{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "One way of generating a neutral time series is considering a lattice of N individuals on which every time step one individual is replaced by another. Each individual of the lattice has an equal probability of being replaced (probability is $1/N$). The disappearance of the 1rst species can be interpreted as the result of either death or emigration. The replacing individual is either the result of immigration or growth. The probability of immigration depends on the immigration rate ($0 \\leq \\lambda \\leq 1$). In case of an immigration event, all species of the external species pool $S$ have an equalprobability of immigrating. The probability of a growth event is thus given by the remaining $1 - \\lambda$. Incase of growth, every individual has an equal probability of growing. Time series generated in this way depend on three variables: the length of the simulation time $T$, the immigration probability $\\lambda$ and the number of individuals $N$. We study the effect of these three variables on both neutrality measures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:53:39.494969Z",
     "start_time": "2020-02-19T06:53:39.100139Z"
    }
   },
   "outputs": [],
   "source": [
    "# Data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Options for pandas\n",
    "pd.options.display.max_columns = 50\n",
    "pd.options.display.max_rows = 30\n",
    "\n",
    "from IPython import get_ipython\n",
    "ipython = get_ipython()\n",
    "\n",
    "# autoreload extension\n",
    "if 'autoreload' not in ipython.extension_manager.loaded:\n",
    "    %load_ext autoreload\n",
    "\n",
    "%autoreload 2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "%matplotlib inline\n",
    "\n",
    "import time\n",
    "np.random.seed(int(time.time()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specific imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:53:40.753233Z",
     "start_time": "2020-02-19T06:53:40.312090Z"
    }
   },
   "outputs": [],
   "source": [
    "from timeseries_plotting import PlotTimeseries\n",
    "from noise_properties_plotting import PiecewiseNormalize\n",
    "from enum import Enum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Settings figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:53:41.234512Z",
     "start_time": "2020-02-19T06:53:41.163865Z"
    }
   },
   "outputs": [],
   "source": [
    "from elife_settings import set_elife_settings, ELIFE\n",
    "\n",
    "set_elife_settings()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure neutrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:53:42.103041Z",
     "start_time": "2020-02-19T06:53:42.057975Z"
    }
   },
   "outputs": [],
   "source": [
    "class NeutralityTest(Enum):\n",
    "    KULLBACKLEIBLER = 1\n",
    "    COVARIANCE = 2\n",
    "\n",
    "\n",
    "def plot_neutrality(f, type=NeutralityTest.KULLBACKLEIBLER, ax=0, ax_clb=0):\n",
    "    if isinstance(f, str):\n",
    "        df = pd.read_csv(f, index_col=0)\n",
    "    elif isinstance(f, list):  # average of all files\n",
    "        df = pd.DataFrame(np.nanmedian([pd.read_csv(fi, index_col=0).values for fi in f], axis=0),\n",
    "                          columns=pd.read_csv(f[0], index_col=0).columns,\n",
    "                          index=pd.read_csv(f[0], index_col=0).index)\n",
    "        df[df == np.inf] = 1e4\n",
    "\n",
    "    if ax == 0:\n",
    "        fig = plt.figure()\n",
    "\n",
    "        gs = gridspec.GridSpec(1, 2, width_ratios=[9, 1], wspace=0.3)\n",
    "\n",
    "        ax = fig.add_subplot(gs[0])\n",
    "        ax_clb = fig.add_subplot(gs[1])\n",
    "\n",
    "    ax.set_facecolor('lightgrey')\n",
    "\n",
    "    if type == NeutralityTest.KULLBACKLEIBLER:\n",
    "        vmin = -1\n",
    "        vmax = 3\n",
    "        with np.errstate(divide='ignore'):\n",
    "            log_KL = np.log10(df.T)\n",
    "        mat = ax.matshow(log_KL, origin='lower', cmap='Blues_r',\n",
    "                         aspect='auto', vmin=vmin, vmax=vmax)\n",
    "    elif type == NeutralityTest.COVARIANCE:\n",
    "        vmin = -5\n",
    "        vmax = 0  # pvalue is max 1 = 1e0\n",
    "        norm = PiecewiseNormalize([vmin, np.log10(0.05), vmax], [0, 0.5, 1])\n",
    "        with np.errstate(divide='ignore'):\n",
    "            log_nct = np.log10(df.T)\n",
    "        mat = ax.matshow(log_nct, origin='lower', norm=norm,\n",
    "                         cmap='seismic_r', aspect='auto', vmin=vmin, vmax=vmax)\n",
    "\n",
    "    skiplabel = 0\n",
    "\n",
