{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis for generating traces with preset ROI having a certain amplitude AP\n",
    "\n",
    "I think it would be best to set the silent ROI to an AP amplitude of -25mV, because there's one per cell and this is high enough to be a reasonable AP and low enough that it won't generate calcium influx\n",
    "- ^ Scratch that -- I'm using an AP amplitude of -10mV for the active ROI (each one, repeating the measurement for silent ROI if a cell has multiple active ROIs), and doing so because this shows a clear separation between silent & active ROI peak.\n",
    "\n",
    "Next steps:\n",
    "1. Create a new hoc file for each cell that performs a \"cut\" experiment. For each pair with optimized kaDensity, do the same experiment on the cut dendrites and show how that affects AP amplitude, calcium conductance, and input resistance in each ROI. "
   ]
  },
  {
   "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\n",
    "\n",
    "from src.collection_uncageMapping import L23\n",
    "import src.morphologyFunctions as mfx\n",
    "import src.neuronFunctions as nfx\n",
    "from src import get_save_dir\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):\n",
    "    saveList = [kaDensity, apAmp, caAmp, vTraces, cTraces, tv, cellID, idxROI, silentID, ires, cutExperiment]\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",
    "    return kaDensity,apAmp,caAmp,vTraces,cTraces,tv,cellID,idxROI,silentID,ires,cutExperiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'h' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 24\u001b[0m\n\u001b[1;32m     20\u001b[0m ires \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(numCells):\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;66;03m# Create cell just to get silent IDs\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m sec \u001b[38;5;129;01min\u001b[39;00m h\u001b[38;5;241m.\u001b[39mallsec(): h\u001b[38;5;241m.\u001b[39mdelete_section(sec\u001b[38;5;241m=\u001b[39msec)\n\u001b[1;32m     25\u001b[0m     cell1 \u001b[38;5;241m=\u001b[39m L23(cellID\u001b[38;5;241m=\u001b[39mn,cutExperiment\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,dendNa\u001b[38;5;241m=\u001b[39m[naDensity,\u001b[38;5;28;01mNone\u001b[39;00m,\u001b[38;5;28;01mNone\u001b[39;00m,\u001b[38;5;28;01mFalse\u001b[39;00m],dendK\u001b[38;5;241m=\u001b[39m[initKaDensity,np\u001b[38;5;241m.\u001b[39minf,\u001b[38;5;28;01mTrue\u001b[39;00m,\u001b[38;5;28;01mFalse\u001b[39;00m],dxSeg\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,fixDiam\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m);\n\u001b[1;32m     26\u001b[0m     cSilentID \u001b[38;5;241m=\u001b[39m cell1\u001b[38;5;241m.\u001b[39msilentID\n",
      "\u001b[0;31mNameError\u001b[0m: name 'h' is not defined"
     ]
    }
   ],
   "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",
    "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",
    "        fast_mode = True\n",
    "        options = {\"maxiter\":10} if fast_mode else {}\n",
    "        results = minimize(determineKaDensity, initKaDensity, args=(targetAmplitude,n,0,naDensity,r),method='Nelder-Mead',bounds=kaBounds, options=options)\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",
    "        # Reset stim program\n",
    "        stim1 = None\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",
    "        print(f'Finished. AP Amps: {apAmp[-1]}, CaAmps: {caAmp[-1]}, Ires: {ires[-1]}')\n",
    "\n",
    "        # Reset stim program\n",
    "        stim1 = 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",
    "        # Reset stim program\n",
    "        stim1 = None\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",
    "        print(f'Finished. AP Amps: {apAmp[-1]}, CaAmps: {caAmp[-1]}, Ires: {ires[-1]}')\n",
    "\n",
    "        # Reset stim program\n",
    "        stim1 = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeStamp = time.strftime(\"saveOptimizeKaDensity_%Y%b%d_%H%M%S\")\n",
    "fname = os.path.join(get_save_dir(), timeStamp+'.pkl')\n",
    "saveKaResults(fname, kaDensity, apAmp, caAmp, vTraces, cTraces, tv, cellID, idxROI, silentID, ires, cutExperiment)\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",
    "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",
    "    matKaDensity[n] = kaDensity[n]\n",
