37_CVplots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "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": [
    "# ST, areas >150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestparams = pd.read_csv(\"/home/slu/spatial/data/hyperparamCV_lasso/ST_bestparams_032421.csv\", sep=',')\n",
    "bestparams = bestparams.set_index(bestparams.columns)\n",
    "bestscore = pd.read_csv(\"/home/slu/spatial/data/hyperparamCV_lasso/ST_bestscore_032421.csv\")\n",
    "meantestscore = np.load('/home/slu/spatial/data/hyperparamCV_lasso/ST_meantestscore_032421.npy',allow_pickle='TRUE').item()\n",
    "teststd = np.load('/home/slu/spatial/data/hyperparamCV_lasso/ST_meanteststd_032421.npy',allow_pickle='TRUE').item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "     632   629   733     9    26   194   943   504  1093   962  ...  1111  \\\n",
       "632    0  0.01  0.01  0.01  0.01  0.05  0.01  0.01  0.01  0.01  ...  0.01   \n",
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       "9      0  0.00  0.00  0.00  0.01  0.01  0.05  0.01  0.01  0.01  ...  0.01   \n",
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       "\n",
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       "632  0.05  0.05  0.01  0.05  0.05  0.01  0.10  0.01  0.10  \n",
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       "9    0.01  0.01  0.01  0.01  0.01  0.01  0.05  0.01  0.01  \n",
       "26   0.01  0.05  0.01  0.01  0.05  0.05  0.01  0.01  0.01  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestparams.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of brain area\\ncomparisons')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histdata = bestparams.values[np.triu_indices_from(bestparams,k=1)]\n",
    "#uniq, counts = np.unique(histdata, return_counts=True)\n",
    "#plt.bar(x=uniq, height=counts)\n",
    "plt.hist(histdata)\n",
    "plt.xlabel(\"best lambda for ST CV areas>150\")\n",
    "plt.ylabel(\"number of brain area\\ncomparisons\")\n",
    "#plt.savefig('032421_1.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1. , 1. , 1. , 0.5, 0.5])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meantestscore['0,1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "basically a really circuitous way to get number of samples below cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "ST_CANTIN_FILT_PATH = \"/home/slu/spatial/data/cantin_ST_filt_v1.h5\"\n",
    "ONTOLOGY_PATH = \"/data/slu/allen_adult_mouse_ISH/ontologyABA.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def read_data():\n",
    "    \"\"\"read in all datasets needed using pandas\"\"\"\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')\n",
    "\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\n",
    "\n",
    "    return STspotsmeta, STspots, STpropont, ontology\n",
    "\n",
    "def filterproponto(sampleonto):\n",
    "    \"\"\"pre-processing for propogated ontology\"\"\"\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": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pre-processing\n",
    "STspotsmeta, STspots, STpropont, ontology = read_data()\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\n",
    "\n",
    "#subset for sufficient samples size for hyperparameter selection\n",
    "leafsizes = leafSTpropont.sum()\n",
    "#filter propogated ontology for brain areas with X number of samples min\n",
    "leafsizes_sub = leafsizes[leafsizes>150]\n",
    "leafSTpropont_sub = leafSTpropont.loc[:,leafsizes_sub.index]\n",
    "#remove areas (rows) from propogated ontology that don't have any samples\n",
    "rowsum = leafSTpropont_sub.sum(axis=1)\n",
    "leafSTpropont_sub = leafSTpropont_sub.loc[rowsum > 0, :]\n",
    "#index samples for rows to match porp ont\n",
    "leafSTspots_sub = leafSTspots.loc[rowsum > 0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "numsamples = pd.DataFrame(index=list(bestparams), columns=list(bestparams))\n",
    "numsamples = numsamples.fillna(0)\n",
    "\n",
    "areas = list(bestparams)\n",
    "#for each column, brain area\n",
    "for i in range(bestparams.shape[1]):\n",
    "    #for each row in each column\n",
    "    for j in range(i+1,bestparams.shape[1]): #upper triangular!\n",
    "        area1 = areas[i]\n",
    "        area2 = areas[j]\n",
    "        numsamples.iloc[i,j] = leafsizes[area1] + leafsizes[area2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>404.0</td>\n",
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       "      <td>380.0</td>\n",
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       "      <td>501.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>426.0</td>\n",
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       "      <td>420.0</td>\n",
       "      <td>402.0</td>\n",
       "      <td>2254.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>580.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>...</td>\n",
       "      <td>384.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>2218.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>544.0</td>\n",
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       "    <tr>\n",
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       "      <td>761.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>...</td>\n",
       "      <td>381.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>2215.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>445.0</td>\n",
       "      <td>541.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     632    629    733      9     26    194    943    504   1093    962  ...  \\\n",
       "632    0  332.0  354.0  318.0  315.0  616.0  449.0  315.0  748.0  374.0  ...   \n",
       "629    0    0.0  384.0  348.0  345.0  646.0  479.0  345.0  778.0  404.0  ...   \n",
       "733    0    0.0    0.0  370.0  367.0  668.0  501.0  367.0  800.0  426.0  ...   \n",
       "9      0    0.0    0.0    0.0  331.0  632.0  465.0  331.0  764.0  390.0  ...   \n",
       "26     0    0.0    0.0    0.0    0.0  629.0  462.0  328.0  761.0  387.0  ...   \n",
       "\n",
       "      1111    258     672    806   1090    155    262    952    284    276  \n",
       "632  368.0  350.0  2202.0  319.0  327.0  320.0  337.0  340.0  432.0  528.0  \n",
       "629  398.0  380.0  2232.0  349.0  357.0  350.0  367.0  370.0  462.0  558.0  \n",
       "733  420.0  402.0  2254.0  371.0  379.0  372.0  389.0  392.0  484.0  580.0  \n",
       "9    384.0  366.0  2218.0  335.0  343.0  336.0  353.0  356.0  448.0  544.0  \n",
       "26   381.0  363.0  2215.0  332.0  340.0  333.0  350.0  353.0  445.0  541.0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numsamples.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "numsamples = numsamples.values[np.triu_indices_from(numsamples,k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#alpha as a function of brain area size\n",
    "plt.scatter(numsamples,histdata)\n",
    "plt.xlabel(\"total number of samples\\nST\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "#plt.savefig('032421_2.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f878fac3048>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(numsamples, histdata, kind='hex').set_axis_labels(\"total number of samples\\nST\",\"best lambda\")\n",
    "#plt.savefig('032421_3.pdf', bbox_inches='tight')"
   ]
  },
  {
   "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": [
    "#define 'sliding window' as a uniform dist\n",
    "uniform = [1/100] * 100\n",
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "#convolve\n",
    "hyperp_conv = np.convolve(hyperp_sorted, uniform, mode='same')\n",
    "\n",
    "plt.scatter(numsamples_sorted, hyperp_conv, s=3, rasterized=True)\n",
    "plt.xlabel(\"total number of samples\\nST > 150\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig(\"033121_1.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "\n",
    "#bin by equal #samples per bin\n",
    "numbins = 50\n",
    "binsize = len(numsamples_sorted)//numbins\n",
    "print(len(numsamples_sorted)%numbins)\n",
    "mean = []\n",
    "sd = []\n",
    "startindex = 0\n",
    "for i in range(numbins):\n",
    "    if i == numbins-1: #last bin\n",
    "        currint = hyperp_sorted[startindex:len(hyperp_sorted)]\n",
    "    else:\n",
    "        currint = hyperp_sorted[startindex:startindex+binsize]\n",
    "\n",
    "    mean.append(np.mean(currint))\n",
    "    sd.append(np.std(currint))\n",
    "    startindex = startindex+binsize\n",
    "\n",
    "plt.scatter(range(1,numbins+1), mean, rasterized=True)\n",
    "plt.errorbar(x=range(1,numbins+1), y=mean, yerr=sd)\n",
    "plt.xlabel(\"bins of total number of samples\\nST>150\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig(\"033121_2.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looking into ties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "lastofties = pd.DataFrame(index=list(bestparams), columns=list(bestparams))\n",
    "lastofties = lastofties.fillna(0)\n",
    "\n",
    "alphas = [0.01, 0.05, 0.1, 0.2, 0.5, 0.9]\n",
    "#for each column, brain area\n",
    "for i in range(bestparams.shape[1]):\n",
    "    #for each row in each column\n",
    "    for j in range(i+1,bestparams.shape[1]): #upper triangular!\n",
    "        currkey = \"%d,%d\" %(i,j)\n",
    "        currscores = meantestscore[currkey]\n",
    "        bestindex = 0\n",
    "        bestscore = -1\n",
