNp6ioiKioKE6fPj3o9dbWVt555x3S09OJjY0lPj6ejIwM3njjDY86fampqWRmZno9Rn+TPX9WtRb/TrKoUwgflZeX89dffxEZGYmiKMOumo6IiCAvLw/oqxLx66+/YrfbycvLo7293WvVaVVV0ev1tLS0UFlZOWgB3licPn2apUuXEhgYiMlkclcRaG5u5tixY0yaNMmnBXlCjJeEjRA+UhSFhIQEHn30UQoKCmhpafFa6TYsLMwjjJ5++mkeeughbDbbkGHz008/cebMGaxWK5988gmqqo47bN577z16eno4cOAA0dHRg95bvXr1qOtuCTFWchlNCB+cOXOGhoYGzGYzWVlZBAYGoqrqqMaYMmXKoEZd11MUxV0myGw2c/LkSY+mfKPV3NzsLityvQkTJnD77bePa3whfCVhI4QPBgaBVqslOTmZAwcOeDTa6nft2jV3h8SOjg4aGhp4++236e7u5plnnvHY/s8//6S8vJz09HRCQkLIzMwkKCgIm802rnnr9Xo6Ozs5fPiwz/sMnPv1fyNVxBbCG7mMJsQIrly5QkVFBWlpae4ClCaTiSNHjlBdXe3u7TFQU1OTR6HLiRMnsm7dOnJycjy2dzgcdHZ2YjKZgL7y/Y888gh2u53c3FyfmvYN5aWXXqK2tpZXX30Vg8HAnDlziImJISEhYchOmd7mLsR4SdgIMYLDhw/jdDrdQQCQnJzMtGnTUFV1yLDR6XRYLBagrz/IhQsXsNlsrF27lsDAQI+meoqioNPpBvVgMZvNOBwOqqurSU5OHtPc4+LiUFWVPXv2UFVVhc1mc/9amjt3Llar1aMz6cC5X++jjz7i+PHjY5qL+G+TsBFiBIqicMsttxAREcHZs2fdrz/44INUVlZy8eJFj2ZfISEhHh0ys7KyMJvNWCwWUlNT0Wq1ALS1tfHtt9+Sk5NDS0uLe/uZM2cSGhqKoihjDhvo65y5adMm97FOnjzJ559/zqlTp3j55ZdRVXVQJ8ih5t7v4MGDY56H+G+TsBFiGK2trXz33Xe4XC6vPYUOHjzo9VHmgQIDA93Nxurr692/iGw2G729vZSWllJaWuqx3zfffMNvv/3GtGnTxnUu0PerRafT8fjjj7N48WLq6uqor6/n/vvvH/fYQgxHwkaIYdhsNlwuFxaLhbCwMI/3t2/fjqqqPoUN9LUbBuju7gb6LrHZ7Xaio6N58cUXPbbv6Ohg/fr1lJWV8fzzz4/9RK4TEBBAbGwsdXV1XLhw4YaNK4Q3EjZCeNHb24vdbsdoNA55Ux/g559/pqioiPr6eu69995hx7ty5QrV1dXA/3q319bW0tbWxtKlS0lPTx9yvz179qCq6pjCpqamhoSEBI8Wx5cvX6ampgbA64MCQtxIEjZCeHH8+HF++eWXYdtRL1iwgKKiIhRFGRQ2XV1d7pI2/Q8IlJeX09raylNPPYXBYAD67gcBzJ8/f9hj7N69mx9//JH77rtvVOewceNGOjs7SU1NxWg0EhwczPnz5ykvL6e5uRmTyTTuNtZC+ELCRggvfAkCo9GIwWDgyy+/5K233iI4OBiA8+fPs2rVKvd2Go2GWbNmsWbNGvc6m87OThwOB7NnzyYyMtLrMfrDRlXVUYfNm2++ydGjR/nhhx84dOgQXV1dhIWFYTQaWbFiBU888cSoxhNirAJcLpfrn56EEEKIfzepICCEEMLvJGyEEEL4nYSNEEIIv5OwEUII4XcSNkIIIfxOwkYIIYTfSdgIIYTwOwkbIYQQfidhI4QQwu8kbIQQQvjd3+oGmn90Dw9GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ABAmaxsize_upper, ABA_test_avg_upper)\n",
    "plt.xlabel(\"maximum size of the two brain areas\\nABA ISH\")\n",
    "plt.ylabel(\"AUROC\")\n",
    "plt.savefig(\"051220_9.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(ABAmaxsize_upper, ABA_test_avg_upper, kind='hex') \\\n",
    "   .set_axis_labels(\"maximum size of the two brain areas\\nABA ISH\", \"AUROC\")\n",
    "plt.savefig(\"051220_10.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(STmaxsize_upper, ST_test_avg_upper)\n",
    "plt.xlabel(\"maximum size of the two brain areas\\nST\")\n",
    "plt.ylabel(\"AUROC\")\n",
    "plt.savefig(\"051220_11.png\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(STmaxsize_upper, ST_test_avg_upper, kind='hex') \\\n",
    "   .set_axis_labels(\"maximum size of the two brain areas\\nST ISH\", \"AUROC\")\n",
    "plt.savefig(\"051220_12.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion \\\n",
    "Looking at minimum or maximum size, there seems to be no correlation between size and performance in general when looking at minimum size of the two areas. The ABA ISH has all performance very near AUROC=1. The ST does seem to have a slight pattern where very small brain areas have a range of performance, but then there are very high performances for all other sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Does performance rise as you downsample in a particular brain region?__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by looking at caudoputamen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Is the CP more transcriptionally diverse in general? (average correlation of voxel profiles)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>id</th>\n",
       "      <th>acronym</th>\n",
       "      <th>name</th>\n",
       "      <th>parent</th>\n",
       "      <th>allencolor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1109</th>\n",
       "      <td>672</td>\n",
       "      <td>CP</td>\n",
       "      <td>Caudoputamen</td>\n",
       "      <td>485.0</td>\n",
       "      <td>#98D6F9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id acronym          name  parent allencolor\n",
       "1109  672      CP  Caudoputamen   485.0    #98D6F9"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ontology.loc[ontology.acronym==\"CP\",:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get indices of the samples that map to CP\n",
    "ABACPindex = ABApropont.loc[ABApropont['672']==1,'672'].index\n",
    "STCPindex = STpropont.loc[STpropont['672']==1,'672'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get the actual samples for all genes\n",
    "CPvox = ABAvox.loc[ABACPindex, :]\n",
    "CPspots = STspots.loc[STCPindex, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABAcorr = np.corrcoef(CPvox, rowvar=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "STcorr = np.corrcoef(CPspots, rowvar=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6652694737905721\n",
      "0.6652694737905721\n"
     ]
    }
   ],
   "source": [
    "#mean upper triangular \n",
    "CPABAmeancorr = np.mean(ABAcorr[np.triu_indices_from(ABAcorr)])\n",
    "CPABAabsmeancorr = np.mean(np.abs(ABAcorr[np.triu_indices_from(ABAcorr)]))\n",
    "print(CPABAmeancorr)\n",
    "print(CPABAabsmeancorr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7273221133508818\n",
      "0.7273221133508818\n"
     ]
    }
   ],
   "source": [
    "CPSTmeancorr = np.mean(STcorr[np.triu_indices_from(STcorr)])\n",
    "CPSTabsmeancorr = np.mean(np.abs(STcorr[np.triu_indices_from(STcorr)]))\n",
    "print(CPSTmeancorr)\n",
    "print(CPSTabsmeancorr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looked at both the raw mean of correlation coefficients and the mean of the absolute value and they are identical in both datasets, so it seems that all correlations were positive. \\\n",
    "\\\n",
    "Need to compare to the average correlation of voxel profiles for other brain areas- see following section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probing difference in performance b/t ST and ABA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Distribution of average correlation expresison profiles within brain regions (i.e. is every brain region made up of voxels that look the same?)__ \\\n",
    "These took a bit of time to run, so they were quickly scripted and run from 9_STcorr.py and 11_ABAcorr.py. \\\n",
    "Outfiles: STareacorr_0504.csv and ABAareacorr_0504.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "#infiles\n",
    "#ABACORR_IN = \"ABAareacorr_0504_2.csv\"\n",
    "#STCORR_IN = \"STareacorr_0504_2.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ABAcorr = pd.read_csv(ABACORR_IN)\n",
    "#STcorr = pd.read_csv(STCORR_IN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getareacorr(data, propont):\n",
    "    #initialize zeros dataframe to store entries\n",
    "    allbyall_corr = pd.DataFrame(index=list(propont), columns=[\"mean\", \"absmean\"])\n",
    "    \n",
    "    areas = list(propont)\n",
    "    #for each column, brain area\n",
    "    for i in range(propont.shape[1]):\n",
    "        area1 = areas[i]\n",
    "\n",
    "        #subset train and test sets for only samples in curr area\n",
    "        Xcurr = data.loc[propont[area1] != 0, :]\n",
    "            \n",
    "        #calculate within brain area correlations\n",
    "        corrmat = np.corrcoef(Xcurr, rowvar=1) #corr b/t samples\n",
    "        #corrmat = np.cov(Xcurr, rowvar=0) #cols are features, rows are samples\n",
    "        absmean = np.mean(np.abs(corrmat[np.triu_indices_from(corrmat)]))\n",
    "        mean = np.mean(corrmat[np.triu_indices_from(corrmat)])\n",
    "            \n",
    "        allbyall_corr.iloc[i,0] = mean\n",
    "        allbyall_corr.iloc[i,1] = absmean\n",
