{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:22.898021Z",
     "start_time": "2021-05-02T02:49:12.057586Z"
    }
   },
   "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",
    "\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",
      "\t1 \n",
      "\t1 \n",
      "\t1 \n",
      "**********************\n",
      "cADpyr232_L5_TTPC1_0fb1ca4724[0].soma[0]\n",
      "1 \n",
      "1 \n",
      "1 \n",
      "\t1 \n",
      "\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",
    "    \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.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": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2500 5000\n",
      "-76.83591653122456 -82.09473537606753\n",
      "105.17637689685955\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = 0.3\n",
    "init_settings(hcn=p)\n",
    "init_stim(stim_start=500, stim_dur=500, sweep_len=1500, dt=0.2, amp=-0.05)\n",
    "Vm, I, t = run_model()\n",
    "start = int(0.5/0.2e-3)\n",
    "end = int(1/0.2e-3)\n",
    "print(start, end)\n",
    "print(Vm[start], Vm[end])\n",
    "print((Vm[end]-Vm[start])/-0.05)\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(t, Vm, label=np.round(p, 2))\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.  0.8 0.6 0.4 0.2 0. ]\n"
     ]
    }
   ],
   "source": [
    "step_amp = -0.05\n",
    "percentages = np.round(np.arange(1, -0.1, -0.2), 1)\n",
    "print(percentages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "plot_resting_path = './n50pA/'\n",
    "os.mkdir(plot_resting_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-76, -70))\n",
    "for p in percentages:\n",
    "    init_settings(nav12=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=step_amp)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Voltage (mV)')\n",
    "plt.title('Nav1.2')\n",
    "plt.savefig(plot_resting_path+'Nav12.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-76, -70))\n",
    "for p in percentages:\n",
    "    init_settings(nav16=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=step_amp)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Nav1.6')\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Voltage (mV)')\n",
    "plt.savefig(plot_resting_path+'Nav16.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-76, -70))\n",
    "for p in percentages:\n",
    "    init_settings(dend_nav12=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=step_amp)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Nav1.2 Dendrite')\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('Voltage (mV)')\n",
    "plt.savefig(plot_resting_path+'Nav12_dendrite.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(soma_nav12=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Nav1.2 Soma')\n",
    "plt.savefig(plot_resting_path+'Nav12_soma.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(ais_nav12=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Nav1.2 AIS')\n",
    "plt.savefig(plot_resting_path+'Nav12_ais.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(dend_nav16=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Nav1.6 Dendrite')\n",
    "plt.savefig(plot_resting_path+'Nav16_dendrite.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(soma_nav16=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Nav1.6 Soma')\n",
    "plt.savefig(plot_resting_path+'Nav16_soma.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(ais_nav16=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Nav1.6 AIS')\n",
    "plt.savefig(plot_resting_path+'Nav16_AIS.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(axon_Kp=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('K persistent Axon')\n",
    "plt.savefig(plot_resting_path+'Kp_axon.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o7bL2U00mk8zks/jMKPOZLGDyZwTkz8xQRnamsvJyldOhg9p376bOvfZTrwP7K79LF5nt8fcsAACAlooiCW3bM3+6VevmL9Xlf3vE6ygAgDRTWVWp9cXLtOHLL7V17RqVbt6syu0lCpVVKFIZUrQqIrfzW+9iTi4mSXXfolfTzplSsq9u3TN/YuZUwC9/hl++zAxlZGUqMydbWXl5yunQQfnduqhT917q1ruP2nXqLJ/P12x/FwAAAAksto22q6y0VGs+WKKjLvqp11EAAGkoKzNL+x14iPY78JCUnXNH6XZtXFOsrWtWq2TjBpVu2ayK7aUKlZUrXFGlaFVI0XBUke0hVW3ZodJojVlTLv6ntPeiaqeds6lMiv9pihdYPsXXnfL7ZH6ffAG/fBl+BYIZCmRlKCMzS5m5ucrKa6es/PbK69hR+V26qnOPnsrr1EWBILcAAgCA3VEkodX76y+vVma7oAqPH+51FABAG9Eur73aDWivAwYMarL3cLGYdpSWaNumddq2Yb12bN6ksq1bVV66Q1VlZQpXVChSWaVIKKxYKKJYJKZweVihkvJ4YRV10s7SKlFgKf7fRIG1b7PWE1XWLrOwZDW2zaTEbYM+v8l8PlmGXz6/T/5AQP5gQP5ghjKCQWVkZysjO1PBnFzl5OUpMzdX2e3aKTuvvXLy85XbLk9Zee2VEQym/O8VAADsHUUSWrWi1/+hqrIqXfiXe72OAgBASpnPp7z2HZTXvoN69zvYsxzhcFhlJdu0ffN67di6TeUl21VeUqKqHaWqqihXqKxSkVDVV6VWJKJYOKpYJKpYJKJoVUzhaI1v9It9VWrtfOycmfXVigzVexqZvrr+2vnfXUqwr2Z61ZjlZYkZYDtvVfT5qmd97Xzu8/vkCwTk8/vlD/jlCwQUyMiQLyP+ZyAzU4FgpgJZWcoIZiiYna3M7Bxl5sQfWbntlJPbTpk5eQpkBrm9EQCQViiS0Kq988gz6vWtg5TL10UDANAkMjIy1KFzV3Xo3HK+ETUWjam8bIfKSreqfHuJyku3q6Jsh0JlO1RVXq5wZaVCVZWKVFYpXBVSLBRWJBRWNBKOl2CxmFwkqlg0Khd1iobDclVOiiVmdTnVmO3lvirAds782vlckjlVl2K7Ll1avbdJ/g6s5tIXVmN7Z6FWXabtYdtqzTjzxV80M5lPibLNJ/PFS09Z4jZLn08+n08WSBRwPr/8fn/81kt/QL5AQP6AX/6MDPkzAvJlBOPlWzBYXcIFMzOVkZmljKwsZWZnK5iZo6ycHGVmZSsjO0eBYAaL4ANAE6JIQqv15K03SZJGXvtbb4MAAIC04vP71C4/X+3y86VeXqdpPrFoTFWhClXu2KHyHSUKVVSqsmKHKsvLFC4vV7gqpHBlhcJVVYpUVSkcCisaCikajSgWichFYonnUblYTLFoTC4ak4vF5KJOziWeOxffjkUTi9RLiQXAqm+llEtUZLuUbomgrkZ95qp3yVRzDtpXt2Kqxr7UzFTbN7uUctJXM9nq2t7Z19muRV7N2W+7jKl9e2hiHbT4nyb5fdXrosWLu8Sf5osv7O/3x4s7f7zA8/n9Ml/8dlLbOWMuIyC/P17a+f2JEi+YoYxAUIGsoAL++DdX+jOCCmZlKxDIUDAnPosumJGpjJxcBQJ++fz+lP/dAkhPFElolarKy7V+4XIdPfbnXkcBAABICz6/T9nZucrOzlXHrt29jtNixaIxVYUrVVVerqqKMoUqyhWqqFQoVKlQZaXClZWKVIUUDlUqUlWlSCikSDgsF40qGo0qGo4knicKulhMsXCihIvFZ7xVl3QuVr2eWcw5KVHUaeetoOHwLmucaedaZ87VKOGcJNttdlziRdUxEa5WcbfrraXx/U51jEj1X/U+qF3n1Z5ht3OX1X5aR/Gn3Yq96uJvl2LPl/hT1TPw5LNdiz2/T2YWvz3VV6vYSzz3Bfzy+fyJ22F9uxV7voBfgczMeMkXiM/UCwaz4uvKZQQVCAQVzMpSIJipYDCoQGamMnNy5M/IpOBDk6FIQqv02NXXKCM7Q4cdN8zrKAAAAGhFfH6fsv05ys7KkdTF6zhtkovFFI3FVFVZoXCoSqHKMoWrQgpVxtdkC1VWxou8UFiRUEjRcPzPWDSiaKLYi0WjioQjikWjikXC8QKvRrEXi0Yk5xSNRuNruNUo9lwsFq/NorH492vunJEXju1yW2vNW113KfZqFHbxJ4n/42ps1niys9iL76pV2O0yM6/2HL3d93lrt/l71ft3Kf2qt3cWlrsOrX07bvxPq1UQ7qkATJSJvkQLmDjO/Dtn/NlXt+KaxWf3+Sxe/ln8T/l88idm9+28XXfnLL/uB/bTd358auP/qtIcRRJanRWffayKrWU6564/eh0FAAAAQIqZz6eAz6dAuzxJeaLQS08uUcRFouH4+nOhkMJVlYqEqxSqqFQ0ElK4KqRoJKxQVVW80AuHFQ6HFQ2FFY1F5MIRxaIRRcIRRWOJW2ujMUXDUcViUblYdJdZfbFYNHF7bY3iz8XkYjVm8u3cdqr+oodYuI417qq/yrRWyVfjCyGqb9FNvLqleBVFEtASvXTrn5Xfq5M6de/hdRQAAAAAaJPMTIFAQIFAQFmZ2V7HQQrxXaJoVd588q+KRmI6+w/MRgIAAAAAINUaXCSZ2Wgzm1/j8aWZhc2se40xvc1stZntca6hmW2qdZ6zGpoJWDjrDfU75jBlBINeRwEAAAAAoNVp8K1tzrnpkqZLkpllSJor6Tbn3PrEvtGSbpbUc0/nMLMBkrY454Y0NAew0+M3XCefz6cfXXyF11EAAAAAAGiVUnVr268lbXDOPShJZtZT0k8knVDPcUdKiprZW2b2kZndaGZ8PyH2WVV5uTYvWa1jLzvX6ygAAAAAALRa9RZJZjbczCJ1PEYnXu8i6WpJV+48xjm3xjl3mnPu83pOH5D0uqQTJR2tePF0eUM/DNqu6Vdfo4zsoL5+xNFeRwEAAAAAoNWq99Y259zL9YwbK2mWc27Zvr65c+4vNbfNbLKkKyTdWWv/2MT77KJPnz77+pZohTauLlb51jL9fNItXkcBAAAAAKBVa/AaSTWcoXj5s8/M7BxJC5xzH+3cJSlce5xzbqqkqbX3FxYWuoa8L1qXZ66fqJyOuerai2IRAAAAAICm1Kg1ksyso6QDJb3dwFMMkjTRzPxmli3pMklPNSYT2pZF772tcEVIoyfd4XUUAAAAAABavcYutn2gpLXOud1mEe2JmU00s4mJzZslbZG0UNJHihdSDzUyE9qQN+56WJ0O6K7MnByvowAAAAAA0Oo16tY259x7ipdJextjtbZvrPG8XNL5jcmAtmvuzCcUi8Z0zh/+5HUUAAAAAADahMbOSAI8s2Dma+rznUO8jgEAAAAAQJtBkYQW6fm7bpck/eQX13qcBAAAAACAtoMiCS1S8buLNGTkMK9jAAAAAADQplAkocV5/Ibr5PP79L3TzvQ6CgAAAAAAbQpFElqUcCikzV+s0dHjzvY6CgAAAAAAbQ5FElqUGdf9RoFAQIO/e6zXUQAAAAAAaHMoktBihCsqtX3VZg27epzXUQAAAAAAaJMoktBiTP/1tcrIylD/IYVeRwEAAAAAoE2iSEKLUFZaqh0bS3TKb6/yOgoAAAAAAG0WRRJahBnX/krBnKAK+g/0OgoAAAAAAG1WwOsAQH22btygim3lOmvy772OAgAAAABAm8aMJKS9p35zg7Lys9WlZ2+vowAAAAAA0KYxIwlpbd2KL1VVVqkx9032OgoAAAAAAG0eM5KQ1p676f+U0ylX+Z06ex0FAAAAAIA2jxlJSFsrFn+icEVIF9x3l9dRAAAAAACAmJGENDb7D3eqXfd8ZebkeB0FAAAAAACIGUlIUysWf6JwZUgX3M9sJAAAAAAA0gUzkpCWZv/hTrXrxmwkAAAAAADSCTOSkHaKP1/EbCQAAAAAANIQM5KQdv7+f39mNhIAAAAAAGmIGUlIK6uWMBsJAAAAAIB0xYwkpJUXb52s3K7MRgIAAAAAIB0xIwlpIz4bKawL7r/b6ygAAAAAAKAOzEhC2njx1j8rt2ses5EAAAAAAEhTzEhCWlj9xWLWRgIAAAAAIM0xIwlpYdbv72A2EgAAAAAAaY4ZSfDc6i8+ZzYSAAAAAAAtADOS4LlZt96u3C7MRgIAAAAAIN0xIwmeWrN0icIVIZ1/751eRwEAAAAAAPVgRhI89cLv/6ScLu2UlZvrdRQAAAAAAFAPZiTBM2uXxWcjjbl7stdRAAAAAABAEpiRBM+8cEt8NlJ2Xp7XUQAAAAAAQBKYkQRPrF2+VKGKkEYzGwkAAAAAgBaDGUnwxAs336aczrnKZTYSAAAAAAAtBjOS0OyYjQQAAAAAQMvEjCQ0uxcm/lHZHZmNBAAAAABAS8OMJDSr9cXLFaqo0gV33uF1FAAAAAAAsI+YkYRm9dxN/6fs9jlql5/vdRQAAAAAALCPmJGEZrNpTbFC5VU6//4/ex0FAAAAAAA0ADOS0GyeveEWZeVnK69jJ6+jAAAAAACABmBGEprF5rWrVVVepTH38U1tAAAAAAC0VMxIQrN45oaJysrLVn6nzl5HAQAAAAAADcSMJDS5LevXqaqsUufdO8nrKAAAAAAAoBGYkYQm98z1v1NWuyy179zF6ygAAAAAAKARmJGEJrV98yZVllbqnLv+6HUUAAAAAADQSMxIQpN68rrrldUuS5269/A6CgAAAAAAaKQGF0lmNtrM5td4fGlmYTPrbmbZZvaImX1sZp8knmfXcQ6/md1pZp+Z2RdmNq5xHwfpZPvmTaosqdDI3//O6ygAAAAAACAFGlwkOeemO+eGOOeGSBoqaZ2ky5xz6yX9VvHb5gYnHtmSflPHaS6WdJCkQYlz/NLMvt3QTEgvT113gzJzstS5R0+vowAAAAAAgBRI1RpJv5a0wTn3YGJ7rqTlzrmYJJnZh5K+Xsdxp0qa6pyLSNpqZk9KOlvS/1KUCx4p3bpFFSXlOmvS772OAgAAAAAAUqTeGUlmNtzMInU8Ride7yLpaklX7jzGOTfHOfd54vX9JP1S0jN1nL63pOIa26skFTT407RQ951zoTatKa5/YAvyxHW/VWZOprr07O11FAAAAAAAkCL1zkhyzr1cz7ixkmY555bVfsHMviXpeUn3OudequNYnyRX8xBJ0TrOMzbxPrvo06fP3sO3EC7m9P6rs3XCmNaxRFTp1i2q2FauM/90s9dRAAAAAADYJyUlJdqwYYPC4bDXUZpUbm6uCgoK5PPt26pHqbi17QxJV9TeaWZnSrpf8XWT/raHY1dKqrmATk/FZyXtwjk3VdLU2vsLCwtd7X0tkT/Lr3WLlngdI2WemBCfjdS99/5eRwEAAAAAIGklJSVav369evXqpezsbJmZ15GaRCwW0+rVq7Vp0yZ169Ztn45t8GLbkmRmHSUdKOntWvtPlnS3pGF7KZEkaZak880sYGYdJJ0p6YXGZGqJsjvkqmJLqdcxUmL75k2q2F6ukbfc4HUUAAAAAAD2yYYNG9SrVy/l5OS02hJJknw+n7p3767t27fv+7GNfO8DJa11ztWe73WH4repPWRm8xOP+yTJzCaa2cTEuAckLZW0QNJ7kh52zv27kZlanG4D+ilSudsdfS3SkxN+q6y8LNZGAgAAAAC0OOFwWNnZ2V7HaBYZGRmKRCL7fFyjbm1zzr2neJlUe/+AvRxzY43nEcUX4m7TDht2kr54832vYzTaxtXFqiyt1Hn3TfI6CgAAAAAADdKaZyLV1NDP2dgZSUiBr+3fT04tf7mnZ2+8RdkdctS+cxevowAAAAAAgCZAkZRGtm/e5HWEBlu77AuFyqv08z/d5nUUAAAAAABaLeeczj33XN1xxx11vj579mwNHjxYAwYM0KhRo1RSUpLS96dIShN+v1//mvGo1zEa7PmJf1RO53Zql9/e6ygAAAAAALRKn376qY4//ng9++yzdb6+ceNGjRkzRjNnztTixYvVt29fTZgwIaUZKJLSRGb7LK1ftNTrGA2y4rOPFa4MafSfbvc6CgAAAAAArdZ9992nCy+8UKNGjarz9Tlz5mjo0KHq37+/JGn8+PGaMWOGnEvdcjqNWmwbqdNryCFa9q8PvI7RIC/94S61656vzJwcr6MAAAAAAJBSv3nuI60vqWqy83fPz9QfThuc1Nh7771XUrwwqktxcbF69/7qW9QLCgpUUlKi0tJS5efnNz6sKJLSxvfPHK0lb7zndYx9tujduYpUhTX69vu8jgIAAAAAQMolW/Kkg1gsVue3sfn9/pS9B7e2pYncvDyZTKuWLPI6yj55497H1Klfd2UEg15HAQAAAACgTevTp4/WrFlTvb169Wp17NhRubm5KXsPiqQ0Esj0681HHvU6RtLmPveEYtGYzrn1j15HAQAAAACgzRs2bJjeffddLVmyRJI0ZcoUjRgxIqXvQZGURroM6KOSVVu8jpG0Bc++pj6HH+J1DAAAAAAA2qyioiINGTJEktStWzdNmzZNI0eO1MCBA7Vw4UJNmjQppe9nqVy5u7kVFha6oqIir2OkzPbNm/ToZdfoF0886nWUes26Z5JWvvOJLv/bI15HAQAAAAAgJT799FMNHDjQ6xjNZi+fd/eFlhKYkZRG2nfuIpNpyfz0L8dWvP2xBp16nNcxAAAAAABAM6JISjPBdkH9+6FHvY6xV4/f8Bv5fD4dO+psr6MAAAAAAIBmRJGUZg77yUkq31zudYw9qiov1+Yv1uiYS87xOgoAAAAAAGhmFElp5vAf/UTOxVS6NT0X3X7sqqsVzApq0FHHeB0FAAAAAAA0M4qkNBQIBvTinaldVT0Vln28QBXby/WzP93qdRQAAAAAAOABiqQ01POw/tr6xTqvY+zm5dvuVvuCLurQtZvXUQAAAAAAgAcoktLQjy+9UtFoRGWlpV5Hqfb64w8rFo3pvNvTb6YUAAAAAABoHhRJaSgjGFQgmKHn/u8Wr6NUWzT7Pxow7HCvYwAAAAAA0GbNnj1bgwcP1oABAzRq1CiVlJTsNub555/X4MGDNWTIEB133HFaunRpSjNQJKWpQ046WttWbPI6hiTp0Wuvls/v0wljxnkdBQAAAACANmnjxo0aM2aMZs6cqcWLF6tv376aMGHCLmMqKip09tln67nnntP8+fN18skn64orrkhpDoqkNHXsz0Yr5mJauzy1zeG+WrVkkbYXb9JPfne1pzkAAAAAAGjL5syZo6FDh6p///6SpPHjx2vGjBlyzlWPiUajcs5p+/btkqQdO3YoKysrpTkCKT0bUiorL0t//8PtGvvgFM8yvHDTJLUv6KyC/od4lgEAAAAAgLauuLhYvXv3rt4uKChQSUmJSktLlZ+fL0lq166dpkyZoiOPPFKdO3dWNBrVf//735TmoEhKYyf/+mo989tbPXv/5yb/UbGY03l3TPYsAwAAAAAAnnrxCqm0Cb9ZPa+HdMrd9Q6LxWIys932+/3+6ucLFy7UxIkTtWjRIvXr10933323Tj/9dM2fP7/OYxuCIimN9ezXX36/X8/88fca9evrm/W9S7duUfF7i3TEuac36/sCAAAAAJBWkih5mkOfPn00b9686u3Vq1erY8eOys3Nrd736quv6qijjlK/fv0kSZdeeqmuvPJKbd68WV26dElJDtZISnP9fzBU6xYsa/b3nf6LXykrP1vfPvGUZn9vAAAAAACwq2HDhundd9/VkiVLJElTpkzRiBEjdhnzzW9+U//+97+1fv16SdILL7ygAw44IGUlkkSRlPZOGDNOzjnNfe6pZnvPmZNuUzQc0/n3pEfrCgAAAABAW9etWzdNmzZNI0eO1MCBA7Vw4UJNmjRJRUVFGjJkiCTpuOOO07XXXqtjjjlGhx56qO69917NmjUrpTm4ta0F+NqhB2jBzDk6+rQzmvy91hcv16qiT3X02LOUEQw2+fsBAAAAAIDkDB8+XMOHD99lX6dOnTR//vzq7UsvvVSXXnppk2VgRlILMGrCjYrFYnpvzktN/l5PT7hF+T076rBjhzX5ewEAAAAAgJaFIqmF6DFoP8179PkmfY+/XHqJzKQxk+5s0vcBAAAAAAAtE0VSC3HGb29SzDm9eM+kJjn/Sw/cpfIt5Tr3ntub5PwAAAAAAKDlo0hqQQ776Qn68u2FCodCKT3vonfmauncD3TEuacrr2OnlJ4bAAAAAAC0HhRJLcj3Tj1DGZkZeuTSy1N2zvUrv9Rrdz2i3kMH6tsnnpyy8wIAAAAAgNaHIqmFOfeeyarcUaXXHnuo0efavnmTnvr1RHXo01mnXTUhBekAAAAAAEBrRpHUwuTm5WnwT47Xon+8pRWfLWzwebasX6fHLvuVcrrk6tw/Nc26SwAAAAAAoHWhSGqBjj3jHHU9uECzbp6s0q1b9vn4ZR8v0OO//I3a9cjThffc2wQJAQAAAABAqs2ePVuDBw/WgAEDNGrUKJWUlOw2ZuHChTrmmGN02GGHqbCwUO+//35KM1AktVA/v+lW5XbJ1bRLrtHa5UuTPu7VR6bo77dOVpcBPXX+n+9qwoQAAAAAACBVNm7cqDFjxmjmzJlavHix+vbtqwkTdl2mpry8XMOGDdOvfvUrffjhh7rhhht01llnpTQHRVILdsE996rDfp309ISJennq3mcWbVpTrPvPu0ifvTZP3/zpcP38plubKSUAAAAAAGisOXPmaOjQoerfv78kafz48ZoxY4acc7uM6devn4YPHy5JOuWUU/T000+nNEcgpWdDsxt92x3676xn9P6T/9A9/zpfXQ7upRPHXaqO3Xqoqrxc/5wxTV/+50NFqsLK7piri6bep4xg0OvYAAAAAABgHxQXF6t3797V2wUFBSopKVFpaany8/MlSZ9//rl69OihCy64QAsWLFCHDh30pz/9KaU5KJJagaNGjNJRI0bppQfu1oq3P9L0X/xGUryR9Pv8yuvVQSNvuEm5eXneBgUAAAAAoIW56e2btLFiY5Odv2t2V9105E31jovFYjKz3fb7/f7q5+FwWC+//LLefPNNHX744Zo1a5aGDx+uFStWKDMzMyV5KZJakR+Pv0Ia73UKAAAAAABaj2RKnubQp08fzZs3r3p79erV6tixo3Jzc6v39ezZUwMHDtThhx8uSRoxYoQuvPBCLVu2TAMHDkxJDtZIAgAAAAAASHPDhg3Tu+++qyVLlkiSpkyZohEjRuwy5qSTTtKXX35Z/U1tc+fOlZnpgAMOSFkOZiQBAAAAAACkuW7dumnatGkaOXKkQqGQ+vXrp+nTp6uoqEgXXnih5s+frx49euiFF17QJZdcorKyMmVmZuq5555TVlZWynJYzdW9W5rCwkJXVFTkdQwAAAAAANAKfPrppym7Bawl2Mvn3X0xpgRubQMAAAAAAEBSKJIAAAAAAACQFIokAAAAAAAAJKXBi22b2WhJV9XY1V5SQeJRIuk+Sd9W/L66eZIudc5V1HGeTZJW1dh1u3NuRkNzAQAAAAAAoGk0uEhyzk2XNF2SzCxD0lxJtznn1pvZ7xPnHqx4kfS4pN9IurHmOcxsgKQtzrkhDc0BAAAAAACA5tHgIqmWX0va4Jx7MLE9V9Jy51xMkszsQ0lfr+O4IyVFzewtxWc0PSvpVudcNEW5AAAAAAAAkCL1rpFkZsPNLFLHY3Ti9S6SrpZ05c5jnHNznHOfJ17fT9IvJT1Tx+kDkl6XdKKkoyWdIOnyRn4mAAAAAAAANIF6ZyQ5516uZ9xYSbOcc8tqv2Bm35L0vKR7nXMv1XHuv9QaP1nSFZLurLV/bOJ9dtGnT5/64gMAAAAAACBFUvGtbWdImlZ7p5mdKek1SROcc/9X14Fmdo6ZDa65S1K49jjn3FTnXGHtR9euXVMQHwAAAAAAIP3Nnj1bgwcP1oABAzRq1CiVlJTscewLL7ygvLy8lGdoVJFkZh0lHSjp7Vr7T5Z0t6Rhzrm/7eUUgyRNNDO/mWVLukz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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(axon_Kt=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('K transient Axon')\n",
    "plt.savefig(plot_resting_path+'Kt_axon.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(axon_K=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('K Axon')\n",
    "plt.savefig(plot_resting_path+'K_axon.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(soma_K=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('K Soma')\n",
    "plt.savefig(plot_resting_path+'K_soma.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(dend_K=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('K Dendrite')\n",
    "plt.savefig(plot_resting_path+'K_dendrite.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-73, -70))\n",
    "for p in percentages:\n",
    "    init_settings(gpas_all=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('G passive All')\n",
    "plt.savefig(plot_resting_path+'Gpas.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 6))\n",
    "plt.ylim((-100, -70))\n",
    "for p in percentages:\n",
    "    init_settings(hcn=p)\n",
    "    init_stim(stim_start=100, stim_dur=50, sweep_len=500, dt=0.2, amp=0)\n",
    "    Vm, I, t = run_model()\n",
    "    start = int(0.5/0.2e-3)\n",
    "    end = int(0.9/0.2e-3)\n",
    "#     print(start, end)\n",
    "#     print(Vm[start], Vm[end])\n",
    "    # print((Vm[end]-Vm[start])/-0.05)\n",
    "    plt.plot(t, Vm, label=p, lw=0.7)\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('HCN')\n",
    "plt.savefig(plot_resting_path+'HCN.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2500 5000\n",
      "-71.43535817772103 -71.47047249882233\n"
     ]
    },
    {
     "data": {
      "image/png": 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9urYDAAAAYInNOCOpqs5JcvUUuy5orV1RVUcl+d0kZ07Yd1ySuyas353k9CnG6NSuqjYm2Th5+/r162cqHwAAAIB5MmOQ1Fq7ZoZ2G5Nc1Vr7wYRtfUnahPVKMjpF307tWmuXJbls8vahoaE2eRsAAAAAC2M+bm37lSSXT9r2oyTHTFg/JuOzjSbr2g4AAACAJTanIKmq1iY5Ocn1k3ZdleSCqhqoqiOSnJ/kyimG6NoOAAAAgCU2629t6zk5yX2ttZFJ2z+Q5KQk30qyIsmlrbUvJ0lVXZwkrbWL9tUOAAAAgANLtfb0fczQ0NBQGx4eXuoyAAAAAA4mNd2O+XhGEgAAAADLgCAJAAAAgE4ESQAAAAB0IkgCAAAAoJO5fmsb7DY21vLwEzuy6fHt2fT49jzyxI5sGxnNjp1j2b5zLDtGxzLQVxns78tAf19W9FfWHDKQw1cN5rCVgzl81VNLX9+0z/UCAAAAloggiVl5+PHtufFHj+Vbdz+W2x58PD946Inc8fAT2bFzbM5jD/RVnnnoITn6sJV51qGH5FmHrcyzDhtfX3fEqqxbuzrPOWJlBvtNqAMAAIDFJEiik20jo/na7Q/n7773QL5666b88OEnkyT9fZXjf2J1TnzmmrxswzOzbu2qHPWMQ3LkmhU58hkrsmrFQA4Z6MshA30Z7O/LzrGWnaPjs5NGRlse37Yzm7eN5MdPjuTHW8eXh5/Yngc2b88Dm7flzoefzA13PJIfbx3Zo56+Sp5z+Kocu3ZV1q0dD5fW9V4ft3Z1nn24oAkAAADmmyCJae0cHctXbtuUT33j7vzdLQ9k28hYVq/oz0tOOjLn/8z6/PT6tXnhsYdn1Yr+Ba9l28hoHti8Lfc8ujV3P7o1dz/6ZO/n1nz99odz/+Z7Mtaeat9XybMPW5lj167KMUesyrFHjIdOx054vXqF0x8AAAD2h/8nzV4e2rI9V3zth/n4P96Vh7Zsz9rVg3ntmcflX576rPzsiT+RQwYWPjiabOVgf44/ck2OP3LNlPt37BzL/T/eNiFgGv95z2Nb8407H81nb74vOycmTUnWrh6cMmR61uErc/Shh+SZhx6yJO8VAAAADlSCJHb7wUOP57LrfpDP3HhPRkbH8j8+7+i8dui4vHzD0VkxcGDfJrZioC/rj1yd9UeunnL/6FjLg1vGZzTd89j4TKZ7Hxt/fcemJ/LV2zblyR2je/U7YvVgjj70kBx9aC9cOuyp10c945D8xJoVWbt6MEesXnHAf0YAAAAwV4Ik8sDmbbnki7fmvw7flYG+ymvPXJc3/fcn5MRnPmOpS5s3/X2V5xy+Ks85fFWGptjfWsuPt47k7ke35sEt2/Lg5u15cMv2PV7fsemJPLRle3aMTv1A8WccMpC1awazdvWK3jKYtWvGXx+6ciCHrhzMMw4Z6L0e6L0ezKErx58jVeWb6gAAADiwCZKWsa07RvP+a2/Lh77yg4yOtbz+Z9fnwv/huXnmoYcsdWmLrqpyxOoVOWL1iiSHT9uutZbHnhzJg1u2Z9Pj2/Pokzvy6JMjefSJHeOvnxhff+zJHfnBpsfz2BMj2bJ954zHH+ir8XBp5UDWrBjIqhX9WTU4vqyc8HrViv6s3PV6sG/3+srB/qwY6MuK/r6s6D3YfPx1ZUV/fwYHanzbrjb9fenrE1wBAACwfwRJy9T/9/0Hc9FV38ldj2zNL73omPzeWRumvS2Mp1TV+CyjNSuyIYd26jMyOpbHt+3Mlm07s2X7yO7Xj2/fmS3bxoOmLdt25vHetse378y2kdFsGxnN5m0j2bpjNNtGxrJ1ZDRbd4xm68jet+DNRn9fZUV/Xwb7a3f41N9Xu5eBvkpfVQb6K/19femvZKBvvM1Af2/fhPa7+/Tt2t6X/r7xPlVJX1X6ej9rwuu+SjJpfXz/xPZ7/uzS5qm2E9uP/+wdcnxJpfefPfelej/T27/n2LvaJRPX9+6fyePtMdZ+9J+htr3e2673tT+1Zc/PCQAAYDJB0jLz8OPbc9FV381nv31fTnrmmvzVb/yL/HcnHbnUZR3UBvv7dodP86G1lu07x3aHSrsCph2jYxnZOTb+c3QsO3a2Kbb1Xu9s2TE6mpHRNmHbWEZby+jYU8vOsZax3s/x9bGMjrVs3zk63qa17Bxtu1+Pjo2vj7UJfUbH0pK0loy11lvG38dYb1trM75tlsh0QdRT4dukYCpPhVC1+78mba/dm3ePu2v7rj17hnXTHyMT2+yqbR/HyBTbpzrGrv1PHW/fx8ik9zHd+6sJBUz7/iYdI9OONfX27PVZ7fsYU30m+zrGxGB1pmNM+IT2fj/TbN81zkx9dtU1nT37TT3evuqYPHZNs1Id3+Nsa5q4cz7G29d7nG7sfY0/25r27NP1Pe6j3z7a7esz7GI2ufrk82LhjjOLPov0fnodF6PLrP7xY9E+uwP4/JmNA/mzHj/WwfW/vdkd6enhYP03y76q/MtTn7XUZSw4QdIycv3tm/L2j9+Ux54cye+ddUo2vvQkD4h+Gqqq3bezrV3qYubRxGBpV7g0NjFsGuv9zDRtxtoeYVWbPObYU6FVy66f4212BV3ZY/ue+1rGd4xN0z8Tt3cZO08Fapmwbar+2euYe65nQjj31P5pxp60vuuzn6q2THr/k9tkyuNkj2Puej3xz3m6NhO3Z4/6pm+3e7Q9tk8+xt7bJ36mXWrZ11iTt2eP7RP6TnGM1nbVMtb5GNnXZ9i1lmk/k+mOvecx0qVd9rRnYNym2b5nvz3ewz7Ga5MGmeZQU4wx9fh71zR1vZPH2/NYk2qapt++aheyA8DTy4r+vvzzu//VUpex4ARJy8DO0bFc8sVb875rb8sJR63Jf37jz+TUYw5b6rJgD1WV/kr6D+J/eQGYL5ODwt2v99Vuj+2T2u0j3NtzvKn77KuOzmHZftQ0q8Bx7+hvZovTZVah4Wzez+yOMzuT/9y79ZnlwRbhOAfy5z2rz3pWx5lFp0X63GZ3pAP8XDiI/zFhVr+POaAIkg5y9zy2Nf/mr27M8J2P5peH1uWPf+kFWb3CHzsAPJ1Nd1vXFC0XvBYAYHmRKBzE/tt37su//dTNGWvJfzj/jJx7xrFLXRIAAADwNCZIOghtGxnNn3z2n/LRr/8op687PP/xf/mpHH/kmqUuCwAAAHiaEyQdZG57cEsu/C835nv3b8lv/PwJ+f2zn+eB2gAAAMC8ECQdAG57cEuOesYhOWL17L8evrWWT/zjXfnjv/lu1qwYyOVvfHFevuHoeawSAAAAWO5MVVliP3r4yfxPf35dPjl896zH+PGTI7nwr27MOz7z7Zx5/Np87t/8vBAJAAAAmHeCpCW2/sjV+an1R+T/+fqd2TYyut/9v3b7w3nFf7gu/+937s/vn70hV1zwszn6sJULUCkAAACw3AmSDgC/f9aG/OiRJ/PHV383rbVOfR7fvjP/7m//Kf/rh7+elYP9+cxbXpK3vvzk9Pf5ml8AAABgYXhG0gHgJScflbf8wkl5/7W3Z+Vgf/7ofz512kBodKzlb2++N//nNd/L/Zu35XU/uz5/eM7zs+YQf5QAAADAwpp1+lBVv5rkdyZsOjzJut6yOcn7kvxMkkpyQ5K3tta