{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis for generating traces with preset ROI having a certain amplitude AP\n",
    "\n",
    "This file runs optimizeKaDensity. It finds the KaDensity that brings the AP amplitude in the target ROI to a requested amplitude. \n",
    "It does this for the intact cell and the cut cell, reparameterizing the KaDensity for the cut cell. \n",
    "\n",
    "In this run, we're saving KaDensity, ApAmp, CaAmp, InputResistance (computed with a -10pA injection into the site), and EPSP size in dendrite and soma (using an alpha synapse with properties TBD). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload\n",
    "\n",
    "import numpy as np\n",
    "from neuron import h, gui\n",
    "\n",
    "from src.collection_uncageMapping import L23\n",
    "import src.morphologyFunctions as mfx\n",
    "import src.neuronFunctions as nfx\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "from scipy.io import savemat, loadmat\n",
    "\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from contextlib import contextmanager\n",
    "import sys, os\n",
    "\n",
    "@contextmanager\n",
    "def suppress_stdout():\n",
    "    with open(os.devnull, \"w\") as devnull:\n",
    "        old_stdout = sys.stdout\n",
    "        sys.stdout = devnull\n",
    "        try:  \n",
    "            yield\n",
    "        finally:\n",
    "            sys.stdout = old_stdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def determineKaDensity(kaDensity, targetAmplitude, cellID, cutExperiment, naDensity, idxROI):\n",
    "    # Create cell\n",
    "    for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "    with suppress_stdout():\n",
    "        cell1 = L23(cellID=cellID,cutExperiment=cutExperiment,dendNa=[naDensity,None,None,False],dendK=[kaDensity,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "\n",
    "    # Record response of AP at all desired sites\n",
    "    stim1 = nfx.attachCC(cell1.soma, delay=1, dur=1, amp=3.5, loc=0.5) # set stim up for somatic injection\n",
    "    \n",
    "    tv = h.Vector() # Time stamp vector\n",
    "    tv.record(h._ref_t)\n",
    "    \n",
    "    vsec = h.Vector()\n",
    "    vsec.record(getattr(cell1.sectionList[idxROI](cell1.segmentList[idxROI]),'_ref_v'))\n",
    "    \n",
    "    # Simulate Data\n",
    "    nfx.simulate(tstop=15,v_init=-75,celsius=35)\n",
    "\n",
    "    # Analyze Data\n",
    "    vData = np.array(vsec)\n",
    "    apAmp = np.amax(vData)\n",
    "\n",
    "    # Reset stim program\n",
    "    stim1 = None\n",
    "    \n",
    "    return np.abs(apAmp - targetAmplitude)\n",
    "\n",
    "def saveKaResults(fname, kaDensity, apAmp, caAmp, vTraces, cTraces, tv, cellID, idxROI, silentID, ires, cutExperiment, epspAmpDend, epspAmpSoma, epspDendTraces,epspSomaTraces,tvEpsp):\n",
    "    saveList = [kaDensity, apAmp, caAmp, vTraces, cTraces, tv, cellID, idxROI, silentID, ires, cutExperiment, epspAmpDend, epspAmpSoma, epspDendTraces,epspSomaTraces,tvEpsp]\n",
    "    fid = open(fname,'wb')\n",
    "    pickle.dump(saveList, fid)\n",
    "    fid.close()\n",
    "    return None\n",
    "\n",
    "def loadKaResults(fname):\n",
    "    fid = open(fname,'rb')\n",
    "    loadedData = pickle.load(fid)\n",
    "    fid.close()\n",
    "    kaDensity=loadedData[0]\n",
    "    apAmp=loadedData[1]\n",
    "    caAmp=loadedData[2]\n",
    "    vTraces=loadedData[3]\n",
    "    cTraces=loadedData[4]\n",
    "    tv=loadedData[5]\n",
    "    cellID=loadedData[6]\n",
    "    idxROI=loadedData[7]\n",
    "    silentID=loadedData[8]\n",
    "    ires=loadedData[9]\n",
    "    cutExperiment=loadedData[10]\n",
    "    epspAmpDend=loadedData[11]\n",
    "    epspAmpSoma=loadedData[12]\n",
    "    epspDendTraces=loadedData[13]\n",
    "    epspSomaTraces=loadedData[14]\n",
    "    tvEpsp=loadedData[15]\n",
    "    return kaDensity,apAmp,caAmp,vTraces,cTraces,tv,cellID,idxROI,silentID,ires,cutExperiment,epspAmpDend,epspAmpSoma,epspDendTraces,epspSomaTraces,tvEpsp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n",
      "Working on cell 1/8, ROI 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/landauland/anaconda3/lib/python3.7/site-packages/scipy/optimize/_minimize.py:522: RuntimeWarning: Method Nelder-Mead cannot handle constraints nor bounds.\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n",
      "Finished. AP Amps: [-10.00358794  20.27465247  25.56092725], CaAmps: [0.81316706 0.82157671 0.82321652], Ires: [173.44511708 217.64758335 231.6342793 ], EpspDend: [ 34.94748895  95.66144076 100.9685486 ], EpspSoma: [131.10942751 131.10942751 131.10942751]\n",
