analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "03527b49",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "from scipy.stats import ttest_rel, ttest_ind\n",
    "from os import listdir, getcwd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "752b42d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data for specific guild\n",
    "\n",
    "date = '03_13_2023'\n",
    "dir_AF1 = 'AF1'\n",
    "dir_AF0 = 'AF0'\n",
    "guild = 'pollinator'\n",
    "allfiles_AF1 = listdir('%s/results/%s/%s/motifs/' % (getcwd(), date, dir_AF1))\n",
    "allfiles_AF0 = listdir('%s/results/%s/%s/motifs/' % (getcwd(), date, dir_AF0))\n",
    "motif_files_AF1 = [file for file in allfiles_AF1 if guild in file]\n",
    "motif_files_AF0 = [file for file in allfiles_AF0 if guild in file]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5af1ccf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge colonizers across all simulations within a group for motif analysis\n",
    "\n",
    "# with adaptive foraging\n",
    "species_af1 = pd.DataFrame()\n",
    "for p in motif_files_AF1:\n",
    "    p_AF1 = pd.read_csv('%s/results/%s/%s/motifs/%s' % (getcwd(), date, dir_AF1, p))\n",
    "    p_AF1 = p_AF1.iloc[9:] #remove first 9 plants in each simulation\n",
    "    species_af1 = species_af1.append(p_AF1)\n",
    "species_af1.removed.apply(lambda x: 1000 if x == math.inf else x)\n",
    "species_af1['lifespan'] = species_af1.removed - species_af1.added\n",
    "\n",
    "# without adaptive foraging\n",
    "species_af0 = pd.DataFrame()\n",
    "for p in motif_files_AF0:\n",
    "    p_AF0 = pd.read_csv('%s/results/%s/%s/motifs/%s' % (getcwd(), date, dir_AF0, p))\n",
    "    p_AF0 = p_AF0.iloc[9:] #remove first 9 plants in each simulation\n",
    "    species_af0 = species_af0.append(p_AF0)\n",
    "species_af0.removed.apply(lambda x: 1000 if x == math.inf else x)\n",
    "species_af0['lifespan'] = species_af0.removed - species_af0.added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "15e7d14a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# How frequently is each motif introduced?\n",
    "\n",
    "#only consider species who have indirect partners\n",
    "species_af1 = species_af1[species_af1.GenPart == 1]\n",
    "species_af0 = species_af0[species_af0.GenPart == 1]\n",
    "\n",
    "#separate between specialists and generalists\n",
    "spc_af1 = species_af1[species_af1.Spc == 1]\n",
    "spc_af0 = species_af0[species_af0.Spc == 1]\n",
    "gen_af1 = species_af1[species_af1.Gen == 1]\n",
    "gen_af0 = species_af0[species_af0.Gen == 1]\n",
    "\n",
    "#separate between specialists with generalists partners with specialist and generalist partners\n",
    "spc_spc_af1 = spc_af1[spc_af1.SpcPartPart == 1]\n",
    "spc_spc_af0 = spc_af0[spc_af0.SpcPartPart == 1]\n",
    "spc_gen_af1 = spc_af1[spc_af1.GenPartPart == 1]\n",
    "spc_gen_af0 = spc_af0[spc_af0.GenPartPart == 1]\n",
    "spc_gen_af1 = spc_gen_af1[spc_gen_af1.SpcPartPart == 0]\n",
    "spc_gen_af0 = spc_gen_af0[spc_gen_af0.SpcPartPart == 0]\n",
    "\n",
    "#separate between generalists with generalists partners with specialist and generalist partners\n",
    "gen_spc_af1 = gen_af1[gen_af1.SpcPartPart == 1]\n",
    "gen_spc_af0 = gen_af0[gen_af0.SpcPartPart == 1]\n",