    "        for k in range(len(currscores)):\n",
    "            if currscores[k] >= bestscore:\n",
    "                bestscore = currscores[k]\n",
    "                bestindex = k\n",
    "        lastofties.iloc[i,j] = alphas[bestindex]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "histdata = lastofties.values[np.triu_indices_from(lastofties,k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "\n",
    "#bin by equal #samples per bin\n",
    "numbins = 10\n",
    "binsize = len(numsamples_sorted)//numbins\n",
    "print(len(numsamples_sorted)%numbins)\n",
    "mean = []\n",
    "sd = []\n",
    "startindex = 0\n",
    "for i in range(numbins):\n",
    "    if i == numbins-1: #last bin\n",
    "        currint = hyperp_sorted[startindex:len(hyperp_sorted)]\n",
    "    else:\n",
    "        currint = hyperp_sorted[startindex:startindex+binsize]\n",
    "\n",
    "    mean.append(np.mean(currint))\n",
    "    sd.append(np.std(currint))\n",
    "    startindex = startindex+binsize\n",
    "\n",
    "plt.scatter(range(1,numbins+1), mean)\n",
    "plt.errorbar(x=range(1,numbins+1), y=mean, yerr=sd)\n",
    "plt.xlabel(\"bins of total number of samples\\nST>150\")\n",
    "plt.ylabel(\"best lambda\\n(last of ties)\")\n",
    "plt.savefig(\"040121_1.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ST, areas >100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestparams = pd.read_csv(\"/home/slu/spatial/data/hyperparamCV_lasso/ST_bestparams_032521.csv\", sep=',')\n",
    "bestparams = bestparams.set_index(bestparams.columns)\n",
    "bestscore = pd.read_csv(\"/home/slu/spatial/data/hyperparamCV_lasso/ST_bestscore_032521.csv\")\n",
    "meantestscore = np.load('/home/slu/spatial/data/hyperparamCV_lasso/ST_meantestscore_032521.npy',allow_pickle='TRUE').item()\n",
    "teststd = np.load('/home/slu/spatial/data/hyperparamCV_lasso/ST_meanteststd_032521.npy',allow_pickle='TRUE').item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65, 65)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestparams.head()\n",
    "bestparams.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of brain area\\ncomparisons')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CQkJgYmJSpiC0tLQwffp0TJ8+vUzHExERfYjcV3gODg44ceKEpBvTw8MDN2/eRPv27dG5c2dERUVh/PjxSg2OC8MSEZGyyH2FN3nyZHz55ZfQ0NAAAPTv3x/Vq1fHwYMHUa1aNfTt2xf9+/dXOICzZ8/i1q1bUvfp/Pz8sGbNGrx69Qr9+vXDihUroK6urnDdRERExeROeCKRSJLsivXp0wd9+vQpVwDbtm2Dvr6+5Oe4uDgsW7YMJiYmMDY2xtGjR2FlZSW1bh4REZGi5OrSzM7ORsuWLbFx40alB/DgwQNYWlpKfj569Chq1KiBwMBAbN26Ff3790dISIjSz0tERFWLXAmvdu3a0NbWlroSU5aMjAypUZjh4eHo2LGj5Fm/9u3bS1ZQICIiKiu5B6106NABERERSg9AV1cXiYmJAICsrCzcvn0bbdu2lex//fo1CgsLlX5eIiKqWuROeLNmzcK1a9ewfv16ZGVlKS0AW1tb/Pnnnzh+/DiWLVuGwsJCdOvWTbI/Pj4e9erVU9r5iIioapJ70IqHhwfy8vLg6+sLX19f6OnpoWbNmlJlRCIRwsLCFApg2rRpcHd3x/fffw8AGDp0KJo1awYAEAQBYWFh6NChg0J1EhERvUvuhNewYUOVBNCsWTMcPXoU169fR506dWBvby/Zl5mZifHjxzPhERFRuYmE4jV+6KNhYWEBALh3716Zjjebe0SZ4Sjk0YoBlXZuIqL3kfseHhER0adMoRXPAeD58+c4c+YMnjx5AgAwMTFBjx49UL9+fbmOd3JyQrVq1XDs2DGoq6ujZ8+eHzymLPcGiYiI3qZQwtu4cSN8fX1RWFiIt3tCvb29MXnyZEydOvWDdTRq1AjAmyQGqO7eIBER0dvkTnh79uzBhg0bJNN8NW3aFAAQGxuLnTt3YuPGjahbty7Gjh373np279793p+JiIhUQe5BK87OztDR0cHevXslC8AWKygogJubG16+fIkTJ07IffJXr17h+PHjaNKkCWxsbBSL/DPGQStERMon96CVf//9FwMGDJBJdsCbxV8HDRoktSCsPDQ0NPDjjz8iOjpaoeOIiIgUJXfCMzIyQnZ2dqn7s7OzYWRkpNjJq1WDkZGRUmdukVdERAQmT56MLl26wMLCAsHBwVL7BUHAhg0b4OjoCGtra4wbNw7379+XKpORkYFZs2ahbdu2aNu2LWbNmiWz6vu9e/cwduxYWFtbo0uXLvDx8QGfBCEiqnhyJ7yxY8fC398fSUlJMvueP3+OP//8E+PGjVM4ABcXFxw8eLDCF3vNycmBWCzGggULZGaMAYAtW7Zg+/btWLhwIQIDA6Gnp4cJEyZIJeeZM2ciOjoaW7ZswdatWxEdHY3Zs2dL9mdlZcHT0xP6+voIDAzEjz/+iG3btmHHjh0V0kYiIvo/pQ5aeXdJnjp16kBfXx/9+vXD4MGDYW5uDpFIhNjYWBw6dAhmZmaSFQ4U0aZNG4SGhmLIkCEYPXo0GjdujFq1asmUe3sGFmXo1q2bZM7OefPmSe0TBAF//PEHJk2aBGdnZwDAypUr0alTJxw+fBiurq6Ii4vD+fPnsXfvXrRp0wYAsHjxYowZMwYPHjyAubk5Dh48iNzcXKxcuRI1a9aEWCxGXFwcduzYgQkTJkhGqhIRkeqVmvDmzp0LkUhUYvfbvn37ZLbduXMH8+bNg4uLi0IBTJgwQfL9zz//LJMEBEGASCRCTEyMQvWWR0JCAl68eAEHBwfJtpo1a8Le3h6RkZFwdXVFZGQkNDU1JckOANq2bQtNTU1ERkbC3NwcN27cQLt27aSuIB0dHfHbb78hISEBJiYmFdYmIqKqrtSE98cff1RIAMuXL6+Q8yjixYsXAAADAwOp7fr6+pIu3eTkZOjp6UklaJFIBD09PSQnJ0vKvPtAfnGdycnJTHhERBWo1ITXvn37Cglg6NChFXKesvhQl2NJ+4uvSEsrU3zFzO5MIqKKxbk0S2BoaAjg/670iqWkpEiu0AwMDJCSkiLV5SsIAtLS0iQrwxsYGEiu9t6uA4BKVo8nIqLSKTyXpqokJycjKioKGRkZJd43VPTeYHkYGxvD0NAQ4eHhsLa2BgDk5eXh6tWrklGYdnZ2yMnJQWRkpOQ+XmRkJHJycmBnZwfgzeK2q1evRl5eHmrUqAEACA8PR7169WBsbFxh7SEioo8g4RUVFWHx4sUIDAxEUVFRqeWUnfCys7Px+PFjSQyJiYmIiYmBjo4OGjZsCHd3d2zatAnm5uYwMzODr68vNDU1MXDgQABA06ZN0aVLFyxatAhLly6FIAhYtGgRevToAXNzcwDAoEGDsHHjRsydOxfffPMNHj16hM2bN2Pq1Kns0iQiqmCVvh7eli1bsGbNGgwePBgODg6YM2cOfvjhB9SuXRu7du1CnTp1MGPGDHTq1Emp5718+TLc3d1ltg8dOhQrVqyAIAjw8fGBv78/MjIyYGNjg59++glisVhSNj09Hd7e3jh9+jSANytB/PTTT9DW1paUuXfvHpYsWYJbt25BR0cHrq6u8PLyem/C49RiRETKV+kJb8CAATAyMsLWrVuRlpaGTp06YceOHejUqRNycnIwePBguLq6YuLEiZUZZoViwiMiUr5SB624u7vj4sWLkp9DQkKQkJCg9ACePHmCrl27vgmm2ptwXr9+DQDQ1NTEsGHDsH//fqWfl4iIqpZSE96VK1ekRhjOmzcPkZGRSg+gZs2akgmpNTU1IRKJJCMZgTcjJp89e6b08xIRUdVSasIzNDSUuqJTVc9nw4YNJaunq6urw9TUFOfPn5fsDw8P5xB+IiIqt1JHaXbq1Am+vr6IioqSDMIICAhAeHh4qZWJRCIsW7ZMoQA6duyI0NBQzJkzBwAwZMgQrF+/XjKjydWrV+Hp6alQnURERO8qNeHNmzcPIpEI4eHhSE5OhkgkQkREBCIiIkqtrCwJz9PTEw4ODsjPz4eGhga+/vprpKam4uDBg6hWrRpGjRqFadOmKVQnERHRu+QepdmiRQv88ssvGDRokKpjqvI4SpOISPnknlps6tSpkg9iIiKiT43cM61MnTpV6ufU1FQAgJ6enlICOXr0KMLCwiQDWExMTNCrVy/0799fKfUTEVHVptDUYs+fP8fatWtx6tQpZGdnAwC0tLTQs2dPTJ8+XWYpHHnk5uZiypQpuHTpEgRBgLa2NgRBwO3bt3Hs2DH4+/tLpvUiIiIqK7kTXmJiIkaNGoXk5GS0bNkSzZo1AwDExcUhJCQEFy5cQEBAAIyMjBQKYO3atbh48SLGjRuHSZMmSa1UsHnzZuzevRu//vorFixYoFC9REREb5M74f3222/IzMzE77//jm7dukntO3v2LL799lv89ttvWLFihUIBHDt2DH379pVJaIaGhliwYAGeP3+OY8eOMeEREVG5yD1o5cKFCxg9erRMsgOAbt26wc3NTeqBcXllZWWhQ4cOpe7v2LEjsrKyFK6XiIjobXInvIyMDDRu3LjU/Y0bN0ZmZqbCAVhYWCA+Pr7U/fHx8VIrFBAREZWF3AmvQYMGuHLlSqn7r169igYNGigcwPfff4+AgADJEjtvCwsLw/79+zF9+nSF6yUiInqb3Pfw+vbti61bt8LY2BiTJk1CnTp1ALzpkty8eTOOHTuGSZMmKRzAwYMHYWxsDC8vLzRp0gRNmzaFSCRCbGwsHj58CLFYjIMHD+LgwYOSY8oyowsREVVtcs+0kpubC09PT0RGRkJNTQ316tUDACQlJaGwsBBt2rTB9u3bUbNmTYUCaNGiheJBi0SIiYlR+LhPBWdaISJSPrmv8GrVqoU9e/YgKCgIYWFhSEhIgCAIcHR0RK9evTB06FDJMj+KuHv3rsLHEBERKUqhDKWmpoZRo0Zh1KhRqoqHiIhIJeQetEJERPQpU7wPUgWuX78OPz8/xMfHIz09XWaxWZFIhLCwsEqKjoiIPgeVnvACAgKwaNEiqKuro0mTJgpPTUZERCSPSk94mzZtQsuWLbF161alrbxARET0rkq/h5eSkoLhw4cz2RERkUrJlfDy8/MRERGBR48eKT2Apk2blmlKMiIiIkXIlfCqVasGDw8PnDt3TukBTJ48GXv37sXz58+VXjcREVExue7hVa9eHQYGBjKjJ5WhT58+yM3NRf/+/dGrVy80atQI1apJ52GRSAQvLy+ln5uIiKoOhebSPHbsGMaNGyeTkMrj4cOHWL9+PbKzs/HXX3+VWIYJj4iIykvuhDdy5EhcvnwZEyZMwPjx49G4cWPUqlVLplzDhg0VCmDx4sVITU3FggUL0K5dO2hrayt0PBERkTzkTngDBw6ESCSCIAjvXSZI0Umdb968CU9PT4wbN06h44iIiBQhd8Lz8vKCSCRSegC1a9fmIwlERKRycie8b7/9ViUB9OvXDydPnsSYMWNUUj8RERHwETx47urqiuzsbEyZMgUXL17EkydPkJiYKPNFRERUHgpNLZaVlYWdO3fiwoULSElJwcqVK2FnZ4fU1FTs3bsX/fr1Q9OmTRUKYMCAARCJRIiKisKZM2dKLfc5L/hKRESqJ3fCS01NhZubGxISEmBqaoonT57g1atXAAA9PT2EhITg5cuXmDdvnkIBqOreIBER0dvkTnjr1q1DcnIyAgICYGRkhM6dO0vt79mzJy5evKhwAKq6N0hERPQ2ue/hnTlzBqNHj0br1q1LvCIzMTHBs2fPlBocERGRssh9hZeWlgZTU9NS94tEIuTl5ZUpiKKiIhw4cAChoaFISEgAABgbG6NPnz5wcXFR6swuRERUNcmd8AwNDfHkyZNS98fExJRp8dZXr17hq6++wtWrVyESiWBoaAgAOHfuHM6ePYuQkBBs2bIFNWrUULhuIiKiYnJfOnXt2hWBgYFISkqS2Xfz5k2EhISgZ8+eCgfg6+uLiIgITJgwARcvXsTZs2dx9uxZXLp0CZ6enrhy5Qp8fX0VrpeIiOhtIkHOJRBevHiBYcOGoaioCE5OTggMDMTgwYNRUFCAkydPol69eggODkbdunUVCqB3796wtLTEr7/+WuL+6dOnIyoqCqGhoQrV+ymzsLAAANy7d69Mx5vNPaLMcBTyaMWASjs3EdH7yH2FZ2hoiICAAFhbWyMoKAiCIOCvv/7CsWPH4OjoiL179yqc7ADg2bNnaN++fan77e3tORiGiIjKTaEHz42MjODr64usrCw8ePAAAGBqalqmRFdMW1sb8fHxpe5//PgxV1AgIqJyK9PwRy0tLVhbW8Pa2rpcyQ4AOnfujL179+L8+fMy+/7++2/s27cPjo6O5ToHERGRQld4AHDr1i2EhoZKRmyamJigV69esLGxKVMA33//Pf7++29MmjQJLVu2RPPmzQEA9+/fR0xMDHR1dTFt2rQy1U1ERFRM7oRXWFiIhQsX4sCBA3h3nMvWrVvh4uICb29vqKmpKRRAo0aNEBQUhDVr1uDMmTOIjo4G8GbZoAEDBmDGjBkKLypLRET0LrkTnq+vL4KDg9GrVy9MnDgRzZo1A/DmSmzr1q0ICQlBo0aNMHXqVIWDaNiwIdasWQNBEJCamgpBEKCvr1+pc2xu2LABPj4+UtsMDAxw4cIFAIAgCPDx8YG/vz8yMzNhY2ODn376SXKFCgAZGRnw9vbG6dOnAQBOTk5YuHAh70kSEVUCue/hBQUFwcHBAT4+PrC1tYWWlha0tLRgZ2eHjRs3omPHjggKCipXMCKRCPr6+jAwMPgoJpRu0qQJ/v77b8nXoUOHJPu2bNmC7du3Y+HChQgMDISenh4mTJiArKwsSZmZM2ciOjoaW7ZswdatWxEdHY3Zs2dXRlOIiKo8uRNeSkoKnJycSt3fq1cvpKSkKByAn58fPDw8St3v6emJP//8U+F6laF69eowNDSUfBWvzC4IAv744w9MmjQJzs7OEIvFWLlyJbKzs3H48GEAQFxcHM6fP48lS5agTZs2sLOzw+LFi3HmzBnJCFciIqo4cic8MzMzvHjxotT9SUlJMDMzUziA4OBgNG7c+L3nLe+VY1k9efIEXbp0gZOTE6ZPny4ZqJOQkIAXL17AwcFBUrZmzZqwt7dHZGQkACAyMhKamppo06aNpEzbtm2hqdiJEXgAACAASURBVKkpKUNERBVH7oT39ddfY+/evbh7967MvujoaOzbtw+TJ09WOID4+HiIxeJS9zdr1uy9z+mpirW1NZYvX44tW7bA29sbycnJcHV1RVpamiTxGxgYSB2jr6+P5ORkAEBycjL09PSkumZFIhH09PQkZYiIqOKUOmjl3QEbwJsVDIYPHw4HBweYm5tDJBIhNjYW4eHhsLCwwMOHDxUO4PXr18jPzy91f35+fplXYSiPbt26Sf1sY2ODXr16ISQkRPIIxofuM5a0XxCEj+L+JBFRVaNQwit27tw5nDt3TmpbdHQ0YmJi4OXlpVAAZmZmuHDhAiZMmFDi/r///vu9yxJVlNq1a6NZs2Z49OgRevXqBeDN/KJvrxCRkpIiueozMDBASkqKVIITBAFpaWnQ19ev+AYQEVVxpSa8U6dOVUgAAwYMwNq1a7Fu3TpMmTIFGhoaAICCggL4+vriwoUL+P777ysklvfJy8vDw4cP0aFDBxgbG8PQ0BDh4eGwtraW7L969apkFKadnR1ycnIQGRkpuY8XGRmJnJwc2NnZVVo7iIiqqlITXqNGjSokAA8PD5w7dw6bNm3Cvn37JF2lcXFxyMjIQLt27Uq9+lOllStXokePHjAyMkJqair++9//IicnB0OHDoVIJIK7uzs2bdoEc3NzmJmZwdfXF5qamhg4cCAAoGnTpujSpQsWLVqEpUuXQhAELFq0CD169IC5uXmFt4eIqKpTeGoxZVNXV8f27duxc+dOHD58GDExMQDedHVOmjQJ7u7uUFdXr/C4nj17hhkzZiA9PR26urqwtbVFQECA5B+Br776Cnl5eViyZAkyMjJgY2OD7du3Q0tLS1LH6tWr4e3tDU9PTwBvHjz/6aefKrwtRESkwHp4APD06VMEBATg0aNHSE9Pl5liTCQSYdeuXUoPsqrhenhERMon9xXeqVOn8N133+H169fQ0tLi9FhERPRJkTvhrV69GkZGRvDx8ZFcgRAREX0q5H7w/OnTpxg3bhyTHRERfZLkTnjGxsbvfUCciIjoYyZ3whs/fjz279