      "\t1 \n",
      "Finished. AP Amps: [ -9.99981673 -51.9566557  -54.45340835], CaAmps: [0.64156271 0.00677922 0.00578491], Ires: [264.68293639 227.73625695 240.55948951], EpspDend: [37.17442189 22.46168563 20.00340135], EpspSoma: [129.51728057 129.51728057 129.51728057]\n",
      "Working on cell 1/8, ROI 1\n",
      "\t1 \n",
      "Finished. AP Amps: [-57.77896986  -9.99952549  -3.42569343], CaAmps: [0.0047663  0.60698104 0.74083222], Ires: [170.48564315 214.37690715 228.20678035], EpspDend: [16.5303956  27.13959734 24.37369643], EpspSoma: [130.2486427 130.2486427 130.2486427]\n",
      "\t1 \n",
      "Finished. AP Amps: [ 23.44408435 -10.00017837  -2.79654075], CaAmps: [0.79569948 0.57029439 0.70992424], Ires: [266.02436535 229.10905701 241.97457746], EpspDend: [97.45947954 26.2465154  23.82762364], EpspSoma: [129.77047204 129.77047204 129.77047204]\n",
      "Working on cell 1/8, ROI 2\n",
      "\t1 \n",
      "Finished. AP Amps: [-57.77924877 -16.26857822  -9.99994683], CaAmps: [0.00467028 0.47264053 0.660179  ], Ires: [170.48550013 214.37676439 228.20663232], EpspDend: [16.5301656  27.13876555 24.37286961], EpspSoma: [130.2486139 130.2486139 130.2486139]\n",
      "\t1 \n",
      "Finished. AP Amps: [ 23.44147847 -16.67510757 -10.00008904], CaAmps: [0.79569061 0.43043413 0.61972856], Ires: [266.02355531 229.10821469 241.97371106], EpspDend: [97.45725308 26.24278075 23.82321988], EpspSoma: [129.77033218 129.77033218 129.77033218]\n",
      "\t1 \n",
      "Working on cell 2/8, ROI 0\n",
      "\t1 \n",
      "Finished. AP Amps: [-9.9994982  21.67898148 25.30716274 25.60730384], CaAmps: [0.61883929 0.75937512 0.75744845 0.75848416], Ires: [178.85497661 235.97973047 242.69187934 236.92221756], EpspDend: [ 61.94324126  97.55320359 101.27774712 101.48265825], EpspSoma: [124.63293095 124.63293095 124.63293095 124.63293095]\n",
      "\t1 \n",
      "Finished. AP Amps: [ -9.99979756 -45.08139138  -8.77700403  -3.45883269], CaAmps: [0.36741098 0.00805294 0.40653597 0.48341459], Ires: [258.17752629 242.11577812 248.78040894 243.70857964], EpspDend: [62.04916264 31.09594583 63.27014822 71.17456681], EpspSoma: [121.43433487 121.43433487 121.43433487 121.43433487]\n",
      "Working on cell 2/8, ROI 1\n",
      "\t1 \n",
      "Finished. AP Amps: [-46.97428306 -10.00014861  16.62735588  16.93332001], CaAmps: [0.00671409 0.50050005 0.68904549 0.6911003 ], Ires: [172.60947614 229.73840405 236.83524624 231.19012787], EpspDend: [28.12216816 45.23752137 92.87971345 93.0696185 ], EpspSoma: [123.23374712 123.23374712 123.23374712 123.23374712]\n",
      "\t1 \n",
      "Finished. AP Amps: [ 13.39000323 -10.0001525   13.08848117  13.59699012], CaAmps: [0.65751438 0.47111259 0.65604237 0.65896841], Ires: [263.95985509 248.40900115 254.62818048 249.45039633], EpspDend: [89.48449744 52.52895903 89.50292588 89.81550118], EpspSoma: [122.66824302 122.66824302 122.66824302 122.66824302]\n",
      "Working on cell 2/8, ROI 2\n",
      "\t1 \n",
      "Finished. AP Amps: [-57.15541556 -47.48945324 -10.00012429  -3.6262314 ], CaAmps: [0.00235164 0.00616053 0.38798033 0.48423977], Ires: [165.53822437 222.72990445 230.35794971 224.85548901], EpspDend: [19.08353214 28.80002819 60.83139166 70.63703058], EpspSoma: [121.65064082 121.65064082 121.65064082 121.65064082]\n",
      "\t1 \n",
      "Finished. AP Amps: [-11.16957448 -45.20451024  -9.9999478   -4.06381041], CaAmps: [0.34257766 0.00793675 0.38391365 0.47424609], Ires: [258.10183893 242.03376448 248.70379283 243.63351088], EpspDend: [60.68727765 30.98516279 61.55290034 70.41755525], EpspSoma: [121.41785135 121.41785135 121.41785135 121.41785135]\n",
      "Working on cell 2/8, ROI 3\n",
      "\t1 \n",
      "Finished. AP Amps: [-57.52882852 -48.03237021 -22.29478611 -10.00009463], CaAmps: [0.00221497 0.0056818  0.12181056 0.36805753], Ires: [164.99894973 222.19222411 229.85892604 224.36800353], EpspDend: [18.76609661 28.3083936  50.61517574 62.36868337], EpspSoma: [121.52214498 121.52214498 121.52214498 121.52214498]\n",
      "\t1 \n",
      "Finished. AP Amps: [-20.62242762 -46.02411927 -21.55372433  -9.99925258], CaAmps: [0.13442361 0.00698934 0.13504389 0.36542222], Ires: [257.53959579 241.42354624 248.13460984 243.0751355 ], EpspDend: [52.44137946 30.24825537 51.3038887  62.7949101 ], EpspSoma: [121.29484276 121.29484276 121.29484276 121.29484276]\n",
      "\t1 \n",
      "Working on cell 3/8, ROI 0\n",
      "\t1 \n",