    "gen_gen_af1 = gen_af1[gen_af1.GenPartPart == 1]\n",
    "gen_gen_af0 = gen_af0[gen_af0.GenPartPart == 1]\n",
    "gen_gen_af1 = gen_gen_af1[gen_gen_af1.SpcPartPart == 0]\n",
    "gen_gen_af0 = gen_gen_af0[gen_gen_af0.SpcPartPart == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "832daf8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Species_Guild Assembly_Model Motif_Group  Num_Colonizers  Est_Num  Est_Rate\n",
      "0    pollinator            AF1     Spc_Spc            1251      488  0.390088\n",
      "1    pollinator            AF1     Spc_Gen            6761     1689  0.249815\n",
      "2    pollinator            AF1     Gen_Spc            3087     1142  0.369938\n",
      "3    pollinator            AF1     Gen_Gen            5277     1597  0.302634\n",
      "4    pollinator            AF0     Spc_Spc            1251      488  0.390088\n",
      "5    pollinator            AF0     Spc_Gen            6761     1689  0.249815\n",
      "6    pollinator            AF0     Gen_Spc            3087     1142  0.369938\n",
      "7    pollinator            AF0     Gen_Gen            5277     1597  0.302634\n"
     ]
    }
   ],
   "source": [
    "# Relative Establishment Table\n",
    "\n",
    "data = pd.DataFrame(columns = ['Species_Guild', 'Assembly_Model', 'Motif_Group', 'Num_Colonizers', 'Est_Num', 'Est_Rate'])\n",
    "data.Species_Guild = np.repeat(guild, 8)\n",
    "data.Assembly_Model = np.concatenate((np.repeat('AF1', 4), np.repeat('AF0', 4)))\n",
    "data.Motif_Group = ['Spc_Spc', 'Spc_Gen', 'Gen_Spc', 'Gen_Gen', 'Spc_Spc', 'Spc_Gen', 'Gen_Spc', 'Gen_Gen']\n",
    "data.Num_Colonizers = [len(spc_spc_af1), len(spc_gen_af1), len(gen_spc_af1), len(gen_gen_af1), len(spc_spc_af0), len(spc_gen_af0), len(gen_spc_af0), len(gen_gen_af0)]\n",
    "data.Est_Num = [sum(spc_spc_af1.lifespan > 4000), sum(spc_gen_af1.lifespan > 4000), sum(gen_spc_af1.lifespan > 4000), sum(gen_gen_af1.lifespan > 4000), sum(spc_spc_af0.lifespan > 4000), sum(spc_gen_af0.lifespan > 4000), sum(gen_spc_af0.lifespan > 4000), sum(gen_gen_af0.lifespan > 4000)]\n",
    "data.Est_Rate = data.Est_Num / data.Num_Colonizers\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07de5b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# How frequent is each motif after establishment? \n",
    "est_species_af1 = species_af1[species_af1.lifespan > 4000]\n",
    "est_species_af0 = species_af0[species_af0.lifespan > 4000]\n",
    "\n",
    "# Only consider species that retain their indirect partners\n",
    "est_species_af1 = est_species_af1[est_species_af1.GenPart2000 == 1]\n",
    "est_species_af0 = est_species_af0[est_species_af0.GenPart2000 == 1]\n",
    "est_species_af1 = est_species_af1[est_species_af1.GenPart4000 == 1]\n",
    "est_species_af0 = est_species_af0[est_species_af0.GenPart4000 == 1]\n",
    "\n",
    "#separate between specialists and generalists\n",
    "spc_af1 = est_species_af1[est_species_af1.Spc == 1]\n",
    "spc_af0 = est_species_af0[est_species_af0.Spc == 1]\n",
    "gen_af1 = est_species_af1[est_species_af1.Gen == 1]\n",
    "gen_af0 = est_species_af0[est_species_af0.Gen == 1]\n",
    "\n",
    "#separate between specialists with generalists partners with specialist and generalist partners\n",
