{
 "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": [
    "#### Allen Cell Database\n",
    "\n",
    "Another source of electrophysiological data is the [allen cell database](https://celltypes.brain-map.org/overview). They got cells from both human and mouse brain. What is nice is that they got their own fitting procedure, which allows to test and compare the fitting procedures used here. \n",
    "\n",
    "Stimuliwise, they applied a whole range of stimuli (long square pulses, white noise, ramp up). Interestingly, some cells show stuttering, which means that maybe the database could provide material to study bifurcations.\n",
    "\n",
    "\n",
    "Browsing, I selected two cells for testing, one with \n",
    "- a [prinicpal cell like firing pattern](https://celltypes.brain-map.org/experiment/electrophysiology/327962063)\n",
    "- an [interneuron like firing pattern](https://celltypes.brain-map.org/experiment/electrophysiology/485184849). \n",
    "\n",
    "What I need to know to proceed is \n",
    "* how to download and access the data? (use the prvoided [SDK](http://alleninstitute.github.io/AllenSDK/cell_types.html))\n",
    "* how did they select the channels for fitting (read white papers)\n",
    "* test fitting with current model\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Artficial data"
   ]
  },
  {
   "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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V4iYAkbh9hof7N/XUa8ggGaEIXWG+UfUIOoVMq4kPIXMEroWqkzUkorm/rUJerML3t/PGSztrKPMYiVmoQN2bhr/KYsfQMB4a8rjZVhTS52ab+Xg0IioXGrJ5vomodsJJN1MEmjwCJPGoGhqtoSFHNtsUQehksSQWNWXhBttsK4KAYQRpwtAodROwdNycnd2bbM7mH4uICoeGLMsRYE5JdS9FgISIY3WHSxZHTSxUDUrKCEIniyEWyShO7cnizMK00kJzyeZjvqWlJGLaI8jKFrV73DxVFqvHeDRivsWE1YaHo0D1h/u6lyKQaTVIEModlhiI4cq0Sngi7J1gAWVLu3IqxuoIIjHLLTR/802gjA3jvYYMbBrh4S9/4dBtoxGRx1Azgq4QGjKg3LuZIpCuOHy4ZLET6tAWXpUShOgWFlrR1yXEt+bQkM/EopQSIQRRIcwfTGPlhuYvpOZMqYqGhmxMFhtcC91LEWRDQ/5YHEVXcYU69FlsGUXgkTJXUbjDCAHpo2Y9AkeBWrYwQySLIwIiEZO8+K6gQD3ON5nzCCrGGrIxR2BQSXUvReDeNEL2GtJOH5Vpl2yWbWhIFbYSwWTLU5wmeg0JoeSzbkNzJT199RpyewSmcwRdnzXkzhFUWUMY+U27nyJwrO6Q3UezoSFt9FGZ29CsW5iZ/EXAsJViWZlKfro9AtsWZrBksZQqRxAxGeqwmjXkN1mcYQ1FKx0asmzcsl6e/qhC91IEaPIIjBT8uK1uyyaY460EtLrTplhWkK/cbRs3d9LT5wllQpjmxVts2fpNFleyjsDq0FCVPuoNeR5BCNaQlEQjuusInNCQha0SpAznEchc3YWRZHEI2YwicLKYyoWGPHL1Kwqf1FZn849HIpVrOheJ2+m5g5FqcR1tqO2BE0+OhIsn59FHdYeGsNCydUJDAa1ut0dgpo5ABM5fGEU2J+VPuUsps6EhYxau1bFuF2vIx28arQhryOKQWl5oyLI21HYhszBD5gikxEClrGvTsG5DcxRo0GSxzCpO7TmCLO3WRk8qHShslf0vYXJj85eQrSic/7LnZHEuR2D+KA9zXP3QqNYReIQ7xBG2jkC3hevIZmUXTVyyBTu8PuaEhrQvVJdyt9FCy843H6EhJBEhiEaEvjbnJUQD7PYIfFYWx4wW4GVgc4uJvNBQlTXUMdwWWqheQ276qAHZrLPQ3OGXkJXFRjwCi3MEAZR7OssaqkSLCZtDHN5kk1lFUOEcgc3jVvUIOoMe1lBaShcv3kBoyLoNzdlsg8mWV1m8NbGGAirQvGSxsX2tC+QIfCaLY9EKsoa6UQGjF3QvRZANv4RlDWGgjgC76aMhNluVLDbIGrK2jsDtrfgLDUEmWbxV1hEEC/dVJlnsTmTbNm7hOgB0hm6mCAywhrQ2nbN1Qwsnm5SSuOnQkK0htSBVz1JN0cpUFts637wrUGdOxaOVpo9aliNwUA0NlYMe1hBG6JCObBayhkIWu+WxrEzQR7MBdcs2NAiUv0hLiUBUoPuoxTkpJ7fio8VErCIH01h8VGU1NOQRmlhDqsWE5lCH7bHu7Lj5//8aTRbbzBoKWH+RV1lsMjRkK+0224DRmyflhNIq0mLCYBuH0MhTUl0sNCSEOFoIMUcIMU8I8QujN9PUa8h8aMhCCw2h5AvUawgEgojIWW/6ZJNYHVIL0EjQ6TVk3COwOifl3ZNy5lREVKCOwObK4q7aa0gIEQX+DhwD7AKcKYTYxdwd3ayhsC0mDISGsu6wbQsz3Kahumma2ths3tBclq3PXkMRIYgYzxHYqkB9enlG51fhvWyuIzBXJGjaI9gHmCelXCClbAfuA040dreAC7PoMhg4jD1kh0+jCNt9VOY2Nv3JYveGZpmFFrA2JJ1xCYw2UesqoUiPHkHEdG+mnHDqwUq2VWaOhc2BloBpRbAdsNT1fFnmtSyEEBcIIaYJIaatXbs23N3yWiWEbTGh+WCavHiybRtauE1Dui02Uy0mbPSkspXFPg0PJzRkdGMLRm2tCPLmm7cWE9neTMZZQ5lHGw02g8ePmlYEosRreb+klPIWKeVkKeXkQYMGhbxdeNaQcyaq9t45eUwJCyeYiASmpeVZbCZCQ9aGOIImi9XBNBGTR1OErBY3C3eYtPwAOKE0M/Or8GYFHT7NNzfyDoOHDZlWBMuAka7nI4AVxu7mTiwGnPzu3udg4PB6aze04JutUYstZP7CKALG4VUozXDM22bWkLvex2tlsXAO8jEunHqIxjNPLfKmDBa7mVYEU4FxQoixQoga4AzgCXO3c1kaASd/tvd5JjSkv+mcjaGhcAfTZFsmmNjYrC7EC6aknDoCo8lisFiB+k0WO4rTQJvzkrKh5hvYNecMNp0z2oZaSpkUQlwMPAdEgdullDPN3TB873pnmpnxCDJWULJdzzV1QcPBNBGh3Hf9+5rtdQT+E9mOsW42WWwza8hvslhWLlnsTsiCXXPOZ9dWPzB+HoGU8mngadP3ydws9Gab63So+2CacIe/GEXIXkNOgVREmGgxQaDNtjLwF+t2fQsw3WLCYtYQ/mRzurUKUaGmc06+DOyikGbj1tXK4jIIH092qhi1V8r6LKuvKELmL7IWm5GmYF2h/sKfbFm6rdEmau4ku83zzUOy2CEjVIQ15PKkwC5vyh0a6mL00cpCgzvsKN3cGbwaZbM1WZynQP3/h9OOI2aC1WF7iCNQsjhDtzVp4dpMu83LSXk8oaxiBWUubyUrqyVwh4aQWhlN3UwRhHeHs7kisZWFhkKcleBOFpthDWHpuAWbb060y2hb5S6RZPeupHIFi4Zlc9dfgF1j5zYmneea0L0UAYQuonFCQ9pj3j7d4YoiJEXTSRYri023bGkll5UbWrDNVmZCaerwemPCYXeS3W+yWLGGKlNH4A4N2ZQjcIzJTHmWxvXQvRRBSBokuBtcaXZFNchmDGFbTECu6ZyJFhO20m4DhtSc5GdUmDjIxxEtHBPMLPwmi2XuRLdKnEeQFxqyaOzcawG0ytbNFIEMZKHlXyLjEaC5d05ei2zLNrTsuMUCWUBpt0dgShHYuKEFlE0CmE4Wu5lgmuPJoVG4FsrI5hTgRYy1Os+7W9cJDVU9go6gIUeQedQew3XHRW2aXED+uAVIFqczLRNMJD9lymLWkL+kZ/ZrmUpso8liN2sI7Jpz7oKy7POOoZag8gjUc4OKwJHNoY/aNOeqHoFHaGQN5Q4X11xQZnXMNmCymBxrSL8iSKvf00qPIDi11ZgHlSdbRkmBXXPOfcQneJBN5nkERvMEhaEhm+acLPBWqsniDqCFNeSEhjT3zrE6WRyuQZmb522m+6jFbKsAoUgn5h0xevSi7SGODEsNysqWTudYQ85zs7JFPMtWUbhrkUDrQHQvRaCh9a7RZLG1G1r4FhOO4tTOGkqncputh3hyZRFMSamNrRKH11ua9HSPG5SVLXfwkXpuljlUGLayaNx8e1Le0b0UgRbWUK6yWDUF0yibY2lYlywOp6TSUimBqDHWUNTSAp9Cq9ubbGYrsR3ZyClQsNOy9SibJN8jMMocchMnPMhWUVSTxR6hgTXkbGSqQMpQiwmbJhcUhDiCVBbL3Bm8xlhDlrrqAaxHRxGYadKXFa5ANssUqI9xcxtnYJg15F4LYGkdQTVZXAbhi2icORaNmEgWWxoaCtmjSRmfwlyLCffkt2phOoaHPyWVlpn5FTFZR5BWcnUFj6DcnJHqvxKtRLLYagVarSz2Bg1FNM4kc1gKZpLFFi1KCM8actUR6A8NpQomv0Vjl8fVx7NsqbTM6A+DvXPSLtqtD9kqgsJYd7lkscuDAtMegcRqDzQvWVz1CEoj4GHibjiTzInhJrXXEVhaUOZwpwMfTGOov36WPmphzNbdGRU8/65SyqzHaWxTkymyrTnA8nErpwjIhh6d5+ZEc0JDNrahLgwNVT2CDuAqogl5Qpn2M1K7RBOwaIjKYoEQJnoNFTJMLFKiAZN3KXey2OTBNJGob2+lIpCudQo+ksXq+dbNGqomi8sjRIGPg7wcgYnQkBB2bWaQr6QCUDTdvXOM9Rqy0bItShZ7ZA25ePFS5mpXtCKdsnjc/NNHEVQ4NGTzuFXpo50jncolyILmCLKsId0tJmxPFgdLkGUL8EzRIZ06Aitj3cE2DXdvJjCUMLaadlvIzCkzbq6CRTBNHw2W96kICsetGhrqAE5cVENlsVNHoO/w+i6SLAafLZXVY44OaaiOwEoLLViBT9rJEZhkwdjMGsoaHt5yK0WKsxKhIRtzUtVksUekU7m+NAGrUFPuHMHW4hGEqEJNS0MelFs2Q9zp8AjmEaTShSwYA6JZzxrynvdRdSoVYg2lHXKCzayhah1B53B7BBDIdXIWZfZMWV1zrsgdtslVDx4XdYbHodsaqyOw0bL1Get2fy2SqSMAUx6Bxawh94l4UD5Z7GKlgemmcyl7DY+itVANDZWGBm2eYw1pTn4apH6FRghLI+3OEZigQxYtTMvGDf8x21Qm1GG0ZYLtrCFfyWKyBYtgOEeQjSrYHBqyzCMQQvxJCPGpEOJjIcSjQoj+rvcuFULME0LMEUIcFV5UDyhKkAVXBE4MV9+kcya/CCybMfhN3rm/mhmeXB2BAdkitlq2BbFuH8niqCv5aSTUYTVryO98y88RmE0RFHpSFtYRRLzlVvwgrEfwAjBRSjkJ+Ay4FEAIsQtwBrArcDRwkxDO7mwQMhV4Q3OQchWUaW2ZYDMtLURZvTtZLEwcvWiQMhcaRQwT7/RRISqQLLaVNVTUfbRcjqCgjqASHoGN42ZrslhK+byU0lGZ7wIjMn+fCNwnpWyTUi4E5gH7hLmXJxT9iME6aYITw9VdR2ApLS2dUq5wACsoGxrC0Ilb6fDK3RyCtSNQrCHDvHgZPkxqDIUbmsc21JU5mMZhqdk4bsFyUl6gM0fwDeCZzN/bAUtd7y3LvFYEIcQFQohpQohpa9euDSeBhgSZdOcITJxHYOOG5q6/cJ57RC5ZbOpgmvDhPmMIGLN1WENGPYJs/YWN41ZYWdy51S1lLgelPm7aI4hg71GVZoyiWLkPCCFeBIaWeOsyKeXjmc9cBiSBe5yvlfh8yV9PSnkLcAvA5MmTw/3CGtw6ZyOLCt2H19ucLM54BCHpo0ZO3LJZgRYmi310H41EchubmWSxzawhv72GZF6vIbMFZQXMQxtzBAaUe1lFIKU8vLP3hRDnAscDh8lcrfwyYKTrYyOAFUGF9AwNbp0zx7KVstrbUOtP9IRGOhl40yg649l4HYFF4xbQVXcKpCLZZLEJ2WxmDfknAOSxrEyGhty1SB5lqxj8tu/2gbCsoaOBnwMnSCmbXW89AZwhhKgVQowFxgFTwtzLE7TkCPIri42cUObIagvShR6B9/90rlurqYKylL3jFrA2JMcaUs/NhYYs9aSKWpp0LlthKM3smcXha5GMwd2vDCobGiqDG4Fa4AWhhHtXSvkdKeVMIcQDwCxUyOgiKStgkhT2rg9bRxAxWUdg0cIMYQU5m1gsYujELastW0eB+vPy1HkEhnnxtm9oPuovUmmnJUfm+dbuEWyJ0FBnkFLu2Ml7VwFXhbm+b2jwCLL00Yih0JCVEyx4O4JsTiUSISIwkCOwmHabnW/+xk1K8noNaR8z5ybWsoYKcgRlZEulJbXxSI5lVRHWkMU5gmob6jLQMFDOHNOfLDZH/QqNIvqod9mcg3ti2gvwMrCadpsMNG4q1IHZZLHVrCF/eZ9kWmYMjQqyhrrEuFUVQWlocIfdBWVGPQKbXPV0MrAnlUrlPCgjrCGbY92OIvBLH5UyO16wNbKGnNyKd48garptd1Y2m8dNGttDuo8iyHaL09RrKIJeFkzROa0WKQIn1h3geD53jsAca8jSWHdAj0A6J5SZDHXYnFvxqUBThR6B0RxBsmAtWDRuRZXFVUVQDGcyaeo15HQf1Z8strBVQrrQCvI+wVKZz0azHkGuKE8LbGdbBZhvysI1fNCK1Z6Uv1BkKi2zoUf13LRslirQgOdfeEH3UQTOZAp5GEfeUZW6zywOUHxUEWTjov4nWF6OIGuxaZTN5rbATkjNN32U/DoCk22obfakPOcI0kSjFWINWd1iopoSADEFAAAgAElEQVQsLg9NHkEuR6AWqjbro4irb9MEC5EsTrm7tarXtFq4NrOtsr+pd/po2s1KEwYtXJtZQ+lEwbh58wgqmiwGJaNV6zT4AVLl0H0UQdYjCBbiyF4m74QyTdaalMWbrW0WWkAFmqOPCjMWrrOh2ahAs5atd08qW7AoRHa/2epYQ0W5FQ+soYqdWZzKjZmI2kUfLRy3arK4BAzkCLQlP1MJ9Rh1eQS2TDApM4nFcPTRqNtiM5IjsNgj8CFb9ijUiOlkscXsl3QqsxY89hrKFJRVJlmcyo2ZbeeLp5IQjRvZQ7qRInD6R4dkDWWUbDb5qUMRpDOKIBK3j43g9qRCNJ2LRSJmePFFlq1lnpTPcXOf32DUwu0KrCGPSiqZlsSihjzOQhR6BFbNt0xILRpXz1NVRVAMZzKFTCw6FpvIFPxoSUw5mjsaV5YQ5LyELQ3pGrcAITV3jsBIE7WsZWtjrLswWezBIyjIQcFW2Gso5eQI/NBHDedUHDjH3YKFHkFC7SFZY1LfHtJ9FEHJDS1IZbE7+akrNJRRBJGY8gpA648YCmmXbCFaTMSigqhzgpTOjc2Z/DZbtj68lbyjUI0eTGMzayiV8Y59eASRSC6nUimPIGJjjiDu8giqiqAYIUMcDhxrI1tHoMUjcEJDwYq2jKJkkt1PjiBXR6A91JFOARKiNfZZtuk0IDO/qXdvxfGWhPHQkM2sIceTiueed4Js99FKs4ZE1D7DIxrzPG5+0H0UgTtZHII15BRIaa2UzSaL3aEhSxSBM25BK4vdB/nojuGm2nOy2eYRyGCGh6M441HDyU8nNGTbuIEr1u0tTJpMp5XHWWnWkI2hIbcnVfUISkCTR9CeiXnHYxF9lbLZ8EvcwtBQOLaVmz6qPVmcVaA2egTucJ932RLO/IpGzFbKBshfVAzpDPvF41pIpw3Sk4tu5mYNxSwLqTm5FaEeqx5BCTg/WMjEYiKzKmuiEX2Vsu5Nw0B8LxRKVmT7aTHhyhFkFqqTQA4NtydlW6w7HSzv48wvpQjUa9pj3lIqbypaa59HkE7n6Moe10IynSYWEcQy8yuha36VQh5rKGJPCBdy9FFQc66aLC6BUlz9AJM/kSyxUMNqArdsBuJ7oRAyWexuMRHPDFhCF20o7VIEtsW6nXETUV90vvZUidCQ7lBHydyKbQo06ikUmU5L0pnzG2KZOZA0SRuymTXkeASg5lyVPloCqTb1GK0N5Q4nUunssYvaXFF3HYG19FF3GCFAjiASIRbV7RE4OQILWUPpUrkV7x5BTV5oSLMiyK4FG8etMKQmOp1v7u628cz8ShhNFrs2WyuTxY5HEKt6BCWRzGwasXDucHtKZi1bbTFvR3P7iItWDHmsIf/eStKVLHYstoQui83qHIFr3HyE+xLJXI7A2GHsKddasNWTcuZaNN7puLlPDBRChYeMeQRSQrIVYnUZGfXG4UNByhx9FMqOm190H0XgTP5oPJQ7nEilqXEUga6CH7c7bFuOwL3ZBpAt24Y6mrPYktqTxRa25iiZI/ARGorlPALtoSF3bgXssmzd4waZWHfH4+YOPYLKRWmbX4Vwxi1Wk5Etal9ILept3PyiGykCV2goRL/uRCpNPKa+ry2GW7LFhCUbWrJVPcZqA9JH1WNU5HIE2iy2dAmPwLpksSuk5sUjcOUItBkahcgaRe4NzTZFkBmzaMybR5BZi/FIRJ/HWXQz1x4Cah+xRYGmXHsIlB03v+g+iiAbGqoJtdkmUumsZasthuu20KxTBJnJH6vLbRzORuIB7Um1UGpjuRyBNlZHqRyBLRtadrOtzdD5vLE43DkCY8niZOGGZqFH4JH90ubMr7j6/WNRoS8HVQh3eBksU6AFXl6VNdQB3No8u6H5H6i2ZDpr2WrrBeOOi1oXGnIUQW0g2VozLKvaeCTnEehyp/NyKxFA2LOhOZ5UPBNP9hizzaePmkoWF24aFoU4spatm/3SiSJIZOZXxkuPRSP65leRbM4e4nhSFuUIUoUhNQvrCIQQPxVCSCHEwMxzIYT4qxBinhDiYyHEXjru0ync2jyEIkikZC5HkLXYQsqWF0awjD6aDQ3VBZItt1Cj2TiudtZQ3oZmiSJIuMYNPMds29305GyyWLNsRaEhizY0twcKZcfN8QjqMh5BPCLM1REkXUYR2OlJZRVozC76qBBiJHAEsMT18jHAuMy/C4B/hL1PWbg3jaxl6z3E4SDh8ghyoY6QmsA9wQyUh4dCnmyZ4yp9eQSpbLw7W0egjTVUsKHZtDCTBYrAY8zWqVyviQkX3VazhZsqCHFEawKtBSNINKvHeA/1WGbcWkt5BMZyBKVyK7Z4UoWy2Rcaug64BHCr6ROBf0uFd4H+QohhGu7VMUqGhgIoglQ6u0BrsqGOkBZIokU9xnv6iidXBIXxZJ+ytSXS1MVy8VvQyBoqpBra5BEUKgKvOQKXR6BdcToo9KSiNRYZHv7GrdAjiEWFuTqCIm/FIsMju4c4CtQi+qgQ4gRguZTyo4K3tgOWup4vy7xW6hoXCCGmCSGmrV27NrgweaGh4HH49lTOI3AeHXc+MLJWUE/1qPlHDIVCd9hnxWJrMkVtPGOt6a4jcCZ/zK2kbAlxlMoRlJfNnSNwSAntukMdhdZjNG6RR1BqQ+skNFTgEcQjFfAI3KEhW+Zb4R6ieS3Eyn1ACPEiMLTEW5cBvwSOLPW1Eq+VnO1SyluAWwAmT54cfEW4Ez1O47kAk78tmc5NOl2hocLJb+OG5i6i8TFubYk0tRmPIK67stiZ/DW91KNmylwoZHMEzm/qrdKzLWNU1MQiCKFqL7R7BEkXownsCg1lFai3cWst4RGYYw0VJost8kBLhdTam7VdvqwikFIeXup1IcRuwFjgI6GSXiOA94UQ+6A8gJGuj48AVoSWtjMUUr8CTv6mtiRD+6pN0aknaNetCGza0Io8ghpfoaE8j0A3a6i9ST1mFYE/2YzCXX8Bnr28pnZlAPSuVUuvJhrJhou0IeGMm+OBWhQacja0mLcQR6kcgbHQUMriZHGhUWRLjkBKOUNKOVhKOUZKOQa1+e8lpVwFPAGck2EP7QfUSylX6hG5A7Rvzq+ODRh+aW5P0TOzSGudGK6O0JCbOqq5T0goFE5+n6Ehd44grrs7ZCl32JYNrciy9eblNbeliEZEzuuMGSiQamtUj1kFalNoqCCkFoA1ZCw0lA1FultMWJIsLhVSq2RoKCCeBo4F5gHNwHmG7pNDWyPU9sk9Dzj5m9qS9KrJTLqYE/PWkCx2NjPITH5LLI32JkD4DnE4aCvlEehaqI5HYGVupdAj8OblNbYl6VkTRTiVstGI/hxBu6MIemdksyk05CJOgBq3ZMeyFXsEBkNDbZvVY11f9RixqA11e6FRFNVKH9WmCDJegfO3BC7SdW1PaG/MTXwIPPmb21P0rFHDoo3VkWjOaXKwKzTU2qAUqNOczOdm29iWzIY5tLOG2pvUxM+TzZINra0REBD356o3tyfpVZNbdjVRAx5BkSKwSIGWrL9o6vDjTW1qs+tVm1uTjUlDm7OjCGozisDG0FCeB2pBaMg6lPIIfGpzKSVN7Ul61eYnP8PnCJpz8Vqwiz7a1pCb+OBbtoaWBH3rVMgrHtHkQTlINOd7Uprd4VBorVfj5lOBNrWn6JmZX4CZZHFbozKEnOZpNnkEbQ2A8KykGlqTCAF9HGMjYtIjaFCPzj5idbLYIvqoVWhrCO0RtCRSSJmzPmp0eQQtG6Guv0s2iyy01vqcKwy+KxY3tybp26PAI9AZGnLi3JDJEViyoRWOm8fq3WaXBwXKwjXiEWjwjo2gZZMaN0eBlhm3hpYEvWtj2XYvMRPj5aBts5pjWbpyzB6PoLUeEDmjrdp9tAO0F3oE/id/Q0s+o0NbaKhlI/TYJvfcppL/toZQ49bQmvMIskcJ6goNNW/IHzfrFGi/3HOPsjW0FiuC9qRmC7dtM9S6FYFN47bJl1Hknl+gPChjbajbNuevBWGRR9CyUc03j11b/aL7KAJnoBwEmPzrmxSDZkAv5VJnk8VhF2qhIrBqYQYPDbUlU7Qm0vTtoRaq9oNDWjZAzwG559EaexRooSLwOG4bmtrZNjO/wBBrqHl98bjZ5BH0cCmCMuSEhpZkdn6BKlo0xhpyvJWsbBF7FEGhUVTNEXSApnXQe3DueYDJv6FJfd5ZqNpyBEUegUU5gub10Gtg7rkP+mh9s/o/5C1UnRZb4Ybms9jNKFo2BAr3rW9syxoaADVREb5yvRBN66Cn+ze1qI6g0COIdD7fNjW3069HzoOKRQ02nWtaA71ce4hNnnvLRui5be559cziEmhvVqGhXoNyrwWY/I4iGNBbLVQtOYJUQlkaBn/EwJASGlfnK1Af9NE1m5UHNaRPbfY1rQeHNBdOfos2tM2roM+Q3PNIeVc9kUrT0JpkG7dHYCLm3bSuYC1YlFspnG/RzjfbNZvbGJIp8IRMiwlT3P5CY9Iqg20d9HCthYje9hfdQxE0ZXoUuS3bANbj2szGNrC32ti05Ag2rwIk9B3uks2SHiatm9QY9XZtaD7CVqsbFBVwsGuhauN5tzdBW33BpmFJSC3ZpjyC3q7OK9Hym8a6RjW/BrkVp25FIKVaD70sDA1JCQ0roY9r3DrZbKWUrG5ozVMERusIGteUMCYtWKegxi1vD6mGhopRv0w99nX1tQsw+ZdtbKF3bYx+mVBHThGEmHibVxbLZoul0ZCRza0IfLARVtYrRTCkb25j03ZwiPOb9huVe83DZlsRNK5Wj30Kxq3MprF8oyqmGrFNjhKrvaCsaa2qFs8bN0s8qZaNSrY+rg2tE+94U3OCtmSawSYVp4NEi7K63Ztt1JJQZLJNha3y9rfMOtV0zGn3UASbFqvHbcbkXgsw+ZdtbGHENj1cVZ/qsS1MDHdT5piGfiNcslkSGtq4UD1uOzb3mg82wuL1TdTGIgzp43bdNcVwN2Wa17rHzZYWExsy45Y338orqaUbFRd8xDa54sJa3cnijZm10L9AgdqwoTnrNO837TgUuWi9KjQbPSBHIY5FDLGGnHXq/k1tMdiyRlGBMQnaIgvdQxFsWKAOVCnabP39iPPXNjLGNemEEOErP9fNVbJt49psbek1tGGBenTL5sOTWrC2iTEDemU53qDx4JD1c9XjgB1dstmiCOarx213yL3mQbZPV22mJhphZJ5HoLmgLCtbid9Uk/UYGOvmqceB43KvdTJuC9cpRTB2oEsRRCNmQkPZtTAmXzaZ3vL9htZnxm2Ae9wyCXRN66F7KILVs9SGEcu5kH5DQ83tSRatb2LnoX3yXo9HRbimc2tmQf/RuSZbYM+GtnqWCgu5E7I+QkOzVzawU8F4aTs4ZM1sxbQqZDTZoEBXfaIot0Xhvs7H7ZPl9ew8tA81sdyyi+vuPrpmtpr7226fey1aA8gtT4VcM1MZQW7ZHKu7hJKavbKBmliE0QMKFKeJjXn1TPU4aLxLtsxmu6Xn3JpZ6tGtQLMeQVURKEgJKz+Eobvlv+4zNDRt0UakhD1H9c97PRTPW0pYNhVGTM5/3RaXc8UHJcbNW2hodUMrK+pbmbRdv7zXtR0csmwqDN9TnejmwJbKYke2iGv5lBk3KSUzltUzsXC8YppzBMunw+AJuU63EOroVq1YOhWGTiow2JwNrVhJfbBkE7sM65vN1YGqI5ASUrrDQys+UN5AXpV98AOutGLpVKU8C5mHoC3E3PUVwYYF0LAcRh+Y/7rPuOjb89cTjwr2Gbtt3uuhknmblqhk8ch9C2TTe/B0IDSuhbWzYfQB+a97VFKvzVFMrYPGDcx7XQuro3mDsoJGFchmA4ujvVlZjz6V+6yVDTS0JosMDa1N5xKtSkmNPij/9RBHt2pDKgkr3oeR++S/3oHVXd+c4IOlm/h8ifkFmo/3TKdh0ZswxsJxkxKWvguj9s9/XbO30vUVwcLX1ePYL+S/7iM0JKXkpdmr2WvUNtnOow5CLdSFr6nHoh/RAo/AkW37g/Nf95jIfnH2aob2rWN8UWhIw8Ehi99Sj6MLxs0GFsfC11X/mSIl1TmL49lPVhERcNj4wXmva80RLJuq2mMXbmhO87ktadkum6Iap43aL//1DqzuVz9bQyotOXjn4vECjR1uAVbPUFTqMQV7SHaz3YLGx+qZqrCycNw0y9b1FcGsx1QM3p1UBF9x+PeXbGTumka+slfxscqhFurMx5S7OWTXwLIZw6zHVfXpsD3yX/eQyF7d0MrLn67h+EnDsgwrB1oODpn5qKooLvKkLDihbPYTUNuv2PDohMWRSKV55P3l7Lf9AAb0rs17Tysdcuaj6lyJsZ/Pf90Gy3bmY6r19I5H5L/ewbjdN2Up2/XvwR4j8z0o51xsrW0mZj6m+grtcEj+6zaEhmY+qsgmOx2T/7pm2bq2Iti0FBa8BpNOz48lg6/Q0N9enke/HnGOmzS86L3AC3XTEljwKkw8uVi2SGzLJu6a1sNnz8Kk03JNrBx4UFK3vL6AlJScvd/oovdCh4aaN8CnT8MuJ+bHuUFtGjK95cauZaPaNCYcn7OyHXTC4nj8wxUs39TCNw4cW/Seml+SdFgLt70JZj4C44/Nb5wGLkXQFu4eQdHeDDMehHFH5jfDg5LjNn3xRt5ZsJ6v7T+aaKTA0MiGhjR5BKkEfHw/7HhYfvEiaE/I+oYj29gvQO9B+e9V6aMuvP03tZHt9bXi9xzrsQxl7ukZK3l1zlouPHiHvK6QDgJ3h3z7RqUAJn+j+L0tTR999+9qkn3u68XvlRm3T5bX8+93FnH65JGMcdH6HMSjkXCsjvf+qU6x2ueCErJtYQtt2h3qPOD9vlv8Xgebxoamdv7w7KfsPqIfhxaEhYAsgyg0E2b6XUpR7f2t4ve29Lh9eI+qxN7vwuL3CsatLZniV49/wuA+tXytpKGh+Vzsjx9QOcbJ5xe/pzkh6xufPAz1S2HfEvPNpUD/O2VJ6FuZOqrSPOqXw/t3we5n5BfPOHBP/kLrLYP5axu59JEZ7D6iH984qNhag4Csofpl8P6/YbfT8msb3LJtqUXZtA7euwV2PQkG7Vz8vtvSKLDINzS1c/G97zOgVy2XHD2++LuEPDikeQO8dzPsfJxivhQi6t406orfN4nmDfDOjbD9IcVMKyi5aSRSab7/3w+ob0lw53l759VbOHBbuCXsEG9oa4S3/6oIE4V5FdiyoaH2JnjzOhixd3GcG/LWqZSSK5+cxcwVDdx6zuTsuSBuOK3OtdQSJNvhjT8rJtNORxW/vyXpo8l2eP3PMGiC8qQKkVmnT3+0hEtfDu8hd02PQEp4+qeAgC/8rPRnykz++WsbOee2KcQigr+euWceRc2NmiA5gud+qR4P/kXp9yNxlXDcEgU+z1+uEooHX1r6/Q5CHBua2vnqre+xsr6VG8/aM6+VshuhDg558QrVE/7Qy0q/H9mClu1Lv1EW95G/Lf1+wabRnkxz0T3v8+a8dfzupInsOrxfya9l25iEqSV47Q+KnXbYr0q/vyUVwRt/URb3Eb8tDpFC9jeVqQRX/W829763hO98cQcO32VI8WfReEYIKOW5YYEat1Kybclxe/fvqqjyiCvzacoOMgr0llfncPDOg4rf94muqQhmPQZznoZDLs2vBHTD+RFLaPNX5qzhKze9TVsyxV3f2CevhL0QvnMEMx5Sidgv/AS2KXZtlWx6qwI9Y84z8NG9cOD3S3sDUDLEMWNZPV/625vMX9vIv86ZzOQx25b+LiEODvnsOeXh7ffd4uS6gy0V4lj4Oky/E/b9TmlvAPJkW7GphTNueYfnZ63myhN25bTJIzu8dOiNbcm78M7fYc+zS1vcBbJVFEunwpvXw+5nlfZUILsWfvP4R9z65kLO3X80Pz+6g7mJxnOxV82A1/4IE06AcUeU/syWCg2tngmvXK084xKeSiKV5r7pqk/Y50b24eazPxf6ll0vNLR+PjzxfRi+V+mYo4MSk7+xLcnVT8/mnveWsPOQPtx67mRGbtuzgwso1MQiNLV7dL02LICnfgQj9oEDf9Tx5/I229KWtXZsWgqPfke5wV+4pOPPuSZ/WzLFza8u4O+vzGNg7xoe/Pb+7F7A4ihEoINDGlbAY9+FIRPh0MvLy1ZJV33zanjofMVKO6QDTwWyv+n/PlrCZa/NJpmS/P2svThu0rBOL+/kCAKdedG8QcnWfxQcdXXHn9sSlm3LRnjkm6r6+phrOvzYJ6uamQhMW7iGy48/lm8cOKaIieZGLKLBI2jbDA9+XVWuH3dtx5/bEqGh9ib1m9b1gy/dUPT2zBX1XPboJ/RYvoYzauDnR+5ITTxa4kL+0LUUQXszPHCuShCfdlcxq8QN1+RPptLcP20p173wGeub2rngC9vz4yN2os7DAKpksYdJ17wB7jlVUb1O/lfO6i8pW4UttPZmePBcxbY59c78dheFyEz+V2cv57evzWD+2iaOnzSMK0/YtYj6WAq+Dw5pb4YHzlHdH0+5o3PZKr2hJdvh4fPVxnHOY8WMFxeW1LczCrj22ZnsMGoifzplEtsP6vjzDmqCdrhNJeDhb6pOqN98Ib8ithAVH7c2uO9speDPfSr/JLcM5q9t5NrnP6N55kLuqIE/njSBCXuXztO5ka0jCJojSKfh8YuU0Xbuk8VsHDcqvU5TSXjoG7BuDnz1oTzZ1je28beX5/HvdxaxTc8a/nrYeHgDatDDoAutCIQQ3wMuBpLA/6SUl2RevxQ4H0gB35dSPhfqRumUmvhrZsJZD5ROELuRmfxPfbCY69+fz7w1jew9ZhtuPXfvIm5yZ/BUUJZogf+eqazucx7vOFzlQDP1q1M447b8fTj9bhiwQ4cflVIyb30744DLHnqf2oFjuOPre3NICbZLR/B1cEg6BY98C5ZNU4p90E6df96x0CrhqkupNoxFb8CXb+kwXDVjWT3Xv/gZNZ/N5x81cMnhO3DEIfuXTAyXQqDQkJTw1A9h/ktwwt9Uu4vOUMkNLZ2Gxy6ExW/CV26FUfm1IMs2NvPXl+by0PRl1MWjXLXnaJgJE4Z07pk7CM0aeuFyFbo98nfFhXeFqCR9VEp45meK1n3cXxSdFZWb++fr8/n324tpS6Y4c59RXHLUePqt/xDeQNseEkoRCCEOAU4EJkkp24QQgzOv7wKcAewKDAdeFELsJKUMpr6khGd+DnP+B8f+ueOYXgarG1qZ8sk6vgRc99xM4kPGc/PZn+OoXYd06naWQtmCsnQaHv22KgM/9c6OY6FuONx90wtTSnjmEjVux/xR8d9LIJFK87+PV3LrmwsYt3Ix19XAZUeP48jP759deF7huY5ASnj2F/DpU3D0H1TdQDlU0rJ96Tcw4wEVqtr99Ly3pJS8OW8dt7+5kFfmrKVfjzhX7zkKZsJREwaARyUAruNQ/SSLX/sDfHA3fPHnsNc55T9fyXF7+TfwyUNw2K9h0qnZlz9auolb31zI0zNWEo0IzjtwLN89eAcGrnkXZuJ5LcQjIeoI3rlJMb/2/Q7sf3H5z1cyR/DW9TDtdjjwh7D3N1m8vom73l7M/VOX0JxI8aVJw/n+YePYcXDGy9ykN88Y1iP4LnCNlLINQEq5JvP6icB9mdcXCiHmAfsA7wS6y9t/han/ggO+D/uU4Eln8NHSTdzx1kKe+nglh4u1fCkOf/nKBHaffJBvBeCgbHdIt4Wx65e9XTRaIY/grRtg6q1wwPdg328XvV3fojjId761iFUNrewwqBen7DMWPoRjdx0IPpUA+Eiuv/03mHKLWpD7fcfbxSuVI5h6G7x5LXzuPPj8T7IvN7UleeSD5dz19iLmrWlkYO8afnLETnz9wDH0WfKK2tB8/qbxmE+P4P3/wKtXwx5f7Zj5VYisIqjEuF2namcO+hGptOTF2au57Y2FTFm0gT61Mc4/aCznHTiGYf0yZzKs9/ebxoIm12c+pth8E74ER/2+NEuoEJVq1vfxg/DiFciJJ/PW6Iu4866pvPTpGqJCcNykYVx8yI6MG1JQJKjZWwmrCHYCPi+EuApoBX4qpZwKbAe86/rcssxrRRBCXABcADBqVIlwz4yH4IVfqQrdw68sejuZSvPszFXc8dYipi/eSO/aGF/bfzTfHdYOT8Eew3t6+9E7QKfdId+9WVkY+3zbm4XhoBIu54yH4MVfw65fgcN/k/fWvDWN/OedRTw4fRnN7SkO3HEAV39lN7640yAin9bDhwTeNGJeDqaZ8ZBSoLt+WdEKvaIS9NE5zyhq8k5HK+9TCJZuaObf7yzivqlL2dyaZLft+nHtabtz3KRh1MYy3l1AJpivHMHcF+HJH8AOh6pEotd5XYkNbc6z2XGrP+T3PPTWIv7zziIWrW9mu/49uPz4XTht8gj61JWoFgfP1eKxIDmCxe/AIxeohndf+VdxNX1HqMQ6XfQm8vELWbPtZM5bfBazpk1lQK8avnfIjnx1v9F5x3TmQXO4r6wiEEK8CAwt8dZlme9vA+wH7A08IITYHig1Q0v+clLKW4BbACZPnpz/mUVvKjbJ6APhpH/k8Wk3Nbdz39Sl/PvtRayob2XUtj351fG7cKoz2eatUh8MOVAd5ghmP6lCG+OPh6Ov9qdsTLucC19XDKHRB8GXb4ZIhFRaNdb79zuLeXPeOmqiEY7ffRjnHzQ2n+MecvKXParS+U1HHQAn3VyaI90RTMe6l02HB8+DYXsgT76NdxZu4o63F/Hi7NVEhOCYiUM578Cx7DWqf7GHGXDcnBxB2dDQig9VUn3ILnBqGaJEIUyHhpa/Dw+dR+vAiVxT8xMe+MNrNLen2GtUf3521HiO2nVIxyFGnwo07pc1tPYz+O8Z0H8knHkfxHuU/05WNrPrdOXcD+h/3xmsSA3iyysuYNR2tfzl1PEcN2lYeSKL5qZzZRWBlPLwjt4TQnwXeERKKd6Ru+cAACAASURBVIEpQog0MBDlAbjJ0yOAFb4kWz0L/nuWOj3r9LuzPcznrdnMHW8t4uH3l9GaSLP/9gO48sSJHDp+cH5fEk2Tv2SOYOkUlYAdMdmfheHAJC1tzWzF2BiwA5xxNxvbBPe/NZ//vLOY5ZtaGNavjp8dtTNn7D2yNAso5OSPZ1hDUsrizXLNbLgv85uecU/nDKFSMBkaWj8f7j2NdO8hPDbhWv550/vMWb2ZbXvVcNHBO/LV/UblwhmdyeZTScW9tFXeuFgx0npuC2c92DlDqKRs5kJDyXULSf37FOrTfThu6XeoX7meE3cfzrkHjCk6e6EkfCpQX3UEm1fB3Ser3+bsh/P7+XuBgXUqpeSdBet59PXp/HDRhWwmyp3b/5nbv7gfnxu9jfcQdqU9gjJ4DDgUeFUIsROKFL8OeAK4VwhxLSpZPA6Y4vmq9cvhnlOgpiec/TDpum14fc4abn9rEa9/tpaaWIST9hjOeQeOZcKwDhaFpslfFPNePx/uPR36DFMWRo03tkO+bIYs24aVcPcpEO/BnMNu59anlvDERytoSyqFefnxEzh8QifWGYSe/A7PO5WW2UWbJ1usDs5+yP+iBHMbWtM6Ev8+mUR7gtObf8yM/61gwrC+/PGUSZyw+3BPNOOgTLCsR9CRImjeoNZCqk3RHft2XpdQEgZCQ+sb23j07U844p2v0S/dyg9qr+EbR+/H6XuP7LDqvHPZPHoEXs8jaGtUyrN5PXz9qfJMPg2ydYaW9hSPfbicO99axLLVa3io7ncMijZRf8bj/HanfctfoBCalVRYRXA7cLsQ4hOgHTg34x3MFEI8AMxC0Uov8swYatmkJn5rA23n/I+H50hue/M15q9tYnCfWn5yxE6cte+o8px2TT+i0x1SSoloXq9kE0JZGL0Glr9AKZiIdbc2IO85hVTzBn7e9488fOciesSjnPK5EZyz/5iiIzg7REhPym2xOeFz2jbDvaeqnu/nPV2e+tsRInqZElJKpny2nEGPnMLw1mWck/w/Ru4ykcsPGMveY3xYZxA8R9BZsjjRCvd9FTYugq89CoNL93cqL5u+0NAny+u5/a2FPP/RYm6L/p7hkTV8cMid3P2F44o6hXqCzxBHrg11Jx5BKqkKxlbPVMbadnv5lwu0jNuyjc38593F3DdlKfUtCXYd2osXR97J0HVLEGfez6BxAZQAQDSz/2kKW4VSBFLKduDsDt67CrjK5wXh/rOR6+by0ITrufqONWxoWsbE7fpy3em7c9xuw/POe+0UmiZ/dqG2tVBz31mZIpknO+Xjl4XTBE+TNm9sbmHTracydMMsvtH+M5bUjuDy48dwyudG0K+Hj1gyaAkNgdrY6uJR16Kcpeo/hu0e6LpKNj2/aVsyxeMfrODON+fxgw2/Y+/opzy28zXccOy5bNffRwzZjZA5giJFkE7D4xfCkrfh5NvKc947Q0hPKp2WvPbZWm55fQHvLFhP7xrBvYP/w6SNn8Ipt7PPxNK0ZG+y+TOKcoZGBx6BlPC/H8O8F+D462GnEg3bvCJEHH7aog3c+sZCnp+lcpVH7TqUr+8/mn1mXYWY/oZK9pehwXcKzV6eVZXFqY2LYNE8fpK8mEff34bDxm/Dtz4/ln3Gbuuf/qkxRyBIE3n8u7D0PZWoKzxuzy80ybayvoU731zIhKmXchLvclO/H3POUedz6PjBnguaiqApNJRMSdeifDEz8TtMN3lDSNptQ2uCe99bwu1vLmTN5lZu6PtfjopOI3Hk1XzlAI8U1nKy+VSgWcVZ2Or8pStVG+LDr4DdTgknWySqKt59zrfWRIrHP1zOv95YyLw1jQztW8cvjx3Pua13U/v2C6pWYOLJ4WTzuRbKsqzevFb1rDroxzD5vJCy+VNS6bTkpU/XcPNr85m+eCP9esS54As78LX9RysD460bYPrtcNCPSreA9yWb3jMmrFIE0dZN/DH9bXpNPpOXDhzjqUS/44vpCb/URCNcEruf2OwnFdVx15NCXU/JlvkRk8EUwZxVm/nna/N54qMVfC/yICfFXmXVnj/kwhN/rUG2cOOW3djS6dyi/PxPwk98CBwaWtPQyu1vLeKedxezuS3JgTsO4L7dprH9+0/BfhcRP6CTnlV+ZfOpQEv2Gpp6qyowmvwNVWCkAz6Obq1vSfCfdxZx59uLWdfYxi7DXB75x3fDy9fCXueqDU2HXODDI+jkhLKPH1CFgLud2nnPKq/w6OW1J9M8/uFybnl9AXPXNLJd/x5c8aVdOG3vkbmjb2c+qmjwu34FDu2gS6wfaM6XWaUImmoG8q1LrmMbDz1tykKT6zR+5SPsF3uS5t3PpecB3wsvFwT2CGauqOfGl+fxzCer6FkT5c87zeSkRY/AHmcz9IQr9MgWkj7qhDriMx/WuyjB97gt3dDMTa/O4+Hpy0mm0xwzcRjf/uL2TKp/FR68WlUzH/m7LSKbg5rC0NCcZ+Hpn8G4o+CYP4WqgSmSr8ymsam5ndvfXMgdby1ic1uSL+40iAu+sD0H7DBAeeTzX4Ynf6jqGI77ix7ZfFq2HbKGFr6hWluMPghO/Ls/WnKHsnXu5bW0p7jnvcXc+sZCVjW0Mn5oH244Yw+O3W1Yflv7Je/BI9+GkfsV0eADIxJRxkd3DA31GjhSjxIAPeGXeS+yz8yreCW1Ozt98Sp66lyUPmSbsayev748lxdmraZPbYzvH7oj3xy9hr73/wHGfhG+dL3GDSNsjiDCXuIz+j//+9yi1C1bGSW1dEMzN748j4ffX0YkIjh18gi+9fnt1YlqKz5UNRYj9lE9hHQsSsjSm/16eXk5gtWzVJO7obvBKbd33rjQLzo5unVDUzu3vrGAu95eRFN7imMmDuXiQ3fMry9ZPUs1fBw03n8dQ2fwOW41pVhWGxbAA1+DbcfCGTmqeWhEooAomm+tiRR3v7uYm19bwLrGNvbbfluuOVkVZBaFsDcsgPvOhH7bwRn3+qdMd4ZojWrwpwFWKQKtCBsaWjcXHjyPzX125OI13+eptMajGzx6Kx8v28T1L87l5U/X0Lcuxo8OV+0M+rWthH+dp9g35bqwGpKtI/RLrOGfNdeR6D2MmtP/o29R5slW+jddtrGZv78yjwenLSMiBF/ddxTfPXhHhvbLLL7Nq1UdQ88BweoYOpUtWMzWUQSR1o3w329ATW/FdOmk02lg+Qp+0w1N7fzztfn8593FtCRSHLfbML536LhihlnTevjv6RDvCV99wH8dQ2eIxADhO0fQlsgogtYG1fBRSjjrftVaWidc49aaSHHve0v4x2vzWbu5jQN2GMBNX92LfcZ2QIVubYB7z1DnbH/1Ieg1QLNs+k467MaKIIRH0FqvJle0hmkH/IOmx1bpORHJo2wL1jbyl+c/438zVtK/Z5yfHbUz5+w/WlVMtzWqQrtkO3z9Pv0TP5bZHIMkoRIt7P3exURoZ/GRtzEuSK1AZ+iAdrtmcyt/fWku909dikBw5j6juPCQHfILwJJtcP/Zqk/+N54tPqg8LJxx82mhxaOCGEmO+uQSaFql6LV9h+uVDfI2jaa2JLe9uZBbXl9Ac3uSE3YfzsWH7siOg0tQjFMJ1cJ882o475nSR6+GgRCZzdbbuEUignhUKI8gnVKtI9bNVfTabbfXKxtANE4qmeDedxfzt5fmsmaz8gD+duae7Ld9Jxu704xy/TwlWxiWYYey1XbP0JBWBE2mpFPw8Ldg40I45wlSTdsBq/x1hywHx0ou+BFXN7RyQ2ZDq41F+P5h4/jW58fm+rOk0/DYdzKtuB8s37Y5jGx+XU4p4fGL6btxFt9M/JgL++yoX7YCBdrUluRfbyzgltcX0J5Mc/reI7nokB0ZXkgBlVLFtpdNUWGNMBTWDmXL/EY+x00IwRXx/zCyYboKVY2YrF82gGgNqUQb97yziL++NI91jW0cucsQfnbUzsUNzdx4/v9UK+6TboYR4U/CKolYra+QWm0sqtbjy7+Dz55RPaG2/6J2saSUJIjx9PuLuHzzJ+w9ZhuuP2MPDtjBQ/3Qq1erUxSP+aMR2QBPeR+v6L6KICgN8uXfwdzn1MlFYw4kPkc1VNXrETibhpr8m1sT/OPV+dz+1kJSacnZ+47i4kPHMahPQVjltWtUj6MjrwpPxewIWcu21d/33roePnmIJXv8lJfe3Ytv6lScDjLjlkomuO+9xVz3wlzWNbZx3G7D+NlRO6scQCm8c6M6ovPgS/WwvkpBiIyF5lOBTr2Ns6Mv8PbQszmgoN21Lkgp2ZwQfDBnBb96fyb7jt2WW875HHuNKuNNvv8feO9m2O8i2ONMI7IBvjwCUEyrndc+C4uvVWy0vb+pXaTpizfw+6c/5e9tgtqadm49ZzKHTRjsjcY+63F4/Y+wx9mwzwXaZcsiGu+e9FGtyLqcPlynTx7OtR/e+3wgxAlSnSFj2aaT7Tw0bSl/fHYO6xrbOHGP4fzkiJ0ZNaBE24rPnlN96Hc/C/a/SJ8sHcjmy7Kd+wK8eCVMPJl1e1wE775DW1LPyUl5EIK0iPLfd+fzfw3KQiu7oS18XdH2djmx8yM6dSBW5y9ZvHQKPHMJr7MXLw+9gAMMiDRjWT1XPjmTX29KUFuT4o7z9ubgUknNItmmqhqQ7Q+GI37T+WfDIuYvxLGLWMKXl1ytmlHqZFYBS9Y38/unZ/PszFUM6lNLr169OWr7/kR2GeLtAqs+gUe/CyP2huOv1SpbEfzub52g+yoC8Oc6rZ4Fj10Eo/ZX7lwGoQ8XLymXsvTveXsel2/8mL1G9ef2r09m0ogOTk7btETFQodMND+5hMhsaB49gk1L1SljQybCCTdSu1aNt9ZQGrBoXRO/eWoWf09Hickk/zpnMoeXs9AaVqij/waMgxNv0scQ6gixGu/j1rReVVz3G8GvGn7I/jrJCMC6xjb+9OwcHpi+lAG9ahi2bV8GbNsHsbOH3EjjWpVP6TtcHR+qk71UCtEa7wq0tYGrU3+mOdqHfqfelavSD4mW9hT/eG0+N782n1hE8KPDd+JbXxhLz1v7eP9NWzbB/V9VyfTTNbKXOkKsGhryBq8827ZGlRCr66tiyK7JlT1BSpMiWFXfyjVPz+JaKUi0tXD96Xtw4h7DO97Qku2qNXI6Baf9218b3aCI1XrzCJLt8NB5imp62l1Q05Pa2GZA33g1tye56ZX53PL6AlXlXVPHqXsMIlrOQksl1Li1N8PX/6efhVMKsTpv881JJDathfNfoP2u9doMjUQqzV1vL+KGF+fSkkjxzYPG8r3DxtH3v9d5C5OmU+rQ+dZNcPZLwZoD+oXX0JCU8MT3GC5Xcf2Qv/CTzs4b9ggpJc/NXM1vn5rF8k0tnLjHcC49ZkKOaebVKHKONq1fppLqfUp17teMKn3UI7y4Ts75r+vnwTlPQJ/8DSbrEYS0cFNpyd3vLuZPz82hPZXmz/E4Z+89jJo9S57Xk8OLv4bl09QxmCaYB6XgdfK/dCUsm5onm1Mpm6X3BYSUkmc/WcVvn5rFivpWvrzndlx6zHjqbu0DyebyF3jh1+r40JNvg0E7h5LFM7wuzLeuU71wjrsWhu9BPPaKFkXw/pKN/PKRGXy6ajMH7zyIy4/fhR2c6vxoXJ2tXQ6v/xkWvKrOQh46MbRMnuDVsp16K8x6jH/3+Dqz4ruFvu2CtY38+omZvDF3HeOH9uG+C/YrZgLFe6jmf+Xw3s3q2NUjfxe+BY1XVFlDHuElNDT9TpjxIBzyfzD280Vv57pDBs8RzF7ZwKWPzODDpZv4/LiB/O6kicRu7QXpMgtz1uPw7k3qBDSvx2DqQKyu/IY2+6nM6WwX5MnmnNgVxiNYVd/K/z32CS/OXs34oX24/ow9c1ztml7Q3tT5BWY+Cu/+XZ1NG7ZPjx/Eassr0IVvKELCxFNUCwl8HO/ZARpaE/zp2Tnc/d5ihvat459f+xxH7VpgkUZrFC26Myx4VbFdJp0Be34tsDy+EfXggS6fDs9eCuOO4qlNp9AjhGGWSKW55fUF3PDSXGpjEa740i6cvd/o0u3ZY3XKO+oMy6YpdtXOx/k7qTAsanpB8zotl+rmiqDjakoAVn4Mz/xclcy7zqZ1o6gFgA+0tKe4/qXPuPWNhfTvEc8PA9X0USGpjrB+Pjx+MQzfC470cZyjDsTqOrceNyxU5fzD9yxq0ZDtnRNgoabTknunLOEPz3xKIp3ml8eO5xsHjs1foPGenSuCdXPVuI3Yx98xmDpQLum5ebWqHN52h7xq8JpohPbCpnMe4HhNVzw5kzWb2zh3/zH89Kid6V1bYlmXM4o2r1KHLQ3cyXweqhA1ZX7Tlo0qn9JnKHz5ZuJ3zwmcg/p42SYueehjPl21meN2G8avT9iFwX06KSyM91Bj0xGaNyjZ+g6HkzRW0XtBbW/VolwDurki6CQ01Nqg8gI9t82cMlY6WRcv1RTMA6Yt2sCPH/iIJRuaOW3yCC49ZgLbuA/sqO0D7R0ogkSrkk1EVNjFdNKpEJ3lCJJtauILSspWF1fj1ZLwxxqav7aRSx+ewZRFGzhgB3WG8ugBJeigNb1U3L8UEi3qOMdYbUY2PYlEz4h34q2kU0oJtDaoAqPaHHc/HvPvEaxvbOOyRz/h2Zmr2GVYX2752mR2H9kB2QA6XwuppFIC7U2qxXpNBzRcU6jtC00LSr8npTI6GlaqQsCe21IXj7K+0V9IpLk9yXUvfMZtby5kUJ9abvna5ziy0GsqhVgdJDswihzZNq+C85/TX9xZDjW9Ot5DfGIrUAQlrCAp4cnvqyMAv/5UpwfMeD4RKYP2ZJobXvqMf7w6n+H9e/Dfb+3H/juUqECs7a0ObCmFZ38Bq2bAmffDNqM93VcrOptgz10GKz9UfVNKnPrUIx4lFhE0tHhv3Xv7Wwv543NzqItF+OPJkzh18oiOk+c1vaBxTen3nvk5rJmlDg3qVyb3YgJ1/WDT4tLvvXqNKsw68SYYsmveWzWljkPtBC9/uppLHppBQ0uCXxwznm8eNLbzU+egc0XwWka2k/4Bgyd4lkMb6vp1vBbeuVEVZh19TbbYrm