decodingCallTypesNaiveBayesPart2-PerRegion.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies \n",
    "import os\n",
    "import glob\n",
    "import re\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pickle as pk\n",
    "import multiprocessing as mp\n",
    "from scipy.stats import t\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from scipy.special import comb\n",
    "from scipy.stats import norm\n",
    "from decoders import naiveBayes\n",
    "import itertools\n",
    "import h5py\n",
    "\n",
    "import config\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some paths\n",
    "rootPath = '/Users/frederictheunissen/Code/songephys/'\n",
    "dataPath = 'data/birds/'\n",
    "\n",
    "\n",
    "# Color code for call type\n",
    "callColor = {'Be': (0/255.0, 230/255.0, 255/255.0), 'LT': (0/255.0, 95/255.0, 255/255.0), 'Tu': (255/255.0, 200/255.0, 65/255.0), 'Th': (255/255.0, 150/255.0, 40/255.0), \n",
    "             'Di': (255/255.0, 105/255.0, 15/255.0), 'Ag': (255/255.0, 0/255.0, 0/255.0), 'Wh': (255/255.0, 180/255.0, 255/255.0), 'Ne': (255/255.0, 100/255.0, 255/255.0),\n",
    "             'Te': (140/255.0, 100/255.0, 185/255.0), 'DC': (100/255.0, 50/255.0, 200/255.0), 'So': (255/255.0, 255/255.0, 255/255.0)}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data base\n",
    "outPath = rootPath+dataPath+'HerminaDataBase.pkl'\n",
    "fileIn = open(outPath,\"rb\")\n",
    "dfDataBase = pk.load(fileIn)\n",
    "pcKDE = pk.load(fileIn)\n",
    "fileIn.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfDataBaseNW = dfDataBase[dfDataBase['call'] != 'Wh']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of Units without Whines\n",
    "unitNames = np.unique(dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'])\n",
    "nunits = unitNames.shape[0]\n",
    "\n",
    "# Number of Calls\n",
    "callNames = np.unique(dfDataBaseNW['call'])\n",
    "ncalls = callNames.shape[0]\n",
    "\n",
    "# Stims per Units\n",
    "nstimsUnit = dict()\n",
    "unitNamesFull = []\n",
    "for unit in unitNames:\n",
    "    nstimsUnit[unit] = len(np.unique(dfDataBaseNW['stim'][dfDataBaseNW['site']+'_'+ dfDataBaseNW['unit'] == unit]))\n",
    "    # Select only Units with 110 stimuli (for now) or 97  if NW\n",
    "    if (nstimsUnit[unit] == 97):\n",
    "        unitNamesFull.append(unit)\n",
    "    \n",
    "nunitsFull = len(unitNamesFull)\n",
    "\n",
    "print(nunitsFull)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191\n"
     ]
    }
   ],
   "source": [
    "# Number of Units with Whines\n",
    "unitNames = np.unique(dfDataBase['site']+'_'+ dfDataBase['unit'])\n",
    "nunits = unitNames.shape[0]\n",
    "\n",
    "# Number of Calls\n",
    "callNames = np.unique(dfDataBase['call'])\n",
    "ncalls = callNames.shape[0]\n",
    "\n",
    "# Stims per Units\n",
    "nstimsUnit = dict()\n",
    "unitNamesFull = []\n",
    "for unit in unitNames:\n",
    "    nstimsUnit[unit] = len(np.unique(dfDataBase['stim'][dfDataBase['site']+'_'+ dfDataBase['unit'] == unit]))\n",
    "    # Select only Units with 110 stimuli (for now) or 97  if NW\n",
    "    if (nstimsUnit[unit] == 110):\n",
    "        unitNamesFull.append(unit)\n",
    "    \n",
    "nunitsFull = len(unitNamesFull)\n",
    "\n",
    "print(nunitsFull)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the results of Part 1 Runs that includes all neurons\n",
    "\n",
    "# Read the PC and Confusion matrices data base\n",
    "# Use the following data if NW\n",
    "#inPath = rootPath+dataPath+'HerminaDataBase1UnitPCNW.pkl'\n",
    "\n",
    "inPath = rootPath+dataPath+'HerminaDataBase1UnitPC.pkl'\n",
    "fileIn = open(inPath,\"rb\")\n",
    "unitNamesFull = pk.load(fileIn)\n",
    "confMatTot = pk.load(fileIn)\n",
    "pcc = pk.load(fileIn)\n",
    "fileIn.close()\n",
    "\n",
    "# These are the neurons that we will be keeping  Use 13 if NW 12 otherwise.\n",
    "indGood = np.argwhere(pcc > 12).flatten()\n",
    "nGood = indGood.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the csv data file that has the area information\n",
    "\n",