2TjHOpiQTHxD0Z621j822rqer3z97Q7bvHMtHvnpHbrzrsfzeWafk5046Kn29QOnBzdvyue/cnyu+9sPc/tATef5zDsv7X//T+en1a5e4cgAAAGC5qK63Uu1zkKrBJNcl+c+ttUur6k+SrE/yaxkPkj6a5NbW2kWT+m1I8jettVNmc9yhoaE2PDw8p9oPNFfddE/+5LO35KEt27N6RX+ec/jKPL59Zx7YvD1JcupzDstbXn5S/tVpz3EbGwAAALAQpg0c5ut+qD9I8mBr7dLe+nVJfthaG0uSqroxyQum6PeSJKNV9ZWMz2j6VJJ3t9b2/6nTB4lzzzg2Z7/g2fnCPz2Qb9z5aB7csi2rBgey4dnPyMtOOTobnn3oUpcIAAAALFMzzkiqqnOSXD3Frgtaa1dU1VFJbk1yZmvtB1P0Pz7J15JsbK397aR9v5Hk9CTvSDKY5LNJPtlau2RSu41JNk4ee/369Wfeeeed+6wfAAAAgP0y7YykOd/aVlV/mOSU1tqvTbHvzCR/neSDrbV/32GsVyd5W2vtZV2OfTDe2gYAAACwxKYNkvrmYfBfSXL5XkesOj/JF5K8Y7oQqareUFWnT9yUZGQeagIAAABgns3pGUlVtTbJyUmun7T9F5P8RZKzWmv7mjJ0WpJX92YirUhyYZJl941tAAAAAE8Hc52RdHKS+1prk2cRvSfjs4s+XFU39Zb3JUlVXVxVF/favSvJI0m+neTmjAdSH55jTQAAAAAsgDk/I2kpeUYSAAAAwLxb0GckAQAAALAMCJIAAAAA6ESQBAAAAEAngiQAAAAAOhEkAQAAANCJIAkAAACATgRJAAAAAHQiSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCeCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdzDpIqqpfraqbJix3VNVIVT2rt3/TpP2vm2KM/qq6pKq+V1W3VdWb5/JmAAAAAFg4A7Pt2Fq7IskVSVJVg0muS/KnrbUHqmpDkkdaa2fMMMxvJjklyWlJDk3ytar6ZmvtH2ZbFwAAAAALY75ubfuDJA+21i7trb8kyWhVfaWqbq6qi6qqf4p+r0xyeWttZ2vt0SQfT/L6eaoJAAAAgHk044ykqjonydVT7LqgtXZFVR2V5HeTnDlp3C8meUeSwSSfTbI5ySWTxjguyV0T1u9OcvoUNWxMsnHy9vXr189UPgAAAADzZMYgqbV2zQztNia5qrX2gwl9PjSxQVX9eZK3Ze8gqS9Jm9g0yegUNVyW5LLJ24eGhtrkbQAAAAAsjPm4te1Xklw+cUNVvaGqJs4sqiQjU/T9UZJjJqwfk/FZSQAAAAAcYOYUJFXV2iQnJ7l+0q7Tklzc+1a2VUkuTPKJKYa4KskFVTVQVUckOT/JlXOpCQAAAICFMdcZSScnua+1Nnm20buSPJLk20luznjQ9OEkqaqLq+riXrsPJLk9ybeS/GOSj7TWvjzHmgAAAABYANXa0/cxQ0NDQ214eHipywAAAAA4mNR0O+bjGUkAAAAALAOCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOhEkAQAAANCJIAkAAACATgRJAAAAAHQiSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCcDs+1YVb+a5HcmbDo8ybre8r4kJ0/Yd0KSL7fWfmmKcTYluXvCpj9rrX1stnUBAAAAsDBmHSS11q5IckWSVNVgkuuS/Glr7YEkr9nVrqpenORTSd46eYyq2pDkkdbaGbOtAwAAAIDFMV+3tv1Bkgdba5dO3FhVK5L8ZZK3t9bumqLfS5KMVtVXqurmqrqoqvrnqSYAAAAA5tGMM5Kq6pwkV0+x64LW2hVVdVSS301y5hRt3pTk3tbaX+/j+F9M8o4kg0k+m2Rzkksm1bAxycbJndevXz9T+QAAAADMk2qtzW2Aqj9Mckpr7dem2PfPSTa21q7tONark7yttfayLu2Hhoba8PDwflQLAAAAwAxquh3zcWvbryS5fK8jVv1UxmccfXnaqqreUFWnT9yUZGQeagIAAABgns0pSKqqtRn/drbrp9j9siRfavue8nRakourqr+qViW5MMkn5lITAAAAAAtjrjOSTk5yX2ttqllEz03yw8kbq+riqrq4t/quJI8k+XaSmzMeSH14jjUBAAAAsADm/IykpeQZSQAAAADzbkGfkQQAAADAMiBIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOhEkAQAAANCJIAkAAACATgRJAAAAAHQiSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCeCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdDKnIKmqXllVN1fVTVX1pao6qbe9v6ouqarvVdVtVfXmafp3agcAAADA0huYbceqWpXko0le1Fq7rar+tyR/keRfJ/nNJKckOS3JoUm+VlXfbK39w6RhurYDAAAAYInNZUZSf5JKcnhv/RlJtvVevzLJ5a21na21R5N8PMnrpxijazsAAAAAltiMM5Kq6pwkV0+x64Ikb05yfVU9nPFg6ed6+45LcteEtncnOX2KMTq1q6qNSTZO0f/xqvr+TO/haeKoJJuWuggOSM4NpuPcYF+cH0zHucF0nBtMx7nBvjg/Dk7/rbX2iql2zBgktdaumapdVb0wyV8nObW1dntVvS3Jp6vqjIzPdGoTmycZnWL4Tu1aa5cluWymWp/Oqmq4tTa01HVw4HFuMB3nBvvi/GA6zg2m49xgOs4N9sX5sfzM5da2s5P8fWvt9t76+zL+rKMjk/woyTET2h6T8dlGk3VtBwAAAMASm0uQ9M0kL6uqZ/XWz0tyR2ttU5KrklxQVQNVdUSS85NcOcUYXdsBAAAAsMRm/a1trbUvVdWfJbm2qnYkeSTJub3dH0hyUpJvJVmR5NLW2peTpKou7vW/aF/tAAAAADiwzDpISpLW2vsyfkvb5O07k7x9mj4XdWkHAAAAwIFlLre2Mb8O6oeJMyfODabj3GBfnB9Mx7nBdJwbTMe5wb44P5aZaq3N3AoAAACAZc+MJAAAAAA6ESQBAAAA0IkgaRFV1b+uqpur6vtV9cmqOmw2bTj4dDw3Xl9V36qqm6rq+qoaWopaWVz78zuhqs6rqi2LWR9Lq+PvjhdW1bVVdWNVDVfVmUtRK4ur47nxyl6bm6rqS1V10lLUyuKrcX9ZVb83zX7Xo8tUh3PD9egyNtP5MaGda9KDnCBpkVTVM5NcnuTVrbUNSX6Q5E/3tw0Hn47nxoYkf5bkFa21M5L8SZLPLHKpLLL9+Z1QVc9N8p4ktXgVspQ6/u5YneTzSf6v1tpPJfl3ST622LWyuDqeG6uSfDTJq3p/r/xNkr9Y5FJZAlX1/CR/l+Q10+x3PbpMdTg3XI8uYzOdHxPauSZdBgRJi+esJP/YWru1t/6BJK+rqtrPNhx8uvy5b0/y6621+3rrw0meXVUrFrFOFl+n3wm9sOCjSX5nketjaXX9e+X21to1vfWrk/zyItbI0uhybvRn/CL/8N76M5JsW7wSWUJvTfLhJJ+cZr/r0eVrpnPD9ejyNtP54Zp0GRlY6gKWkeOS3DVh/e4khyU5NMnm/WjDwWfGP/fW2g+T/DAZn1Ka5M+TXN1a27GYhbLouv5OuLS33Lx4pXEA6HJ+nJLk/qr6SJIXJXksyb9dxBpZGl3+Xnm8qt6c5PqqejjjwdLPLXahLL7W2oVJUlVnTdPE9egyNdO54Xp0eevwuyNxTbpsmJG0ePqStCm2j+5nGw4+nf/cq2pNkv+a5OQkv77AdbH0Zjw3quotSXa21v7TolXFgaLL747BJOckuay1NpTkPya5pqoOWYT6WDpdfne8MMlFSU5trR2T5N1JPm3WCXE9ygxcjzIV16TLiyBp8fwoyTET1o9N8mhr7Yn9bMPBp9Ofe1WtT3J9xi/kXt5ae2zRKmSpdDk3fi3Ji6vqpiTXJFnVewDmxH4cnLqcH/cmuaW1dkOStNauyvjMkxMXrUqWQpdz4+wkf99au723/r4kpyU5cnFK5ADmepRpuR5lH34trkmXDUHS4vl8kn/Re/hYkrw5yVWzaMPBZ8Y/96o6NMm1ST7TWju/tbZ1cUtkicx4brTWfqa1dlrvoZfnJNnaWjujtXbv4pbKEujyd8bnkpyw65vaquqlGZ9pcMeiVclS6HJufDPJy6rqWb3185Lc0VrbtDglcgBzPcqUXI+yL65JlxdB0iJprT2Y5I1JPlVVtyR5YZLfraqhXmo7bZslKplF0uXcSHJhkuOTvLKX7O9a/MvxQazjucEy1fHvlfszHhC8v6q+k+T/zvi3dHmo8kGs47nxpYx/+9K1VfWtjP89c+4SlcwScz3KdFyPsi+uSZevam2qW6ABAAAAYE9mJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOvn/ASEroIX0KIPcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "init_settings()\n",
    "init_stim(stim_start=500, stim_dur=500, sweep_len=1500, dt=0.2, amp=0)\n",
    "Vm, I, t = run_model()\n",
    "start = int(0.5/0.2e-3)\n",
    "end = int(1/0.2e-3)\n",
    "print(start, end)\n",
    "print(Vm[start], Vm[end])\n",
    "# print((Vm[end]-Vm[start])/-0.05)\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.ylim((-80, -60))\n",
    "plt.plot(t, Vm, label=np.round(p, 2))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "# Model NaV distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:45.302086Z",
     "start_time": "2021-05-02T02:49:44.384121Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "init_settings()\n",
    "\n",
    "NaV12 = []\n",
    "NaV16 = []\n",
    "distance = []\n",
    "nseg = h.cell.axon[0].nseg\n",
    "\n",
    "# distance.append(-.4)\n",
    "# distance.append(-.2)\n",
    "# NaV12.append(h.dend_na12)\n",
    "# NaV16.append(h.dend_na16)\n",
    "# NaV12.append(h.dend_na12)\n",
    "# NaV16.append(h.dend_na16)\n",
    "\n",
    "# distance.append(-.2)\n",
    "# distance.append(0)\n",
    "# NaV12.append(h.soma_na12)\n",
    "# NaV16.append(h.soma_na16)\n",
    "# NaV12.append(h.soma_na12)\n",
    "# NaV16.append(h.soma_na16)\n",
    "\n",
    "for i in range(nseg):\n",
    "    x = i/nseg\n",
    "    distance.append(x)\n",
    "    NaV12.append(h.cell.axon[0](x).gbar_na12 + h.cell.axon[0](x).gbar_na12mut)\n",
    "    NaV16.append(h.cell.axon[0](x).gbar_na16)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "NaV12 = np.asarray(NaV12)\n",
    "NaV16 = np.asarray(NaV16)   \n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5,2), sharex=False, sharey=False)\n",
    "ax.set_title('Baseline model AIS NaV distribution')\n",
    "\n",
    "ax.set_ylabel(\"Distance\")\n",
    "ax.set_xlabel(\"Channel density\")\n",
    "\n",
    "ax.fill_between(distance, NaV16, label = 'NaV16', color = '#922A8E', alpha=0.5)\n",
    "ax.fill_between(distance, NaV12, label = 'NaV12', color = '#059552', alpha=0.5)\n",
    "\n",
    "ax.set_xlim(0, 0.55)\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('Model_ais_distribution.pdf')\n",
    "# ax = plot_AIS_NaV_distribution(ax)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5,2), sharex=False, sharey=False)\n",
    "ax.set_title('Model soma NaV distribution')\n",
    "\n",
    "ax.set_ylabel(\"Distance\")\n",
    "ax.set_xlabel(\"Channel density\")\n",
    "\n",
    "ax.fill_between( [0, 1], [h.soma_na16, h.soma_na16], label = 'NaV16', color = '#922A8E', alpha=0.5)\n",
    "ax.fill_between([0, 1], [h.soma_na12, h.soma_na12], label = 'NaV12', color = '#059552', alpha=0.5)\n",
    "\n",
    "plt.savefig('Model_soma_distribution.pdf')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5,2), sharex=False, sharey=False)\n",
    "ax.set_title('Model dendrite NaV distribution')\n",
    "\n",
    "ax.set_ylabel(\"Distance\")\n",
    "ax.set_xlabel(\"Channel density\")\n",
    "\n",
    "ax.fill_between( [0, 1], [h.dend_na16, h.dend_na16], label = 'NaV16', color = '#922A8E', alpha=0.5)\n",
    "ax.fill_between([0, 1], [h.dend_na12, h.dend_na12], label = 'NaV12', color = '#059552', alpha=0.5)\n",
    "\n",
    "# plt.savefig('Model_dend_distribution.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-29T23:49:23.626910Z",
     "start_time": "2020-12-29T23:49:23.621980Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0130725"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.dend_na16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4e-05\n",
      "8e-05\n",
      "0.00016\n"
     ]
    }
   ],
   "source": [
    "for p in [0.5, 1, 2]:\n",
    "    init_settings(hcn=p)\n",
    "    print(h.hcn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-04T01:55:40.565529Z",
     "start_time": "2020-12-04T01:55:40.561247Z"
    }
   },
   "source": [
    "# AP Waveform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:49:52.450483Z",
     "start_time": "2021-05-02T02:49:52.428377Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def AP1_phase_plane(ax1, ax2, label):\n",
    "    ax1.set_xlabel('Time (sec)')\n",
    "    ax1.set_ylabel('Vm (mV)')\n",
    "\n",
    "    ax2.set_xlabel('Vm (mV)')\n",
    "    ax2.set_ylabel('dVdt (V/s)')\n",
    "    \n",
    "    Vm, I, t = run_model()\n",
    "    \n",
    "    spikes = detect_spike_properties(Vm, t)\n",
    "    \n",
    "    Vm = spikes[0]['Vm']\n",
    "    t = np.arange(len(Vm))*h.dt\n",
    "    dvdt = np.gradient(Vm)/h.dt\n",
    "    \n",
    "    ax1.plot(t[:int(2/h.dt)], Vm[:int(2/h.dt)], linewidth = 1, label = label)\n",
    "    ax1.set_ylim(-55,40)\n",
    "    ax1.set_xlim(0,2)\n",
    "    ax2.plot(Vm, dvdt, linewidth = 1)\n",
    "    ax2.set_ylim(-150,550)\n",
    "    ax2.set_xlim(-55,40)\n",
    "    return spikes[0]['peak_dVdt'], spikes[0]['thresh']\n",
    "\n",
    "def AP1_phase_plane_comparison_axes(percents, cmap):\n",
    "    fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(nrows=2, ncols=2, figsize=(10,8), sharex=False, sharey=False)\n",