9dnIXryoQHXZi+eAM/ul8ZZWftO4pfHDOevnUe27LE6zrOEbz9N1XQdvQfYDtDxXadoVxUwQe6tyKI9yi9oU29VcWRD78CRnfO3q7xkSyeu3ozP7z/Q2auaOC0ySO4/PhdclXBhajto1zeQnz8AEy/Aw78Aex8dNl7GkHPASr8U4hPHoap/1Jx0PHHlfyqEIK+PeI0tJZXBCvrW/jJAx/x9vz1HD5hCL//ysTO3XRQoaFECY9gxkOZPvQ/gh0NFduVQ4/+sPKj4tfnvQiv/0kVGO351aK3veYImtuTXPW/2dzz3hLGD+3Df87fp+OjWgsRjZf28ua9pBLEe5wNe5zl7Vq6UdtHeUqFWPKuSvpP+JLK92TQt0eMeg+GRiKV5m8vzeXGV+YxvH+P0sngcqjrXzq3suQ9ePEKmHAC7Pttf9fUhdrean+T4Wucurci6DVItYpwY8UH8NwvYdyRcMAPyl7COa+2qb3jUEc6LbnrnUVc88yn9KqNlU7WFaKmt6oPcGPNp/DkD1Qtg67WzUHQYxtomZ7/2rp58MQPVOz98Cs6/Xq/HnHqWzrvXvrUxyv45SMzSKQk13xlN07fe6S3qs1SntT6+WrcRu6nkv5bCnX9ixOL9ctVDcjgCXDsn0p/LR5lU3PnG9sHSzby4wc+YtH6Ji74wvb85Midsol5T+ixjYpnS5mLYzesUDUgnchWEdT2hbYGRat1QrRN6xT9t//Iog62/XrEaWhJqONjO5gzC9Y28qP7P+SjZfWcvNcIrjihE6OsM/QaCIkmFTZzQmZN6xVtuv8oOPHGyuYF3KjpDcjShpFPdG9F0HuQOlXMQfMGFUPuNRi+/E9PBUZ18Sh9amOs3Vw6Zr6yvoWfPfgxb85bx6HjB3PNybuVt2pBWY/N63PPnVqGeM9MC2KNHUX9oue2SjZn02hvVrJF43DqHWVl69sj3qHF1tCa4IrHZ/LIB8vZfWR/rj99D8Z21BqiFHoPhcbVKq4djeXyKdE4nHKb+eKnztCjv7LQUomMBZ6ps0i2qRqQmhIV48DgPrXMWF6a0ZNIpbnx5Xnc+Mo8hvat495vdhBqLIc+QxVXv2Wj+n2T7aqtdLJN1c50IFtFUNcPkNC+Wf2dTikF1bwezn8+834O/XrESUtobEsWbe5SSv47ZSm/fWoWNbEIfz9rL46bNCy4bD0zYeOmdUoROHUWTetKylZRODUeTWtDX6p7K4Jeg9VkSiVV4vXRb2eSTs/5KpQZ3LeW1Q3FccInP1rBZY8qq/b3X96NM/fxaNUC9B+tZGvbrDT74xfCus/g7EfMdJ/0g54DVTy5dZOycp/8gToI/OyHPHWf3KZnvKTinLJwAz+6/0NW1rfwg8PGcfGhO+Yf4OEF/bZTbX0bV0Hf7eCZSzL5lPv0d8b0C6ejacMKldt5/jJliJxyBwwc1+HXBvetY11jG4lUOm883FbtV/bcjitO3NV7bLtItkx9TONqNfdf+JVqwnfKHWaaF/qB87ttXAzDJsErv1eH4Bx/PQzfo+jj/XsqEsD6xvY8RbCusY1fPPwxL85ew0E7DuTPp+6eKwwLil6ZorWmteo3ffUaJduX/lpStoqi/yj1WBhZCIDurQgG7AhIWPspfHwfzH1enazks4vi0H51LN+UYw7UNyf41ROf8PiHK9hjZH+u82vVQi7Run6+6iM063HV02WHQ/xdxwSchOHqmSpOO+MBFXLxGHsfO7AXUxZuIJ2WRCKCtmSK61+cy82vzWfkNj158DsH8LnRARkWzuRfP0/leZzzaXc+Jtj1dGLQePW49lOVF5hyi8qnTPxKp18b2rcOKVX9xMhte5JOS+6ZsoSr/jeLung0vFULsM3YnGxL3oX3/gH7fresbBXBgEyn2nWfwdo58MafYa9zOjxzePvMWluwrjHbaPDFWav5xSMf09Ca5PLjd+G8A8YEP7fbDUeBr/5EUTWdZnJ7nRP+2mGR3UPmhb5U91YEo/ZTj/eeBg3LVZfCyef7vsykEf259Y0FNLQmeHPuOn79xEw2NLXz4yN24sKDdyjP2OhMtvvOUrLtfiYc8H3/1zGB4Xup9hyPXKBk2+3UDussSmHnIX1obk/x2ZrNNLenuPThGcxZvZnTJ4/k8i/tUprn7hUj91XnEjzybeUVTDhhy+ZT3BiyK8R6wFM/hoZl6rjJw68s+7VJI1R4YcrCDbQl01z+2Ce8s2A9nx+nrNohfTUcoDNskmKZPPNzxboad6T5ZnJeMXAn5Xk+/3/KY3GayXWAcZlzEz5csolJI/pzzTOf8tD0ZYwf2oe7v7kv44dqPDhn2+2VN/Xy71RoefSBcNyft1xewI3+o6HvCHjvltCXEjJExlkIsQdwM1AHJIELpZRThIqP3AAcCzQDX5dSvl/uepMnT5bTpk0LLE9JvPBr+OBu1U30i78I1Hjs42WbOOHGt4hFBMm0ZMKwvvzplElM3C5kfPCFXyuG0F7nqgRsxEfyzzRe/xO8c5Nikhx+pa/Y+7rGNg685mUiQtCSSDG4Ty3XnLwbh4732MGxHN66QVH3Jp6sisYqXS/QGd6+UVmNu35ZNS/0UAOSSksO+8urLN3YQiot6VMb45fHTeAMrwl0r5h+l9rQxh+r6JiV6FvlFVNvUx1sdz4ajr+ubC3DObdP4a1564gKQUpKLjx4B7536LhsQaNWzHhIEUx2OFQl1Ws7Ob+h0vj4QXjy/9u72xCpqjiO499frlpR5lOa1C5qZOCr2kx90YNZ9CBlvRIzSCgSJESFMsXodT6QGAQlKSiJlWglkWhKFka7ouZT2OZGiubWIkEJkWT9e3HONtfdWde8szPTnP8Hhrlz7mU49z9n73/PfThnLnq5bZ+ZXfZkFnkTwXZghZltlTQFWGBmk+LyHEIimACsNLMJPX1frySCEvn40Gm+bD3DxNFDuk5O7brYfewMm78+xdgRA5g+viFfLyABre1nWffVCYYPuJJp4+q7zkXhLtAeJ3G6qm8fZkxoYPT1ZRhUsFr9dR7V9a1oItgGrDGz9yQ9CTxmZjMkvQXsMrMNcbsWYJKZtV3s+6o5ETjnXLWSlCsR5P03bR6wTdJy4Ar4d26NG4GTme1OxbIuiUDSLGAWQENDQ87qOOec+696TASSdgDFno5aDNwPzDezTZKmAauBBwgj0XRWtOthZquAVRB6BJdYb+eccyXSYyIws27vGZS0Duh4PHcj8HZcPgXUZza9CTh9mXV0zjnXi/Je8TwN3BuXJwPH4vIW4GkFE4Ffe7o+4JxzrjLyXiN4DlgpqQ74g3iuH/iEcMdQK+H20eJPhjjnnKu4XInAzHYDXR7TtXAr0vN5vts551x5+M3wzjmXOE8EzjmXuFwPlJWapLNAS6XrUSWGAqWZmfr/z2NR4LEo8FgU3Gpmlz32RbU999+S5+m4WiJpr8ci8FgUeCwKPBYFknINyeCnhpxzLnGeCJxzLnHVlgjyD6xdOzwWBR6LAo9FgceiIFcsqupisXPOufKrth6Bc865MitrIpC0RlK7pCOZssGSPpV0LL4PiuWS9LqkVkmHJDWWs669rZtYLJP0bdzfDyQNzKxbFGPRIumhytS6dxSLRWbdC5JM0tD4Obl2EcvnxN/+G0lLM+VJtQtJt0lqknRA0l5J42N5zbYLSfWSPpN0NP7+c2N56Y6dZla2F3AP0AgcyZQtBRbG5YXAkrg8BdhKGNJ6ItBczrpWKBYPAnVxeUkmFmOBg0B/YBTwPdCn0vvQm7GI5fXANuAEMDThdnEfsAPoHz8PS7VdANuBRzJtYVettwtgBNAYl68Fvou/fcmOnWXtEZjZF8AvnYofB9bG5bXAE5nydRY0AQMljShPTXtfsViY2XYzOx8/NhGGYQKPXAAAAkJJREFU74YQi3fN7JyZ/UAYzG982Srby7ppFwArgAVcOJdFcu0CmA28ambn4jbtsTzFdmFAx+z011EY3r5m24WZtVmc893MzgJHCRN9lezYWQ3XCIZbHKI6vg+L5d3NcpaKZwhZHRKMhaSpwI9mdrDTquRiAYwB7pbULOlzSXfG8hRjMQ9YJukksBxYFMuTiIWkkcDtQDMlPHZWQyLoziXPclZrJC0GzgPrO4qKbFazsZB0NWEGvFeKrS5SVrOxiOqAQYRu/ovA+5JEmrGYTZgVsR6YT5gVERKIhaRrgE3APDP77WKbFim7aCyqIRH83NFtie8d3d4kZzmTNBN4FHjK4gk/0ovFzYRz3gclHSfs735JN5BeLCDs8+bY1d8D/E0YZyfFWMwENsfljRROhdV0LCT1JSSB9WbWsf8lO3ZWQyLYQvhxie8fZcqTmuVM0sPAS8BUM/s9s2oLMF1Sf0mjgFuAPZWoYzmY2WEzG2ZmI81sJKFhN5rZTyTYLoAPCTMAImkM0I8w2FpS7SJKblbE2PtbDRw1s9cyq0p37Czz1e8NQBvwJ+GP+1lgCLCT8IPuBAbHbQW8QbgT4jAwrpx1rVAsWgnn9g7E15uZ7RfHWLQQ75qolVexWHRaf5zCXUMptot+wDvAEWA/MDnVdgHcBewj3C3VDNxR6+0i7rMBhzLHhimlPHb6k8XOOZe4ajg15JxzroI8ETjnXOI8ETjnXOI8ETjnXOI8ETjnXOI8ETjnXOI8ETjnXOI8ETjnXOL+Aa7e+8u4Nw1sAAAAAElFTkSuQmCC\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": [
    "## Fit to target dynamics"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 0: Best parameters EK=-80.72970268 mV, ENa=93.28475067 mV, g_kd=11.94292419 uS, g_na=18.65090046 uS (error: 535.13167236 mV^2)\n",
      "Round 1: Best parameters EK=-80.72970268 mV, ENa=93.28475067 mV, g_kd=11.94292419 uS, g_na=18.65090046 uS (error: 535.13167236 mV^2)\n",
      "Round 2: Best parameters EK=-80.72970268 mV, ENa=93.28475067 mV, g_kd=11.94292419 uS, g_na=18.65090046 uS (error: 535.13167236 mV^2)\n",
      "Round 3: Best parameters EK=-79.93536198 mV, ENa=98.90291024 mV, g_kd=4.90649368 uS, g_na=12.4663984 uS (error: 521.20696045 mV^2)\n",
      "Round 4: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 5: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 6: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 7: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 8: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 9: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 10: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 11: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 12: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 13: Best parameters EK=-79.27860726 mV, ENa=90.67703194 mV, g_kd=4.02634386 uS, g_na=10.23542998 uS (error: 443.4301886 mV^2)\n",
      "Round 14: Best parameters EK=-80.4990432 mV, ENa=101.66477246 mV, g_kd=3.6033472 uS, g_na=12.51018389 uS (error: 403.40016701 mV^2)\n",
      "Round 15: Best parameters EK=-80.4990432 mV, ENa=101.66477246 mV, g_kd=3.6033472 uS, g_na=12.51018389 uS (error: 403.40016701 mV^2)\n",
      "Round 16: Best parameters EK=-80.4990432 mV, ENa=101.66477246 mV, g_kd=3.6033472 uS, g_na=12.51018389 uS (error: 403.40016701 mV^2)\n",