    "electrodeH = pd.read_csv(rootPath+dataPath+'HerminaGoodUnitsInfoRegion.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Unnamed: 0.1</th>\n",
       "      <th>unitName</th>\n",
       "      <th>birdName</th>\n",
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       "      <th>electrode</th>\n",
       "      <th>cluster</th>\n",
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       "      <th>rMean</th>\n",
       "      <th>rMin</th>\n",
       "      <th>rMax</th>\n",
       "      <th>rStd</th>\n",
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       "      <th>region</th>\n",
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       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>25.980101</td>\n",
       "      <td>0.047651</td>\n",
       "      <td>0.080397</td>\n",
       "      <td>22.554656</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>16.203165</td>\n",
       "      <td>1.272586</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>L_NCM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>ZF4F_2t_190612_101201_goodPlayback-e16-c66.pkl</td>\n",
       "      <td>ZF4F</td>\n",
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       "      <td>17</td>\n",
       "      <td>55</td>\n",
       "      <td>17.988497</td>\n",
       "      <td>0.069760</td>\n",
       "      <td>0.099178</td>\n",
       "      <td>8.152294</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>10.661459</td>\n",
       "      <td>0.920148</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>CMM</td>\n",
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       "    <tr>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>ZF4F_2t_190612_101201_goodPlayback-e17-c36.pkl</td>\n",
       "      <td>ZF5M</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12</td>\n",
       "      <td>76</td>\n",
       "      <td>17.937830</td>\n",
       "      <td>0.070974</td>\n",
       "      <td>0.103737</td>\n",
       "      <td>2.468599</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.208191</td>\n",
       "      <td>0.729407</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>ZF4F_2t_190612_101201_goodPlayback-e17-c38.pkl</td>\n",
       "      <td>ZF7F</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>17.927170</td>\n",
       "      <td>0.186117</td>\n",
       "      <td>0.678361</td>\n",
       "      <td>1.594718</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.217952</td>\n",
       "      <td>0.414690</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>ZF4F_2t_190612_101201_goodPlayback-e19-c2.pkl</td>\n",
       "      <td>ZF4F</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>62</td>\n",
       "      <td>17.566820</td>\n",
       "      <td>0.024578</td>\n",
       "      <td>0.618425</td>\n",
       "      <td>3.368159</td>\n",
       "      <td>-46.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>11.679969</td>\n",
       "      <td>0.288012</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>CLM</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>ZF7F_7t_190806_115742_goodPlayback-e17-c31.pkl</td>\n",
       "      <td>ZF6M</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15</td>\n",
       "      <td>32</td>\n",
       "      <td>11.411733</td>\n",
       "      <td>0.068468</td>\n",
       "      <td>0.524894</td>\n",
       "      <td>0.569120</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.299242</td>\n",
       "      <td>0.247303</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CMM</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>ZF7F_7t_190806_115742_goodPlayback-e19-c32.pkl</td>\n",
       "      <td>ZF5M</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20</td>\n",
       "      <td>66</td>\n",
       "      <td>11.253594</td>\n",
       "      <td>0.077864</td>\n",
       "      <td>0.475473</td>\n",
       "      <td>-0.272182</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.363807</td>\n",
       "      <td>-0.196279</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NCM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>97</td>\n",
       "      <td>ZF7F_7t_190806_115742_goodPlayback-e19-c34.pkl</td>\n",
       "      <td>ZF6M</td>\n",