+PnJwcVcZDRESkEnLfw/viiy+QlZWFgQMHwsXFBY0aNSpxsVcXFxelBkhERKQMcie85ORkhIaGIjExEf/9739LLCMSiZjwiIjooyR3wlu0aBFu374NDw8PtGvXjo8lEBHRJ0XuhHfx4kW4u7tjzpw5qoyHiIhIJeQetKKhofFRrFpARERUFnInvO7duyM8PFyVsRAREamM3Alv7ty5+Pfff+Ht7Y3Hjx/LzKNJRET0MZP7Hl7Hjh0hEolw584d+Pn5lVhGJBIhOjpaacEREREpi9wJz8XFRbJyNxER0adG7oS3YsUKVcZBRESkUnLfwyMiIvqUyX2Fl5iYKFe5hg0bljkYIiIiVZE74Tk5Ocl1Dy8mJqZcAREREamC3AnPy8tLJuG9fv0aT548walTpyAWi9G1a1elB0hERKQMcie8b7/9ttR9T548wRdffAFLS0ulBEVERKRsShm0YmJigi+++ALr169XRnVERERKp7RRmvXr10dcXJyyqiMiIlIqpSW8sLAwLhlEREQfLbnv4fn4+JS4PSMjA5cuXcL9+/cxceJEpQVGRESkTOVOeABgYGCA77//Hl999ZVSgiIiIlI2uRPeqVOnZLaJRCLo6Oigdu3aSg2KiIhI2eROeI0aNVJlHERERCold8IjkofZ3COVct5HKwZUynmJ6NOhUMKLjIzEnj17EB8fj/T0dJlFYEUiEcLCwpQaIBERkTLInfBCQkIwb948VK9eHWZmZjAyMlJlXEREREold8Lz9fVFkyZNsGPHDtSvX1+VMRERESmd3A+eJyYmws3NjcmOiIg+SXInvAYNGiA/P1+VsRAREamM3AnP1dUVhw4dQmFhoSrjISIiUgm57+G1bt0aJ0+exMiRIzF69GgYGxtDTU1Nppy9vb1SAyQiIlIGuROeh4eH5Psff/xRZjFYQRAgEom44jkREX2U5E54y5cvV2UcREREKiV3whs6dKgq4yAiIlIppa2HR0RE9DFjwiMioiqBCY+IiKoEJjwiIqoSmPCIiKhKYMIjIqIqgQmPiIiqBK54XgH8/Pywbds2vHjxAs2bN8f8+fPRrl27yg6LlISrvBN9GniFp2JHjx7FsmXLMHnyZISEhMDOzg5fffUVEhMTKzs0IqIqhQlPxXbs2IGhQ4di1KhRaNq0KRYuXAhDQ0Ps27evskMjIqpS2KWpQvn5+bhz5w48PT2ltjs4OCAyMvKDx1tYWJTpvDXKdNSnzeLAjEo7d2W93pXZZqKP2b1790rczis8FUpLS0NhYSEMDAyktuvr6+PFixeVFBURUdXEK7wK8O5SSqVtK1bafydERFR2vMJTIV1dXaipqclczaWkpMhc9RERkWox4amQhoYGWrdujfDwcKnt4eHhsLOzq6SoiIiqJnZpqtiECRMwe/ZsWFtbo02bNti3bx+SkpLg6upa2aEREVUpTHgq1r9/f6SlpcHX1xdJSUkQi8XYvHkzGjVqVNmhERFVKSJBEITKDoKIiEjVeA+PiIiqBCY8IiKqEpjwPmJ+fn5wcnKClZUVhg0bhqtXr763/JUrVzBs2DBYWVmhZ8+en+z0ZYq0OykpCTNnzkTfvn3RsmVLzJ07twIjVS5F2n3y5El4enqiY8eOsLOzw8iRI3Hq1KkKjFZ5FGn3lStX4Orqig4dOsDa2hp9+/bFtm3bKjBa5VL0b7zY1atX0apVKwwcOFDFEaqGIu2+fPkyLCwsZL7i4uIUP7FAH6UjR44IrVq1Evz9/YXY2FhhyZIlgq2trfD06dMSyz9+/FiwsbERlixZIsTGxgr+/v5Cq1athOPHj1dw5OWjaLufPHkiLF26VAgKChK++OILYc6cORUcsXIo2u6lS5cKv//+u3Dz5k3h0aNHwoYNG4QWLVoIERERFRx5+Sja7tu3bwuHDx8W/vnnH+Hx48dCSEiIYGNjI+zZs6eCIy8/RdteLD09XXBychI8PT2FAQMGVFC0yqNouy9duiSIxWLh/v37QlJSkuTr9evXCp+bCe8jNWLECGHBggVS23r37i2sXr26xPKrVq0SevfuLbVt/vz5wqhRo1QWoyoo2u63TZo06ZNNeOVpd7Hhw4cLy5cvV3ZoKqWMdnt5eQnTp09XdmgqV9a2e3l5CRs2bBDWr1//SSY8RdtdnPBSUlLKfW52aX6EiieddnBwkNr+vkmnb9y4IVPe0dERUVFRKCgoUFmsylSWdn8OlNXu7OxsaGtrKzs8lVFGu6OjoxEZGQl7e3tVhKgyZW27n58fkpOT8c0336g6RJUoz+98xIgRcHR0xPjx43Hp0qUynZ8J7yNUlkmnk5OToa+vL7XNwMAAr1+/RlpamspiVaaqOtm2Mtrt5+eHZ8+eYciQIaoIUSXK0+6uXbvC0tISw4cPh5ubG9zc3FQZqtKVpe337t3Dxo0b8csvv0BNTa0iwlS6srTb0NAQ//nPf7B+/Xps2LABTZo0gYeHByIiIhQ+Px88/4gpOun0u/uE//+I5fuO+Rgp2u7PRVnbfeLECaxatQpr1679JCc0KEu7/fz8kJOTg5s3b2L16tUwNjaGi4uLqkJUGXnbnp+fjxkzZmD27NkwMTGpiNBUSpHfubm5OczNzSU/29nZ4enTp9i2bZvCV/ZMeB+hskw6bWBggOTkZJny1atXR926dVUWqzJV1cm2y9PuEydOYPbs2Vi5ciV69uypyjCVrjztLv7Qt7CwQHJyMnx8fD6phKdo25OSkhAbG4v58+dj/vz5AICioiIIgoBWrVph8+bNcHR0rJDYy0NZf+M2NjY4cuSIwudnl+ZHqCyTTtva2pZY3tLSEurq6iqLVZmq6mTbZW330aNHMWvWLCxfvhx9+/ZVdZhKp6zfd1FREfLz85Udnkop2vb69evj0KFDCAkJkXy5urqicePGCAkJ+WT+PpT1O4+JiYGhoaHC5+cV3kfqQ5NOz549GwCwatUqAICrqyv8/Pzw888/w9XVFdevX8eBAwewZs2aSmtDWSjabuDNmx8AsrKyIBKJEBMTA3V1dTRr1qziG1BGirb7yJEjmD17NmbPng17e3vJf8zq6uqfzBU9oHi7d+/eDWNjYzRp0gQAEBERge3bt2P06NGV04ByUKTt6urqEIvFUsfr6+tDQ0NDZvvHTtHf+c6dO2FsbIxmzZqhoKAABw8eRFhYGDZs2KDwuZnwPlIfmnT633//lSpvYmKCzZs3Y/ny5di3bx/q1auHBQsWwNnZuTLCLzNF2w1ApivrzJkzaNSoEU6fPl0hMSuDou3+888/8fr1ayxbtgzLli2TbG/fvj12795dobGXh6LtLiwsxOrVq/H06VOoqanB1NQUM2fO/OQGrQBle69/DhRtd0FBAVauXInnz5+jZs2aaNasGTZv3oxu3bopfG5OHk1ERFUC7+EREVGVwIRHRERVAhMeERFVCUx4RERUJTDhERFRlcCER0REVQITHhERVQlMeEREVCUw4RERUZXAhEdERFUCEx4REVUJTHhERFQlMOEREVGVwIRHRERVAhMeERFVCUx4RERUJTDhfYaCg4NhYWGBy5cvV3YoctuwYQMsLCyQkJBQqXE4OTlh3LhxH00978rNzYW3tze6d++Oli1bwsnJSennIPpcVa/sAOjzExMTg7CwMAwdOhTGxsaVHc5nZcuWLdi9ezc8PT1hYWEBLS2tCjt3REQEtm3bhn/++QdJSUnQ1tZGgwYN0KZNG4wfPx4mJiZwcnLC06dP5arvjz/+QIcOHd5bJjc3F/7+/jh58iRiY2ORnZ0NHR0dtG7dGv369cPgwYPx8OFDDBw4ED179sR///vfUusKCgrC/PnzMX36dEyePFmhtlc12dnZ2LFjB6KiohAdHY3nz5+jffv22L17d6nHnD17Fr6+vrh79y40NDTQsWNHzJo1CyYmJjJlHzx4gNWrVyMiIgIFBQVo1aoVvv32W3Tq1EmVzWLCI+WLiYmBj48P2rdvz4SnZOHh4RCLxZgzZ06Fnnfv3r1YvHgxTExM4OLiAiMjI6SmpuKff/5BcHAw2rVrBxMTE8yfPx/Z2dmS4x48eIBNmzahd+/e6N27t1SdTZs2fe854+PjMWnSJDx69AidO3fGpEmToKuri5SUFFy8eBHz5s1DbGwsZs+eDRsbG5w9exYpKSnQ19cvsb7g4GCoqalh6NCh5X9BPnNpaWnYsGEDDAwM0Lp1a6SkpLy3/MmTJzFt2jS0aNECs2bNQlZWFnbt2gU3NzcEBQWhfv36krKPHz+Gm5sb1NTUMHHiRGhpaWH//v2YOHEitmzZgs6dO6usXUx4RJ+QFy9eoGHDhkqvNysrq9SrxdevX+PXX39Fw4YNERISIlPu1atXePXqFQCgV69eUvsuX76MTZs2wcLCAkOGDJE7nlevXuHrr79GQkICNmzYgD59+kjtnzRpEm7duoXbt28DAEaMGIGbN2/i4MGDmDBhgkx98fHxuHr1Krp16yb14asKr169QvXq1VG9euV+vKampiI/Px8NGjRQ+Nh69erh7NmzkmPt7OxKLVtQUIClS5fCyMgIfn5+qF27NgCga9euGDZsGHx8fLB06VJJ+TVr1iAzMxPBwcFo2bIlAMDFxQUDBw7E4sWLcfz4cYhEIoVjlgfv4X3GCgsLsWHDBvTo0QOWlpYYNGgQjhw5UmLZ27dvw8vLCx06dIClpSWcnZ3h6+uL169fS5W7f/8+pk2bhi5dusDS0hIODg4YN24c/ve//wF4cy9u3rx5AAB3d3dYWFjAwsICc+fOVTj+58+fY8WKFRgyZAjs7e1hZWWF/v37Y/PmzSgsLJQqW3zf8uLFi/Dx8UGPHj1gbW2NkSNH4saNGwCAK1euwM3NDba2tnB0dMTGjRtLPfedO3fg7u4OOzs7tG/fHnPmzCnxv9x///0X3333Hdq2bYs2bdpg8uTJePz4cYl1Hj16FJMnT0b37t1haWmJDh06YMqUKbh79+4HX4vi9iUkJODKlSuS13XDhg2SMmFhYXB1dYWdnR3s7Ozg6uqKsLAwmbqK7y9GR0fjyy+/RNu2bTF48OBSz52WlobMzExYWVmVmBRr1qyJunXrfrANiti/fz8ePnyICRMmyCS7YtbW1hgzZgwAoH///tDU1ERwcHCJZYu3Dx8+/IPnvnXrFubOnQtnZ2fY2NhIXsvQ0FCZsnPnzoWFhQVSU1Mxb948dO7cGba2tnj27JmkzNGjR+Hm5gY7OzvY2Nhg5MiROH78uExdirw/rl+/jokTJ8LBwQFWVlbo0qULvvrqK8l7HXjzt9qjRw+4u7sjMDAQWVlZH2x7MQ0NDbkTZUREBJKSkjBixAhJsgOAli1bon379jh69CgKCgoAADk5OTh9+jTat28vSXYAULt2bYwYMQKPHj2S/BOjCrzC+4ytXr0aOTk5cHNzA/Dmj37GjBnIy8vDsGHDJOXOnj0LLy8vNG7cGJ6entDR0cGNGzewfv16xMTEYP369QDefPCNHz8eAODq6oqGDRsiLS0NUVFRuHnzJrp3747evXvjxYsX8Pf3x+TJk2Fubg4AMDU1VTj+e/fu4eTJk+jduzdMTU1RUFCA8+fPY82aNUhISMCSJUtKbHNRURHc3d1RUFCA7du348svv8TKlSuxYMECjBo1CoMGDcKxY8ewfv16GBsby1x5PHv2DB4eHujTpw+cnZ0RHR2NoKAgREVFITAwELVq1QIAZGZmYsyYMXj27BlcXV3RtGlTREREwN3dXXLF87Y9e/ZAR0cHo0aNgqGhIR4/foyAgAC4ubnhwIEDMDMzK/W1sLe3x6pVq7B8+XLo6upK7kFZWFgAAPz8/LBkyRKYm5vjm2++AQAcOHAAXl5eWLJkCb744gup+hITEzF+/Hj07dsXffr0QU5OTqnnNjAwgKamJiIiIvDgwQPJ71SVTpw4AQAycZdGS0sLzs7OOHDgAG7dugVra2vJvqKiIoSEhEBPT0+uQT6hoaF48OAB+vbti0aNGiE9PR0HDhzA1KlTsXr1agwaNEjmmAkTJsDAwABTpkxBTk4ONDU1AQC//vorNm3ahC5duuC7775DtWrVEBoaiu+++w4//fSTJGED8r8/Hjx4AE9PTxgYGMDd3R36+vpITk7G9evXcffuXdja2gIAWrVqhenTp+PQoUNYsGABlixZgh49emDw4MHo2rUr1NXV5XptP6Q4QZV0FWhra4tLly7h0aNHaN68Oe7du4f8/HxJjO+WLa7v7d+fUgn02QkKChLEYrHQvXt3ITMzU7I9MzNT6N69u2Bvby/k5uYKgiAIr169Ejp37iyMHj1aKCgokKpnx44dglgsFi5duiQIgiCEhYUJYrFYOHLkiFznLz5OHuvXrxfEYrHw5MkTybbc3FyhqKhIpuwPP/wgtGjRQnj+/LnMOV1cXIS8vDzJ9uKYW7ZsKdy8eVOyPS8vT3BwcBBGjRolVXePHj0EsVgs7NixQ2p78Wvx+++/S7atWbNGEIvFQmBgoFRZb29vQSwWC2PHjpXanp2dLdOW2NhYoXXr1sKiRYtKeFVk9ejRQ6be9PR0wdbWVujVq5fw8uVLyfaXL18KPXv2FGxtbYWMjAyZNgYEBMh1TkEQhG3btklex+HDhwtLly4V/vrrLyEpKem9x126dEkQi8XC+vXr5T6XIAhC+/btBTs7O4WOiYiIEMRiscxree7cOUEsFgvLli2Tq56Sfk85OTlCnz59hH79+kltnzNnjiAWi4WZM2fKHBMVFSWIxWJhzZo1Mvu++eYbwc7OTur3Je/7Y9euXYJYLJZ6P3/I3bt3hV9++UXo3r27IBaLhfbt2wuLFi0Srl27Jtfxtra2Mu+7YkuWLBHEYrEQGxsrs2/Pnj2CWCwWzp8/LwiCIBw/flwQi8WCn5+fTNn79++X+nopC7s0P2Nubm6oU6eO5Oc6derA1dUVGRkZkkcWLly4gOTkZAwbNgyZmZlITU2VfHXt2lVSpvh4ADh//rxC3SNlVbNmTUlffn5+PtLT05GamgpHR0cUFRUhKipK5hg3NzdoaGhIfm7Xrh0AwMbGRuq/Rg0NDVhZWeHRo0cydWhpaWH06NFS20aPHg0tLS2pbq2wsDAYGBjAxcVFquxXX31VYnuK/+sXBAFZWVlITU2Frq4umjRpglu3br3vpXivCxcuICcnB+PGjZPqctTS0sLYsWORk5OD8PBwqWPq1q0rdZX/IZ6envD19YWDgwPi4uKwe/duzJo1C926dcP8+fORm5tb5vhL8r57iqVp164dzMzMcOTIEeTl5Um2K9KdCfzf7wl4M0o0LS0Nubm56NixI+Li4kp873/55Zcy2w4dOgSRSAQXFxepv6vU1FQ4OTkhOztbqgtS3vdH8d/hqVOnpNr5PhYWFvjhhx9w+vRp+Pn5wdnZGceOHYObmxt69eol6cUpi+Lf/dt/d8Vq1KgBAJIeD3nKKvu99DZ2aX7GSup6Kh4ZV/y8W1xcHABg/vz5pdaTnJwMAGjfvj1cXFwQHByMQ4cOwdLSEp07d0b//v3RrFkzZYeP169fY/Pmzfjrr78QHx8PQRCk9mdmZsoc8+4QaB0dHQAocbSojo4O0tPTS6zj3T9IDQ0NmJiY4MmTJ5JtT548gZWVFdTU1KTK1qtXD9ra2jL1RkdH47fffsOVK1dkuhDLM5q1+HfZvHlzmX1isVgS69tMTExk4v4QJycnODk5obCwELGxsbh48SL++OMPBAUFoXr16iV2MZeVlpaW1GhPeQ0fPhxr1qxBaGgoBg4ciIyMDISFhcHa2lryWnxISkoK1q1bh1OnTpV43zYzM1MmGZfUHR0XFwdBENCvX79Sz1X8twXI//4YMGAADh48iE2bNmHnzp2wsbGBo6MjBgwYgEaNGr23bSKRCO3atUO7du0wfvx4LFiwAJGRkdi4cSOmTZv23mNLU9zFn5+fL7OvOCHXrFlT7rLFZVSBCe8zJs9Ip+IkMnv2bKmbyG+rV6+e5PuVK1fiyy+/xNmzZ3Ht2jXs2LEDmzZtwvz58zF27FjlBP7/rVixArt370b//v0xefJk6OnpQV1dHXfu3JHcq3tXtWold1oo8uFe2uv2bsJVpGxiYiLGjBkDLS0tfPPNNzA3N0etWrUgEomwbNmy995DU4XyfKioqalJBs0MHjwYffr0wYEDB7Bo0SKFk2hpmjdvjoiICDx58qTE57hKM3ToUPz2228IDg7GwIEDcfjwYeTn52PEiBFyHS8IAjw9PREXF4dx48bBysoKderUgZqaGoKCgnD48OES33clvZ6CIEAkEmHLli2lvi7F/ygq8v7Q0NDAjh07cOvWLZw/fx5Xr17F+vXr4ePjgzVr1sg8/vG25ORkHD16FIcPH8bNmzdRo0YNODs7v3fQ0ocUfz48f/5c5lGTpKQkAJCMjH277LuKt6lyFC0T3mcsLi4OPXv2lNkG/N9/jMX/mdaqVUvu51/EYjHEYjG++uorZGZmYuTIkVizZg3GjBkDkUiktCHFf/31F+zt7fHrr79KbY+Pj1dK/aV5/Pgx8vPzpa7y8vPzkZCQIHXVbGJigkePHqGwsFDqAy0pKQkvX76UqjM0NBQ5OTnw9fVFx44dpfalp6eX2MUjr+KEcP/+fZkHd2NjY6XKKJuenh5MTU1x584dpKWlwcDAQCn19unTBxEREdi/fz9mzJgh93GGhobo0qULzp49i3///RdBQUGoVasWBgwYINfx9+7dw927d+Hl5SVzxbN//36F2mBmZobz58+jYcOGH3zmsCzvD2tra0k3/b///gsXFxesW7dOJuFlZWUhNDQUhw4dwqVLl1BUVAR7e3t4e3ujb9++Urc9ysLKygoAEBkZKfMZcuPGDWhpaUk+Z8RiMTQ0NKS6ct8uCwCWlpbliud9eA/vM7Zv3z6pD96XL1/izz//hLa2Ntq3bw8AcHR0hL6+PrZs2VJi996rV68k9yzS09Nl/rvV1taGsbExcnNzJV0SxfciMjIyyhV/tWrVZK6UcnJysHPnznLV+yFZWVnYu3ev1La9e/ciKytL6jmznj17Ijk5GSEhIVJlt2zZIlNncUJ8tz0BAQF48eJFueJ1cHCApqYm9uzZI3V/KSsrC3v27IGmpiYcHBzKXH9ubi6uXLlS4r5Hjx4hNjYWurq60NPTK/M53jVy5Eg0adIE27dvL/HRCgCIioqCn5+fzPYRI0agqKgIK1aswJ07d+Ds7Cz3/cDiHoJ3f0///PNPiY8lvE/xVdPatWtlHqMBINVdqsj7IzU1VaauBg0aQE9PT+pv7tmzZ5g2bRo6d+6MuXPn4vnz5/juu+9w+vRp7N69GyNHjix3sgPejCA2NDREYGCgVDf03bt3ceXKFfTt21cyIrR27dro0aMHrly5IvW4RXZ2NgIDA2FmZqa6EZrgFd5nTVdXFyNHjsTw4cMhCAKCg4ORmJgIb29vSReMpqYmVq5cCS8vL/Tt2xfDhw9H48aNkZmZiQcPHiA0NBQ+Pj7o0KEDQkJCsGvXLvTq1QuNGzdG9erVERERgb///hv9+vWT9NNbWVmhWrVq2LRpEzIyMqCpqQljY2PY2NgoFL+zszP8/f3x/fffo3PnzkhOTkZQUJDSn/l6l6mpKTZu3Ij79++jdevWuHPnDoKCgmBubi41P+bEiRNx+PBhLFy4EHfu3EGzZs1w5coV3LhxA7q6ulJ1du3aFbVq1cLs2bMxduxYaGtr4/r16zh37hxMTU1L/ECUl7a2Nn744QcsWbIEo0aNkswkcuDAAcTHx2PJkiXl+mDLzc3FuHHjIBaL4ejoCDMzMwiCgAcPHuCvv/5CXl4efvrpp1K7k8uiVq1a+P333zFp0iR4eXnB0dERnTt3Rt26dZGamorLly/j77//xsSJE2WO7d69OwwN6RXgBwAAAypJREFUDSXPusk7WAV4c4+7efPm2Lp1K169eoUmTZrg4cOH8Pf3h1gsxp07d+Suy9raGt9++y02bNgAFxcXODs7o379+khKSsKdO3dw7tw5ycArRd4fvr6+uHDhArp37w5jY2MIgoAzZ87gwYMHUq9HfHw8rl+/DldXVwwZMgStW7eWO3bgzWMSxffJCwoK8PTpU8nUbS1atJA84qGuro4FCxZg+vTpGDNmDEaOHIns7Gzs3LkTenp6MlfKM2fOxKVLl+Dp6QkPDw/Url0b+/fvx/Pnz/H777+r7KFzgAnvs/bDDz/g6tWr8PPzQ3JyMszMzEp8jqhLly4IDAzE5s2bcfDgQaSlpUFbWxumpqbw8PCQPOvVoUMHxMTE4H//+x9evHiBatWqwdjYGHPmzJG6f9ewYUMsW7YMW7ZsweLFi1FQUIChQ4cqnPDmzZuH2rVr4/jx4zh16hSMjIzwxRdfwMrKCh4eHuV+fUrToEEDrFu3DitXrsSRI0egrq6OQYMGYc6cOVIj+HR0dODn54cVK1YgJCQEgiCgQ4cO+OOPP2TiMzU1xZYtW7B27Vps2rQJampqaNOmDXbv3o2lS5fKPf9kacaMGYN69eph27ZtkgfqW7RogY0bN8rMfqIobW1tLFu2DBcuXMDp06fx4sUL5OfnQ1dXF/b29hg7dqxMN5wyNG7cGCEhIfD398eJEyewadMm5OTkQEdHB5aWllixYkWJz8RVr14dQ4YMwdatW2Fqagp7e3u5z6mmpobff/8dK1euxIEDB5Cbm4vmzZtj5cqVuHv3rkIJDwCmTp0KS0tL7N69G3/88QdycnKgr6+P5s2bSw0UU+T90atXL7x48QLHjx9HcnIyatasicaNG8Pb21vqXmXxdGtlva+6fft2qfM+ffoUv/32G4A390rffqax+B9eX19frFq1ChoaGujUqRN++OEHmXtyjRs3xr59+7B69Wps3rxZMpfm1q1bVTqtGACIhJLuxBMREX1meA+PiIiqBCY8IiKqEpjwiIioSmDCIyKiKoEJj4iIqgQmPCIiqhKY8IiIqEpgwiMioiqBCY+IiKqE/wdveJz96UeHhwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histdata = bestparams.values[np.triu_indices_from(bestparams,k=1)]\n",
    "#uniq, counts = np.unique(histdata, return_counts=True)\n",
    "#plt.bar(x=uniq, height=counts)\n",
    "plt.hist(histdata)\n",
    "plt.xlabel(\"best lambda for ST CV areas>100\")\n",
    "plt.ylabel(\"number of brain area\\ncomparisons\")\n",
    "#plt.savefig('032621_1.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#meantestscore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get num samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#pre-processing\n",
    "STspotsmeta, STspots, STpropont, ontology = read_data()\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\n",
    "\n",
    "#subset for sufficient samples size for hyperparameter selection\n",
    "leafsizes = leafSTpropont.sum()\n",
    "#filter propogated ontology for brain areas with X number of samples min\n",
    "leafsizes_sub = leafsizes[leafsizes>100]\n",
    "leafSTpropont_sub = leafSTpropont.loc[:,leafsizes_sub.index]\n",
    "#remove areas (rows) from propogated ontology that don't have any samples\n",
    "rowsum = leafSTpropont_sub.sum(axis=1)\n",
    "leafSTpropont_sub = leafSTpropont_sub.loc[rowsum > 0, :]\n",
    "#index samples for rows to match porp ont\n",
    "leafSTspots_sub = leafSTspots.loc[rowsum > 0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "numsamples = pd.DataFrame(index=list(bestparams), columns=list(bestparams))\n",
    "numsamples = numsamples.fillna(0)\n",
    "\n",
    "areas = list(bestparams)\n",
    "#for each column, brain area\n",
    "for i in range(bestparams.shape[1]):\n",
    "    #for each row in each column\n",
    "    for j in range(i+1,bestparams.shape[1]): #upper triangular!\n",
    "        area1 = areas[i]\n",
    "        area2 = areas[j]\n",
    "        numsamples.iloc[i,j] = leafsizes[area1] + leafsizes[area2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "numsamples = numsamples.values[np.triu_indices_from(numsamples,k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#alpha as a function of brain area size\n",
    "plt.scatter(numsamples,histdata)\n",
    "plt.xlabel(\"total number of samples\\nST\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig('032621_2.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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(numsamples, histdata, kind='hex').set_axis_labels(\"total number of samples\\nST\",\"best lambda\")\n",
    "plt.savefig('032621_3.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define 'sliding window' as a uniform dist\n",
    "uniform = [1/100] * 100\n",
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "#convolve\n",
    "hyperp_conv = np.convolve(hyperp_sorted, uniform, mode='same')\n",
    "\n",
    "plt.scatter(numsamples_sorted, hyperp_conv, s=3, rasterized=True)\n",
    "plt.xlabel(\"total number of samples\\nST > 100\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig(\"033121_3.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "\n",
    "#bin by equal #samples per bin\n",
    "numbins = 50\n",
    "binsize = len(numsamples_sorted)//numbins\n",
    "print(len(numsamples_sorted)%numbins)\n",
    "mean = []\n",
    "sd = []\n",
    "startindex = 0\n",
    "for i in range(numbins):\n",
    "    if i == numbins-1: #last bin\n",
    "        currint = hyperp_sorted[startindex:len(hyperp_sorted)]\n",
    "    else:\n",
    "        currint = hyperp_sorted[startindex:startindex+binsize]\n",
    "\n",
    "    mean.append(np.mean(currint))\n",
    "    sd.append(np.std(currint))\n",
    "    startindex = startindex+binsize\n",
    "\n",
    "plt.scatter(range(1,numbins+1), mean, rasterized=True)\n",
    "plt.errorbar(x=range(1,numbins+1), y=mean, yerr=sd)\n",
    "plt.xlabel(\"bins of total number of samples\\nST>100\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig(\"033121_4.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looking into ties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "lastofties = pd.DataFrame(index=list(bestparams), columns=list(bestparams))\n",
    "lastofties = lastofties.fillna(0)\n",
    "\n",
    "alphas = [0.01, 0.05, 0.1, 0.2, 0.5, 0.9]\n",
    "#for each column, brain area\n",
    "for i in range(bestparams.shape[1]):\n",
    "    #for each row in each column\n",
    "    for j in range(i+1,bestparams.shape[1]): #upper triangular!\n",
    "        currkey = \"%d,%d\" %(i,j)\n",
    "        currscores = meantestscore[currkey]\n",
    "        bestindex = 0\n",
    "        bestscore = -1\n",
    "        for k in range(len(currscores)):\n",
    "            if currscores[k] >= bestscore:\n",
    "                bestscore = currscores[k]\n",
    "                bestindex = k\n",
    "        lastofties.iloc[i,j] = alphas[bestindex]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "histdata = lastofties.values[np.triu_indices_from(lastofties,k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": 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Bh1t55Uh9WIK0RyW4/KgUVS8bBMdNdbpgvKspPHvqwbOnHnoaqAt2snlSVit0D67JMBtDPCquwW9/P8bhq08wa1AvzBncCxoqUs1rIoSQVpP40yYiIgLTp08Hh8NBUFAQlJSUADQ+0iY6OhqJiYlSPy6nibKyMoYNG4Zhw4a16nwinZq6Blx7XCbooV17XIaXDXzB8V4G6vBzNoZnTz14WOihu27zz/lb7msHANhzMQc8PoPuul0EsygZAPvTcvF90gP8kPwAMak5mD/cGsGe5lCiGZaEkLdMqid66+joIDIyEj/88APMzMwAND4nrqqqCubm5li7dq1UT/Qm7YMBAz6fQW0dD12U2SLHy2vrcSWnRHAP7VZeORr4jRN7WCzApqsm+vbUg2dPfXj01IWRpuTLPRTZCvjKrzeO33kGPp9B4sIhQm2Y0q8HAlxNsfP8Q2w/k4Uvj9zBL+cf4vNRNhjraNym5+8RQkhLpHqiNwAYGxsDgGB6v6amJjQ1NVFfX//GRdmhoaFgsVjYuXMnFBUVxQbR17FYLAqazWhuDVrY4F64KuihlSD9WQWaJqqyFVhwMNVuDGgWenC30IWOmnKb28ICC2wFltgAq66iiPnDrTG5rzk2Jz9AdOpjzIu5hh3dsxExxg79LfXbXD8hhLxO4id6t4e8vDywWCzBsoDmgiiRTHNr0F5NU1ZUgIeF3j89ND24metCXUb3wQw0VPC1vwNmePXEhhMZ+PPmUwTv+BtDbQyxZLQt7IzfzqbahJDOqUM/6V4Pmm8jiHYWtXU8JN59LvaYiqICwoZYwsvKAE7dtaGqJNqrkiULA3VsmeyG2YPKsPbYPZzOKMSZ+4UY79odi0ZyYKpDk4sIIW1Hd/rfUwWVL5BfViv2WAOPj/FujbMe37Xg9ipnMx3EzO6LX2d4wKarJuKu5mHYhtOI5KajrKbuzQUQQkgLpApwf/zxB4KCgtC/f3/Y2dmJ/GtpnZw4dnZ2+OOPP5o9zuVyYWdnJ1WZnYWRpipMmunpGOt0kWqiiCyxWCwMtTHC0fmDsDHQGYYaKog6m43B609h25ksvKin3VAIIa0j8RDlTz/9hM2bN0NfXx+urq7Q1tZuc+XituiS5nhn1kWZjRH2XcWuQRth31XsZI93GVuBhfFu3eHraIy9F3Ow5VQm1h67h90pj7BwBAcT3LqDrUAzLgkhkpM4wMXExMDT0xM///yzYA3c25afnw91dfUOqet91NIatPeVqhIbswf3QqC7GbaeycKuCw8Rfugmdp57iCVjbDDMxoiWFhBCJCJxgKuursaYMWPaHNySkpKQnJwseH3gwAGkpKSI5CsvL8fFixfh5ubWpvrk2ZvWoL3PtNWUEDHGFqH9e+D7pPs4dCUPM3+9jL499RAxxhau5qLPDySEkFdJHODs7Ozw9OnTNld47949xMfHA2i8/5KWloa0tDSRfGpqanB1dcWXX37Z5jrlXUtr0N53JjpdsH6iMz4c2Avr/7qH5HsFCPgpBb6O3fD5SBv0MtSQdRMJIe8oiQPcggUL8Omnn2LkyJHo3bv1z/maN28e5s2bBwCwtbXFt99+Cz8/v1aXRzoHm26a2DndA6nZxVhz7B64t57h+J3nCH5tM+c37epCCOk8JA5wnp6eWL16NT744AO4uLjA1NQUCgrCkzBZLBYiIyMlrjw5OZkeaEqk0reXPuI/GYC/bj/Dt8czBJs5z/TqibKaOnqyOCFEQOIAd+PGDSxduhQNDQ24fPkyLl++LJJH2gBnamoqcV5CmrBYLIxxNIaPfVfBZs5bTmUK5aEnixNCJA5wq1evhqKiIn766Se4u7tDS4u2VSKypcRWwJR+PTC6dzd4f3caFS8aRPIk3n2O8FG2NFxJSCck8dhNRkYGZs6cCW9vbwpu5J1SXdcg9Py6V+WV1iLheh54fFpTSUhnI3GA09fX77D1b4RIo6VdXQBg6eHbGLjuJDYl3m92ezNCiPyROMCNHz8ev//+OxoaxH9TJkRWmnZ1EcfP2Rghfc1R+aIB/5f8AAPXncTMX9OQePc5Gnh8secQQuSDxPfg+vTpg9OnTyMwMBCTJ09G9+7dwWaL3tfw8PBotgw7OzusX79esCxg6dKlCAoKgrOzcyuaTsj/tLSriyJbAct87fDnzXzEXMrFyXsFOHmvAF21VPCBuxkCPcxafGo5IeT9JHGAmzFjhuD/V6xYIbJdEsMwYLFYSE9Pb7YMBQUF8Hj/2zw3Pj4eAwYMoABH2uxNu7qoqyjiAw9zfOBhjrv5FdiX9hjxV5/gh5OZ2HwqE0M4hgj2NIe3rRGUaFkBIXJB4gC3Zs2aNldmbGyMK1euYNy4cYI02leQtCdJdnWxN9HCN/4OiBhji6M3nyLm0mOczijE6YxCGGmqINDdDB94mMFMj3p1hLzPJA5wAQEBba7M398fP/74I44fPw5NTU0AQGRkJDZt2tTsOSwWC0lJSW2um5DXqSkrYpK7GSa5m+Heswrsu5SLuKt52HIqEz+ezsQga0NM9jTDcLuu1Ksj5D3UoU/0njdvHkxMTJCSkoKioiLk5+dDR0cH+vr6HdkMQkTYdtPCf/7dG0tG24J7q7FXd/Z+Ic7eL4SBhgoC3bsjyMMc5vrUqyPkfSF1gCsqKsLt27dRXl4u9nltrw4/vo7FYmHChAmYMGECgMa9KD/++GPai5K8M7ooszGhT3dM6NMd959XIvbSY8RdycNPp7Pw0+ksDLI2QLCnOXzsukJZkXp1hLzLJA5wfD4fX3/9NQ4dOgQ+v/np1S