{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import os\n",
    "\n",
    "plt.rcParams['axes.spines.right'] = False\n",
    "plt.rcParams['axes.spines.top'] = False\n",
    "plt.rcParams['font.sans-serif'] = \"Arial\"\n",
    "plt.rcParams['font.family'] = \"sans-serif\"\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "tick_major = 6\n",
    "tick_minor = 4\n",
    "plt.rcParams[\"xtick.major.size\"] = tick_major\n",
    "plt.rcParams[\"xtick.minor.size\"] = tick_minor\n",
    "plt.rcParams[\"ytick.major.size\"] = tick_major\n",
    "plt.rcParams[\"ytick.minor.size\"] = tick_minor\n",
    "\n",
    "font_small = 12\n",
    "font_medium = 13\n",
    "font_large = 14\n",
    "plt.rc('font', size=font_small)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=font_medium)    # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=font_medium)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=font_small)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=font_small)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=font_small)    # legend fontsize\n",
    "plt.rc('figure', titlesize=font_large)   # fontsize of the figure title\n",
    "\n",
    "import matplotlib.colors as clr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Controls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:40:38.619248Z",
     "start_time": "2021-05-02T02:40:30.161499Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t1 \n",
      "\t1 \n",
      "Setting temperature to 34.000000 C\n",
      "Setting simulation time step to 0.100000 ms\n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "Km value: 0.000010\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "**********************\n",
      "cADpyr232_L5_TTPC1_0fb1ca4724[0].soma[0]\n",
      "1 \n",
      "1 \n",
      "1 \n",
      "\t1 \n",
      "Km value: 0.000010Km value: 0.000010\t1 \n",
      "\t1 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from neuron import h\n",
    "h.load_file(\"runModel.hoc\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Model Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T03:22:53.581851Z",
     "start_time": "2021-05-02T03:22:53.571750Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def init_settings(nav12=1,\n",
    "                  nav16=1,\n",
    "                  dend_nav12=1, \n",
    "                  soma_nav12=1, \n",
    "                  ais_nav12=1, \n",
    "                  dend_nav16=1, \n",
    "                  soma_nav16=1,\n",
    "                  ais_nav16=1, \n",
    "                  axon_Kp=1,\n",
    "                  axon_Kt =1,\n",
    "                  axon_K=1,\n",
    "                  soma_K=1,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1,\n",
    "                  hcn=1,\n",
    "                  km=1):\n",
    "    \n",
    "    # create default model parameters to avoid loading the model\n",
    "    \n",
    "    h.dend_na12 = 0.026145/2 \n",
    "    h.dend_na16 = h.dend_na12 \n",
    "    h.dend_k = 0.004226 * soma_K\n",
    "\n",
    "\n",
    "    h.soma_na12 = 0.983955/10 \n",
    "    h.soma_na16 = h.soma_na12 \n",
    "    h.soma_K = 0.303472 * soma_K\n",
    "\n",
    "    h.ais_na16 = 4 \n",
    "    h.ais_na12 = 4 \n",
    "    h.ais_ca = 0.000990\n",
    "    h.ais_KCa = 0.007104\n",
    "\n",
    "    h.node_na = 2\n",
    "\n",
    "    h.axon_KP = 0.973538 * axon_Kp\n",
    "    h.axon_KT = 0.089259 * axon_Kt\n",
    "    h.axon_K = 1.021945 * axon_K\n",
    "\n",
    "    h.cell.axon[0].gCa_LVAstbar_Ca_LVAst = 0.001376286159287454\n",
    "    \n",
    "    #h.soma_na12 = h.soma_na12/2\n",
    "    h.naked_axon_na = h.soma_na16/5\n",
    "    h.navshift = -10\n",
    "    h.myelin_na = h.naked_axon_na\n",
    "    h.myelin_K = 0.303472\n",
    "    h.myelin_scale = 10\n",
    "    h.gpas_all = 3e-5 * gpas_all\n",
    "    h.cm_all = 1\n",
    "    \n",
    "    \n",
    "    h.dend_na12 = h.dend_na12 * nav12 * dend_nav12\n",
    "    h.soma_na12 = h.soma_na12 * nav12 * soma_nav12\n",
    "    h.ais_na12 = h.ais_na12 * nav12 * ais_nav12\n",
    "    \n",
    "    h.dend_na16 = h.dend_na16 * nav16 * dend_nav16\n",
    "    h.soma_na16 = h.soma_na16 * nav16 * soma_nav16\n",
    "    h.ais_na16 = h.ais_na16 * nav16 * ais_nav16\n",
    "    \n",
    "    h.hcn = hcn\n",
