FIGURES FOR SIR EPIDEMIC THRESHOLDS AND THRESHOLDS ESTIMATIONS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.use('TkAgg')\n",
    "from pylab import *\n",
    "import numpy as np\n",
    "import csv\n",
    "from scipy import interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def extraer_valores(lista_beta,lista_R):\n",
    "    \n",
    "    f = interpolate.interp1d(lista_beta,lista_R)\n",
    "    lista_beta_adap = np.linspace(lista_beta[0], lista_beta[-1], num=1000)\n",
    "    lista_R_adap = f(lista_beta_adap)\n",
    "    \n",
    "    k = 0\n",
    "\n",
    "    while lista_R_adap[k] < 0.0025 and k < len(lista_R_adap)-1:\n",
    "\n",
    "        k += 1\n",
    "    a10 = lista_beta_adap[k]  \n",
    "    \n",
    "    return a10\n",
    "\n",
    "def extraer_valores2(lista_beta,lista_R):\n",
    "    \n",
    "    f = interpolate.interp1d(lista_beta,lista_R)\n",
    "    lista_beta_adap = np.linspace(lista_beta[0], lista_beta[-1], num=1000)\n",
    "    lista_R_adap = f(lista_beta_adap)\n",
    "    \n",
    "    k = 0\n",
    "\n",
    "    while lista_R_adap[k] < 0.0012 and k < len(lista_R_adap)-1:\n",
    "\n",
    "        k += 1\n",
    "    a10 = lista_beta_adap[k]  \n",
    "    \n",
    "    return a10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#######################################################################################################\n",
    "### MELLIN s= 2.5, gamma = 2.0\n",
    "#######################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s=2.5, gamma = 2.0\n",
      "Fila: 0.089049049049\n",
      "Columna: 0.108668668669\n",
      "Chess: 1.28780780781\n",
      "Random: 0.0713913913914\n",
      "Patches: 0.0537337337337\n",
      "Strips: 0.0753153153153\n",
      "Monocrop: 0.04\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print 's=2.5, gamma = 2.0'\n",
    "\n",
    "cmap = plt.get_cmap('jet_r')\n",
    "colores = [cmap(i) for i in np.linspace(0, 1, 7)]\n",
    "marca = ['o','s','>','d','H','.','+']\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ROW_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "    \n",
    "\n",
    "i=0\n",
    "print 'Fila:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Row}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_COLUMNA_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "i=1\n",
    "print 'Columna:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Column}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ALTERNADOS_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=2\n",
    "print 'Chess:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Alter}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ALEATORIO_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "i=3\n",
    "print 'Random:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Random}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_PATCHES_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=4\n",
    "print 'Patches:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Patches}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_STRIPS_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=5\n",
    "print 'Strips:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Patches}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_MONOCROP_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "\n",
    "with open(name) as inputfile:\n",
    "\n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=6\n",
    "print 'Monocrop:', extraer_valores2(lista_beta,lista_R)\n",
    "semilogx(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "        markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=2,\\\n",
    "        label=r'$\\mathrm{SIN}$')\n",
    "\n",
    "\n",
    "xticks(fontsize = 20)\n",
    "yticks(fontsize = 20)\n",
    "\n",
    "xlabel(r'$\\beta/\\mu$',fontsize=30)\n",
    "ylabel(r'$R$',fontsize=30)\n",
    "\n",
    "ylim([0,1])\n",
    "xlim([0,1])\n",
    "gca().xaxis.set_major_locator( MaxNLocator(nbins = 5,prune= 'lower' ))\n",
    "#legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\",frameon=False,fontsize = 20,numpoints = 1)\n",
    "#tight_layout(rect=[0,0,0.75,1])\n",
    "\n",
    "tight_layout()\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#######################################################################################################\n",
    "### MELLIN s=4.0, gamma=2.0\n",
    "#######################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s=4.0, gamma = 2.0\n",
      "Fila: 0.12044044044\n",
      "Columna: 0.112592592593\n",
      "Chess: 2.0\n",
      "Random: 0.14986986987\n",
      "Patches: 0.0694294294294\n",
      "Strips: 0.0968968968969\n",