0FuNft2bMHVlZWEucnpCNxumriK7/GXt2x208Rk/oY5x4U4dyDIhhoKGNiHzMEeZjBwkD0mYW06TMhsidxgNu5cyf279+Pf//73/Dy8sKSJUvw+eefQ11dHbt374ampiYWLVokVeWenp4AGmdg3r17F7m5uQAAMzMz2Nvb0wQU8k5QVWIjwLU7Aly748HzSuxLa7xXt+1MFradyYKXlT6CPc0x0r4bFFjAam46bfpMyDtA4gCXkJCAgQMHYv369SgtLQUA9O7dG/3794e/vz/+/e9/486dO+jfv79UDTh79iy+/vpr5OfnC6Wbmpriq6++wqBBg6Qqj5C3ybqrJlb+yx5fjLLB8TvPEJP6GBcyi3Ehsxj66sow1lHF7ScVgvy06TMhsiPxV8rc3FwMHjy48aR/HpPTtKuJmpoaxo8fj4MHD0pV+ZUrV/DJJ5+goqICU6dOxTfffINvvvkGoaGhqKiowMcff4yrV69KVSYhHUFViQ1/F1Ps/6g/khYNwexBPcFjGKHg9qrEu89RW8cTe4wQ8nZI3INTVVWFomJjdjU1NbBYLBQXFwuOGxoa4tmzZ1JV/tNPP8HAwAAHDhyAkZGR0LEPP/wQgYGB+PHHH7Fz506pyiWkI1kZaWD5WHsEupth5KazELet85PSWsRfy4O/iynUVTp08jIhnZbEPTgTExPBPTIlJSWYm5vj3LlzguMpKSlST/e/ceMGAgMDRYIbABgZGWHSpEm4ceOGVGUSIivdddVgqit+02cGwLL423D55gQ+2H4RP57KxK28cvDpKQeEvDUSB7h+/fohMTFR8Nrf3x9Hjx7F1KlTMXXqVPz1118YM2aMVJXX19dDXV10BloTDQ0N1NfXS1WmNKKjo+Ht7Q1HR0eMHz9e7ENcmxQUFGDx4sUYPXo07OzsEBERITbf8ePH4evrCwcHB/j6+gr9zIh8a2nT5zEO3TBvmBXsjbVw6VEJvj2eAb8t5+G+OgnzY6/h4OVcPK940cEtJkS+STxWMnPmTHh5eaGurg7Kysr46KOPUFJSgt9//x0KCgoIDAzE/Pnzparc0tISXC4XISEhguHPJg0NDTh27BgsLS2lKlNSXC4XkZGR+Oqrr9CnTx/ExMRg9uzZOHr0KExMTETy19XVQVdXF3PmzMGBAwfElnnt2jUsXLgQn376KUaOHIkTJ07gs88+Q2xsLO232Um8adPnz0fZoKS6Dhcyi3D2fiHOPSjC7zfy8fuNxklWNl01MZhjgEHWhvDsqQdVJVpiIG9oCUnHkTjAGRkZCQ0lstlsrFixAitWrGh15cHBwVi5ciWmT5+OWbNmCYJZZmYmdu7ciRs3buCbb75pdfkt2bVrFwICAhAYGAgAWLlyJc6dO4fY2FgsXrxYJH/37t0F13r8+HGxZe7evRt9+/bFxx9/DAD4+OOPkZqait27d2Pjxo1v5TrIu+VNmz4DgJ66MvycTeDnbAKGYZBZUIUz/wS71IfF2HGuEjvOPYSKogI8e+phsLUhBnMMwemqQUtn3mMNPD4tIelgMr3bPWnSJDx69Ai//PILrly5InL8ww8/xKRJk9q93rq6Oty5cwczZ84USvfy8sK1a9daXe7169cxZcoUobSBAwciOjq61WWS95Mkmz4Djbv7WHfVhHVXTcwa1Asv6nm4/KgUZx8UCnp45x4UYTU3HUaaKhhkbYjBHAMMtDKAvoZKB10NaQ+ruemCJSMALSEB3n5vttkA9/q6NEmJG95ryRdffIGJEyciOTkZeXl5YBgG5ubm8Pb2Rs+ePVvVhjcpLS0Fj8eDgYGBULq+vj5SUlJaXW5RUZFImQYGBigsLGx1maRzUVViY6C1AQZaG2CZrx0KKl78E+Qag13c1TzEXc0DiwU4mGhjkLUBBnMM4WauK3brsHdhOOxdaIOs1dbxcOKO+Fnmf91+hvBRtp3qZ9NRvdlmA5y3t3erhkNaeh5cc3r27IlZs2ZJfV5bibu+9h4CanpOHiGtYaSlKtgbk89ncPdpBc49aLx/dzmnBLeelOOn01lQU2ajfy99DOYYYpC1Acx0uyDy2D2ZDofRkFyjF/U8/HQ6E0/KxE8ielr+At7fnUa/Xvro00MX7ha64BhpQkFBfj83Oqo322yAmzt3rtx+MOvq6oLNZov0rIqLi0V6YNIwMDBAUVFRu5ZJSBMFBRYcTLXhYKqNj4daovplA1IfFuPs/SKcfVCI5HsFSL5XAADQUFFE1csGwblNHyBFVS8xd5gVFBUUoMxWgCKbBSW2ApT++a8imwUlBYV2+XDt7ENyL+p5iE59jG1nslBY+RIsQOwaSVUlBdTU8RB/7Qnirz0BAGiqKsLNXBfuPXTRx0IXLmY6UFN+f9dPFlW9RPrTCqQ/rcDtJxU4dvup2HyJd5+3a2+22Z/Yp59+2i4VvIuUlZXRu3dvpKSkCC1tSElJwciRI1tdrouLC1JSUoR6oykpKXB1dW1TewkRR11FEd62XeFt27g0Ia+0BucfFOFURgFO3H0u9pw/bjzFHzfEf7i8iq3Aagx6CgpQUlSAosLrgVABymwWFF9JU2L/k09RAQqAINi+rr0/xN41tXU8RKfmYPvZbBRWvoS6MhsfD7VEWU0dYi/liuQP9jTHyrH2yC6qwuVHpbicU4rLj0pw5n4hztxv/BKuqMCCvYkW+vTQhYeFHtx76MJIS7WjL+2N6hr4yCqswr1nFUh/WvlPUKtEUdVLic5/WlaLgsoX6KHf/PIxaby/XwnaaMaMGQgPD4eTkxPc3NwQGxuLgoICBAUFAQDCw8MBAOvXrxec0zT8WlVVBRaLhfT0dCgpKQmeiBAaGoopU6Zg+/bt8PHxQVJSElJTUxETE9PBV0c6o+66agjyNEd/S32cuCM+wLEAjHM1haoSGw08Pup5fNTzGdQ38NHAZxpf8/io5zFo4PFR989/m9Jq6nho4DegvoGPen5jGk/KxepPSmuRV1oD666a7XDV746augZE//0Y289mo6iqMbB9MtQSswb1gp66Mhp4fKgqscUuIVFQYMHKSBNWRpoI8jQHABRWvsSVnFJcySnB5ZxS3H5Sjpt55YJesJleF7j30JPZsGZTr+xeUyB7VonMgkrU84R/H0x1usDHzgh2xlqwM9aChb46Zu9JEztka6zTBUaa7Re4O22A8/X1RWlpKbZu3YqCggJwOBxERUXB1NQUAPD0qei33NcfBXTq1CmYmpri5MmTAAA3Nzds3LgR33//PTZv3gwzMzNs2rSJ1sCRDmWkqQpT3S7IK60VOWaq2wWRAY7t2nvi8xnU8/lo4DGCQFj5oh6Td/yNZxWi39wZAIHbL335K/gAACAASURBVGJin+4I8jSHpaFGu7VFFmrqGvDb3zmIOpuNoqo6aKgoYt4wK3w4sCd01ZUF+SRZQvIqQ00VjHbohtEO3QA0DnnezCvH5ZwSXHlUiiuPS1s9rCnNxJ963j+9sn8C2d2nFbj3rBKFlcLvraqSAuxNtGFvrAnbbo3BzKabJrS7KImUObJ3N6Hh6yYj7Lu26+9mpw1wABASEoKQkBCxx/bu3SuSlpGR8cYyR48ejdGjR7e5bYS0VtOOKh3xAQI03htUUWDj1S02DTVVMMbRWGwbHEy0kF/+AjvOPcSOcw/h2VMPkz3NMdqh23u1sL2mrgF7LzYGtuLqxsD2qXdjYNNRU272PEmXkLxOVYkNz5568OypB6Dxi8Wrw5pXckqbHdZ076EHdwtd6Ksrtzjxp7jq5f+GFv8ZZnxTr6wxmGmih7462BL2IFvaEKE9yTTApaWlwdLSEnp6emKPl5SUICsrCx4eHh3cMkLebx31AdLaNvAYBol3nyP2UuPjhi49LIH270oY72aKYE9zcN7h4cvqlw3YczEHO85lo6S6DpoqipjvbYWZbwhs7a01w5rNTT46cecZ6niM+F7ZP0OLjcFME7bGWmJ7ZdKQtjfb6nravUQphIaGYv369fDz8xN7/O+//8bixYtbtfSAkM6soz5AWtsGRQD/cjLBv5xMkFNcjX1puTh4OQ+7LjzCrguP0KeHLoI9zTHW0fidmYxS9bIBey4+wo6z2SitqYemqiI+G26NmV49oa3Wtg/89tLSsOal7BKcfSB+Te6Tshcw1laFj52RYHjR1lgTFlL0ylqjtb1ZSck0wDFMyzeneTye4NlzhBDpve0PkPZoQw99dSwZbYtFIzhITn+OmEu5OPegEFdySvH1H3cQ4GqKIA9z2JtodXDLG1W+qBf02Mpq6qGlqogFPtaY4dWzzT2Zt+3VYc2xjtUY+u1psfkUAOyb06/dZi++KyQOcEuXLkVQUFCzEyZu3ryJ2NhYrFmzRqoGtLTW7tq1a9DV1ZWqPELI+0mJrYDRDsYY7WCM3JIaHLici/1pudhzMQd7LubA2UwHkz3N8C8nkw55pl7li3rsTnmEn88/FAS2RSM4mO5lAS3VdzuwidPS5CMT3fadvfiukPi3JD4+HgMGDGg2wOXl5SEhIeGNAW737t3Ys2eP4HVkZCQ2bdokkq+iogJVVVWYMGGCpE0khMgJMz01LB5pg8+GW+NURiFiLz3G6YwCLMktw3//TMe/XUww2dMcDqba7V53xYt6/HrhEXaef4jy2npod1HC4hEcTHtPA1uTjp589C5ot69BNTU1Io+8EUdLS0uwX+WTJ0+go6Mj8qBUFosFa2truLi4YNq0ae3VRELIe0aRrYAR9l0xwr4r8stqceByLg6k5SIm9TFiUh/DwVQLwZ7m+LezCTTbGHzKa5sCWzYqXjRAR00JX4yyQWj/Hm0u+13xLkw+6kgtRqT8/Hw8efJE8Do7OxtpaWki+crLyxEbG4sePXq8scKAgAAEBAQAaNzvcvHixRg+fLi07SaEdDImOl2wwIeDT72tcfZ+IWIuPcbJewVYHn8bq4+mw8/JBMF9zeHcXVuqbQbLa+vxy/mH+OXCQ1S+aIDuP4Ft2gALaHTAUGhHehcmH3WkFt+9w4cPY8uWLWCxWGCxWNi2bRu2bdsmko9hGCgoKCAyMlKqypsWSBNCiKTYCiwMszXCMFsjPK94gYOXc7EvLRf7Lzf+s+2micl9zeHvYio0CeT1xc3lNfXYeeEhdr0S2JaMtsXU/j3kLrC97l2YfNQRWnwXfXx8YGpqCoZhsGzZMgQGBorsq8hisaCmpgZHR0cYGxtLVXlpaSlKSkqEntqdm5uLX3/9FWVlZRg3bhwGDRokVZmEkM6jq5Yq5nlb45OhVjifWYTYS4+RePc5vjxyB5HcdIx1NMEH7t3Bvf1UsLh5+HenYaipgqzCalS9bICeujIixthiar8eHTJ5hXScFt9NW1tb2NraAmgcrhw5ciQ4HE67Vb569Wo8evQIhw4dAgBUV1cjJCQEBQWNm7QeO3YMu3fvpoXehJAWKSiwMJjT+OTzwsqXOHQlD/vSHguen/eq/PIXyC9/AVUlBSwdY4spFNjklsSLzObNm9dscKurq2tV5devX8fgwYMFr7lcLgoKChAVFYVz587B0tISP//8c6vKJoR0ToaaKvh4qCVOLR6KXdM90KWZ7b/01JUR2t+CgpsckzjAnTlzBps3bxZKi46OhpubG1xcXLB48WLU19dLVXlxcbHQsOa5c+fg4OCAwYMHw9DQEAEBAbh7965UZRJCCNDYq+tlqI6XDTyxx5+Xv0BBpfiHkBL5IHGA27lzJ7KzswWvs7KyEBkZCSMjIwwYMABcLhfR0dFSVa6oqIiXL/+399mlS5eEhiM1NTVRVlYmVZmEENLESFMVJjpdxB5r70ezkHePxAEuOzsbDg4OgtdcLhcqKio4dOgQfv75Z/j6+iIhIUGqyi0sLHD8+HEwDIPk5GSUl5ejf//+guPPnj2Dtnb7L+QkhHQOTYubxZHXxc3kfyQefC4vLxfaNislJQX9+vWDhkbjs5w8PT1x5swZqSoPCQlBREQEPDw88OLFC5iZmQkFuLS0NNjY2EhVJiGEvKqzLW4m/yNxgNPV1UV+fj6Axida37p1CwsXLhQcb2hoAI8nfqy7OePGjQOLxUJSUhI0NDQQFhYGJaXGdSulpaWoqqpCcHCwVGUSQsirOtviZvI/Egc4FxcX7Nu3D1ZWVjh79ix4PB6GDBkiOJ6TkwMjIyOpG+Dv7w9/f3+RdF1dXRw+fFjq8gghRJzOsriZ/I/E9+Dmz58PPp+PBQsW4PDhwxg3bhysrKwANO5kkpSUBDc3t7fWUEIIIUQaEvfgrKyswOVycfXqVWhqagrNdqyoqMC0adPQt29fqRvQ0NCApKQk3LhxAxUVFeDz+ULHWSyW1FuAEUIIIVKtcNTR0YG3t7dIura2dqt2/S8rK0NoaCgePHgAhmHAYrEED0Ft+n8KcIQQQlpD6sdlp6WlYdOmTVixYgWysrIANG6xlZaWhoqKCqnK+v7775GdnY1Vq1YhMTERDMNg586d4HK5GDt2LBwdHZGamiptEwkhhBDJAxyPx8OCBQsQGhqK7du3Iy4uTrBnpKKiIubOnYuYmBipKj9z5gzGjRuHCRMmCJYbKCgooFevXtiwYQNUVVXx3XffSVUmIYQQAkgR4Hbs2IETJ04gIiICXC5XMJQIACoqKvDx8ZF6HVxhYSEcHR0BQPCw1Ff3tRw+fDg9UocQQkirSBzgEhIS4O/vj2nTpgkt+G5iaWmJ3NxcqSrX0dFBbW0tAEBdXR2Kiop4+vSp4LiSkpLUw56EEEIIIEWAe/Lkiciz4F6lpaWF8vJyqSq3sLBAZmZmY0MUFGBvb4/4+HjU1dWhtrYWCQkJMDMzk6pMQgghBJAiwKmrq7e48XFOTg709PSkqtzLywvHjx8XDEtOnz4dN27cgKenJwYMGIDbt2+3anYmIYQQIvEygT59+uCPP/7A7NmzRY6Vl5cjLi5O6qdvh4WF4cMPP4SysjIAwNfXF4qKivj999+hoKCA0aNHw9fXV6oyiWxciBBdPkIIIbIkcYALCwvD5MmTERoaivHjxwMAMjIykJOTg6ioKNTW1mLOnDlSVc5isQTBrcnIkSMxcuRIqcohhBBCXidxgHN0dMSWLVuwfPlyLF26FACwbt06MAwDfX19bNmyRbB1FyGEECJrUu1kMmTIEJw8eRLnz59HdnY2GIaBhYUFBg4ciC5dxD9U8FXSPi+uybhx41p1HiGEkM5LqgAHAMrKyvD29ha7ZdebRERECG3HJQkWi0UBjhBCiNSkDnB1dXVITU0VrHkzMzODp6cnVFRU3njunj17pG8hIYQQ0gpSBbiEhASsWbMGFRUVQpsia2lpYcmSJYLJJ83x9PRsfUsJIYQQKUgc4LhcLiIiImBiYoIPP/wQlpaWYBgGWVlZ2LdvH5YvXw5VVVWa1k8IIeSdIHGA27ZtG3r16oUDBw4INkYGAB8fH0yePBmTJk3C1q1bWwxwFy9eRP/+/VvV0JSUFAwYMKBV58o7WoNGCCGiJN7J5OHDhxg/frxQcGuiqamJ8ePHIycnp8UyZs2ahdDQUJw6dQo8Hu+NddbX1yMxMRFTpkyReo0dIYSQzk3iHpyhoWGLsx8VFBRgYGDQYhnx8fFYu3YtPv74Y+jq6mLAgAFwcnKCubk5tLW1wTAMysvLkZOTg+vXr+Pvv/9GRUUFvLy8Wr3EgBBCSOckcYALCAhAfHw8Jk+eDHV1daFjVVVViIuLe+MkEw6Hg19++QXXrl1DTEwMkpOTcfToUbBYLKF8DMNAQ0MDI0aMQHBwMJycnKS4JEIIIaSFAJeWlib02t3dHadOnYKfnx8mT56MXr16gcViITMzE7GxsdDV1UWfPn0kqtTV1RWurq7g8Xi4c+cOMjMzUVJSAhaLBT09PVhbW8Pe3h4KClI/cJwQQggB0EKAmzp1qtieFQBs2LBBcKwpLT8/HzNnzkR6errElbPZbDg5OcmshxYdHY2dO3eisLAQ1tbWWLZsGdzd3ZvNf+nSJaxduxYPHjyAkZERZs2aheDgYMHxzZs3Y8uWLULnGBgY4MKFC2/tGgghhIjXbIBbs2ZNR7ajw3G5XERGRuKrr75Cnz59EBMTg9mzZ+Po0aMwMTERyZ+bm4s5c+ZgwoQJ+Pbbb3HlyhV8/fXX0NPTw6hRowT5evbsib179wpes9nsDrkeQgghwpoNcAEBAR3Zjg63a9cuBAQEIDAwEACwcuVKnDt3DrGxsVi8eLFI/n379sHIyAgrV64E0PgE8xs3buCXX34RCnCKioowNDTsmIsghBDSrE55k6uurg537tyBl5eXULqXlxeuXbsm9pzr16+L5B84cCBu376N+vp6QVpubi4GDRoEb29vLFy4ULClGSGEkI7VKQNcaWkpeDyeyLIGfX19FBYWij2nqKgI+vr6QmkGBgZoaGhAaWkpAMDJyQlr1qzBjh07sGrVKhQVFSEoKEhwnBBCSMeRerNlefL6JJrm0po79up+nEDj44Re5ezsDB8fHyQkJGDGjBltbS6RAO3qQghp0il7cLq6umCz2SK9teLi4mYXqxsYGKCoqEgkv6KiInR0dMSeo66uDisrKzx69Khd2k0IIURynTLAKSsro3fv3khJSRFKT0lJgaurq9hzXFxcxOZ3cHCAkpKS2HNevnyJhw8f0qQTQgiRgU4Z4ABgxowZiI+Px8GDB5GVlYVVq1ahoKAAQUFBAIDw8HCEh4cL8gcFBeH58+dYvXo1srKycPDgQcTHx2PmzJmCPOvWrcOlS5eQm5uLGzduYP78+aipqZH7GamEEPIu6rT34Hx9fVFaWoqtW7eioKAAHA4HUVFRMDU1BQA8ffpUKL+ZmRmioqKwZs0axMbGwsjICMuXLxdaIvDs2TMsWrQIZWVl0NXVhYuLCw4cOCAokxBCSMfptAEOAEJCQhASEiL22KuLtZt4enoiPj6+2fI2bdrUbm0jhBDSNp12iJIQQoh8owBHCCFELlGAI4QQIpc69T04Qt4WWnBOiOxRD44QQohcogBHCCFELlGAI4QQIpcowBFCCJFLNMmEEPJW0YQbIivUgyOEECKXqAdHiByj3hPpzCjAEUJIB6IvHR2HhigJIYTIJerBEUJIJ9NZepHUgyOEECKXKMARQgiRSxTgCCGEyCUKcIQQQuQSBThCCCFyiQIcIYQQuUQBjhBCiFyiAEcIIUQuUYAjhBAilyjAEUIIkUu0VRchpFPoLNtTkf+hHhwhhBC5RAGOEEKIXKIARwghRC5RgCOEECKXKMARQgiRSxTgCCGEyCUKcIQQQuQSBThCCCFyicUwDCPrRnR2NjY2AICMjAwZt4QQQuQH9eAIIYTIJQpwhBBC5BIFOEIIIXKJAhwhhBC5RAGOEEKIXKIARwghRC5RgCOEECKXKMARQgiRS/RE73dI04JvQgghkmtukwzqwRFCCJFLtFUXIYQQuUQ9OEIIIXKJAhwhhBC5RAGOEEKIXKIARwghRC5RgCOEECKXKMARQgiRSxTgCCGEyCUKcKRNtm/fjgkTJsDNzQ39+vVDWFgY7t+/L+tmvRO2bdsGGxsbfPPNN7JuiswUFBRgyZIl6NevHxwdHeHr64tLly7Julkdjsfj4fvvv4e3tzccHR3h7e2NTZs2oaGhQdZN6xBpaWkICwvDoEGDYGNjg8OHDwsdZxgGmzdvxsCBA+Hk5ISpU6fiwYMHba6XAhxpk0uXLmHy5MnYt28fdu/eDTabjRkzZqCsrEzWTZOp69ev48CBA516+7WKigoEBweDYRhERUWBy+Vi5cqV0NfXl3XTOtyOHTsQExODFStW4NixY1i+fDliYmKwfft2WTetQ9TU1IDD4WD58uVQVVUVOb5jxw788ssvWLlyJQ4dOgQ9PT3MmDEDVVVVbauYIaQdVVVVMba2tkxycrKsmyIzFRUVzPDhw5mUlBRmypQpzNdffy3rJsnEd999x3zwwQeybsY7Yc6cOUx4eLhQWnh4ODNnzhwZtUh2XFxcmLi4OMFrPp/PeHl5MT/99JMgrba2lnFxcWFiY2PbVBf14Ei7qq6uBp/Ph5aWlqybIjMrV67EqFGj0L9/f1k3RaaSkpLg7OyMBQsWoH///vD398dvv/0GphPuDtinTx+kpqYiKysLAJCZmYm///4bgwcPlnHLZC8vLw+FhYXw8vISpKmqqsLDwwPXrl1rU9n0NAHSrlavXg07Ozu4urrKuikyceDAATx+/Bjr16+XdVNkLjc3FzExMZg+fTrmzJmD9PR0rFq1CgAwZcoUGbeuY82ePRvV1dUYO3Ys2Gw2GhoaEBYWhpCQEFk3TeYKCwsBAAYGBkLp+vr6KCgoaFPZFOBIu1mzZg2uXLmC2NhYsNlsWTenw2VnZ2Pjxo2Ijo6GsrKyrJsjcwzDwMHBAYsXLwYA2NvbIycnB9HR0Z0uwHG5XCQkJOC7776DlZUV0tPTERkZie7du2PSpEmybt47gcVitXuZFOBIu4iMjASXy8Xu3bthZmYm6+bIxPXr11FaWgo/Pz9BGo/HQ1paGvbt24fr1693qsBnaGgIS0tLobRevXrh6dOnMmqR7Kxfvx4zZ87E2LFjATQ++zE/Px9RUVGdPsAZGhoCaOzJGRsbC9KLi4tFenXSogBH2mzVqlXgcrnYu3evyAdaZ+Lj4wMHBwehtKVLl8LCwgIfffQRlJSUZNQy2XBzc8PDhw+F0h49egQTExMZtUh2Xrx4ITKqwWazwefzZdSid0f37t1haGiIlJQUODk5AQBevnyJy5cvIzw8vE1lU4AjbfL111/jyJEj+PHHH6GlpSUYT1dTU4O6urqMW9extLS0RCbXqKmpQVtbGxwOR0atkp1p06YhODgYW7duha+vL+7evYu9e/di0aJFsm5ahxs2bBiioqLQvXt3wRDlrl27MG7cOFk3rUNUV1fj8ePHAAA+n4/8/Hykp6dDW1sbJiYmCA0NxbZt29CrVy9YWFhg69atUFNTw7/+9a821UsPPCVt0tw6r3nz5uHTTz/t4Na8e6ZOnQpra2t8+eWXsm6KTJw+fRobN27Ew4cPYWJigpCQEEydOvWt3G95l1VVVeH//u//kJSUhOLiYhgaGmLs2LGYO3cuVFRUZN28ty41NRWhoaEi6QEBAVi7di0YhsGWLVuwf/9+lJeXw9nZGV9++WWbvxhSgCOEECKXaB0cIYQQuUQBjhBCiFyiAEcIIUQuUYAjhBAilyjAEUIIkUsU4AghhMglCnCEEELkEgU4QgghcokCHCGEELlEAY4QQohcogBHCCFELlGAI4QQIpcowBFCCJFLFOAIIYTIJQpwhBBC5BIFOEIIIXKJAlwncvjwYdjY2CA1NVWi/FOnToW3t/dbblX7KCkpQXh4OAYOHAgbGxtMnTpV1k2Sio2NDSIiImTdjHbxPl9LbW0tVq1ahaFDh8LOzu69+f1vi82bN8PGxgZ5eXmybkq7U5R1AwhpD+vWrQOXy0VYWBjMzMxgYGDQbN68vDzEx8fDx8cHdnZ2ra6zvcoh744dO3Zg7969mDlzJmxsbKChoSHrJpE2oABHmrVz505ZN0FiFy5cwMCBAzFv3rw35n3y5Am2bNkCU1PTNgWm9iqHvDtSUlLA4XCwZMkSWTeFtAMaoiTNUlZWhrKysqybIZGioiLo6OjIuhmkg/F4PNTW1rZbeYWFhfR7JEcowHVCPB4PmzdvxrBhw+Dg4AA/Pz8cPXpUJJ+4e3BNac+fP8eiRYvg4eEBFxcXfPjhh3j48KFQ3pcvX2Lz5s0YNWoUnJ2d4e7uDj8/P6xbt06idtbU1OC7776Dj48PHBwc4OXlhfDwcDx58kSQp+n+AcMwiI+Ph42NDWxsbHD48GGxZR4+fBihoaEAgKVLlwryv3rPTpJ631QOn8/H1q1bERISAi8vLzg4OGDo0KH46quvUFpaKtH1i5Oamiq4vri4OIwdOxYODg4YNmwYduzYIZK/ufth4u7HNv0sMzMzsXr1agwcOBAuLi6YNm0asrOzAQAnTpxAQEAAnJyc4O3tjf379zfb1pSUFAQGBsLZ2RleXl5YtWoVampqRPJVVlbi22+/xYgRI+Dg4IB+/fph0aJFyM3NFdvmlJQU/Pjjj/Dx8YGTkxOOHTvW4s+soaEBUVFR8PX1haOjI/r27Yu5c+ciIyNDpOy8vDxcunRJ8H5u3ry5xbITEhIwceJEuLu7w8XFBcOHD8fixYtRUlIiyHPz5k1EREQI/g5cXV0RFBSExMREkfIiIiJgY2OD0tJSREREoG/fvnB1dcUnn3yCwsJCAMD+/fsxZswYODo6YvTo0UhKShIqIy8vT9D2P//8E35+fnB0dMTQoUOxefNmNDQ0tHhNTSR9X9r6d/420RBlJ7RhwwbU1NQgODgYQOMf96JFi/Dy5UuMHz/+jefX1NRgypQpcHZ2xsKFC5GXl4c9e/bgk08+wZ9//gk2mw0A+PrrrxEXF4dx48bBxcUFfD4fjx49kmiSS0NDAz788ENcvXoVo0aNwowZM5CTk4PY2FhcuHABcXFx6NatG0aMGAFzc3OEh4fD3d0dgYGBAAA3Nzex5Xp4eCAsLAzbtm3DBx98gD59+gCA4J6dpPW+qZz6+nrs3LkTI0eOxPDhw9GlSxfcunULcXFxuHr1KuLi4trUO963bx+KioowceJEaGlp4ffff8eGDRvQrVs3+Pn5tbpcAFiyZAnU1NTw0UcfoaSkBLt27cKsWbMwf/58bNiwAUFBQZgwYQIOHTqEL7/8EpaWlnB3dxcq486dOzh+/DgmTZoEf39/pKamYu/evXjw4AF27doFBYXG79aVlZUICgpCfn4+JkyYAGtraxQWFiImJgaTJk1CXFwcTE1Nhcpet24dGhoaEBgYCHV1dfTs2bPF6/n8889x7NgxeHl5ITg4GEVFRYiOjkZQUBCio6Nhb28PDw8PrF+/HmvWrIGuri7CwsIANH5BaM6RI0ewZMkSuLu7Y/78+VBVVUV+fj7Onj2L4uJi6OnpAQASExORnZ2N0aNHw9TUFGVlZYiPj8e8efOwYcMGse/XrFmz0K1bN8yfPx+PHz/G3r17MW/ePIwYMQIHDhzAxIkToaysjL179+Kzzz7DX3/9BTMzM6EyTp06hd27dyMkJAQGBgY4efIktmzZgvz8fKxZs6bFn5k070tb/s7fOoZ0GnFxcQyHw2GGDh3KVFRUCNIrKiqYoUOHMh4eHkxtba0gfcqUKcywYcOEypgyZQrD4XCYqKgoofQdO3YwHA6HOXv2rCDNw8ODmTVrVqvaun//fobD4TDr1q0TSj916hTD4XCYzz//XCidw+EwS5Yskajsv//+m+FwOExcXFyb6m2pHD6fL/SzbHLgwAGGw+EwR48ebVX7m+r08vJiysvLBek1NTVM3759mcDAQInKbfpd+PvvvwVpP/zwA8PhcJiPPvqI4fP5gvTdu3czHA6HcXFxYZ48eSJILy4uZhwcHJiFCxeK1MnhcJjExESh9P/+978Mh8Nh/vzzT6E0R0dHJj09XShvXl4e4+rqKtT2pjaPHDmSqampafHn1OT8+fMMh8NhPvvsM6FrSk9PZ+zs7Jjg4GCh/MOGDWOmTJkiUdlz585lXF1dmfr6+hbzVVdXi6TV1NQwI0eOZMaMGSOUvmTJEobD4TD/+c9/hNIjIyMZDofDDBkyhKmsrBS6Dg6Hw2zYsEGQlpuby3A4HMbW1pa5ffu2IJ3P5zOffPIJw+FwmGvXrgnSm9733NxcQZo070tb/s7fNhqi7ISCg4OhqakpeK2pqYmgoCCUl5dL9K1LQUFBMDzXpF+/fgCAnJwcQZqGhgYyMzNx//59qduYmJgIBQUFfPTRR0LpTdO3k5OTwefzpS63o+plsVhQVVUF0DgkXFFRgZKSEsHP6ebNm21q54QJE6ClpSV43aVLF7i4uODRo0dtKhdoHIZmsViC1029s+HDh8PExESQrqenh549e4qts2fPnvDx8RFKmzNnDgAIhuYYhsEff/wBDw8PGBkZoaSkRPCv6XrOnz8vUnZwcDC6dOki0bU01RUWFiZ0Tba2thg6dCiuXLkiNJwoDU1NTbx48QKnT58GwzDN5lNTUxP8f21tLUpLS1FbW4t+/fohKysLVVVVIudMmzZN6HXTe+Dv7y80s9PW1hYaGhpCf3dNBgwYgN69ewtes1gszJo1CwDEDo82kfZ9acvf+dtGQ5SdUK9evUTSLC0tAUCitTBGRkZQUVERSmu6MV9WViZIW7ZsGcLDw+Hn5wczMzP07dsXw4YNg7e3t2CIqjl5eXkwMjKCtra2yDErKyukp6ejtLQU+vr6b2yvNNqzXi6XO+az1wAADG1JREFUi127diE9PR319fVCx8rLy9vUzu7du4uk6ejoCP38W+v1oa6mQCquTm1tbaF7k02afp9eZWRkBC0tLcE9nJKSEpSVleH8+fPo37+/2LaI+z1505Dkq/Ly8qCgoCC2PdbW1khOTkZeXp5gOFEaH330EdLS0jB37lzo6OjA09MTgwcPxpgxY4SCUHFxMb7//nskJyejuLhYpJyKigqR5QjSvgfi7uuKu2YrKysAELmP9ipp35e2/J2/bRTgOqFXv8m2RtM9NnFe/Sbr4+ODkydP4syZM0hLS0NKSgoOHToEd3d37Nq1q8V7UC19I36b2qveEydOYOHChXBycsKyZctgbGwMFRUV8Hg8zJo1q831tPQeSILH4zV7rLkPJWnqbO537NXrbvr/AQMGYPbs2RKX3dQzlsTb/D2ysLAAl8vFxYsXcfHiRVy6dAkrVqzADz/8gOjoaJibm4NhGMycORNZWVmYOnUqHB0doampCTabjbi4OPz5559iRwSa+1m3x3vwJtK+L235O3/bKMB1QllZWRg+fLhIGiD+G2Jb6OjowN/fH/7+/mAYBhs2bMDPP/+M5ORkjBkzptnzzM3Nce7cOVRUVAgNxTW1VUNDA7q6uq1qU0t/+NLU21I5R44cgYqKCvbs2SM0nNb0c+4ozfXqWvoG3x4yMzNF0goKClBZWSnonejp6UFLSwtVVVUYMGDAW2mHubk5zp8/j6ysLNja2goda4/feWVlZQwZMgRDhgwBAJw5cwZz5szBrl278NVXXyEjIwP37t3D3LlzMX/+fKFzDx482Op6JSHuPWhKe72H+KrWvC+t/Tt/2+geXCcUGxuLyspKwevKykrs27cPWlpa8PT0bJc6mu47vYrFYsHe3h7Am4fofHx8wOfzERUVJZR+5swZ3L17t03DH033RMS1QZp6WyqHzWaDxWIJfTtnGAZbt25tVZtby8LCAtevXxdaK1ZeXt7sMor28vDhQ5Hp603LGJruzSkoKMDPzw83b97EX3/9JbYccUN60miqKyoqSqg3d//+fZw8eRJ9+vRp1fAkALH37l7//W76XXm9J3n//v0W74O1h5SUFNy5c0fwmmEY/PzzzwAgcn/0VdK8L239O3/bqAfXCenq6mLSpEmYMGECGIbB4cOHkZ+fj1WrVkl88/5NqqurMXDgQHh7e8Pe3h56enrIy8tDbGwstLW1MWzYsBbPDwgIQHx8PHbs2IEnT57A3d0djx8/RkxMDAwMDLBo0aJWt83Kygrq6uqIiYmBqqoqtLS0oKenh/79+0tVb0vljBo1CsePH8e0adMwbtw4NDQ0ICkpqV0XJUsiJCQEX3zxBaZNmwZ/f39UVFTg4MGDMDExEayrehs4HA6++OILTJo0CT169EBqaiqOHz8OT09P+Pr6CvItXLgQV69exYIFCzBmzBg4OztDSUlJMN2+d+/eWLt2bavb4eXlhTFj/r+9uw1p6n3jAP6djyMnROZ0iJXr56xEluRD9UpnVJo4nYRkPhRR0kg0kkC0FxG0DQzzRYlaOotVUjl1lqFMC3Qg5UMzokjUrHCbBZosN1H2e/HH8d/fh59u9s/fvD7vzr37XOeGcbg45z73fcXj2bNnmJycRGxsrPVzd09PTxQXF9sd+8yZM2CxWIiMjASHw8HPnz+hVCrBYDAgFAoB/GceLDg4GHfu3IHJZEJQUBCGh4dRV1cHHo9nk4DW2q5du5CdnY2TJ0/C19cXarUaGo0GQqEQ4eHhy5670v/F0fv8d6MEtwEVFBTgzZs3UCgU+P79O3bs2LHkehx7MZlMZGdnW+cnjEYj2Gw2BAIBcnJy4Ofnt+z57u7uuHv3LsrLy/H8+XO0tbXB29sbR48eRX5+PjgcjkNjKy0txc2bN3H9+nXMzMwgKioKBw4cWNV1l4tz7NgxGI1GyOVyyGQy681+6dIlREdH2z321UpKSoLBYIBCoYBEIkFgYCDEYjFcXFzw9u3b33bd0NBQFBYWorS0FI8ePQKLxUJGRgYuXrxo8+Tt7e2Nhw8forq6Gi9evIBarYarqyv8/f2xb98+HD9+3OGxlJSUYM+ePVAqlZBKpdi0aRMiIyORl5e37Dq3f3LixAm0tLSgrq4Ok5OT2Lx5M3bv3o3i4mLr17Kurq6oqKiATCaDUqnE9PQ0goODIZPJ8OHDh9+a4AQCAYKCglBRUYHh4WH4+PhALBZDLBb/47kr/V8cvc9/N4blT83mE0IIWXNfv35FXFwcLly4gNzc3D89nD+K5uAIIYQ4JUpwhBBCnBIlOEIIIU6J5uAIIYQ4JXqCI4QQ4pQowRFCCHFKtA6OkHXsy5cvqKysxOvXrzE2NgYPDw/4+voiLCwMKSkpaGhogFKpXFGstfhsXKvVoqmpCe/evcPHjx/x69cvSCSSJesIzszMoLy8HI2NjTAYDPD394dIJMLZs2fh7u6+oH9DQwPkcjmGhobAYrGsawft3W2EbGyU4AhZpwYGBpCZmQk3NzckJyfjr7/+gslkwsjICDo6OuDl5YW0tLQFO75fvnwZXC7XWrRzniOLmue9evUKCoUCXC4XISEh6OvrW7Z/fn4+1Go1UlNTER4ejr6+PpSVlWF0dHTBDiVyuRwSiQRRUVEoKiqCTqeDXC5Hf38/Hj9+bFN2hpAV+T/WniOErEJOTo6Fx+NZ3r9/v+C3ubk5i06nW/Q8Ho+3bNHOwcFBi9lstmtM4+Pj1gKeLS0tSxZ8tVgslpcvX1p4PJ5FIpHYtEskEguPx7P09PRY2378+GHh8/mW1NRUy+zsrLVdrVZbeDyepby83K7xko2N5uAIWadGRkas2z/9LxcXF7u3QaqqqsLBgwdRVFSE7u7uVZWU2bp164qfpFQqFYCFxTvnj5uamqxtarUa09PTyMjIsCkJIxAIEBgYaNOXkJWiBEfIOrVt2zZMTEygtbV1TeNmZmYiPj4ebW1tyMrKQmxsLG7cuIFPnz6t6XUGBgbg5+e3YN9QDocDNpuNgYEBm74AFt0EmM/nY2hoCEajcU3HR5wfJThC1qnz58/D3d0dubm5OHLkCAoLC/HgwQOHa8qFhobi2rVr6OzsxK1bt8Dn81FbW4vExEQkJyejuroaer3e4fEbDIYlnzL9/PxsrmEwGKzti/W1WCzWPoSsFCU4Qtap8PBwPH36FCkpKZiamkJ9fT2uXr2KhIQEpKenO1y01MPDA4cOHUJZWRk0Gg2kUim2bNmCkpISxMTE4PTp0+js7LQ7vslkWrKas6enJ0wmk/V4vozQYv09PT2t8QhZDUpwhKxjISEhkEql0Gg0aG9vh0wmQ0REBHp6eiAWizEzM7Mm12GxWEhJSUFVVRWkUilYLBY0Gg2am5vtjslkMpccn9lsBpPJtB7P1yFcrL/ZbLbGI2Q1aJkAIf8SAQEBCAgIgFAoRHp6Onp7e6HVahEREeFwbK1WC5VKhZaWFoyPj4PD4SAtLQ2pqal2x2Sz2Uu+6tTr9TavI9lstrV9+/btC/oyGAxrH0JWihIcIf8yDAYDfD4fvb29Ds1LDQ0Nobm5Gc3Nzfj8+TNYLBYOHz4MoVCI6OhoMBgMh8YZFhYGlUqFsbExmw9NxsbGYDAYIBAIbPrW1dWhr69vQYLTarUICgqCl5eXQ+MhGw+9oiRknerq6sLs7OyCdpPJhK6uLgDAzp07Vx23tbUVIpEI8fHxqKysBJfLRWlpKTQaDSQSCfbv3+9wcgOAxMREAEBtba1N+/zxf1eQj4uLA5PJhEKhwNzcnLW9vb0do6Oja1ptnmwc9ARHyDolkUgwMTEBgUAAHo8HJpMJnU4HlUqFkZERJCcn27U7SUdHB9zc3HDlyhUkJCSsahusb9++obGxEQAwODhojafT6QAAQqEQAQEBAICYmBjExsaipqYGU1NT2Lt3L/r7+/HkyRMkJSXZvFrdsmUL8vLyIJPJcOrUKSQmJkKv16OmpgZcLnfBWjpCVoLK5RCyTnV2dkKtVqOnpwd6vR5TU1Pw9vYGj8eDUCiESCSCi8vClzAhISGIiorC/fv3F41rNBrtft3X3d2NrKysJX+/d+8eoqOjrcdmsxm3b9+GSqWyLhsQiUQ4d+7contR1tfXQy6XY3h4GCwWCzExMSgoKICPj49d4yUbGyU4QgghTonm4AghhDglSnCEEEKcEiU4QgghTokSHCGEEKdECY4QQohTogRHCCHEKVGCI4QQ4pQowRFCCHFKlOAIIYQ4JUpwhBBCnNLfSwMwAEEbPYcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "\n",
    "#bin by equal #samples per bin\n",
    "numbins = 10\n",
    "binsize = len(numsamples_sorted)//numbins\n",
    "print(len(numsamples_sorted)%numbins)\n",
    "mean = []\n",
    "sd = []\n",
    "startindex = 0\n",