      "Finished. AP Amps: [-9.99991004 21.94819448 22.81358159], CaAmps: [0.50389988 0.70635377 0.70593155], Ires: [191.5909998  257.25476629 260.31380613], EpspDend: [51.07535391 98.15862808 98.95282409], EpspSoma: [119.65881695 119.65881695 119.65881695]\n",
      "\t1 \n",
      "Finished. AP Amps: [-10.00007338 -21.14152835 -12.79621622], CaAmps: [0.27701659 0.09001632 0.21868132], Ires: [242.75577368 256.68992155 257.27744857], EpspDend: [67.18656822 55.84470096 64.36107681], EpspSoma: [113.38142334 113.38142334 113.38142334]\n",
      "Working on cell 3/8, ROI 1\n",
      "\t1 \n",
      "Finished. AP Amps: [-49.53511265  -9.9999354   -4.20625572], CaAmps: [0.00368988 0.28924386 0.37440721], Ires: [169.93302015 236.86767942 240.08256082], EpspDend: [27.63443728 66.9510365  73.06067674], EpspSoma: [114.25676378 114.25676378 114.25676378]\n",
      "\t1 \n",
      "Finished. AP Amps: [ -3.36770905 -10.00005461  -5.17527384], CaAmps: [0.39097101 0.287805   0.35814577], Ires: [246.05636982 260.05452501 260.60563848], EpspDend: [73.77800268 66.98117693 72.02498917], EpspSoma: [114.22738138 114.22738138 114.22738138]\n",
      "Working on cell 3/8, ROI 2\n",
      "\t1 \n",
      "Finished. AP Amps: [-50.76513913 -19.68823967 -10.00010959], CaAmps: [0.00324316 0.11258228 0.27103719], Ires: [167.53838244 234.60019435 237.83006075], EpspDend: [26.55976273 57.24953608 67.21593663], EpspSoma: [113.57131925 113.57131925 113.57131925]\n",
      "\t1 \n",
      "Finished. AP Amps: [ -7.60553151 -17.42610815  -9.99994506], CaAmps: [0.32019299 0.14838802 0.27105885], Ires: [243.83289364 257.78838114 258.36373926], EpspDend: [69.53389313 59.47062516 67.14555977], EpspSoma: [113.66040653 113.66040653 113.66040653]\n",
      "\t1 \n",
      "Working on cell 4/8, ROI 0\n",
      "\t1 \n",
      "Finished. AP Amps: [-10.00174743  10.58288708], CaAmps: [0.73644911 0.79232089], Ires: [169.45024236 226.53852854], EpspDend: [30.40155151 84.34659807], EpspSoma: [129.43027007 129.43027007]\n",
      "\t1 \n",
      "Finished. AP Amps: [-10.00056601 -17.8247695 ], CaAmps: [0.58746071 0.30920457], Ires: [228.27913056 249.68258535], EpspDend: [41.28677686 51.75582955], EpspSoma: [128.32743672 128.32743672]\n",
      "Working on cell 4/8, ROI 1\n",
      "\t1 \n",
      "Finished. AP Amps: [-53.45004226  -9.99981833], CaAmps: [0.00898926 0.61301499], Ires: [167.39027859 224.66758828], EpspDend: [21.44606999 56.52131716], EpspSoma: [129.00318801 129.00318801]\n",
      "\t1 \n",
      "Finished. AP Amps: [  8.76871785 -10.00023765], CaAmps: [0.76198808 0.71349437], Ires: [228.63486659 250.03088878], EpspDend: [43.81351835 56.24013328], EpspSoma: [128.39328266 128.39328266]\n",
      "\t1 \n",
      "Working on cell 5/8, ROI 0\n",
      "\t1 \n",
      "Finished. AP Amps: [-9.99994739 34.70807523], CaAmps: [0.75667812 0.8141135 ], Ires: [172.39270877 230.64656881], EpspDend: [ 34.16294555 110.09097792], EpspSoma: [128.26647168 128.26647168]\n",
      "\t1 \n",
      "Finished. AP Amps: [-10.00016201 -34.83392167], CaAmps: [0.3342954  0.01420653], Ires: [271.40206206 262.16547344], EpspDend: [64.81187548 41.28263098], EpspSoma: [121.71211815 121.71211815]\n",
      "Working on cell 5/8, ROI 1\n",
      "\t1 \n",
      "Finished. AP Amps: [-56.67358078 -10.00012214], CaAmps: [0.00306301 0.45990527], Ires: [155.95538891 214.98268599], EpspDend: [18.9951047  51.11047094], EpspSoma: [124.15946475 124.15946475]\n",
      "\t1 \n",
      "Finished. AP Amps: [ 3.00607433 -9.99416671], CaAmps: [0.54216653 0.37911187], Ires: [275.11535375 265.98503167], EpspDend: [79.15783721 63.54560147], EpspSoma: [122.4591558 122.4591558]\n",
      "\t1 \n",
      "Working on cell 6/8, ROI 0\n",
      "\t1 \n",
      "Finished. AP Amps: [-9.99988779 28.7399846 ], CaAmps: [0.548827 0.781827], Ires: [200.00884091 283.70076141], EpspDend: [ 49.33850619 104.44628239], EpspSoma: [128.16006753 128.16006753]\n",
      "\t1 \n",
      "Finished. AP Amps: [ -9.99949269 -32.41366883], CaAmps: [0.29987057 0.01564067], Ires: [309.6309943  307.81328947], EpspDend: [65.99566085 44.10084152], EpspSoma: [121.19332073 121.19332073]\n",
      "Working on cell 6/8, ROI 1\n",
      "\t1 \n",
      "Finished. AP Amps: [-55.48794028 -10.00007288], CaAmps: [0.00235222 0.36000998], Ires: [177.84084455 262.57200618], EpspDend: [20.85267292 64.2213922 ], EpspSoma: [122.94339894 122.94339894]\n",
      "\t1 \n",
      "Finished. AP Amps: [ 2.91712794 -9.99963041], CaAmps: [0.50581443 0.34115331], Ires: [315.26893074 313.63825276], EpspDend: [79.4869945  65.54309263], EpspSoma: [122.21717156 122.21717156]\n",