    "    ax1.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "    ax2.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "    \n",
    "    ax1.set_title = 'AP Waveform'\n",
    "    ax1.set_ylabel('Vm (mV)')\n",
    "    ax1.set_xlabel('Time (ms)')\n",
    "    \n",
    "    ax2.set_title = 'Phase Plane'\n",
    "    ax2.set_ylabel('dVdt (V/s)')\n",
    "    ax2.set_xlabel('Vm (mV)')\n",
    "\n",
    "    ax3.set_title = 'Peak dVdt'\n",
    "    ax3.set_ylabel('dVdt (V/s)')\n",
    "    ax3.set_xlabel('Conductance fraction')\n",
    "    ax3.set_ylim(0,550)\n",
    "    \n",
    "    ax4.set_title = 'Threshold'\n",
    "    ax4.set_ylabel('Threshold (mV)')\n",
    "    ax4.set_xlabel('Conductance fraction')\n",
    "    ax4.set_ylim(-55,-50)\n",
    "    \n",
    "    return fig, ax1, ax2, ax3, ax4\n",
    "\n",
    "def init_stim_for_phase_plane(amp=0.5):\n",
    "    sweep_len = 50\n",
    "    stim_dur = 25\n",
    "    stim_start = 25\n",
    "    amp = amp\n",
    "    dt = 0.01\n",
    "\n",
    "    init_stim(sweep_len=sweep_len, \n",
    "              stim_start=stim_start,\n",
    "              stim_dur=stim_dur,\n",
    "              amp=amp,\n",
    "              dt=dt)\n",
    "\n",
    "def highResPoints(x,y,factor=10):\n",
    "    '''\n",
    "    Take points listed in two vectors and return them at a higher\n",
    "    resultion. Create at least factor*len(x) new points that include the\n",
    "    original points and those spaced in between.\n",
    "\n",
    "    Returns new x and y arrays as a tuple (x,y).\n",
    "    '''\n",
    "\n",
    "    # r is the distance spanned between pairs of points\n",
    "    r = [0]\n",
    "    for i in range(1,len(x)):\n",
    "        dx = x[i]-x[i-1]\n",
    "        dy = y[i]-y[i-1]\n",
    "        r.append(np.sqrt(dx*dx+dy*dy))\n",
    "    r = np.array(r)\n",
    "\n",
    "    # rtot is a cumulative sum of r, it's used to save time\n",
    "    rtot = []\n",
    "    for i in range(len(r)):\n",
    "        rtot.append(r[0:i].sum())\n",
    "    rtot.append(r.sum())\n",
    "\n",
    "    dr = rtot[-1]/(NPOINTS*RESFACT-1)\n",
    "    xmod=[x[0]]\n",
    "    ymod=[y[0]]\n",
    "    rPos = 0 # current point on walk along data\n",
    "    rcount = 1 \n",
    "    while rPos < r.sum():\n",
    "        x1,x2 = x[rcount-1],x[rcount]\n",
    "        y1,y2 = y[rcount-1],y[rcount]\n",
    "        dpos = rPos-rtot[rcount] \n",
    "        theta = np.arctan2((x2-x1),(y2-y1))\n",
    "        rx = np.sin(theta)*dpos+x1\n",
    "        ry = np.cos(theta)*dpos+y1\n",
    "        xmod.append(rx)\n",
    "        ymod.append(ry)\n",
    "        rPos+=dr\n",
    "        while rPos > rtot[rcount+1]:\n",
    "            rPos = rtot[rcount+1]\n",
    "            rcount+=1\n",
    "            if rcount>rtot[-1]:\n",
    "                break\n",
    "\n",
    "    return xmod,ymod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "# percents = np.arange(2,0.9,-.1)\n",
    "percents = np.arange(4,-0.1,-.4)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Changing HCN – All Compartments')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(hcn = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "\n",
    "    \n",
    "\n",
    "ax3.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax3.scatter(percents[i:i+2],dvdt[i:i+2])\n",
    "ax3.set_xlim(1.1,-0.1)    \n",
    "\n",
    "ax4.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax4.scatter(percents[i:i+2],thresh[i:i+2])\n",
    "ax4.set_xlim(1.1,-0.1)\n",
    "    \n",
    "# plt.savefig('Phase-plane - Changing HCN – All Compartments.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reducing NaV12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-04T02:20:43.574042Z",
     "start_time": "2020-12-04T02:20:43.569842Z"
    }
   },
   "source": [
    "### Reducing NaV12 – All Compartments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T02:50:11.467288Z",
     "start_time": "2021-05-02T02:49:57.846848Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV12 – All Compartments')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "\n",
    "    \n",
    "\n",
    "ax3.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax3.scatter(percents[i:i+2],dvdt[i:i+2])\n",
    "ax3.set_xlim(1.1,-0.1)    \n",
    "\n",
    "ax4.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax4.scatter(percents[i:i+2],thresh[i:i+2])\n",
    "ax4.set_xlim(1.1,-0.1)\n",
    "    \n",
    "# plt.savefig('Phase-plane - Reducin/g NaV12 – All Compartments.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SectionList[3]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h.cell.apical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV12 – All Compartments')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = p, hcn=1) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "\n",
    "    \n",
    "\n",
    "ax3.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax3.scatter(percents[i:i+2],dvdt[i:i+2])\n",
    "ax3.set_xlim(1.1,-0.1)    \n",
    "\n",
    "ax4.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax4.scatter(percents[i:i+2],thresh[i:i+2])\n",
    "ax4.set_xlim(1.1,-0.1)\n",
    "    \n",
    "# plt.savefig('Phase-plane - Reducing NaV12 – All Compartments.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-04T02:20:43.576596Z",
     "start_time": "2020-12-04T02:20:07.627Z"
    }
   },
   "source": [
    "### Reducing NaV12 – AIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:36:10.705288Z",
     "start_time": "2020-12-27T01:35:54.627294Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV12 – AIS')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(ais_nav12 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "# plt.savefig('Phase-plane - Reducing NaV12 – AIS.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reducing NaV12 – Soma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:36:25.210621Z",
     "start_time": "2020-12-27T01:36:10.707590Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV12 – Soma')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(soma_nav12 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "plt.savefig('Phase-plane - Reducing NaV12 – Soma.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-04T02:20:43.579706Z",
     "start_time": "2020-12-04T02:20:30.388Z"
    }
   },
   "source": [
    "### Reducing NaV12 – Dendrites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:36:39.472719Z",
     "start_time": "2020-12-27T01:36:25.213260Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV12 – Dendrites')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(dend_nav12 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "plt.savefig('Phase-plane - Reducing NaV12 – Dendrites.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NaV16 compensation in Nav12 hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-31T01:01:19.583660Z",
     "start_time": "2020-12-31T01:01:10.069540Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,1.6,0.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('NaV16 compensation in Nav12 hom')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = 0, nav16 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "plt.show('Phase-plane - NaV16 compensation in Nav12 hom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:30:45.173129Z",
     "start_time": "2020-12-27T01:30:45.170480Z"
    }
   },
   "source": [
    "## NaV16 AIS compensation in Nav12 hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T01:21:52.081545Z",
     "start_time": "2021-01-03T01:21:42.148454Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'red'),\n",
    "                                          (1,    'grey')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,1.6,0.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('NaV16 AIS compensation in Nav12 hom')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = 0, ais_nav16 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "\n",
    "ax3.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax3.scatter(percents[i:i+2],dvdt[i:i+2])\n",
    "\n",
    "ax4.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax4.scatter(percents[i:i+2],thresh[i:i+2])\n",
    "    \n",
    "plt.savefig('Phase-plane - NaV16 AIS compensation in Nav12 hom.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T01:22:01.963641Z",
     "start_time": "2021-01-03T01:21:52.084347Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'red'),\n",
    "                                          (1,    'grey')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,1.6,0.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('NaV16 total compensation in Nav12 hom')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = 0, nav16 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "\n",
    "ax3.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax3.scatter(percents[i:i+2],dvdt[i:i+2])\n",
    "\n",
    "ax4.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax4.scatter(percents[i:i+2],thresh[i:i+2])\n",
    "    \n",
    "plt.savefig('Phase-plane - NaV16 total compensation in Nav12 hom.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Restoring Somatic NaV1.2 in NaV1.2 KO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:37:21.414317Z",