    "for i in range(numbins):\n",
    "    if i == numbins-1: #last bin\n",
    "        currint = hyperp_sorted[startindex:len(hyperp_sorted)]\n",
    "    else:\n",
    "        currint = hyperp_sorted[startindex:startindex+binsize]\n",
    "\n",
    "    mean.append(np.mean(currint))\n",
    "    sd.append(np.std(currint))\n",
    "    startindex = startindex+binsize\n",
    "\n",
    "plt.scatter(range(1,numbins+1), mean)\n",
    "plt.errorbar(x=range(1,numbins+1), y=mean, yerr=sd)\n",
    "plt.xlabel(\"bins of total number of samples\\nST>100\")\n",
    "plt.ylabel(\"best lambda\\n(last of ties)\")\n",
    "plt.savefig(\"040121_2.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABA, areas > 150"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestparams = pd.read_csv(\"/home/slu/spatial/data/hyperparamCV_lasso/ABA_bestparams_032421.csv\", sep=',')\n",
    "bestparams = bestparams.set_index(bestparams.columns)\n",
    "bestscore = pd.read_csv(\"/home/slu/spatial/data/hyperparamCV_lasso/ABA_bestscore_032421.csv\")\n",
    "meantestscore = np.load('/home/slu/spatial/data/hyperparamCV_lasso/ABA_meantestscore_032421.npy',allow_pickle='TRUE').item()\n",
    "teststd = np.load('/home/slu/spatial/data/hyperparamCV_lasso/ABA_meanteststd_032421.npy',allow_pickle='TRUE').item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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       "632    0  0.01  0.01  0.01  0.01  0.01  0.01  0.01  0.01  0.01  ...   0.01   \n",
       "821    0  0.00  0.01  0.01  0.01  0.01  0.01  0.01  0.01  0.01  ...   0.01   \n",
       "733    0  0.00  0.00  0.01  0.01  0.01  0.01  0.01  0.01  0.01  ...   0.01   \n",
       "381    0  0.00  0.00  0.00  0.01  0.01  0.01  0.01  0.01  0.01  ...   0.01   \n",
       "26     0  0.00  0.00  0.00  0.00  0.01  0.01  0.01  0.01  0.01  ...   0.01   \n",
       "\n",
       "     10683  10713   262  10687   952   854   284   276   202  \n",
       "632   0.01   0.01  0.01   0.01  0.01  0.01  0.01  0.01  0.01  \n",
       "821   0.01   0.01  0.01   0.01  0.01  0.01  0.01  0.01  0.01  \n",
       "733   0.01   0.01  0.01   0.01  0.01  0.01  0.01  0.01  0.01  \n",
       "381   0.01   0.01  0.01   0.01  0.01  0.01  0.01  0.01  0.01  \n",
       "26    0.01   0.01  0.01   0.01  0.01  0.01  0.01  0.01  0.01  \n",
       "\n",
       "[5 rows x 86 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestparams.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "histdata = bestparams.values[np.triu_indices_from(bestparams,k=1)]\n",
    "#uniq, counts = np.unique(histdata, return_counts=True)\n",
    "#plt.bar(x=uniq, height=counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of brain area\\ncomparisons')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(histdata)\n",
    "plt.xlabel(\"best lambda for ABA CV areas>150\")\n",
    "plt.ylabel(\"number of brain area\\ncomparisons\")\n",
    "#plt.savefig('032521_1.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1. , 1. , 1. , 0.5, 0.5])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meantestscore['0,1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#meantestscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALLEN_FILT_PATH = \"/home/slu/spatial/data/ABAISH_filt_v6_avgdup.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data():\n",
    "    \"\"\"read in all datasets needed using pandas\"\"\"\n",
    "    metabrain = pd.read_hdf(ALLEN_FILT_PATH, key='metabrain', mode='r')\n",
    "    voxbrain = pd.read_hdf(ALLEN_FILT_PATH, key='avgvoxbrain', mode='r')\n",
    "    propontvox = pd.read_hdf(ALLEN_FILT_PATH, key='propontology', mode='r')\n",
    "    #geneIDName = pd.read_hdf(ALLEN_FILT_PATH, key='geneIDName', mode='r')\t\n",
    "\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\n",
    "\n",
    "    return metabrain, voxbrain, propontvox, ontology\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86\n",
      "(28170, 19934)\n",
      "(28170, 86)\n"
     ]
    }
   ],
   "source": [
    "#pre-processing\n",
    "metabrain, voxbrain, propontvox, ontology = read_data()\n",
    "propontvox = filterproponto(propontvox)\n",
    "#get leaf areas only\n",
    "leaves = getleaves(propontvox,ontology)\n",
    "leafpropontvox = propontvox.loc[metabrain.ids.isin(leaves),leaves] #subset propontvox for leaf areas\n",
    "leafvoxbrain = voxbrain.loc[metabrain.ids.isin(leaves),:] #subset voxbrain for voxels from leaves\n",
    "\n",
    "#subset for sufficient samples size for hyperparameter selection\n",
    "leafsizes = leafpropontvox.sum()\n",
    "#plt.hist(leafsizes, bins=100)\n",
    "#plt.xlabel(\"leaf brain area sizes ABA\")\n",
    "#plt.ylabel(\"number of brain areas\")\n",
    "#plt.savefig(\"032321.pdf\", bbox_inches='tight')\n",
    "#filter propogated ontology for brain areas with X number of samples min\n",
    "leafsizes_sub = leafsizes[leafsizes>150]  #139 areas\n",
    "leafpropontvox_sub = leafpropontvox.loc[:,leafsizes_sub.index]\n",
    "#remove areas (rows) from propogated ontology that don't have any samples\n",
    "rowsum = leafpropontvox_sub.sum(axis=1)\n",
    "leafpropontvox_sub = leafpropontvox_sub.loc[rowsum > 0, :]\n",
    "#index samples for rows to match porp ont\n",
    "leafvoxbrain_sub = leafvoxbrain.loc[rowsum > 0, :]\n",
    "print(len(leafsizes_sub))\n",
    "print(leafvoxbrain_sub.shape)\n",
    "print(leafpropontvox_sub.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "numsamples = pd.DataFrame(index=list(bestparams), columns=list(bestparams))\n",
    "numsamples = numsamples.fillna(0)\n",
    "\n",
    "areas = list(bestparams)\n",
    "#for each column, brain area\n",
    "for i in range(bestparams.shape[1]):\n",
    "    #for each row in each column\n",
    "    for j in range(i+1,bestparams.shape[1]): #upper triangular!\n",
    "        area1 = areas[i]\n",
    "        area2 = areas[j]\n",
    "        numsamples.iloc[i,j] = leafsizes[area1] + leafsizes[area2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "numsamples = numsamples.values[np.triu_indices_from(numsamples,k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#alpha as a function of brain area size\n",
    "plt.scatter(numsamples,histdata)\n",
    "plt.xlabel(\"total number of samples\\nABA\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "#plt.savefig('032521_2.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f878ca0f940>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(numsamples, histdata, kind='hex').set_axis_labels(\"total number of samples\\nABA\",\"best lambda\")\n",
    "#plt.savefig('032521_3.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define 'sliding window' as a uniform dist\n",
    "uniform = [1/1000] * 1000\n",
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "#convolve\n",
    "hyperp_conv = np.convolve(hyperp_sorted, uniform, mode='same')\n",
    "\n",
    "plt.scatter(numsamples_sorted, hyperp_conv, s=3, rasterized=True)\n",
    "plt.xlabel(\"total number of samples\\nABA > 150\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig(\"033121_5.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "\n",
    "#bin by equal #samples per bin\n",
    "numbins = 100\n",
    "binsize = len(numsamples_sorted)//numbins\n",
    "print(len(numsamples_sorted)%numbins)\n",
    "mean = []\n",
    "sd = []\n",
    "startindex = 0\n",
    "for i in range(numbins):\n",
    "    if i == numbins-1: #last bin\n",
    "        currint = hyperp_sorted[startindex:len(hyperp_sorted)]\n",
    "    else:\n",
    "        currint = hyperp_sorted[startindex:startindex+binsize]\n",
    "\n",
    "    mean.append(np.mean(currint))\n",
    "    sd.append(np.std(currint))\n",
    "    startindex = startindex+binsize\n",
    "\n",
    "plt.scatter(range(1,numbins+1), mean, rasterized=True)\n",
    "plt.errorbar(x=range(1,numbins+1), y=mean, yerr=sd)\n",
    "plt.xlabel(\"bins of total number of samples\\nABA>150\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig(\"033121_6.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looking into ties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "lastofties = pd.DataFrame(index=list(bestparams), columns=list(bestparams))\n",
    "lastofties = lastofties.fillna(0)\n",
    "\n",
    "alphas = [0.01, 0.05, 0.1, 0.2, 0.5, 0.9]\n",
    "#for each column, brain area\n",
    "for i in range(bestparams.shape[1]):\n",
    "    #for each row in each column\n",
    "    for j in range(i+1,bestparams.shape[1]): #upper triangular!\n",
    "        currkey = \"%d,%d\" %(i,j)\n",
    "        currscores = meantestscore[currkey]\n",
    "        bestindex = 0\n",
    "        bestscore = -1\n",
    "        for k in range(len(currscores)):\n",
    "            if currscores[k] >= bestscore:\n",
    "                bestscore = currscores[k]\n",
    "                bestindex = k\n",
    "        lastofties.iloc[i,j] = alphas[bestindex]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "histdata = lastofties.values[np.triu_indices_from(lastofties,k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "\n",
    "#bin by equal #samples per bin\n",
    "numbins = 10\n",
    "binsize = len(numsamples_sorted)//numbins\n",
    "print(len(numsamples_sorted)%numbins)\n",
    "mean = []\n",
    "sd = []\n",
    "startindex = 0\n",
    "for i in range(numbins):\n",
    "    if i == numbins-1: #last bin\n",