      "\t1 \n",
      "Working on cell 7/8, ROI 0\n",
      "\t1 \n",
      "Finished. AP Amps: [-9.99991957 -2.21454191], CaAmps: [0.51872126 0.64697309], Ires: [243.77394913 280.09601461], EpspDend: [37.89105292 66.29417507], EpspSoma: [125.32374633 125.32374633]\n",
      "\t1 \n",
      "Finished. AP Amps: [-10.00010507  -9.30218558], CaAmps: [0.45540055 0.49232245], Ires: [288.68523686 306.53027773], EpspDend: [52.91112391 60.2210779 ], EpspSoma: [123.7939791 123.7939791]\n",
      "Working on cell 7/8, ROI 1\n",
      "\t1 \n",
      "Finished. AP Amps: [-38.30456964 -10.00005607], CaAmps: [0.02541142 0.50043958], Ires: [243.33081723 279.66997271], EpspDend: [34.27771949 56.06630814], EpspSoma: [125.25293765 125.25293765]\n",
      "\t1 \n",
      "Finished. AP Amps: [-11.12040929 -10.00154391], CaAmps: [0.43410629 0.47597374], Ires: [288.64588396 306.49120783], EpspDend: [51.66962102 59.4545978 ], EpspSoma: [123.78750374 123.78750374]\n",
      "\t1 \n",
      "Working on cell 8/8, ROI 0\n",
      "\t1 \n",
      "Finished. AP Amps: [-9.99994849 38.52653672], CaAmps: [0.76116028 0.81940599], Ires: [188.00353155 329.47466669], EpspDend: [ 30.58830897 114.02001704], EpspSoma: [129.0684195 129.0684195]\n",
      "\t1 \n",
      "Finished. AP Amps: [-10.00007487 -34.38886575], CaAmps: [0.34174012 0.01458382], Ires: [346.97114953 360.28490827], EpspDend: [62.70400029 41.36428812], EpspSoma: [122.7672284 122.7672284]\n",
      "Working on cell 8/8, ROI 1\n",
      "\t1 \n",
      "Finished. AP Amps: [-65.07212314 -10.00005082], CaAmps: [0.00128662 0.44348814], Ires: [169.30810097 311.80379583], EpspDend: [10.73394538 49.58711453], EpspSoma: [124.91292233 124.91292233]\n",
      "\t1 \n",
      "Finished. AP Amps: [ 3.94146211 -9.99993548], CaAmps: [0.54988597 0.36962686], Ires: [349.49340391 362.87556392], EpspDend: [79.94432977 60.260821  ], EpspSoma: [123.25777047 123.25777047]\n"
     ]
    }
   ],
   "source": [
    "numCells = 8\n",
    "naDensity = 5\n",
    "initKaDensity = 0.01\n",
    "kaBounds = (0,None)\n",
    "roiType = False # True means silent, False means active (will get all from each category)\n",
    "targetAmplitude = -10\n",
    "\n",
    "cellID = []\n",
    "cutExperiment = []\n",
    "idxROI = []\n",
    "silentID = []\n",
    "tv = []\n",
    "vTraces = []\n",
    "cTraces = []\n",
    "\n",
    "kaDensity = []\n",
    "apAmp = []\n",
    "caAmp = []\n",
    "\n",
    "ires = []\n",
    "\n",
    "epspAmpDend = []\n",
    "epspAmpSoma = []\n",
    "epspDendTraces = []\n",
    "epspSomaTraces = []\n",
    "tvEpsp = []\n",
    "\n",
    "for n in range(numCells):\n",
    "    # Create cell just to get silent IDs\n",
    "    for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "    cell1 = L23(cellID=n,cutExperiment=0,dendNa=[naDensity,None,None,False],dendK=[initKaDensity,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "    cSilentID = cell1.silentID\n",
    "    \n",
    "    # Trade out these two lines to use all or just active/silent type of interest\n",
    "    listTarget = [n for n in range(len(cSilentID))]\n",
    "    #listTarget = [n for n in range(len(cSilentID)) if cSilentID[n]==roiType]\n",
    "    \n",
    "    for r in listTarget:\n",
    "        print(f'Working on cell {n+1}/{numCells}, ROI {r}')\n",
    "        \n",
    "        # -- do it for the normal cell -- \n",
    "        \n",
    "        # Optimize kaDensity for this cell\n",
    "        results = minimize(determineKaDensity, initKaDensity, args=(targetAmplitude,n,0,naDensity,r),method='Nelder-Mead',bounds=kaBounds)\n",
    "        kaDensity.append(results.x) # Store optimal kaDensity\n",
    "\n",
    "        # Run cell with optimal kaDensity and record voltage/calcium traces for all ROIs in the cell\n",
    "        for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "        cell1 = L23(cellID=n,cutExperiment=0,dendNa=[naDensity,None,None,False],dendK=[results.x,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "        \n",
    "        # Record response of AP at all desired sites\n",
    "        stim1 = nfx.attachCC(cell1.soma, delay=1, dur=1, amp=3.5, loc=0.5) # set stim up for somatic injection\n",
    "        \n",
    "        # Record peak of AP in all the sites\n",
    "        vsec,ctv = mfx.recordSites(cell1.sectionList,cell1.segmentList)\n",
    "\n",
    "        # Record ica in all sites + soma\n",
    "        csec = mfx.recordSites(cell1.sectionList,cell1.segmentList,recordVariable='_ref_ica')[0]\n",