    "            \n",
    "        #if i == 1:\n",
    "        #break\n",
    "     \n",
    "    #return temp\n",
    "    return allbyall_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "STcorr = getareacorr(leafSTspots, leafSTpropont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABAcorr = getareacorr(leafABAvox, leafABApropont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ABAcorr['mean'])\n",
    "plt.xlabel(\"mean correlation within brain areas\\nABA\")\n",
    "plt.ylabel(\"counts\")\n",
    "#caudoputamen\n",
    "plt.vlines(x=CPABAmeancorr, ymin=0, ymax=250, linestyles='--', colors='orange')\n",
    "plt.text(0.6, 250, \"CP\", fontsize=16)\n",
    "#mean\n",
    "plt.vlines(x=ABAcorr['mean'].mean(), ymin=0, ymax=250, linestyles='--')\n",
    "plt.text(0.7, 250, \"$\\mu = %.3f$\" %ABAcorr['mean'].mean(), fontsize=16)\n",
    "\n",
    "plt.savefig(\"051220_13.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ABAcorr['absmean'])\n",
    "plt.xlabel(\"mean absolute value of correlation within brain areas\\nABA\")\n",
    "plt.ylabel(\"counts\")\n",
    "#plt.savefig(\"050420_7.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAakAAAE1CAYAAABdiB9jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3dfVyN9/8H8NeR1Eo3VMYKkc4h3VtCsWmsEZv724kYs2HM3dfNsJlRxmxyt7IYy9233KYxYqwImZttErmtuaukFN1fvz/6db6Obo86XeecXs/H4zxyPtfnXOf9Pudc3ufzuW6ORBAEAURERGqontgBEBERlYdFioiI1BaLFBERqS0WKSIiUlssUkREpLZYpIiISG2xSBERkdrSiCJ17tw5TJw4EV27doVMJsPu3bvly/Lz8/Htt9+ib9++cHZ2hqenJ2bMmIF79+4prCMvLw9ff/013N3d4ezsjIkTJ+LBgwe1nQoRESlBI4rUs2fPIJVKMX/+fOjr6yssy8nJwZUrV/DJJ59g9+7dWLduHe7fv4+PPvoIBQUF8n7ffPMNDh8+jO+++w6hoaHIzs7Gxx9/jMLCwtpOh4iIqkiiaVeccHFxwYIFCzBgwIBy+yQmJsLHxwf79++HTCbD06dP0blzZyxduhTvv/8+AOD+/fvo3r07goOD0bVr13LXJZPJAAAJCQk1mwjRSy5cuIDNmzfj/PnzePLkCQwNDWFnZ4f3338f77//PuLi4uDr6yvvr6enBysrK/Tu3RsfffRRqS9wpOj+/ftYtmwZYmJiIAgCunTpgnnz5uGNN96o8HGjRo3C2bNny1zm6emJn376SX7//PnzWLt2LeLj45Gbm4uWLVti5MiRGDRokLzPH3/8geDgYNy4cQMZGRlo3LgxXFxcMGXKFLRp06ZmktUi9cUOQBWysrIAACYmJgCAv//+G/n5+fD09JT3adasGWxsbHDhwoUKixRRbdi8eTP8/f3RqVMnzJw5E5aWlsjIyEBMTAy+/PJLGBkZwcjICADwxRdfwMHBAc+fP0d0dDTWrl2Lu3fvYvny5SJnob6eP3+O0aNHo0GDBggICAAA/PDDD/D19cX+/fthYGBQ7mMXLVok/z+lxMWLF7Fs2TJ4eXnJ265evQo/Pz84OTnh66+/xmuvvYbDhw9j/vz5yMvLw4gRIwAAGRkZaN++PUaMGIHGjRvj3r17CA4OxpAhQ3DgwAFYWlqq4BXQXFpXpPLy8uDv74/u3bujadOmAIDU1FTo6OigUaNGCn3NzMyQmpoqRphEcufOnYO/vz8+/PBDfPHFFwrLevToAT8/Pzx79gwZGRkAABsbGzg7OwMAOnfujMePH2P37t2YN28eTE1Naz1+TbBr1y4kJSXh0KFDaNmyJYDiWRJvb2/s3LkTfn5+5T62rNHNrl27oKurCx8fH3lbZGQkioqKsGHDBhgaGgIAPDw8cPXqVezbt09epPr06YM+ffoorM/R0RG9evXC4cOHMXbs2Grnq000Yp9UVRUUFGDWrFl4+vQpli1bVml/DZvpJC0VFBQEExMTzJo1q8zlLVq0QNu2bct9vL29PQDg7t27KomvJhUVFcHFxQVr1qxRaM/IyIBMJsOePXtU8rzHjh2Dk5OTvEABQPPmzeHq6oqoqCil1pWTk4NDhw7By8tL4UtBfn4+6tevX2ra1cjICEVFRRWus2Q99etr3bih2rSmSBUUFGD69OlISEjA5s2bFUZN5ubmKCwsRHp6usJjHj9+DHNz89oOlUiusLAQZ8+ehaenJ/T09F5pHcnJyQAgnw5UZ7du3cKzZ89gZ2en0H7lyhUAQLt27Uo9RhAEFBQUVHqr6CCoxMRESKXSUu1t2rRBYmKiUjn89ttvyM7ORr9+/RTa+/fvDwBYsmQJHj58iMzMTOzatQuxsbEYM2ZMqfUUFhYiLy8Pt2/fxqJFi2BhYaEwMqNiWlG28/PzMX36dFy7dg1bt26FhYWFwnJ7e3vo6uoiJiYGffv2BQA8ePAAN27cgIuLixghEwEA0tPTkZOTU+nO+xcVFRWhoKAAOTk5iI6Oxo4dO9CuXTu0atVKhZHWjJJi1L59e4X2+Ph4NGjQADY2NqUec/bsWYUDRsrTsWNHbN26tcxlGRkZMDY2LtVuYmKCzMzMqoQut2/fPpiZmaFbt24K7VKpFFu2bMHkyZOxbds2AICuri6+/PLLMovP4MGD8c8//wAAWrZsiZ9//hlmZmZKxVIXaESRys7Olk9lFBUV4d69e4iPj4eJiQmaNGmCqVOn4q+//sKGDRsgkUiQkpICoPibpb6+PoyMjDBw4EAsX74cZmZmMDU1xbJlyyCTydClSxcxUyNS2rhx4xTud+/eHQsWLBApGuX8888/MDMzw+uvv16q3dbWFrq6uqUe0759e4SFhVW67pL9QOWRSCTKBVuGhw8f4tSpU/D19S01NXf79m189tlnsLW1xVdffQV9fX1ERUXhyy+/hJ6envzI4hLffvstsrKykJSUhJCQEPj5+WHbtm2wsrKqdpzaRCOK1N9//63wTSowMBCBgYHo378/Jk+eLJ9Tfvmw9GXLlsnb5s2bh/r16+Pzzz9HTk4OOnfujOXLl0NHR6f2EiF6iampKfT19UudfF6RhQsXwtHREfr6+rC0tKzwyDR1888//5Sa6gOKR1LlzWoYGhqWOQ34soqKkLGxsfzAkxeVN8Iqz/79+1FUVCSf2nvRd999h/r162PDhg3yYtu5c2ekp6fjm2++QZ8+fVCv3v/2sJSMGp2cnNCtWzd4eXkhKCgIixcvrnI8dYFGFCl3d/cKz1OqyjlMenp6WLBggcZ846S6oX79+ujYsSNiYmKQl5eHBg0aVPqYVq1awcHBoRaiq1mCIODq1asYPny4QntaWhpu3bqFkSNHlvm4mpjua9OmDa5fv16q/caNG0qdm7Rv3z60bdu2zANZrl27hrZt25YaDTo6OiIiIgJpaWmldkWUMDY2RosWLTTi4JfaphFFikibTZgwAaNGjcLy5ctLHYIOAElJScjOzhYhspp19+5dZGZmKowmAGDr1q0oKioq9wjGmpju8/LywvLly5GUlITmzZsDKD7g5M8//8SMGTOqFP9ff/2F69evY+7cuWUut7CwQHx8fKkvG5cvX4aenp78vM2ypKam4tatW/J95vQ/LFJEInNzc8OcOXPg7++PGzduoH///njjjTeQkZGB06dPIywsDCtWrNCIo/cqUnKQQFhYGJo1awYzMzNER0dj7969AIqn9du3b1/qEO6GDRtWe+Q4ZMgQhIaG4tNPP8XUqVMhkUjwww8/oGnTphg6dKi839mzZzFmzBgsXbq01NF7+/btQ/369Uud41Ri5MiRmDp1Kj755BMMHz4c+vr6OHbsGCIiIjBmzBh54Zo0aRLs7Owgk8nQsGFD3L59G5s3b4aOjk6F52vVVSxSRGLYZ13894PbAIAxY8bA0dERmzdvxvLly5Geng5DQ0PY29vjq6++gpeXF86dOydauK/K2toaQPFBBVeuXIGpqSlmzJiBlStXIjs7Gz169MAPP/yA6dOn49dff8Xo0aNVEoeBgQF+/vlnLFu2DLNnz4YgCOjcuTPmzZunMAITBAGFhYWlzmvKz89HREQEunbtWu5pK++99x6CgoKwceNGfPHFF8jNzUWLFi2wcOFCDBs2TN7PyckJhw4dwqZNm5Cfn4+mTZvC3d0dEyZM4EETZdC4a/fVNl67j1TipSKlrV4sUiWjhE2bNokYEWkarTmZl0ij1Dcsvmk5Q0ND+UjlypUrpc6PIqoMp/uIxODzj9gR1IqS/VD//vsvnjx5wiJFSmORIiKVs7S05JQ5vRJO9xGJITW2+KblYmNjERur/XmS6nAkRSSGmP8/2kvLD5woOart9u3b4gZCGosjKSIiUlssUkREpLZYpIiISG2xSBERkdpikSIiIrXFo/uIxNA1XOwIakV4eN3Ik1SHRYpIDI07iB1BrejQoW7kSarD6T4iIlJbLFJEYthvW3zTcra2trC11f48SXU43UckBiFf7AhqRX5+zedpPedgja+zKm77+4jyvHUdR1JERKS2WKSIiEhtsUgREZHa4j4pInolVdk3lJz+vMp9icrCIkUkBtk0sSOoFcZvfiB2CKThWKSIxNC2jhQpNxYpqh6N2Cd17tw5TJw4EV27doVMJsPu3bsVlguCgMDAQHh6esLR0RGjRo3C9evXFfpkZGRg1qxZ6NChAzp06IBZs2YhMzOzNtMgIiIlaUSRevbsGaRSKebPnw99ff1Sy4ODgxESEoIFCxYgLCwMjRs3hp+fH7KysuR9ZsyYgStXriA4OBgbN27ElStXMHv27NpMg+h/YscV37RcauQPSI38QewwSINpRJF66623MH36dLz33nuoV08xZEEQsGXLFkyYMAHe3t6QSqUICAhAdnY2IiIiAAA3btzAH3/8gcWLF8PV1RUuLi746quvcPz4cdy8eVOMlKiuexhVfNNyOXcuIefOJbHDIA2mEUWqIsnJyUhJSYGHh4e8TV9fH25ubrhw4QIA4MKFCzAwMICrq6u8T4cOHWBgYCDvQ0RE6kfji1RKSgoAwNzcXKHdzMwMqampAIDU1FQ0btwYEolEvlwikaBx48byPkREpH40vkiVeLEAVXW5IAiVPo6IiMSj8UXKwsICwP9GVCXS0tLkoytzc3OkpaVBEAT5ckEQkJ6eDjMzs9oLloiIlKLxRcrKygoWFhY4deqUvC03NxdxcXFwcXEBALi4uODZs2cK+58uXLiAZ8+eyfsQ1apGrsU3LdegqQ0aNLUROwzSYBpxMm92djbu3r0LACgqKsK9e/cQHx8PExMTvPHGG/D19cWGDRvQunVrWFtbY/369TAwMECfPn0AADY2NujatSsWLVqEr7/+GoIgYNGiRejevTtat24tZmpUV3XbXXkfLdCk/3yxQyANJxFenANTU2fOnIGvr2+p9v79+8Pf3x+CIGDNmjXYuXMnMjIy4OTkhIULF0Iqlcr7PnnyBEuWLMGxY8cAAF5eXli4cCGMjY0rfG6ZTAYASEhIqMGMiDRfXbseH39PShwaUaTExCJFKnFzc/Hf1mPEjKJaqlKksv46CgBo6NBD1eGoHIuUODRiuo9I6/z1ZfFfDS5SVfEkehsA7ShSJA6NP3CCiIi0F4sUERGpLRYpIiJSWyxSRESktlikiIhIbfHoPiIx9P5b7AhqxRvj1oodAmk4FikiMeg2FDuCWlGvwWtih0AajtN9RGLIull803L5Tx4g/8kDscMgDcaRFJEYoryK/35wW9QwVO3h9nkAAKtPQkSOhDQVR1JERKS2WKSIiEhtsUgREZHaYpEiIiK1xSJFRERqi0f3EYnBbb3YEdQKs3c/FTsE0nAsUkRieKOX2BHUitds3hQ7BNJwnO4jIiK1xSJFJIYjnsU3Lffgl9l48MtsscMgDcbpPiIxPEsWO4JaUfA0VewQSMNxJEVERGqLRYqIiNQWixQREaktFikiIlJbPHCCSAwth4sdQa0wtOsmdgik4bSiSBUWFiIwMBD79+9HSkoKLCws0LdvX0yZMgX16xenKAgC1qxZg507dyIzMxNOTk5YuHAhbG1tRY6e6iTnZTWyGus5B2tkParS6K0xYodAGk4rpvuCg4Oxbds2fPHFF/j1118xf/58bNu2DT/++KNCn5CQECxYsABhYWFo3Lgx/Pz8kJWVJWLkRERUEa0oUhcuXED37t3h5eUFKysrvPPOO/Dy8sLly5cBFI+itmzZggkTJsDb2xtSqRQBAQHIzs5GRESEyNFTnXRxbvFNy6Wf2Iz0E5vFDoM0mFYUqQ4dOuDMmTO4ceMGACAxMRGxsbHo1q14Pjw5ORkpKSnw8PCQP0ZfXx9ubm64cOGCKDFTHXdne/FNy2VfOYnsKyfFDoM0mFbskxo/fjyys7Ph4+MDHR0dFBQUYOLEiRg5ciQAICUlBQBgbm6u8DgzMzM8evSo1uMlIqKq0YoiFRkZib1792LlypVo06YN4uPjsXTpUlhZWWHw4MHyfhKJRMQoiYhIWVpRpJYvX46xY8fCx8cHACCTyXDv3j0EBQVh8ODBsLCwAFA8omrWrJn8cWlpaaVGV0REpD60Yp9UTk4OdHR0FNp0dHRQVFQEALCysoKFhQVOnTolX56bm4u4uDi4uLjUaqxERFR1WjGS6t69O4KCgmBlZSWf7tu0aRP69esHoHiaz9fXFxs2bEDr1q1hbW2N9evXw8DAAH369BE5eqqTDKzEjqBW1DfiTAVVj0QQBEHsIKorKysLP/zwA44ePYq0tDRYWFjAx8cHkyZNgp6eHgDFk3kzMjLkJ/NKpdIK1y2TyQAACQkJKs+DSFnqfjKvNrnt7yN2CHWSVhQpVWKRInXGIlV7WKTEoRX7pIg0zr1fi29a7vmNODy/ESd2GKTBaqxI5eXl1dSqiLTfuU+Kb1ou7bd1SPttndhhkAZTqkidOHECgYGBCm2hoaFwdXWFs7MzZsyYgfz8/BoNkIiI6i6litRPP/2Emzdvyu/fuHEDS5