      "Monocrop: 0.0498098098098\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print 's=4.0, gamma = 2.0'\n",
    "\n",
    "cmap = plt.get_cmap('jet_r')\n",
    "colores = [cmap(i) for i in np.linspace(0, 1, 7)]\n",
    "marca = ['o','s','>','d','H','.','+']\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ROW_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "    \n",
    "\n",
    "i=0\n",
    "print 'Fila:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Row}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_COLUMNA_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "i=1\n",
    "print 'Columna:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Column}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ALTERNADOS_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=2\n",
    "print 'Chess:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Alter}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ALEATORIO_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "i=3\n",
    "print 'Random:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Random}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_PATCHES_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=4\n",
    "print 'Patches:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Patches}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_STRIPS_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=5\n",
    "print 'Strips:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Patches}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_MONOCROP_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5_ATENUADO.txt'\n",
    "resultado = []\n",
    "\n",
    "with open(name) as inputfile:\n",
    "\n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=6\n",
    "print 'Monocrop:', extraer_valores2(lista_beta,lista_R)\n",
    "semilogx(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "        markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=2,\\\n",
    "        label=r'$\\mathrm{SIN}$')\n",
    "\n",
    "\n",
    "xticks(fontsize = 20)\n",
    "yticks(fontsize = 20)\n",
    "\n",
    "xlabel(r'$\\beta/\\mu$',fontsize=30)\n",
    "ylabel(r'$R$',fontsize=30)\n",
    "\n",
    "ylim([0,1])\n",
    "xlim([0,1])\n",
    "gca().xaxis.set_major_locator( MaxNLocator(nbins = 5,prune= 'lower' ))\n",
    "#legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\",frameon=False,fontsize = 20,numpoints = 1)\n",
    "#tight_layout(rect=[0,0,0.75,1])\n",
    "\n",
    "tight_layout()\n",
    "\n",
    "show()"
   ]
  },
  {
   "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": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#######################################################################################################\n",
    "### MELLIN s=2.5, gamma=1.0 (there are no trap crops)\n",
    "#######################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s=2.5, gamma = 1.0\n",
      "Fila: 0.0596196196196\n",
      "Columna: 0.0655055055055\n",
      "Chess: 0.122402402402\n",
      "Random: 0.0635435435435\n",
      "Patches: 0.0458858858859\n",
      "Strips: 0.0635435435435\n",
      "Monocrop: 0.04\n"
     ]
    }
   ],
   "source": [
    "print 's=2.5, gamma = 1.0'\n",
    "\n",
    "cmap = plt.get_cmap('jet_r')\n",
    "colores = [cmap(i) for i in np.linspace(0, 1, 7)]\n",
    "marca = ['o','s','>','d','H','.','+']\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ROW_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "    \n",
    "\n",
    "i=0\n",
    "print 'Fila:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Row}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_COLUMNA_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "i=1\n",
    "print 'Columna:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Column}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ALTERNADOS_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=2\n",
    "print 'Chess:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Alter}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ALEATORIO_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "i=3\n",
    "print 'Random:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Random}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_PATCHES_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=4\n",
    "print 'Patches:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Patches}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_STRIPS_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=5\n",
    "print 'Strips:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Patches}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_MONOCROP_MELLIN_SEXY_s_2p5_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "\n",