      "Round 17: Best parameters EK=-80.4990432 mV, ENa=101.66477246 mV, g_kd=3.6033472 uS, g_na=12.51018389 uS (error: 403.40016701 mV^2)\n",
      "Round 18: Best parameters EK=-80.4990432 mV, ENa=101.66477246 mV, g_kd=3.6033472 uS, g_na=12.51018389 uS (error: 403.40016701 mV^2)\n",
      "Round 19: Best parameters EK=-80.4990432 mV, ENa=101.66477246 mV, g_kd=3.6033472 uS, g_na=12.51018389 uS (error: 403.40016701 mV^2)\n",
      "Round 20: Best parameters EK=-79.51921081 mV, ENa=101.66477246 mV, g_kd=3.92580048 uS, g_na=10.54136112 uS (error: 384.44272758 mV^2)\n",
      "Round 21: Best parameters EK=-79.84819886 mV, ENa=98.90291024 mV, g_kd=3.5189427 uS, g_na=11.77498632 uS (error: 332.37870658 mV^2)\n",
      "Round 22: Best parameters EK=-80.12376037 mV, ENa=98.90291024 mV, g_kd=3.26929368 uS, g_na=11.77498632 uS (error: 305.30684053 mV^2)\n",
      "Round 23: Best parameters EK=-80.12376037 mV, ENa=98.90291024 mV, g_kd=3.26929368 uS, g_na=11.77498632 uS (error: 305.30684053 mV^2)\n",
      "Round 24: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 25: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 26: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 27: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 28: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 29: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 30: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 31: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 32: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 33: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 34: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 35: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 36: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 37: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 38: Best parameters EK=-80.13456744 mV, ENa=111.76223105 mV, g_kd=3.52687664 uS, g_na=11.5911939 uS (error: 164.58388185 mV^2)\n",
      "Round 39: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 40: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 41: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 42: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 43: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 44: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 45: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 46: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 47: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 48: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 49: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 50: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 51: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 52: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 53: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 54: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 55: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 56: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 57: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 58: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 59: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 60: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 61: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 62: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 63: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 64: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 65: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 66: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 67: Best parameters EK=-80.57629328 mV, ENa=92.3130725 mV, g_kd=3.36284425 uS, g_na=13.35824019 uS (error: 106.929503 mV^2)\n",
      "Round 68: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 69: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 70: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 71: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 72: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 73: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 74: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 75: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 76: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 77: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 78: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 79: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 80: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 81: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 82: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 83: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 84: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 85: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 86: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 87: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 88: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 89: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 90: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 91: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 92: Best parameters EK=-80.16013747 mV, ENa=104.25254492 mV, g_kd=3.73747797 uS, g_na=12.65996397 uS (error: 85.0442541 mV^2)\n",
      "Round 93: Best parameters EK=-79.80793375 mV, ENa=116.05993741 mV, g_kd=5.20046815 uS, g_na=12.63195592 uS (error: 80.91336816 mV^2)\n",
      "Round 94: Best parameters EK=-79.80793375 mV, ENa=116.05993741 mV, g_kd=5.20046815 uS, g_na=12.63195592 uS (error: 80.91336816 mV^2)\n",
      "Round 95: Best parameters EK=-79.80793375 mV, ENa=116.05993741 mV, g_kd=5.20046815 uS, g_na=12.63195592 uS (error: 80.91336816 mV^2)\n",
      "Round 96: Best parameters EK=-79.80793375 mV, ENa=116.05993741 mV, g_kd=5.20046815 uS, g_na=12.63195592 uS (error: 80.91336816 mV^2)\n",
      "Round 97: Best parameters EK=-79.80793375 mV, ENa=116.05993741 mV, g_kd=5.20046815 uS, g_na=12.63195592 uS (error: 80.91336816 mV^2)\n",
      "Round 98: Best parameters EK=-79.80793375 mV, ENa=116.05993741 mV, g_kd=5.20046815 uS, g_na=12.63195592 uS (error: 80.91336816 mV^2)\n",
      "Round 99: Best parameters EK=-79.80793375 mV, ENa=116.05993741 mV, g_kd=5.20046815 uS, g_na=12.63195592 uS (error: 80.91336816 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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ENa': 116.05993741 * mvolt,\n",
