{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aim\n",
    "\n",
    "The prevalence of the interneuron hub mircorcircuit led us to the hypothesis that the interneurons mediate lateral inhibition. For effective lateral inhibition, the more active excitatory neuron has to be less sensitive to the inhibitory inputs than the less active one. This difference in sensitivity could stem from the timing of inhibition. The more active neuron is more likely to produce a spike in the interneuron, so that it receives the inhibition at a short time after spiking. In contrast, the less active neuron has a high chance to receive the inhibition in a later phase. How this timing of inhibition translates into sensitivity depends on the neuron model. Generally, however, most neuron models are less senstive to input directly after the spike than input in the middle of the phase.\n",
    "\n",
    "We demonstrated the lateral inhibition with a model of Hodgkin-Huxley dynamics. Here, one important factor for the difference in senstivity is the relatively strong after-hyperpolarisation. When the membrane is hyperpolarised, inhibitory conductances cannot further inhibit the neuron, so that their effect is diminished. However, most pyramidal cells exhibit a reduced after-hyperpolarisation. Therefore, we would like to test the robustness of our results in more pyramidal-like neuron models.\n",
    "\n",
    "For a different neuron model, I read Pospischil, 2008, an article claiming to provide model classes for four different neuron types (regular spiking, fast spiking, bursting and low threshold spiking). However, for their model fitting, they focus more on **spike timing** (\"The error function consisted of a weighted sum over the absolute value of the differences in the time of the first spike after DC onset, the first, second and last interspike intervals, all values taken at three different DC levels\" and \"In order to avoid that an (experimentally) unreliable feature strongly impacts on the error function, we chose the weights wi to be the inverse of the SD of the experimental values. Large SDs thus lead to a reduced contribution to the error. However, in order to prevent an error that predomi-nantly consists of the contribution of a very reliable feature, we introduced a cut-off: whenever the SD of a given feature was smaller than 3% of the mean experimental value, the weight was taken as the inverse of these 3%, rather than as the inverse of the SD itself.\"). In almost all figures, there is a clear difference between the experimentally observed spike shapes and the modelled ones. \n",
    "\n",
    "1. Pospischil, M. et al. Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons. Biol. Cybern. 99, 427–441 (2008).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Brian2Model Fitting\n",
    "I thought one interesting route could be to use Yanfangs recordings to do a custom fitting that puts more emphasis on the spike shape. For this reason, I will try the [Brian2 model fitting toolbox](https://brian2modelfitting.readthedocs.io/en/stable/index.html).\n",
    "\n",
    "Installation\n",
    "\n",
    "pip install brian2modelfitting did not work for me, because I did not get the right nevergrad version, which was related to this bug https://github.com/brian-team/brian2modelfitting/issues/24. So I cloned the repro and installed it from there."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate target dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from brian2.units import *\n",
    "import brian2 as br"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = 20000 * umetre ** 2\n",
    "hodgkin_huxley_params = {\n",
    "    \"Cm\": 1 * ufarad * cm ** -2 * area,\n",
    "    \"gl\": 5e-5 * siemens * cm ** -2 * area,\n",
    "    \"El\": -65 * mV,\n",
    "    \"EK\": -80 * mV,\n",
    "    \"ENa\": 50 * mV,\n",
    "    \"g_na\": 100 * msiemens * cm ** -2 * area,\n",
    "    \"g_kd\": 30 * msiemens * cm ** -2 * area,\n",
    "    \"VT\": -63 * mV\n",
    "}\n",
    "\n",
    "hodgkin_huxley_eqs = '''\n",
    "dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I )/Cm : volt\n",
    "dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/\n",
    "    (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/\n",
    "    (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1\n",
    "dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/\n",
    "    (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1\n",
    "dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1\n",
    "'''\n",
    "\n",
    "I_eqs = '''\n",
    "I: amp'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [0.2*nA, 0.5*nA]\n",
    "dt = 0.1*ms\n",
    "duration = 100*ms\n",
    "\n",
    "br.brian_prefs.dt = dt \n",
    "number_of_inputs = len(inputs)\n",
    "\n",
    "neuron = br.NeuronGroup(N=number_of_inputs, model=hodgkin_huxley_eqs+I_eqs, method=\"exponential_euler\", threshold=\"v>-40*mV\", refractory=\"v>-40*mV\")\n",
    "\n",
    "trace_monitor = br.StateMonitor(neuron, ['v'], record=True)\n",
    "net = br.Network(neuron)\n",
    "net.run(100*ms, namespace=hodgkin_huxley_params)\n",
    "\n",
    "v_eq = neuron.v[0]\n",
    "m_eq = neuron.m[0]\n",
    "h_eq = neuron.h[0]\n",
    "n_eq = neuron.n[0]\n",
    "\n",
    "net.add(trace_monitor)\n",
    "for neuron_idx in range(number_of_inputs):\n",
    "    neuron.I[neuron_idx]=inputs[neuron_idx]\n",
    "    \n",
    "net.run(duration,namespace=hodgkin_huxley_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 200)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx in range(number_of_inputs):\n",