    "\n",
    "        # Simulate Data\n",
    "        nfx.simulate(tstop=15,v_init=-75,celsius=35)\n",
    "\n",
    "        # Convert calcium current to conductance\n",
    "        gca_sec = []\n",
    "        for ica,v in zip(csec,vsec):\n",
    "            gca_sec.append(nfx.conductanceFromCurrent(ica,v,cell1.Eca))\n",
    "        \n",
    "        # Store Data\n",
    "        cellID.append(n)\n",
    "        cutExperiment.append(0)\n",
    "        idxROI.append(r)\n",
    "        silentID.append(cSilentID[r])\n",
    "        vData = np.array(vsec)\n",
    "        gcaData = np.array(gca_sec)\n",
    "        apAmp.append(np.amax(vData,axis=1))\n",
    "        caAmp.append(np.amax(gcaData,axis=1))\n",
    "        vTraces.append(vData)\n",
    "        cTraces.append(gcaData)\n",
    "        tv.append(np.array(ctv))\n",
    "        \n",
    "        # --------- And also measure input resistance for sites!! ---------\n",
    "        stim = None\n",
    "        amplitude=-0.01\n",
    "        vsection,ctv,stim = mfx.injectSites(cell1.sectionList,cell1.segmentList,stim=stim,amplitude=amplitude)\n",
    "\n",
    "        # Delay is 50ms, duration is 50ms\n",
    "        dvm = (np.array(vsection)[:,np.where(np.array(ctv)<=100)[0][-1]] - np.array(vsection)[:,np.where(np.array(ctv)<=50)[0][-1]])\n",
    "        ires.append(dvm/amplitude)        \n",
    "        \n",
    "        # --------- And also measure EPSP size and transfer function for sites! ---------\n",
    "        syn = None\n",
    "        stim = None\n",
    "        onset=50\n",
    "        tau=1\n",
    "        gmax=0.0005\n",
    "        tstop = 80\n",
    "        epspDendrite,epspSoma,epspTV,syn = mfx.injectAlphaSites(cell1.sectionList,cell1.segmentList,syn=syn,onset=onset,tau=tau,gmax=gmax,tstop=tstop)\n",
    "        vEpspDend = np.array(epspDendrite)\n",
    "        vEpspSoma = np.array(epspSoma)\n",
    "        cTvEpsp = np.array(epspTV)\n",
    "        cEpspAmpDend = np.amax(vEpspDend,axis=1) - vEpspDend[:,np.where(cTvEpsp>=onset-1)[0][0]]\n",
    "        cEpspAmpSoma = np.amax(vEpspSoma,axis=1) - vEpspSoma[:,np.where(cTvEpsp>=onset-1)[0][0]]\n",
    "        epspAmpDend.append(cEpspAmpDend)\n",
    "        epspAmpSoma.append(cEpspAmpSoma)\n",
    "        epspDendTraces.append(vEpspDend)\n",
    "        epspSomaTraces.append(vEpspSoma)\n",
    "        tvEpsp.append(cTvEpsp)\n",
    "        \n",
    "        print(f'Finished. AP Amps: {apAmp[-1]}, CaAmps: {caAmp[-1]}, Ires: {ires[-1]}, EpspDend: {epspAmpDend[-1]}, EpspSoma: {epspAmpSoma[-1]}')\n",
    "\n",
    "        # Reset stim programs\n",
    "        syn = None\n",
    "        stim = None\n",
    "        \n",
    "        \n",
    "        # -- now measure it again for the cut experiment -- (this time redoing the fit)\n",
    "        results = minimize(determineKaDensity, initKaDensity, args=(targetAmplitude,n,2,naDensity,r),method='Nelder-Mead',bounds=kaBounds)\n",
    "        kaDensity.append(results.x) # Store optimal kaDensity\n",
    "\n",
    "        # Run cell with optimal kaDensity and record voltage/calcium traces for all ROIs in the cell\n",
    "        for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "        cell1 = L23(cellID=n,cutExperiment=2,dendNa=[naDensity,None,None,False],dendK=[results.x,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "        \n",
    "        # Record response of AP at all desired sites\n",
    "        stim1 = nfx.attachCC(cell1.soma, delay=1, dur=1, amp=3.5, loc=0.5) # set stim up for somatic injection\n",
    "        \n",
    "        # Record peak of AP in all the sites\n",
    "        vsec,ctv = mfx.recordSites(cell1.sectionList,cell1.segmentList)\n",
    "\n",
    "        # Record ica in all sites + soma\n",
    "        csec = mfx.recordSites(cell1.sectionList,cell1.segmentList,recordVariable='_ref_ica')[0]\n",
    "\n",
    "        # Simulate Data\n",
    "        nfx.simulate(tstop=15,v_init=-75,celsius=35)\n",
    "\n",
    "        # Convert calcium current to conductance\n",
    "        gca_sec = []\n",
    "        for ica,v in zip(csec,vsec):\n",
    "            gca_sec.append(nfx.conductanceFromCurrent(ica,v,cell1.Eca))\n",
    "        \n",
    "        # Store Data\n",
    "        cellID.append(n)\n",
    "        cutExperiment.append(1)\n",
    "        idxROI.append(r)\n",
    "        silentID.append(cSilentID[r])\n",
    "        vData = np.array(vsec)\n",
    "        gcaData = np.array(gca_sec)\n",