       "      <td>7.5</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>11.112370</td>\n",
       "      <td>0.138303</td>\n",
       "      <td>0.324483</td>\n",
       "      <td>0.607692</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.519388</td>\n",
       "      <td>0.184150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "      <td>ZF7F_8_5t_190809_133540_goodPlayback-e17-c29.pkl</td>\n",
       "      <td>ZF4F</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12</td>\n",
       "      <td>35</td>\n",
       "      <td>10.659376</td>\n",
       "      <td>0.019704</td>\n",
       "      <td>0.858307</td>\n",
       "      <td>0.223950</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.856264</td>\n",
       "      <td>0.172524</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CLM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
       "      <td>ZF7F_8_5t_190809_133540_goodPlayback-e19-c2.pkl</td>\n",
       "      <td>ZF6M</td>\n",
       "      <td>9.5</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>10.335949</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>0.264680</td>\n",
       "      <td>1.159744</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.826174</td>\n",
       "      <td>0.491433</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>L_NCM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Unnamed: 0  Unnamed: 0.1  \\\n",
       "0            0             0   \n",
       "1            1             1   \n",
       "2            2             2   \n",
       "3            3             3   \n",
       "4            4             4   \n",
       "..         ...           ...   \n",
       "95          95            95   \n",
       "96          96            96   \n",
       "97          97            97   \n",
       "98          98            98   \n",
       "99          99            99   \n",
       "\n",
       "                                            unitName birdName  depth  \\\n",
       "0     ZF4F_2t_190612_101201_goodPlayback-e11-c31.pkl     ZF5M    9.5   \n",
       "1     ZF4F_2t_190612_101201_goodPlayback-e16-c66.pkl     ZF4F    2.0   \n",
       "2     ZF4F_2t_190612_101201_goodPlayback-e17-c36.pkl     ZF5M    8.0   \n",
       "3     ZF4F_2t_190612_101201_goodPlayback-e17-c38.pkl     ZF7F    7.0   \n",
       "4      ZF4F_2t_190612_101201_goodPlayback-e19-c2.pkl     ZF4F    2.0   \n",
       "..                                               ...      ...    ...   \n",
       "95    ZF7F_7t_190806_115742_goodPlayback-e17-c31.pkl     ZF6M    2.0   \n",
       "96    ZF7F_7t_190806_115742_goodPlayback-e19-c32.pkl     ZF5M   11.0   \n",
       "97    ZF7F_7t_190806_115742_goodPlayback-e19-c34.pkl     ZF6M    7.5   \n",
       "98  ZF7F_8_5t_190809_133540_goodPlayback-e17-c29.pkl     ZF4F    3.0   \n",
       "99   ZF7F_8_5t_190809_133540_goodPlayback-e19-c2.pkl     ZF6M    9.5   \n",
       "\n",
       "    electrode  cluster  decodingpcc  selectivityH  selectivityZ      rMean  \\\n",
       "0          10      115    25.980101      0.047651      0.080397  22.554656   \n",
       "1          17       55    17.988497      0.069760      0.099178   8.152294   \n",
       "2          12       76    17.937830      0.070974      0.103737   2.468599   \n",
       "3          13        0    17.927170      0.186117      0.678361   1.594718   \n",
       "4          11       62    17.566820      0.024578      0.618425   3.368159   \n",
       "..        ...      ...          ...           ...           ...        ...   \n",
       "95         15       32    11.411733      0.068468      0.524894   0.569120   \n",
       "96         20       66    11.253594      0.077864      0.475473  -0.272182   \n",
       "97         11        7    11.112370      0.138303      0.324483   0.607692   \n",
       "98         12       35    10.659376      0.019704      0.858307   0.223950   \n",
       "99         21       27    10.335949      0.051546      0.264680   1.159744   \n",
       "\n",
       "    rMin  rMax       rStd         z  xpos  ypos region  \n",
       "0  -35.0  81.0  16.203165  1.272586   2.0   1.0  L_NCM  \n",