       " 'EK': -79.80793375 * mvolt,\n",
       " 'g_kd': 5.20046815 * usiemens,\n",
       " 'g_na': 12.63195592 * usiemens}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ENa: 0.05 (target) 0.11605993741166562 (fit)\n",
      "EK: -0.08 (target) -0.07980793374984355 (fit)\n",
      "g_kd: 6e-06 (target) 5.200468147161877e-06 (fit)\n",
      "g_na: 2e-05 (target) 1.2631955922035222e-05 (fit)\n"
     ]
    }
   ],
   "source": [
    "for key in res.keys():\n",
    "    print(\"{:s}: {} (target) {} (fit)\".format(key, hodgkin_huxley_params[key], res[key]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$20.000000000000004\\,\\mathrm{\\mu}\\,\\mathrm{S}$"
      ],
      "text/plain": [
       "20. * usiemens"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hodgkin_huxley_params[\"g_na\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$6.0\\,\\mathrm{\\mu}\\,\\mathrm{S}$"
      ],
      "text/plain": [
       "6. * usiemens"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hodgkin_huxley_params[\"g_kd\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "traces = fitter.generate_traces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7ab1d7a240>"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7ab1b69f60>"
      ]
     },
     "execution_count": 22,
     "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Real traces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from allensdk.core.cell_types_cache import CellTypesCache\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from brian2.units import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "ctc = CellTypesCache(manifest_file='../../alleninstitute_cell_db/manifest.json')\n",
    "\n",
    "excitatory_neuron_test_id = 327962063\n",
    "\n",
    "data_set = ctc.get_ephys_data(excitatory_neuron_test_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/allensdk/core/nwb_data_set.py:386: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  sweep_metadata[field] = stim_details[field].value\n",
      " [py.warnings]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "{'aibs_stimulus_amplitude_pa': 400.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "11\n",
      "{'aibs_stimulus_amplitude_pa': 350.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "12\n",
      "{'aibs_stimulus_amplitude_pa': 370.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "14\n",
      "{'aibs_stimulus_amplitude_pa': 380.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "15\n",
      "{'aibs_stimulus_amplitude_pa': 390.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "16\n",
      "{'aibs_stimulus_amplitude_pa': 390.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "18\n",
      "{'aibs_stimulus_amplitude_pa': 390.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "20\n",
      "{'aibs_stimulus_amplitude_pa': -110.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "21\n",
      "{'aibs_stimulus_amplitude_pa': -90.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "23\n",
      "{'aibs_stimulus_amplitude_pa': -50.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "24\n",
      "{'aibs_stimulus_amplitude_pa': -29.999998, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "25\n",
      "{'aibs_stimulus_amplitude_pa': -10.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "26\n",
      "{'aibs_stimulus_amplitude_pa': 10.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "27\n",
      "{'aibs_stimulus_amplitude_pa': 29.999998, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "28\n",
      "{'aibs_stimulus_amplitude_pa': 50.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "29\n",
      "{'aibs_stimulus_amplitude_pa': 70.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "30\n",
      "{'aibs_stimulus_amplitude_pa': 90.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "31\n",
      "{'aibs_stimulus_amplitude_pa': 110.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "32\n",
      "{'aibs_stimulus_amplitude_pa': 130.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "33\n",
      "{'aibs_stimulus_amplitude_pa': 150.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "34\n",
      "{'aibs_stimulus_amplitude_pa': 170.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "35\n",
      "{'aibs_stimulus_amplitude_pa': 190.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "36\n",
      "{'aibs_stimulus_amplitude_pa': 40.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "37\n",
      "{'aibs_stimulus_amplitude_pa': 50.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "38\n",
      "{'aibs_stimulus_amplitude_pa': 50.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "39\n",
      "{'aibs_stimulus_amplitude_pa': 50.0, 'aibs_stimulus_name': 'Long Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "4\n",
      "{'aibs_stimulus_amplitude_pa': 800.0, 'aibs_stimulus_name': 'Ramp', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "40\n",
      "{'aibs_stimulus_amplitude_pa': 115.875, 'aibs_stimulus_name': 'Noise 1', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "41\n",
      "{'aibs_stimulus_amplitude_pa': 117.875, 'aibs_stimulus_name': 'Noise 2', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "42\n",
      "{'aibs_stimulus_amplitude_pa': 115.875, 'aibs_stimulus_name': 'Noise 1', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "43\n",
      "{'aibs_stimulus_amplitude_pa': 117.875, 'aibs_stimulus_name': 'Noise 2', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "44\n",
      "{'aibs_stimulus_amplitude_pa': 115.875, 'aibs_stimulus_name': 'Noise 1', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "46\n",
      "{'aibs_stimulus_amplitude_pa': 115.875, 'aibs_stimulus_name': 'Noise 1', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "49\n",
      "{'aibs_stimulus_amplitude_pa': -200.0, 'aibs_stimulus_name': 'Square - 0.5ms Subthreshold', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "50\n",
      "{'aibs_stimulus_amplitude_pa': -200.0, 'aibs_stimulus_name': 'Square - 0.5ms Subthreshold', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "53\n",
      "{'aibs_stimulus_amplitude_pa': 0.0, 'aibs_stimulus_name': 'Test', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "7\n",
      "{'aibs_stimulus_amplitude_pa': 100.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "8\n",
      "{'aibs_stimulus_amplitude_pa': 200.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n",