cuRZMmTdClSxdERkYiNDS0xoMkIqK6SakidfPmTdjb28vvR0ZGQk9PD2FhYdi4cSN69+6NvXv31niQRERUNylVpDIyMtCoUSP5/VOnTqFTp05o2LAhAKBjx45ITk6u2QiJiKjOUqpINWrUCPfu3QNQfG7SX3/9hQ4dOsiXFxQUoLCwsGYjJCKiOkupK044Oztjx44daNOmDU6ePInCwkK89dZb8uV37txBkyZNajxIIq3zzjGxI6gVrw9fKnYIpOGUKlKfffYZfH19MW3aNABA//790aZNGwDFV3Q4evQo3N3daz5KIm3TsLXYEdQKXdOmYodAGk6pItWmTRtERkbizz//hJGREdzc3OTLMjMzMXr0aBYpoqrIzyr+q9tQ3DhUrCjvOQCgXoPXRI6ENJVSRercuXOwsbGBl5dXqWUmJibo27ev/NdxiagCkf9/lOwHt0UNQ9Xu/TQJAGD1SYjIkZCmUurACV9fX8TExJS7PDY2Fr6+vtUOioiICFCySFV2LdrCwkLUq8fLARIRUc1QuqJU9BPsFy5cUDiPioiIqDoq3Sf1888/Y8uWLfL7S5cuxapVq0r1y8zMRFZWFgYOHFizERIRUZ1VaZEyNjbGG2+8AQD4999/YWpqCjMzM4U+EokEtra2cHZ2xujRo1UTKRER1TmVFqn+/fujf//+AAAvLy/MmDED77zzjsoDI9JqDl+KHUGtMPUcIXYIpOGUOgT92LG6cZY8kcq1HiN2BLWioUMPsUMgDadUkXrR8+fP8eTJkzKP+CuZHiQiIqoOpYpUUVERNm7ciK1btyI1NbXcfvHx8dUOjEirnRxQ/LfbbnHjULFHe74BADTpP1/kSEhTKVWkVqxYgZCQENja2sLb2xumpqaqiotIu6X/KXYEtSLvAa9AQ9WjVJHav38/unbtiuDgYFXFQ0REJKfUybyZmZk8so+IiGqNUkVKKpUiJSVFVbEQEREpUKpITZ48GTt27MD9+/dVFQ8REZGcUvuk/v77b7zxxhvo3bs3evbsCSsrq1IXlJVIJJg0aVKNBlkVjx49wsqVK3HixAlkZ2ejefPm+PLLL9GxY0cAxRfHXbNmDXbu3InMzEw4OTlh4cKFsLW1rfVYifB63Zg212/pJHYIpOEkQmWXNn9B27ZtK1+hRFLrh6BnZmaif//+6NChAz788EM0atQIycnJaNKkCWxsbAAAQUFBWL9+Pfz9/dGqVSusXbsW58+fx6FDh9CwYfk/PCeTyQAACQkJtZILkTKs5xwUO4Q647a/j9gh1ElKjaSioqJUFUe1bNy4ERYWFli+fLm8rXnz5vJ/C4KALVu2YMKECfD29gYABAQEoHPnzoiIiMCwYcNqPWYiIqqcUkXK0tJSVXFUy9GjR9G1a1dMmzYNZ86cQZMmTTB48GCMHDkSEokEycnJSElJgYeHh/wx+vr6cHNzw4ULF1ikqPZd/b74b9tp4sahYpnn9gEAjN0+EDkS0lRa8QuFSUlJ2LZtG5o3b46ffvoJvr6+WLlyJUJDQwFAfkSiubm5wuPMzMwqvHIGkcokfF9803KZcfuQGbdP7DBIgyk1kpo7d26lfSQSCZYuXfrKAb0KQRBgb2+PGTNmAADs7Oxw584dhIaG4sMPP1SIjYiINIdSRWrPnj2V9hGjSFlYWMgPkCjRunVr+aHyFhYWAIpHVM2aNZP3SUtLKzW6IiIi9aFUkbp69WqptsLCQiQlJSEkJATXrl3Dxo0bayy4qnJ1dcWtW7cU2m7fvi2/GruVlRUsLCxw6tQpODo6AgByc3MRFxeH2bNn13q8RERUNdXeJ6WjowNra2ssXrwYpqam+Pbbb2siLqWMHj0aly5dwvr163Hnzh38+uuv2Lp1K0aOHAmgeHTn6+uLoKAg/Pbbb7h27RrmzJkDAwMD9OnTp9bjJSKiqqnRAye6du2K3377rSZXWSWOjo5Yu3Ytfv31V/Tp0wfff/89pk6dihEj/veroOPHj4efnx8WL16MgQMHIiUlBSEhIRWeI0WkMhLd4puWk+joQKKjI3YYpMGUOpm3MmvXrkVQUBAuXbpUU6sUHU/mJXXGk3lrD0/mFccr/zLvizIzM3Hq1Cn8/PPPaN++fU2skoiISLki1bZt23IP4xYEASYmJpgzZ06NBEak1R6fL/7buIO4cahY7oNEAIBe0zYiR0KaSqki1a9fvzKLlKmpKaytreHj48N9PERV8cfA4r8f3BY1DFVL2VN8OorVJyEiR1J9Yk6t1uWpRqWKlL+/v6riICIiKkUrLotERETaSekDJ4qKirBnzx4cOXIEycnJAIpPln333XfRr1+/Ur8vRURE9KqUKlI5OTkYP3484uLiIJFI5JcbOnnyJE6cOIG9e/ciODgYenp6KgmWiIjqFqWGPevXr8e5c+fg5+eH06dP48SJEzhx4gRiY2MxduxYnD17FuvXr1dVrEREVMcoNZKKjIxEr169Sl3vztjYGLNmzcK9e/dw8OBBTJum3b+RQ1RtHjvEjqBWWLzPa2NS9Sg1knrw4AE6duxY7nI3Nzc8ePCg2kERaT3zTsU3Ladn2RZ6lm3FDoM0mFJFytjYGHfu3Cl3+d27d2FsbFztoIiIiAAli1SXLl2wbds2/PHHH6WWRUdHY/v27fD09Kyx4Ii01sH2xTctd2/jp7i38VOxwyANptQ+qWnTpiE6OhoTJkxAu3btYGtrCwC4fv064uPj0ahRI3z22WcqCZRIqxRkix1BrSjKzxE7BNJwShUpS0tLhIeHY+XKlTh+/DiuXLkCADA0NISPjw+mT58u/6FBIiKi6lL6ZN433ngDK1euhCAIePz4MQRBgJmZWbkXniUiInpVr/xTHRKJBGZmZjUZCxERkQKlDpwIDQ3FmDFjyl0+duxY7NhRN87/ICIi1VOqSO3evRstW7Ysd7m1tTXCw8OrHRSR1rP9tPim5Yxce8PItbfYYZAGU2q6786dOxgwYEC5y9u0aYOIiIhqB0Wk9ezqxpUYTNwHiR0CaTilRlIFBQXIy8srd3leXh5yc3OrHRQRERGgZJGytrZGTExMucujo6PRokWLagdFpPXOTiy+abm0w2uQdniN2GGQBlOqSPn4+CAmJgbff/+9wogqPz8fq1evRkxMDPr06VPjQRJpnfuHim9a7vnNP/H85p9ih0EaTKl9UmPGjMHJkyexYcMGbN++Ha1bt4ZEIsGNGzeQkZGBN998E35+fqqKlYiI6hilipSuri5CQkKwefNmREREID4+HkDxNOCECRPg6+sLXV1dlQRKRER1j9In8+rq6mL8+PEYP368KuIhIiKSU2qflKbYsGEDZDIZFi9eLG8TBAGBgYHw9PSEo6MjRo0ahevXr4sYJRERVUbritTFixexa9cuyGQyhfbg4GCEhIRgwYIFCAsLQ+PGjeHn54esrCyRIqU6zcS++KblGli0RAOL8i8AQFSZV752nzp6+vQpZs6ciW+++Qbr1q2TtwuCgC1btmDChAnw9vYGAAQEBKBz586IiIjAsGHDxAqZtIT1nINKPuKT4j+HlH2cZmkyaJHYIZCG06qR1IIFC+Dt7Y3OnTsrtCcnJyMlJQUeHh7yNn19fbi5ueHChQu1HSYREVWR1hSpXbt24e7du5g6dWqpZSkpKQAAc3NzhXYzMzOkpqbWSnxEL3rf9He8b/q72GGoXPaV35F95XexwyANphXTfTdv3sR3332H0NBQNGjQoNx+/M0rUhezm24BAOx/8ra4gahY+oniPA3t3hY3ENJYWlGkLl68iPT0dPTt21feVlhYiHPnzmHHjh3yi96mpKSgWbNm8j5paWmlRldERKQ+tKJI9ejRA/b2ikdKzZ07F9bW1vj444/RqlUrWFhY4NSpU3B0dAQA5ObmIi4uDrNn142rURMRaSKtKFLGxsYwNjZWaDMwMICJiQmkUikAwNfXFxs2bEDr1q1hbW2N9evXw8DAgNcaJCJSY1pRpKpi/PjxyM3NxeLFi5GRkQEnJyeEhISgYcOGYodGRETl0NoitXXrVoX7EokEU6ZMwZQpU0SKiIiIlKW1RYpInfW+vlrsEGpFM7+6kSepDosUkQgyC+vGNLOOft3Ik1RHa07mJdIkTXVT0VRX+08kL8hMRUGm9udJqsORFJEIwmyKT33wvBoiciSq9SC0OE+rT7Q7T1IdjqSIiEhtsUgREZHaYpEiIiK1xSJFRERqi0WKiIjUFo/uIxLBV/cmiB1CrWjco27kSarDIkUkgiOZncQOoVYY2NaNPEl1ON1HRERqi0WKSATbW8/F9tZzxQ5D5R5sn4sH27U/T1IdTvcRiaB5g4dih1ArCp7UjTxJdTiSIiIitcUiRUREaotFioiI1BaLFBERqS0eOEEkgsgMD7FDqBWGsrqRJ6kOixSRCJbeHyd2CLWikVfdyJNUh9N9RESktlikiETw+euh+Pz1ULHDULknf4TiyR/anyepDosUkQgGNorCwEZRYoehcll/RyHrb+3Pk1SHRYqIiNQWD5wgrWE956DYIRBRDdOKIvXjjz/it99+w61bt5RJ/SEAACAASURBVNCgQQM4Oztj+vTpkEql8j6CIGDNmjXYuXMnMjMz4eTkhIULF8LW1lbEyImIKifWF7Db/j6iPO+LtGK67+zZsxgxYgR27NiBn3/+GTo6OvDz88OTJ0/kfYKDgxESEoIFCxYgLCwMjRs3hp+fH7KyskSMnIiIKqIVReqnn37CwIEDIZVKIZPJsHz5cjx+/Bh//vkngOJR1JYtWzBhwgR4e3tDKpUiICAA2dnZiIiIEDl6qotSC0yQWmAidhgqp2NoAh1D7c+TVEcrpvtelp2djaKiIhgbGwMAkpOTkZKSAg+P/539rq+vDzc3N1y4cAHDhg0TK1Sqo/olrhI7hFrRzLdu5EmqoxUjqZd98803aNeuHVxcXAAAKSkpAABzc3OFfmZmZkhNTa31+IiIqGq0biS1bNkynD9/Htu3b4eOjo7CMolEIlJURIo8Gl4EAMRkOYsciWo9v12c52vW2p0nqY5WjaSWLl2KgwcP4ueff0bz5s3l7RYWFgD+N6IqkZaWVmp0RVQbAqxWI8BqtdhhqFzar6uR9qv250mqozVFasmSJYiIiMDPP/8MGxsbhWVWVlawsLDAqVOn5G25ubmIi4uTTwkSEZH60Yrpvq+++gr79u3D2rVrYWxsLB8xGRgYwNDQEBKJBL6+vtiwYQNat24Na2trrF+/HgYGBujTp4/I0RMRUXm0okht27YNADBmzBiF9smTJ2PKlCkAgPHjxyM3NxeLFy9GRkYGnJycEBISgoYNG9Z2uEREVEVaUaQSEhIq7SORSDBlyhR50SIiIvWnNfukiIhI+2jFSIpI04y+9ZXYIdSK14fUjTxJdVikiERwI7d55Z20gK5Z3ciTVIfTfUQi0JXkQ1eSL3YYKicU5kMo1P48SXU4kqIax991qtxx2ccAAM+rISJHolr/BhXnafWJdudJqsORFBERqS0WKSIiUlssUkREpLZYpIiISG2xSBERkdri0X1EIljzaIjYIdQKk851I09SHRYpIhHsePye2CHUCiPnupEnqQ6n+4iISG2xSBGJILBFAAJbBIgdhsql7AtAyj7tz5NUh9N9RCJwMaj852W0Qe69upEnqQ5HUkREpLY4ktJSvH4eEWkDjqSIiEhtsUgREZHa4nQfkQjOZrcXO4Raod+8buRJqsMiRSSC6UkzxA6hVpj3qRt5kupwuo+IiNQWixSRCEaZRWCUWYTYYajc0z8j8PRP7c+TVIfTfUQi+NhiNwBga1ofkSNRrYwzxXkauWp3nqQ6LFIqxvOViIheXZ2b7gsNDYWXlxccHBwwYMAAxMXFiR0SERGVo04VqcjISCxduhQTJ07E3r174eLigvHjx+PevXtih0ZERGWoU0Vq06ZN6N+/P4YMGQIbGxssWLAAFhYW2L59u9ihERFRGerMPqm8vDz8888/GDt2rEK7h4cHLly4UOnjZTLZKz2v3is9irTdOzAFAOhhusiRqJZN0+I8sUe789RWshp43xISqncl/DozkkpPT0dhYSHMzc0V2s3MzJCSkiJSVEREVJE6M5IqIZFIqtRWorrfAoiI6NXVmZFUo0aNoKOjU2rUlJaWVmp0RURE6qHOFKkGDRqgffv2OHXqlEL7qVOn4OLiIlJURERUkTo13efn54fZs2fD0dERrq6u2L59Ox49eoRhw4aJHRoREZWhThWp3r17Iz09HevXr8ejR48glUoRFBQES0tLsUMjIqIySARBEMQOgoiIqCx1Zp8UERFpHhYpIiJSWyxSr0jZC9Xm5eXhhx9+gJeXF+zt7fH2229jy5YtCn0OHz6M3r17w97eHr1798aRI0dUmYKCms5n9+7dkMlkpW65ubmqTgWAcvnMmTOnzFidnZ0V+p09exYDBgyAg4MD3nnnnVq9nFZN53PmzJky+9y4caM20lH683bgwAF88MEHcHJygoeHB2bOnFnqdBJN2n4qy0eTtp+S/r169YKjoyO8vb2xd+/eUn1e+f0RSGkHDx4U7OzshJ07dwqJiYnC4sWLBWdnZ+Hff/8t9zGTJ08WBg4cKERHRwtJSUnCxYsXhdjYWPnyP//8U2jXrp2wbt06ITExUVi3bp3Qrl074eLFixqZT3h4uODk5CQ8evRI4VYblM0nMzOzVJzvvPOOMGfOHHmfu3fvCk5OTsLixYuFxMREYefOnYKdnZ1w6NAhjcwnNjZWkEqlwvXr1xX6FRQUqF0+cXFxQtu2bYVNmzYJd+/eFS5cuCD069dP8PX1lffRpO2nKvlo0vYTGhoqODs7CwcOHBDu3r0rRERECM7OzkJUVJS8T3XeHxapVzBo0CBh/vz5Cm09e/YUVqxYUWb/P/74Q3B1dRXS0tLKXefUqVOFMWPGKLSNHj1a+Pzzz6sfcCVUkU94eLjg7Oxco3FWlbL5vCwuLk6QSqXC+fPn5W3Lly8XevbsqdBv3rx5wpAhQ6ofcCVUkU9JkaroPVQVZfPZuHGj8Pbbbyu0hYWFKXy+NGn7qUo+mrT9DB06VPjmm28U2pYtWyYMGzZMfr867w+n+5RUcqFaDw8PhfaKLlR79OhRODg4YPPmzejWrRveffddLFmyBNnZ2fI+Fy9eLLVOT0/PKl38tjpUlQ8A5OTkoHv37ujWrRs+/vhjXLlyRWV5lHiVfF723//+F7a2tnB1dZW3lff+/P3338jPz69+4OVQVT4lBg0aBE9PT4wePRqxsbE1EnNFXiUfV1dXpKSk4NixYxAEAY8fP0ZkZCS6desm76NJ209V8gE0Z/vJy8uDnp7ipbT19PTw119/ybeN6rw/LFJKepUL1SYlJeH8+fO4evUqAgMDsWDBAvzxxx+YO3euvE9qamqpdZqbm6v84reqyqdVq1ZYunQp1q1bh++++w56enoYPnw4bt++rcp0qn0h4adPn+LQoUMYPHiwQntqairMzMwU2szNzVFQUID09PTqB14OVeVjYWGBL7/8EqtXr0ZgYCBatWqFMWPG4Ny5czUa/8teJR8XFxesXLkSM2fOhL29PTp37gxBEBAQECDvo0nbT1Xy0aTtx9PTE+Hh4bh8+TIEQcBff/2FsLAw5Ofny7eN6rw/depk3pqkzIVqBUGARCLBypUrYWRkBABYsGABxo0bV+ab9/LjakNN5+Pi4qJwuSkXFxf069cPv/zyC7744gvVJFFJ7FV5Lffv34/CwkJ88MEHlT5e+P9TDGvjParpfFq3bo3WrVvL77u4uODff//FTz/9BDc3t+oHXAll8klMTMSSJUvw6aefwtPTEykpKVi+fDkWLlyI5cuXl/sc6rr9VCUfTdp+Pv30U6SkpGD48OEQBAFmZmbo168fNm7cCB0dnXKfo6rvD0dSSnqVC9VaWFjg9ddfl/+HDgA2NjYAIP9VYHNzc6SmplZ5nTVFVfm8TEdHB/b29ir/JljdCwnv2rUL7777LkxNTRXay3t/6tevX6pvTVJVPmVxcnLCnTt3XjnWqniVfH788Uc4Ojrio48+Qtu2bdG1a1csWrQI+/btw/379wFo1vZTlXxeps7bj76+PpYtW4aLFy/i2LFj+P3332FpaQlDQ0M0atQIQPXeHxYpJb3KhWpdXV3x6NEjhX02JR+2kksyOTs7i3LxW1Xl8zJBEJCQkAALC4uaCbwc1bmQ8OXLl3H16lUMGTKk1LLy3h97e3vo6upWP/ByqCqfssTHx6vl+5OTk1PqG3nJ/ZLRrCZtP1XJ52WasP3o6uqiadOm0NHRQWRkJLp374569YpLTLXen0oPraBSDh48KLRv317YtWuXkJiYKHz99deCs7OzkJycLAiCIMyaNUuYNWuWvH9WVpbQrVs3YcqUKcK1a9eEuLg4wcfHR5gyZYq8z/nz54V27doJGzZsEBITE4UNGzYIdnZ2tXYIbU3nExgYKJw8eVK4e/eucOXKFWHOnDmCnZ2dcOnSJbXLp8S8efOEd999VygqKiq1rOQQ9CVLlgiJiYnCrl27hPbt29faIeg1nc+mTZuEI0eOCLdu3RKuXbsmrFixQpBKpcLhw4fVLp/w8HDBzs5OCA0NFe7evSvExcUJAwYMEPr37y/vo0nbT1Xy0aTt5+bNm8KePXuEW7duCZcuXRKmTZsmdOzYUUhKSpL3qc77w31Sr6CyC9W+PGQ3NDTEpk2bsGTJEgwaNAjGxsbo0aMHZsyYIe/j6uqK7777Dt9//z0CAwPRvHlzrFq1Ck5OThqZT2ZmJhYuXIiUlBQYGRnBzs4Ov/zyCxwdHdUuHwDIyspCZGQkPv300zLnyZs3b46goCAsW7YM27dvR5MmTTB//nx4e3trZD75+fkICAjAw4cPoa+vjzZt2iAoKAhvvfWW2uUzYMAAZGdnIzQ0FAEBATAyMoK7uztmzZol76NJ209V8tGk7aeoqAibN2/GrVu3UL9+fbi7u2P79u2wsrKS96nO+8MLzBIRkdriPikiIlJbLFJERKS2WKSIiEhtsUgREZHaYpEiIiK1xSJFRERqi0WKiIjUFosUERGpLRYpIiJSWyxSRESktlikiIhIbbFIERGR2mKRIiIitcUiRUREaotFioiI1BaLFBERqS0WKdJau3fvhkwmw5kzZ2p83cnJyZDJZAgMDKzxddcGZeNXtv+cOXMgk8mqE2KZAgMDIZPJkJycXOPrrsiZM2cgk8mwe/fuWn1eYpEiKldycjICAwMRHx8vdii1IjMzE4GBgSop6kSvqr7YARCpq3///Rdr1qyBpaUl2rVrp7DM0tISly9fho6OjkjRVU9Z8WdmZmLNmjWYPHky3N3dq7X+r7/+Gl999VV1w1Qbbm5uuHz5MurX53+ZtY0jKVILOTk5KCgoKHd5VlZWLUZTOYlEAj09PY39T0vV8evq6kJPT08l666uV/ks1atXD3p6emr5pUTdto2axiJVDSX7PE6fPo01a9age/fucHR0xODBg3Hx4kUAwNmzZzF8+HA4OzvD09MTa9euLXNdf/31FyZNmgR3d3fY29vD29sb69evL/Uf9+XLlzFnzhx4e3vDyckJLi4uGDZsGI4cOVJqnSX7BZ4+fYpFixahc+fOcHBwwLBhw3Dp0qUq55mVlYVVq1ahV69ecHBwgLu7O4YPH46DBw8q9Lt69ao8BwcHB/Tu3RvBwcEoLCwsM67Hjx9j7ty56NKlC5ydnfHgwQOFfR+RkZEYMGAAHB0dsWTJEvnjHz16hEWLFuHtt9+Gvb09PD09sWDBAqSlpVU5l8GDB8tf6549e2LFihV4/vy5vN/u3bvh6+sLAJg7dy5kMhlkMhlGjRoFoPx9NAUFBQgKCkLv3r3lr9WkSZOQkJCg0O/Fxx8/fhwDBw6Eg4MDPD09ERAQUGHBLjFq1Ch4eXkptB04cAAymQwffPCBQvu2bdsgk8lw+fLlMuM/c+YM3nnnHQDAmjVr5Pm+vH4AVYq3rH1SNfV5BIDnz59jyZIl8PDwkG9zp0+fLtVPJpNhzpw5OH36NIYPHw4XFxd88sknAICHDx/C398fH3zwAdzc3OSf2aCgoFKf2bL2Sb3YFh4eDh8fH9jb26N79+4IDg6uci6hoaEYO3YsunbtKv88z5w5s8z9bhXlAwBPnz7Ft99+i549e8Le3h6dOnXC9OnTkZSUpLCeqm4HACAIAjZv3oy+ffvCxcUFrq6u8Pb2xrx585Cfn1/lPF+VZn4NVDMrVqxAUVERfH19kZ+fj5CQEIwbNw4BAQGYP38+hgwZgr59++LXX3/F6tWrYWVlpfCfyIkTJzBp0iS0bNkSY8eOhYmJCS5evIjVq1cjPj4eq1evlvc9cuQIbt68iffeew+WlpZ48uQJ9uzZg8mTJ2PFihXo27dvqfjGjRuHxo0bY9KkSXjy5Ak2bdqECRMmICoqCg0bNqwwt8zMTIwYMQLXr1+Ht7c3hg8fjqKiIly5cgXHjx+Hj48PgOIiO2rUKNSvXx8jR46Eubk5jh8/jhUrVuDq1atYuXJlqXX7+fnB3Nwcn376KZ49ewYDAwM8e/YMAHD06FFs3boVw4cPx7Bhw+Rx3rt3D0OHDkV+fj4GDRqEFi1a4M6dO9i+fTvOnDmD8PBwGBkZlZvPw4cPERYWhnfffRd9+vRB/fr1cfbsWWzcuBHx8fH46aefABRP70ycOBEbNmzA0KFD0aFDBwCAubl5ha/XzJkz8euvv8LDwwPDhw9HamoqQkNDMWzYMISGhsLOzk6h/4kTJ7Bt2zYMGzYMAwcORFRUFEJCQmBiYoKJEydW+Fzu7u4IDAzE3bt30aJFCwBAbGws6tWrh4SEBDx+/BiNGzeWtzds2BDt27cvc102NjaYO3culi1bhp49e6Jnz54AAENDwxqLt0R1Po8l/vOf/6BevXoYP348srKysHPnTnz00UcIDg5Gly5dFPr+/fffOHz4MIYMGYL+/fvL2xMSEvDbb7+hZ8+eaNGiBfLz8/HHH39g5cqVSE5OxuLFi6sUy44dO5CamopBgwbB2NgY+/fvx4oVK9C0adMyt8eXhYSEwNnZGaNGjYKpqSmuXbuGsLAwxMbG4sCBA2jUqFGV8nn69CmGDRuGe/fuYeDAgbC1tUVKSgq2bduGwYMHIzw8HJaWlgCqvh0AwLp167B69Wp0794dw4YNg46ODpKTk3Hs2DHk5eVBV1e3Sq/TKxPolYWHhwtSqVTo16+fkJubK28/evSoIJVKhXbt2gmXLl2St+fm5goeHh7CkCFD5G05OTlCly5dhBEjRgj5+fkK69+0aZMglUqF2NhYeVt2dnapOJ49eya8++67Qq9evRTa//Of/whSqVRYtGiRQntkZKQglUqF7du3V5rjokWLBKlUKuzYsaPUssLCQvm/hw4dKrRr106Ij4+XtxUVFQmfffaZIJVKhVOnTpWKa8aMGaXWmZSUJEilUsHOzk5ITEwstXzixIlCp06dhPv37yu0X758WWjXrp2wevVqeVvJ+/Pi65ebmyvk5eWVWu+qVasEqVSq8H7FxsYKUqlUCA8PLzfOF58vOjpakEqlwtSpU4WioiJ5e3x8vNCuXTth+PDhpR7v5OQkJCUlKbxmPj4+goeHR6nnfNm5c+cEqVQq7Ny5U97m5eUlzJw5U5BKpcLBgwfl63R3dxc+/vjjCuMvq+1V4y15j8tqq87ncfXq1YJUKhUGDRqksM3dv39fcHZ2Ft577z2F/lKpVJBKpUJMTEypdT1//lzhfSoxc+ZMoW3btsLDhw/lbWV9FkraPDw8hIyMDHn7s2fPBHd3d4XtvCJlbdOnTp0SpFKpEBQUVOV8vv76a8HBwUFhGxQEQUhOThZcXFyE//znP/I2ZbaDfv36lfq/pTZxuq8GDB8+HA0aNJDff/PNNwEATk5OcHR0lLc3aNAADg4OuH37trwtJiYGqampGDBgADIzM/H48WP5rVu3bvI+JQwMDOT/fv78OdLT0/H8+XN06tQJN27cKHN+esyYMQr3O3XqBAC4c+dOhXkVFRUhMjISNjY2GDJkSKnl9eoVf3zS0tJw4cIFeHl5oW3btvLlEolE/u26rOnIcePGlfvcb731FmxsbBTanj59it9//x1eXl5o0KCBwmtlaWmJFi1aKLxWZWnQoIH8m19BQQEyMjLw+PFj+bdvZaedXlSS48SJEyGRSOTtbdu2xdtvv43z58/j8ePHCo955513YGVlJb8vkUjg7u6OlJQUZGdnV/h8Tk5OMDAwQGxsLIDiAz2Sk5PRp08fSKVSeXtCQgLS09Pl73t1VCfeEq/6eXx5HS9ucyWjlps3b+LGjRsKfdu2bVtqdAUA+vr68vcpLy8PT548wePHj+Hp6YmioiL8/fffVYpl4MCBMDY2lt9/7bXX4OzsrLCdV6Rkmy4qKsLTp0/x+PFjyGQyGBkZyadnK8tHEAQcOHAAbm5uaNKkicK2URJPdHS0vL8y20HDhg3x8OFDxMXFVSmfmsbpvhrQvHlzhfsmJiYAoLAxv7jsyZMn8vslG9S8efPKXX9qaqr832lpafj+++8RFRVV5j6YzMzMUlMmL8dXMn3wYhxlSU9PR0ZGBrp27arwn+7LSubO27RpU2qZjY0N6tWrV2pOHACsra3LXWdZy27duoWioiKEhYUhLCyszMe9nGtZQkNDsWPHDiQmJqKoqEhhWUZGRqWPL09ycjLq1atXqrgCgK2tLaKiopCcnCyfgisvXlNTUwDF78/L020v0tXVhaurq/yQ8dOnT6N+/fp488034e7ujpMnTwKAvFjVRJGqTrzlraOqn8cXlfUal7QlJSUpLC/vc1ay/3Dfvn24c+cOBEFQWJ6ZmVmlWMrazk1NTaucz+nTp7Fu3TpcunQJubm5CsvK+jyWlc/jx4/x5MkTREdHo3PnzmU+T8mXyhJV3Q6mT5+OSZMmYeTIkWjSpAk6duyIt99+G97e3gpfFFSFRaoGvPzml6jKkUAlG8bs2bNLHeZcokmTJvK+Y8eOxY0bNzBq1Cg4ODjAyMgIOjo6CA8PR0RERKkPW0VxvLxRKrtc2X4ve+2115RaVvI877//vsJc/IsqO6Js06ZN8Pf3h6enJ3x9fdGkSRPo6uri4cOHmDNnzivn8mJ8yqjoM1KV9XXq1AnR0dG4fv06YmNj4eDgAENDQ3Tq1Albt27FvXv3EBsbi0aNGtXIybXVjbeidVTnta/o8eV9zvz9/bF161b07t0bEydOROPGjaGrq4t//vlHvp+5KqpzxN/ly5cxbtw4tGjRAjNmzICVlZV8hPf555+XmVNF20aXLl0wfvz4Sp9Xme3AxcUFR44cQXR0NM6cOYMzZ84gIiIC69evx7Zt2+RfUlSFRUpkJd+KXnvttTKnJF6UkJAgP4Lus88+U1j23//+t8Zja9y4MUxMTHD16tUK+5V8M05MTCy17ObNmygqKqrSCKcyLVq0gEQiQX5+fqWvVXn27dsHS0tLBAcHK3y5KBl1vKii0WN58UVHR+PGjRsK057A/0bMZX3rro6S0dHp06cRGxuLQYMGASg+qEJHRwcxMTGIi4uDh4dHpfkom6+YynqNb968CaBqo2mg+LPg5uaGVatWKbQrM+1YXRERESgsLERwcLBC3M+ePavySA4o3laNjY2RlZVVpW1Dme0AKD6AxtvbG97e3gCKR2GLFy9GWFgYPvrooyrH+Sq4T0pknp6eMDMzQ3BwcJnTAzk5OfL9TCUfppe/XV27dq3MfT7VVa9ePfj4+CAxMbHMIlgSh5mZGVxcXHD8+HFcu3ZNYXlQUBAAyI8Wq45GjRrhrbfewpEjR+SH+L8cz8v7fF5Wr149SCQShdewoKCgzEOGS/YVVHUKsEePHgCAoKAghfVfu3YNx44dQ4cOHRSm+mpC+/btYWJigh07diAlJUVetIyMjGBnZ4fNmzfj6dOnVZrqUzZfMW3evBl5eXny+w8ePMCBAwfQqlWrMqcCy1KvXr1S29KzZ8+wefPmmgy1QuWNwn788ccqj+SA4lz69u2Ly5cv49ChQ2X2eXH3gDLbQVnbVMlRorXxWeFISmQGBgYICAjApEmT8N5772HgwIFo2bIlMjMzcfPmTRw5cgRr1qyBu7s7bGxsYGtri40bNyInJwetWrXCrVu3sHPnTkilUvzzzz81Ht+0adMQGxuLL774AjExMejQoQMEQUB8fDwKCgrw7bffAgDmz5+PUaNGYeTIkRgxYgQsLCxw/PhxREdHo0+fPuXOkyvryy+/xIgRI/Dhhx/igw8+gJ2dHYqKipCUlISoqCj069cPU6ZMKffx7733HlauXInx48ejZ8+eyMrKQkRERJkntbZp0waGhobYtm0b9PX1YWxsjMaNG5ebi4eHB3r16oWDBw8iIyMD3bt3lx8CrKenhy+++KJGXoMX1atXD2+++SaioqKgp6cHV1dX+bJOnTrJ/9OpSpFq1KgRWrZsiYMHD6J58+YwNzfHa6+9Vua5UmIrLCzEyJEj4ePjg+zsbOzYsQO5ublKvcbe3t7YuXMnpk2bhi5duiA1NRXh4eEqn756UY8ePbB582aMHz8eQ4cOha6uLmJiYpCQkFDq0PPKfP755/jzzz8xbdo09OrVC05OTtDV1cW9e/dw8uRJtG/fHv7+/gCU2w569+4NZ2dnODo6okmTJkhJScGuXbugq6srPwVFlVik1EDXrl0RFhaGoKAg7N+/H+np6TA2NkaLFi0wZswY+b4EHR0d/PjjjwgICMCePXvw/Plz2NraIiAgAFevXlVJkTIxMcHOnTuxYcMGHDlyBEePHoWhoSFsbGzw4Ycfyvs5ODhgx44dWL16NbZv345nz56hefPmmDlzJsaOHVtj8TRr1gzh4eEIDg7GsWPHsH//fujp6aFZs2bo3r07evXqVeHjx40bB0EQEBYWhm+++QYWFhbo1asXBg4ciN69eyv01dfXx6pVq/D9999j6dKlyMvLQ8eOHSssuCtWrICdnR327NkDf39/GBgYwM3NDVOnTlXJBVcBoHPnzoiKioKLi4vCjuzOnTsjODgYr7/+Olq1alWlda1YsQJLly7FqlWr8Pz5c1haWqplkQoICMCOHTsQHByMzMxMyGQy+Pv7w8PDo8rrmDt3LgwNDXHo0CFERUWhWbNmGDp0KBwcHEodgagqHTp0QGBgINatW4cffvgBenp66NKlC3755ReF7asqjIyMsH37doSEhMhz0tHRQdOmTdGhQwcMHjxY3leZ7WDs2LE4ceIEtm7diqdPn8LMzAxOTk74+OOPS025qoJEqO7eSiIiIhXhPikiIlJbLFJERKS2WKSIiEhtsUgREZHaYpEiIiK1xSJFRERqi0WKiIjUFk/mJVJTSUlJCAoKwrlz53D//n00aNAAFhYWcHBwQP/+/bF3717s2bOnSuuaPHlyhVfiIFJXLFJEaujFXzru168f2rRpg5ycHNy+fRvHjx+HoaEhhg4dWurqF7Nnz0br1q1L/Uquqq52QaRqvOIEkRqaxE9UjgAAAaRJREFUOHEijh8/jr1795b6CZeioiKkpKTg9ddfL/U4mUyGjh07YuvWrbUVKpFKcZ8UkRq6ffs2TE1Ny/yNsXr16pVZoIi0EYsUkRpq0aIFnjx5gt9++03sUIhExX1SRGrok08+walTpzBlyhRYW1vD1dUVDg4O8p9sIaoruE+KSE0lJCRg06ZNOHnypMIP1nXo0AEBAQFl/gIt90mRtmGRItIA//77L86dO4f//ve/iIuLg1QqRXh4uMLvRwEsUqR9uE+KSANYWlqiX79++OWXX+Dq6opr167h8uXLYodFpHIsUkQaRCKRwMnJCQDw6NEjkaMhUj0WKSI1FBMTg4KCglLtOTk5iImJAQAeQEF1Ao/uI1JDy5Ytw5MnT+Dl5QWpVAp9fX08ePAABw4cwO3bt9GvXz9eRYLqBBYpIjU0Z84cREVF4fz58zh8+DCePn0KIyMjSKVSjB8/HgMGDBA7RKJawaP7iIhIbXGfFBERqS0WKSIiUlssUkREpLZYpIiISG2xSBERkdpikSIiIrXFIkVERGqLRYqIiNQWixQREamt/wOHI3BBu0T6vwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(STcorr['mean'])\n",
