rule_30_circuit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.integrate import odeint\n",
    "import math\n",
    "import random\n",
    "import time\n",
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 25})\n",
    "matplotlib.rc('xtick', labelsize=20) \n",
    "matplotlib.rc('ytick', labelsize=20) \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d_m=math.log(2)/5\n",
    "d_p=math.log(2)/600\n",
    "factor=60 #conv sec to min\n",
    "features={\n",
    "    #RNAP binding free energy, kcal/mol\n",
    "    'dG_RNAP':{\"offtarget\":-4,\n",
    "                 \"Phlf\":-4.7,\n",
    "                 \"Srpr\":-4.4,\n",
    "                 \"BetI\":-4.6,#\n",
    "                 \"HlyllR_1\":-4.7,\n",
    "                 \"HlyllR_2\":-4.7,\n",
    "                 \"AmtR\":-4.8,\n",
    "                 \"LacI\":-4.5,#\n",
    "                 \"TetR\":-4.5,#\n",
    "                 \"AraC_1\":-4.5,#\n",
    "                 \"AraC_2\":-4.5,#\n",
    "                 \"Con_A\":-4,#\n",
    "                 \"Con_I\":-4,#\n",
    "                 \"KanR\":-4,#\n",
    "                 \"StrepR\":-4#\n",
    "                },#tbd\n",
    "    #TF binding free energy, kcal/mol\n",
    "    'dG_TF':{\"offtarget\":-6,\n",
    "                 \"Phlf\":-9.2,\n",
    "                 \"Srpr\":-7.4,\n",
    "                 \"BetI\":-8,#\n",
    "                 \"HlyllR_1\":-6.4,\n",
    "                 \"HlyllR_2\":-6.4,\n",
    "                 \"AmtR\":-8.9,\n",
    "                 \"TetR\":-8,#\n",
    "                 \"AraC_1\":-8,#\n",
    "                 \"AraC_2\":-8,#\n",
    "                 \"LacI\":-8#\n",
    "                }, #tbd\n",
    "    #inactive binding free energy\n",
    "    'dG_Inactive':{'TetR':-6.0,#\n",
    "                  'LacI':-6.0,#\n",
    "                  'AraC':-6.0},#\n",
    "    #mRNA deg rate, 1/min\n",
    "    'd_mRNA':{\"Phlf\":d_m,#\n",
    "                  \"Srpr\":d_m,#\n",
    "                  \"BetI\":d_m,#\n",
    "                  \"HlyllR\":d_m,#\n",
    "                  \"AmtR\":d_m,#\n",
    "                  \"YFP\":d_m,#\n",
    "                  \"LacI_TetR\":d_m,#\n",
    "                  \"AraC\":d_m,#\n",
    "                  \"KanR\":d_m,#\n",
    "                  \"StrepR\":d_m #\n",
    "                }, #tbd github/cidarlab?\n",
    "    #Translation rate, protein/mRNA/min\n",
    "    'beta':{\"Phlf\":10,#\n",
    "                  \"Srpr\":10,#\n",
    "                  \"BetI\":10,#\n",
    "                  \"HlyllR\":10,#\n",
    "                  \"AmtR\":10,#\n",
    "                  \"YFP\":10,#\n",
    "                  \"LacI\":10,#\n",
    "                  \"AraC\":10,#\n",
    "                  \"TetR\":10,#\n",
    "                  \"KanR\":10,#\n",
    "                  \"StrepR\":10#\n",
    "                }, #tbd\n",
    "    #number of TF binding sites in promoter\n",
    "    'N_ontarget':{\"Phlf\":2,#\n",
    "                  \"Srpr\":2,#\n",
    "                  \"BetI\":2,#\n",
    "                  \"HlyllR_1\":2,#\n",
    "                  \"HlyllR_2\":2,#\n",
    "                  \"AmtR\":2,#\n",
