{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.append('../')\n",
    "sys.path.append('../anmodel')\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import anmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on package anmodel:\n",
      "\n",
      "NAME\n",
      "    anmodel\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    analysis\n",
      "    channels\n",
      "    models\n",
      "    params\n",
      "    search\n",
      "\n",
      "FILE\n",
      "    /Users/tyamada/ueda_lab/averaged_neuron/anmodel/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(anmodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ion = anmodel.params.Ion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sleep_ion = ion.sleep_ion\n",
    "an_sleep = anmodel.models.ANmodel(ion=True, concentration=sleep_ion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1c52b290>]"
      ]
     },
     "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": [
    "an_sleep.set_sws_params()\n",
    "s, info = an_sleep.run_odeint()\n",
    "plt.plot(s[4999:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "awake_ion = ion.awake_ion\n",
    "an_awake = anmodel.models.ANmodel(ion=True, concentration=awake_ion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x151ead1750>]"
      ]
     },
     "execution_count": 9,
     "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": [
    "an_awake.set_sws_params()\n",
    "s, info = an_awake.run_odeint()\n",
    "plt.plot(s[4999:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hu': array([0.16502991, 0.11759439, 0.02257834, ..., 0.10359893, 0.1996568 ,\n",
       "        0.1996568 ]),\n",
       " 'tcur': array([2.01277532e+00, 3.02158546e+00, 4.01594080e+00, ...,\n",
       "        9.99804192e+03, 9.99906815e+03, 1.00000664e+04]),\n",
       " 'tolsf': array([4.48163865e-313, 4.48163865e-313, 4.48163865e-313, ...,\n",
       "        4.48163865e-313, 4.48163865e-313, 4.48163865e-313]),\n",
       " 'tsw': array([1.00000000e+00, 1.00000000e+00, 1.00000000e+00, ...,\n",
       "        9.99539839e+03, 9.99539839e+03, 9.99539839e+03]),\n",
       " 'nst': array([    12,     18,     69, ..., 148288, 148295, 148300], dtype=int32),\n",
       " 'nfe': array([    25,     43,    163, ..., 359709, 359723, 359733], dtype=int32),\n",
       " 'nje': array([   0,    0,    0, ..., 6761, 6761, 6761], dtype=int32),\n",
       " 'nqu': array([4, 4, 5, ..., 4, 5, 5], dtype=int32),\n",
       " 'imxer': -1,\n",
       " 'lenrw': 212,\n",
       " 'leniw': 30,\n",
       " 'mused': array([1, 1, 1, ..., 1, 1, 1], dtype=int32),\n",
       " 'message': 'Integration successful.'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}