    "plt.xlabel(\"mean correlation within brain areas\\nST\")\n",
    "plt.ylabel(\"counts\")\n",
    "#caudoputamen\n",
    "plt.vlines(x=CPSTmeancorr, ymin=0, ymax=115, linestyles='--', colors='orange')\n",
    "plt.text(CPSTmeancorr+0.0001, 115, \"CP\", fontsize=16)\n",
    "#mean\n",
    "plt.vlines(x=STcorr['mean'].mean(), ymin=0, ymax=115, linestyles='--')\n",
    "plt.text(STcorr['mean'].mean()+0.001, 115, \"$\\mu = %.3f$\" %STcorr['mean'].mean(), fontsize=16)\n",
    "\n",
    "plt.savefig(\"051220_14.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAExCAYAAABoL0v0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3deVyN6f8/8Nep0IQslfFRSDiF9mSrDBnTEB9hmAyyTcZYhrENM2OZGGRk5iNDUyZkN8iSxt7YEiLMjGxZY0ZFe2Q51+8Pv87XcU6rTset1/Px8Hjouq9zX+/r3t7nvq/7PrdMCCFAREREkqWn6wCIiIjo9TCZExERSRyTORERkcQxmRMREUkckzkREZHEMZmXA2tra1hbW+s6DCIiqqSYzImIiCSOyZyIiEjimMyJiIgkjsmciIhI4pjMiYiIJI7JnIiISOKYzImIiCSOyZyIiEjimMyJiIgkjsmciIhI4gx0HQARkTZYTtutk3ZvLvDWSbtUufHMnIiISOKYzImIiCSOyZyIiEjiOGZORFqjq3FrosqGZ+ZEREQSx2ROREQkcUzmREREEvfWJPPTp09j1KhR8PDwgLW1NbZt26YyXQiB4OBguLu7w97eHoMHD8bVq1dV6mRmZmLKlClwcXGBi4sLpkyZgqysrIrsBhERUam9Nck8Ly8Pcrkc33zzDQwNDdWmh4WFITw8HDNmzMCWLVtQt25dDBs2DDk5Oco6kyZNwsWLFxEWFoYVK1bg4sWLmDp1akV2g4iIqNTemmT+3nvvYeLEifjwww+hp6faLSEEIiIiMHLkSHh5eUEulyMwMBC5ubmIiooCACQlJeHo0aMICAiAs7MznJyc8N133yEmJgbXr1/XRZeIiIhK5K1J5kVJTk5Gamoq3NzclGWGhoZwdXVFQkICACAhIQFGRkZwdnZW1nFxcYGRkZGyDhER0ZuoUiTz1NRUAICpqalKuYmJCdLS0gAAaWlpqFu3LmQymXK6TCZD3bp1lXWIiIjeRJUimRd4OVGXdLoQotjPERER6VKlSOZmZmYA/u8MvcCDBw+UZ+umpqZ48OABhBDK6UIIpKenw8TEpOKCJSIiKqVKkcwtLCxgZmaG2NhYZVl+fj7i4+Ph5OQEAHByckJeXp7K+HhCQgLy8vKUdYiIiN5Eb81vs+fm5uL27dsAAIVCgXv37iExMRG1atVCgwYN4Ofnh5CQEFhZWcHS0hLLly+HkZERevToAQBo2rQpPDw8MGvWLMyZMwdCCMyaNQudO3eGlZWVLrtGRERUJJl4+bqyhJ08eRJ+fn5q5b1798aCBQsghMDSpUuxadMmZGZmwsHBATNnzoRcLlfWzcjIwNy5c3Ho0CEAgKenJ2bOnAljY+Mi27a2tgYAXL58uRx7RCR9lfFFKzcXeOs6BKqE3ppkrktM5kSaMZkTVYxKMWZORET0NmMyJyIikjgmcyIiIoljMiciIpI4JnMiIiKJYzInIiKSOCZzIiIiiWMyJyIikjgmcyIiIoljMiciIpI4JnMiIiKJYzInIiKSOCZzIiIiiWMyJyIikjgmcyIiIoljMiciIpI4JnMiIiKJYzInIiKSOCZzIiIiiWMyJyIikjgmcyIiIoljMiciIpI4JnMiIiKJYzInIiKSOCZzIiIiiWMyJyIikjgmcyIiIoljMiciIpI4A10HUJGeP3+O4OBg7Ny5E6mpqTAzM0PPnj0xbtw4GBi8WBRCCCxduhSbNm1CVlYWHBwcMHPmTDRv3lzH0ROVneW03boOgYi0qFKdmYeFhWH9+vX49ttv8fvvv+Obb77B+vXr8csvv6jUCQ8Px4wZM7BlyxbUrVsXw4YNQ05Ojg4jJyIiKlylSuYJCQno3LkzPD09YWFhgS5dusDT0xMXLlwA8OKsPCIiAiNHjoSXlxfkcjkCAwORm5uLqKgoHUdPRESkWaVK5i4uLjh58iSSkpIAANeuXUNcXBw6duwIAEhOTkZqairc3NyUnzE0NISrqysSEhJ0EjMREVFxKtWYub+/P3Jzc+Ht7Q19fX08e/YMo0aNwsCBAwEAqampAABTU1OVz5mYmCAlJaXC4yUiIiqJSpXMo6OjsX37dgQFBaFZs2ZITEzEvHnzYGFhgX79+inryWQyHUZJRERUOpUqmS9cuBDDhw+Ht7c3AMDa2hr37t1DaGgo+vXrBzMzMwAvztD/85//KD/34MEDtbN1IiKiN0WlGjN//Pgx9PX1Vcr09fWhUCgAABYWFjAzM0NsbKxyen5+PuLj4+Hk5FShsRIREZVUpToz79y5M0JDQ2FhYaG8zL5y5Ur4+PgAeHF53c/PDyEhIbCysoKlpSWWL18OIyMj9OjRQ8fRExERaVapkvm3336L//3vf/juu+/w4MEDmJmZoX///hgzZoyyjr+/P/Lz8xEQEIDMzEw4ODggPDwcNWrU0GHkREREhZMJIYSug5A6a2trAMDly5d1HAmRZvwFuIpzc4G3rkOgSqhSjZkTERG9jZjMiYiIJI7JnIiISOKYzImIiCSOyZyIiEjimMyJiIgkjsmciIhI4pjMiYiIJI7JnIiISOKYzImIiCSOyZyIiEjimMyJiIgkjsmciIhI4io8mT958qSimyQiInqraSWZHz58GMHBwSpl69atg7OzMxwdHTFp0iQ8ffpUG00TERFVOlpJ5r/++iuuX7+u/DspKQnz5s1DvXr10KFDB0RHR2PdunXaaJqIiKjS0Uoyv379OmxtbZV/R0dHo1q1atiyZQtWrFiB7t27Y/v27dpomoiIqNLRSjLPzMxEnTp1lH/HxsaiXbt2qFGjBgCgTZs2SE5O1kbTRERElY5WknmdOnVw7949AEBOTg7+/PNPuLi4KKc/e/YMz58/10bTRERElY6BNmbq6OiIjRs3olmzZjhy5AieP3+O9957Tzn91q1bqFevnjaaJiIiqnS0cmb+xRdfQKFQYMKECdi2bRt8fHzQrFkzAIAQAgcOHICzs7M2miYiIqp0tHJm3qxZM0RHR+Ps2bOoWbMmXF1dldOysrIwZMgQtG3bVhtNExERVTpaOTM/ffo0FAoFPD09VRI5ANSqVQs9e/ZEdna2NpomIiKqdLSSzP38/HD8+PFCp8fFxcHPz08bTRMREVU6WknmQogipz9//hx6evxZeCIiovKgtYwqk8kKnZaQkKDyHDoRERGVXbndALd69WpEREQo/543bx5+/PFHtXpZWVnIyclB3759y6tpIiKiSq3ckrmxsTEaNGgAALh79y5q164NExMTlToymQzNmzeHo6MjhgwZUl5NExERVWrllsx79+6N3r17AwA8PT0xadIkdOnSpbxmX25SUlIQFBSEw4cPIzc3Fw0bNsTs2bPRpk0bAC/G+5cuXYpNmzYhKysLDg4OmDlzJpo3b67jyImIiDTTynPmhw4d0sZsX1tWVhYGDBgAFxcXhIaGok6dOkhOTla5ghAWFobw8HAsWLAATZo0wc8//4xhw4Zhz549yt+WJyIiepNoJZm/7NGjR8jIyNB4h3vBZfmKsmLFCpiZmWHhwoXKsoYNGyr/L4RAREQERo4cCS8vLwBAYGAg2rdvj6ioKPj6+lZovERERCWhlWSuUCiwYsUKrFmzBmlpaYXWS0xM1EbzhTpw4AA8PDwwYcIEnDx5EvXq1UO/fv0wcOBAyGQyJCcnIzU1FW5ubsrPGBoawtXVFQkJCUzmRET0RtJKMl+0aBHCw8PRvHlzeHl5oXbt2tpoptTu3LmD9evXY+jQoRg5ciQSExMxd+5cAMCgQYOQmpoKADA1NVX5nImJCVJSUio8XiIiopLQSjLfuXMnPDw8EBYWpo3Zl5kQAra2tpg0aRIAoGXLlrh16xbWrVuHQYMGKesV9Yw8ERHRm0YrPxqTlZX1Rt7JbmZmhqZNm6qUWVlZ4Z9//lFOB6A8Qy/w4MEDtbN1IiKiN4VWkrlcLldLiG8CZ2dn3LhxQ6Xs5s2byhvxLCwsYGZmhtjYWOX0/Px8xMfHw8nJqUJjJSIiKimtJPOxY8di48aNyjPeN8WQIUNw/vx5LF++HLdu3cLvv/+ONWvWYODAgQBeXF738/NDaGgo9u3bhytXrmDatGkwMjJCjx49dBw9ERGRZloZM//rr7/QoEEDdO/eHV27doWFhYXai1VkMhnGjBmjjeYLZW9vj59//hmLFy/GsmXL0KBBA4wfPx6ffPKJso6/vz/y8/MREBCAzMxMODg4IDw8nM+YExHRG0sminvFWRnY2NgU37BMVuGPpmmLtbU1AODy5cs6joRIM8tpu3UdQqVxc4G3rkOgSkgrZ+YHDx7UxmyJiIhIA60kc3Nzc23MloiIiDTQ2vvMiYiIqGJo5cx8+vTpxdaRyWSYN2+eNponItIZXd2fwLH6yk0ryTwyMrLYOkzmRERE5UMryfzSpUtqZc+fP8edO3cQHh6OK1euYMWKFdpomoiIqNKpsDFzfX19WFpaIiAgALVr18YPP/xQUU0TERG91XRyA5yHhwf27duni6aJiIjeOjpJ5hkZGcjLy9NF00RERG8drYyZFyYrKwuxsbFYvXo1WrVqVZFNExERvbW0ksxtbGwKfSe4EAK1atXCtGnTtNE0ERFRpaOVZO7j46MxmdeuXRuWlpbw9vbmi0uIiIjKiVaS+YIFC7QxWyIiItKAP+dKREQkcVq7AU6hUCAyMhL79+9HcnIyAMDCwgIffPABfHx81N5vTkRERGWjlWT++PFj+Pv7Iz4+HjKZDGZmZgCAI0eO4PDhw9i+fTvCwsJQrVo1bTRPRERUqWjl9Hj58uU4ffo0hg0bhhMnTuDw4cM4fPgw4uLiMHz4cJw6dQrLly/XRtNERESVjlaSeXR0NLp164apU6eiVq1aynJjY2NMmTIF3bp1w+7dunmzEBER0dtGK8n833//RZs2bQqd7urqin///VcbTRMREVU6WknmxsbGuHXrVqHTb9++DWNjY200TUREVOloJZl36NAB69evx9GjR9WmHTt2DBs2bIC7u7s2miYiIqp0tHI3+4QJE3Ds2DGMHDkSLVq0QPPmzQEAV69eRWJiIurUqYMvvvhCG00TERFVOlpJ5ubm5ti6dSuCgoIQExODixcvAgCqV68Ob29vTJw4EQ0aNNBG00RERJWO1n40pkGDBggKCoIQAg8fPoQQAiYmJoW+gIWIiIjKRuuvQJXJZDAxMdF2M0RERJWWVm6AW7duHYYOHVro9OHDh2Pjxo3aaJqIiKjS0Uoy37ZtGxo3blzodEtLS2zdulUbTRMREVU6Wknmt27dglwuL3R6s2bNinwOnYiIiEpOK8n82bNnePLkSaHTnzx5gvz8fG00XWIhISGwtrZGQECAskwIgeDgYLi7u8Pe3h6DBw/G1atXdRglERFR8bSSzC0tLXH8+PFCpx87dgyNGjXSRtMlcu7cOWzevBnW1tYq5WFhYQgPD8eMGTOwZcsW1K1bF8OGDUNOTo6OIiUiIiqeVu5m9/b2xuLFi/HTTz9h9OjRqFq1KgDg6dOnWL58OY4fP44JEyZoo+liZWdnY/Lkyfj++++xbNkyZbkQAhERERg5ciS8vLwAAIGBgWjfvj2ioqLg6+urk3jp7WE5jS8XIiLt0EoyHzp0KI4cOYKQkBBs2LABVlZWkMlkSEpKQmZmJlq3bo1hw4Zpo+lizZgxA15eXmjfvr1KMk9OTkZqairc3NyUZYaGhnB1dUVCQgKTORERvbG0ksyrVKmC8PBwrFq1ClFRUUhMTATw4vL7yJEj4efnhypVqmij6SJt3rwZt2/fxsKFC9WmpaamAgBMTU1Vyk1MTJCSklIh8REREZWF1n40pkqVKvD394e/v7+2miiV69evY/HixVi3bp3ysr8m/IU6IiKSGq3/Atyb4ty5c0hPT0fPnj2VZc+fP8fp06exceNGREVFAXhxhv6f//xHWefBgwdqZ+tERERvkkqTzN9//33Y2tqqlE2fPh2Wlpb47LPP0KRJE5iZmSE2Nhb29vYAgPz8fMTHx2Pq1Km6CJmIiKhEKk0yNzY2hrGxsUqZkZERatWqpfyBGz8/P4SEhMDKygqWlpZYvnw5jIyM0KNHD12ETEREVCKVJpmXhL+/P/Lz8xEQEIDMzEw4ODggPDwcNWrU0HVoREREhZIJIYSug5C6gh+fuXz5so4joTcZnzMnbbq5wFvXIZAOaeUX4IiIiKjiMJkTERFJHJM5ERGRxDGZExERSRyTORERkcQxmRMREUkckzkREZHEMZkTERFJHJM5ERGRxDGZExERSRyTORERkcQxmRMREUkckzkREZHEMZkTERFJHJM5ERGRxDGZExERSRyTORERkcQxmRMREUkckzkREZHEMZkTERFJHJM5ERGRxDGZExERSRyTORERkcQxmRMREUkckzkREZHEMZkTERFJnIGuAyCqaJbTdus6BCKicsUzcyIiIomrVGfmv/zyC/bt24cbN26gatWqcHR0xMSJEyGXy5V1hBBYunQpNm3ahKysLDg4OGDmzJlo3ry5DiMnIiqaLq843VzgrbO26YVKdWZ+6tQpfPLJJ9i4cSNWr14NfX19DBs2DBkZGco6YWFhCA8Px4wZM7BlyxbUrVsXw4YNQ05Ojg4jJyIiKlylSua//vor+vbtC7lcDmtrayxcuBAPHz7E2bNnAbw4K4+IiMDIkSPh5eUFuVyOwMBA5ObmIioqSsfRExERaVapkvmrcnNzoVAoYGxsDABITk5Gamoq3NzclHUMDQ3h6uqKhIQEXYVJRERUpEqdzL///nu0aNECTk5OAIDU1FQAgKmpqUo9ExMTpKWlVXh8REREJVGpboB72fz583HmzBls2LAB+vr6KtNkMpmOoiIiIiq9SnlmPm/ePOzevRurV69Gw4YNleVmZmYA/u8MvcCDBw/UztaJiIjeFJUumc+dOxdRUVFYvXo1mjZtqjLNwsICZmZmiI2NVZbl5+cjPj5eeSmeiIjoTVOpLrN/99132LFjB37++WcYGxsrz8CNjIxQvXp1yGQy+Pn5ISQkBFZWVrC0tMTy5cthZGSEHj166Dh6IiIizSpVMl+/fj0AYOjQoSrlY8eOxbhx4wAA/v7+yM/PR0BAADIzM+Hg4IDw8HDUqFGjosMlIiIqEZkQQug6CKmztrYGAFy+fFnHkVBJ8LfZicoXfwFO9yrdmDkREdHbhsmciIhI4pjMiYiIJI7JnIiISOKYzImIiCSuUj2aRm8O3lFORFR+eGZOREQkcUzmREREEsdkTkREJHFM5kRERBLHZE5ERCRxTOZEREQSx2ROREQkcUzmREREEsdkTkREJHFM5kRERBLHZE5ERCRxTOZEREQSxxetVHJ84QkRkfTxzJyIiEjimMyJiIgkjsmciIhI4pjMiYiIJI7JnIiISOKYzImIiCSOyZyIiEji+Jz5G4LPexMRUVnxzJyIiEjimMw1WLduHTw9PWFnZ4c+ffogPj5e1yEREREVisn8FdHR0Zg3bx5GjRqF7du3w8nJCf7+/rh3756uQyMiItKIyfwVK1euRO/evdG/f380bdoUM2bMgJmZGTZs2KDr0IiIiDTiDXAvefLkCf7++28MHz5cpdzNzQ0JCQnFft7a2rrMbVcr8yeJiHTLOnLia33+8uXL5RRJ5cUz85ekp6fj+fPnMDU1VSk3MTFBamqqjqIiIiIqGs/MNZDJZCUqK8BvlUREpEs8M39JnTp1oK+vr3YW/uDBA7WzdSIiojcFk/lLqlatilatWiE2NlalPDY2Fk5OTjqKioiIqGi8zP6KYcOGYerUqbC3t4ezszM2bNiAlJQU+Pr66jo0IiIijZjMX9G9e3ekp6dj+fLlSElJgVwuR2hoKMzNzXUdGhERkUYyIYTQdRBERERUdhwzJyIikjgmcyIiIoljMtey0r605cmTJ/jf//4HT09P2NraolOnToiIiFCps3fvXnTv3h22trbo3r079u/fr80uqCjv/mzbtg3W1tZq//Lz87XdFQCl68+0adM0xuro6KhS79SpU+jTpw/s7OzQpUuXCv0p4PLuz8mTJzXWSUpKqojulHp727VrF3r16gUHBwe4ublh8uTJao+aSmn/Ka4/Utp/Cup369YN9vb28PLywvbt29Xq6HL9SJogrdm9e7do2bKl2LRpk7h27ZoICAgQjo6O4u7du4V+ZuzYsaJv377i2LFj4s6dO+LcuXMiLi5OOf3s2bOiRYsWYtmyZeLatWti2bJlokWLFuLcuXOS7M/WrVuFg4ODSElJUflXEUrbn6ysLLU4u3TpIqZNm6asc/v2beHg4CACAgLEtWvXxKZNm0TLli3Fnj17JNmfuLg4IZfLxdWrV1XqPXv27I3rT3x8vLCxsRErV64Ut2/fFgkJCcLHx0f4+fkp60hp/ylJf6S0/6xbt044OjqKXbt2idu3b4uoqCjh6OgoDh48qKyjy/UjdUzmWvTRRx+Jb775RqWsa9euYtGiRRrrHz16VDg7O4sHDx4UOs/x48eLoUOHqpQNGTJEfPnll68fcDG00Z+tW7cKR0fHco2zpErbn1fFx8cLuVwuzpw5oyxbuHCh6Nq1q0q9r7/+WvTv3//1Ay6GNvpTkMyLWofaUtr+rFixQnTq1EmlbMuWLSrbl5T2n5L0R0r7z8cffyy+//57lbL58+cLX19f5d+6XD9Sx8vsWlLw0hY3NzeV8qJe2nLgwAHY2dlh1apV6NixIz744APMnTsXubm5yjrnzp1Tm6e7u3uJXgTzOrTVHwB4/PgxOnfujI4dO+Kzzz7DxYsXtdaPAmXpz6t+++03NG/eHM7OzsqywtbPX3/9hadPn75+4IXQVn8KfPTRR3B3d8eQIUMQFxdXLjEXpSz9cXZ2RmpqKg4dOgQhBB4+fIjo6Gh07NhRWUdK+09J+gNIZ/958uQJqlVTfaVUtWrV8Oeffyr3DV2tn7cBk7mWlOWlLXfu3MGZM2dw6dIlBAcHY8aMGTh69CimT5+urJOWlqY2T1NTU62/CEZb/WnSpAnmzZuHZcuWYfHixahWrRoGDBiAmzdvarM7r/1SnezsbOzZswf9+vVTKU9LS4OJiYlKmampKZ49e4b09PTXD7wQ2uqPmZkZZs+ejSVLliA4OBhNmjTB0KFDcfr06XKN/1Vl6Y+TkxOCgoIwefJk2Nraon379hBCIDAwUFlHSvtPSfojpf3H3d0dW7duxYULFyCEwJ9//oktW7bg6dOnyn1DV+vnbcAfjdGy0ry0RQgBmUyGoKAg1KxZEwAwY8YMjBgxQuNG/urnKkJ598fJyUnlp3KdnJzg4+ODtWvX4ttvv9VOJ4qJvSTLcufOnXj+/Dl69epV7OfF//8ph4pYR+XdHysrK1hZWSn/dnJywt27d/Hrr7/C1dX19QMuRmn6c+3aNcydOxejR4+Gu7s7UlNTsXDhQsycORMLFy4stI03df8pSX+ktP+MHj0aqampGDBgAIQQMDExgY+PD1asWAF9ff1C26jI9SNlPDPXkrK8tMXMzAzvvvuuMvEBQNOmTQEA9+7dA/DiW2paWlqJ51letNWfV+nr68PW1lbrZxav+1KdzZs344MPPkDt2rVVygtbPwYGBmp1y5O2+qOJg4MDbt26VeZYS6Is/fnll19gb2+PTz/9FDY2NvDw8MCsWbOwY8cO/PPPPwCktf+UpD+vepP3H0NDQ8yfPx/nzp3DoUOH8Mcff8Dc3BzVq1dHnTp1AOhu/bwNmMy1pCwvbXF2dkZKSorKmHLBTlnwc7KOjo46eRGMtvrzKiEELl++DDMzs/IJvBCv81KdCxcu4NKlS+jfv7/atMLWj62tLapUqfL6gRdCW/3RJDEx8Y1cP48fP1Y7wyv4u+DqiJT2n5L051VS2H+qVKmC+vXrQ19fH9HR0ejcuTP09F6kIl2tn7dCxd9zV3ns3r1btGrVSmzevFlcu3ZNzJkzRzg6Oork5GQhhBBTpkwRU6ZMUdbPyckRHTt2FOPGjRNXrlwR8fHxwtvbW4wbN05Z58yZM6JFixYiJCREXLt2TYSEhIiWLVtW2KM15d2f4OBgceTIEXH79m1x8eJFMW3aNNGyZUtx/vz5N64/Bb7++mvxwQcfCIVCoTat4NG0uXPnimvXronNmzeLVq1aVdijaeXdn5UrV4r9+/eLGzduiCtXrohFixYJuVwu9u7d+8b1Z+vWraJly5Zi3bp14vbt2yI+Pl706dNH9O7dW1lHSvtPSfojpf3n+vXrIjIyUty4cUOcP39eTJgwQbRp00bcuXNHWUeX60fqOGauRcW9tOXVS2XVq1fHypUrMXfuXHz00UcwNjbG+++/j0mTJinrODs7Y/Hixfjpp58QHByMhg0b4scff4SDg4Mk+5OVlYWZM2ciNTUVNWvWRMuWLbF27VrY29u/cf0BgJycHERHR2P06NEax/EaNmyI0NBQzJ8/Hxs2bEC9evXwzTffwMvLS5L9efr0KQIDA3H//n0YGhqiWbNmCA0NxXvvvffG9adPnz7Izc3FunXrEBgYiJo1a6Jt27aYMmWKso6U9p+S9EdK+49CocCqVatw48YNGBgYoG3bttiwYQMsLCyUdXS5fqSOL1ohIiKSOI6ZExERSRyTORERkcQxmRMREUkckzkREZHEMZkTERFJHJM5ERGRxDGZExERSRyTORERkcQxmRMREUkckzkREZHEMZkTERFJHJM5ERGRxDGZExERSRyTORERkcQxmRMREUkckzkREZHEMZmX0rZt22BtbY2TJ09WyvZ15eTJk7C2tsa2bdt0HUqJKRQKBAcHo0uXLmjZsiWsra11HZLWeHp6YvDgwVqZ97Rp0yS97Eobf2nqJycnw9raGsHBwWUNr1DW1taYNm1auc+3OIMHD4anp2eFtyt1BroOgCpecHAwWrRogffff1/XobzVIiMjsXTpUvTt2xeurq7Q0+N358Js27YNWVlZGDp0qK5DqRAHDhxAYmIixo0bp+tQ6C3Bo0sltHTpUhw4cEDXYbz1YmNjUbNmTXz//ffo3bs3evXqpeuQ3liRkZGIiIjQOG3OnDm4cOFCBUdUfjTFf+DAASxduvS1521ubo4LFy7g888/f+15vSl+/fVX7OhPH40AABsYSURBVNmzR9dhSA7PzIm0JDU1FcbGxpDJZLoORUVOTg5q1Kihcdrjx49hYGAAA4M359BQpUoVXYfwWrQZv0wmQ7Vq1bQ2/9fx9OlTKBSKUsdXtWpVLUX0esran4pS7Jl5wRjtiRMnsHTpUnTu3Bn29vbo168fzp07BwA4deoUBgwYAEdHR7i7u+Pnn3/WOK8///wTY8aMQdu2bWFrawsvLy8sX74cz549U6l34cIFTJs2DV5eXnBwcICTkxN8fX2xf/9+tXkWjC9lZ2dj1qxZaN++Pezs7ODr64vz58+XaCHcv38fCxYsQK9eveDq6go7Ozt0794doaGheP78ucbPPH/+HMHBwejcuTNsbW3Rs2dP7N69W63e2bNn8emnn8LNzQ12dnbw8PCAv7+/ctkVSE5OxpQpU9ChQwfY2tri/fffx+LFi/Ho0aNi4w8ODoa1tTWSk5PVpr08llkwvga8OBOytrZW/ntZbGwshg8fjtatW8POzg49e/bEhg0bio0jKysLdnZ2GDt2rMbpQUFBsLa2RmJiIoCyLfeXFXX/QGHjbiXdBovy22+/oXfv3rC3t4eLiwuGDx+O+Ph45fSC8f2TJ0/i7t27ymVckvHHuLg4jBw5Em3btoWdnR26dOmCr7/+Gg8fPlTWefbsGUJDQ9G9e3fY2dmhbdu2GDNmDC5fvqwyr5fHU6Ojo9GnTx/Y29tj7ty5AP5v33n48CGmT5+ODh06wNHREf/++69yHtHR0RgwYACcnJzg4OCAfv36lfis6dixY5gwYQK6dOkCe3t7tG7dGsOHD8epU6dU6nl6euLUqVMqy+rl9VrYGPKlS5eU67Jg2wkLC1Pbdl73GFHY/RpeXl6wtrZWu8rl5uYGf39/tfYLDB48GJGRkQCg0t9X51+SeDWNmb9cFhMTg759+8LOzg7u7u4IDAws1bYOvDge9O/fHw4ODnBzc8PcuXORl5enUqfgGHT16lXMnz8fHTt2hL29vfI4Fx0djVGjRqFTp06wtbVF27ZtMXr0aFy6dEmtPU37bkHZ/fv3MXHiRLi6usLR0REjRozAjRs3StSPpKQkzJ49G97e3srtuU+fPti8ebNa3eL6U7BcSnKcLOl+AABXr17FF198AQ8PD9ja2sLNzQ2DBw/GH3/8UWz/Svz1e9GiRVAoFPDz88PTp08RHh6OESNGIDAwEN988w369++Pnj174vfff8eSJUtgYWGhclnx8OHDGDNmDBo3bozhw4ejVq1aOHfuHJYsWYLExEQsWbJEWXf//v24fv06PvzwQ5ibmyMjIwORkZEYO3YsFi1ahJ49e6rFN2LECNStWxdjxoxBRkYGVq5ciZEjR+LgwYOFnoUUuHz5Mvbt24euXbuiUaNGePr0KY4ePYqgoCAkJycjICBA4/LIy8vDgAEDALxILBMnTkR+fj769OkDALh+/TqGDx8OU1NT+Pn5wcTEBGlpaTh79iwuXboER0dHAMDdu3fRr18/ZGdnY8CAAbC0tMSpU6fwyy+/4OzZs1i1alW5nCnVrVsXCxcuxNSpU9G6dWv0799frc6mTZswa9YsODo6YtSoUXjnnXcQGxuL2bNn4/bt2/jqq68Knb+xsTE8PT1x8OBBZGRkoHbt2sppCoUCu3btgrW1NVq0aAGgbMv9dZRmGyzMDz/8gBUrVsDe3h4TJ05ETk4ONm/ejCFDhmDZsmV477330LRpUyxcuBAhISFIT0/H9OnTAQCNGjUqct4bN27E7Nmz8e6778LX1xfm5ua4d+8eYmJicP/+fdStWxcAMHnyZPz+++9wc3PDgAEDkJaWhnXr1sHX1xfr1q1Dy5YtVeZ74MABrFmzBgMGDICvr6/a/jBs2DCYmppi9OjRyMvLg5GREQDgxx9/REhICDw8PDB+/Hjo6elh//79GD9+PGbOnImBAwcW2Z/IyEhkZmbCx8cH9evXx/379/Hbb79h6NChiIiIQOvWrQEAX3/9NYKCglSWFQA0bdq00Hn/+eefGDx4MAwMDDBw4ECYmpoiJiYGixYtwqVLlxAUFKT2mbIeI5ycnGBoaIgTJ04o9+379+/j5s2b0NPTw4kTJ5T3n1y9ehVpaWlo165dofMbNWoUFAoF4uPjsXDhQmW5s7NzucRb4PDhw1i/fj18fX3Rt29fHDx4EOHh4ahVqxZGjRpV7OcB4O+//8bevXvRr18/9OrVCydPnsSaNWtw9epVrFy5Uu0+kMmTJ8PQ0BDDhw8HAJiZmQEA1q5di1q1aqF///4wMzPD7du3sXnzZgwYMACRkZGwtLQsNpa8vDwMGjQIDg4O+PLLL5GcnIyIiAiMHj0aUVFR0NfXL/Lzp06dQnx8PDp16gQLCws8evQIe/bswYwZM5Ceno7PPvtM7TOF9ac0x8mS7gfp6ekYMmQIAMDX1xcNGjRAeno6/vrrL5w/fx6dOnUqegGJYmzdulXI5XLh4+Mj8vPzleUHDhwQcrlctGjRQpw/f15Znp+fL9zc3ET//v2VZY8fPxYdOnQQn3zyiXj69KnK/FeuXCnkcrmIi4tTluXm5qrFkZeXJz744APRrVs3lfKvvvpKyOVyMWvWLJXy6OhoIZfLxYYNG4rronj06JFQKBRq5ZMnTxY2Njbi/v37yrKC5dGpUyeRlZWlLM/KyhKdOnUSrq6u4tGjR0IIIVavXi3kcrnK8tFk4sSJQi6Xiz/++EOlfMGCBUIul4vNmzertf/y8lqyZImQy+Xizp07avPu3LmzGDRokEqZXC4XX331lVrd+/fvC1tbWzFx4kS1aXPmzBE2Njbi1q1bRfYlJiZGyOVysXbtWpXy2NhYIZfLRXh4uLKsNMs9Li5OyOVysXXrVmWZpmVRYNCgQaJz587Kv