    "                  \"LacI\":2,#\n",
    "                  \"TetR\":2,#\n",
    "                  \"AraC_1\":2,#\n",
    "                  \"AraC_2\":2#\n",
    "                }, #tbd\n",
    "    # number of free RNAP\n",
    "    'N_RNAP':750,#\n",
    "    # number of free Ribosomes\n",
    "    'N_RIBO':1000, #\n",
    "    #protein deg rates, 1/min\n",
    "    'd_Protein':{\"Phlf\":d_p,#\n",
    "                  \"Srpr\":d_p,#\n",
    "                  \"BetI\":d_p,#\n",
    "                  \"HlyllR\":d_p,#\n",
    "                  \"AmtR\":d_p,#\n",
    "                  \"YFP\":d_p,#\n",
    "                  \"LacI\":d_p,#\n",
    "                  \"AraC\":d_p,#\n",
    "                  \"TetR\":d_p,#\n",
    "                  \"KanR\":d_p,#\n",
    "                  \"StrepR\":d_p#\n",
    "                }, #tbd\n",
    "    #growth rate, 1/min\n",
    "    'mu':math.log(2)/45, #tbd\n",
    "    #max promoter transcription rate mRNA/promoter/min\n",
    "    'a_max':{\"Phlf\":2.84*factor,\n",
    "                  \"Srpr\":2.92*factor,\n",
    "                  \"BetI\":2*factor,#\n",
    "                  \"HlyllR_1\":2.41*factor,\n",
    "                  \"HlyllR_2\":2.41*factor,\n",
    "                  \"AmtR\":1.42*factor,\n",
    "                  \"LacI\":2*factor,#\n",
    "                  \"TetR\":2*factor,#\n",
    "                  \"AraC_1\":2*factor,#\n",
    "                  \"AraC_2\":2*factor,#\n",
    "                  \"Con_A\":2*factor,\n",
    "                  \"Con_I\":2*factor,\n",
    "                  \"KanR\":2*factor,\n",
    "                  \"StrepR\":2*factor\n",
    "                },\n",
    "    #genome length\n",
    "    'genome_len':4600000,\n",
    "    #copy number of circuit    \n",
    "    \"copy_number\":{\"Phlf\":10,\n",
    "                  \"Srpr\":10,\n",
    "                  \"BetI\":10,\n",
    "                  \"HlyllR\":10,\n",
    "                  \"AmtR\":10,\n",
    "                  \"YFP\":5,\n",
    "                  \"LacI_TetR\":10,\n",
    "                  \"AraC\":10,\n",
    "                  \"KanR\":10,\n",
    "                  \"StrepR\":5},\n",
    "    #cooperativity\n",
    "    'co_op':{'AraC':2,#\n",
    "             'LacI':2,#\n",
    "             'TetR':2#\n",
    "                },\n",
    "    #inducer concentrations used mM\n",
    "    'inducers':{'AraC':0,\n",
    "                'TetR':0,\n",
    "                'LacI':0\n",
    "                },\n",
    "    #kd of inducers\n",
    "    'iKd':{'AraC': 0.005,#\n",
    "           'LacI': 0.05,#\n",
    "           'TetR':0.005 #\n",
    "                },\n",
    "    #kd for hill functions\n",
    "    'Kd':{'AraC':5,#\n",
    "          'LacI':5,#\n",
    "          'TetR':5},#\n",
    "    # temperature\n",
    "    'T':310\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Partition_fxn(a_max,dG_RNAP,N_RNAP,dG_TFoff,dG_Active,dG_Inactive, \n",
    "                  N_Bindingsites,N_Repressors,fractionActive,genome_len,T):\n",
    "    RT=T*1.987/1000 #temperature(kelvin)*R(kcal/K/mol)\n",
    "\n",
    "    P_RNAP=N_RNAP*math.exp(-dG_RNAP/RT)/genome_len\n",
    "    P_Active=(1 + N_Repressors * fractionActive * math.exp(- (dG_Active-dG_TFoff)) / genome_len)**N_Bindingsites - 1\n",
    "    P_Inactive=(1 + N_Repressors * (1 - fractionActive) * math.exp(-(dG_Inactive-dG_TFoff)) / genome_len)**N_Bindingsites -1;\n",
    "    \n",
    "    Pbound=P_RNAP / (1 + P_RNAP + P_Active + P_Inactive)\n",
    "    \n",
    "    alpha=a_max*Pbound\n",
    "    return alpha\n",
    "\n",
    "def Hill_function_R(a_max,Kd,cooperativity,N_Repressor):\n",
    "    alpha = a_max/(1 + (N_Repressor/Kd)**cooperativity);\n",
    "    return alpha\n",
    "\n",
    "def Hill_function_A(a_max,Ka,cooperativity,N_Activator):\n",
    "    alpha = a_max*((N_Activator)**cooperativity)/(Ka**cooperativity + (N_Repressor)**cooperativity);\n",
    "    return alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def rule_30_circuit(y,t,features):\n",
    "    n_strep=y[0]\n",
    "    m_strep=y[1]\n",
    "    p_strep=y[2]\n",
    "    \n",
    "    n_kan=y[3]\n",
    "    m_kan=y[4]\n",
    "    p_kan=y[5]\n",
    "    \n",
    "    n_ara=y[6]\n",
    "    m_ara=y[7]\n",
    "    p_ara=y[8]\n",
    "    \n",
    "    n_lac=y[9]\n",
    "    m_lac=y[10]\n",
    "    p_lac=y[11]\n",
    "    p_tet=y[12]\n",
    "    \n",
    "    n_Phlf=y[13]\n",
    "    m_Phlf=y[14]\n",
    "    p_Phlf=y[15]\n",
    "    \n",
    "    n_Srpr=y[16]\n",
    "    m_Srpr=y[17]\n",
    "    p_Srpr=y[18]\n",
    "    \n",
    "    n_BetI=y[19]\n",
    "    m_BetI=y[20]\n",
    "    p_BetI=y[21]\n",
    "\n",
    "    n_HlyllR=y[22]\n",
    "    m_HlyllR=y[23]\n",
    "    p_HlyllR=y[24]\n",
    "    \n",
    "    n_AmtR=y[25]\n",
    "    m_AmtR=y[26]\n",
    "    p_AmtR=y[27]\n",
    "    \n",
    "    n_YFP=y[28]\n",
    "    m_YFP=y[29]\n",
    "    p_YFP=y[30]\n",
    "    \n",
    "    fractionActive_ara=1/(1+features[\"inducers\"][\"AraC\"]/features['iKd']['AraC']) \n",
    "    fractionActive_lac=1/(1+features[\"inducers\"][\"LacI\"]/features['iKd']['LacI'])\n",
    "    fractionActive_tet=1/(1+features[\"inducers\"][\"TetR\"]/features['iKd']['TetR'])\n",
    "    \n",
    "    #print fractionActive_lac\n",
    "    \n",
    "    alpha_strep=Partition_fxn(features['a_max']['StrepR'],features['dG_RNAP']['StrepR'],features['N_RNAP'],0,0,0, \n",
    "                  0,0,0,features['genome_len'],features['T'])\n",
    "    alpha_kan=Partition_fxn(features['a_max']['KanR'],features['dG_RNAP']['KanR'],features['N_RNAP'],0,0,0, \n",
    "                  0,0,0,features['genome_len'],features['T'])\n",
    "    alpha_con_a=Partition_fxn(features['a_max']['Con_A'],features['dG_RNAP']['Con_A'],features['N_RNAP'],0,0,0, \n",
    "                  0,0,0,features['genome_len'],features['T'])\n",
    "    alpha_con_L_T=Partition_fxn(features['a_max']['Con_I'],features['dG_RNAP']['Con_I'],features['N_RNAP'],0,0,0, \n",
    "                  0,0,0,features['genome_len'],features['T'])\n",
    "    alpha_ara_1=Partition_fxn(features['a_max']['AraC_1'],features['dG_RNAP']['AraC_1'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['AraC_1'],features['dG_Inactive']['AraC'], \n",
    "                  features['N_ontarget']['AraC_1'],p_ara,fractionActive_ara,features['genome_len'],features['T'])\n",
