EcoEvo_agriculture_1SEQ.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eco-evolutionary dynamics in agriculture: a study in crop rotations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "\n",
    "from __future__ import division\n",
    "from subprocess import call\n",
    "from scipy import *\n",
    "from scipy.linalg import eigh as largest_eigh\n",
    "from scipy.integrate import odeint\n",
    "import scipy.integrate as spi\n",
    "import numpy as np\n",
    "from itertools import combinations\n",
    "from itertools import repeat\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import io\n",
    "import csv\n",
    "import sys, os\n",
    "import pandas as pd\n",
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## For crop rotation cash yield and soil quality\n",
    "\n",
    "q0 = 1 # init soil quality\n",
    "y0 = 0 # init cash yield\n",
    "K = 2 #carrying capacity\n",
    "beta_h1 = -1.5  #soil contribution h1\n",
    "beta_h1_r = -1.5 #soil contribution h1_r\n",
    "beta_h2 = 1 #soil contribution h2\n",
    "y_h1 = 1 #cash contribution h1\n",
    "y_h1_r = 0.9 #cash contribution h1_r\n",
    "y_h2 = 0 #cash contribution h2\n",
    "\n",
    "\n",
    "\n",
    "## For eco-evo dynamics\n",
    "epsilon = 0.5 # pathogen clearance\n",
    "k = 0.1 # cost of resistance\n",
    "mu = 0.1 # transition probability\n",
    "a = 0.5 # GfG proportion\n",
    "sigma = 0.04 # pathogen virulence of p1\n",
    "H_dens = 50 # host density\n",
    "dp = 0.5 # pathogen deathrate\n",
    "\n",
    "\n",
    "\n",
    "## Time (needed for simulation)\n",
    "t_step = 0.005\n",
    "t_last = int(1/t_step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Infection environment (pathogen yes/no, time of infection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pathogen true? 1 = true, 0 = false1\n",
      "infection time?0\n"
     ]
    }
   ],
   "source": [
    "pathogen_true = 1 # int(raw_input(\"pathogen true? 1 = true, 0 = false\")) #presence or absence of pathogen\n",
    "infection_time = 0 #int(raw_input(\"infection time?\")) # time the pathogen infects\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sequence population (create all possible patterns of a certain length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_seq = 1\n",
    "l = 10  #length\n",
    "#write your sequence of interest, as a combination of h1, h2 and h1_r of length l\n",
    "seq = ['h2','h2','h2','h1','h2','h1','h2','h1','h1','h1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## binary sequence for each crop type\n",
    "\n",
    "def get_h_seq(seq):\n",
    "    crop_types = ['h1','h2','hr']\n",
    "    seq_elements = seq\n",
    "    binaries = {k:[int(k in v) for v in seq_elements]for k in crop_types}\n",
    "    h1_seq,h2_seq,hr_seq = binaries['h1'],binaries['h2'],binaries['hr']\n",
    "    \n",
    "    return h1_seq,h2_seq,hr_seq\n",
    "\n",
    "h1_seq,h2_seq,hr_seq = get_h_seq(seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transition and fitness matrices (section 2.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def transition_matrix():\n",
    "    d = np.array([\n",
    "    [1 - 2*mu ,mu,0,0,0,mu,0,0,0,0],\n",
    "    [mu,1 - 3*mu,mu,0,0,0,mu,0,0,0],\n",
    "    [0,mu,1 - 3*mu,mu,0,0,0,mu,0,0],\n",
    "    [0,0,mu,1 - 3*mu,mu,0,0,0,mu,0],\n",
    "    [0,0,0,mu,1 - 2*mu,0,0,0,0,mu],\n",
    "    [mu,0,0,0,0,1 - 2*mu,mu,0,0,0],\n",
    "    [0,mu,0,0,0,mu,1 - 3*mu,mu,0,0],\n",
    "    [0,0,mu,0,0,0,mu,1 - 3*mu,mu,0],\n",
    "    [0,0,0,mu,0,0,0,mu,1 - 3*mu,mu],\n",
    "    [0,0,0,0,mu,0,0,0,mu,1 - 2*mu]\n",
    "    ])\n",
    "    # d = np.identity(10) ### in case we don't want any transition between types\n",
    "    return(d)\n",
    "\n",