    "    matCellID[n] = cellID[n]\n",
    "    matIdxROI[n] = idxROI[n]\n",
    "    matCutExp[n] = cutExperiment[n]\n",
    "\n",
    "matname = os.path.join(get_save_dir(), timeStamp+'.mat')\n",
    "matdict = {\"matVoltage\":matVoltage, \"matCalcium\":matCalcium, \"matApAmp\":matApAmp,\"matCaAmp\":matCaAmp,\"matIres\":matIres,\"matKaDensity\":matKaDensity,\"matCellID\":matCellID,\"matIdxROI\":matIdxROI, \"matTV\":matTv,\"matCutExp\":matCutExp,\"silentID\":np.array(silentID)}\n",
    "savemat(matname,matdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = os.path.join(get_save_dir(), \"saveOptimizeKaDensity_2025Jan28_213409.pkl\")\n",
    "kaDensity, apAmp, caAmp, vTraces, cTraces, tv, cellID, idxROI, silentID, ires, cutExperiment = loadKaResults(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n"
     ]
    }
   ],
   "source": [
    "# Get section list for each cell\n",
    "cell_silentID = []\n",
    "for n in range(numCells):\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",
    "    cell_silentID.append(cell1.silentID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7d/qc682nsj13d_bz11bztzd5hm0000gn/T/ipykernel_38790/75636609.py:4: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.\n",
      "  cmap = cm.get_cmap('jet')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "use_cut_experiment = 0\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "for n in range(numFits):\n",
    "    if cutExperiment[n] != use_cut_experiment: continue\n",
    "    c_cellID = cellID[n]\n",
    "    numROI = vTraces[n].shape[0]\n",
    "    for r in range(numROI):\n",
    "        if cell_silentID[c_cellID][r]==False and idxROI[n]!=r: continue\n",
    "        ccol = 'b'\n",
    "        if cell_silentID[c_cellID][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",
    "    if cutExperiment[n] != use_cut_experiment: continue\n",
    "    c_cellID = cellID[n]\n",
    "    numROI = cTraces[n].shape[0]\n",
    "    for r in range(numROI):\n",
    "        if cell_silentID[c_cellID][r]==False and idxROI[n]!=r: continue\n",
    "        ccol = 'b'\n",
    "        if cell_silentID[c_cellID][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",
    "    if cutExperiment[n] != use_cut_experiment: continue\n",
    "    c_cellID = cellID[n]\n",
    "    numROI = cTraces[n].shape[0]\n",
    "    for r in range(numROI):\n",
    "        if cell_silentID[c_cellID][r]==False and idxROI[n]!=r: \n",
    "            continue\n",
    "        ccol = 'b'\n",
    "        if cell_silentID[c_cellID][r]==False: \n",
    "            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": "markdown",
   "metadata": {},
   "source": [
    "## Run example for EPSP simulation"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Run example of K-Density AP fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n",
      "\t1 \n",
      "[-57.15541556 -47.48945324 -10.00012429  -3.6262314 ]\n",
      "[0.00235164 0.00616053 0.38798033 0.48423977]\n",
      "\t1 \n",
      "[ -1.22404467 -43.55398527  -0.3795742    1.49898493]\n",
      "[0.51479738 0.00920293 0.52791063 0.54793361]\n"
     ]
    }
   ],
   "source": [
    "cellID = 1\n",
    "cutExp = 0\n",
    "targetROI = 2\n",
    "naDensity = 5\n",
    "initKaDensity = 0.01\n",
    "kaBounds = (0,None)\n",
    "targetAmplitude = -10\n",
    "\n",
    "# Create cell just to get silent IDs\n",
    "for sec in h.allsec(): h.delete_section(sec=sec)\n",
    "cell1 = L23(cellID=cellID,cutExperiment=cutExp,dendNa=[naDensity,None,None,False],dendK=[initKaDensity,np.inf,True,False],dxSeg=1,fixDiam=None);\n",
    "cSilentID = cell1.silentID\n",
    "\n",
    "results = minimize(determineKaDensity, initKaDensity, args=(targetAmplitude,cellID,cutExp,naDensity,targetROI),method='Nelder-Mead')\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=cellID,cutExperiment=cutExp,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",
    "print(np.amax(np.array(vsec),axis=1))\n",
    "print(np.amax(np.array(gca_sec),axis=1))\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=cellID,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",
    "print(np.amax(np.array(vsec),axis=1))\n",
    "print(np.amax(np.array(gca_sec),axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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