{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Jupyter Notebook for simulation and data collection only (main figures 2-4)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "import numpy as np\n", "import random as rd\n", "import functions_plant_pollinator_evolution as ppe\n", "import time\n", "%pylab inline\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "start_total_time = time.time()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.224860760096506, 1.22497197337306] 0.5124022081433903 0.5121786393274811\n", "0.9031806625597399\n" ] } ], "source": [ "c_P = 1 # intraspecific competition of the plant [1/(time*P-Pop)]\n", "c_A = 1 # intraspecific competition of the pollinator [1/(time*A-Pop)]\n", "gamma_A = 1 # mutualistic gain of the plant (e.g. benefits of pollination)\n", "gamma_P = 1 # mutualistic gain of the pollinator (e.g. energy gain of nectar)\n", "eco_parameter = [c_P,c_A,gamma_A,gamma_P]\n", "\n", "alpha_max = 0.8 # maxiumum value for the plant attractiveness\n", "s_a = 3\n", "beta_max = 0.8 # maxiumum value for the pollinator effort\n", "s_b = 3\n", "tof_parameter = [alpha_max,s_a,beta_max,s_b]\n", "\n", "# defining evolutionary parameter\n", "mut_step_a = 0.016 # mutant and resident trait differ for alpha \n", "mut_step_b = 0.016 # mutant and resident trait differ for beta\n", "P_mut_prop = 1\n", "A_mut_prop = 1\n", "evo_parameter = [mut_step_a,mut_step_b,P_mut_prop,A_mut_prop]\n", "\n", "env_change = 1 # test value if environment is changing\n", "r_A_change = 0.001 # Amount of decrease per time step\n", "time_point = 0\n", "env_parameter = [env_change,r_A_change,time_point]\n", "\n", "\n", "# calculate optimal alpah and beta value and starting population for parameter setting \n", "\n", "max_num_mut = 1000\n", "count_mut = 0\n", "alpha = 0.5127149146621419 # using the optimal strategie of no insect decline as a beginning\n", "beta = 0.5127149146621419 # using the optimal strategie of no insect declineas a beginning\n", "alpha_m = alpha + mut_step_a * np.random.normal(0,1/2.5758) # initial mutant strategy\n", "beta_m = beta + mut_step_b * np.random.normal(0,1/2.5758) # initial mutant strategy\n", "X0 = [1.2252774066850036,1.2252774066850036] # starting population\n", "\n", "alpha_0,beta_0,X0 = ppe.starting_values(0.5,0.5,eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", "print(X0, alpha_0, beta_0)\n", "r_P = ppe.rp_parameter(alpha,tof_parameter)\n", "r_A = ppe.ra_parameter( beta,tof_parameter)\n", "print(r_P)\n", "\n", "\n", "Cmap=cm.get_cmap('seismic_r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Figure 2\n", "\n", "# 2 heatmaps with different intaction gain & competition combinations" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "start_time = time.time()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "A_ext = 0.01\n", "t_limit = 1000000\n", "limits = [A_ext, t_limit]\n", "\n", "num_val = 150\n", "\n", "fig_2_gain = [np.zeros((num_val,num_val))]\n", "gamma_list = np.linspace(0,1.5,num_val) # propability of a plant/pollinator mutation\n", "\n", "mut_prob_list = [0.1]\n", "\n", "np.random.seed(14) # select seed for random -> to achieve same results \n", "\n", "for n, mut_prob in enumerate(mut_prob_list):\n", " \n", " extinction_time = np.zeros((num_val,num_val))\n", "\n", " for i, gamma_A in enumerate(gamma_list):\n", "\n", " for j, gamma_P in enumerate(gamma_list):\n", " \n", " \n", " eco_parameter = [c_P,c_A,gamma_A,gamma_P]\n", " alpha_0,beta_0,x = ppe.starting_values(alpha,beta,eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", " \n", " \n", " evo_parameter = [mut_step_a,mut_step_b,0,0]\n", " x,A_list,x,x,x = ppe.coevolution_seperate(alpha_0,beta_0, limits,\n", " eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", " ref_time = len(A_list)\n", "\n", " evo_parameter = [mut_step_a,mut_step_b,mut_prob,mut_prob]\n", " x,A_list,x,x,x = ppe.coevolution_seperate(alpha_0,beta_0, limits,\n", " eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", "\n", " extinction_time[j,i] = len(A_list)/ref_time\n", " \n", " fig_2_gain = np.vstack([fig_2_gain,[extinction_time]])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "numpy.savetxt(\"Fig_2_hm_gain_0.1.csv\",fig_2_gain[1],delimiter=\",\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "env_change = 1 # test value if environment is changing\n", "r_A_change = 0.001 # Amount of decrease per time step\n", "time_point = 0\n", "env_parameter = [env_change,r_A_change,time_point]\n", "\n", "c_P = 1 # intraspecific competition of the plant [1/(time*P-Pop)]\n", "c_A = 1 # intraspecific competition of the pollinator [1/(time*A-Pop)]\n", "gamma_A = 1.4 # mutualistic gain of the plant (e.g. benefits of pollination)\n", "gamma_P = 1.4 # mutualistic gain of the pollinator (e.g. energy gain of nectar)\n", "eco_parameter = [c_P,c_A,gamma_A,gamma_P]\n", "\n", "num_val = 150 # number of values to go through\n", "\n", "fig_2_comp = [np.zeros((num_val,num_val))] # \"Heatmap of competition\" dummy\n", "c_list = np.linspace(0.92,2.5,num_val) # probability of a plant/pollinator mutation\n", "\n", "A_ext = 0.01\n", "t_limit = 1000000\n", "limits = [A_ext, t_limit]\n", "\n", "list_mut_prob = [0.1]\n", "\n", "np.random.seed(14) # select seed for random -> to achieve same results \n", "\n", "for n, mut_prob in enumerate(list_mut_prob):\n", " \n", " extinction_time = np.zeros((num_val,num_val))\n", "\n", " for i, c_P in enumerate(c_list):\n", "\n", " for j, c_A in enumerate(c_list):\n", " \n", " #np.random.seed(1) # select seed for random -> to achieve same results \n", " \n", " \n", " eco_parameter = [c_P,c_A,gamma_A,gamma_P]\n", " alpha_0,beta_0,x = ppe.starting_values(alpha,beta,eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", " \n", " \n", " evo_parameter = [mut_step_a,mut_step_b,0,0]\n", " x,A_list,x,x,x = ppe.coevolution_seperate(alpha_0,beta_0, limits,\n", " eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", " ref_time = len(A_list)\n", " \n", "\n", " evo_parameter = [mut_step_a,mut_step_b,mut_prob,0.5]\n", " x,A_list,x,x,x = ppe.coevolution_seperate(alpha_0,beta_0, limits,\n", " eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", "\n", " extinction_time[j,i] = len(A_list)/ref_time\n", " \n", " fig_2_comp = np.vstack([fig_2_comp,[extinction_time]])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "numpy.savetxt(\"Fig_2_hm_comp_0.1.csv\",fig_2_comp[1],delimiter=\",\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It took 238.69778519471487 min to run this figure.\n" ] } ], "source": [ "end_time = time.time()\n", "\n", "print(\"It took \"+str((end_time-start_time)/60)+\" min to run this figure.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Figure 3\n", "\n", "# 3 heatmaps with different mut probability combinations" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "start_time = time.time()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gamma = 1.0\n", "gamma = 1.4\n", "gamma = 1.5\n" ] } ], "source": [ "env_change = 1 # test value if environment is changing\n", "r_A_change = 0.001# Amount of decrease per time step\n", "time_point = 0\n", "env_parameter = [env_change,r_A_change,time_point]\n", "\n", "c_P = 1 # intraspecific competition of the plant [1/(time*P-Pop)]\n", "c_A = 1 # intraspecific competition of the pollinator [1/(time*A-Pop)]\n", "\n", "A_ext = 0.01\n", "t_limit = 1000000\n", "limits = [A_ext, t_limit]\n", "\n", "num_val = 150\n", "\n", "fig_3_hm = [np.zeros((num_val,num_val))]\n", "mut_prob_list = np.linspace(0,1,num_val) # probability of a plant/pollinator mutation\n", "\n", "gamma_list = [1.0,1.4,1.5]\n", "\n", "for n, gamma in enumerate(gamma_list):\n", " \n", " extinction_time = np.zeros((num_val,num_val))\n", " \n", " print(r'gamma = '+str(gamma))\n", "\n", " eco_parameter = [c_P,c_A,gamma,gamma]\n", "\n", " #np.random.seed(14)\n", "\n", " alpha_0,beta_0,x = ppe.starting_values(alpha,beta,eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", "\n", " for i, P_mut_prob in enumerate(mut_prob_list):\n", "\n", " for j, A_mut_prob in enumerate(mut_prob_list):\n", "\n", " evo_parameter = [mut_step_a,mut_step_b,P_mut_prob,A_mut_prob]\n", "\n", " x,A_list,x,x,x = ppe.coevolution_seperate(alpha_0,beta_0, limits,\n", " eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", "\n", " if P_mut_prob == 0 and A_mut_prob == 0:\n", " ref_pop = A_list[-1] \n", " ref_time = len(A_list)\n", "\n", " #print(ref_time)\n", "\n", " extinction_time[j,i] = len(A_list)/ref_time\n", "\n", " fig_3_hm = np.vstack([fig_3_hm,[extinction_time]])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Save heatmaps:\n", "\n", "numpy.savetxt(\"Fig_3_hm_gain_prob_1.0.csv\",fig_3_hm[1],delimiter=\",\")\n", "numpy.savetxt(\"Fig_3_hm_gain_prob_1.4.csv\",fig_3_hm[2],delimiter=\",\")\n", "numpy.savetxt(\"Fig_3_hm_gain_prob_1.5.csv\",fig_3_hm[3],delimiter=\",\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It took 21.08595624367396 min to run this figure.\n" ] } ], "source": [ "end_time = time.time()\n", "\n", "print(\"It took \"+str((end_time-start_time)/60)+\" min to run this figure.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Figure 4\n", "\n", "# 3 heatmaps of changing environmental decay and mut. probabilities" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "start_time = time.time()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1/3 done\n", "2/3 done\n", "3/3 done\n" ] } ], "source": [ "c_P = 1 # intraspecific competition of the plant [1/(time*P-Pop)]\n", "c_A = 1 # intraspecific competition of the pollinator [1/(time*A-Pop)]\n", "gamma_P = 1.4 # mutualistic gain of the plant (e.g. benefits of pollination)\n", "gamma_A = 1.4 # mutualistic gain of the pollinator (e.g. energy gain of nectar)\n", "eco_parameter = [c_P,c_A,gamma_P,gamma_A]\n", "\n", "alpha_max = 0.8 # maxiumum value for the plant attractiveness\n", "s_a = 3\n", "beta_max = 0.8 # maxiumum value for the pollinator effort\n", "s_b = 3\n", "tof_parameter = [alpha_max,s_a,beta_max,s_b]\n", "\n", "# defining evolutionary parameter\n", "mut_step_a = 0.016 # mutant and resident trait differ for alpha \n", "mut_step_b = 0.016 # mutant and resident trait differ for beta\n", "P_mut_prob = 1\n", "A_mut_prob = 1\n", "evo_parameter = [mut_step_a,mut_step_b,P_mut_prob,A_mut_prob]\n", "\n", "P_mut_prob = 0\n", "A_mut_prob = 0\n", "evo_parameter_0 = [mut_step_a,mut_step_b,P_mut_prob,A_mut_prob]\n", "\n", "env_change = 1 # state if environment is changing\n", "r_A_change = 0 # value how much r_A is changing per time step \n", "time_point = 0 # counter for time step\n", "env_parameter = [env_change,r_A_change,time_point]\n", "\n", "A_ext = 0.01\n", "t_limit = 1000000\n", "limits = [A_ext, t_limit]\n", "\n", "num_val = 150\n", "\n", "ref_etp = np.zeros((num_val,num_val))\n", "mut_etp = np.zeros((num_val,num_val))\n", "rel_etp = np.zeros((num_val,num_val))\n", "\n", "evo_speed = np.linspace(0,1,num_val)\n", "env_speed = np.geomspace(0.1,0.00001,num_val,endpoint=True)\n", "\n", "np.random.seed(14)\n", "\n", "for