    "    alpha_ara_2=Partition_fxn(features['a_max']['AraC_2'],features['dG_RNAP']['AraC_2'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['AraC_2'],features['dG_Inactive']['AraC'], \n",
    "                  features['N_ontarget']['AraC_2'],p_ara,fractionActive_ara,features['genome_len'],features['T'])\n",
    "    alpha_tet=Partition_fxn(features['a_max']['TetR'],features['dG_RNAP']['TetR'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['TetR'],features['dG_Inactive']['TetR'], \n",
    "                  features['N_ontarget']['TetR'],p_tet,fractionActive_tet,features['genome_len'],features['T'])\n",
    "    alpha_lac=Partition_fxn(features['a_max']['LacI'],features['dG_RNAP']['LacI'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['LacI'],features['dG_Inactive']['LacI'], \n",
    "                  features['N_ontarget']['LacI'],p_lac,fractionActive_lac,features['genome_len'],features['T'])\n",
    "    alpha_Phlf=Partition_fxn(features['a_max']['Phlf'],features['dG_RNAP']['Phlf'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['Phlf'],0, \n",
    "                  features['N_ontarget']['Phlf'],p_Phlf,1,features['genome_len'],features['T'])\n",
    "    alpha_Srpr=Partition_fxn(features['a_max']['Srpr'],features['dG_RNAP']['Srpr'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['Srpr'],0, \n",
    "                  features['N_ontarget']['Srpr'],p_Srpr,1,features['genome_len'],features['T'])\n",
    "    alpha_BetI=Partition_fxn(features['a_max']['BetI'],features['dG_RNAP']['BetI'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['BetI'],0, \n",
    "                  features['N_ontarget']['BetI'],p_BetI,1,features['genome_len'],features['T'])\n",
    "    alpha_AmtR=Partition_fxn(features['a_max']['AmtR'],features['dG_RNAP']['AmtR'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['AmtR'],0, \n",
    "                  features['N_ontarget']['AmtR'],p_AmtR,1,features['genome_len'],features['T'])\n",
    "    alpha_HlyllR_1=Partition_fxn(features['a_max']['HlyllR_1'],features['dG_RNAP']['HlyllR_1'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['HlyllR_1'],0, \n",
    "                  features['N_ontarget']['HlyllR_1'],p_HlyllR,1,features['genome_len'],features['T'])\n",
    "    alpha_HlyllR_2=Partition_fxn(features['a_max']['HlyllR_2'],features['dG_RNAP']['HlyllR_2'],features['N_RNAP'],features['dG_TF']['offtarget'],features['dG_TF']['HlyllR_2'],0, \n",
    "                  features['N_ontarget']['HlyllR_2'],p_HlyllR,1,features['genome_len'],features['T'])\n",
    "    #print alpha_lac, alpha_tet ,alpha_ara_1\n",
    "    #print alpha_HlyllR_1\n",
    "    \n",
    "    dy=np.zeros(len(y))\n",
    "    \n",
    "    dy[0]=0\n",
    "    dy[1]=alpha_strep*n_strep-features['d_mRNA']['StrepR']*m_strep-features['mu']*m_strep\n",
    "    dy[2]=features['beta']['StrepR']*m_strep-features['d_Protein']['StrepR']*p_strep-features['mu']*p_strep\n",
    "\n",
    "    dy[3]=0\n",
    "    dy[4]=alpha_kan*n_kan-features['d_mRNA']['KanR']*m_kan-features['mu']*m_kan\n",
    "    dy[5]=features['beta']['KanR']*m_kan-features['d_Protein']['KanR']*p_kan-features['mu']*p_kan\n",
    "                                                                                    \n",
    "    dy[6]=0\n",