    "        currint = hyperp_sorted[startindex:len(hyperp_sorted)]\n",
    "    else:\n",
    "        currint = hyperp_sorted[startindex:startindex+binsize]\n",
    "\n",
    "    mean.append(np.mean(currint))\n",
    "    sd.append(np.std(currint))\n",
    "    startindex = startindex+binsize\n",
    "\n",
    "plt.scatter(range(1,numbins+1), mean)\n",
    "plt.errorbar(x=range(1,numbins+1), y=mean, yerr=sd)\n",
    "plt.xlabel(\"bins of total number of samples\\nABA>100\")\n",
    "plt.ylabel(\"best lambda\\n(last of ties)\")\n",
    "plt.savefig(\"040121_3.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABA, areas > 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestparams = pd.read_csv(\"/home/slu/spatial/data/hyperparamCV_lasso/ABA_bestparams_032521.csv\", sep=',')\n",
    "bestparams = bestparams.set_index(bestparams.columns)\n",
    "bestscore = pd.read_csv(\"/home/slu/spatial/data/hyperparamCV_lasso/ABA_bestscore_032521.csv\")\n",
    "meantestscore = np.load('/home/slu/spatial/data/hyperparamCV_lasso/ABA_meantestscore_032521.npy',allow_pickle='TRUE').item()\n",
    "teststd = np.load('/home/slu/spatial/data/hyperparamCV_lasso/ABA_meanteststd_032521.npy',allow_pickle='TRUE').item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139, 139)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestparams.head()\n",
    "bestparams.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "histdata = bestparams.values[np.triu_indices_from(bestparams,k=1)]\n",
    "#uniq, counts = np.unique(histdata, return_counts=True)\n",
    "#plt.bar(x=uniq, height=counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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(histdata)\n",
    "plt.xlabel(\"best lambda for ABA CV areas>100\")\n",
    "plt.ylabel(\"number of brain area\\ncomparisons\")\n",
    "plt.savefig('033121_7.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1. , 1. , 1. , 0.5, 0.5])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meantestscore['0,1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#meantestscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "ALLEN_FILT_PATH = \"/home/slu/spatial/data/ABAISH_filt_v6_avgdup.h5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data():\n",
    "    \"\"\"read in all datasets needed using pandas\"\"\"\n",
    "    metabrain = pd.read_hdf(ALLEN_FILT_PATH, key='metabrain', mode='r')\n",
    "    voxbrain = pd.read_hdf(ALLEN_FILT_PATH, key='avgvoxbrain', mode='r')\n",
    "    propontvox = pd.read_hdf(ALLEN_FILT_PATH, key='propontology', mode='r')\n",
    "    #geneIDName = pd.read_hdf(ALLEN_FILT_PATH, key='geneIDName', mode='r')\t\n",
    "\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\n",
    "\n",
    "    return metabrain, voxbrain, propontvox, ontology\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "139\n",
      "(34661, 19934)\n",
      "(34661, 139)\n"
     ]
    }
   ],
   "source": [
    "#pre-processing\n",
    "metabrain, voxbrain, propontvox, ontology = read_data()\n",
    "propontvox = filterproponto(propontvox)\n",
    "#get leaf areas only\n",
    "leaves = getleaves(propontvox,ontology)\n",
    "leafpropontvox = propontvox.loc[metabrain.ids.isin(leaves),leaves] #subset propontvox for leaf areas\n",
    "leafvoxbrain = voxbrain.loc[metabrain.ids.isin(leaves),:] #subset voxbrain for voxels from leaves\n",
    "\n",
    "#subset for sufficient samples size for hyperparameter selection\n",
    "leafsizes = leafpropontvox.sum()\n",
    "#plt.hist(leafsizes, bins=100)\n",
    "#plt.xlabel(\"leaf brain area sizes ABA\")\n",
    "#plt.ylabel(\"number of brain areas\")\n",
    "#plt.savefig(\"032321.pdf\", bbox_inches='tight')\n",
    "#filter propogated ontology for brain areas with X number of samples min\n",
    "leafsizes_sub = leafsizes[leafsizes>100]  #139 areas\n",
    "leafpropontvox_sub = leafpropontvox.loc[:,leafsizes_sub.index]\n",
    "#remove areas (rows) from propogated ontology that don't have any samples\n",
    "rowsum = leafpropontvox_sub.sum(axis=1)\n",
    "leafpropontvox_sub = leafpropontvox_sub.loc[rowsum > 0, :]\n",
    "#index samples for rows to match porp ont\n",
    "leafvoxbrain_sub = leafvoxbrain.loc[rowsum > 0, :]\n",
    "print(len(leafsizes_sub))\n",
    "print(leafvoxbrain_sub.shape)\n",
    "print(leafpropontvox_sub.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "numsamples = pd.DataFrame(index=list(bestparams), columns=list(bestparams))\n",
    "numsamples = numsamples.fillna(0)\n",
    "\n",
    "areas = list(bestparams)\n",
    "#for each column, brain area\n",
    "for i in range(bestparams.shape[1]):\n",
    "    #for each row in each column\n",
    "    for j in range(i+1,bestparams.shape[1]): #upper triangular!\n",
    "        area1 = areas[i]\n",
    "        area2 = areas[j]\n",
    "        numsamples.iloc[i,j] = leafsizes[area1] + leafsizes[area2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "numsamples = numsamples.values[np.triu_indices_from(numsamples,k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#alpha as a function of brain area size\n",
    "plt.scatter(numsamples,histdata)\n",
    "plt.xlabel(\"total number of samples\\nABA > 100\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig('033121_8.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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(numsamples, histdata, kind='hex').set_axis_labels(\"total number of samples\\nABA > 100\",\"best lambda\")\n",
    "plt.savefig('033121_9.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define 'sliding window' as a uniform dist\n",
    "uniform = [1/1000] * 1000\n",
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "#convolve\n",
    "hyperp_conv = np.convolve(hyperp_sorted, uniform, mode='same')\n",
    "\n",
    "plt.scatter(numsamples_sorted, hyperp_conv, s=3, rasterized=True)\n",
    "plt.xlabel(\"total number of samples\\nABA > 100\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig(\"033121_10.pdf\", bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "\n",
    "#bin by equal #samples per bin\n",
    "numbins = 50\n",
    "binsize = len(numsamples_sorted)//numbins\n",
    "print(len(numsamples_sorted)%numbins)\n",
    "mean = []\n",
    "sd = []\n",
    "startindex = 0\n",
    "for i in range(numbins):\n",
    "    if i == numbins-1: #last bin\n",
    "        currint = hyperp_sorted[startindex:len(hyperp_sorted)]\n",
    "    else:\n",
    "        currint = hyperp_sorted[startindex:startindex+binsize]\n",
    "\n",
    "    mean.append(np.mean(currint))\n",
    "    sd.append(np.std(currint))\n",
    "    startindex = startindex+binsize\n",
    "\n",
    "plt.scatter(range(1,numbins+1), mean, rasterized=True)\n",
    "plt.errorbar(x=range(1,numbins+1), y=mean, yerr=sd)\n",
    "plt.xlabel(\"bins of total number of samples\\nABA>100\")\n",
    "plt.ylabel(\"best lambda\")\n",
    "plt.savefig(\"033121_11.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looking into ties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "lastofties = pd.DataFrame(index=list(bestparams), columns=list(bestparams))\n",
    "lastofties = lastofties.fillna(0)\n",
    "\n",
    "alphas = [0.01, 0.05, 0.1, 0.2, 0.5, 0.9]\n",
    "#for each column, brain area\n",
    "for i in range(bestparams.shape[1]):\n",
    "    #for each row in each column\n",
    "    for j in range(i+1,bestparams.shape[1]): #upper triangular!\n",
    "        currkey = \"%d,%d\" %(i,j)\n",
    "        currscores = meantestscore[currkey]\n",
    "        bestindex = 0\n",
    "        bestscore = -1\n",
    "        for k in range(len(currscores)):\n",
    "            if currscores[k] >= bestscore:\n",
    "                bestscore = currscores[k]\n",
    "                bestindex = k\n",
    "        lastofties.iloc[i,j] = alphas[bestindex]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "histdata = lastofties.values[np.triu_indices_from(lastofties,k=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#alpha as a function of brain area size\n",
    "plt.scatter(numsamples,histdata)\n",
    "plt.xlabel(\"total number of samples\\nABA > 100\")\n",
    "plt.ylabel(\"best lambda\\n(last of ties)\")\n",
    "#plt.savefig('033121_8.pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort both lists by total number of samples\n",
    "numsamples_sorted = sorted(numsamples)\n",
    "hyperp_sorted = [x for y, x in sorted(zip(numsamples_sorted, histdata))]\n",
    "\n",
    "#bin by equal #samples per bin\n",
    "numbins = 10\n",
    "binsize = len(numsamples_sorted)//numbins\n",
    "print(len(numsamples_sorted)%numbins)\n",
    "mean = []\n",
    "sd = []\n",
    "startindex = 0\n",
    "for i in range(numbins):\n",
    "    if i == numbins-1: #last bin\n",
    "        currint = hyperp_sorted[startindex:len(hyperp_sorted)]\n",
    "    else:\n",
    "        currint = hyperp_sorted[startindex:startindex+binsize]\n",
    "\n",
    "    mean.append(np.mean(currint))\n",
    "    sd.append(np.std(currint))\n",
    "    startindex = startindex+binsize\n",
    "\n",
    "plt.scatter(range(1,numbins+1), mean)\n",
    "plt.errorbar(x=range(1,numbins+1), y=mean, yerr=sd)\n",
    "plt.xlabel(\"bins of total number of samples\\nABA>100\")\n",
    "plt.ylabel(\"best lambda\\n(last of ties)\")\n",
    "plt.savefig(\"040121_4.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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