{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from scipy import linalg\n",
"from scipy.integrate import odeint\n",
"sns.set()\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"t = 0;dt = 0.005; s4 = 0;\n",
"chi=2; ci=chi; s5=0.7; factive=0.2; s7=0.01; s6=1-factive-s7-s4;\n",
"tnow =[]\n",
"ixnow = []\n",
"#S1=[]\n",
"\n",
"for i in range(1,150000+1):\n",
" t = t + dt\n",
" ni = 0\n",
" if t>30 and t<80:\n",
" ni = 100\n",
" ci = 2\n",
" if t>130 and t<180:\n",
" ni = 75\n",
" ci = 2\n",
" if t>230 and t<280:\n",
" ni = 50\n",
" ci = 2\n",
" if t>330 and t<380:\n",
" ni = 30\n",
" ci = 2\n",
" if t>430 and t<480:\n",
" ni = 15\n",
" ci = 2\n",
" if t>530 and t<580:\n",
" ni = 10\n",
" ci = 2\n",
" if t>630 and t<680:\n",
" ni = 5\n",
" ci = 2\n",
" if t>730 and t<780:\n",
" ni = 0\n",
" ci = 2\n",
" #if t>280 and t<310:\n",
" #ni = 100\n",
" #ci = 0\n",
" #if t>310 and t<340:\n",
" #ni = 0\n",
" #ci = 0\n",
" #if t>340 and t<370:\n",
" #ni = 100\n",
" #ci = 0\n",
" #if t>370 and t<400:\n",
" #ni = 0\n",
" #ci = 0\n",
" #if t>400 and t<430:\n",
" #ni = 100\n",
" #ci = chi\n",
" #if t>430 and t<460:\n",
" #ni = 0\n",
" #ci = 0\n",
" \n",
" \n",
" f3n=ni**2.5/(ni**2.5+17**2.5)\n",
" kcon1=0.1\n",
" kcoff1=0.05\n",
" kcon2=20 \n",
" kcoff2=0.3\n",
" #kinact=0.2 \n",
" #kinact=1\n",
" kinact=0 \n",
" #kinact=1\n",
" #kinact=0.2\n",
" \n",
" s5=s5+ (s6*ci*kcon1-s5*kcoff1+factive*f3n*kinact-s5*0.3) *dt\n",
" factive=factive+(s7*ci*kcon2-factive*kcoff2+s5*0.15-factive*f3n*kinact)*dt\n",
" s7=s7+(factive*kcoff2+s6*0.1-s7*ci*kcon2-s7*f3n*kinact*25)*dt\n",
" s6=1-s5-factive-s7\n",
" \n",
" #S1.append(s1)\n",
" \n",
" incx=factive*f3n\n",
" tnow.append(t)\n",
" ixnow.append(incx)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Data frame for the data extract\n",
"df = pd.DataFrame()\n",
"df['time'] = tnow\n",
"df['current'] = ixnow"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(150000, 2)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" time | \n",
" current | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0.005 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 0.010 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.015 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.020 | \n",
" 0.0 | \n",
"
\n",
" \n",
" | 4 | \n",
" 0.025 | \n",
" 0.0 | \n",
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\n",
" \n",
"
\n",
"