    "    dy[7]=alpha_con_a*n_ara-features['d_mRNA']['AraC']*m_ara-features['mu']*m_ara\n",
    "    dy[8]=features['beta']['AraC']*m_ara-features['d_Protein']['AraC']*p_ara-features['mu']*p_ara\n",
    "    \n",
    "    dy[9]=0\n",
    "    dy[10]=alpha_con_L_T*n_lac-features['d_mRNA']['LacI_TetR']*m_lac-features['mu']*m_lac\n",
    "    dy[11]=features['beta']['LacI']*m_lac-features['d_Protein']['LacI']*p_lac-features['mu']*p_lac\n",
    "\n",
    "    dy[12]=features['beta']['TetR']*m_lac-features['d_Protein']['TetR']*p_tet-features['mu']*p_tet\n",
    "                                                                                    \n",
    "    dy[13]=0\n",
    "    dy[14]=(alpha_Srpr+alpha_BetI)*n_Phlf-features['d_mRNA']['Phlf']*m_Phlf-features['mu']*m_Phlf\n",
    "    dy[15]=features['beta']['Phlf']*m_Phlf-features['d_Protein']['Phlf']*p_Phlf-features['mu']*p_Phlf\n",
    "    \n",
    "    dy[16]=0\n",
    "    dy[17]=alpha_ara_1*n_Srpr-features['d_mRNA']['Srpr']*m_Srpr-features['mu']*m_Srpr\n",
    "    dy[18]=features['beta']['Srpr']*m_Srpr-features['d_Protein']['Srpr']*p_Srpr-features['mu']*p_Srpr\n",
    "\n",
    "    dy[19]=0\n",
    "    dy[20]=alpha_HlyllR_1*n_BetI-features['d_mRNA']['BetI']*m_BetI-features['mu']*m_BetI\n",
    "    dy[21]=features['beta']['BetI']*m_BetI-features['d_Protein']['BetI']*p_BetI-features['mu']*p_BetI\n",
    "                                                                        \n",
    "    dy[22]=0\n",
    "    dy[23]=(alpha_tet+alpha_lac)*n_HlyllR-features['d_mRNA']['HlyllR']*m_HlyllR-features['mu']*m_HlyllR\n",
    "    dy[24]=features['beta']['HlyllR']*m_HlyllR-features['d_Protein']['HlyllR']*p_HlyllR-features['mu']*p_HlyllR\n",
    "\n",
    "    dy[25]=0\n",
    "    dy[26]=(alpha_ara_2+alpha_HlyllR_2)*n_AmtR-features['d_mRNA']['AmtR']*m_AmtR-features['mu']*m_AmtR\n",
    "    dy[27]=features['beta']['AmtR']*m_AmtR-features['d_Protein']['AmtR']*p_AmtR-features['mu']*p_AmtR\n",
    "    \n",
    "    dy[28]=0\n",
    "    dy[29]=(alpha_Phlf+alpha_AmtR)*n_YFP-features['d_mRNA']['YFP']*m_YFP-features['mu']*m_YFP\n",
    "    dy[30]=features['beta']['YFP']*m_YFP-features['d_Protein']['YFP']*p_YFP-features['mu']*p_YFP\n",
    "    \n",
    "    return dy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plots the solution to circuit model\n",
    "def circuit_char(time_points, solution):\n",
    "    \n",
    "    fig, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(24, 24))\n",
    "    \n",
    "    ax1.set_ylim((0, 3000))\n",
    "    ax2.set_ylim((0, 3000))\n",
    "    ax3.set_ylim((0, 3000))\n",
    "\n",
    "    \n",
    "    ax1.set_title('YFP mRNA levels')\n",
    "    ax1.plot(time_points, solution[:, 29], 'orange')\n",
    "    ax1.set_xlabel('Time (minutes)')\n",
    "    ax1.set_ylabel('mRNAs')\n",
    "\n",
    "    ax2.set_title('YFP protein levels')\n",
    "    ax2.plot(time_points, solution[:, 30], 'orange')\n",
    "    ax2.set_xlabel('Time (minutes)')\n",
    "    ax2.set_ylabel('Proteins')\n",
    "    \n",
    "    ax3.set_title('TF mRNA levels')\n",
    "    ax3.plot(time_points, solution[:, 14], 'r')\n",
    "    ax3.plot(time_points, solution[:, 17], 'y')\n",
    "    ax3.plot(time_points, solution[:, 20], 'g')\n",