    "        apAmp.append(np.amax(vData,axis=1))\n",
    "        caAmp.append(np.amax(gcaData,axis=1))\n",
    "        vTraces.append(vData)\n",
    "        cTraces.append(gcaData)\n",
    "        tv.append(np.array(ctv))\n",
    "        \n",
    "        # And also measure input resistance for sites!!\n",
    "        stim = None\n",
    "        amplitude=-0.01\n",
    "        vsection,ctv,stim = mfx.injectSites(cell1.sectionList,cell1.segmentList,stim=stim,amplitude=amplitude)\n",
    "\n",
    "\n",
    "        # Delay is 50ms, duration is 50ms\n",
    "        dvm = (np.array(vsection)[:,np.where(np.array(ctv)<=100)[0][-1]] - np.array(vsection)[:,np.where(np.array(ctv)<=50)[0][-1]])\n",
    "        ires.append(dvm/amplitude)        \n",
    "        \n",
    "        # --------- And also measure EPSP size and transfer function for sites! ---------\n",
    "        syn = None\n",
    "        stim = None\n",
    "        onset=50\n",
    "        tau=1\n",
    "        gmax=0.0005\n",
    "        tstop = 80\n",
    "        epspDendrite,epspSoma,epspTV,syn = mfx.injectAlphaSites(cell1.sectionList,cell1.segmentList,syn=syn,onset=onset,tau=tau,gmax=gmax,tstop=tstop)\n",
    "        vEpspDend = np.array(epspDendrite)\n",
    "        vEpspSoma = np.array(epspSoma)\n",
    "        cTvEpsp = np.array(epspTV)\n",
    "        cEpspAmpDend = np.amax(vEpspDend,axis=1) - vEpspDend[:,np.where(cTvEpsp>=onset-1)[0][0]]\n",
    "        cEpspAmpSoma = np.amax(vEpspSoma,axis=1) - vEpspSoma[:,np.where(cTvEpsp>=onset-1)[0][0]]\n",
    "        epspAmpDend.append(cEpspAmpDend)\n",
    "        epspAmpSoma.append(cEpspAmpSoma)\n",
    "        epspDendTraces.append(vEpspDend)\n",
    "        epspSomaTraces.append(vEpspSoma)\n",
    "        tvEpsp.append(cTvEpsp)\n",
    "        \n",
    "        print(f'Finished. AP Amps: {apAmp[-1]}, CaAmps: {caAmp[-1]}, Ires: {ires[-1]}, EpspDend: {epspAmpDend[-1]}, EpspSoma: {epspAmpSoma[-1]}')\n",
    "\n",
    "        # Reset stim programs\n",
    "        syn = None\n",
    "        stim1 = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeStamp = time.strftime(\"optKaDensity_%Y%b%d_%H%M%S_redoFits\")\n",
    "fname = './'+timeStamp+'.pkl'\n",
    "saveKaResults(fname, kaDensity, apAmp, caAmp, vTraces, cTraces, tv, cellID, idxROI, silentID, ires, cutExperiment, epspAmpDend, epspAmpSoma, epspDendTraces, epspSomaTraces, tvEpsp)\n",
    "\n",
    "# Stack saved results in format matlab will like\n",
    "numFits = len(kaDensity)\n",
    "maxROI = 4\n",
    "NT = tv[0].shape[0]\n",
    "matVoltage = np.empty((NT,numFits,maxROI))\n",
    "matVoltage[:] = np.NAN\n",
    "matCalcium = np.empty_like(matVoltage)\n",
    "matCalcium[:] = np.NAN\n",
    "matTv = tv[0]\n",
    "matApAmp = np.empty((numFits,maxROI))\n",
    "matApAmp[:] = np.NAN\n",
    "matCaAmp = np.empty_like(matApAmp)\n",
    "matCaAmp[:] = np.NAN\n",
    "matIres = np.empty_like(matApAmp)\n",
    "matIres[:] = np.NAN\n",
    "matKaDensity = np.empty_like(matApAmp)\n",
    "matCellID = np.empty(numFits)\n",
    "matIdxROI = np.empty(numFits)\n",
    "matCutExp = np.empty(numFits)\n",
    "matEpspDend = np.empty_like(matApAmp)\n",
    "matEpspSoma = np.empty_like(matApAmp)\n",
    "matEpspDend[:] = np.NAN\n",
    "matEpspSoma[:] = np.NAN\n",
    "NT = tvEpsp[0].shape[0]\n",
    "matEpspDendTraces = np.empty((NT,numFits,maxROI))\n",
    "matEpspSomaTraces = np.empty((NT,numFits,maxROI))\n",
    "matEpspDendTraces[:] = np.NAN\n",
    "matEpspSomaTraces[:] = np.NAN\n",
    "for n in range(numFits):\n",
    "    cNumROI = vTraces[n].shape[0]\n",
    "    matVoltage[:,n,:cNumROI] = vTraces[n].T\n",
    "    matCalcium[:,n,:cNumROI] = cTraces[n].T\n",
    "    matApAmp[n,:cNumROI] = apAmp[n]\n",
    "    matCaAmp[n,:cNumROI] = caAmp[n]\n",
    "    matIres[n,:cNumROI] = ires[n]\n",
    "    matEpspDend[n,:cNumROI] = epspAmpDend[n]\n",
    "    matEpspSoma[n,:cNumROI] = epspAmpSoma[n]\n",
    "    matKaDensity[n] = kaDensity[n]\n",
    "    matCellID[n] = cellID[n]\n",
    "    matIdxROI[n] = idxROI[n]\n",
    "    matCutExp[n] = cutExperiment[n]\n",
    "    matEpspDendTraces[:,n,:cNumROI] = epspDendTraces[n].T\n",
    "    matEpspSomaTraces[:,n,:cNumROI] = epspSomaTraces[n].T\n",
    "\n",
    "matname = './'+timeStamp+'.mat'\n",
    "matdict = {\"matVoltage\":matVoltage, \"matCalcium\":matCalcium, \"matApAmp\":matApAmp,\"matCaAmp\":matCaAmp,\"matIres\":matIres,\"matKaDensity\":matKaDensity,\"matCellID\":matCellID,\"matIdxROI\":matIdxROI,\\\n",