    "    ax.set_xticks(range(0, df.shape[0], (skiplabel+1)))\n",
    "    ax.set_xticklabels(['%d' % i for i in df.index][::(skiplabel+1)])\n",
    "    ax.set_yticks(range(0, df.shape[1], (skiplabel+1)))\n",
    "    ax.set_yticklabels(\n",
    "        ['%.3f' % i for i in df.columns.astype(float)][::(skiplabel+1)])\n",
    "    ax.set_xlabel('Size community')\n",
    "    ax.set_ylabel(r'Immigration probability $\\lambda$')\n",
    "\n",
    "    if ax_clb != 0:\n",
    "        plt.colorbar(mat, cax=ax_clb)\n",
    "\n",
    "        if type == NeutralityTest.KULLBACKLEIBLER:\n",
    "            ax_clb.set_title(r'log$_{10}$(D$_{KL}$)')\n",
    "\n",
    "            ax_clb2 = ax_clb.twinx()\n",
    "            ax_clb2.yaxis.set_ticks_position('right')\n",
    "            ax_clb.yaxis.set_ticks_position('left')\n",
    "            ax_clb2.yaxis.set_ticks([0.05, 0.95])\n",
    "            ax_clb2.set_ylim([0, 1])\n",
    "            ax_clb2.yaxis.set_ticklabels(['neutral', 'niche'])\n",
    "\n",
    "        elif type == NeutralityTest.COVARIANCE:\n",
    "            ax_clb.set_title(r'log$_{10}$($p_{NCT}$)')\n",
    "            ax_clb2 = ax_clb.twinx()\n",
    "            ax_clb2.yaxis.set_ticks_position('right')\n",
    "            ax_clb.yaxis.set_ticks_position('left')\n",
    "            ax_clb2.yaxis.set_ticks([1+(vmin + np.log10(0.05))/(vmax - vmin)/2,\n",
    "                                     1+(vmax + np.log10(0.05))/(vmax - vmin)/2])\n",
    "            ax_clb2.set_ylim([0, 1])\n",
    "            ax_clb2.yaxis.set_ticklabels(['niche', 'neutral'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating neutral timeseries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:53:42.977829Z",
     "start_time": "2020-02-19T06:53:42.943049Z"
    }
   },
   "outputs": [],
   "source": [
    "new = False\n",
    "\n",
    "lamda = 0.01  # immigration probability\n",
    "\n",
    "T = int(1e7)\n",
    "tskip = 999\n",
    "\n",
    "S = 50  # amount of different species\n",
    "J = 5000  # Number of individuals in the community\n",
    "\n",
    "f = 'test_neutral4.txt'\n",
    "\n",
    "\n",
    "def neutral_timeseries(S, lamda, J, tskip=1e3, T=int(1e6), f=0):\n",
    "    initcond = np.arange(J/S, J+1, J/S)\n",
    "\n",
    "    x = np.copy(initcond)\n",
    "    x_ts = np.copy(initcond)\n",
    "\n",
    "    # save x as cumulative distribution, it makes simulations faster\n",
    "\n",
    "    for i in range(T):\n",
    "        if i % 1e6 == 0:\n",
    "            print(i)\n",
    "\n",
    "        if np.random.uniform(0, 1) < lamda:  # immigration from outside pool\n",
    "            immi = int(np.random.uniform()*S)\n",
    "            x[immi:] += 1\n",
    "        else:\n",
    "            growing = int(np.random.uniform()*J)\n",
    "\n",
    "            x[x > growing] += 1\n",
    "\n",
    "        dead = int(np.random.uniform()*(J+1))\n",
    "\n",
    "        x[x > dead] -= 1\n",
    "\n",
    "        if i % (tskip + 1) == 0:\n",
    "            x_ts = np.vstack((x_ts, x))\n",
    "\n",
    "    # transform cumulative distribution into abundances\n",
    "\n",
    "    for i in range(1, S):\n",
    "        x_ts[:, -i] = x_ts[:, -i] - x_ts[:, -i-1]\n",
    "\n",
    "    if f != 0:\n",
    "        np.savetxt(f, x_ts, fmt='%d')\n",
    "\n",
    "    return x_ts\n",
    "\n",
    "\n",
    "if new:\n",
    "    neutral_timeseries(S, lamda, J, tskip, T, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:45:04.218729Z",
     "start_time": "2020-02-19T06:45:04.187146Z"
    }
   },
   "source": [
    "Plot some neutral timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:53:45.453046Z",
     "start_time": "2020-02-19T06:53:43.887910Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Open Sans'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "for i, l in enumerate(['', '2', '3', '4'], start=1):\n",
    "    ax = fig.add_subplot(2, 2, i)\n",
    "    ts_np = np.loadtxt('results/neutral_timeseries/ts_neutral' + l + '.txt').T\n",
    "    ts = pd.DataFrame(\n",
    "        ts_np, columns=['species_%d' % i for i in range(1, ts_np.shape[1]+1)])\n",
    "    ts['time'] = range(len(ts))\n",
    "\n",
    "    PlotTimeseries(ts, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate neutrality measures for timeseries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:53:46.104548Z",
     "start_time": "2020-02-19T06:53:46.071378Z"
    }
   },
   "outputs": [],
   "source": [
    "def neutral_measures(fKL, fNCT,\n",
    "                     Js=[50, 100, 500, 1000, 2500, 5000],\n",
