{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Make the figures for the eLife draft."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:47:52.838017Z",
     "start_time": "2020-02-20T09:47:52.422684Z"
    }
   },
   "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": "2020-02-20T09:47:54.252484Z",
     "start_time": "2020-02-20T09:47:52.905037Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "from noise_analysis import noise_color\n",
    "from scipy import stats\n",
    "from noise_properties_plotting import noise_cmap_ww, noise_lim, PiecewiseNormalize, \\\n",
    "    PlotTimeseriesComparison, PlotNoiseColorComparison\n",
    "from generate_timeseries import Timeseries\n",
    "from noise_parameters import NOISE, MODEL\n",
    "\n",
    "from matplotlib import font_manager\n",
    "font_manager._rebuild()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:29:47.356497Z",
     "start_time": "2020-02-18T20:29:47.336571Z"
    }
   },
   "source": [
    "## Settings figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:47:54.439059Z",
     "start_time": "2020-02-20T09:47:54.367264Z"
    }
   },
   "outputs": [],
   "source": [
    "from elife_settings import set_elife_settings, ELIFE\n",
    "\n",
    "set_elife_settings()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figures\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 1: analysis of experimental data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the experimental data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:47:55.135961Z",
     "start_time": "2020-02-20T09:47:54.550622Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load dataframes\n",
    "\n",
    "# MartinPlatero plankton data\n",
    "\n",
    "df_ts = {}\n",
    "\n",
    "path = 'Data/MartinPlatero/'\n",
    "files = ['41467_2017_2571_MOESM5_ESM_MartinPlatero_Plankton_Eukarya.csv']\n",
    "    #['41467_2017_2571_MOESM4_ESM_MartinPlatero_Plankton_Bacteria.csv']\n",
    "keys = ['plankton_eukarya']\n",
    "    #['plankton_bacteria'] \n",
    "\n",
    "for i, f in enumerate(files):\n",
    "    x = pd.read_csv(path+f, na_values='NAN', index_col=0)\n",
    "    x = x.iloc[:, :-1] # delete last columns which contains details on the otu's\n",
    "    \n",
    "    # only keep 200 most abundant species\n",
    "    sum_x = x.sum(axis='columns')\n",
    "    \n",
    "    x = x[sum_x >= np.sort(sum_x)[-200]]\n",
    "    \n",
    "    x = x.T # species are in rows instead of columns\n",
    "    \n",
    "    x.insert(0, 'time', [int(j[4:7]) for j in x.index]) # day\n",
    "    \n",
    "    x = x.groupby('time').agg('mean').reset_index()\n",
    "    \n",
    "    x.columns = ['time'] + ['species_%d' % j for j in range(1, len(x.columns))]\n",
    "    \n",
    "    df_ts[keys[i]] = x\n",
    "\n",
    "\n",
    "# David stool data\n",
    "\n",
    "files = ['Data/Faust/25_timeseries/25_timeseries.txt']\n",
    "keys = ['David_stool_A']\n",
    "\n",
    "for i, f in enumerate(files):\n",
    "    x = pd.read_csv(f, na_values='NAN', delimiter='\\t', header=None)\n",
    "    \n",
    "    x = x.T\n",
    "    \n",
    "    x.insert(0, 'time', range(len(x)))\n",
    "    \n",
    "    x.columns = ['time'] + ['species_%d' % j for j in range(1, len(x.columns))]\n",
    "    \n",
    "    df_ts[keys[i]] = x\n",
    "    \n",
    "# Caporaso body sites data\n",
    "\n",
    "sites = ['F4_L_palm_L6', 'F4_tongue_L6']\n",
    "\n",
    "for site in sites:\n",
    "    file = 'Data/Caporaso/' + site + '.txt'\n",
    "    key = 'Caporaso_' + site\n",
    "\n",
    "    x = pd.read_csv(file, delimiter='\\t', skiprows=1, index_col=0, header=None)\n",
    "    #x = x[x.sum(axis='rows') > 0]\n",
    "\n",
    "    x.index = ['time'] + ['species_%d' % j for j in range(1, len(x.index))]\n",
    "\n",
    "    x = x.T\n",
    "\n",
    "    # only keep 200 most abundant species\n",
    "    if len(x.columns) > 201:\n",
    "        sum_x = x.sum(axis='rows')\n",
    "\n",
    "        sum_x['time'] = np.inf\n",
    "\n",
    "        sum_x.sort_values(ascending=False, inplace=True)\n",
    "\n",
    "        x = x[sum_x.index.tolist()[:201]]\n",
    "\n",
    "    x.columns = ['time'] + ['species_%d' % j for j in range(1, len(x.columns))]\n",
    "\n",
    "    df_ts[key] = x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate noise color of all time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:48:02.475534Z",
     "start_time": "2020-02-20T09:47:55.566158Z"
    }
   },
   "outputs": [],
   "source": [
    "df_ns = {}\n",
    "\n",
    "keys = ['plankton_eukarya', 'David_stool_A',\n",
    "        'Caporaso_F4_L_palm_L6', 'Caporaso_F4_tongue_L6']\n",
    "\n",
    "for i, key in enumerate(keys):\n",
    "    ts = df_ts[key]\n",
    "    df_ns[key] = noise_color(ts)['slope_linear']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate width distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:48:03.595901Z",
     "start_time": "2020-02-20T09:48:02.994246Z"
    }
   },
   "outputs": [],
   "source": [
    "df_disdx = {}\n",
    "\n",
    "#keys = ['Caporaso_F4_L_palm_L6'] \n",
    "keys =  ['plankton_eukarya', 'David_stool_A',\n",
    "        'Caporaso_F4_L_palm_L6', 'Caporaso_F4_tongue_L6']\n",
    "\n",
    "def fit_ratio(x):\n",
    "    # ratios of succesive time points\n",
    "    x = x = [x1/x2 for x1, x2 in zip(x[:-1], x[1:]) if x1 != 0 and x2 != 0 ] \n",
    "    \n",
    "    if len(x) > 5:\n",
    "        a, b, c = stats.lognorm.fit(x, floc=0)  # Gives the paramters of the fit\n",
    "        stat, pval = stats.kstest(x, 'lognorm', args=((a, b, c))) # get pvalue for kolmogorov-smirnov test \n",
    "        # (null hypothesis: ratios of succesive time points follow lognorm distribution)\n",
    "\n",
    "        return a, b, c, stat, pval\n",
    "    else:\n",
    "        return (np.nan, np.nan, np.nan, np.nan, np.nan)\n",
    "\n",
    "count = 0\n",
    "\n",
    "for i, key in enumerate(keys):\n",
    "    ts = df_ts[key]\n",
    "\n",
    "    dx_ratio = pd.DataFrame(index=ts.columns, columns=['s', 'loc', 'scale', 'ks-stat', 'ks-pval'])\n",
    "    dx_ratio.drop('time', inplace=True)\n",
    "\n",
    "    for idx in dx_ratio.index:\n",
    "        fit_par = fit_ratio(ts[idx].values)\n",
    "        dx_ratio.loc[idx] = fit_par\n",
    "        \n",
    "        if False and fit_par[-1] > 0.5 and count < 10:\n",
    "            print(key, idx, fit_par[-1])\n",
    "            \n",
    "            print(x[:5])\n",
    "\n",
    "            x = ts[idx].values\n",
    "            x_transf = x[:-1] / x[1:] # ratios of succesive time points\n",
    "            x_transf = x_transf[np.isfinite(x_transf)] # remove infinities\n",
    "            \n",
    "            a, b, c, _, pval = fit_par\n",
    "            \n",
    "            x_fit = np.logspace(-1.5,1.5,100)\n",
    "            pdf_fitted = stats.lognorm.pdf(x_fit,a,b,c) #Gives the PDF\n",
    "            plt.figure()\n",
    "            plt.hist(x_transf, alpha=0.4, normed=True, bins = np.logspace(-1.5,1.5,30))\n",
    "            plt.plot(x_fit, pdf_fitted, label='%.2f, %.2f, %.2f'%(a,b,c))\n",
    "            plt.xscale('log')\n",
    "            plt.legend()\n",
    "            plt.show()\n",
    "            \n",
    "            count += 1\n",
    "    \n",
    "    if count == 10:\n",
    "        break;\n",
    "        \n",
    "    df_disdx[key] = dx_ratio\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:48:07.806407Z",