    "q_matrix = transition_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fitness_matrix():\n",
    "    w_matrix = np.array([[1,0,0],[1.25,0,0],[1.5,0,0],[1.75,0,0],[2,0,0],\n",
    "    [a*(1 - a*k),1 - a*k,0],[a*(1.25 - a*k),1.25 - a*k,0],[a*(1.5 - a*k),1.5 - a*k,0],[a*(1.75 - a*k),1.75 - a*k,0],\n",
    "    [a*(2 - a*k),2 - a*k,0]])\n",
    "\n",
    "    return(w_matrix)\n",
    "\n",
    "w_matrix = fitness_matrix()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eco-Evo dynamics (section 2.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def infection(h1_seq,hr_seq,h2_seq):\n",
    "    def HP_evol(t,init): ## equations for host-pathogen coevolution\n",
    "        h = np.array(init[0:3],dtype='float16')\n",
    "        p = np.array(init[3:13],dtype='float16')\n",
    "        hdot = np.zeros(3)\n",
    "        pdot = np.zeros(10)\n",
    "        \n",
    "        for h_type in range(0,3):    \n",
    "            hdot[h_type] = h[h_type] * np.sum((-sigma * np.multiply(w_matrix[:,h_type], p,dtype='float16')))\n",
    "\n",
    "        for p_type in range(0,10):\n",
    "            pdot[p_type] = np.sum((np.multiply(q_matrix[:,p_type], p,dtype='float16') * np.sum((sigma * np.multiply(w_matrix[p_type,:], h,dtype='float16'))))) - p[p_type]*dp\n",
    "        \n",
    "        ydot = np.append(hdot,pdot)\n",
    "        return(ydot)\n",
    "    \n",
    "    \n",
    "    t_i = 0 #init time (start of season)\n",
    "    t_f = 1 #final time (end of season)\n",
    "    general_ts = []\n",
    "    general_zs = []\n",
    "    z0 = np.zeros(13)\n",
    "    for i in range(0,l):\n",
    "\n",
    "      h1 = H_dens * (h1_seq[i])\n",
    "      hr = H_dens * (hr_seq[i])\n",
    "      h2 = H_dens * (h2_seq[i])\n",
    "      \n",
    "      z0[0:3] = [h1,hr,h2]\n",
    "\n",
    "      if t_i == infection_time and pathogen_true == 1: ## introducing the pathogen at infection time with a density of 1\n",
    "        p1 = 1\n",
    "        z0[3] = p1\n",
    "\n",
    "      ## ODE solver\n",
    "    \n",
    "      t_end = 1\n",
    "      t_start = 0.\n",
    "      t_step = 0.005\n",
    "      t_interval = np.arange(t_start, t_end, t_step)\n",
    "\n",
    "      myode =  spi.ode(HP_evol)\n",
    "      myode.set_integrator('lsoda',nsteps = 500000000)\n",
    "      myode.set_initial_value(z0,t_start)\n",
    "\n",
    "      ts = [0]\n",
    "      zs = [z0]  \n",
    "\n",
    "      while myode.successful() and myode.t < t_end:\n",
    "         myode.integrate(myode.t + t_step)\n",
    "         ts.append(myode.t)\n",
    "         zs.append(myode.y)\n",
    "\n",
    "      z_final = zs[t_last]\n",
    "      #\n",
    "      h1_f = z_final[0]\n",
    "      h1_r_f = z_final[1]\n",
    "      h2_f = z_final[2]\n",
    "      \n",
    "      p = np.zeros(10)\n",
    "      for n in range(0,10):\n",
    "        p[n] = epsilon * z_final[(n + 3)]\n",
    "      \n",
    "      z0[3:13] = p\n",
    "  \n",
    "      t_extra = np.full((t_last + 1,),i)\n",
    "      general_ts.append(ts + t_extra)\n",
    "      general_zs.append(zs)\n",
    "\n",
    "      t_i = t_i + 1  #discrete time increase\n",
    "      t_f = t_f + 1\n",
    "    return general_ts,general_zs\n",
    "\n",
    "ts,zs = infection(h1_seq,hr_seq,h2_seq)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soil quality and cash yield in crop rotation patterns (section 2.1 and 2.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('cash', array([ 0.        ,  0.        ,  0.        ,  0.        ,  1.90023504,\n",
      "        1.90023504,  3.74491629,  3.74491629,  5.50323875,  6.73805887,\n",
      "        7.25029433]))\n",
      "('soil', array([ 1.        ,  1.46211716,  1.76159416,  1.90514825,  1.63514895,\n",