    "    plt.plot(trace_monitor.t/ms, trace_monitor.v[idx][:]/mV)\n",
    "plt.xlim(100, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_traces = trace_monitor.v[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1000)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_traces.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_traces = np.repeat(np.array([inputs]), output_traces.shape[1], axis=0).T*amp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_traces.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a fitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /home/pfeiffer/.local/lib/python3.7/site-packages/tqdm-4.31.1-py3.7.egg/tqdm/autonotebook/__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n",
      " [py.warnings]\n"
     ]
    }
   ],
   "source": [
    "import brian2modelfitting as mf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameter_eqs = '''\n",
    "g_na : siemens (constant)\n",
    "g_kd : siemens (constant)\n",
    "ENa: volt (constant)\n",
    "EK: volt (constant)\n",
    "'''\n",
    "\n",
    "Cm =  1 * ufarad * cm ** -2 * area\n",
    "gl =  5e-5 * siemens * cm ** -2 * area\n",
    "El =  -65 * mV\n",
    "VT =  -63 * mV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitter = mf.TraceFitter(dt=0.1*ms,\n",
    "                        model=hodgkin_huxley_eqs+parameter_eqs,\n",
    "                        input_var=\"I\",\n",
    "                        output_var=\"v\",\n",
    "                        input=input_traces,\n",
    "                        output=output_traces,\n",
    "                        method='exponential_euler',\n",
    "                        n_samples = 10,\n",
    "                        param_init={\"v\":v_eq, \"m\": m_eq, \"n\": n_eq, \"h\": h_eq})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = mf.NevergradOptimizer(num_workers=3)\n",
    "metric = mf.MSEMetric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 0: Best parameters EK=-78.34272938 mV, ENa=27.27076459 mV, g_kd=13.46178134 uS, g_na=22.89360602 uS (error: 402.19317278 mV^2)\n",
      "Round 1: Best parameters EK=-78.34272938 mV, ENa=27.27076459 mV, g_kd=13.46178134 uS, g_na=22.89360602 uS (error: 402.19317278 mV^2)\n",
      "Round 2: Best parameters EK=-78.28625021 mV, ENa=24.17809884 mV, g_kd=15.53226421 uS, g_na=17.6488482 uS (error: 386.69997268 mV^2)\n",
      "Round 3: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 4: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 5: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 6: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 7: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 8: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 9: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 10: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 11: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 12: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 13: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 14: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 15: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 16: Best parameters EK=-80.29287141 mV, ENa=25.47313302 mV, g_kd=9.31321283 uS, g_na=31.45285497 uS (error: 193.02621214 mV^2)\n",
      "Round 17: Best parameters EK=-80.29287141 mV, ENa=22.41855064 mV, g_kd=9.31321283 uS, g_na=31.16288791 uS (error: 129.55934835 mV^2)\n",
      "Round 18: Best parameters EK=-80.29287141 mV, ENa=22.41855064 mV, g_kd=9.31321283 uS, g_na=31.16288791 uS (error: 129.55934835 mV^2)\n",
      "Round 19: Best parameters EK=-80.29287141 mV, ENa=22.41855064 mV, g_kd=9.31321283 uS, g_na=31.16288791 uS (error: 129.55934835 mV^2)\n",
      "Round 20: Best parameters EK=-80.29287141 mV, ENa=22.41855064 mV, g_kd=9.31321283 uS, g_na=31.16288791 uS (error: 129.55934835 mV^2)\n",
      "Round 21: Best parameters EK=-80.29287141 mV, ENa=22.41855064 mV, g_kd=9.31321283 uS, g_na=31.16288791 uS (error: 129.55934835 mV^2)\n",
      "Round 22: Best parameters EK=-80.29287141 mV, ENa=22.41855064 mV, g_kd=9.31321283 uS, g_na=31.16288791 uS (error: 129.55934835 mV^2)\n",
      "Round 23: Best parameters EK=-80.29287141 mV, ENa=22.41855064 mV, g_kd=9.31321283 uS, g_na=31.16288791 uS (error: 129.55934835 mV^2)\n",
      "Round 24: Best parameters EK=-80.29287141 mV, ENa=22.41855064 mV, g_kd=9.31321283 uS, g_na=31.16288791 uS (error: 129.55934835 mV^2)\n",
      "Round 25: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.66071616 uS, g_na=26.52755915 uS (error: 74.24025354 mV^2)\n",
      "Round 26: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.66071616 uS, g_na=26.52755915 uS (error: 74.24025354 mV^2)\n",
      "Round 27: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.66071616 uS, g_na=26.52755915 uS (error: 74.24025354 mV^2)\n",
      "Round 28: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.66071616 uS, g_na=26.52755915 uS (error: 74.24025354 mV^2)\n",
      "Round 29: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.66071616 uS, g_na=26.52755915 uS (error: 74.24025354 mV^2)\n",