0u7DWqSlJQkrK2tha+vr8q+8O+//woXFxfRuXNn8ezZs0JjKMo///wjWrVqJbp16yYyMzPVpj9//lwIIcSxY8eEXC4X48ePV1l2iYmJokWLFmLAgAHKsjt37gi5XC5atmwprl27pjbPgn1n0qRJatP++usvIZfLRVBQkNq0zz//XDg5OYns7GxlmabtTNN+nJqaKtq0aSM+/fRTlfKillVBnC/7+OOPRYsWLURiYqKyTKFQiC+++ELI5XIRGxur9vnXOUYMGTJEeHh4KP+OjIwUNjY2YuLEiSrHpIiICCGXy8Vff/1VZPyaysoSb8E6XrJkiVqZg4ODyjFBoVAIb29v4ebmVmx/hXhxnJDL5WL//v0q5XPmzBFyuVxERUUpywqOQYMGDVLbv4TQvC1cu3ZNtGrVSq2fmraFQYMGCblcLkJDQ1XKw8LChFwuF0eOHCm2P5pieP78uRg0aJBwdnYWT548KVF/SnucLOl+UJBTd+/eXWxfNCnxDXADBgxQGcso+Dbh4OAAe3t7ZXnVqlVhZ2eHmzdvKsuOHz+OtLQ09OnTB1lZWXj48KHyX8eOHZV1ChScGQDAo0ePkJ6ejkePHqFdu3ZISkpCTk6OWnyv3gVb8M341q1bxfbN0NBQOa755MkTZGRk4OHDh3B3d4dCocBff/2lcXnUrFlT+XfNmjXh6+uLzMxM5eXBgukHDx5Efn6+xrYVCgUOHTqEli1b4r333lOZ9tlnn0FPT6/Cblbbu3cvnjx5go8++khlHT18+BCenp5QKBQ4ceJEkfNwd3eHqakpduzYoVK+fft26Ovrq1xVKctyL6vSboOaHDx4EEIIfPrppyr7wrvvvovevXvj7t27uHjxYpni27NnD54+fYqxY8fC2NhYbXrBGVDBUNOoUaNUxuJtbGzQqVMnnDlzRuWSPADl1YLCjBgxQq1s165dkMlk8PHx0bgt5Obmqg0Vverl/Tg3Nxfp6enQ09ODg4PDa93Q9uDBAyQkJMDT0xM2NjbKcplMpjzj1DQk9zrHiHbt2uH+/fu4fv06gBfDIa1atcIHH3yApKQkpKSkAHhxSb5WrVrKq0+v43XiBYAuXbrAwsJC+bdMJkPbtm2RmpqK3NzcEs2jSZMmak+9jBw5EoDmZTxkyBCNVxELtgUhBHJycvDw4UPUqVMHTZo0KfG2oKenBz8/P5Wy0iyTl7fH/Px8pKenIyMjA25ubsjJyVGu2+L6U9rjZEn3g4J8cfToUY05rjglvnbbsGFDlb9r1aoFACoby8vTMjIylH8nJSUBeHE5rTBpaWnK/z948AA//fQTDh48iAcPHqjVzcrKUrvM9Gp8derUAQCVOApTMAa5Y8cO3Lp1C0IItfZeZWVlpVZWcMAsGLv29vbGzp07ERISglWrVsHBwQHu7u7w9vaGubk5AODhw4fIy8tDs2bN1OZXu3ZtmJmZ4c6dO8X2oTwUrKeiHg96eT1pYmBggB49emDVqlW4ceMGmjRpgry8POzbtw8eHh4wNTVV1i3Lci+r0m6DmhSs1+bNm6tNk8vlAIA7d+7Azs6u1PEVfPktLgkkJydDT09PY3Ju3rw5Dh48iOTkZOUleQDFXsLUND0pKQlCCHTr1q3QzxW3vG7fvo0ff/wRx44dU1uXr3NTYMF60LTPNG3aFHp6ehr3mdc5RhQkjbi4OFhZWeHkyZPw9vZG27ZtIZPJEBcXhx49euD06dNo06ZNuTyG+Drxavo8AOXQV0ZGBqpXr17sPDRtZ/Xq1YOxsbHGZVzYtnbx4kX873//w6lTp9TG2zXlEE3q1aundvPZy/0pTm5uLpYuXYrff/8d//zzj9p0TcebwvYNoOTHyZLuB23atIGPjw+2bduGXbt2wdbWFh06dED37t01buuvKnEyL2zjLG6cAoDyID116tRCD1b16tVT1h0+fDiSkpIwePBg2NnZoWbNmtDX18fWrVsRFRUFhUJR4jheTRCaLFiwAGvWrEH37t0xatQo1K1bF1WqVMHff/+tvFfgVSU5GFWtWhUrV67EhQsXcPToUcTHx2PJkiVYunQpgoKC0LVr1xLFV5yiYinNzS4FsQQGBirXx6s0HSBe1bt3b6xatQrbt2/Hl19+iX379iEvL0/t0ayyLPeXlabfpdkGC1Me66q4eRe3XZUlhnfeeafU04UQkMlkCAsLK3TfKuoAk5ubi4EDB+LRo0cYMmQI5HI5qlevDj09Pfzyyy+Ii4srXSdeia0sXucYYWdnhxo1aiAuLg5ubm64d+8e2rVrh9q1a8PGxgYnTpxA06ZNkZGRUeR4eUXFW9TnSzOPwrbHwj5vaGioVnbv3j0MHDgQNWrUwOeffw4rKyu88847kMlkmDdvnlpyL8zr9mfSpEn4448/0L9/f7i6uqJWrVowMDDA4cOHsWrVKo3HG039Kc1xsrT7QWBgIEaMGIHDhw/jzJkzWLlyJUJCQvD1119j0KBBRfavQp4/Kfh2884776BDhw5F1r18+bLyLtUvvvhCZdpvv/2mlfh27NgBV1dX/PjjjyrlRV26SUpKQpcuXdTKAPVvmvb29sqhiH/++Qc+Pj746aef0LVrV5iYmKB69eq4du2aWhuZmZlITU0t9myt4CpJZmamStv5+flITU1F48aNi/x8gYL1VKdOnWLXU1FsbGxgY2ODXbt2YcKECdixYweMjY3VlldZlvvLXu73q5KTk1UeCSrNNliYghvYrl69qnYzW8H6K8mXHU2aNGkC4MUZTFFn0o0aNcKxY8eQlJSkcokZKHz7KwtLS0scPXoUDRo0KPISfWFOnDiBlJQUzJs3D3379lWZ9tNPP71WbAXLWNM+c/36dSgUijKvh8Lo6+ujdevWOHnyJGJjY1GlShW4uLgAANq3b489e/Yor9iUJJm/aY8rFkbTMk5JSUF2dnaJl/H+/fuRl5eH5cuXqy2bjIyMCnkULSsrC3/88Qd69eqldmNtbGxsqeZVmuNkWfYDuVwOuVwOf39/ZGVloV+/fggKCsLAgQOL3G4q5Edj3N3dYWJigrCwMI2XQx4/fqwcIyi4AvDqN60rV65oHKMpD3p6emrt5eXlYdWqVYV+ZsOGDcjOzlb+nZ2djY0bN8LY2Bht2rQBALWxSwCoX78+6tatq0xAenp66Ny5My5evIgjR46o1A0NDYVCoSj2l9oKNq5XN8rCvm0aGRlpXA/dunVD1apVERwcjMePH6tNz87OxpMnT4qMpUDBGPKuXbsQFxeHbt26qV0iK8tyf1lh/Y6KilKOYRYozTZYGE9PT8hkMvz66694+vSpsjwlJQXbtm2Dubm52p3kJfXhhx+iSpUq+PnnnzXGUbCcCraF0NBQlWV35coVHDp0CC4uLiqX2Mvqv//9LwBg8eLFGh8T1DT89bKCs6hX1++xY8c0Pg5WvXp1ZGZmlugMy8TEBE5OToiJicGVK1eU5UIIhIaGAgC6du1a7HxKq127dsjIyMCaNWvg6OiovKLRrl073Lt3D1u3boWZmVmJLokWjKOW9JK5rty4cUPtnp2wsDAAKPEvSBa2LWzevBmpqanlEGXxCssrKSkppT5JLM1xsjT7QUZGhtrx2tjYWHnnfWH3XRWokDNzIyMjBAYGYsyYMfjwww/Rt29fNG7cGFlZWbh+/Tr279+PpUuXom3btmjatCmaN2+OFStW4PHjx2jSpAlu3LiBTZs2QS6X4++//y73+Ly8vLBp0yZMmDABHTp0QFpaGrZu3aryaNWr6tSpg379+qFv374QQmDbtm24d+8e5s6dq9zJly9fjuPHjysfhRBCICYmBtevX8enn36qnNfEiRMRGxuLMWPG4JNPPkGjRo0QHx+P6OhouLq6onfv3kXG36FDB1hZWWHJkiXIyMiAhYUFzpw5g/PnzyvH2V7m6OiIEydOIDQ0FA0aNIBMJoO3tzfq16+P2bNn49tvv0X37t3x3//+F+bm5nj48CGuXLmCAwcOYPfu3SU68+vZsyd++OEHfPfdd1AoFBr7UJbl/jIrKyt06NABmzZtghACLVq0QGJiIg4cOIDGjRurXGovzTZYVHsjRozAihUrMGjQIHTr1g25ubnYvHkz8vLysGjRohINO2lSv359fP311wgICEDPnj3Rq1cvmJub4/79+zh48CDmzZuHFi1awM3NDd26dcPu3buRmZmJzp07IzU1FevXr0e1atXw7bfflqn9V9nb22PcuHEIDg6Gj48PvLy88O677yIlJQV///03jhw5UuQNii4uLjAzM0NgYCDu3r2L+vXrIzExETt27IBcLldJwsCLG2ljYmIQEBAAJycn6Ovro127djAxMdE4/2+++QaDBw/GwIED8cknn8DMzAwxMTE4duwYevTogfbt25fLcnhZwVllUlISunfvrixv3bo1DAwMcO3aNfTo0aNE83JwcMDatWvx3Xff4b333kOVKlVgb29f7lcUXpdcLseUKVPQr18/NG7cGCdPnsTevXvRpk0blWVQlI4dO+Kdd97B1KlTMWjQIBgbG+Ps2bM4cuQIGjVqVKLflHhdNWrUgJubG3bu3AlDQ0PY2dnh7t272LRpEywsLEr1pao0x8nS7Afbt2/H6tWr8f7776Nx48YwMDDA6dOncezYMXTr1k3jJf+XVdjPPHl4eGDLli0IDQ3Fzp07kZ6eDmNjYzRq1AhDhw5V/qiCvr4+fvnlFwQGBiIyMhKPHj1C8+bNERgYiEuXLmklmU+fPh3Vq1fHnj17cPDgQfznP//Bxx9/DDs7u0Jvcpg8eTLi4+Oxbt06pKWlwdLSUu0Z+Pfffx+pqanYs2cP0tLSYGhoiMaNG2Pu3Ln46KOPlPXMzc2xefNmLFmyBDt37kR2djbeffddfPbZZ/j888+LfcZcX18fy5Ytw9y5c7F27VpUqVIFbm5uWLt2rfI5+JfNmjULAQEBCAkJUd7V6u3tDQDo27cvLC0tER4ejk2bNiE7Oxu1a9dGkyZNMH78eOVzlsUxMTGBh4cHYmJiYGlpCScnJ7U6ZVnur1q4cCHmzJmDXbt2YefOnXBxcUFERARmz56Nu3fvqtQt6TZYlClTpqBx48ZYv349goKCUKVKFTg4OCAoKEj5hEdZFXyR+/XXX7FmzRo8efIE9erVQ/v27VG/fn1lvUWLFqFly5aIjIzEggULYGRkBFdXV4wfP75cX0gyduxY2NraYs2aNYiIiEBeXh5MTEzQvHnzIm8kBF6cUaxYsQI//PAD1q5di2fPnsHW1hZhYWHYsmWLWjIfMmQI7ty5g71792Ljxo1QKBSIiIgoNJnb2dlh48aNWLJkCTZs2IC8vDw0bNgQkydPVj4TXN5sbGxQp04dpKenq1wurl69Ouzs7JCQkFDi8fIePXogMTERu3fvxp49e6BQKDB//vw3Lpm3atUK06dPx48//oiNGzeiRo0aGDRoEL788ssS3+TXqFEjhIWFYfHixQgJCYG+vj6cnZ2xZs0azJkzR20/1ZYffvgBQUFBOHTokPLZ9i+//BIGBgYqv29QEiU9TpZmP2jbti0SExPxxx9/IDU1FXp6erCwsMBXX31V7Hg5AMiENu/qISIiIq3ji1aIiIgkjsmciIhI4pjMiYiIJI7JnIiISOKYzImIiCSOyZyIiEjiKuw5cyLSjjt37iA0NBSnT5/GP//8g6pVq8LMzAx2dnbo3bs3tm/fjsjIyBLNa+zYsRg3bpyWIyai8sZkTiRhf/75JwYPHgwDAwP4+PigWbNmePz4MW7evImYmBhUr14dH3/8sdovok2dOhVWVlbKV4YWKM8fnSGiisMfjSGSsFGjRiEmJgbbt29XeyGPQqFAamoq3n33XbXPWVtbo02bNlizZk1FhUpEWsQxcyIJu3nzJmrXrq3xzXp6enoaEzkRvX2YzIkkrFGjRsjIyMC+fft0HQoR6RDHzIkk7PPPP0dsbCzGjRsHS0tLODs7w87OTvkGQiKqHDhmTiRxly9fxsqVK3HkyBGV94y7uLggMDBQ45u4OGZO9HZhMid6i9y9exenT5/Gb7/9hvj4eMjlcmzduhVVq1ZVqcdkTvR24Zg50VvE3NwcPj4+WLt2LZydnXHlyhVcuHBB12ERkZYxmRO9hWQyGRwcHAAAKSkpOo6GiLSNyZxIwo4fP45nz56plT9+/BjHjx8HAN4IR1QJ8G52IgmbP38+MjIy4OnpCblcDkNDQ/z777/YtWsXbt68CR8fH/6qG1ElwGROJGHTpk3DwYMHcebMGezduxfZ2dmoWbMm5HI5/P390adPH12HSEQVgHezExERSRzHzImIiCSOyZyIiEjimMyJiIgkjsmciIhI4pjMiYiIJI7JnIiISOKYzImIiCSOyZyIiEjimMyJiIgkjsmciIhI4v4freJmB+mw+wgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(STcorr['absmean'])\n",
    "plt.xlabel(\"mean absolute value of correlation within brain areas\\nST\")\n",
    "plt.ylabel(\"counts\")\n",
    "#plt.savefig(\"050420_10.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/slu/miniconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py:447: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "/home/slu/miniconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py:447: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#combine ST and ABA means w/o absolute value\n",
    "sns.kdeplot(ABAcorr['mean'],label='ABA ISH')\n",
    "sns.kdeplot(STcorr['mean'], label='ST')\n",
    "plt.xlabel(\"mean correlation within brain areas\")\n",
    "plt.ylabel(\"density\")\n",
    "\n",
    "#caudoputamen\n",
    "plt.vlines(x=CPABAmeancorr, ymin=0, ymax=8, linestyles='--',colors='C0')\n",
    "#plt.text(CPABAmeancorr+0.0001, 8, \"CP\", fontsize=16)\n",
    "plt.vlines(x=CPSTmeancorr, ymin=0, ymax=8, linestyles='--',colors='C1')\n",
    "#plt.text(CPSTmeancorr+0.0001, 8, \"CP\", fontsize=16)\n",
    "\n",