     "start_time": "2020-02-20T09:48:03.599815Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Open Sans'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Open Sans'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 471.969x504 with 28 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotts = True\n",
    "plotra = True\n",
    "plotnc = True\n",
    "plotdx = True\n",
    "plotdisdx = True\n",
    "\n",
    "keys = ['plankton_eukarya', 'David_stool_A',\n",
    "        'Caporaso_F4_L_palm_L6', 'Caporaso_F4_tongue_L6']\n",
    "\n",
    "titles = ['Plankton eukarya', 'Microbiome stool', \n",
    "          'Microbiome palm', 'Microbiome tongue']\n",
    "\n",
    "fig = plt.figure(figsize=(0.9*ELIFE.FULLWIDTH,7))\n",
    "\n",
    "lm = 0.15 # left margin\n",
    "rm = 0.85 # right margin\n",
    "\n",
    "gs_ts = gridspec.GridSpec(2, len(keys), top=0.97, bottom=0.7, left = lm, right=rm, hspace=0.5, wspace=0.1)\n",
    "gs_ma = gridspec.GridSpec(3, len(keys), top=0.65, bottom=0.22, left = lm, right=rm, hspace=0.05, wspace=0.1)\n",
    "gs_legend = gridspec.GridSpec(2, 1, top=0.97, bottom=0.7, left = 0.88, right=0.99, hspace=0.5, wspace=0.1)\n",
    "gs_cbar = gridspec.GridSpec(3, 1, top=0.65, bottom = 0.22, left = 0.88, right=0.9, hspace=0.05, wspace=0.1)\n",
    "\n",
    "gs1 = gridspec.GridSpec(2,1,hspace=0, left=lm, right= rm, top=0.15, bottom=0.11) # for neutrality\n",
    "gs2 = gridspec.GridSpec(1,2,wspace=0.2, left=lm, right = rm, top = 0.07, bottom=0.05) # for colorbars neutrality\n",
    "\n",
    "#axes = np.empty([5, len(keys)])\n",
    "\n",
    "axes = [[0 for i in range(len(keys))] for j in range(5)]\n",
    "for i in range(2):\n",
    "    for j in range(len(keys)):\n",
    "        if j == 0: # share axes except for timeseries\n",
    "            axes[i][j] = fig.add_subplot(gs_ts[i,j])\n",
    "            if i == 0:\n",
    "                axes[i][j].set_title(titles[i])\n",
    "        elif i == 0:\n",
    "            axes[i][j] = fig.add_subplot(gs_ts[i,j], sharey=axes[i][0])\n",
    "            axes[i][j].set_title(titles[j])\n",
    "        else:\n",
    "            axes[i][j] = fig.add_subplot(gs_ts[i,j], sharey=axes[i][0], sharex=axes[i][0])\n",
    "        axes[i][j].grid()\n",
    "        \n",
    "for i in range(2,5):\n",
    "    for j in range(len(keys)):\n",
    "        if i-2 == 0 and j == 0: # share axes except for timeseries\n",
    "            axes[i][j] = fig.add_subplot(gs_ma[i-2,j])\n",
    "        elif i-2 == 0:\n",
    "            axes[i][j] = fig.add_subplot(gs_ma[i-2,j], sharey=axes[i][0], sharex=axes[i][0])\n",
    "        elif j == 0:\n",
    "            axes[i][j] = fig.add_subplot(gs_ma[i-2,j], sharex=axes[i-1][j])\n",
    "        else:\n",
    "            axes[i][j] = fig.add_subplot(gs_ma[i-2,j], sharey=axes[i][0], sharex=axes[i-1][j])\n",
    "        axes[i][j].grid()\n",
    "axes = np.array(axes)\n",
    "\n",
    "axes_cbar = fig.add_subplot(gs_cbar[-1])\n",
    "axes_legend = fig.add_subplot(gs_legend[-1])\n",
    "axes_legend.axis('off')\n",
    "\n",
    "for i, key in enumerate(keys):\n",
    "    ts = df_ts[key]\n",
    "    mean = df_ts[key].mean()\n",
    "    mean.drop('time', inplace=True)\n",
    "    ts['time'] -= ts['time'].min()\n",
    "    \n",
    "    vmin = 1e-4\n",
    "    vmax = 1e5\n",
    "\n",
    "    # timeseries\n",
    "    \n",
    "    if plotts:\n",
    "        ax = axes[0,i]\n",
    "\n",
    "        sorted_species = mean.sort_values().index.tolist()[::-1]\n",
    "\n",
    "        skip = max(1, int(len(ts) / 500))\n",
    "        for species in sorted_species[::int((len(ts.columns)-1) / 4)]:\n",
    "            ax.plot(ts['time'][::skip], ts[species][::skip])\n",
    "        \n",
    "        ax.set_yscale('log')\n",
    "        \n",
    "    # Rank abundance\n",
    "    \n",
    "    if plotra:\n",
    "        ax = axes[1][i]\n",
    "\n",
    "        selected_times = np.arange(ts['time'].min(), ts['time'].max(), 50)[:4]\n",
    "        \n",
    "        for t in selected_times:\n",
    "            abundance_profile = ts[ts['time'] == t-1].values.flatten()[1:]\n",
    "            ax.plot(range(1, len(abundance_profile) + 1), np.sort(abundance_profile)[::-1],\n",
    "                       label='Day %d' % int(t))\n",
    "        \n",
    "\n",
    "        ax.set_xscale('log')\n",
    "        ax.set_yscale('log')\n",
    "        if i == 1:\n",
    "            handles, labels = ax.get_legend_handles_labels()\n",
    "            axes_legend.legend(handles, labels, handlelength=1, fontsize=ELIFE.FONTSIZE)\n",
    "        ax.set_ylim([vmin, vmax])\n",
    "    \n",
    "    # Noise color\n",
    "    \n",
    "    if plotnc:\n",
    "        ax = axes[2][i]\n",
    "\n",
    "        ax.set_xscale('log')\n",
    "\n",
    "        ns = df_ns[key]\n",
    "        sc = ax.scatter(mean, ns, vmin=0, vmax=10, s=3)\n",
    "\n",
    "        xx = np.linspace(2, -3, 500).reshape([500, 1])\n",
    "        ax.imshow(xx, cmap=noise_cmap_ww, vmin=noise_lim[0], vmax=noise_lim[1], extent=(vmin, vmax, -3, 2),\n",
    "                     aspect='auto', alpha=0.75)\n",
    "        ax.set_xlabel('Abundance')\n",
    "        \n",
    "        plt.setp(ax.get_xticklabels(), visible=False)\n",
    "        \n",
    "    # absolute timestep\n",
    "    \n",
    "    if plotdx:\n",
    "        ax = axes[3][i]\n",
    "\n",
    "        dx = (ts.values[1:, 1:] - ts.values[:-1, 1:])  # / x.values[:-1, 1:];\n",
    "        dx[~np.isfinite(dx)] = np.nan\n",
    "        mean_dx = np.nanmean(abs(dx), axis=0)\n",
    "\n",
    "        p_lin = np.polyfit(np.log10(mean), np.log10(mean_dx), deg=1, cov=False)\n",
    "\n",
    "        xx = [np.nanmin(mean.values), np.nanmax(mean.values)]\n",
    "        ax.plot(xx, 10 ** (p_lin[1] + p_lin[0] * np.log10(xx)), c='k', linewidth=0.5)\n",
    "        ax.annotate(r'y $\\propto$ x$^{%.2f}$' % p_lin[0],(0.3,0.01))\n",
    "        ax.scatter(mean, mean_dx, s=3)\n",
    "\n",
    "        ax.set_xscale('log')\n",
    "        ax.set_yscale('log')\n",
    "\n",
    "        ax.set_xlabel('Mean abundance')\n",
    "        plt.setp(ax.get_xticklabels(), visible=False)\n",
    "        #ax.set_xticklabels(['']*10) # no ticklabels\n",
    "    \n",
    "    # distribution timestep\n",
    "    \n",
    "    if plotdisdx:\n",
    "        ax = axes[4][i]\n",
    "        \n",
    "        dx_ratio = df_disdx[key]\n",
    "        \n",
    "        sc = ax.scatter(mean, dx_ratio['s'], c=dx_ratio['ks-pval'], vmin=0, vmax=1, cmap='coolwarm', s=3)\n",
    "\n",
    "        # ax_disdx.legend()\n",
    "        ax.set_xscale('log')\n",
    "        ax.set_yscale('log')\n",
    "        \n",
    "        if i == 0:\n",
    "            fig.colorbar(sc, cax=axes_cbar)\n",
    "            axes_cbar.set_ylabel('p-value lognormal fit')\n",
    "        \n",
    "    if i == 0:\n",
    "        axes[0][i].set_ylabel('Abundance')\n",
    "        axes[0][i].set_ylim([1e-4,1e5])\n",
    "        axes[1][i].set_ylabel('Abundance')\n",
    "        axes[1][i].set_ylim([1e-1,1e3])\n",
    "        axes[1][i].set_xlim([5e-1,3e2])\n",
    "        axes[2][i].set_ylabel('Slope power \\n spectral density')\n",
    "        axes[3][i].set_ylabel('Difference \\n time points \\n' + r'$\\left< \\mid x(t+\\delta t) - x(t) \\mid \\right>$')\n",
    "        # \\langle \\rangle\n",
    "            #'Mean absolute \\n difference between successive \\n time points')\n",
    "        axes[3][i].set_ylim([1e-3, 5e4])\n",
    "        axes[4][i].set_ylabel('Width distribution \\n of ratios \\n' + r'$x(t + \\delta t) / x(t)$')\n",
    "        axes[4][i].set_xlim([1e-3, 5e4])\n",
    "        axes[4][i].set_ylim([8e-2, 8e0])\n",