    "with open(name) as inputfile:\n",
    "\n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=6\n",
    "print 'Monocrop:', extraer_valores2(lista_beta,lista_R)\n",
    "semilogx(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "        markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=2,\\\n",
    "        label=r'$\\mathrm{SIN}$')\n",
    "\n",
    "\n",
    "xticks(fontsize = 20)\n",
    "yticks(fontsize = 20)\n",
    "\n",
    "xlabel(r'$\\beta/\\mu$',fontsize=30)\n",
    "ylabel(r'$R$',fontsize=30)\n",
    "\n",
    "ylim([0,1])\n",
    "xlim([0,1])\n",
    "gca().xaxis.set_major_locator( MaxNLocator(nbins = 5,prune= 'lower' ))\n",
    "#legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\",frameon=False,fontsize = 20,numpoints = 1)\n",
    "#tight_layout(rect=[0,0,0.75,1])\n",
    "\n",
    "tight_layout()\n",
    "\n",
    "show()"
   ]
  },
  {
   "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": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#######################################################################################################\n",
    "### MELLIN s=4.0, gamma=1.0 (there are no trap crops)\n",
    "#######################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s=4.0, gamma = 1.0\n",
      "Fila: 0.110630630631\n",
      "Columna: 0.0968968968969\n",
      "Chess: 0.53049049049\n",
      "Random: 0.092972972973\n",
      "Patches: 0.0635435435435\n",
      "Strips: 0.0949349349349\n",
      "Monocrop: 0.0498098098098\n"
     ]
    }
   ],
   "source": [
    "print 's=4.0, gamma = 1.0'\n",
    "\n",
    "cmap = plt.get_cmap('jet_r')\n",
    "colores = [cmap(i) for i in np.linspace(0, 1, 7)]\n",
    "marca = ['o','s','>','d','H','.','+']\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ROW_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "    \n",
    "\n",
    "i=0\n",
    "print 'Fila:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Row}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_COLUMNA_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "i=1\n",
    "print 'Columna:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Column}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ALTERNADOS_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=2\n",
    "print 'Chess:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Alter}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_ALEATORIO_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "i=3\n",
    "print 'Random:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Random}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_PATCHES_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=4\n",
    "print 'Patches:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Patches}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_STRIPS_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "    \n",
    "with open(name) as inputfile:\n",
    "    \n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=5\n",
    "print 'Strips:', extraer_valores(lista_beta,lista_R)\n",
    "plot(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "         markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=1,\\\n",
    "         label=r'$\\mathrm{Patches}$')\n",
    "\n",
    "\n",
    "name =  'SIR_THRESHOLDS_MEAN_MONOCROP_MELLIN_SEXY_s_4_F20_C50_mu_0p5_phi_0p01_SEED_BORDE_beta_0p5.txt'\n",
    "resultado = []\n",
    "\n",
    "with open(name) as inputfile:\n",
    "\n",
    "    for line in inputfile:\n",
    "\n",
    "        resultado.append(line.strip().split(','))\n",
    "\n",
    "    lista_R = [float(x.strip(' \"')) for x in resultado[0]]\n",
    "\n",
    "lista_beta = [i/0.5 for i in np.logspace(log10(0.02), log10(1.0), num=50, endpoint=True, base=10.0)]\n",
    "\n",
    "\n",
    "\n",
    "i=6\n",
    "print 'Monocrop:', extraer_valores2(lista_beta,lista_R)\n",
    "semilogx(lista_beta,lista_R,color=colores[i],linewidth=0,marker = marca[i], markersize=10,\\\n",
    "        markerfacecolor='None', markeredgecolor=colores[i], markeredgewidth=3, markevery=2,\\\n",
    "        label=r'$\\mathrm{SIN}$')\n",
    "\n",
    "\n",
    "xticks(fontsize = 20)\n",
    "yticks(fontsize = 20)\n",
    "\n",
    "xlabel(r'$\\beta/\\mu$',fontsize=30)\n",
    "ylabel(r'$R$',fontsize=30)\n",
    "\n",
    "ylim([0,1])\n",
    "xlim([0,1])\n",
    "gca().xaxis.set_major_locator( MaxNLocator(nbins = 5,prune= 'lower' ))\n",
    "#legend(bbox_to_anchor=(1.04,0.5), loc=\"center left\",frameon=False,fontsize = 20,numpoints = 1)\n",
    "#tight_layout(rect=[0,0,0.75,1])\n",
    "\n",
    "tight_layout()\n",
    "\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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