      "        1.84828364,  1.46211716,  1.76159416,  1.24491866,  0.53788284,\n",
      "        0.15171636]))\n",
      "('fitness', 7.2502943323471634)\n"
     ]
    }
   ],
   "source": [
    "def getdeltas(seq,zs):  ## section 2.3, delta as the ratio of host at the end of season and initial host density\n",
    "    zs = np.array(zs)\n",
    "    zz = zs[:,t_last]\n",
    "    zz_all = []\n",
    "    for j in range(0,l):\n",
    "        zz_1 = zz[j]\n",
    "        zz_2 = zz_1[0:3]\n",
    "        zz_all.append(zz_2)\n",
    "    zz_all = np.array(zz_all)\n",
    "    x = np.sum(zz_all,axis = 1)    \n",
    "    deltas = x/H_dens \n",
    "    return deltas\n",
    "\n",
    "\n",
    "deltas = getdeltas(seq,zs)\n",
    "\n",
    "\n",
    "def fitness_function(init_cash,init_soil,seq,deltas):\n",
    "\n",
    "    soil_vector = []\n",
    "    cash_vector = []\n",
    "    n_h2 = 0 #n of consecutive cover crops\n",
    "    n_h1_hr = 0 #n of consecutive cash crops\n",
    "    \n",
    "    \n",
    "    soil_seq = np.zeros(l + 1) #vector that will have soil changes\n",
    "    soil_seq[0] = init_soil\n",
    "\n",
    "    cash_seq = np.zeros(l + 1) #vector that will have cash changes\n",
    "    cash_seq[0] = init_cash\n",
    "\n",
    "    for t in range(0,l):\n",
    "        d = deltas[t]      \n",
    "        if seq[t] == 'h2': #soil quality and cash yield for cv crops\n",
    "            n_h2 = n_h2 + 1\n",
    "            n_h1_hr = 0\n",
    "            soil_seq[t + 1] = max(0.01, min(1.99,((K * soil_seq[t] * math.exp(beta_h2)) / (K + soil_seq[t] * (math.exp(beta_h2) - 1)))))\n",
    "            cash_seq[t + 1] = cash_seq[t] + y_h2 * d * soil_seq[t]\n",
    "        elif seq[t]=='h1': \n",
    "            n_h1_hr = n_h1_hr + 1\n",
    "            n_h2 = 0\n",
    "            soil_seq[t + 1] = max(0.01, min(1.99,((K * soil_seq[t] * math.exp(beta_h1)) / (K + soil_seq[t] * (math.exp(beta_h1) - 1)))))\n",
    "            cash_seq[t + 1] = cash_seq[t] + y_h1 * d * soil_seq[t]\n",
    "        elif seq[t]=='hr': \n",
    "            n_h1_hr = n_h1_hr + 1\n",
    "            n_h2 = 0\n",
    "            soil_seq[t + 1] = max(0.01, min(1.99,((K * soil_seq[t] * math.exp(beta_hr)) / (K + soil_seq[t] * (math.exp(beta_hr) - 1)))))\n",
    "            cash_seq[t + 1] = cash_seq[t] + y_hr * d * soil_seq[t]\n",
    "    \n",
    "    fitness = cash_seq[l]\n",
    "    return cash_seq,soil_seq,fitness\n",
    "\n",
    "\n",
    "init_soil = np.full(1,q0)    #init soil is 1\n",
    "init_cash = np.full(1,y0)   #init cash is 0\n",
    "\n",
    "cash_seq,soil_seq,fitness = fitness_function(init_cash,init_soil,seq,deltas)\n",
    "\n",
    "print(\"cash\", cash_seq)\n",
    "print(\"soil\", soil_seq)\n",
    "print(\"fitness\",fitness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEBCAYAAABmCeILAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYQQOv0FfGh2mQ00P4YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x150b98a490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## soil quality and cash yield\n",
    "# soil quality and cash yield\n",
    "\n",
    "t_sc = np.linspace(0,l,l+1)\n",
    "fig2,ax = plt.subplots()\n",
    "plt.xticks(fontname = \"Arial\")\n",
    "plt.yticks(fontname = \"Arial\")\n",
    "plt.tick_params(labelsize=16)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(t_sc,np.array(cash_seq).T,color='darkred',marker='o')\n",
    "ax.plot(t_sc,(np.array(soil_seq).T),color ='xkcd:dark blue',marker='o')\n",
    "for i in range(0,l):\n",
    "    ax.axvline(i,linestyle = '--', lw = 1, c = 'xkcd:black')\n",
    "    if seq[i] == 'h1':\n",
    "        ax.axvspan(i,i+1,facecolor = 'xkcd:golden rod', alpha = 0.15)\n",
    "    if seq[i] == 'h2':\n",