    "    \n",
    "    h.km = km\n",
    "    \n",
    "    h.working()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Stimulation Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:48:59.026404Z",
     "start_time": "2021-05-02T02:48:59.022102Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def init_stim(sweep_len = 800, stim_start = 100, stim_dur = 500, amp = 0.5, dt = 0.01):\n",
    "    # updates the stimulation params used by the model\n",
    "    # time values are in ms\n",
    "    # amp values are in nA\n",
    "    \n",
    "    h(\"st.del = \" + str(stim_start))\n",
    "    h(\"st.dur = \" + str(stim_dur))\n",
    "    h(\"st.amp = \" + str(amp))\n",
    "    h.tstop = sweep_len\n",
    "    h.dt = dt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:00.722086Z",
     "start_time": "2021-05-02T02:49:00.715276Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def run_model(start_Vm = -72):\n",
    "\n",
    "    h.finitialize(start_Vm)\n",
    "    timesteps = int(h.tstop/h.dt)\n",
    "    \n",
    "    Vm = np.zeros(timesteps)\n",
    "    I = {}\n",
    "    I['Na'] = np.zeros(timesteps)\n",
    "    I['Ca'] = np.zeros(timesteps)\n",
    "    I['K'] = np.zeros(timesteps)\n",
    "    t = np.zeros(timesteps)\n",
    "    \n",
    "    for i in range(timesteps):\n",
    "        Vm[i] = h.cell.soma[0].v\n",
    "        I['Na'][i] = h.cell.soma[0](0.5).ina\n",
    "        I['Ca'][i] = h.cell.soma[0](0.5).ica\n",
    "        I['K'][i] = h.cell.soma[0](0.5).ik\n",
    "        t[i] = i*h.dt / 1000\n",
    "        h.fadvance()\n",
    "        \n",
    "    return Vm, I, t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AP analysis code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:02.498832Z",
     "start_time": "2021-05-02T02:49:02.478323Z"
    },
    "code_folding": [
     81,
     90,
     94,
     99,
     113
    ]
   },
   "outputs": [],
   "source": [
    "def detect_spikes(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0002, properties=True):\n",
    "    '''\n",
    "    Method for idenifying spikes based on rates of change in the membrane potential\n",
    "    INPUTS:\n",
    "    Vm: array-like - membrane potential (mV)\n",
    "    time: array-like - time corresponding to Vm (sec)\n",
    "    dVdt_thresh: float - Threshold for determining spike initiation (V/s)\n",
    "    min_spike_len: float - Minimum length of time dVdt must be above dVdt_thresh to be considered a spike (sec)\n",
    "    properties: Bool - If true, returns spike_times and spike_properties. Otherwise returns only spike_properties\n",
    "\n",
    "    Output:\n",
    "    array of spike times\n",
    "\n",
    "    Identification of spike start times:\n",
    "    dVdt is first quanitified from Vm and time\n",
    "    Continuous tretches (runs) of dVdt above dVdt_thresh are identified, and then esured to last longer than min_spike_len\n",
    "\n",
    "    Spike Property measurement:\n",
    "    spike_end is determined by finding the second zero crossing of dVdt after spike dVdt_threshold\n",
    "    First zero crossing is AP peak, second should be end of repolarization phase\n",
    "    Persistent zero crossing (must stay above zero for 3 continous points) is used to make sure that noise in slowly repolarizing spikes doesn't trigger spike end\n",
    "    '''\n",
    "    if len(Vm) != len(time):\n",
    "        raise Exception(\"Vm and time must be the same length\")\n",
    "\n",
    "    #determine stretches where dVdt exceeds dVdt_thresh\n",
    "    dVdt = np.gradient(Vm,time)/1e3\n",
    "    runs = group_consecutives(np.argwhere((dVdt>=dVdt_thresh)).flatten())\n",
    "\n",
    "    #If runs are longer than min_spike_len count as a spike\n",
    "    dt = time[1]-time[0] #sample rate\n",
    "    min_run_len = np.ceil(min_spike_len/dt)\n",
    "    spike_times = []\n",
    "    for run in runs:\n",
    "        if len(run) > min_run_len:\n",
    "            spike_times.append(time[run[0]])\n",
    "    spike_times = np.asarray(spike_times)\n",
    "\n",
    "    if not properties: #just return spike_times\n",
    "        return spike_times\n",
    "\n",
    "    #get spike properties\n",
    "    spike_properties=[]\n",
    "    for spike_time in spike_times:\n",
    "        #find index of spike_time in time\n",