    "    ax3.plot(time_points, solution[:, 23], 'b')\n",
    "    ax3.plot(time_points, solution[:, 26], 'm')\n",
    "    ax3.legend(['Phlf', 'Srpr', 'BetI','HlyllR', 'AmtR'])\n",
    "    ax3.set_xlabel('Time (minutes)')\n",
    "    ax3.set_ylabel('mRNAs')\n",
    "\n",
    "    ax4.set_title('TF protein levels')\n",
    "    ax4.plot(time_points, solution[:, 15], 'r')\n",
    "    ax4.plot(time_points, solution[:, 16], 'y')\n",
    "    ax4.plot(time_points, solution[:, 21], 'g')\n",
    "    ax4.plot(time_points, solution[:, 24], 'b')\n",
    "    ax4.plot(time_points, solution[:, 27], 'm')\n",
    "    ax4.legend(['Phlf', 'Srpr', 'BetI','HlyllR', 'AmtR'])\n",
    "    ax4.set_xlabel('Time (minutes)')\n",
    "    ax4.set_ylabel('Proteins')\n",
    "\n",
    "    fig.subplots_adjust(hspace=0.5)\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solves circuit model function\n",
    "def solve_circuit(induction_conditions,features):\n",
    "    \n",
    "    t = np.linspace(0, 1000, 1000)\n",
    "\n",
    "    h0 = np.zeros(31)\n",
    "    h0[0]=features['copy_number']['StrepR']\n",
    "    h0[3]=features['copy_number']['KanR']\n",
    "    h0[6]=features['copy_number']['AraC']\n",
    "    h0[9]=features['copy_number']['LacI_TetR']\n",
    "    h0[13]=features['copy_number']['Phlf']\n",
    "    h0[16]=features['copy_number']['Srpr']\n",
    "    h0[19]=features['copy_number']['BetI']\n",
    "    h0[22]=features['copy_number']['HlyllR']\n",
    "    h0[25]=features['copy_number']['AmtR']\n",
    "    h0[28]=features['copy_number']['YFP']\n",
    "    \n",
    "    features['inducers']['LacI']=0\n",
    "    features['inducers']['TetR']=0\n",
    "    features['inducers']['AraC']=0\n",
    "    \n",
    "    y = odeint(rule_30_circuit, h0, t, (features,))\n",
    "\n",
    "    # circuit_char(t, y)\n",
    "\n",
    "    h0=y[-1,:]\n",
    "\n",
    "    # induction\n",
    "    \n",
    "    features['inducers']['LacI']=induction_conditions[0]\n",
    "    features['inducers']['TetR']=induction_conditions[1]\n",
    "    features['inducers']['AraC']=induction_conditions[2]\n",
    "    \n",
    "    y = odeint(rule_30_circuit, h0, t, (features,))\n",
    "\n",
    "    circuit_char(t, y)\n",
    "                                                  \n",
    "#solve_circuit([0,0,0],features)\n",
    "#print \"done!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x9dKq+vwWHttqd6sk31vpQQBbhOsZ1gbXMqwdrmdYG1zLsHZM2/V8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xgo7z1vKLd31hw7qNy2lafwj0AzsF9EjOlind+2KrMxdSRJkiRJkiRJwMiB7sBQFREnAxMo0qrM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\nS4bFhYVdG0Zv6xo5OY4yliRJk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2FQLkmSJEmSJGWs6upqPv74Y0IIjBgxgrPOOoubbrop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KSVFJc0703pBSorahltr6WmobatncsLlnHufaaHq8sW4jdY111DXUNe/rG+u3Ote0T3302XZJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11230d410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done!\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "matplotlib.rcParams.update({'font.size': 25})\n",
    "solve_circuit([1,0,0],features)\n",
    "print \"done!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}