    "    else:\n",
    "        for j in range(5):\n",
    "            plt.setp(axes[j][i].get_yticklabels(), visible=False)\n",
    "    \n",
    "    if i == len(keys) - 1:\n",
    "        axes[0][i].set_xlabel('Time (days)', ha='right', x=1)\n",
    "        axes[1][i].set_xlabel('Rank', ha='right', x=1)\n",
    "        axes[1][i].set_xticks([10,100])\n",
    "        axes[1][i].set_xticklabels([10,100])\n",
    "        axes[4][i].set_xlabel('Mean abundance', ha='right', x=1)\n",
    "        axes[4][i].set_xticks([1e-2, 1e1, 1e4])\n",
    "\n",
    "for ax, label in zip(axes[:,0], ('A', 'B', 'C', 'D', 'E')):\n",
    "    ax.text(-0.72, 1.05, label, transform=ax.transAxes,\n",
    "      fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "    \n",
    "# neutrality\n",
    "\n",
    "neutrality =pd.read_csv('results/experimental/neutrality.csv', index_col=0)\n",
    "neutrality = neutrality.loc[keys]\n",
    "\n",
    "ax_KL = fig.add_subplot(gs1[0])\n",
    "ax_clb_KL = fig.add_subplot(gs2[0])\n",
    "ax_NCT = fig.add_subplot(gs1[1])\n",
    "ax_clb_NCT = fig.add_subplot(gs2[1])\n",
    "ax_KL.set_facecolor('lightgrey')\n",
    "ax_NCT.set_facecolor('lightgrey')\n",
    "\n",
    "ax_KL.text(-0.72*0.23, 1.05, 'F', transform=ax_KL.transAxes,\n",
    "      fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "x = np.log10(neutrality['KL'].values.astype(np.float64))\n",
    "x = x.reshape([1, len(x)])\n",
    "x[np.isinf(x)] = 3.0\n",
    "mat_KL = ax_KL.matshow(x, origin='lower', \n",
    "                    cmap='Blues_r', aspect='auto', vmin=-1, vmax=3)\n",
    "ax_KL.set_yticks([0])\n",
    "ax_KL.set_yticklabels([r'log$_{10}$($D_{KL}$)'])\n",
    "\n",
    "ax_KL.tick_params(axis=\"both\", bottom=False, top=False, labelbottom=False, labeltop=False, left=True, labelleft=True)\n",
    "\n",
    "fig.colorbar(mat_KL, cax=ax_clb_KL, orientation='horizontal')\n",
    "#ax_clb_KL.set_title(r'log$_{10}$($D_{KL}$)')\n",
    "ax_clb_KL.set_xlabel(r'log$_{10}$($D_{KL}$)', ha='right', x=1)\n",
    "\n",
    "x = np.log10(neutrality['NCT'].values.astype(np.float64))\n",
    "x = x.reshape([1, len(x)])\n",
    "\n",
    "vmin = -5; vmax = 0 # pvalue is max 1 = 1e0\n",
    "norm = PiecewiseNormalize([vmin, np.log10(0.05), vmax], [0, 0.5, 1])\n",
    "mat_NCT = ax_NCT.matshow(x, origin='lower', norm=norm, \n",
    "                     cmap='seismic_r', aspect='auto', vmin=vmin, vmax=vmax)\n",
    "fig.colorbar(mat_NCT, cax=ax_clb_NCT, orientation='horizontal')\n",
    "\n",
    "ax_NCT.set_yticks([0])\n",
    "ax_NCT.set_yticklabels([r'log$_{10}$($p_{NCT}$)'])\n",
    "\n",
    "# Remove ticks\n",
    "ax_NCT.tick_params(axis=\"both\", bottom=False, top=False, labelbottom=False, labeltop=False, left=True, labelleft=True)\n",
    "\n",
    "ax_clb_NCT.set_xlabel(r'log$_{10}$($p_{NCT}$)', ha='right', x=1)\n",
    "ax_clb2 = ax_clb_KL.twiny()\n",
    "ax_clb_KL.xaxis.set_ticks_position('bottom')\n",
    "#ax_clb2.xaxis.set_ticks_position('top')\n",
    "ax_clb2.xaxis.set_ticks([0.05,0.95])\n",
    "ax_clb2.set_xlim([0,1])\n",
    "ax_clb2.xaxis.set_ticklabels(['neutral','niche'])\n",
    "ax_clb2.tick_params(axis='x', direction='out')\n",
    "\n",
    "ax_clb2 = ax_clb_NCT.twiny()\n",
    "ax_clb_NCT.xaxis.set_ticks_position('bottom')\n",
    "ax_clb2.xaxis.set_ticks_position('top')\n",
    "#ax_clb2.xaxis.set_tick_params(direction='out', which='top')\n",
    "ax_clb2.xaxis.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_xlim([0,1])\n",
    "ax_clb2.xaxis.set_ticklabels(['niche','neutral'])\n",
    "ax_clb2.tick_params(axis='x', direction='out')\n",
    "    \n",
    "#fig.align_labels()\n",
    "fig.align_ylabels(axes[:,0])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 2: Noise color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:48:09.952169Z",
     "start_time": "2020-02-20T09:48:07.808796Z"
    }
   },
   "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 396.85x288 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(ELIFE.TEXTWIDTH, 4))\n",
    "\n",
    "ymin = -3\n",
    "ymax = 1.5\n",
    "\n",
    "# without interactions\n",
    "\n",
    "yoff = 0.01\n",
    "\n",
    "gs = gridspec.GridSpec(1, 3, top=0.88+yoff, bottom=0.6+yoff,\n",
    "                       left=0.1, right=0.95, wspace=0.05, hspace=0.05)\n",
    "\n",
    "gs_comb = gridspec.GridSpec(\n",
    "    1, 1, top=0.95+yoff, bottom=0.55+yoff, left=0.07, right=0.95)\n",
    "\n",
    "ax = fig.add_subplot(gs_comb[0], frameon=False)\n",
    "ax.set_xlabel(r'Mean abundance $\\times$ self-interaction', ha='right', x=1)\n",
    "ax.set_ylabel('Slope power \\n spectral density')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "# ax.text(-0.02, 1.05, 'A', transform=ax.transAxes,\n",
    "#      fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "ax.text(0, 1.08, 'Logistic model (without interactions)', transform=ax.transAxes,\n",
    "        fontsize=9, va='top', ha='left')\n",
    "# ax.text(0.5, 1.1, 'Logistic model (without interactions)', transform=ax.transAxes,\n",
    "#        fontsize=9, va='top', ha='center')\n",
    "\n",
    "# implementation\n",
    "\n",
    "ax = fig.add_subplot(gs[0])\n",
    "ax.text(0, 1.05, 'A', transform=ax.transAxes,\n",
    "        fontsize=10, fontweight='bold', va='bottom', ha='left')\n",
    "\n",
    "path = 'results/noise_color/no_interaction/'\n",
    "files_noise = [path + 'noise_abundance_Langevin_linear.csv',\n",
    "               path + 'noise_abundance_Langevin_sqrt.csv',\n",
    "               path + 'noise_abundance_Langevin_constant.csv'][::-1]\n",
    "# [path + 'noise_abundance_Ricker_linear.csv',\n",
    "#[path + 'noise_abundance_Arato_linear.csv'][::-1]\n",
    "\n",
    "# , 'Ricker linear', 'Arato linear'][::-1]\n",
    "labels = ['Linear multiplicative', 'Sqrt multiplicative', 'Additive']\n",
    "\n",
    "PlotNoiseColorComparison(\n",
    "    files_noise, labels, legend_title='Noise implementation', ax=ax, masi=True)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "ax.set_ylim([ymin, ymax])\n",
    "ax.set_xlim([2e-2, 2e2])\n",
    "\n",
    "# sampling dt\n",
    "\n",
    "ax = fig.add_subplot(gs[1], sharex=ax, sharey=ax)\n",
    "ax.text(0, 1.05, 'B', transform=ax.transAxes,\n",
    "        fontsize=10, fontweight='bold', va='bottom', ha='left')\n",
    "\n",
    "files_noise_samp = [\n",
    "    path + 'noise_abundance_Langevin_samp%d.csv' % i for i in range(1, 5)]\n",
    "\n",
    "labels = ['0.005', '0.01', '0.025', '0.05']  # , '0.25']\n",
    "\n",
    "PlotNoiseColorComparison(\n",
    "    files_noise_samp[::-1], labels[::-1], legend_title='Sampling dt', ax=ax, masi=True)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "ax.set_ylim([ymin, ymax])\n",
    "ax.tick_params(axis=\"both\", left=True, labelleft=False)\n",
    "ax.set_xlim([2e-2, 2e2])\n",
    "\n",
    "# noise strength\n",
    "\n",
    "ax = fig.add_subplot(gs[2], sharex=ax, sharey=ax)\n",
    "ax.text(0, 1.05, 'C', transform=ax.transAxes,\n",
    "        fontsize=10, fontweight='bold', va='bottom', ha='left')\n",
    "\n",
    "files_noise_sigma = [\n",
    "    path + 'noise_abundance_Langevin_linear_sigma%d.csv' % i for i in range(1, 6)]\n",
    "\n",
    "labels = ['0.01', '0.1', '0.2', '0.25', '0.3']\n",
    "\n",
    "PlotNoiseColorComparison(\n",
    "    files_noise_sigma[::-1], labels, legend_title='Noise strength $\\sigma$', ax=ax, masi=True)\n",
    "ax.tick_params(axis=\"both\", left=True, labelleft=False)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('')\n",
    "\n",
    "# with interactions\n",
    "\n",
    "gs = gridspec.GridSpec(1, 2, top=0.38+yoff, bottom=0.08 +\n",