      "9\n",
      "{'aibs_stimulus_amplitude_pa': 300.0, 'aibs_stimulus_name': 'Short Square', 'gain': 0.01, 'initial_access_resistance': 18.771164, 'seal': 1.31769}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for sweep_id in data_set.get_experiment_sweep_numbers():\n",
    "    print(sweep_id)\n",
    "    print(data_set.get_sweep_metadata(sweep_id))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_sweep_ids = data_set.get_experiment_sweep_numbers()\n",
    "long_square_sweep_ids = list(filter(lambda sweep_id: data_set.get_sweep_metadata(sweep_id)[\"aibs_stimulus_name\"] == \"Long Square\", all_sweep_ids))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/allensdk/core/nwb_data_set.py:255: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  return f[ds].value\n",
      " [py.warnings]\n"
     ]
    }
   ],
   "source": [
    "sweeps_with_spiking = list(filter(lambda sweep_id: len(data_set.get_spike_times(sweep_id))>0, long_square_sweep_ids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[28, 29, 30, 31, 32, 33, 34, 35, 37, 38, 39]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweeps_with_spiking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/allensdk/core/nwb_data_set.py:108: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  stimulus = stimulus_dataset.value\n",
      " [py.warnings]\n",
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/allensdk/core/nwb_data_set.py:109: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  response = swp['response']['timeseries']['data'].value\n",
      " [py.warnings]\n",
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/allensdk/core/nwb_data_set.py:125: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  swp_idx_start = swp['stimulus']['idx_start'].value\n",
      " [py.warnings]\n",
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/allensdk/core/nwb_data_set.py:126: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  swp_length = swp['stimulus']['count'].value\n",
      " [py.warnings]\n",
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/allensdk/core/nwb_data_set.py:135: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  exp_idx_start = exp['stimulus']['idx_start'].value\n",
      " [py.warnings]\n",
      "WARNING    /home/pfeiffer/Applications/miniconda2/envs/interneuron_polarity/lib/python3.7/site-packages/allensdk/core/nwb_data_set.py:136: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  exp_length = exp['stimulus']['count'].value\n",
      " [py.warnings]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sweep_id = sweeps_with_spiking[2]\n",
    "\n",
    "sweep_data = data_set.get_sweep(sweep_id)\n",
    "sweep_len = sweep_data[\"index_range\"][1]\n",
    "\n",
    "ts = np.arange(0, sweep_len+1) * 1.0/sweep_data[\"sampling_rate\"]\n",
    "current = sweep_data[\"stimulus\"][:]*1e12\n",
    "voltage = sweep_data[\"response\"][:]*1e3\n",
    "\n",
    "fig,axes = plt.subplots(2,1)\n",
    "axes[0].plot(ts, current)\n",
    "axes[1].plot(ts, voltage)\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_xlim(0.9, 2.1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling_rate = sweep_data[\"sampling_rate\"]\n",
    "start_idx = int(0.8*sweep_data[\"sampling_rate\"])\n",
    "end_idx = int(2.2*sweep_data[\"sampling_rate\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_input_trace(sweep_id):\n",
    "    sweep_data = data_set.get_sweep(sweep_id)\n",
    "    return sweep_data[\"stimulus\"][:]\n",
    "\n",
    "def get_output_trace(sweep_id):\n",
    "    sweep_data = data_set.get_sweep(sweep_id)\n",
    "    return sweep_data[\"response\"][:]\n",
    "\n",
    "selected_sweeps = [28, 29, 30]\n",
    "    \n",
    "input_traces = [get_input_trace(sweep_id)[start_idx:end_idx] for sweep_id in selected_sweeps]\n",
    "output_traces = [get_output_trace(sweep_id)[start_idx:end_idx] for sweep_id in selected_sweeps]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_traces_as_array = np.vstack(tuple(input_traces))*amp\n",
    "output_traces_as_array = np.vstack(tuple(output_traces))*volt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test fitting with trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import brian2modelfitting as mf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "area = 20000 * umetre ** 2\n",
    "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitter = mf.TraceFitter(dt=1.0/(sampling_rate*hertz),\n",
    "                        model=hodgkin_huxley_eqs+parameter_eqs,\n",
    "                        input_var=\"I\",\n",
    "                        output_var=\"v\",\n",
    "                        input=input_traces_as_array,\n",
    "                        output=output_traces_as_array,\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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = mf.NevergradOptimizer(num_workers=3)\n",
    "metric = mf.MSEMetric()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Round 0: Best parameters EK=-75.94370134 mV, ENa=101.09358748 mV, g_kd=9.77489769 uS, g_na=40.02886521 uS (error: 490.68526208 mV^2)\n",
      "Round 1: Best parameters EK=-79.96226185 mV, ENa=81.74897498 mV, g_kd=8.86173338 uS, g_na=32.28912981 uS (error: 467.78003885 mV^2)\n",
      "Round 2: Best parameters EK=-79.96226185 mV, ENa=81.74897498 mV, g_kd=8.86173338 uS, g_na=32.28912981 uS (error: 467.78003885 mV^2)\n"
     ]
    }
   ],
   "source": [
    "res, error = fitter.fit(n_rounds = 3, 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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitted_traces = fitter.generate_traces()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7aab589fd0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, len(selected_sweeps), figsize=(16, 9))\n",
    "for idx, ax in enumerate(axes):\n",
    "    ax.plot( fitted_traces[idx,:], label=\"fit\")\n",
    "    ax.plot( output_traces_as_array[idx,:], linestyle= '--', label=\"data\")\n",
    "axes[-1].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some obs:\n",
    "- the data shows spike trains on top of a relatively high plateau, this could be due to morphology, if the axon was remote\n",
    "- passive data like leak and capacitance could be taken from subthreshold data\n",
    "- how do they choose the ionic currents to fit"
   ]
  }
 ],
 "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
}