    "        spike_start_idx = np.argwhere(spike_time == time)[0][0]\n",
    "\n",
    "        #find zero crossings of dVdt after spike dVdt_threshold\n",
    "        zero_crosses = find_zero_crossing(dVdt[spike_start_idx:-1])\n",
    "        #make sure zero cross is persistent to account for noise\n",
    "        if len(zero_crosses) > 1:\n",
    "            spike_end_idx = spike_start_idx\\\n",
    "                            + zero_crosses[np.argwhere(np.diff(zero_crosses)>3)[0] + 1][0]\n",
    "        else: #Vm ends before spike can repolarize, therefore assigned Vm[-1] as spike end\n",
    "            spike_end_idx = len(Vm)-1\n",
    "\n",
    "        spike_Vm = Vm[spike_start_idx:spike_end_idx]\n",
    "        spike_time = time[spike_start_idx:spike_end_idx] - time[spike_start_idx]\n",
    "        spike_dVdt = dVdt[spike_start_idx:spike_end_idx]\n",
    "        spike = {}\n",
    "        spike['start_idx'] = spike_start_idx\n",
    "        spike['start_time'] = time[spike_start_idx]\n",
    "        spike['end_idx'] = spike_end_idx\n",
    "        spike['end_time'] = time[spike_end_idx]\n",
    "        spike['Vm'] = spike_Vm\n",
    "        spike['time'] = spike_time\n",
    "        spike['thresh'] = spike_Vm[0]\n",
    "        spike['peak_Vm'] = spike_Vm.max()\n",
    "        spike['height'] = np.max(spike_Vm)-spike_Vm[0]\n",
    "        spike['AHP'] = spike_Vm[0]-spike_Vm[-1]\n",
    "        spike['peak_dVdt'] = spike_dVdt.max()\n",
    "        spike['min_dVdt'] = spike_dVdt.min()\n",
    "        try:\n",
    "            half_pnts = find_zero_crossing(spike_Vm - (spike_Vm[0]+(np.max(spike_Vm)-spike_Vm[0])/2))\n",
    "            spike['half_width'] = (half_pnts[1]-half_pnts[0])*dt*1000\n",
    "        except: #For slowly repolarizing spikes this can sometimes fail\n",
    "            spike['half_width'] = np.nan\n",
    "\n",
    "        spike_properties.append(spike)\n",
    "    return spike_times,spike_properties\n",
    "\n",
    "def detect_spike_times(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0002):\n",
    "    '''\n",
    "    Wrapper of detect_spikes to only get spike times\n",
    "    '''\n",
    "    return detect_spikes(Vm, time,\n",
    "                          dVdt_thresh = dVdt_thresh,\n",
    "                          min_spike_len = min_spike_len,\n",
    "                          properties=False)\n",
    "\n",
    "def detect_spike_properties(Vm, time, dVdt_thresh = 15, min_spike_len = 0.0001):\n",
    "    '''\n",
    "    Wrapper of detect_spikes to only get spike properties\n",
    "    '''\n",
    "    return detect_spikes(Vm, time,\n",
    "                          dVdt_thresh = dVdt_thresh,\n",
    "                          min_spike_len = min_spike_len,\n",
    "                          properties=True)[1]\n",
    "\n",
    "def group_consecutives(vals, step=1):\n",
    "    \"\"\"Return list of consecutive lists of numbers from vals (number list).\"\"\"\n",
    "    run = []\n",
    "    result = [run]\n",
    "    expect = None\n",
    "    for v in vals:\n",
    "        if (v == expect) or (expect is None):\n",
    "            run.append(v)\n",
    "        else:\n",
    "            run = [v]\n",
    "            result.append(run)\n",
    "        expect = v + step\n",
    "    return result\n",
    "\n",
    "def find_zero_crossing(x):\n",
    "    '''\n",
    "    returns array of indicies before a zero crossing occur\n",
    "    If your input array starts and stops with zeros, it will find a zero crossing at the beginning, but not at the end\n",
    "    '''\n",
    "    zero_crossings = np.where(np.diff(np.signbit(x)))[0]\n",
    "    return zero_crossings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FI Curve support functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T03:14:42.207959Z",
     "start_time": "2021-05-02T03:14:42.200132Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def FI_curve(stims, iclamp, orig, stim_start = 50, stim_dur = 300, sweep_len = 350, dt = 0.1):\n",
    "             \n",
    "    f = []\n",
    "    i = []\n",
    "    for amp in stims:\n",
    "        i.append(amp)\n",
    "        init_stim(stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt, amp=amp)\n",