    "                       yoff, left=0.1, right=0.95, wspace=0.05, hspace=0.05)\n",
    "\n",
    "gs_comb = gridspec.GridSpec(\n",
    "    1, 1, top=0.45+yoff, bottom=0.03+yoff, left=0.07, right=0.95)\n",
    "\n",
    "ax = fig.add_subplot(gs_comb[0], frameon=False)\n",
    "ax.set_xlabel(r'Mean abundance $\\times$ self-interaction', ha='right', x=1)\n",
    "ax.set_ylabel('Slope power \\n spectral density')\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "# ax.text(-0.02, 1.05, 'B', transform=ax.transAxes,\n",
    "#      fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "ax.text(0, 1.08, 'Generalized Lotka-Volterra model (with interactions)', transform=ax.transAxes,\n",
    "        fontsize=9, va='top', ha='left')\n",
    "# ax.text(0.5, 1.05, 'Generalized Lotka-Volterra model (with interactions)', transform=ax.transAxes,\n",
    "#        fontsize=9, va='top', ha='center')\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=0.21, clip=True)\n",
    "mapper = mpl.cm.ScalarMappable(norm=norm, cmap='summer')\n",
    "\n",
    "ax = fig.add_subplot(gs[0])\n",
    "ax.text(0, 1.05, 'D', transform=ax.transAxes,\n",
    "        fontsize=10, fontweight='bold', va='bottom', ha='left')\n",
    "ax.text(0.1, 1.05, 'Equal abundances', transform=ax.transAxes,\n",
    "        fontsize=ELIFE.FONTSIZE, va='bottom', ha='left')\n",
    "#ax.set_title('Equal abundances')\n",
    "\n",
    "path = 'results/noise_color/with_interaction/'\n",
    "files_noise_int = [\n",
    "    path + 'noisecolor_Langevin_linear_interaction%d.csv' % i for i in [1, 2, 3, 6]]\n",
    "\n",
    "labels = ['0.01', '0.05', '0.1', '0.15']\n",
    "\n",
    "PlotNoiseColorComparison(files_noise_int, labels,\n",
    "                         legend_title=r'Interaction strength $\\alpha$', ax=ax, masi=True, interaction_colors=True)\n",
    "\n",
    "ax.set_ylabel('')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylim([ymin, ymax])\n",
    "\n",
    "ax = fig.add_subplot(gs[1], sharex=ax, sharey=ax)\n",
    "ax.text(0, 1.05, 'E', transform=ax.transAxes,\n",
    "        fontsize=10, fontweight='bold', va='bottom', ha='left')\n",
    "ax.text(0.1, 1.05, 'Lognormally distributed abundances', transform=ax.transAxes,\n",
    "        fontsize=ELIFE.FONTSIZE, va='bottom', ha='left')\n",
    "#ax.set_title('Lognormally distributed abundances')\n",
    "\n",
    "files_noise_pl = [path + 'noisecolor_Langevin_linear_powerlaw_sigma1.csv',\n",
    "                  path + 'noisecolor_Langevin_linear_powerlaw_sigma2.csv',\n",
    "                  path + 'noisecolor_Langevin_linear_powerlaw_sigma3.csv',\n",
    "                  path + 'noisecolor_Langevin_linear_powerlaw_sigma4.csv']\n",
    "\n",
    "labels = ['0', '0.1', '0.15', '0.2']\n",
    "\n",
    "PlotNoiseColorComparison(files_noise_pl, labels,\n",
    "                         legend_title=r'Interaction strength $\\alpha$', ax=ax, masi=True, interaction_colors=True)\n",
    "ax.tick_params(axis=\"both\", left=True, labelleft=False)\n",
    "ax.set_ylabel('')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylim([ymin, ymax])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:59:42.215269Z",
     "start_time": "2020-02-18T20:59:42.180678Z"
    }
   },
   "source": [
    "## Fig 3 : Width ratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:48:11.532285Z",
     "start_time": "2020-02-20T09:48:11.484909Z"
    }
   },
   "outputs": [],
   "source": [
    "path = 'results/width_ratios/'\n",
    "df1 = pd.read_csv(path +  'width_lognormal_fit_1.csv')\n",
    "df2 = pd.read_csv(path +  'width_lognormal_fit_1_interaction0.05.csv')\n",
    "df3 = pd.read_csv(path +  'width_lognormal_fit_1_interaction0.1.csv')\n",
    "df4 = pd.read_csv(path +  'width_lognormal_fit_1_interaction0.15.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:48:12.885794Z",
     "start_time": "2020-02-20T09:48:12.161403Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x165.6 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas = [0.01, 0.1, 1.0]\n",
    "\n",
    "cmap = mpl.cm.get_cmap('coolwarm') #viridis')\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=0.21, clip=True)\n",
    "mapper = mpl.cm.ScalarMappable(norm=norm, cmap='summer')\n",
    "\n",
    "fig = plt.figure(figsize=(ELIFE.TEXTWIDTH,2.3))\n",
    "\n",
    "gs_l = gridspec.GridSpec(2,1, height_ratios=[8,1], hspace= 0.8, \n",
    "                         right=0.95, left=0.8, top=0.85, bottom=0.2)\n",
    "gs_r = gridspec.GridSpec(2,2, height_ratios=[8,1], hspace= 0.8, wspace=0.05, \n",
    "                         right=0.65, left=0.15, top=0.85, bottom=0.2)\n",
    "\n",
    "ax_mat = fig.add_subplot(gs_l[0])\n",
    "ax = fig.add_subplot(gs_r[0])\n",
    "ax2 = fig.add_subplot(gs_r[1], sharey=ax)\n",
    "\n",
    "ax_mat.text(-0.2, 1.08, 'B', transform=ax_mat.transAxes,\n",
    "      fontsize=10, fontweight='bold', va='bottom', ha='right')\n",
    "ax.text(-0.2, 1.08, 'A', transform=ax.transAxes,\n",
    "      fontsize=10, fontweight='bold', va='bottom', ha='right')\n",
    "\n",
    "ax_mat_cbar = fig.add_subplot(gs_l[1])\n",
    "ax_legend = fig.add_subplot(gs_r[2])\n",
    "ax_cbar = fig.add_subplot(gs_r[3])\n",
    "\n",
    "\n",
    "df_slopes2 = pd.read_csv('results/slopes/slopes_equal_abundances.csv', index_col=0, na_values='NAN')\n",
    "df_slopes2['slope'] = df_slopes2.iloc[:,2:12].mean(axis=1)\n",
    "df_slopes2['slope_std'] = df_slopes2.iloc[:,2:12].std(axis=1)\n",
    "df_slopes2.drop(['%d'%i for i in range(10)], axis=1, inplace=True)\n",
    "\n",
    "slope = df_slopes2.drop(['implementation', 'interaction', 'slope_std'], axis=1)\n",
    "std_slope = df_slopes2.drop(['implementation', 'interaction', 'slope'], axis=1)\n",
    "\n",
    "slope = slope.groupby(['noise_lin', 'noise_sqrt']).agg('mean')\n",
    "std_slope = std_slope.groupby(['noise_lin', 'noise_sqrt']).agg('mean')\n",
    "\n",
    "slope = slope.unstack() #.iloc[:4, :4]\n",
    "\n",
    "val = slope.values\n",
    "\n",
    "mat = ax_mat.matshow(val, cmap='coolwarm', vmin=0.65, vmax=1.1)\n",
    "ax_mat.set_xlabel(r'$\\sigma_\\mathregular{sqrt}$')\n",
    "ax_mat.set_ylabel(r'$\\sigma_\\mathregular{lin}$')\n",
    "ax_mat.set_xticks([0,1,2,3,4])\n",
    "ax_mat.set_yticks([0,1,2,3,4])\n",
    "\n",
    "ax_mat.set_xticklabels([0, 0.01, 0.1, 0.5, 1.0], rotation=90)\n",
    "ax_mat.set_yticklabels([0, 0.01, 0.1, 0.5, 1.0])\n",
    "\n",
    "cbar = plt.colorbar(mat, cax=ax_mat_cbar, orientation='horizontal')\n",
    "cbar.set_label(r'Slope $\\left< \\mid x(t+\\delta t) - x(t) \\mid \\right>$') #'Slope steps')\n",
    "\n",
    "#for i, df, alpha in zip(range(4), [df1, df2, df3, df4], [0, 0.05, 0.1, 0.15]):\n",
    "for i, df, alpha in zip(range(3), [df1, df3, df4], [0, 0.1, 0.15]):\n",
    "    for j, sigma in enumerate(sigmas):\n",
    "        w = df['sigma_%.2f_width_mean' % sigma]\n",
    "        pval = df['sigma_%.2f_pval' % sigma]\n",
    "        ss = df['ss']\n",
    "        \n",
    "        col = mapper.to_rgba(alpha)\n",
    "        \n",
    "        ax.plot(ss.values, w.values, c=col, alpha=0.3, marker='o', markersize=3, label=alpha if j==0 else \"\")\n",
    "        ax2.plot(ss.values, w.values, c='lightgrey', alpha=0.3) #, label=alpha if j==0 else \"\")\n",
    "        s_ax2 = ax2.scatter(ss.values, w.values,s=3, c = pval, cmap=cmap, vmin=0, vmax=1)\n",
    "                        #c=col, label=alpha if j==0 else \"\")\n",
    "        x = 2e-1 #ss.values[0]\n",
    "        y = w.values[0]\n",
    "        \n",
    "        if i == 0:\n",