i, mut_prob in enumerate(evo_speed):\n", " \n", " evo_parameter = [mut_step_a,mut_step_b,mut_prob,mut_prob]\n", " \n", " for j, r_A_change in enumerate(env_speed):\n", "\n", " env_parameter = [env_change,r_A_change,time_point]\n", "\n", " alpha,beta,X0 = ppe.starting_values(0.5,0.5,eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", "\n", " P,A1,a,b,r = ppe.coevolution_seperate(alpha,beta,limits,eco_parameter,tof_parameter,evo_parameter_0,env_parameter)\n", " ref_etp[j,i] = len(A1)\n", "\n", " P,A2,a,b,r = ppe.coevolution_seperate(alpha,beta,limits,eco_parameter,tof_parameter,evo_parameter ,env_parameter)\n", " mut_etp[j,i] = len(A2)\n", "\n", " rel_etp[j,i] = len(A2)/len(A1)\n", " \n", "numpy.savetxt(\"Fig_4_hm_env_evo_speed_=_1.4.csv\",rel_etp,delimiter=\",\")\n", "print('1/3 done')\n", "\n", "c_P = 1 # intraspecific competition of the plant [1/(time*P-Pop)]\n", "c_A = 1 # intraspecific competition of the pollinator [1/(time*A-Pop)]\n", "gamma_P = 1.5 # mutualistic gain of the plant (e.g. benefits of pollination)\n", "gamma_A = 1.5 # mutualistic gain of the pollinator (e.g. energy gain of nectar)\n", "eco_parameter = [c_P,c_A,gamma_P,gamma_A]\n", "\n", "for i, mut_prob in enumerate(evo_speed):\n", " \n", " evo_parameter = [mut_step_a,mut_step_b,mut_prob,mut_prob]\n", " \n", " for j, r_A_change in enumerate(env_speed):\n", "\n", " env_parameter = [env_change,r_A_change,time_point]\n", "\n", " alpha,beta,X0 = ppe.starting_values(0.5,0.5,eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", "\n", " P,A1,a,b,r = ppe.coevolution_seperate(alpha,beta,limits,eco_parameter,tof_parameter,evo_parameter_0,env_parameter)\n", " ref_etp[j,i] = len(A1)\n", "\n", " P,A2,a,b,r = ppe.coevolution_seperate(alpha,beta,limits,eco_parameter,tof_parameter,evo_parameter ,env_parameter)\n", " mut_etp[j,i] = len(A2)\n", "\n", " rel_etp[j,i] = len(A2)/len(A1)\n", " \n", "numpy.savetxt(\"Fig_4_hm_env_evo_speed_=_1.5.csv\",rel_etp,delimiter=\",\")\n", "print('2/3 done')\n", "\n", "for i, mut_prob in enumerate(evo_speed):\n", " \n", " evo_parameter = [mut_step_a,mut_step_b,mut_prob,0]\n", " \n", " for j, r_A_change in enumerate(env_speed):\n", "\n", " env_parameter = [env_change,r_A_change,time_point]\n", "\n", " alpha,beta,X0 = ppe.starting_values(0.5,0.5,eco_parameter,tof_parameter,evo_parameter,env_parameter)\n", "\n", " P,A1,a,b,r = ppe.coevolution_seperate(alpha,beta,limits,eco_parameter,tof_parameter,evo_parameter_0,env_parameter)\n", " ref_etp[j,i] = len(A1)\n", "\n", " P,A2,a,b,r = ppe.coevolution_seperate(alpha,beta,limits,eco_parameter,tof_parameter,evo_parameter ,env_parameter)\n", " mut_etp[j,i] = len(A2)\n", "\n", " rel_etp[j,i] = len(A2)/len(A1)\n", " \n", "numpy.savetxt(\"Fig_4_hm_env_evo_speed_0_1.5.csv\",rel_etp,delimiter=\",\")\n", "print('3/3 done')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It took 476.25492590268453 min to run this figure.\n" ] } ], "source": [ "end_time = time.time()\n", "\n", "print(\"It took \"+str((end_time-start_time)/60)+\" min to run this figure.\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It took 44162.52937531471 seconds to run the complete code.\n", "It took 736.0421562552452 minutes to run the complete code.\n", "It took 12.267369270920753 hours to run the complete code.\n" ] } ], "source": [ "total_time = time.time() -start_total_time\n", "print(\"It took \"+str(total_time)+\" seconds to run the complete code.\")\n", "print(\"It took \"+str(total_time/60)+\" minutes to run the complete code.\")\n", "print(\"It took \"+str(total_time/3600)+\" hours to run the complete code.\")" ] } ], "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }