{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "import operator\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import cm\n",
    "import os\n",
    "import seaborn as sns\n",
    "import json\n",
    "\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "CM = 1/2.54  # centimeters in inches\n",
    "\n",
    "clColorStr = 'Blues'\n",
    "kColorStr = 'Oranges'\n",
    "expColorStr = 'Purples'\n",
    "\n",
    "kColors = getattr(cm, kColorStr)(np.linspace(0.3, 0.8, 4))\n",
    "clColors = getattr(cm, clColorStr)(np.linspace(0.3, 0.8, 4))\n",
    "expColors = getattr(cm, expColorStr)(np.linspace(0.3, 0.8, 4))\n",
    "\n",
    "kColor = kColors[-2]\n",
    "clColor = clColors[-1]\n",
    "expColor = expColors[-1]\n",
    "\n",
    "plt.rc('xtick',labelsize=7)\n",
    "plt.rc('ytick',labelsize=7)\n",
    "plt.rcParams['lines.linewidth'] = 0.75\n",
    "plt.rc('font', size=8)\n",
    "\n",
    "def tauFunc(x, A, tau, b): \n",
    "    return A * np.exp(-x/tau) + b\n",
    "\n",
    "def Ifunc(I, p1, p2, p3=1):\n",
    "    p3 = 1 \n",
    "    return p3/(1+np.exp(p1/p2)*I**(-1/(p2*np.log(10))))\n",
    "\n",
    "def fit(xs, ys, func, p0):\n",
    "    params, cv = scipy.optimize.curve_fit(func, xs, ys, p0)\n",
    "    r2 =  1 - np.sum(np.square(ys - func(xs, params[0], params[1], params[2]))) / np.sum(np.square(ys - np.mean(ys)))\n",
    "    return params, r2\n",
    "\n",
    "def fitOpsinmodel(X, Y, Vs, xI, yI, tStimStart = 275, tStimEnd = 755):\n",
    "\n",
    "    Iss = np.zeros(Vs.size)\n",
    "    Ipeaks = np.zeros(Vs.size)\n",
    "    tauOffs = np.zeros(Vs.size)\n",
    "    tauOns = np.zeros(Vs.size)\n",
    "    tauInacts = np.zeros(Vs.size)\n",
    "    tauOff_R2s = np.zeros(Vs.size)\n",
    "    tauOn_R2s = np.zeros(Vs.size)\n",
    "    tauInact_R2s = np.zeros(Vs.size)\n",
    "\n",
    "    xI, yI  = xI[~np.isnan(xI)], yI[~np.isnan(yI)]\n",
    "    \n",
    "    maxi = np.max(np.max(Y[~np.isnan(Y)]))\n",
    "\n",
    "    fig, axs = plt.subplots(1,2, figsize = (10,3))\n",
    "    colors = getattr(cm, 'Spectral')(np.linspace(0.1, 0.9, 7))\n",
    "    for i, V in enumerate(Vs):\n",
    "        x, y = X[i,:], Y[i,:]\n",
    "        x, y  = x[~np.isnan(x)], y[~np.isnan(y)]\n",
    "        \n",
    "        axs[0].plot(x,y,color = colors[i], alpha = 0.2)\n",
    "        \n",
    "        i_peak = np.argmax(y)\n",
    "        i_ss = np.where(x>tStimEnd)[0][0]\n",
    "        i_on = np.where(x<tStimStart+0.1)[0][-1]\n",
    "        Ipeaks[i] = y[i_peak]\n",
    "        Iss[i] = y[i_ss]\n",
    "\n",
    "        xOn, yOn = x[i_on:i_peak+1], y[i_on:i_peak+1] \n",
    "        if len(xOn)<4:\n",
    "            print('V = ' + str(V) + ': too little points to accurately estimate tauOn')\n",
    "            tauOns[i], tauOn_R2s[i] = np.nan, np.nan\n",
    "        else:\n",
    "            yOn = np.interp(np.arange(xOn[0], xOn[-1], 0.01),xOn,yOn)\n",
    "            xOn = np.arange(xOn[0], xOn[-1], 0.01)-xOn[0]\n",
    "            params, tauOn_R2s[i] = fit(xOn, yOn, tauFunc, (50, -1, maxi))\n",
    "            A, tauOns[i], b = params\n",
    "            axs[0].plot(x[i_on:i_peak+1], y[i_on:i_peak+1], '.', color = colors[i])\n",
    "            print(x[i_on:i_peak+1].shape, tauFunc(xOn, A, tauOns[i], b).shape)\n",
    "            axs[0].plot(xOn+x[i_on], tauFunc(xOn, A, tauOns[i], b), ':', color = colors[i], label = str(V)+ ' mV, Off, r2 = ' +str(np.round(tauOff_R2s[i],2)))\n",
    "            \n",
    "        xOff, yOff = x[i_ss:], y[i_ss:] \n",