    "            ax.annotate(r\"$\\sigma_\\mathregular{lin} =$ %.2f\" % sigma, xy=(x, y), xytext=(0.2*x, 1.5*y))\n",
    "            ax2.annotate(r\"$\\sigma_\\mathregular{lin} =$ %.2f\" % sigma, xy=(x, y), xytext=(0.2*x, 1.5*y))\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax_legend.legend(handles, labels, title='Interaction ' + r'strength $\\alpha$', \n",
    "                 loc=9, ncol=3, columnspacing=0.5)\n",
    "ax_legend.axis('off')\n",
    "\n",
    "cbar = plt.colorbar(s_ax2, cax=ax_cbar, orientation='horizontal')\n",
    "cbar.set_label('p-value lognormal fit')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "#ax.set_xlabel(r'Mean abundace $\\times$ self-interaction', ha='right', x=1)\n",
    "\n",
    "ax.set_ylabel('Width distribution \\n of ratios \\n' + r'$x(t + \\delta t) / x(t)$') #'Width distribution \\n ratios of time points')\n",
    "ax.set_xlim([2e-2,2e2])\n",
    "ax2.set_ylim([5e-4,1e0])\n",
    "ax.set_yscale('log')\n",
    "ax.grid()\n",
    "\n",
    "ax2.set_xscale('log')\n",
    "ax2.tick_params(axis=\"both\", left=True, labelleft=False)\n",
    "ax2.set_xlabel(r'Mean abundace $\\times$ self-interaction', ha='right', x=1)\n",
    "#ax2.set_ylabel('Scale lognormal fit')\n",
    "ax2.set_xlim([2e-2,2e2])\n",
    "\n",
    "ax2.set_yscale('log')\n",
    "ax2.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:48:13.970939Z",
     "start_time": "2020-02-20T09:48:12.887554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x165.6 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas = [0.01, 0.1, 1.0]\n",
    "\n",
    "cmap = mpl.cm.get_cmap('coolwarm') #viridis')\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=0.21, clip=True)\n",
    "mapper = mpl.cm.ScalarMappable(norm=norm, cmap='summer')\n",
    "\n",
    "fig = plt.figure(figsize=(ELIFE.TEXTWIDTH,2.3))\n",
    "\n",
    "gs_l = gridspec.GridSpec(2,1, height_ratios=[8,1], hspace= 0.8, \n",
    "                         right=0.25, left=0.1, top=0.9, bottom=0.2)\n",
    "gs_r = gridspec.GridSpec(2,2, height_ratios=[1,3], width_ratios=[10,1], hspace= 0.15, wspace=0.2, \n",
    "                         right=0.9, left=0.4, top=0.9, bottom=0.2)\n",
    "\n",
    "ax_mat = fig.add_subplot(gs_l[0])\n",
    "\n",
    "ax = fig.add_subplot(gs_r[:,0], sharey=ax)\n",
    "\n",
    "ax_mat_tot = fig.add_subplot(gs_l[:])\n",
    "ax_mat_tot.axis('off')\n",
    "\n",
    "ax_mat_tot.text(-0.2, 1.1, 'A', transform=ax_mat_tot.transAxes,\n",
    "      fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "ax.text(-0.2, 1.1, 'B', transform=ax.transAxes,\n",
    "      fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "ax_mat_cbar = fig.add_subplot(gs_l[1])\n",
    "#ax_legend = fig.add_subplot(gs_r[0,1])\n",
    "ax_cbar = fig.add_subplot(gs_r[:,1])\n",
    "\n",
    "df_slopes2 = pd.read_csv('results/slopes/slopes_equal_abundances.csv', index_col=0, na_values='NAN')\n",
    "df_slopes2['slope'] = df_slopes2.iloc[:,2:12].mean(axis=1)\n",
    "df_slopes2['slope_std'] = df_slopes2.iloc[:,2:12].std(axis=1)\n",
    "df_slopes2.drop(['%d'%i for i in range(10)], axis=1, inplace=True)\n",
    "\n",
    "slope = df_slopes2.drop(['implementation', 'interaction', 'slope_std'], axis=1)\n",
    "std_slope = df_slopes2.drop(['implementation', 'interaction', 'slope'], axis=1)\n",
    "\n",
    "slope = slope.groupby(['noise_lin', 'noise_sqrt']).agg('mean')\n",
    "std_slope = std_slope.groupby(['noise_lin', 'noise_sqrt']).agg('mean')\n",
    "\n",
    "slope = slope.unstack() #.iloc[:4, :4]\n",
    "\n",
    "val = slope.values\n",
    "\n",
    "mat = ax_mat.matshow(val, cmap='coolwarm', vmin=0.65, vmax=1.1)\n",
    "ax_mat.set_xlabel(r'$\\sigma_\\mathregular{sqrt}$')\n",
    "ax_mat.set_ylabel(r'$\\sigma_\\mathregular{lin}$')\n",
    "ax_mat.set_xticks([0,1,2,3,4])\n",
    "ax_mat.set_yticks([0,1,2,3,4])\n",
    "\n",
    "ax_mat.set_xticklabels([0, 0.01, 0.1, 0.5, 1.0], rotation=90)\n",
    "ax_mat.set_yticklabels([0, 0.01, 0.1, 0.5, 1.0])\n",
    "\n",
    "ax_mat.tick_params(axis='both', top=False, bottom=True, labelbottom=True, labeltop=False)\n",
    "\n",
    "cbar = plt.colorbar(mat, cax=ax_mat_cbar, orientation='horizontal')\n",
    "cbar.set_label(r'Slope $\\left< \\mid x(t+\\delta t) - x(t) \\mid \\right>$') #'Slope steps')\n",
    "\n",
    "df1 = pd.read_csv('results/width_ratios/width_lognormal_fit_experimental.csv')\n",
    "df2 = pd.read_csv('results/width_ratios/width_lognormal_fit_experimental_interaction.csv')\n",
    "\n",
    "#for i, df, alpha in zip(range(4), [df1, df2, df3, df4], [0, 0.05, 0.1, 0.15]):\n",
    "for i, df, alpha, m in zip(range(1), [df1], [0], ['o']):\n",
    "    #zip(range(3), [df1, df2], [0, 0.15], ['o', '^']):\n",
    "    for j, sigma in enumerate(sigmas):\n",
    "        w = df[['sigma_%.2f_width_mean_%d' % (sigma, d) for d in range(20)]].median(axis=1).values\n",
    "        pval = df[['sigma_%.2f_pval_%d' % (sigma, d) for d in range(20)]].median(axis=1).values\n",
    "        ss = df['ss'].values\n",
    "        si = df['selfints'].values\n",
    "\n",
    "        x = ss * si\n",
    "\n",
    "        p = x.argsort()\n",
    "\n",
    "        x = x[p]\n",
    "        w = w[p]\n",
    "        pval = pval[p]\n",
    "        ss = ss[p]\n",
    "        si = si[p]\n",
    "\n",
    "        col = mapper.to_rgba(alpha)\n",
    "\n",
    "        #ax.plot(x, w, c=col, alpha=0.3) #, label=alpha if j==0 else \"\")\n",
    "        #ax.scatter(x, w, c=col, label=label if j==0 else \"\", s=3)\n",
    "        ax.plot(x, w, c='lightgrey', alpha=0.3) #, label=alpha if j==0 else \"\")\n",
    "        s_ax = ax.scatter(x, w, s=3, c = pval, cmap=cmap, vmin=0, vmax=1, marker=m)\n",
    "                        #c=col, label=alpha if j==0 else \"\")\n",
    "            \n",
    "        x = 1e0 #ss.values[0]\n",
    "        y = w[0]\n",
    "        \n",
    "        if i == 0:\n",
    "            ax.annotate(r\"$\\sigma_\\mathregular{lin} =$ %.2f\" % sigma, xy=(x, y), xytext=(x, 1.5*y), ha='left')\n",
    "\n",
    "#handles, labels = ax.get_legend_handles_labels()\n",
    "#ax_legend.legend(handles, labels, #title='Interaction ' + r'strength $\\alpha$', \n",
    "#                 loc=9, ncol=3, columnspacing=0.5)\n",
    "\n",
    "#legend_elements = [Line2D([0], [0], marker='o', color='w', label='No interaction',\n",
    "#                          markerfacecolor='grey', markersize=5),\n",
    "#                   Line2D([0], [0], marker='^', color='w', label='With interaction',\n",
    "#                          markerfacecolor='grey', markersize=5),]\n",
    "\n",
    "#ax_legend.legend(handles=legend_elements, loc=2) #loc='center')\n",
    "#ax_legend.axis('off')\n",
    "\n",
    "cbar = plt.colorbar(s_ax, cax=ax_cbar, orientation='vertical') #orientation='horizontal')\n",
    "cbar.set_label('p-value lognormal fit')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "#ax.set_xlabel(r'Mean abundace $\\times$ self-interaction', ha='right', x=1)\n",
    "\n",
    "ax.set_ylabel('Width distribution \\n of ratios ' + r'$x(t + \\delta t) / x(t)$') #'Width distribution ratios \\n of successive time points')\n",
    "ax.set_xlim([5e-1,9e1])\n",
    "ax.set_ylim([9e-4,2e0])\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlabel(r'Mean abundace $\\times$ self-interaction', ha='right', x=1)\n",
    "ax.set_xlim([5e-1,5e1])\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T09:48:16.309767Z",
     "start_time": "2020-02-20T09:48:14.800762Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x165.6 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmas = [0.01, 0.1, 1.0]\n",
    "\n",
    "cmap = mpl.cm.get_cmap('coolwarm') #viridis')\n",