    "spc_spc_af1 = spc_af1[spc_af1.SpcPartPart == 1]\n",
    "spc_spc_af0 = spc_af0[spc_af0.SpcPartPart == 1]\n",
    "spc_gen_af1 = spc_af1[spc_af1.GenPartPart == 1]\n",
    "spc_gen_af0 = spc_af0[spc_af0.GenPartPart == 1]\n",
    "spc_gen_af1 = spc_gen_af1[spc_gen_af1.SpcPartPart == 0]\n",
    "spc_gen_af0 = spc_gen_af0[spc_gen_af0.SpcPartPart == 0]\n",
    "\n",
    "#separate between generalists with generalists partners with specialist and generalist partners\n",
    "gen_spc_af1 = gen_af1[gen_af1.SpcPartPart == 1]\n",
    "gen_spc_af0 = gen_af0[gen_af0.SpcPartPart == 1]\n",
    "gen_gen_af1 = gen_af1[gen_af1.GenPartPart == 1]\n",
    "gen_gen_af0 = gen_af0[gen_af0.GenPartPart == 1]\n",
    "gen_gen_af1 = gen_gen_af1[gen_gen_af1.SpcPartPart == 0]\n",
    "gen_gen_af0 = gen_gen_af0[gen_gen_af0.SpcPartPart == 0]\n",
    "\n",
    "# How frequent is each motif after extinctions? \n",
    "\n",
    "#separate between specialists and generalists\n",
    "spc_af1_ext = est_species_af1[est_species_af1.Spc2000 == 1]\n",
    "spc_af0_ext = est_species_af0[est_species_af0.Spc2000 == 1]\n",
    "gen_af1_ext = est_species_af1[est_species_af1.Gen2000 == 1]\n",
    "gen_af0_ext = est_species_af0[est_species_af0.Gen2000 == 1]\n",
    "\n",
    "#separate between specialists with generalists partners with specialist and generalist partners\n",
    "spc_spc_af1_ext = spc_af1_ext[spc_af1_ext.SpcPartPart2000 == 1]\n",
    "spc_spc_af0_ext = spc_af0_ext[spc_af0_ext.SpcPartPart2000 == 1]\n",
    "spc_gen_af1_ext = spc_af1_ext[spc_af1_ext.GenPartPart2000 == 1]\n",
    "spc_gen_af0_ext = spc_af0_ext[spc_af0_ext.GenPartPart2000 == 1]\n",
    "spc_gen_af1_ext = spc_gen_af1_ext[spc_gen_af1_ext.SpcPartPart2000 == 0]\n",
    "spc_gen_af0_ext = spc_gen_af0_ext[spc_gen_af0_ext.SpcPartPart2000 == 0]\n",
    "\n",
    "#separate between generalists with generalists partners with specialist and generalist partners\n",
    "gen_spc_af1_ext = gen_af1_ext[gen_af1_ext.SpcPartPart2000 == 1]\n",
    "gen_spc_af0_ext = gen_af0_ext[gen_af0_ext.SpcPartPart2000 == 1]\n",
    "gen_gen_af1_ext = gen_af1_ext[gen_af1_ext.GenPartPart2000 == 1]\n",
    "gen_gen_af0_ext = gen_af0_ext[gen_af0_ext.GenPartPart2000 == 1]\n",
    "gen_gen_af1_ext = gen_gen_af1_ext[gen_gen_af1_ext.SpcPartPart2000 == 0]\n",
    "gen_gen_af0_ext = gen_gen_af0_ext[gen_gen_af0_ext.SpcPartPart2000 == 0]\n",
    "\n",
    "# How frequent is each motif after establishment and extinctions?\n",
    "\n",
    "#separate between specialists and generalists\n",
    "spc_af1_est = est_species_af1[est_species_af1.Spc4000 == 1]\n",
    "spc_af0_est = est_species_af0[est_species_af0.Spc4000 == 1]\n",
    "gen_af1_est = est_species_af1[est_species_af1.Gen4000 == 1]\n",
    "gen_af0_est = est_species_af0[est_species_af0.Gen4000 == 1]\n",
    "\n",
    "#separate between specialists with generalists partners with specialist and generalist partners\n",
    "spc_spc_af1_est = spc_af1_est[spc_af1_est.SpcPartPart4000 == 1]\n",
    "spc_spc_af0_est = spc_af0_est[spc_af0_est.SpcPartPart4000 == 1]\n",
    "spc_gen_af1_est = spc_af1_est[spc_af1_est.GenPartPart4000 == 1]\n",
    "spc_gen_af0_est = spc_af0_est[spc_af0_est.GenPartPart4000 == 1]\n",
    "spc_gen_af1_est = spc_gen_af1_est[spc_gen_af1_est.SpcPartPart4000 == 0]\n",