       "1  -18.0  73.0  10.661459  0.920148   1.0   1.0    CMM  \n",
       "2   -4.0  14.0   3.208191  0.729407   2.0   4.0      L  \n",
       "3  -10.0  23.0   4.217952  0.414690   1.0   2.0      L  \n",
       "4  -46.0  42.0  11.679969  0.288012   2.0   3.0    CLM  \n",
       "..   ...   ...        ...       ...   ...   ...    ...  \n",
       "95  -8.0  11.0   2.299242  0.247303   1.0   0.0    CMM  \n",
       "96  -7.0   8.0   1.363807 -0.196279   1.0   4.0    NCM  \n",
       "97 -20.0  37.0   3.519388  0.184150   1.0   4.0      L  \n",
       "98 -14.0  11.0   2.856264  0.172524   2.0   4.0    CLM  \n",
       "99  -7.0  17.0   2.826174  0.491433   2.0   1.0  L_NCM  \n",
       "\n",
       "[100 rows x 18 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "electrodeH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Border : 6 units\n",
      "L-CML : 56 units\n",
      "NCM-CMM : 38 units\n"
     ]
    }
   ],
   "source": [
    "pccGood = pcc[indGood]\n",
    "unitNamesGood = [unitNamesFull[i] for i in indGood]\n",
    "\n",
    "zoneName = []\n",
    "regionName = []\n",
    "for i, unit in enumerate(unitNamesGood):\n",
    "    region = electrodeH.loc[electrodeH['unitName'] == unit]['region']\n",
    "    if len(region):\n",
    "        reg = region.array[0]\n",
    "    else:\n",
    "        reg = 'NA'\n",
    "    regionName.append(reg)\n",
    "    if (('CMM' in reg) | ('NCM' in reg) & ~('L' in reg)):\n",
    "        zoneName.append('NCM-CMM')\n",
    "    elif ( reg == 'L_NCM'):\n",
    "        zoneName.append('Border')\n",
    "    else:\n",
    "        zoneName.append('L-CML')\n",
    "\n",
    "indSort = np.flip(np.argsort(pccGood))\n",
    "\n",
    "# Printing the name of the most discriminating units\n",
    "zoneUnique = np.unique(zoneName)\n",
    "for izone, zone in enumerate(zoneUnique):\n",
    "    nGoodZone = len([x for x in zoneName if x == zone])\n",
    "    print(zone, ':', nGoodZone, 'units')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decoding for a variable number of units.  \n",
    "# For 1 and 2 and when combinations are smaller that maximum number of tests, all permutations are done. \n",
    "\n",
    "# FIrst make the list of arguments so that the multiprocessing can be called out.\n",
    "# We are doing 1,2,3,4,5,10,20\n",
    "\n",
    "numUnits = 20\n",
    "code = 'PC'\n",
    "maxTest = 1000\n",
    "mpflag = True\n",
    "nZones = len(zoneUnique)\n",
    "argListZones = [[] for i in range(nZones)]\n",
    "ntests = np.zeros((nZones,), dtype=int)\n",
    "\n",
    "for izone, zone in enumerate(zoneUnique):\n",
    "    nGoodZone= len([x for x in zoneName if x == zone])\n",
    "    \n",
    "    if (nGoodZone < numUnits) | (zone == 'Border'):\n",
    "        continue\n",
    "        \n",
    "    ntests[izone] = int(comb(nGoodZone,numUnits))\n",
    "    if ntests[izone] > maxTest:\n",
    "        ntests[izone] = maxTest + 1\n",
    "    \n",
    "    # Generate the good index for this bird\n",
    "    indGoodZone = [ind for ind, zonelist in enumerate(zoneName) if zonelist == zone]\n",
    "    unitListZone = [unitNamesGood[i] for i  in indGoodZone]\n",
    "\n",
    "    # Generate the list of arguments to run naiveBayes in parallel\n",
    "    argList = argListZones[izone]\n",
    "    if numUnits == 1:\n",
    "        for i, unit in enumerate(unitListZone):\n",
    "            if i == ntests[izone]:\n",
    "                break\n",
    "            unitChoice = [unit]\n",
    "            argList.append(unitChoice)\n",
    "    elif numUnits == 2:  \n",
    "        i = 0\n",
    "        for i1, unit1 in enumerate(unitListZone):\n",
    "            if i == ntests[izone]:\n",
    "                break\n",
    "            for i2, unit2 in enumerate(unitListZone):\n",
    "                if i2 <= i1:\n",
    "                    continue\n",
    "                if i == ntests[izone]:\n",
    "                    break\n",
    "                unitChoice = [unit1, unit2]\n",
    "                argList.append(unitChoice)\n",
    "                i += 1\n",
    "    elif ntests[izone] <= maxTest:\n",
    "        # Generate all possible combinations\n",