     "start_time": "2020-12-27T01:37:07.444033Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane()\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Restoring NaV12 in the soma')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(ais_nav12 = 0, soma_nav12 = p,dend_nav12 = 0) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "plt.show('Phase-plane - Restoring NaV12 in the soma')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reducing NaV16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reducing NaV16 – All Compartments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T01:17:54.286628Z",
     "start_time": "2021-01-03T01:17:39.363594Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'blue')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane(amp=0.8)\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV16 – All Compartments')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav16 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax3.scatter(percents[i:i+2],dvdt[i:i+2])\n",
    "ax3.set_xlim(1.1,-0.1)    \n",
    "\n",
    "ax4.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "for i in range(len(percents)-1):\n",
    "    ax4.scatter(percents[i:i+2],thresh[i:i+2])\n",
    "ax4.set_xlim(1.1,-0.1)\n",
    "ax4.set_ylim(-55,-43)\n",
    "plt.savefig('Phase-plane - Reducing NaV16 – All Compartments.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reducing NaV16 – AIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:37:50.220919Z",
     "start_time": "2020-12-27T01:37:35.600785Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'blue')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane(amp=0.8)\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV16 – AIS')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(ais_nav16 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "plt.savefig('Phase-plane - Reducing NaV16 – AIS.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reducing NaV16 – Soma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:38:04.573870Z",
     "start_time": "2020-12-27T01:37:50.222898Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'blue')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane(amp=0.8)\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV16 – Soma')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(soma_nav16 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "plt.savefig('Phase-plane - Reducing NaV16 – Soma.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reducing NaV16 – Dendrites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:38:18.735544Z",
     "start_time": "2020-12-27T01:38:04.576261Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'blue')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane(amp=0.8)\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV16 – Soma')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(dend_nav16 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "plt.savefig('Phase-plane - Reducing NaV16 – Soma')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:38:33.111564Z",
     "start_time": "2020-12-27T01:38:18.737674Z"
    }
   },
   "outputs": [],
   "source": [
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'blue')], N=256)\n",
    "\n",
    "### PLOTS ###\n",
    "init_stim_for_phase_plane(amp=0.8)\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "fig, ax1, ax2, ax3, ax4 = AP1_phase_plane_comparison_axes(percents, cmap)\n",
    "fig.suptitle('Reducing NaV16 – Soma')\n",
    "dvdt = []\n",
    "thresh = []\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(dend_nav16 = p) ### set conductance here\n",
    "    dvdt_temp, thresh_temp = AP1_phase_plane(ax1, ax2, label = '{}%'.format(int(p*100)))\n",
    "    dvdt.append(dvdt_temp)\n",
    "    thresh.append(thresh_temp)\n",
    "ax3.plot(percents, dvdt, color = 'k')\n",
    "ax4.plot(percents, thresh, color = 'k')\n",
    "plt.savefig('Phase-plane - Reducing NaV16 – Soma')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-04T01:57:11.965475Z",
     "start_time": "2020-12-04T01:57:11.961656Z"
    }
   },
   "source": [
    "# FI Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-> 104 mohms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-9-cae3000e971e>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-9-cae3000e971e>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    0 to 300 20 inc\u001b[0m\n\u001b[1;37m      ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "0 to 300 20 inc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4\n",
      "0.6000000000000001\n",
      "0.8000000000000002\n",
      "1.0000000000000002\n",
      "1.2000000000000002\n",
      "1.4000000000000004\n",
      "1.6000000000000005\n",
      "1.8000000000000003\n",
      "2.0000000000000004\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 5))\n",
    "# plt.ylim(())\n",
    "for p in np.arange(0.4, 2.2, 0.2):\n",
    "    init_settings(hcn = p)\n",
    "    print(h.hcn)\n",
    "    init_stim(stim_start=50, stim_dur=500, sweep_len=1000, dt=0.2, amp=-0.5)\n",
    "    Vm, I, t = run_model()\n",
    "    spike_times = detect_spike_times(Vm, t)\n",
    "    plt.plot(t, Vm, label=np.round(p, 2))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FI Curve support functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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, 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",
    "        \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, ax, label='', stim_start = 50, stim_dur = 300, sweep_len = 350, dt = 0.5):\n",
    "    f, i = FI_curve(stims=stims, stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt)\n",
    "    \n",
    "    ax.set_ylim(0,12)\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": [
    "## Reducing NaV12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 1\n",
      "0.9 0.5\n",
      "0.8 0\n"
     ]
    }
   ],
   "source": [
    "hcn_percents = np.arange(1, 0.4, -0.1)\n",
    "na12_percents = [1, 0.5, 0]\n",
    "\n",
    "for x, y in np.(hcn_percents, na12_percents):\n",
    "    print(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "hcn_percents = np.arange(1, 0.4, -0.1)\n",
    "na12_percents = [1, 0.5, 0]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for ih_p in hcn_percents:\n",
    "    for na_p in na12_percents:\n",
    "        init_settings(hcn = ih_p, nav12 = na_p)\n",
    "        FI_curve_plot(stims, ax, label='(hcn: '+str(np.round(ih_p,2))+', nav1.2: '+str(np.round(na_p,2))+')', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "plt.legend()\n",
    "title='FI curve - reducing HCN'\n",
    "plt.title(title)\n",
    "# plt.savefig(title + '.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "4\n",
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = [8, 4, 1]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    print(p)\n",
    "    init_settings(hcn = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "plt.legend()\n",
    "title='FI curve - reducing NaV12'\n",
    "plt.title(title)\n",
    "# plt.savefig(title + '.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T03:15:28.062138Z",
     "start_time": "2021-05-02T03:14:43.726873Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-41ddd68b5690>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpercents\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0minit_settings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnav12\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m     \u001b[0mFI_curve_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstim_start\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m25\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstim_dur\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msweep_len\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m325\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'FI curve - reducing NaV12'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-19-d061ed2e041d>\u001b[0m in \u001b[0;36mFI_curve_plot\u001b[1;34m(stims, ax, label, stim_start, stim_dur, sweep_len, dt)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mFI_curve_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstim_start\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstim_dur\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msweep_len\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m350\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFI_curve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstims\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstim_start\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstim_start\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstim_dur\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstim_dur\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msweep_len\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msweep_len\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-19-d061ed2e041d>\u001b[0m in \u001b[0;36mFI_curve\u001b[1;34m(stims, stim_start, stim_dur, sweep_len, dt)\u001b[0m\n\u001b[0;32m      7\u001b[0m         \u001b[0minit_stim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstim_start\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstim_start\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstim_dur\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstim_dur\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msweep_len\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msweep_len\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mamp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m         \u001b[0mVm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mI\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m         \u001b[0mspike_times\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdetect_spike_times\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mVm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mspike_times\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-5-b275a4ba8e2a>\u001b[0m in \u001b[0;36mrun_model\u001b[1;34m(start_Vm)\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[0mI\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'K'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcell\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msoma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mik\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[0mt\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdt\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m         \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfadvance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mVm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mI\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12 = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "plt.legend()\n",