    "spc_gen_af0_est = spc_gen_af0_est[spc_gen_af0_est.SpcPartPart4000 == 0]\n",
    "\n",
    "#separate between generalists with generalists partners with specialist and generalist partners\n",
    "gen_spc_af1_est = gen_af1_est[gen_af1_est.SpcPartPart4000 == 1]\n",
    "gen_spc_af0_est = gen_af0_est[gen_af0_est.SpcPartPart4000 == 1]\n",
    "gen_gen_af1_est = gen_af1_est[gen_af1_est.GenPartPart4000 == 1]\n",
    "gen_gen_af0_est = gen_af0_est[gen_af0_est.GenPartPart4000 == 1]\n",
    "gen_gen_af1_est = gen_gen_af1_est[gen_gen_af1_est.SpcPartPart4000 == 0]\n",
    "gen_gen_af0_est = gen_gen_af0_est[gen_gen_af0_est.SpcPartPart4000 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d344995",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Species_Guild Assembly_Model Motif_Group  Indirect_Spc_Ext_Num  \\\n",
      "0    pollinator            AF1     Spc_Spc                    16   \n",
      "1    pollinator            AF1     Spc_Gen                     0   \n",
      "2    pollinator            AF1     Gen_Spc                   275   \n",
      "3    pollinator            AF1     Gen_Gen                     0   \n",
      "4    pollinator            AF0     Spc_Spc                    16   \n",
      "5    pollinator            AF0     Spc_Gen                     0   \n",
      "6    pollinator            AF0     Gen_Spc                   275   \n",
      "7    pollinator            AF0     Gen_Gen                     0   \n",
      "\n",
      "   Indirect_Spc_Ext_Rate  Motif_Freq_After_Ext  Indirect_Spc_Est_Num  \\\n",
      "0               0.032787                   567                     0   \n",
      "1               0.000000                  1661                   151   \n",
      "2               0.240806                   969                     0   \n",
      "3               0.000000                  1718                   161   \n",
      "4               0.032787                   567                     0   \n",
      "5               0.000000                  1661                   151   \n",
      "6               0.240806                   969                     0   \n",
      "7               0.000000                  1718                   161   \n",
      "\n",
      "   Indirect_Spc_Est_Rate  Motif_Freq_After_Est  \n",
      "0               0.000000                   515  \n",
      "1               0.090909                  1079  \n",
      "2               0.000000                  1030  \n",
      "3               0.093714                  2291  \n",
      "4               0.000000                   515  \n",
      "5               0.090909                  1079  \n",
      "6               0.000000                  1030  \n",
      "7               0.093714                  2291  \n"
     ]
    }
   ],
   "source": [
    "# Motif Group Transformations Table\n",
    "\n",
    "#How often are indirect specialist excluded?\n",
    "spc_spc2000spc_gen_af1 = len(spc_spc_af1[spc_spc_af1.SpcPartPart2000 == 0])\n",
    "gen_spc2000gen_gen_af1 = len(gen_spc_af1[gen_spc_af1.SpcPartPart2000 == 0])\n",
    "spc_spc2000spc_gen_af0 = len(spc_spc_af0[spc_spc_af0.SpcPartPart2000 == 0])\n",
    "gen_spc2000gen_gen_af0 = len(gen_spc_af0[gen_spc_af0.SpcPartPart2000 == 0])\n",
    "\n",
    "#How often are indirect specialists added?\n",