    "        for unitChoice in itertools.combinations(unitListZone, numUnits):\n",
    "            argList.append(list(unitChoice))\n",
    "    else:\n",
    "        for i in range(ntests[izone]):\n",
    "            unitChoice = np.random.choice(unitListZone, numUnits, replace=False)\n",
    "            argList.append(unitChoice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with chunk 1/11 for zone 2/3\n",
      "Done with chunk 2/11 for zone 2/3\n",
      "Done with chunk 3/11 for zone 2/3\n",
      "Done with chunk 4/11 for zone 2/3\n",
      "Done with chunk 5/11 for zone 2/3\n",
      "Done with chunk 6/11 for zone 2/3\n",
      "Done with chunk 7/11 for zone 2/3\n",
      "Done with chunk 8/11 for zone 2/3\n",
      "Done with chunk 9/11 for zone 2/3\n",
      "Done with chunk 10/11 for zone 2/3\n",
      "Done with chunk 11/11 for zone 2/3\n",
      "Done with chunk 1/11 for zone 3/3\n",
      "Done with chunk 2/11 for zone 3/3\n",
      "Done with chunk 3/11 for zone 3/3\n",
      "Done with chunk 4/11 for zone 3/3\n",
      "Done with chunk 5/11 for zone 3/3\n",
      "Done with chunk 6/11 for zone 3/3\n",
      "Done with chunk 7/11 for zone 3/3\n",
      "Done with chunk 8/11 for zone 3/3\n",
      "Done with chunk 9/11 for zone 3/3\n",
      "Done with chunk 10/11 for zone 3/3\n",
      "Done with chunk 11/11 for zone 3/3\n"
     ]
    }
   ],
   "source": [
    "# Run the decoders using multiprocessors if possible\n",
    "\n",
    "# Run Naive Bayes in chunks of 90 with a pool of 30 workers\n",
    "chunckSize = 100    # A chunk size of 100 is used for memory since it has to keep the results of all or 90 and 1800 tests.\n",
    "poolSize = 30\n",
    "\n",
    "# Allocate memory for results\n",
    "pccZones = []\n",
    "selHZones = []\n",
    "confMatTotZones = []\n",
    "Zones = []\n",
    "\n",
    "for izone, zone in enumerate(zoneUnique):\n",
    "    \n",
    "    # Arglist for this zone\n",
    "    argList = argListZones[izone]\n",
    "    if (len(argList) == 0):\n",
    "        continue\n",
    "\n",
    "    \n",
    "    # Allocate zone specific storage for the results\n",
    "    pcc = np.zeros((ntests[izone],))\n",
    "    selH = np.zeros((ntests[izone],))\n",
    "    confMatTot = np.zeros((ncalls, ncalls))\n",
    "    confMatCount = 0\n",
    "    \n",
    "    # Calculate the number of chunks\n",
    "    nchunks = ntests[izone]//chunckSize\n",
    "    if ntests[izone] % chunckSize:\n",
    "        nchunks += 1\n",
    "\n",
    "        \n",
    "    if mpflag == True:\n",
    "        if __name__ == '__main__':\n",
    "            for ichunk in range(nchunks):\n",
    "                istart = ichunk*chunckSize\n",
    "                iend = (ichunk+1)*chunckSize\n",
    "                if iend > ntests[izone]:\n",
    "                    iend = ntests[izone]         \n",
    "        \n",
    "                with mp.Pool(poolSize) as p:\n",
    "                    results = p.map(naiveBayes, argList[istart:iend])\n",
    "        \n",
    "            # I don't think I need these??\n",
    "            # p.close()\n",
    "            # p.join()\n",
    "        \n",
    "                for i, res in enumerate(results):\n",
    "                    confMat = res[1]\n",
    "                    if (confMat.shape == (ncalls,ncalls) ):\n",
    "                        pcc[istart + i] = res[0]\n",
    "                        pccDiag = np.diag(confMat)\n",
    "                        pccDiag = pccDiag/np.sum(pccDiag)\n",
    "                        pccDiag[pccDiag == 0] = 1. # Because 0log0 = 0\n",
    "                        selH[istart + i] = 1 - np.sum(-pccDiag*np.log2(pccDiag))/(np.log2(ncalls))\n",
    "                        testsPerClass = res[2]\n",
    "                        confMatTot = np.add(confMatTot, confMat)\n",
    "                        confMatCount += testsPerClass\n",
    "                \n",
    "                print('Done with chunk %d/%d for zone %d/%d' % (ichunk+1, nchunks, izone+1, nZones))\n",
    "    else:\n",
    "        for i in range(ntests[izone]):\n",