      "Round 30: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 31: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 32: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 33: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 34: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 35: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 36: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 37: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 38: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 39: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 40: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 41: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 42: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 43: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 44: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 45: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 46: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 47: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 48: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 49: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 50: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 51: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 52: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 53: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 54: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 55: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 56: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 57: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 58: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 59: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 60: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 61: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 62: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n",
      "Round 63: Best parameters EK=-79.67807164 mV, ENa=26.79787859 mV, g_kd=14.34532187 uS, g_na=26.52755915 uS (error: 67.0600397 mV^2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 64: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 65: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 66: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 67: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 68: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 69: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 70: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 71: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 72: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 73: Best parameters EK=-79.25331479 mV, ENa=29.1855946 mV, g_kd=14.38383269 uS, g_na=23.84749322 uS (error: 61.92135698 mV^2)\n",
      "Round 74: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 75: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 76: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 77: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 78: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 79: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 80: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 81: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 82: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 83: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 84: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 85: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 86: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 87: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 88: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 89: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 90: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 91: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 92: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 93: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 94: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 95: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 96: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 97: Best parameters EK=-79.25331479 mV, ENa=34.57521441 mV, g_kd=13.21758273 uS, g_na=22.66965902 uS (error: 54.24210634 mV^2)\n",
      "Round 98: Best parameters EK=-79.56861212 mV, ENa=22.93187477 mV, g_kd=11.44286746 uS, g_na=27.10457024 uS (error: 24.1015132 mV^2)\n",
      "Round 99: Best parameters EK=-79.56861212 mV, ENa=22.93187477 mV, g_kd=11.44286746 uS, g_na=27.10457024 uS (error: 24.1015132 mV^2)\n"
     ]
    }
   ],
   "source": [
    "res, error = fitter.fit(n_rounds = 100, optimizer=opt, metric=metric, g_na=[0.33*hodgkin_huxley_params[\"g_na\"], 3*hodgkin_huxley_params[\"g_na\"]], g_kd = [0.33*hodgkin_huxley_params[\"g_kd\"], 3*hodgkin_huxley_params[\"g_kd\"]], ENa = [0.33*hodgkin_huxley_params[\"ENa\"], 3*hodgkin_huxley_params[\"ENa\"]], EK = [ 3*hodgkin_huxley_params[\"EK\"], 0.33*hodgkin_huxley_params[\"EK\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EK': -79.56861212 * mvolt,\n",
       " 'ENa': 22.93187477 * mvolt,\n",
       " 'g_na': 27.10457024 * usiemens,\n",
       " 'g_kd': 11.44286746 * usiemens}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$20.000000000000004\\,\\mathrm{\\mu}\\,\\mathrm{S}$"
      ],
      "text/plain": [
       "20. * usiemens"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hodgkin_huxley_params[\"g_na\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$6.0\\,\\mathrm{\\mu}\\,\\mathrm{S}$"
      ],
      "text/plain": [
       "6. * usiemens"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hodgkin_huxley_params[\"g_kd\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "traces = fitter.generate_traces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0d2b7374e0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trace_monitor.t/ms, traces[0,:], label=\"fit\")\n",
    "plt.plot(trace_monitor.t/ms, output_traces[0,:], linestyle= '--', label=\"data\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0d2b32a320>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trace_monitor.t/ms, traces[1,:], label=\"fit\")\n",
    "plt.plot(trace_monitor.t/ms, output_traces[1,:], linestyle= '--', label=\"data\")\n",
    "plt.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}