    "        iclamp.amp = -0.025 if orig else 0\n",
    "        \n",
    "        Vm, I, t = run_model()\n",
    "        spike_times = detect_spike_times(Vm, t)\n",
    "        f.append(len(spike_times))\n",
    "    \n",
    "    return f, i\n",
    "\n",
    "def FI_curve_plot(stims, iclamp, orig, ax, label='', stim_start = 50, stim_dur = 300, sweep_len = 350, dt = 0.5):\n",
    "    f, i = FI_curve(stims=stims, iclamp=iclamp, orig=orig, stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt)\n",
    "    \n",
    "    ax.set_ylim(0,18)\n",
    "    ax.set_ylabel('Spikes per Epoch ({}ms)'.format(stim_dur))\n",
    "    ax.set_xlabel('Injected Current (nA)')\n",
    "    ax.plot(i, f, linewidth = 1, label=label) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 25% Ri Increase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_path = './ri increase/'\n",
    "if os.path.isdir(plot_path) is False:\n",
    "    os.mkdir(plot_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpas_reduct = 0\n",
    "hcn_reduct = 0.565"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Km value: 0.000010original: 77 MOhm\n",
      "Km value: 0.000010Ri increase: 96 MOhm\n",
      "diff: 19 MOhm (25%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "step_amp = -0.05\n",
    "init_stim(stim_start=600, stim_dur=300, sweep_len=1500, dt=0.2, amp=step_amp)\n",
    "\n",
    "iclamp = h.IClamp(h.cell.soma[0](0.5))\n",
    "iclamp.delay = 300\n",
    "iclamp.dur = 900\n",
    "iclamp.amp = -0.025\n",
    "\n",
    "init_settings()\n",
    "Vm, I, t = run_model()\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(t, Vm, lw=0.7, label='original')\n",
    "start = int(0.6/0.2e-3)\n",
    "end = int(0.9/0.2e-3)\n",
    "original_ri = (Vm[end]-Vm[start])/step_amp\n",
    "original_title = 'original: %d MOhm' %(np.round(original_ri, 2))\n",
    "print(original_title)\n",
    "\n",
    "iclamp.amp = 0\n",
    "init_settings(gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "Vm, I, t = run_model()\n",
    "plt.plot(t, Vm, lw=0.7, label='Ri increase (gpas=0, hcn=0.565)')\n",
    "gpas0_ri = (Vm[end]-Vm[start])/step_amp\n",
    "gpas0_title = 'Ri increase: %d MOhm' %(np.round(gpas0_ri, 2))\n",
    "print(gpas0_title)\n",
    "diff_ri = gpas0_ri-original_ri\n",
    "diff_title = 'diff: %d MOhm (%d%%)' % (np.round(diff_ri, 2), np.round(diff_ri/original_ri*100, 2))\n",
    "print(diff_title)\n",
    "\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Voltage (mV)')\n",
    "plt.legend()\n",
    "n50pa_step_title = original_title + ', ' + gpas0_title + ', ' + diff_title\n",
    "plt.title(n50pa_step_title)\n",
    "# plt.savefig(plot_path+'n50pa_step.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FI Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(20,8))\n",
    "stims = np.arange(0.1,.51,.02)\n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='original', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims, ax, label='Ri increase (gpas=0, hcn=0.565)', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(nav12=0)\n",
    "FI_curve_plot(stims, ax, label='Hom', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(nav12=0, gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims, ax, label='Hom + Ri increase (gpas=0, hcn=0.565)', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(plot_path+'fi_curve.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(20,8))\n",
    "stims = np.arange(0.1,.51,.02)\n",
    "\n",
    "iclamp = h.IClamp(h.cell.soma[0](0.5))\n",
    "iclamp.delay = 300\n",
    "iclamp.dur = 900\n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=True, ax=ax, label='original', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=False, ax=ax, label='Ri increase (gpas=0, hcn=0.565)', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(nav12=0)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=True, ax=ax, label='Hom', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "init_settings(nav12=0, gpas_all=gpas_reduct, hcn=hcn_reduct)\n",
    "FI_curve_plot(stims=stims, iclamp=iclamp, orig=False, ax=ax, label='Hom + Ri increase (gpas=0, hcn=0.565)', stim_start = 600, stim_dur = 300, sweep_len = 1500, dt = 0.2)\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(plot_path+'fi_curve_vm_hold.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}