    "           \"matTV\":matTv,\"matCutExp\":matCutExp,\"silentID\":np.array(silentID),\"epspAmpDend\":matEpspDend,\"epspAmpSoma\":matEpspSoma,\"epspDendTraces\":matEpspDendTraces,\"epspSomaTraces\":matEpspSomaTraces,\"tvEpsp\":tvEpsp}\n",
    "savemat(matname,matdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = './saveOptimizeKaDensity_2021Aug30_140545.pkl'\n",
    "kaDensity, apAmp, caAmp, vTraces, cTraces, tv, cellID, idxROI, silentID, ires, cutExperiment = loadKaResults(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Everything below this is just for playing with the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Somatic AP\n",
    "fig = plt.figure(figsize=(20,6))\n",
    "\n",
    "cmap = cm.get_cmap('jet')\n",
    "\n",
    "numFits = len(kaDensity)\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "for n in range(numFits):\n",
    "    numROI = vTraces[n].shape[0]\n",
    "    for r in range(numROI):\n",
    "        if silentID[n][r]==False and idxROI[n]!=r: continue\n",
    "        ccol = 'b'\n",
    "        if silentID[n][r]==False: ccol = 'k'\n",
    "        plt.plot(np.array(tv[n]),vTraces[n][r].T,c=ccol)\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "for n in range(numFits):\n",
    "    numROI = cTraces[n].shape[0]\n",
    "    for r in range(numROI):\n",
    "        if silentID[n][r]==False and idxROI[n]!=r: continue\n",
    "        ccol = 'b'\n",
    "        if silentID[n][r]==False: ccol = 'k'\n",
    "        plt.plot(np.array(tv[n]),cTraces[n][r].T,c=ccol)\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "for n in range(numFits):\n",
    "    numROI = cTraces[n].shape[0]\n",
    "    for r in range(numROI):\n",
    "        if silentID[n][r]==False and idxROI[n]!=r: continue\n",
    "        ccol = 'b'\n",
    "        if silentID[n][r]==False: ccol = 'k'\n",
    "        plt.scatter(apAmp[n][r],caAmp[n][r],c=ccol,s=80)\n",
    "    plt.plot([apAmp[n][0],apAmp[n][idxROI[n]]],[caAmp[n][0],caAmp[n][idxROI[n]]],c='k',linewidth=0.2,linestyle='dashed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cellID = 0\n",
    "cutExperiment = 0\n",
    "idxROI = 0\n",
    "\n",
    "naDensity = 6\n",
    "targetAmplitude = -20\n",
    "initKaDensity = 0.01\n",
    "\n",
    "results = minimize(determineKaDensity, initKaDensity, args=(targetAmplitude, cellID, cutExperiment, naDensity, idxROI),method='Nelder-Mead',bounds=(0,None))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In this cell, I'll go through the cells and vary the potassium channel density until the requested ROI has an AP amplitude of a set voltage. \n",
    "\n",
    "numCells = 8 # number of cells to run through\n",
    "naDensity = 6 # Na channel density (in channels / um2)\n",
    "initKaDensity = 0.01 # K channel density (in units of S/cm2)\n",
    "kaMinimum = 0 # minimum k channel density \n",
    "kaMaximum = 0.2 # maximum k channel density\n",
    "stepSize = 0.00001 # ka S/cm2 per mV\n",
    "tolerance = 0.05 # tolerance in mV for AP\n",
    "\n",
    "# Index of ROI to set AP amplitude for\n",
    "targetROI = [0,0,0,0,0,0,0,0]\n",
    "targetAmplitude = 10 # target AP amplitude\n",
    "\n",
    "cAP = np.Inf\n",
    "cKa = initKaDensity\n",
    "while np.abs(cAP - targetAmplitude) > tolerance:\n",
    "    # Create cell\n",
    "    for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "    cell1 = L23(cellID=1,cutExperiment=0,dendNa=[naDensity,None,None,False],dendK=[cKa,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "\n",
    "    # Record response of AP at all desired sites\n",
    "    stim1 = nfx.attachCC(cell1.soma, delay=1, dur=1, amp=3.5, loc=0.5) # set stim up for somatic injection\n",
    "\n",
    "    # Record peak of AP in all the sites\n",
    "    vsec,tv = mfx.recordSites(cell1.sectionList,cell1.segmentList)\n",
    "\n",
    "    # Record ica in all sites + soma\n",
    "    csec = mfx.recordSites(cell1.sectionList,cell1.segmentList,recordVariable='_ref_ica')[0]\n",
    "\n",
    "    # Simulate Data\n",
    "    nfx.simulate(tstop=8,v_init=-75,celsius=35)\n",
    "\n",
    "    gca_sec = []\n",
    "    for ica,v in zip(csec,vsec):\n",
    "        gca_sec.append(nfx.conductanceFromCurrent(ica,v,cell1.Eca))\n",
    "\n",
    "    # Analyze Data\n",
    "    vData = np.array(vsec)\n",
    "    gcaData = np.array(gca_sec)\n",
    "    apAmp = np.amax(vData,axis=1)\n",
    "    gcAmp = np.amax(gcaData,axis=1)\n",
    "\n",
    "    # Reset stim program\n",
    "    stim1 = None\n",
    "\n",
    "    cAP = apAmp[targetROI[0]]\n",