    "title='FI curve - reducing NaV12'\n",
    "plt.title(title)\n",
    "# plt.savefig(title + '.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-04T06:31:20.019700Z",
     "start_time": "2020-12-04T06:31:20.012724Z"
    }
   },
   "source": [
    "## Reducing NaV16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T01:06:31.658299Z",
     "start_time": "2021-01-03T01:05:44.634373Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'blue')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav16 = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "plt.legend()\n",
    "title='FI curve - reducing NaV16'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing AIS_NaV16 in NaV12 Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T01:07:01.750293Z",
     "start_time": "2021-01-03T01:06:31.661521Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,1.6,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'red'),\n",
    "                                          (1,    'grey')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12 = 0, ais_nav16 = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "plt.legend()    \n",
    "title='FI curve - increasing AIS NaV16 in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T01:07:31.686483Z",
     "start_time": "2021-01-03T01:07:01.753217Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,1.6,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'red'),\n",
    "                                          (1,    'grey')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12 = 0, nav16 = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "plt.legend()    \n",
    "title='FI curve - increasing total NaV16 in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing AIS_NaV16 in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T01:08:01.716851Z",
     "start_time": "2021-01-03T01:07:31.688779Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,1.6,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'black'),\n",
    "                                          (1,    'blue')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(ais_nav16 = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "plt.legend()    \n",
    "title='FI curve - increasing AIS NaV16 in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T01:08:31.731339Z",
     "start_time": "2021-01-03T01:08:01.721333Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,1.6,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'black'),\n",
    "                                          (1,    'blue')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav16 = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "plt.legend()    \n",
    "title='FI curve - increasing total NaV16 in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing Kp_axon in NaV12 Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-31T02:01:48.769798Z",
     "start_time": "2020-12-31T02:01:18.395533Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.6,.25)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'k'),\n",
    "                                              (1,    'red')], N=256)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12=0, axon_Kp = p)\n",
    "    FI_curve_plot(stims, ax, label=p, stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "ax.legend()\n",
    "title='FI curve - increasing Kp_Axon in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T00:34:45.152893Z",
     "start_time": "2021-01-03T00:34:15.546928Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.6,.25)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'k'),\n",
    "                                              (1,    'red')], N=256)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12=0,\n",
    "                 axon_Kp = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "ax.legend()\n",
    "# title='FI curve - increasing Kp_Axon in NaV12 Hom'\n",
    "plt.title(title)\n",
    "# plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-04T06:31:20.024565Z",
     "start_time": "2020-12-04T06:11:20.442Z"
    }
   },
   "source": [
    "## Reducing Kp_axon in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T00:35:56.245549Z",
     "start_time": "2021-01-03T00:35:26.271055Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,0.3,-.1)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'K'),\n",
    "                                              (1,    'lightblue')], N=256)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(axon_Kp = p)\n",
    "    FI_curve_plot(stims, ax, label=str(np.round(p,2)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "ax.legend()    \n",
    "title='FI curve - reducing Kp_Axon in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing soma_nav12 in NaV12 Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-04T20:52:33.549401Z",
     "start_time": "2021-01-04T20:51:41.964532Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(0,1.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing ais_nav12 in NaV12 Hom')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(soma_nav12 = p, ais_nav12 = 0, dend_nav12 = 0)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "title='FI curve - increasing soma_nav12 in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing ais_nav12 in NaV12 Hom\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T02:29:29.784872Z",
     "start_time": "2021-01-03T02:28:32.619322Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(0,1.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing ais_nav12 in NaV12 Hom')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(soma_nav12 = 0, ais_nav12 = p, dend_nav12 = 0)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing ais_nav12 in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing ais_ and soma_nav12 in Nav12 Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T02:30:22.164671Z",
     "start_time": "2021-01-03T02:29:29.787876Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(0,1.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing ais_nav12 in NaV12 Hom')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(soma_nav12 = p, ais_nav12 = p, dend_nav12 = 0)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing soma_nav12 and ais_nav12 in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing dend_nav12 in Nav12 Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T02:31:13.546805Z",
     "start_time": "2021-01-03T02:30:22.167463Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(0,1.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing dend_nav12 in NaV12 Hom')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(soma_nav12 = 0, ais_nav12 = 0, dend_nav12 = p)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing dend_nav12 in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-10T04:57:43.374673Z",
     "start_time": "2021-01-10T04:57:18.717986Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,5.1,1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12 = p)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing NaV12 in WT'\n",
    "plt.title(title)\n",
    "# plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing axon Kt in Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T22:53:10.267977Z",
     "start_time": "2021-05-02T22:52:45.231251Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,5.1,1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12=0,\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 =p,\n",
    "                  axon_K=1,\n",
    "                  soma_K=1,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing axon Kt in Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing axon Kt in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T22:53:50.885122Z",
     "start_time": "2021-05-02T22:53:25.590279Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,5.1,1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    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 =p,\n",
    "                  axon_K=1,\n",
    "                  soma_K=1,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing axon Kt in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing axon K in Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T23:13:35.263227Z",
     "start_time": "2021-05-02T23:12:45.947721Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12=0,\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=p,\n",
    "                  soma_K=1,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing axon K in Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing axon K in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T23:14:23.879945Z",