    "spc_gen4000spc_spc_af1 = len(spc_gen_af1_ext[spc_gen_af1_ext.SpcPartPart4000 == 1])\n",
    "gen_gen4000gen_spc_af1 = len(gen_gen_af1_ext[gen_gen_af1_ext.SpcPartPart4000 == 1])\n",
    "spc_gen4000spc_spc_af0 = len(spc_gen_af0_ext[spc_gen_af0_ext.SpcPartPart4000 == 1])\n",
    "gen_gen4000gen_spc_af0 = len(gen_gen_af0_ext[gen_gen_af0_ext.SpcPartPart4000 == 1])\n",
    "\n",
    "data = pd.DataFrame(columns = ['Species_Guild', 'Assembly_Model', 'Motif_Group', 'Indirect_Spc_Ext_Num', 'Indirect_Spc_Ext_Rate', 'Motif_Freq_After_Ext', 'Indirect_Spc_Est_Num', 'Indirect_Spc_Est_Rate', 'Motif_Freq_After_Est'])\n",
    "data.Species_Guild = np.repeat(guild, 8)\n",
    "data.Assembly_Model = np.concatenate((np.repeat('AF1', 4), np.repeat('AF0', 4)))\n",
    "data.Motif_Group = ['Spc_Spc', 'Spc_Gen', 'Gen_Spc', 'Gen_Gen', 'Spc_Spc', 'Spc_Gen', 'Gen_Spc', 'Gen_Gen']\n",
    "data.Indirect_Spc_Ext_Num = [spc_spc2000spc_gen_af1, 0, gen_spc2000gen_gen_af1, 0, spc_spc2000spc_gen_af0, 0, gen_spc2000gen_gen_af0, 0]\n",
    "data.Indirect_Spc_Ext_Rate = [spc_spc2000spc_gen_af1/len(spc_spc_af1) , 0, gen_spc2000gen_gen_af1/len(gen_spc_af1), 0, spc_spc2000spc_gen_af0/len(spc_spc_af0), 0, gen_spc2000gen_gen_af0/len(gen_spc_af0), 0]\n",
    "data.Motif_Freq_After_Ext = [len(spc_spc_af1_ext), len(spc_gen_af1_ext), len(gen_spc_af1_ext), len(gen_gen_af1_ext), len(spc_spc_af0_ext), len(spc_gen_af0_ext), len(gen_spc_af0_ext), len(gen_gen_af0_ext)]\n",
    "data.Indirect_Spc_Est_Num = [0, spc_gen4000spc_spc_af1, 0, gen_gen4000gen_spc_af1, 0, spc_gen4000spc_spc_af0, 0, gen_gen4000gen_spc_af0]\n",
    "data.Indirect_Spc_Est_Rate = [0, spc_gen4000spc_spc_af1/len(spc_gen_af1_ext), 0, gen_gen4000gen_spc_af1/len(gen_gen_af1_ext), 0, spc_gen4000spc_spc_af0/len(spc_gen_af0_ext), 0, gen_gen4000gen_spc_af0/len(gen_gen_af0_ext)]\n",
    "data.Motif_Freq_After_Est = [len(spc_spc_af1_est), len(spc_gen_af1_est), len(gen_spc_af1_est), len(gen_gen_af1_est), len(spc_spc_af0_est), len(spc_gen_af0_est), len(gen_spc_af0_est), len(gen_gen_af0_est)]\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dc151260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the relative frequency of motifs through each stage of core motif development for assembly model with AF\n",
    "\n",
    "a = len(spc_spc_af1) / len(est_species_af1)\n",
    "b = len(spc_gen_af1) / len(est_species_af1)\n",
    "c = len(gen_spc_af1) / len(est_species_af1)\n",
    "d = len(gen_gen_af1) / len(est_species_af1)\n",
    "\n",
    "e = len(spc_spc_af1_ext) / len(est_species_af1)\n",
    "f = len(spc_gen_af1_ext) / len(est_species_af1)\n",
    "g = len(gen_spc_af1_ext) / len(est_species_af1)\n",
    "h = len(gen_gen_af1_ext) / len(est_species_af1)\n",
    "\n",
    "i = len(spc_spc_af1_est) / len(est_species_af1)\n",
    "j = len(spc_gen_af1_est) / len(est_species_af1)\n",
    "k = len(gen_spc_af1_est) / len(est_species_af1)\n",
    "l = len(gen_gen_af1_est) / len(est_species_af1)\n",
    "\n",
    "# plot\n",
    "labels = ['Spc-Gen-Spc', 'Spc-Gen-Gen', 'Gen-Gen-Spc', 'Gen-Gen-Gen']\n",
    "data = [a, b, c, d]\n",
    "data_ext = [e, f, g, h]\n",
    "data_est = [i, j, k, l]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.20\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 4))\n",