    "plt.savefig(\"051220_15.pdf\",bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This doesn't really make sense that the sample correlation is higher in ST, but the performance is higher in ABA. However it is at least a bit convincing that the CP has a lower sample correlation than the mean in both datasets and is the worst performing area in ABA. Although I expected it to be on the much lower in ABA ISH at least.\\\n",
    "\\\n",
    "Follow-up Q's:\n",
    " - is it actually appropriate to average correlation coefficients? do i have to do fisher's z transform first (arctanh)?\n",
    " - spearman's rho?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Gene variance within a brain region (distribution across all brain regions), average for all genes__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getgenevar(data, propont):\n",
    "    #initialize zeros dataframe to store entries\n",
    "    allbyall_var = pd.Series(index=list(propont))\n",
    "    \n",
    "    #try z-scoring whole dataset\n",
    "    #data = zscore(data)\n",
    "    \n",
    "    areas = list(propont)\n",
    "    #for each column, brain area\n",
    "    for i in range(propont.shape[1]):\n",
    "        #print(\"col %d\" %i)\n",
    "        area1 = areas[i]\n",
    "\n",
    "        #subset train and test sets for only samples in curr area\n",
    "        Xcurr = data.loc[propont[area1] != 0, :]\n",
    "        #try ranking in sample space\n",
    "        #Xcurr = Xcurr.T.apply(lambda x: sp.stats.mstats.rankdata(x),axis=0)\n",
    "        Xcurr = zscore(Xcurr.T) #try z-scoring on samples\n",
    "            \n",
    "        #calculate within brain area gene variance and average for all genes\n",
    "        meangenevar = np.var(Xcurr, axis=1).mean()\n",
    "            \n",
    "        allbyall_var.iloc[i] = meangenevar\n",
    "         \n",
    "    return allbyall_var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABAgenevar = getgenevar(leafABAvox, leafABApropont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "STgenevar = getgenevar(leafSTspots, leafSTpropont)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.31381490674137413\n",
      "0.22612127041550717\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(ABAgenevar))\n",
    "print(np.mean(STgenevar))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ABAgenevar)\n",
    "plt.xlabel(\"mean gene variance per brain areas\\nABA\")\n",
    "plt.ylabel(\"counts\")\n",
    "#plt.savefig(\"050420_13.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(STgenevar)\n",
    "plt.xlabel(\"mean gene variance per brain areas\\nST\")\n",
    "plt.ylabel(\"counts\")\n",
    "#plt.savefig(\"050420_14.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ABAgenevar,label=\"ABA ISH\")\n",
    "sns.distplot(STgenevar,label=\"ST\")\n",
    "plt.xlabel(\"mean gene variance per brain area\")\n",
    "plt.ylabel(\"density\")\n",
    "#plt.ylim([-0.1,0.05])\n",
    "plt.legend()\n",
    "#plt.savefig(\"051220_17.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ABA ISH had higher gene variance within a brain region than the ST. Again... this is the opposite of what I expected. Since the ABA ISH performs higher than the ST and the ABA ISH has a lower dimensionality, I expected the gene variance within each brain area to be lower in ABA than in ST. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__gene variance for whole dataset__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#try z-scoring in sample space first (genes don't work to calculate variance duh)\n",
    "zleafABAvox = zscore(leafABAvox.T)\n",
    "zleafSTspots = zscore(leafSTspots.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABAvar = np.var(zleafABAvox, axis=0)\n",
    "STvar = np.var(zleafSTspots, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ABAvar,label=\"ABA ISH\")\n",
    "#sns.distplot(STvar,label=\"ST\")\n",
    "plt.xlabel(\"gene variance\")\n",
    "plt.ylabel(\"density\")\n",
    "#plt.ylim([-0.0002, 0.012])\n",
    "plt.legend()\n",
    "plt.savefig(\"080520_9.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__pairwise gene-gene correlations for whole dataset__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pearson's r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABAcorrmatrix = np.corrcoef(leafABAvox, rowvar=False) #cols are genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "STcorrmatrix = np.corrcoef(leafSTspots, rowvar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABAcorrmatrix_upper = ABAcorrmatrix[np.triu_indices_from(ABAcorrmatrix, k=1)]\n",
    "#STcorrmatrix_upper = STcorrmatrix[np.triu_indices_from(STcorrmatrix, k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ABAcorrmatrix_upper)\n",
    "plt.xlabel(\"gene-gene pearson's r\\nABA ISH\")\n",
    "plt.ylabel(\"density\")\n",
    "#plt.savefig(\"050820_1.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "STcorrmatrix_upper = STcorrmatrix_upper[np.isnan(STcorrmatrix_upper) == False]\n",
    "sns.distplot(STcorrmatrix_upper)\n",
    "plt.xlabel(\"gene-gene pearson's r\\nST\")\n",
    "plt.ylabel(\"density\")\n",
    "#plt.savefig(\"051220_1.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.distplot(ABAcorrmatrix_upper,label=\"ABA ISH\")\n",
    "sns.distplot(STcorrmatrix_upper,label=\"ST\")\n",
    "plt.xlabel(\"gene-gene correlation\\nPearson's r\")\n",
    "plt.ylabel(\"density\")\n",
    "#plt.ylim([-0.1,0.05])\n",
    "plt.legend()\n",
    "plt.savefig(\"051220_4.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Spearman's rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rankedleafABAvox = leafABAvox.apply(lambda col: sp.stats.mstats.rankdata(col), axis=0)\n",
    "ABAspearman = np.corrcoef(rankedleafABAvox, rowvar=False) #cols are genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABAspearman_upper = ABAspearman[np.triu_indices_from(ABAspearman, k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5249555366038159"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(ABAspearman_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ABAspearman_upper)\n",
    "plt.xlabel(\"gene-gene Spearman's rho\\nABA ISH\")\n",
    "plt.ylabel(\"density\")\n",
    "#plt.savefig(\"050820_2.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "rankedleafSTspots = leafSTspots.apply(lambda col: sp.stats.mstats.rankdata(col), axis=0)\n",
    "STspearman = np.corrcoef(rankedleafSTspots, rowvar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "STspearman_upper = STspearman[np.triu_indices_from(STspearman, k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04919581105860777"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(STspearman_upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(STspearman_upper)\n",
    "plt.xlabel(\"gene-gene Spearman's rho\\nST\")\n",
    "plt.ylabel(\"density\")\n",
    "plt.savefig(\"051220_2.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(ABAspearman_upper,label=\"ABA ISH\")\n",
    "sns.distplot(STspearman_upper,label=\"ST\")\n",
    "plt.xlabel(\"gene-gene correlation\\nSpearman's rho\")\n",
    "plt.ylabel(\"density\")\n",
    "#plt.ylim([-0.1,0.05])\n",
    "plt.legend()\n",
    "#plt.savefig(\"051220_3.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Average difference between a pair of brain regions: is there more DE using ABA data? but is that just because variance is lower rather than higher fold changes? (or effect sizes). Volcano plot like implicaiton of overfitting__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: this is hard to do, becuase the DE takes a long time to look at (limiting factor of CFS), but since I have to re-run that, I'll try to record this info when I get there as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}