     "start_time": "2021-05-02T23:13:35.265746Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    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=p,\n",
    "                  soma_K=1,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing axon K in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing soma K in Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T23:18:26.716675Z",
     "start_time": "2021-05-02T23:17:38.261043Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12=0,\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=p,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing soma K in Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing soma K in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T23:19:15.212083Z",
     "start_time": "2021-05-02T23:18:26.720519Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    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=p,\n",
    "                  dend_K=1,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing soma K in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing dend K in Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T23:18:26.716675Z",
     "start_time": "2021-05-02T23:17:38.261043Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12=0,\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=p,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing dend K in Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing dend K in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T23:19:15.212083Z",
     "start_time": "2021-05-02T23:18:26.720519Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    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=p,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing dend K in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing K in Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T23:18:26.716675Z",
     "start_time": "2021-05-02T23:17:38.261043Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(nav12=0,\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=p,\n",
    "                  soma_K=p,\n",
    "                  dend_K=p,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing K in Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing K in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T23:19:15.212083Z",
     "start_time": "2021-05-02T23:18:26.720519Z"
    }
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,2.1,0.1)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    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=p,\n",
    "                  soma_K=p,\n",
    "                  dend_K=p,\n",
    "                  gpas_all=1)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing K in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing Rin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T03:26:58.261576Z",
     "start_time": "2021-05-02T03:26:34.214571Z"
    },
    "code_folding": [
     5
    ]
   },
   "outputs": [],
   "source": [
    "stims = np.arange(0.1,.31,.02)\n",
    "percents = np.arange(1,10,2)\n",
    "\n",
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,4), sharex=False, sharey=False)\n",
    "ax.set_title('Increasing nav12 in WT')\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                         [(0,    'k'),\n",
    "                                          (0.5, 'skyblue'),\n",
    "                                          (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(1,0,len(percents) + 1)))\n",
    "\n",
    "\n",
    "\n",
    "for p in percents:\n",
    "    init_settings(gpas_all = p)\n",
    "    FI_curve_plot(stims, ax, label=str(int(p*100)), stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)    \n",
    "\n",
    "init_settings()\n",
    "FI_curve_plot(stims, ax, label='Full WT', stim_start = 25, stim_dur = 300, sweep_len = 325, dt = .2)\n",
    "\n",
    "ax.legend()\n",
    "title='FI curve - increasing Rin in WT'\n",
    "plt.title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-02T22:03:08.357608Z",
     "start_time": "2021-05-02T22:02:56.601794Z"
    }
   },
   "outputs": [],
   "source": [
    "sweep_len=1000\n",
    "stim_start=400\n",
    "stim_dur=200\n",
    "dt=0.2\n",
    "amp=-0.300\n",
    "\n",
    "init_stim(stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt, amp=amp)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "percents = np.arange(0,10,1)\n",
    "for p in percents:\n",
    "    init_settings(gpas_all = p)\n",
    "    Vm, I, t = run_model()\n",
    "#     plt.plot(t, Vm, label=str(np.round(p,2)))\n",
    "    \n",
    "    \n",
    "    run = pd.DataFrame(data={'Vm':Vm}, index=t)\n",
    "    \n",
    "    run.plot(ax=ax, label=str(np.round(p,2)))\n",
    "    Rin = np.round(np.abs(run['Vm'][stim_start/1000] - run['Vm'].min())/np.abs(amp), 1)\n",
    "    print(f'gpas_all = {np.round(p,2)} Rin = {Rin}')\n",
    "\n",
    "plt.ylim(-150, 50)\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example spikes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-31T00:40:55.430491Z",
     "start_time": "2020-12-31T00:40:41.370990Z"
    }
   },
   "outputs": [],
   "source": [
    "def FI_curve_spikes(stims, ax, title='', color='k', stim_start = 50, stim_dur = 300, sweep_len = 350, dt = 0.1):\n",
    "    \n",
    "    for i, amp in enumerate(stims):\n",
    "        init_stim(stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len, dt=dt, amp=amp)\n",
    "        Vm, I, t = run_model()\n",
    "        ax.plot(t, Vm + 110 *i, linewidth=1, color=color)\n",
    "        \n",
    "    ax.set_title(title)\n",
    "    ax.set_axis_off()\n",
    "    \n",
    "stim_start = 10\n",
    "stim_dur = 300\n",
    "sweep_len = 350\n",
    "dt = 0.1\n",
    "x_min = .025\n",
    "x_max = 0.34\n",
    "\n",
    "fig, [ax1, ax2, ax3] = plt.subplots(nrows=1, ncols=3, figsize=(24, 16), sharex=False, sharey=True)\n",
    "\n",
    "stims = np.arange(0.1, .31, .05)\n",
    "\n",
    "init_settings()\n",
    "FI_curve_spikes(stims, ax1, color='k', title='100% NaV1.2',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax1.set_xlim(x_min, x_max)\n",
    "\n",
    "init_settings(nav12=0.5)\n",
    "FI_curve_spikes(stims, ax2, color='skyblue', title='50% NaV1.2',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax2.set_xlim(x_min, x_max)\n",
    "\n",
    "init_settings(nav12=0)\n",
    "FI_curve_spikes(stims, ax3, color='red', title='0% NaV1.2',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax3.set_xlim(x_min, x_max)\n",
    "\n",
    "ax1.plot([x_min, x_min], [0, 40], color = 'k')\n",
    "ax1.plot([x_min, x_min + .05], [0, 0], color = 'k')\n",
    "\n",
    "plt.savefig(\"{}.pdf\".format('model_FI_curve_spiking'), transparent=True)\n",
    "\n",
    "plt.show()\n",
    "print(stims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-31T00:44:13.850660Z",
     "start_time": "2020-12-31T00:43:55.058724Z"
    }
   },
   "outputs": [],
   "source": [
    "stim_start = 10\n",
    "stim_dur = 300\n",
    "sweep_len = 350\n",
    "dt = 0.1\n",
    "x_min = .025\n",
    "x_max = 0.34\n",
    "\n",
    "fig, [ax1, ax2, ax3] = plt.subplots(nrows=1, ncols=3, figsize=(24, 16), sharex=False, sharey=True)\n",
    "\n",
    "stims = np.arange(0.1, .31, .05)\n",
    "\n",
    "init_settings()\n",
    "FI_curve_spikes(stims, ax1, color='k', title='100% NaV1.2',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax1.set_xlim(x_min, x_max)\n",
    "\n",
    "init_settings(axon_Kp=0.5)\n",
    "FI_curve_spikes(stims, ax2, color='skyblue', title='50% axon_Kp',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax2.set_xlim(x_min, x_max)\n",
    "\n",
    "init_settings(axon_Kp=.3)\n",
    "FI_curve_spikes(stims, ax3, color='red', title='30% axon_Kp',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax3.set_xlim(x_min, x_max)\n",
    "\n",
    "ax1.plot([x_min, x_min], [0, 40], color = 'k')\n",
    "ax1.plot([x_min, x_min + .05], [0, 0], color = 'k')\n",
    "\n",
    "# plt.savefig(\"{}.pdf\".format('model_FI_curve_spiking'), transparent=True)\n",
    "\n",
    "plt.show()\n",
    "print(stims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-31T00:49:34.488959Z",
     "start_time": "2020-12-31T00:49:18.712036Z"
    }
   },
   "outputs": [],
   "source": [
    "stim_start = 10\n",
    "stim_dur = 300\n",
    "sweep_len = 350\n",
    "dt = 0.1\n",
    "x_min = .025\n",
    "x_max = 0.34\n",
    "\n",
    "fig, [ax1, ax2, ax3] = plt.subplots(nrows=1, ncols=3, figsize=(24, 16), sharex=False, sharey=True)\n",
    "\n",
    "stims = np.arange(0.1, .31, .05)\n",
    "\n",
    "init_settings(nav12=0)\n",
    "FI_curve_spikes(stims, ax1, color='red', title='0% NaV1.2',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax1.set_xlim(x_min, x_max)\n",
    "\n",
    "init_settings()\n",
    "FI_curve_spikes(stims, ax2, color='k', title='100% NaV1,2',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax2.set_xlim(x_min, x_max)\n",
    "\n",
    "init_settings(nav12=0, axon_Kp=2.5)\n",
    "FI_curve_spikes(stims, ax3, color='blue', title='0% NaV1.2, 250% axon_Kp',stim_start=stim_start, stim_dur=stim_dur, sweep_len=sweep_len)\n",
    "ax3.set_xlim(x_min, x_max)\n",
    "\n",
    "ax1.plot([x_min, x_min], [0, 40], color = 'k')\n",
    "ax1.plot([x_min, x_min + .05], [0, 0], color = 'k')\n",
    "\n",
    "# plt.savefig(\"{}.pdf\".format('model_FI_curve_spiking'), transparent=True)\n",
    "\n",
    "plt.show()\n",
    "print(stims)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AHP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T00:45:16.481579Z",
     "start_time": "2021-01-03T00:45:16.474958Z"
    }
   },
   "outputs": [],