    "        ax.axvspan(i,i+1,facecolor = 'xkcd:marine', alpha = 0.15)\n",
    "ax.axvline(l,linestyle = '--', lw = 1, c = 'xkcd:black')\n",
    "\n",
    "#ax2.set_ylim((-0.4,8.4))\n",
    "ax.set_ylim((-0.1,2.1))\n",
    "ax.xaxis.set_tick_params(width=2)\n",
    "ax.yaxis.set_tick_params(width=2)\n",
    "ax2.yaxis.set_tick_params(width=2)\n",
    "for axis in ['top','bottom','left','right']:\n",
    "  ax.spines[axis].set_linewidth(2)\n",
    "plt.yticks(fontname = \"Arial\")\n",
    "\n",
    "plt.tick_params(labelsize=16)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEPCAYAAACp/QjLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1513fdad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEBCAYAAACUmXXrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7ssAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15141efb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## eco evo dynamics\n",
    "\n",
    "\n",
    "t3 = np.vstack(ts).T\n",
    "y = np.array(zs).T\n",
    "\n",
    "y = y[:,1:,:]\n",
    "t3 = t3[1:,:]\n",
    "\n",
    "h1,h1_r,h2,p1,p2,p3,p4,p5,p6,p7,p8,p9,p10 = y\n",
    "\n",
    "\n",
    "p_sum = p1 + p2 + p3 + p4 + p5\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "\n",
    "plt.yticks(fontname = \"Arial\")\n",
    "plt.xticks(fontname = \"Arial\")\n",
    "plt.tick_params(labelsize=16)\n",
    "\n",
    "ax.plot(t3,h1,label='h1',color='xkcd:golden rod',lw =3)\n",
    "ax.plot(t3,h2,label='h2',color='xkcd:marine',lw = 3)\n",
    "\n",
    "ax.plot(t3,p_sum,label='p sum',color='xkcd:medium grey',lw = 3)\n",
    "\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('Host Density')\n",
    "ax.set_xlim((-0.4,l + 0.4))\n",
    "\n",
    "ax.xaxis.set_tick_params(width=2)\n",
    "ax.yaxis.set_tick_params(width=2)\n",
    "for axis in ['top','bottom','left','right']:\n",
    "  ax.spines[axis].set_linewidth(2)\n",
    "\n",
    "for i in range(0,l):\n",
    "     ax.axvline(i,linestyle = '--', lw = 1, c = 'xkcd:black')\n",
    "     if seq[i] == 'h1':\n",
    "         ax.axvspan(i,i+1,facecolor = 'xkcd:golden rod', alpha = 0.15)\n",
    "     if seq[i] == 'h2':\n",
    "         ax.axvspan(i,i+1,facecolor = 'xkcd:marine', alpha = 0.15)\n",
    "ax.axvline(l,linestyle = '--', lw = 1, c = 'xkcd:black')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "freq_p5 = p5 / p_sum\n",
    "freq_p4 = p4 / p_sum\n",
    "freq_p3 = p3 / p_sum\n",
    "freq_p2 = p2 / p_sum\n",
    "freq_p1 = p1 / p_sum\n",
    "\n",
    "freq_min = np.zeros((np.shape(freq_p5)))\n",
    "freq_min_a = np.zeros(200*l)\n",
    "\n",
    "freq_p5_a = freq_p5.reshape(-1)\n",
    "\n",
    "t3_a = t3.reshape(-1)\n",
    "\n",
    "#\n",
    "fig,ax = plt.subplots()\n",
    "#\n",
    "plt.yticks(fontname = \"Arial\")\n",
    "plt.xticks(fontname = \"Arial\")\n",
    "plt.tick_params(labelsize=16)\n",
    "\n",
    "for i in range(0,l):\n",
    "    ax.fill_between(t3[:,i], freq_p5[:,i] + freq_p4[:,i] + freq_p3[:,i] + freq_p2[:,i] + freq_p1[:,i],freq_min[:,i],label='p sum',color='xkcd:black',alpha = 0.5)\n",
    "    ax.fill_between(t3[:,i], freq_p5[:,i] + freq_p4[:,i] + freq_p3[:,i] + freq_p2[:,i],freq_min[:,i],label='p sum',color='xkcd:black',alpha = 0.5)\n",
    "    ax.fill_between(t3[:,i], freq_p5[:,i] + freq_p4[:,i] + freq_p3[:,i],freq_min[:,i],label='p sum',color='xkcd:black',alpha = 0.5)\n",
    "    ax.fill_between(t3[:,i], freq_p5[:,i] + freq_p4[:,i],freq_min[:,i],label='p sum',color='xkcd:black',alpha = 0.5)\n",
    "    ax.fill_between(t3[:,i], freq_p5[:,i],freq_min[:,i],label='p sum',color='xkcd:black',alpha = 0.5)\n",
    "\n",
    "ax.set_xlim((-0.4,l + 0.4))\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "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": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}