    "    print(cAP)\n",
    "    \n",
    "    voltageError = (apAmp[targetROI[0]] - targetAmplitude)\n",
    "    if voltageError>1: \n",
    "        updateValue = voltageError**2 * np.sign(voltageError) * stepSize\n",
    "    else:\n",
    "        updateValue = voltageError * stepSize\n",
    "    newKa = cKa + updateValue\n",
    "    if newKa > kaMaximum or newKa < kaMinimum:\n",
    "        print(f'Out of range!! Ka:{newKa}')\n",
    "        break\n",
    "    cKa = newKa\n",
    "\n",
    "    print(cKa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n",
      "-19.862718519098134\n"
     ]
    }
   ],
   "source": [
    "testKa = 0.01871557\n",
    "for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "cell1 = L23(cellID=0,cutExperiment=0,dendNa=[naDensity,None,None,False],dendK=[testKa,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "\n",
    "# Record response of AP at all desired sites\n",
    "stim1 = nfx.attachCC(cell1.soma, delay=1, dur=1, amp=3.5, loc=0.5) # set stim up for somatic injection\n",
    "\n",
    "# Record peak of AP in all the sites\n",
    "vsec,tv = mfx.recordSites(cell1.sectionList,cell1.segmentList)\n",
    "\n",
    "# Record ica in all sites + soma\n",
    "csec = mfx.recordSites(cell1.sectionList,cell1.segmentList,recordVariable='_ref_ica')[0]\n",
    "\n",
    "# Simulate Data\n",
    "nfx.simulate(tstop=8,v_init=-75,celsius=35)\n",
    "\n",
    "gca_sec = []\n",
    "for ica,v in zip(csec,vsec):\n",
    "    gca_sec.append(nfx.conductanceFromCurrent(ica,v,cell1.Eca))\n",
    "\n",
    "# Analyze Data\n",
    "vData = np.array(vsec)\n",
    "gcaData = np.array(gca_sec)\n",
    "apAmp = np.amax(vData,axis=1)\n",
    "gcAmp = np.amax(gcaData,axis=1)\n",
    "\n",
    "# Reset stim program\n",
    "stim1 = None\n",
    "\n",
    "print(apAmp[targetROI[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n"
     ]
    }
   ],
   "source": [
    "testKa = 0.01871557\n",
    "for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "cell1 = L23(cellID=0,cutExperiment=0,dendNa=[naDensity,None,None,False],dendK=[testKa,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "\n",
    "stim = None\n",
    "syn = None\n",
    "onset=30\n",
    "tau=2\n",
    "gmax=0.0002\n",
    "tstop = 100\n",
    "vsection,vsoma,tv,syn = mfx.injectAlphaSites(cell1.sectionList,cell1.segmentList,syn=syn,onset=onset,tau=tau,gmax=gmax,tstop=tstop)\n",
    "vsec = np.array(vsection)\n",
    "vsoma = np.array(vsoma)\n",
    "tv = np.array(tv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,6))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(tv,vsec[0].T,c='b')\n",
    "for n in range(len(vsec)-1):\n",
    "    plt.plot(tv,vsec[n+1].T,c='k')\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(tv,vsoma[0].T,c='b')\n",
    "for n in range(len(vsoma)-1):\n",
    "    plt.plot(tv,vsoma[n+1].T,c='k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n"
     ]
    }
   ],
   "source": [
    "testKa = 0.07\n",
    "cellID = 7\n",
    "cutExp = 0\n",
    "\n",
    "for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "cell1 = L23(cellID=cellID,cutExperiment=cutExp,dendNa=[naDensity,None,None,False],dendK=[testKa,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "\n",
    "syn = None\n",
    "syn = None\n",
    "onset=50\n",
    "tau=1\n",
    "gmax=0.0005\n",
    "tstop = 120\n",
    "epspDendrite,epspSoma,epspTV,syn = mfx.injectAlphaSites(cell1.sectionList,cell1.segmentList,syn=syn,onset=onset,tau=tau,gmax=gmax,tstop=tstop)\n",
    "vEpspDend = np.array(epspDendrite)\n",
    "vEpspSoma = np.array(epspSoma)\n",
    "tvEpsp = np.array(epspTV)\n",
    "cEpspAmpDend = np.amax(vEpspDend,axis=1) - vEpspDend[:,np.where(tvEpsp>=onset-1)[0][0]]\n",
    "cEpspAmpSoma = np.amax(vEpspSoma,axis=1) - vEpspSoma[:,np.where(tvEpsp>=onset-1)[0][0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fdb91c3ab38>]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,6))\n",
    "plt.subplot(1,3,1)\n",
    "plt.plot(tvEpsp,vEpspDend[0].T,c='b')\n",
    "for n in range(len(vEpspDend)-1):\n",
    "    plt.plot(tvEpsp,vEpspDend[n+1].T,c='k')\n",
    "plt.xlim(40,100)\n",
    "plt.subplot(1,3,2)\n",
    "plt.plot(tvEpsp,vEpspSoma[0].T,c='b')\n",
    "for n in range(len(vEpspSoma)-1):\n",
    "    plt.plot(tvEpsp,vEpspSoma[n+1].T,c='k')\n",
    "plt.xlim(40,100)\n",
    "plt.subplot(1,3,3)\n",
    "plt.plot(range(len(vEpspDend)),cEpspAmpDend,c='k')\n",
    "plt.plot(range(len(vEpspDend)),cEpspAmpSoma,c='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}