   "source": [
    "def ahp_plot_axes():\n",
    "\n",
    "\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,6), sharex=False, sharey=False)\n",
    "\n",
    "    ax.set_ylabel('Vm (mV)')\n",
    "    ax.set_ylabel('Time (sec)')\n",
    "    ax.plot([0, 0], [-40,-20])\n",
    "    ax.plot([0, 0.005], [-40,-40])\n",
    "\n",
    "#     ax.axis('off')\n",
    "\n",
    "    ax.set_ylim(-75,50)\n",
    "    ax.set_xlim(0.035, 0.073)\n",
    "    return fig, ax\n",
    "\n",
    "sweep_len = 100\n",
    "stim_dur = 50\n",
    "stim_start = 25\n",
    "amp = 0.6\n",
    "dt = 0.1\n",
    "init_stim(sweep_len=sweep_len, \n",
    "          stim_start=stim_start,\n",
    "          stim_dur=stim_dur,\n",
    "          amp=amp,\n",
    "          dt=dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reducing NaV12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T00:45:20.904141Z",
     "start_time": "2021-01-03T00:45:17.618633Z"
    }
   },
   "outputs": [],
   "source": [
    "percents = np.arange(1,-0.1,-.1)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'k'),\n",
    "                                              (0.5, 'skyblue'),\n",
    "                                              (1,    'red')], N=256)\n",
    "\n",
    "fig, ax = ahp_plot_axes()\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = p) ### set conductance here\n",
    "    Vm, I, t = run_model()\n",
    "    ax.plot(t[int(35/h.dt):int(73/h.dt)], Vm[int(35/h.dt):int(73/h.dt)], linewidth=1, label = '{}'.format(int(p*100)))\n",
    "\n",
    "ax.legend(frameon=False)\n",
    "title='AHP - reducing NaV12'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing axon_Kp in NaV12 Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T00:45:25.402073Z",
     "start_time": "2021-01-03T00:45:22.951029Z"
    }
   },
   "outputs": [],
   "source": [
    "percents = np.arange(1,2.6,.25)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'red'),\n",
    "                                              (1,    'k')], N=256)\n",
    "\n",
    "fig, ax = ahp_plot_axes()\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = 0, axon_Kp = p) ### set conductance here\n",
    "    Vm, I, t = run_model()\n",
    "    ax.plot(t[int(35/h.dt):int(73/h.dt)], Vm[int(35/h.dt):int(73/h.dt)], linewidth=1, label = '{}'.format(int(p*100)))\n",
    "\n",
    "ax.legend(frameon=False)\n",
    "title='AHP - Increasing axon_Kp in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decreasing axon_Kp in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-03T00:45:27.720940Z",
     "start_time": "2021-01-03T00:45:25.405624Z"
    }
   },
   "outputs": [],
   "source": [
    "percents = np.arange(1,0.3,-.1)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'K'),\n",
    "                                              (1,    'lightblue')], N=256)\n",
    "\n",
    "\n",
    "fig, ax = ahp_plot_axes()\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = 1, axon_Kp = p) ### set conductance here\n",
    "    Vm, I, t = run_model()\n",
    "    ax.plot(t[int(35/h.dt):int(73/h.dt)], Vm[int(35/h.dt):int(73/h.dt)], linewidth=1, label = '{}'.format(int(p*100)))\n",
    "\n",
    "ax.legend(frameon=False)\n",
    "title='AHP - Decreasing axon_Kp in WT'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## increasing nav12_soma in NaV12 Hom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:50:01.628911Z",
     "start_time": "2020-12-27T01:49:58.601624Z"
    }
   },
   "outputs": [],
   "source": [
    "percents = np.arange(1,-0.1,-.1)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'k'),\n",
    "                                              (0.5, 'skyblue'),\n",
    "                                              (1,    'red')], N=256)\n",
    "\n",
    "fig, ax = ahp_plot_axes()\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "for i, p in enumerate(percents):\n",
    "#     init_settings(soma_nav12 = p, ais_nav12 = p, dend_nav12 = p) ### set conductance here\n",
    "    init_settings(nav12 = p)\n",
    "    Vm, I, t = run_model()\n",
    "    ax.plot(t, Vm, linewidth=1, label = '{}'.format(int(p*100)))\n",
    "\n",
    "ax.legend(frameon=False)\n",
    "title='AHP - increasing nav12_soma in NaV12 Hom'\n",
    "plt.title(title)\n",
    "plt.savefig(title + '.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-04T01:57:22.624576Z",
     "start_time": "2020-12-04T01:57:22.505244Z"
    }
   },
   "source": [
    "# Currents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reducing NaV12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-12-27T01:50:27.780144Z",
     "start_time": "2020-12-27T01:50:01.631044Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def Na_k_current_plots(label):\n",
    "    ax2.set_title('I_k')\n",
    "    ax2.set_xlabel('Time (sec)')\n",
    "    ax2.set_ylabel('I_k (nA)')\n",
    "#     ax2.set_ylim(0, 0.8)\n",
    "    \n",
    "    ax3.set_title('I_Na')\n",
    "    ax3.set_xlabel('Time (sec)')\n",
    "    ax3.set_ylabel('I_Na (nA)')\n",
    "#     ax3.set_ylim(-2.5, 0)\n",
    "\n",
    "    Vm, I, t = run_model()\n",
    "    \n",
    "    spikes = detect_spike_properties(Vm, t)\n",
    "\n",
    "    \n",
    "    spike_start = spikes[0]['start_idx']\n",
    "    spike_end = spike_start + int(5/dt)\n",
    "#     spike_end = spikes[0]['end_idx']\n",
    "\n",
    "    t = t[spike_start:spike_end]\n",
    "    t = t-t[0]\n",
    "    Vm = Vm[spike_start:spike_end]\n",
    "    I_Na = I['Na'][spike_start:spike_end]\n",
    "    I_K = I['K'][spike_start:spike_end]\n",
    "    \n",
    "    ax1.plot(t + i*.015, Vm, linewidth = 1, label = label)    \n",
    "    ax2.plot(t + i*.015, I_K, linewidth = 1, label = label)\n",
    "    ax3.plot(t + i*.015, I_Na, linewidth = 1, label = label)\n",
    "\n",
    "sweep_len = 100\n",
    "stim_dur = 95\n",
    "stim_start = 5\n",
    "amp = 0.35\n",
    "dt = 0.01\n",
    "\n",
    "init_stim(sweep_len=sweep_len, \n",
    "          stim_start=stim_start,\n",
    "          stim_dur=stim_dur,\n",
    "          amp=amp,\n",
    "          dt=dt)\n",
    "\n",
    "fig1, [ax1, ax2, ax3] = plt.subplots(nrows=3, ncols=1, figsize=(8,6), sharex=False, sharey=False)\n",
    "fig_title = 'Baseline Model'\n",
    "fig1.suptitle(fig_title) \n",
    "\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'k'),\n",
    "                                              (0.5, 'skyblue'),\n",
    "                                              (1,    'red')], N=256)\n",
    "\n",
    "ax1.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "ax2.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "ax3.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "ax1.plot([0, 0], [0, 40], 'k')\n",
    "ax1.plot([0, 0.005], [0, 0], 'k')\n",
    "ax2.plot([0, 0], [0, .2], 'k')\n",
    "ax3.plot([0, 0], [0, -0.5], 'k')\n",
    "\n",
    "ax1.axis('off')\n",
    "ax2.axis('off')\n",
    "ax3.axis('off')\n",
    "\n",
    "\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = p)\n",
    "    Na_k_current_plots(label = '{}'.format(int(p*100)))\n",
    "    \n",
    "# ax1.legend(frameon=False, title='NaV1.2 (%)')\n",
    "plt.savefig(\"{}.pdf\".format('model_currents_and_spikes'), transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-10T17:48:56.256030Z",
     "start_time": "2021-01-10T17:48:53.339630Z"
    }
   },
   "outputs": [],
   "source": [
    "def Na_k_ratio_plot():\n",
    "\n",
    "    Vm, I, t = run_model()\n",
    "    t= t-t[0]\n",
    "    dt = t[1]-t[0]\n",
    "    dvdt = np.gradient(Vm)/h.dt\n",
    "    \n",
    "    spike_start = np.argwhere(dvdt >= 50)[0][0] - int(0.0005 / dt)\n",
    "    spike_end = spike_start + int(0.01 / dt)\n",
    "\n",
    "    t = t[spike_start:spike_end]\n",
    "    t = t-t[0]\n",
    "    Vm = Vm[spike_start:spike_end]\n",
    "    I_Na = I['Na'][spike_start:spike_end]\n",
    "    I_K = I['K'][spike_start:spike_end]\n",
    "    \n",
    "    ratio = np.trapz(np.abs(I_Na),t)/np.trapz(I_K,t)\n",
    "    AP_height = np.max(Vm)\n",
    "    ax.scatter(int(p * 100), ratio)\n",
    "    print(f'{int(p*100)}: {np.round(ratio,2)}')\n",
    "\n",
    "\n",
    "    \n",
    "sweep_len = 100\n",
    "stim_dur = 95\n",
    "stim_start = 5\n",
    "amp = 0.35\n",
    "dt = 0.1\n",
    "init_stim(sweep_len=sweep_len, \n",
    "          stim_start=stim_start,\n",
    "          stim_dur=stim_dur,\n",
    "          amp=amp,\n",
    "          dt=dt)\n",
    "\n",
    "fig1, ax = plt.subplots(nrows=1, ncols=1, figsize=(3,3), sharex=False, sharey=False)\n",
    "fig_title = 'Na/K Ratio'\n",
    "fig1.suptitle(fig_title) \n",
    "ax.set_ylabel('I_Na/I_K ratio')\n",
    "ax.set_xlabel('Percent NaV1.2')\n",
    "ax.set_xlim(105, -5)\n",
    "\n",
    "percents = np.arange(1,-0.1,-.1)\n",
    "cmap = clr.LinearSegmentedColormap.from_list('scn2a', \n",
    "                                             [(0,    'k'),\n",
    "                                              (0.5, 'skyblue'),\n",
    "                                              (1,    'red')], N=256)\n",
    "\n",
    "ax.set_prop_cycle('color',cmap(np.linspace(0,1,len(percents))))\n",
    "\n",
    "\n",
    "for i, p in enumerate(percents):\n",
    "    init_settings(nav12 = p)\n",
    "    Na_k_ratio_plot()\n",
    "    \n",
    "\n",
    "plt.savefig(\"{}.pdf\".format('Na_K_Q_ratio'), transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Increasing axon_Kp in NaV12 Hom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decreasing axon_Kp in WT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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