    "                     lamdas=[0, 0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 1]):\n",
    "    KL = np.zeros([len(Js), len(lamdas)])\n",
    "    NCT = np.zeros([len(Js), len(lamdas)])\n",
    "\n",
    "    for i, J in enumerate(Js):\n",
    "        for j, lamda in enumerate(lamdas):\n",
    "            print(J, lamda)\n",
    "\n",
    "            x_ts = neutral_timeseries(S, lamda, J, tskip=0, T=int(1e4))\n",
    "\n",
    "            KL[i, j] = KullbackLeibler_neutrality(x_ts[:, :-1])\n",
    "\n",
    "            for k in range(len(x_ts)):\n",
    "                s = sum(x_ts[k])\n",
    "                if s > 0:\n",
    "                    x_ts[k] /= s\n",
    "\n",
    "            NCT[i, j] = neutral_covariance_test(\n",
    "                x_ts, ntests=500, method='Kolmogorov')\n",
    "\n",
    "    KL = pd.DataFrame(KL, index=Js, columns=lamdas)\n",
    "    KL.to_csv(fKL)\n",
    "\n",
    "    NCT = pd.DataFrame(NCT, index=Js, columns=lamdas)\n",
    "    NCT.to_csv(fNCT)\n",
    "\n",
    "    return KL, NCT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the neutrality measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-19T06:53:47.553059Z",
     "start_time": "2020-02-19T06:53:46.559077Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Open Sans'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x216 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(ELIFE.TEXTWIDTH, 3))\n",
    "\n",
    "gs = gridspec.GridSpec(2, 4, wspace=0.05, hspace=0.3,\n",
    "                       right=0.82, bottom=0.18, top=0.9)\n",
    "gs_clb = gridspec.GridSpec(2, 1, hspace=0.35, top=0.9,\n",
    "                           bottom=0.18, left=0.88, right=0.9)\n",
    "gs_tot = gridspec.GridSpec(1, 1, top=0.95, bottom=0.08, left=0.07, right=0.82)\n",
    "\n",
    "ax_clb_KL = fig.add_subplot(gs_clb[0])\n",
    "ax_clb_NCT = fig.add_subplot(gs_clb[1])\n",
    "\n",
    "for i, s in enumerate(['1e4', '1e5', '1e6', '1e7']):\n",
    "    ax_KL = fig.add_subplot(gs[0, i])\n",
    "    ax_NCT = fig.add_subplot(gs[1, i])\n",
    "\n",
    "    ax_KL.set_title('T = 10$^{%d}$' % int(s[-1]))\n",
    "\n",
    "    path = 'results/neutral_timeseries/'\n",
    "    if i == 0:\n",
    "        plot_neutrality([path + 'KL-%s-' % s + '%d.csv' % j for j in range(0, 6)],\n",
    "                        ax=ax_KL, ax_clb=ax_clb_KL)\n",
    "        plot_neutrality([path + 'NCT-%s-' % s + '%d.csv' % j for j in range(0, 6)],\n",
    "                        type=NeutralityTest.COVARIANCE,\n",
    "                        ax=ax_NCT, ax_clb=ax_clb_NCT)\n",
    "\n",
    "        ax_KL.tick_params(axis=\"both\", bottom=True, labelbottom=False, top=False, labeltop=False,\n",
    "                          left=True, labelleft=True)\n",
    "        ax_NCT.tick_params(axis=\"both\", bottom=True, labelbottom=True, top=False, labeltop=False,\n",
    "                           left=True, labelleft=True)\n",
    "\n",
    "        ax_KL.text(-0.08, 1.15, 'A', transform=ax_KL.transAxes,\n",
    "                   fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "        ax_NCT.text(-0.08, 1.15, 'B', transform=ax_NCT.transAxes,\n",
    "                    fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "    else:\n",
    "        plot_neutrality([path + 'KL-%s-' % s + '%d.csv' % j for j in range(0, 6)],\n",
    "                        ax=ax_KL)\n",
    "        plot_neutrality([path + 'NCT-%s-' % s + '%d.csv' % j for j in range(0, 6)],\n",
    "                        type=NeutralityTest.COVARIANCE,\n",
    "                        ax=ax_NCT)\n",
    "\n",
    "        ax_KL.tick_params(axis=\"both\", bottom=True, labelbottom=False, top=False, labeltop=False,\n",
    "                          left=True, labelleft=False)\n",
    "        ax_NCT.tick_params(axis=\"both\", bottom=True, labelbottom=True, top=False, labeltop=False,\n",
    "                           left=True, labelleft=False)\n",
    "\n",
    "    ax_NCT.tick_params(axis='x', rotation=90)\n",
    "\n",
    "    ax_KL.set_xlabel('')\n",
    "    ax_NCT.set_xlabel('')\n",
    "    ax_KL.set_ylabel('')\n",
    "    ax_NCT.set_ylabel('')\n",
    "\n",
    "ax = fig.add_subplot(gs_tot[0], frameon=False)\n",
    "ax.tick_params(axis=\"both\", bottom=False, labelbottom=False,\n",
    "               left=False, labelleft=False)\n",
    "ax.set_xlabel('Size community', x=1, ha='right')\n",
    "ax.set_ylabel('Immigration probability $\\lambda$')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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