    "data_fig = ax.bar(x - (width), data, width, label='After Establishment', color = 'black')\n",
    "ax.bar_label(data_fig, fmt='%0.2f')\n",
    "data_ext_fig = ax.bar(x, data_ext, width, label='After Establishment and Residual Extinctions', color = 'black', alpha = 0.7)\n",
    "ax.bar_label(data_ext_fig, fmt='%0.2f')\n",
    "data_est_fig = ax.bar(x + (width), data_est, width, label='After Establishment, Residual Extinctions, and Subsequent Establishment', color = 'black', alpha = 0.2)\n",
    "ax.bar_label(data_est_fig, fmt='%0.2f')\n",
    "\n",
    "ax.set_ylabel('Relative Frequency')\n",
    "ax.set_xticks(x)\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8a72e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the relative frequency of motifs through each stage of core motif development for assembly model without AF\n",
    "\n",
    "a = len(spc_spc_af0) / len(est_species_af0)\n",
    "b = len(spc_gen_af0) / len(est_species_af0)\n",
    "c = len(gen_spc_af0) / len(est_species_af0)\n",
    "d = len(gen_gen_af0) / len(est_species_af0)\n",
    "\n",
    "e = len(spc_spc_af0_ext) / len(est_species_af0)\n",
    "f = len(spc_gen_af0_ext) / len(est_species_af0)\n",
    "g = len(gen_spc_af0_ext) / len(est_species_af0)\n",
    "h = len(gen_gen_af0_ext) / len(est_species_af0)\n",
    "\n",
    "i = len(spc_spc_af0_est) / len(est_species_af0)\n",
    "j = len(spc_gen_af0_est) / len(est_species_af0)\n",
    "k = len(gen_spc_af0_est) / len(est_species_af0)\n",
    "l = len(gen_gen_af0_est) / len(est_species_af0)\n",
    "\n",
    "# plot\n",
    "labels = ['Spc-Gen-Spc', 'Spc-Gen-Gen', 'Gen-Gen-Spc', 'Gen-Gen-Gen']\n",
    "data = [a, b, c, d]\n",
    "data_ext = [e, f, g, h]\n",
    "data_est = [i, j, k, l]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.20\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 4))\n",
    "data_fig = ax.bar(x - (width), data, width, label='After Establishment', color = 'black')\n",
    "ax.bar_label(data_fig, fmt='%0.2f')\n",
    "data_ext_fig = ax.bar(x, data_ext, width, label='After Establishment and Residual Extinctions', color = 'black', alpha = 0.7)\n",
    "ax.bar_label(data_ext_fig, fmt='%0.2f')\n",
    "data_est_fig = ax.bar(x + (width), data_est, width, label='After Establishment, Residual Extinctions, and Subsequent Establishment', color = 'black', alpha = 0.2)\n",
    "ax.bar_label(data_est_fig, fmt='%0.2f')\n",
    "\n",
    "ax.set_ylabel('Relative Frequency')\n",
    "ax.set_xticks(x)\n",
    "ax.legend()\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "562cc45b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_table_af0 = pd.read_csv('%s/results/%s/%s/data_table.csv' % (getcwd(), date, dir_AF0))\n",
    "data_table_af1 = pd.read_csv('%s/results/%s/%s/data_table.csv' % (getcwd(), date, dir_AF1))\n",
    "\n",
    "# analyze final networks produced in each simulation\n",
    "af0_data = data_table_af0[data_table_af0.TimeStep == 100000]\n",
    "af1_data = data_table_af1[data_table_af1.TimeStep == 100000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66724640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3975909413283257\n",
      "0.3975909413283257\n",
      "Ttest_relResult(statistic=nan, pvalue=nan)\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "bplot = ax.boxplot(x = [af0_data.Connectence, af1_data.Connectence], patch_artist = True)\n",
    "ax.set_ylabel('Connectence')\n",
    "colors = ['lightgrey', 'lightgrey', 'lightgrey']\n",
    "for patch, color in zip(bplot['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "    \n",
    "for median in bplot['medians']:\n",
    "    median.set_color('black')\n",
    "    \n",
    "fig.tight_layout()\n",
    "#plt.ylim(0, 4)\n",
    "plt.show()\n",
    "\n",
    "print(np.mean(af0_data.Connectence))\n",
    "print(np.mean(af1_data.Connectence))\n",
    "print(ttest_rel(af0_data.Connectence, af1_data.Connectence, alternative = 'less'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38914500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53.70247933884298\n",
      "53.70247933884298\n",
      "Ttest_relResult(statistic=nan, pvalue=nan)\n"
     ]
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "bplot = ax.boxplot(x = [af0_data.NetworkSize, af1_data.NetworkSize], patch_artist = True)\n",
    "ax.set_ylabel('Richness')\n",
    "colors = ['lightgrey', 'lightgrey', 'lightgrey']\n",
    "for patch, color in zip(bplot['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "    \n",
    "for median in bplot['medians']:\n",
    "    median.set_color('black')\n",
    "    \n",
    "fig.tight_layout()\n",
    "#plt.ylim(0, 4)\n",
    "plt.show()\n",
    "\n",
    "print(np.mean(af0_data.NetworkSize))\n",
    "print(np.mean(af1_data.NetworkSize))\n",
    "print(ttest_rel(af0_data.NetworkSize, af1_data.NetworkSize, alternative = 'less'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6627822d",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'NODFc'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/dz/887wb7q155d3pyd6qzhg8hrm0000gn/T/ipykernel_38225/1480323598.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mbplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0maf0_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNODFc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maf1_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNODFc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpatch_artist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Nestedness'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'lightgrey'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lightgrey'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'lightgrey'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'NODFc'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "bplot = ax.boxplot(x = [af0_data.NODFc, af1_data.NODFc], patch_artist = True)\n",
    "ax.set_ylabel('Nestedness')\n",
    "colors = ['lightgrey', 'lightgrey', 'lightgrey']\n",
    "for patch, color in zip(bplot['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "    \n",
    "for median in bplot['medians']:\n",
    "    median.set_color('black')\n",
    "    \n",
    "fig.tight_layout()\n",
    "#plt.ylim(0, 4)\n",
    "plt.show()\n",
    "\n",
    "print(np.mean(af0_data.NODFc))\n",
    "print(np.mean(af1_data.NODFc))\n",
    "print(ttest_rel(af0_data.NODFc, af1_data.NODFc, alternative = 'less'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00c5360",
   "metadata": {},
   "source": [
    "##### "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}