    "        yOff = np.interp(np.arange(xOff[0], xOff[-1], 0.01),xOff,yOff)\n",
    "        xOff = np.arange(xOff[0], xOff[-1], 0.01) - xOff[0]\n",
    "        params, tauOff_R2s[i] = fit(xOff, yOff, tauFunc, (50, 100, maxi))\n",
    "        A, tauOffs[i], b = params\n",
    "        axs[0].plot(x[i_ss:], y[i_ss:], '.', color = colors[i])\n",
    "        axs[0].plot(xOff+x[i_ss], tauFunc(xOff, A, tauOffs[i], b), color = colors[i], label = str(V)+ ' mV, Off, r2 = ' +str(np.round(tauOff_R2s[i],2)))\n",
    "        \n",
    "        xInact, yInact = x[i_peak:i_ss+1], y[i_peak: i_ss+1]\n",
    "        yInact = np.interp(np.arange(xInact[0], xInact[-1], 0.01),xInact,yInact)\n",
    "        xInact = np.arange(xInact[0], xInact[-1], 0.01)-xInact[0]\n",
    "        params, tauInact_R2s[i] = fit(xInact, yInact, tauFunc, (50, 100, maxi))\n",
    "        A, tauInacts[i], b = params\n",
    "        axs[0].plot(x[i_peak:i_ss+1], y[i_peak: i_ss+1], '.', color = colors[i])\n",
    "        axs[0].plot(xInact+x[i_peak], tauFunc(xInact, A, tauInacts[i], b), '--', color = colors[i], label = str(V)+ ' mV, Inact, r2 = ' +str(np.round(tauInact_R2s[i],2)))\n",
    "\n",
    "    axs[0].set_ylim(0,)\n",
    "    params, r2 = fit(xI, yI, Ifunc, (2.5,0.7, 1))\n",
    "    p1, p2 , _ = params\n",
    "    axs[1].scatter(xI, yI, color = 'black')\n",
    "    axs[1].plot(np.logspace(-3,4,1000), Ifunc(np.logspace(-3,4,1000), p1, p2), label =('p1:', p1, 'p2:', p2, 'r2:', r2), color = 'black')\n",
    "    axs[1].set_xscale('log')\n",
    "    for i in range(2):axs[i].legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    fig, axs = plt.subplots(1,3, figsize = (10,3))\n",
    "    axs[0].scatter(Vs, Iss, label ='Iss', color = 'black')\n",
    "    a_Iss,b_Iss = np.polyfit(Vs, Iss, 1)\n",
    "    R2_Iss = 1 - np.sum(np.square(Iss - (a_Iss*Vs+b_Iss))) / np.sum(np.square(Iss - np.mean(Iss)))\n",
    "    axs[0].plot(np.linspace(np.min(Vs), np.max(Vs), 100), np.linspace(np.min(Vs), np.max(Vs), 100)*a_Iss+b_Iss, '--', label = R2_Iss, color = 'black')\n",
    "\n",
    "    axs[0].scatter(Vs, Ipeaks, label ='Ipeaks', color ='blue')\n",
    "    a_Ipeaks, b_Ipeaks = np.polyfit(Vs, Ipeaks, 1)\n",
    "    R2_Ipeaks = 1 - np.sum(np.square(Ipeaks - (a_Ipeaks*Vs+b_Ipeaks))) / np.sum(np.square(Ipeaks - np.mean(Ipeaks)))\n",
    "    axs[0].plot(np.linspace(np.min(Vs), np.max(Vs), 100), np.linspace(np.min(Vs), np.max(Vs), 100)*a_Ipeaks+b_Ipeaks, '--', label = R2_Ipeaks, color = 'blue')\n",
    "\n",
    "    axs[1].scatter(Vs, Iss/Ipeaks, label ='Iratio', color = 'black')\n",
    "    axs[1].set_ylim(0,1)\n",
    "    axs[2].scatter(Vs, tauInacts, label ='tauInact', color = 'black')\n",
    "    axs[2].scatter(Vs, tauOffs, label ='tauOff', color = 'blue')\n",
    "    if np.sum(np.isnan(tauOns)) ==0 : axs[2].scatter(Vs, tauOns, label ='tauOff', color = 'green')\n",
    "    for i in range(3):axs[i].legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return {'Iratio': np.mean(Iss/Ipeaks), 'tauOff': np.mean(tauOffs), 'tauInact': np.mean(tauInacts), 'tauOn': np.mean(tauOns), 'Oinf_p1': p1, 'Oinf_p2':p2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V = 40.0: too little points to accurately estimate tauOn\n",
      "V = 20.0: too little points to accurately estimate tauOn\n",
      "V = 0.0: too little points to accurately estimate tauOn\n",
      "V = -20.0: too little points to accurately estimate tauOn\n",
      "V = -40.0: too little points to accurately estimate tauOn\n",
      "V = -60.0: too little points to accurately estimate tauOn\n",
      "V = -80.0: too little points to accurately estimate tauOn\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Experimental data preparation\n",
    "tStimStart = 275\n",
    "tStimEnd = 755\n",
    "\n",
    "data = pd.read_csv('Data/WiChRExperimentalData.csv')\n",
    "xI = data['I']\n",
    "yI = data['iPeak']\n",
    "\n",
    "X_col = [col for col in data.columns if 'X' in col]\n",
    "Y_col = [col for col in data.columns if 'Y' in col]\n",
    "Vs = np.array([col.split('V=')[-1].split('mV')[0] for col in X_col]).astype('float')\n",
    "X = np.array(data[X_col]).T\n",
    "Y = np.array(data[Y_col]).T\n",
    "\n",
    "#fit wichr data to opsin model\n",
    "params = fitOpsinmodel(X, Y, Vs, xI, yI, tStimStart = 275, tStimEnd = 755)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Run simulations\n",
    "from Functions.setup import simParams\n",
    "from Functions.utils import Dict\n",
    "from Functions.Simulation import runSimulation\n",
    "\n",
    "inputs = []\n",
    "\n",
    "tStimStart = 275\n",
    "tStimEnd = 755\n",
    "\n",
    "Iratio = params['Iratio']\n",
    "tauOn = 1\n",
    "tauOff = params['tauOff']\n",
    "tauInact = params['tauInact']\n",
    "tauRecov = 5000\n",
    "Oinf_p1 = params['Oinf_p1']\n",
    "Oinf_p2 = params['Oinf_p2']\n",
    "\n",
    "Is = np.logspace(-5, 2, 15)*1e3\n",
    "Vclamp = np.arange(-80, 41, 40).astype(float)\n",
    "\n",
    "Erev = -100\n",
    "\n",
    "id = 0\n",
    "for V in Vclamp:\n",
    "    for I in Is:\n",
    "        inputs.append(Dict(simParams().todict(inplace=False)))\n",
    "        \n",
    "        inputs[-1].stimopt.Estimparams.vClamp = True\n",
    "        \n",
    "        inputs[-1].cellsopt.opsin_options.opsinmech = 'K22OMs'\n",
    "        \n",
    "\n",
    "        inputs[-1].recordDict = {'v': ['allsec'], \n",
    "                                'ecl': ['somatic'], 'icl': ['somatic'], 'cli': ['somatic'], 'clo': ['somatic'],\n",
    "                                'ek': ['somatic'],  'ik':  ['somatic'],  'ki': ['somatic'], 'ko':  ['somatic'],\n",
    "                                'ena': [], 'ina': [], 'nai': [], 'nao': [],\n",
    "                                'iopto': ['somatic'], 'gopto': ['somatic'], 'DAopto': [], 'Oopto': [],\n",
    "                                'tauO': [], 'tauR': [], 'Rinf': [], 'Oinf': []}\n",
    "\n",
    "        inputs[-1].saveDict = {'t': None, 'synapseInput': None, 'regionsDict': None, 'Iopto': None, \n",
    "                        'v': [], 'ecl': [], 'iopto': ['somatic'], 'gopto': [],\n",
    "                        'tauO': [], 'tauR': [], 'Rinf': [], 'Oinf': []}\n",
    "\n",
    "        inputs[-1].analysesopt.featuresDict = { }\n",
    "\n",
    "        inputs[-1].cellsopt.opsin_options.Iratio = Iratio\n",
    "        inputs[-1].cellsopt.opsin_options.tauOn = tauOn\n",
    "        inputs[-1].cellsopt.opsin_options.tauOff = tauOff\n",
    "        inputs[-1].cellsopt.opsin_options.tauInact = tauInact\n",
    "        inputs[-1].cellsopt.opsin_options.tauRecov = tauRecov\n",
    "        inputs[-1].cellsopt.opsin_options.Oinf_p1 = Oinf_p1\n",
    "        inputs[-1].cellsopt.opsin_options.Oinf_p2 = Oinf_p2\n",
    "\n",
    "        #chosen as to match experimental setup\n",
    "        inputs[-1].cellsopt.opsin_options.Erev = Erev\n",
    "        inputs[-1].cellsopt.ion_options.cextra0 =  {'cl':145,  'k': 1,    'na':110}  \n",
    "        inputs[-1].cellsopt.ion_options.cintra0 =  {'cl':7,    'k': 110,   'na': 1} \n",
    "        inputs[-1].cellsopt.opsin_options.EvarFlag = 0\n",
    "        \n",
    "        inputs[-1].duration = 2500\n",
    "        inputs[-1].stimopt.Ostimparams.dur = tStimEnd-tStimStart\n",
    "        inputs[-1].stimopt.Ostimparams.delay = tStimStart\n",
    "        inputs[-1].stimopt.Estimparams.dur = 2250\n",
    "        inputs[-1].stimopt.Estimparams.delay = 100\n",
    "\n",
    "        inputs[-1].returnResults = False\n",
    "\n",
    "        inputs[-1].resultsFolder = 'Results/FitTest'\n",
    "        \n",
    "        inputs[-1].stimopt.Estimparams.amp = V\n",
    "        inputs[-1].stimopt.Ostimparams.amp = I\n",
    "        inputs[-1].id = id\n",
    "\n",
    "        inputs[-1].analysesopt.featuresDict = {}\n",
    "        id+=1\n",
    "\n",
    "#import multiprocessing as mp\n",
    "#import tqdm\n",
    "#pool = mp.Pool(8)\n",
    "#results = list(tqdm.tqdm(pool.imap(runSimulation, inputs), total=len(inputs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216.535x354.331 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#compare model to experiments\n",
    "df = None\n",
    "t_continuous = None\n",
    "\n",
    "folders = ['Results/FitTest']\n",
    "           \n",
    "for folder in folders:\n",
    "    resultfiles = [x for x in os.listdir(folder) if x.endswith(\"results.json\")]\n",
    "    inputfiles = [x for x in os.listdir(folder) if x.endswith(\"input.json\")]\n",
    "    for i in range(len(resultfiles)):\n",
    "        with open(folder + '/' + resultfiles[i]) as f: results = Dict(json.load(f))\n",
    "        with open(folder + '/' + inputfiles[i]) as f: input = Dict(json.load(f)['settings'])\n",
    "        newRow = {\n",
    "            'model': 'fitTest',\n",
    "            'I': float(input.stimopt.Ostimparams.amp),\n",
    "            'V': float(input.stimopt.Estimparams.amp),\n",
    "            'iOpto': [np.array(results.recordings.iopto[results.regionsDict['somatic'][0]])], \n",
    "            'iPeak': np.max(np.array(results.recordings.iopto[results.regionsDict['somatic'][0]])),\n",
    "        }\n",
    "        df = pd.DataFrame(newRow) if df is None else pd.concat([df, pd.DataFrame(newRow)])\n",
    "    if t_continuous is None: t_continuous = np.array(results.t).astype(float)\n",
    "df['I'] = df['I']*1e-3\n",
    "Is = np.sort(df['I'].unique())\n",
    "Imax = np.max(Is)\n",
    "\n",
    "#plot comparison\n",
    "fig, axs = plt.subplots(2,1, figsize = (5.5*CM, 9*CM))\n",
    "df_ = df.loc[(df['I'] == Imax)]\n",
    "imax = np.nanmax(np.array(df_['iOpto'].to_list()))\n",
    "df['iPeak_norm'] = df['iPeak']/imax\n",
    "\n",
    "for i, V in enumerate(Vclamp):\n",
    "    df_select = df_.loc[(df_['V'] == V)]\n",
    "    axs[0].plot(t_continuous.astype(float)*1e-3, np.array(df_select['iOpto'].to_list()).T/imax, label = V, color = kColors[i])\n",
    "\n",
    "for i, V in enumerate(Vs): \n",
    "    if V % 40 == 0: axs[0].scatter(X[i,:]*1e-3, Y[i,:]/np.max(np.max(Y[~np.isnan(Y)])), color = expColors[expColors.shape[0]-i-1], s =2)\n",
    "axs[0].set_xlabel('time [s]')\n",
    "axs[0].set_ylabel('photocurrent')\n",
    "\n",
    "sns.lineplot(df.dropna(), x='I', y='iPeak_norm', hue = 'V', ax = axs[1], palette = kColors, legend = False)\n",
    "axs[1].scatter(xI*1e-3, yI, color = expColor, s= 2)\n",
    "axs[1].set_xscale('log')\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"OpsinFit.svg\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env_netpyne",
   "language": "python",
   "name": "env_netpyne"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