    "            pccval, confMat, testsPerClass = naiveBayes(argList[i], code = code)\n",
    "            pcc[i] = pccval\n",
    "            pccDiag = np.diag(confMat)\n",
    "            pccDiag = pccDiag/np.sum(pccDiag)\n",
    "            pccDiag[pccDiag == 0] = 1. # Because 0log0 = 0\n",
    "            selH[i] = 1 - np.sum(-pccDiag*np.log2(pccDiag))/(np.log2(ncalls))\n",
    "            confMatTot = np.add(confMatTot, confMat)\n",
    "            confMatCount += testsPerClass\n",
    "\n",
    "    # Calculate the average confusion matrix. \n",
    "    for i in range(ncalls):\n",
    "        if confMatCount[i] > 0:\n",
    "            confMatTot[i,:] = confMatTot[i,:]/confMatCount[i]\n",
    "            \n",
    "    # Store the results in the zone lists\n",
    "    pccZones.append(pcc)\n",
    "    selHZones.append(selH)\n",
    "    confMatTotZones.append(confMatTot)\n",
    "    Zones.append(zone)\n",
    "    \n",
    "        \n",
    "# save the PC and Confusion matrices data base\n",
    "outPath = rootPath + dataPath + 'HerminaDataBase%dGoodUnitPCPerZone.pkl' % numUnits\n",
    "fileOut = open(outPath,\"wb\")\n",
    "pk.dump(argListZones, fileOut)\n",
    "pk.dump(confMatTotZones, fileOut)\n",
    "pk.dump(pccZones, fileOut)\n",
    "pk.dump(selHZones, fileOut)\n",
    "pk.dump(Zones, fileOut)\n",
    "fileOut.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.02481977718619\n",
      "38.674254761126434\n"
     ]
    }
   ],
   "source": [
    "nZones = len(Zones)\n",
    "for izone in range(nZones):\n",
    "    print(np.mean(pccZones[izone]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4), dpi=300)\n",
    "ax = fig.subplots()\n",
    "\n",
    "for izone, zone in enumerate(Zones):\n",
    "    ax.plot(pccZones[izone], selHZones[izone], '+', label = zone)\n",
    "\n",
    "ax.set_xlabel('PCC')\n",
    "ax.set_ylabel('Selelectivy (Ent Z)')\n",
    "ax.set_title('%d Unit Good PC' % numUnits)\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('/Users/frederictheunissen/Desktop/scatterPCCSelH%dUnitGoodPCPerZone.eps' % numUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,4), dpi=300)\n",
    "ax = fig.subplots()\n",
    "\n",
    "for izone, zone in enumerate(Zones):\n",
    "   ax.hist(pccZones[izone], bins = 30, range = (0,60), label=zone)\n",
    "\n",
    "ax.set_xlabel('PCC')\n",
    "ax.set_ylabel('Count')\n",
    "ax.set_title('%d Unit Good PC' % numUnits)\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('/Users/frederictheunissen/Desktop/pcchist%dUnitGoodPCPerZone.eps' % numUnits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x3600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the overall confusion matrix\n",
    "\n",
    "cmap='viridis'\n",
    "fig = plt.figure(figsize=(4,12), dpi=300)\n",
    "axZones = fig.subplots(1, nZones)\n",
    "\n",
    "for izone, zone in enumerate(Zones):\n",
    "    ax = axZones[izone]\n",
    "    im_ = ax.imshow(confMatTotZones[izone]*100.0, interpolation='nearest', cmap=cmap, extent = (-0.5, ncalls-0.5, ncalls-0.5, -0.5), vmin=5, vmax = 50)\n",
    "\n",
    "    \n",
    "    if izone == 0 :\n",
    "        ax.set(xticks=np.arange(ncalls),\n",
    "               yticks=np.arange(ncalls),\n",
    "               ylim = (ncalls-0.5, -0.5),\n",
    "               # xticklabels=callNames,\n",
    "               # yticklabels=callNames,\n",
    "               ylabel=\"Actual Call Type\",\n",
    "               xlabel=\"Predicted Call Type\",\n",
    "               title='%s %.0f %%' % (zone, np.mean(pccZones[izone])))\n",
    "    else:\n",
    "        ax.set(xticks=np.arange(ncalls),\n",
    "               yticks=np.arange(ncalls),\n",
    "               ylim = (ncalls-0.5, -0.5),\n",
    "               # xticklabels=callNames,\n",
    "               # yticklabels=callNames,\n",
    "               # ylabel=\"Actual Call Type\",\n",
    "               # xlabel=\"Predicted Call Type\",\n",
    "               title='%s %.0f %%' % (zone, np.mean(pccZones[izone])))\n",
    "        \n",
    "plt.savefig('/Users/frederictheunissen/Desktop/confMat%dUnitGoodPCPerZone.eps' % numUnits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}