    "\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=0.21, clip=True)\n",
    "mapper = mpl.cm.ScalarMappable(norm=norm, cmap='summer')\n",
    "\n",
    "fig = plt.figure(figsize=(ELIFE.TEXTWIDTH,2.3))\n",
    "\n",
    "gs_l = gridspec.GridSpec(2,1, height_ratios=[8,1], hspace= 0.8, \n",
    "                         right=0.25, left=0.1, top=0.9, bottom=0.2)\n",
    "gs_r = gridspec.GridSpec(2,2, height_ratios=[1,3], width_ratios=[10,1], hspace= 0.15, wspace=0.2, \n",
    "                         right=0.9, left=0.4, top=0.9, bottom=0.2)\n",
    "\n",
    "ax_mat = fig.add_subplot(gs_l[0])\n",
    "\n",
    "ax = fig.add_subplot(gs_r[:,0], sharey=ax)\n",
    "\n",
    "ax_mat_tot = fig.add_subplot(gs_l[:])\n",
    "ax_mat_tot.axis('off')\n",
    "\n",
    "ax_mat_tot.text(-0.2, 1.1, 'A', transform=ax_mat_tot.transAxes,\n",
    "      fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "ax.text(-0.2, 1.1, 'B', transform=ax.transAxes,\n",
    "      fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "ax_mat_cbar = fig.add_subplot(gs_l[1])\n",
    "#ax_legend = fig.add_subplot(gs_r[0,1])\n",
    "ax_cbar = fig.add_subplot(gs_r[:,1])\n",
    "\n",
    "df_slopes = pd.read_csv('results/slopes/interaction_005.csv', index_col=0, na_values='NAN')\n",
    "df_slopes2 = df_slopes[df_slopes.implementation == 'NOISE.LANGEVIN_LINEAR_SQRT']\n",
    "\n",
    "Nts = 10\n",
    "\n",
    "pd_opt_orig = pd.options.mode.chained_assignment\n",
    "pd.options.mode.chained_assignment = None # avoid SettingWithCopyWarning\n",
    "df_slopes2['slope'] = df_slopes2.loc[:,['ts_%d' % i for i in range(Nts)]].mean(axis=1)\n",
    "df_slopes2.loc[:,'slope_std'] = df_slopes2.loc[:,['ts_%d' % i for i in range(Nts)]].std(axis=1)\n",
    "df_slopes2.drop(['ts_%d'%i for i in range(Nts)], axis=1, inplace=True)\n",
    "pd.options.mode.chained_assignment = pd_opt_orig # restore SettingWithCopyWarning\n",
    "\n",
    "slope = df_slopes2.drop(['implementation', 'interaction', 'slope_std', 'noise_ct'], axis=1)\n",
    "std_slope = df_slopes2.drop(['implementation', 'interaction', 'slope', 'noise_ct'], axis=1)\n",
    "\n",
    "slope = slope.groupby(['noise_lin', 'noise_sqrt']).agg('mean')\n",
    "std_slope = std_slope.groupby(['noise_lin', 'noise_sqrt']).agg('mean')\n",
    "\n",
    "slope = slope.unstack() #.iloc[:4, :4]\n",
    "std_slope = std_slope.unstack().iloc[:4, :4]\n",
    "\n",
    "val = slope.values\n",
    "val[0][0] = np.nan\n",
    "\n",
    "mat = ax_mat.matshow(val, cmap='coolwarm', vmin=0.65, vmax=1.1)\n",
    "ax_mat.set_xlabel(r'$\\sigma_\\mathregular{sqrt}$')\n",
    "ax_mat.set_ylabel(r'$\\sigma_\\mathregular{lin}$')\n",
    "ax_mat.set_xticks([0,1,2,3,4])\n",
    "ax_mat.set_yticks([0,1,2,3,4])\n",
    "\n",
    "ax_mat.set_xticklabels([0, 0.01, 0.1, 0.5, 1.0])\n",
    "ax_mat.set_yticklabels([0, 0.01, 0.1, 0.5, 1.0])\n",
    "\n",
    "ax_mat.tick_params(axis='both', top=False, bottom=True, labelbottom=True, labeltop=False)\n",
    "\n",
    "cbar = plt.colorbar(mat, cax=ax_mat_cbar, orientation='horizontal')\n",
    "cbar.set_label(r'Slope $\\left< \\mid x(t+\\delta t) - x(t) \\mid \\right>$') #'Slope steps')\n",
    "\n",
    "df1 = pd.read_csv('results/width_ratios/width_lognormal_fit_experimental.csv')\n",
    "df2 = pd.read_csv('results/width_ratios/width_lognormal_fit_experimental_interaction.csv')\n",
    "\n",
    "#for i, df, alpha in zip(range(4), [df1, df2, df3, df4], [0, 0.05, 0.1, 0.15]):\n",
    "for i, df, alpha, m in zip(range(1), [df1], [0], ['o']):\n",
    "    #zip(range(3), [df1, df2], [0, 0.15], ['o', '^']):\n",
    "    for j, sigma in enumerate(sigmas):\n",
    "        w = df[['sigma_%.2f_width_mean_%d' % (sigma, d) for d in range(20)]].median(axis=1).values\n",
    "        pval = df[['sigma_%.2f_pval_%d' % (sigma, d) for d in range(20)]].median(axis=1).values\n",
    "        ss = df['ss'].values\n",
    "        si = df['selfints'].values\n",
    "\n",
    "        x = ss * si\n",
    "\n",
    "        p = x.argsort()\n",
    "\n",
    "        x = x[p]\n",
    "        w = w[p]\n",
    "        pval = pval[p]\n",
    "        ss = ss[p]\n",
    "        si = si[p]\n",
    "\n",
    "        col = mapper.to_rgba(alpha)\n",
    "\n",
    "        #ax.plot(x, w, c=col, alpha=0.3) #, label=alpha if j==0 else \"\")\n",
    "        #ax.scatter(x, w, c=col, label=label if j==0 else \"\", s=3)\n",
    "        ax.plot(x, w, c='lightgrey', alpha=0.3) #, label=alpha if j==0 else \"\")\n",
    "        s_ax = ax.scatter(x, w, s=3, c = pval, cmap=cmap, vmin=0, vmax=1, marker=m)\n",
    "                        #c=col, label=alpha if j==0 else \"\")\n",
    "            \n",
    "        x = 1e0 #ss.values[0]\n",
    "        y = w[0]\n",
    "        \n",
    "        if i == 0:\n",
    "            ax.annotate(r\"$\\sigma_\\mathregular{lin} =$ %.2f\" % sigma, xy=(x, y), xytext=(x, 1.5*y), ha='left')\n",
    "\n",
    "#handles, labels = ax.get_legend_handles_labels()\n",
    "#ax_legend.legend(handles, labels, #title='Interaction ' + r'strength $\\alpha$', \n",
    "#                 loc=9, ncol=3, columnspacing=0.5)\n",
    "\n",
    "#legend_elements = [Line2D([0], [0], marker='o', color='w', label='No interaction',\n",
    "#                          markerfacecolor='grey', markersize=5),\n",
    "#                   Line2D([0], [0], marker='^', color='w', label='With interaction',\n",
    "#                          markerfacecolor='grey', markersize=5),]\n",
    "\n",
    "#ax_legend.legend(handles=legend_elements, loc=2) #loc='center')\n",
    "#ax_legend.axis('off')\n",
    "\n",
    "cbar = plt.colorbar(s_ax, cax=ax_cbar, orientation='vertical') #orientation='horizontal')\n",
    "cbar.set_label('p-value lognormal fit')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "#ax.set_xlabel(r'Mean abundace $\\times$ self-interaction', ha='right', x=1)\n",
    "\n",
    "ax.set_ylabel('Width distribution \\n of ratios \\n' + r'$x(t + \\delta t) / x(t)$') #'Width distribution \\n ratios of time points')\n",
    "ax.set_xlim([5e-1,9e1])\n",
    "ax.set_ylim([9e-4,2e0])\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_xlabel(r'Mean abundace $\\times$ self-interaction', ha='right', x=1)\n",
    "ax.set_xlim([5e-1,5e1])\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 4 Logistic model reproducing noise characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T10:31:00.868617Z",
     "start_time": "2020-02-20T10:30:49.189588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7/rjD9xi+5/YIehtRjHbizHpW/WYKT/+wM1rx65WIjT+s+C0GJSrQLUzrztQd8oo60hT/7romHD4t95DG0UmHf81CiG7tLEZlxXH3if2QUkYFN7WmZdqG1qPYHU8Y8VavjyoLu32GJ4L3RdgzJtPkjLLl74tBqXay4y37reet2dSmbNvvT4rY5Fvjq99O0XNJyIAa3DXU3oBJR1QnYzboGFv1fNRxZoOu3SRuJ/ZLblN27bG5kTepQ6W720+YNT++w/yVczj78de54p0VPPPDDpbuqo2k+NDQOJLpkOIXQjwAvBbafvxQLiyEOEMIcZ8Q4rKOHls+ewa1C++hsF8yrr+pa3MH3LWUfjgtql6OrowcXRlCCBybP8Cx6b3IvoCrEhnw0rDmZWTQz0VpW/nTtAEoyr5t2bfY38ccqEcGvDzjO4c/Dd6O0al2IkGfk4C7beZL9XoVBNxqMrXGDW9Rt/wf7dYrfmN4pNOqaFLNLAVJbSeAw0ifOvlaMfdSPGXL2P1KX/IC66I6O4sSJL/8dUCd+D1/eAbmVnMZALvHPMSJqbU4/3Y6fzy5f6T872cM4dzhh674O7P9HConHzOJX/SR/GvAV9zjuYYJW65lw9f3cuMzT3HiU19x80dreH3ZbjaUNx7w2ggaGr2Fjo6+7MDm0Hb7yV9CCCH6AH8E4qSUM4UQNuA5wAvMl1K+LYT4DrjxQC+etvk25PHL8NZsACFoXPsqtkEXE/RJXNs+xb3nOwKuSjwVKxnir+D6+P9yseUzArZLqPnqtwQ9tTSsfEo9WdCHc+tsqr+5EXPGseQsu4g/37b3xUvC3Bb3HtudpxJwhdw7v7iCWMtw4Ebqlj5Mw8onyL+lMVI/4K6DoI+y2TPwVq7GPvw6/PU78NZuIn7cHe1eQwY8CL2ZjFn3s+vCe8myqWakumV/J/6Yu6LqBn1qziHXtk+wD1fXlFb0ZmJMeny1W4gre4t03enofA6klAzPiOWDX43jga83t5mw9VasRHobsBh0pMY05wiy4AL2bco6QA64/XQ1jcu/gg0bUUZPJXX4+WTExjM4UMGMihU0lc7G2eBmz8aBfPLTQOY19MVvyeOYvATG58QzPje+jWuwhkZvoqOKXwIZQogZQPo+K+4nt4oQ4l3UhbUPaOQX9NRjdq0n4CpHMVgJ+pxUz78d55aP8Dt24wuZbmp/fJDGn55nRGw+4yefh2OlGui1w6kmcvOUq4nUgt4GfHXbACL/axffj7dyNWlnzcbXUISit6CzpiKlZPdLeVj7qrZ+U6CBmgW/D52nHhljw7XjC4TejAx4CPqbcG35iOr5t2PKnIS/bit+h5qPv3HNv1CMcQiTqkhl0A9CR9BThxIu87tBr07get01uCp/xJp/Cn//6SseGnsHQmkerUufA4QCMkjAoeb4z4sVZMZbcGx+jfiyN8iyjUYgIx0KwF0n9OXXx6oTzf7GPTg2vNksD83mLIDiF1PIvHgRxrQxCHFItv4Dbj9dTcLUa9ngsGFMjcVTvA7P8o14y7eC34s+IZPY1FEMt5kZZi/m8rgleFzF1NdmsbluKA8v7MtaVyo+JYbMhDjyk2z0SbRSkGSlT6KVvATrXk1zGho9gY4q/vuAS4Ac4KYOHhuVWwX1bSABmATMbV25dW6V8886mV/lpbF0wVwS3UFMtQsBaCqKTjVcvmM1VsDfsJOSXTuIRQ1pzwMkCgLVrr3h5x8xurZgBzatmEu8MFC56hWUoIcN8+eTte4SvJa+VPZ5AIJe8lxlNK55Sb3m9m9xOj+JXNPYsJryOWfRZB+HBVj+2aOk7HwQgNqKnZidm6NkDHrr8UsTP3zxBlkbr6F0wDNkbL4ZV+x4rMANn7/BabGqB9Gqb55ldMlT+IzpPGK5kFP+NweMibxXtw2LouMiWYI1YRoSAf+7HoCcpp2YF39IsbcSG2Bt3AIK/DD/K4L62ChZ1gP2ytkkFqtzANv/+xvc9tE4HYOAgki9klmTKB78Kn5TVvvf7oFxKO2nU/l30Rr+J72clljA8L6TGBKfhllvQEqJv7YEb9kmPKWb8JZuxFvmJ1APdlHMMTF1jDd+D3o3UvHjbwzgderwFSt4gpI9ASNr/Wac0spO+rJGdyxV+n5YTXosBh0Wgw67SU9qjImUGCOpMUZSY0zkJVjIibfs1RVYo2eRnJws8/Pzu1uM/bJixYoqKWWbiciOKv5zpJQvAAghfgG824Fjo3KrSCkf2Ffl9nKrrH7XwcgBadQ70/EUb233uHiTi7DBJpmteIHjJwxn12oiSh+gX14qnpLNuKohofRVrH3OpKnoa3S2dAoLC9m1xYg9IY5r65bySNEjUdcYEO9CZxyAr3Yz5uypsOc7YgZfChveRhjtbLP52anL5JhACbFWA952MkCbzGaGJRZTC4wZOZTSzWBtUFf32mz0c5H8DoC0nBwoAYO3DCxQMCiDvNyJ3Dp7GXbFzF/65RNwWVFM8VR9/QUxQ68grd9Aaha+ic6WiKvexK+GGxGbLEyaMAa9PTsigwx4ado9n/LVzRO/dqUWS8nLnKGP5w5extjCIjNhwrEY4vc/+b0PDqX9dCpn5w6lbtsuajwuntnwAxvqyvEFg/SNTWJ4QgbDEjIYMX4k+TEJKKG3nKC3CW/ZFrxlm/DV7MbfUEmgsRJjYyWBhgqCfg9J0k+e0oRiNXByfgBp/QJf7UZIHocv/SSako+nPmCh0umhwuFlQ4WD77bXsLPaxe76JqwGHYPSYkiLMSGEGnwXY9JxQt9kxuXEt/Fk0+ge8vPz2+innogQoqi98o4q/uEttkewjx+uECIJeIjm3CpPAc8IIc7gYHOryCAVn12ApeD0vVbx1W5uUV1VWnveHNmmnr9hJ36nukyjPrYAY8pwXNs/RehCtm2h0FT0FT/GDWKNLo1sf7PdXlYuxTL0CuKOuQtDXB9KPziRpJOex7HhbQyxBfy2xMFA01iOcZUQcLfNHxQ6C01F36Cz5xBoqoraU+52UiMdQAwLGp18ajuXt5yzAejzzX8oOd2GqN2IO3EIvvod6CypCIOavsIQ1wfpdxNwVSBQQOixONYTTBmJ9Ltw7VAVkTlzMt7aTVR9eVXUtQMhk5QSbOLV5Ad5sO7KZon90Zk/D4IDbj9dTYo5hnGWFAqHTY2UBYJBtjZWsaa2lNU1xbyxdTlFjhosOgNDEtIZnpDO8IQMho84nSSzba/nDvo8+OtKqf32OVwrVpF81p8xpKXgLvqSpkXPkqgzMDh9PKb08ZiGTUAf0xxT4vT42VDhoMrpjUzy1zT5eHFJEdd9+BNjs+O5b9qAdiO+NTQOlI4q/lghxNWottp9Jm6RUlYD17cqvrK9ugdKfepFpFvr8dVuxpgyCn/DToIe1YtGGGxInxMpAyB0xAz5Ja7tnwMQdFWAzgiB5slbb/lKCKque/ZhVyD06g+ppeIPc4vtdNbWP4sZtb70NiAMVuxDLifodeC2DkHRmym4zcuT7/+SUqfCYCROYwIWV2W7rlPS50T6m5A+J1Xf3ADAw+bJ3J8RR0VDEzWyEYhhbZOHJfqcqGO3zSrEaJ1ORWMpdTufImX6awih2v2F3kzQ5yDoqiSAglQMBJqqMCYPJ+hz4dj0Ls6N76KzphFwRaeQFnpLRLnrgh6mmFdzsqV5jWHpd3OIHHD76Q50isLAuFQGxqUyM795sOD0eVhXV86a2lI+LlrLg6u/ptbbhECQbLaRZYsjyxpHpjWWLKu6nRWTRMLMv5JQW0LVnPvxVmwndeZDJEy6n4C7Dk/5MjylS2hc+28CzlL08X0xpozElDKSUSkj0WVnR5l9Lh2TjZSSrzZXctGbKzihfzI3Tco/IBdhDRBC/Br4M2redIHqZHCXlHLH4ZTjtdde4+mnn2bZsmVs3ryZWbNm8Yc//IHBgwfz1ltvMXHixHbr/PnPf+5UOTqq+G8ETmmxfViR+hgMcX1wlyxCn5aDrNtC7vXlLCjfScrnp2HFiWKMRTHEoI/JJuhuzrypMycTCE3wAsiAG8USMn0pBhRTKI+PosOx8R2CrnICNP/wXMKAWTb7eCt6dcSnGGMoH/AEg0Plv3GmARBTMJ2Re/rwsOtrZgY2RI5LOul57MOuZMKs3+EWJpae9Q9K35+CbcQNvFxk4B5LI3U1XooJKVkZMk/pmmMWPEKHGT97/LBNSSAFgQyElnjUman66hoAAq4y0MWis6YhDFakv4mAU1X2mZcuY/dLudHPt50R/c3Za6Cx+ZkdIt3afg4Wm8HE+JRcxqdEP6+gDFLldlLsaqDYVU+xs57lVbuZ41pHsaueBq8bb9APKcPINCVzwSu/BuDr/lOpTMpHWtOxxM7EkqAnFh/JjgYSqxeRuOJdEj3lJAkfKdKD3ZqEtf+52Pqfx6kDUzm5fwpvrtjDNe//RLXLS4biZpncSr9kG32SrGTYzZj0CnaTfr/uyUcRI4A/SSlfAhBCXA98JIQYI/e1WHcXMHz4cN566y3Gjx8PwJw5c7jnnnsiir+9Op1NRxX/CcDZgAm4ALhq39U7D4fPw3ZvAyOsNqS3gdiRN+DvexYunYWTvn0bLJdRnLmF5Gkv4Nr6MWduKeNaJYvJgd0A2AZeyG3rV3ORdx0nzJyNyZ4BOjNF/8pCKEZ01tTItSq/uAIAT4vHE0gYwu5j7uOBBS9hIMiljX7G1Vcwd/cGRgNlrgZijc2pFOp8qneMVUZ7LRqThuILBlnhloAbc+axZFz4PU6hh6JZVOviUAhSHFRTLFS6XYBAHz+QYe5yBgRrcIfkmu7bguHcb7BlDcZdvACdNR2hjx4BCulHMdgIeuqoWfhH9e0H0NuinWpiR92MYkmibvH9WApOp2nHXEYWv8FPWZdjTB6BYkkmeOimnm5rP12BIhRSLXZSLXZGJx3IpPcjuDYv5ITF7+Be/yl+Rw3EphBIG0BTTDKNwQD1gQD1gTS2yVQWSyiTUOFQqF23lX6r/o8CfQCLOQ6zKZYz822gGNlTUk+st5pNu/L5ZJ2VskYPbl8Qh9ePP6gGOo7IjGVyfiKnDkyJyvN0FDEceCv8QUr5ghDiPlRHg10dPVllZSXjxjVn9r3uuuu47rrrDujYmTNn8sYbbzBixAgAZs+ezWuvvcb8+fNpbGxst05n0+HJXeBOusEHu9Lt4I3azZwbp460rQVnIHQGdK/eGaljmfQAelsGsSNv4IeVd/JDzLkRE40lewrvbAvyjmkE/y4v5qykYXy6ez1Xxt1Krc6AtWA6+be6KJnVnM7BI/TESjcNwkwlRh7fuoF5hj4A+KsdbNu+mvtXf8U3BTPI/+AhfMHmN4Lvy7dHzlEtLCzQ53KObxNIP7XeaAVqzjyWmvpSAIqCRkYZXbzmzSZHF6Dc6wPM+MzJGNxBzNJHmRKDQ4lhnLeE6oBEKDosOYXkXreLhjWvRJ1bCh1S+sHnw1P8w16fr96egy7UGaSf/TE7njDikKFn3f88kIHOsPF3WfsJzR2NA7ZJKd/aX/3uwjpgMtYBahuTUhJoqMC9Zw3+ujII+JABH9LvJehrIuhuJNjUgL++mKaSDTSZ7JTkj2FX7hTcnno8PheBgIdaxcWGupXsdC9EB/TLyGNE2lDyYhIYmpDOkJhsVhXXM39bNQ/+bzNGnUJegpX8RCuDU2O4eHQmJn3bgL4jjGHAulZlTaiehR1W/CkpKYc0uXvLLbfw1FNPIYRgzZo13HrrrZSXlzNr1iwMBkNUndzc3P2creN0VPEXAxagbax/F5NqjqHE78KphNwRlbaiN9py24QZbdEl0SdQy5eOZqVc6fMxZe6zrK8L2bgV1YwiWp3Tg45x/hJipJcXYkcyr6rZVLTJ42eAW1305KmqNVFKv/U5VuoyuNN6KpckDUAfm0etp60CdYW+ivP2uLlNX0Lf5OGMF408Vt4ECngyp2Jq2oPJVcY1trMZpveS7GmiyhPtMhSe8wgjhQEZ8JJ21myK3xqDr7p12w/Vk0GCvubVu5ZMXgPvqRPlit6CDPo7w8Z/0O2nKwICuxshBPq4NGLi0g6ovr++nNTP/sbwFZ+QcdVLKAZ1PqrlClyOuh2s/vZuymvqqLWfxUM//Y+r+o/n4oGjOWVgKn89fTBuX6Qk6+wAACAASURBVIBddU3srHGxuKiWyU8v5LWLRzE4zX5Eeg0JIXKARillQ4syA5ABbBdCzEaNM/o94JJSPiGE+FRKeWb7Zzx0jj/+eP75z38yZ84cFi5cyMSJE/H5fJx//vmcd955UXV6guIfADyCOjkiOYyv6jaDiW3eBm7YE2SSYSgTy7ZRmNEvqk6V20m2LR4AnRAEpGSrkkiNsHD1igWRejU+H/HGZpPI054Uhhat5Zy86CRvpnF3k+BKIMUaz9LqYnAXR/ZV+CWVbgexBjNzGps9pkw6PZ5A8xKFnxkHRGYKEme8j17RUVdRxMSUPIKopsVAMIgz0DwITnQW8ddpd/Dm8lmUKzFYpRdPvwuxNX6D3mmGBlCMsQw47RW2NTbPY6i0MlcKgxq4JQQBVwWWgtNJmfZSeGekvtAZCTZVRg47cVA+L840wxJ10pfOUfwH3X4ONSDwiFlzN+scLOtnU3nvRFzDf0HAkkRTQMd3X36K1BlBZ4CEX9On/F2sO37FMHNfrindwJql75FiSmNMTD5BgzqfZQSmGiG3X4ArXv8Bp08SkJBsFlw+wMSg+CPmLWAEbUf7VwLfAvGAA3gZeENK+V1o7eadXSHIFVdcEdmePXt21D6DwcAnn3wSVda6TmfRIcUvpbxSCJECWGmjYQ4PRp2Be6wnceeejW0Uf4mrgb6xSdgNZkbHJ/PL4jcoUuJI8zui6lX5vBHlbNMb+dOa77myv5tz8oYRcFVgzj2JYFMVxtwzyF71Bbq4DFZWF0edIyAEQSlp8EUrwylpffi6pNmltKVHTo3HRZrFTq3XxXFpBcwvUyOGx8z5J/3jmmMs4gIN6GwZ9M0aA5WrKJANVPmDGBQdpE+ChiV4DbHkxiTw22WfcWmfseTEqB1e3KhbsOSeRMPqZ3GsfwO/MQkl5OoZbKpEH5OFzqpeK3XGLAKuSpAB7EOvIuh3Ysk/FVCXg7z22Dx2RCn+QzP1dHL76VBA4BG15m5hIc713+JY8yX+qrVUFG0mMdZG0OdG+j0Q8INQEKZ00hUH78XtZIEumY9lE42uxVyflkhS4RMooSjuQuDKFmPbNaUN3Dp7Ldfm5nLJmOx2RehlDAfWhj8IIU5BHd2fDoxFnXu6RspQ8AyMAVYebiEPJx1S/EKIR4FjgS1Af+D4rhBqX9hDvpFp5rZ+zG9vX8m80q08Ov5M/FIhK9jAj7osHCI6i+crO5o7/7yYBNbXlZNhUVfECjiKseZPJ/m8/1L1/KVcs+Qd3rzlszbXsuiM7UZZfjrtaq794X3e3LYCgEfXfcqfBp6KxRZPtcepKn5PE8lmG3/49nHWT57J2rqyyOgfIEZ6UcwJDBt8NqxexXGDz6Ta48Kg6HCH3gwUIRiTlM1uZx1fFG/k2oHHAiD0Jkypo5FBH0Jnok5/JgNO+W3k3IpRNZU1/jSXugXvkHPzB5F9Or2JTdenkHPrbOxjzoqUC70FEfQRaPFGcDB0cvvpUEDgkYZtyInYhpwIwJb58xndqgOTwSDS6yLQ1IC7aBUzFr3FdOdu7h9+DqdX7yHpresZpnNxu7kWc+pIbP1noo/LQ2dNZ3hGLJ9ePZ4Tnl9Ev2Qb43O7b63lTmI4UCiEOAn1bXMDMF1KuUkIcQkwE7hbCLFKSlmO2hm8t/fT9X46aupRUO2p9woh2s8w1oU8mTGJf1T/yIBAFdMfOwnP36OjdyuaGqlyqzZvoSikBx2UmTPxZx4HxQ3tnZKUUCBOqkVdTtGQMADFoi6zGAj40SNo9Lr5YNkbXDz2MgKKgj4YYEJyDt+VbmtzPoOi477Rp0QU//TKzXyf1Iep1QEqmi5n4qd/5NK0PI5NTKdfTRHcng2Fd+AO+Lmkzxje2b4Sm/Qi9FbslZv4jT0Ws8lKlduBUafDE5pLsOmNGBQdtw45nnSLHefG76n48A8U3KNO4MaPvQP7kF/huvckuPA3ODZ/ReLUV3Guno8r80c8e9bQuOzDiNyBpgY2Xa/OkOx+8myGvN7cEQmdCZ09u43H0EFw0O2nSwICj2CEoiDMMSjmGAwJmdhHnUHjT3O59+P7iZ/5Vxw5o3lh8yIuLdvOlKCOm9a+inSVEnCWoRhjMecU8sE4A0/Pns/zMaM4bew4zh2e3ivXZZBSXrqP3UOB+4GbgRdDEeVjgCQhhB/4oMWbQIQVK1ZU7S0qtofR7spPHVX82wGdEOIV1Em6w0ruju8YVbeZ1AnnwoLXcXtdfPLjv7n/xNu4eczpPLHue8pDqYyz68uIK3WRERdHWkU946Qfc/oA6oqXsNmYiTekQFPMqsIPe/JmXbYKGdoXCPiRioLHUcMgZyUr+w1j5Pb1fLP4RYoLr+cSr8AgA/iEjmULnuKYKf8HqHb+llxcvJoRjWW8WbyJS7Yv4OIFT5HsiTY/NXqcXP3DC7yTOR57yHzkWv8N1y15jaof3+TLS57BoOhw+dUgNLuio+6HN4i3pZH/10k4Tvg1TVsWRs7XsPQL4idfDoC3cge7HjuVhJNupG7+yxiS8tHbm01LJS9fhTlvTJQ8MhhAKLpI9Kg5c9LBfGWtOej20xUBgUcb9pGno4/PoOarJ1E8L/LQjbPY4azlhY2L+L07idP6DybOaGagEUy1a0kOerhn4h5qN/yZWVvuZcI3cZw+JJMpfRIZlRkXWR60NyOlPC+0uQfV6wzgtAM47sAW4uihdNTG/yyAECIBaD/xfBeS+/O7XKZAYPIlADRu+5GCplqGVm0nRm+iyu2kzNVARdUuBlbvgFIvd5eqaZifzxrKiJl/ZNcrtzE14x5ey8jj8vWLOKZsA996XRGvHMdPc9n9zAWk3vAOtpUfERQK9jI1AEv599Xop9xGoq+JxK8f59yBp/CHLd9y8dhLMQf91Jx+Pd7KHRhsySAlb0y5BOb/gySv6i2zomoPT2xf0Oa+hJQkVReRunkBZI4n96cKGld8jK+2GF/lDuJQJ64NQuG5ieczIDaFst0/U/LSLehu+hhdUwOekg1R56x4/3eRyOQd941V5Ter5iwZ8EEow2fl7PupW/AqtprdUcf768qonf8vPBv91Cd9hK3f2Yf8/XV3+9EAS95osq59jYqP/kTRIydiTuvPbTo9m90udlZuZm18Dk8Hguxx1qFXFBJMcQzMvob82rk8nbQGa10W3688md9/PpBJBSmMzorjlAEpWgRxL+OAFb8Q4gMgC9UH24uaoL1rwsrawVu5E6OrUo2QfUUdyTa8quafz3RUkli6DhrKGR3wUXVHHp5jot/uTB4nNV89iWdzgOQCGxn/vpKvQ/sWDzwFbzBAoKmR3U+qnf6L81/jXECRQa75ptlRJMtdzxZrErlGM0MbyzEH/Xy8TF3kpPiu/sRPvQZf5Q42lG7EP/ViVT6DATzQtGcN7fHzd//kb/1OAMDm9wBQ+vr1BN2NKNY4gq56qtxOYo1mMqyxTBEBYuepHVpysTpn5fjpcwzJeex57mJij7kAAF/NbqTQYUrtg7d8C8GQ66evcgc6m5oxoWHZB8QXXodz7VdRMgVctdQvfAMENC77HC4/sO9pb3R3+9GIJuXc+/FVFRFwVCMDPsY6axmyZSGuJa9xpZQY0wcQM2oG7iEnsa6unPW1I7h96zL6mQz0bfyZNwrm4Uo+g3mOSVz3wU8UN7g5e2g6vy3sh918VAaI9SoO+BuSUl4ghHhQSnkPgBDitq4Tqy2G5DwqTv0HqV+qpmGHwUKMT/UymaRTSH3hF0wacDKBFDV7ZGwLl0oAUbWT+sVvI12SFKMlksMeYEr1DlKWv0eT0nxMSnG091dYAX+29FXW2tNoGjqNzA1tTH/o49Kp++5lACxbF1JssJAYUrgvLnh2r/d3Vtl6hMnGIIc6gRpoUCNsM698iaL3f0eV20GSyUzAWUtqbTG2BjUGIW33KvyxaehDnxt+fI+G5f/BlDMCb/kWvJljSBo6mZqvnsBfW4w+MZuGpe9jSM5X78sSi23ISdTN/1eUPAFHDf6GCoTBhGKKQUp5SCmDu7v9aEQjhMCYkg8p+ZEy+0jVwhFw1uIp3UTddy/hX/Aq46ZcReGI07l6wAS2O6pZXH4Ml/z8BSeWr+DKwFtcN2oaMWPu4OF52yh8fiGKEBybl8Bpg1I5bVCqlmq6B9LRmZp+QoipQoiJEElPc1gQQiDimgMZdmQMiWwPKFmDTMojzeMgTarKO8HfRMzIM6LO4asqAh3cU/YzyCC5d34BwLSqLYz6/iV2PTad+EI17Pq42p3cOaT5eEtB8+DUrRjQJ2QypDE6yRmoI+9wgjfD9qWUmGPB177/uxKaXwAY6ignZujJ/H3nDyScqCZtSz77XmLHXwjxmeTvWMpNL17IphsTyQ1dVxjMJO9Zizt3FAiFQFMDln6TIOAndtz5eMs2I40xiFDMgrd8C4aEbGKGT8dXqUYWK5Y49HZ1nd2c2z9l4HO1JJ/5B/w1u5FeF0Kxo5jsbLhCIeCs3cu3c8B0W/vROHB0tgSs/Y4l8+pXSJ5xN461X7PjwckU3zeWjJ/nctWA8Sw//x5Gjvwl15pPZUVtBVWfnM1v+u1gxe1T+f6myZwxOI0Pfyrlhv+sOSKXrrQKITN7wZ9ViHYffkffyW4BLgpt//GQntxBIM3NcbkVyX1g1wrKM4eRVroO3YgzGLtzNaagOvk5qGwjcefdpyriFlgHTSCnZB1NgCFJnfC+feiZPL5OdQwxpQ/g730LuWvbfL5MHcR/LvoL7qJVBF11ONd9zRZbMk06A0pKAUmhSNeAJQGDAkIouItWETv+QhqWvo9r43z88ZnQTgfR/5+70CdmU/LiL6lf/DYA9nHnk7pyDnETL6X22+dJKPw1Qm9An5BN/1L1DcQ6cArVc/5C8pl/oHHVJwifF6kzIPQGgu5GjKl9adr+I/Yx51A5+z58LVJY+6p2Yhs+HX1MMu7dPwOgGMzoE1VfbUuf8ehs8SiWWIpfVJdCDrobCRgsoeOL0NkOybWvW9uPRsex9puItZ+aOCzobaL4hUup+fopDAlZnD3ufEZMvIC7V35BlUzklB8/5LwFf6F//9OYfuw9nDowhXu+2MjJLy7mgpGZjMyMZUJuwhERHRwPHFhmnu7lX3sp7+iIPw2IAZLojtB4nYHBrwYY8GQpHpM6Wv7m3IdASsy5IxhXv4fhu9W4i7yKzSimmOjDbYmkXfgcTVsWUnDfUnQxqtvmhpjmkPnEabcQF6Mqt/dPuBxL/hgSpl6N0KuxAIbEbJoUPYa8sRGZvJnjEIjI+cwFxwDgryvlxKEntrmNgj8vR5+opt21Dj6BmBGnkX3T+8RNvIT8exdhyhoKqGYYAIM5hjN3LWPl+Y+Qd5c6M2Hpeyy+qiKcsakQ8CN0Rgj4UaxxxE64CFP2MPo+vJGmYRdEXJZ0MUkEGirQx6XjKVE7EmG0YEzrH3k+AMGmZtdX6fMgw3MDdaUH/FXthe5tPxqHhGK0kHPrR/R9YDWpFzyMt2wz8U+fw4fJqXxzxm0MGftrfpt0Cb/Ysoc/zf0LtR4XD502mGfOHY6U8NzCnZzz6lJKGw45AlzjEOmo4v8N8Bkwi24KcBCKgj4+HV8oT8nF/Y9BsSVgT87n+uHnRdcNmTiyrn8b29BpGDMGYckfQ9qlT2LKGoouRlV0L571G9668En6/nUdQm9Ep1OTJJ2f35wZT4bmA0b1GUeTzoApIYuLxl6G0BkQMkjQ544oTlN688Ia7Y2QLQVjI3bPhKlXk3vHXGLHX4BQdFj7TYwo/LApKLZAzQJ4xujTEHojsRMuwpBSQNDdSENiHiLgi3RMQtGTff3bCEXBlDFQjeAM7cu/ZxEZV72MbejJBF31xBdeR9ykXyKEYMjrEhFyQ00+616MLe7BOuREDKl98dc15yo6SLq9/Wh0Duac4aRd+DB5d39L/eK3qXv0FH6RmMH8027kqRn3EeMsYvKH9/Hq+vkMSbdz4+R83rp0DNcdm0fhc4t4d1Xx/i/SC9kJvIrayHe2KF8YKnsVWI6aFe4T4H3gp1Cdl0J1PgNaD7EWoYajf4oa8r4ytP0uUHYQcnbU1LNWSrl2/9W6Hq/JhkfRMTohg632FAzWeBYmFfCfmz5mwEf3MLx0bWTkakztR95dXyGDqvJOOuXWyHmGvC7xVhfzSMUubk3IJk1KdMG2OcR05tDoOyFbVfw6HevtaaqyDAaQPndkxG9IVSeYs2/+EFPmYCr/cw9Jp92Ja/MCFOv+TSVCUfvjcOeQdsqtpE67JfI5+8ZZANjHnsueAccTZ7Ez2BLD7qfPI+Bsu+JX8hl3kTD1agwJmWpBen8G/9sXUfStUYxmDCkF+BvKCbrqybn5A5wb5qkZJA+NHtN+NDoHnS2BnFs+xLVlEbufOJPsW2czIDmP357/Ipct+RtXLn+Lxctf4vGTb8WWOYEzh6YzKiuO2+es5cwhacSYOt8DSAgxCzVCNx9IB26UUn6+z4M6ESPgB1qubh3O+fsf1DShZiA8Y/k+MBLVzW1GO+cLoCr384ClqJ3GmNBfKbAJ9SY7Qofz8QshCgEPIKWUF3bw+E5je84YJp9wJy5AF5saGSXbbQnq4heoYe3xU6/FkKqmUg4r1NYYdapP+6KKImbkDMbYjgky7rjLsY89h4blH3HRwImYQ28FQtGDDIAMogtNkupC6R9ijzk/EgCVdvGjHbq/gj8vi/rcnmdEzq0f0bDqSzxJ2dhzh6qmnHYUv2KyoZiilwrcm9IPo7MlNke1AbbBJ3RE/L3RY9qPRudi7T+JtEueoPi5i5EySPyky0gvvIsvJhp5cOkHzPzqOeacnYQxoR858RY+/NUxXSnOSOBjKeVFQojjgH8Cnar4XUTbz8eG/vJQexsH8BWqsg7TiKpwzS3KFgKjQtv1qKN9UDuK8BDRhZrcCtTOIWyIDaJ2BIUHIX9HA7i6LE1pRzHrDVhCyldvT2m2hwsFf8BHkz0FnS2BzKv2Nr3RjCG0bOEuZy3ugJ/24hGFokNnS8DSdwI5MUn4w4pTp0cEA2Rc+RLm3JHU//A6+rgM0i9XXTeFEAx4tqqdM+4bS8G4/VdqRfplT0eCtA4VY0oBwmjBlFLQKeeDntV+NDof26ApFPxpMUGPk8qP/8LOvxxL1g3vcu+EC7m8voI35lzHr85+EUNC/y6TQQhhAZJR0zAArCekQ4UQu4GrpJRfh+JKLgz9TUY1ezuBjcAE4FTgS2CVlPLF1tex0v7kbnh4ZkEd9bdkFc1KHmAx6oRX2KjaesS/BDXKsRBV+YPaOaShvgXMDQnaOhX9gdDRJG3vopqYYoBcKeWo/RzSZfz9mBncO2oaAHETL8GY1p+/jj2dwfFp7Aj4UNrJ1783wiP+arcTd8DH2oLx3HZ8++k9zNnDMGcPw+lTA60MiTkE9XYSCtXlDvs/WYLQG0g8qXnuUh8yAXU1cRM6bwCdOvMhEk+9vdPOBz2r/Wh0HYrJRtpFj+AcMZ2KD35P9i0fct+xFzN9biWnzb2EpMLHMWcd11WXHwZskVKGZ5DHAD+FcvJ/gapbv0bV0ccCE6SUtwIIIYxSSq8QYg6gl1K2ThGyXzYAW1FfaccDs4FzURv9LmBKqN4mVFt/Aaoyn0L0iH9ESLgwGcB/URX+MahvE9UtzjG0g3J2dMT/i/B2dwfgxBktxIUmb2OPmQnA70acyPq6MnZKMOmN+zo8CkMofYE3GMAd8OOLSSZ27Dn7PCacjyfv7nnsWrio+VzxGR26j55M2L+/s+hJ7Uej67EOKqRx9eeUvXETfa94gZy4NHSjX6Z20d1knPffrrrsSCBXCGEGdKgj/7tQLTHzUbN09gOKgCtQE/8BIKX0hjbHcpBpmQcTHaCSH/ovgMtalA8M/bXk2n2cd2Krz6cejHAt6OiIPzyMNaAuc9fjGByXhiMuBYO7/oCPEaEXNG8wgCfgj9jv94U+1FnoLHbQ9/5kVYeD3tB+NDoPIQTpv3iM7X9SEwBOSi1giUfHeE8D0u9BdM3vZiTwNqqSjwX+KqVcKIR4IFSuoHqXLQROoYVFRgihk1IGUBX/vK4QrqfQ0cnddahvLR72HhvQrQghSDFZCHgd+68cIsMay6cnX83cPRtwB/yY9zPxGSZw5WMHK+bRSpe1HyHEFNSBUYmU8s3OPLfGoSKQwSBT0/vyVfEmJiX0w1e3BWPysP0f2nFGAtdKKX/XqnwAsBnVQeZ54GnUtRz+IYSoBOzA7ahm9ZHAk10hXE+ho4pfjzqnEURdquybg71wV/5Qk0+/C7mXNAl7I81iD5l6fJg6MD+g0SEOuv3sb81dIEtK+YgQovUPXqOb0dmTCTiqGZmYwWNr56MzJRD0tr8+RifQF3WhnyiklOGI8TrUOaYwv2inbpuy1pRC1f2quain0yn5+C9DfVACNd5grz/c7vyh2kd33HnEqOjwBvwdGvFrdJgDbj+t2d+au8CKzhZWo3PQ21MINFZiTumDL+gHxaCmBu8CpJRZXXLittc5OvLxCyGsqIsVZKG+ru8zjPNQf6iHe3Hs3V4HxXWlLHWuoKKpqkPXcTgc3bZo987anRhK67Bvb39ZxO6UrSUdbT8HQOs1d5cIIe4G2g0JPWIWW2+HnvId7w17ZTXbf1xMY3wJtXV17K4txd2wHPe2Iy95W2+hI0PbZ1F/sPejBqfpOnitDv1QD/fi2Nsbq/lkWTUD+w+lsWo3haMP/DrduWj3d6s8DE/KpjC3fYeuHrSg+KG2n9a0XnP3e+D7vVU+ohZbb0VPv4+SbW+SOHYs/szBxH29idz4PpgzhmAtKOxu0Y5aOpKr5wbgW8CG6jb60L6rtyH8Q4XQD1VK+XBPmYgzKjrNxt+1HFL7EUIkCSFeoHnN3Y+A84UQz6OtuduzETp1Kc/wR8WADHaNqUfjwOiI4t8Y+n8JsGR/OVd62w/VqOjwhfz4NRt/l9Ch9tMaKWW1lPJ6KWVfKeXfpJROKeWVUsobpJRvd764Gp2FUHSRRY8kquJHU/zdSkc03LnAr1B9X9OFECYppWdvlXvb4tiGkOL3aIq/q+hQ+9E4glAUdcQvhJq7qgsndzUOjI4svbgKWCWEMAEzUYMhZnaVYIebsKnnQAO4NDrGkd5+NPZOeMQvEOqIX2dEBr37PU6j6+hoPn6klB4p5dtSyiPqR2vU6dnZWMNvln6ijfi7kCO1/WjsA6FEbPxSypCpp3UKM43DiabhQuiFwh6XmubBpCl+DY3OIzziF+qIXzP1dD8dHvEfqbTMd6+N+DU0Og8hFAiP+JGaV08PQFP87aDZ+DU0OhFFhwyP+GVoiU9N8XcrmuJvB23Er6HRiUSN+FFNPZri71Y0xd+Cv49T17/RbPwaGp2HUHTIYDCS/lwoRmRA8+rpTjTF34I7hhcC2ohfQ6NTEYq6LjUhG7/OqJl6uhlN8beDZuPX0Og8hKKDYBAhQMqQH7824u9WNMXfDtqIX0OjE4n48QskEhSjZuPvZjTF3w6ajV9Do/MQLfz4QRvx9wQ0xd8OeqE9Fg2NTiOUqyeMlrKh++lVGu7TT7s+qWfdZQ+SYLJ26JjDIdfB0pNl626OlGfT4+9DNGfnhFB2Tm3E3630KsX/2Wefdfk17AZzh485HHIdLD1Zts5GCHGGEOI+IcRl+6oXVpSH8mwORNnurU575a3LWn7e33ZPvw+hqH78AHV19RFTT7huj++4jkB6leLXOHoQQvQRQrwSXrpTCGETQrwuhHhJCHFpe8dIKT8H/gFk7uvcnaFoepLiPxQOy30ouoipp76+Xp3cDXg0xd+NCCl75rqXQohK2q5inwxUdYM4+6OnygVdL1teVy48LYT4UEo5UwjxS6CuxZrN7wLnhKp9JaV8RwihAPcBj0sp61qdp5HmgY4OKAttH+yziQPqD7JOe+Wty1p+3t/2oXzHh/M+Wn8Ob4f/d2lb0mimx7qvaA1AoxVRazZLKT8GPm5V549AAjAJmNtyh5TS3uUSamj0Enqs4tfQaEXU4urtVZBSPnBYJdLQ6KVoNn6NHklvW7NZQ6M30WNt/C0RQtiA5wAvML+7FtcWQvRBNSfEhezOlwAnACbghlC1bpFTCHEOcAaQCjwLDAcKAAPq2scZwKNAAHhVSjnvcMmmoaHRs+gtij9qYk9KeVE3yxOecPxASnmBEGIGqm2Z7pZTCJEA/BMwSikvFULcDPwEFKLaxNcBb0kpLzncsvVUhBBTgIlAiZTyze6W52AJ3ceNUsqLu1sWjZ5NbzH1ZAO7Q9uBfVU8zIR7zSJUGXuCnPcALwOVoc9RsknZIpLmKOEAXEOPlVI+wn7cQLub/d2HlPJ71DkQDY190lsUf3hiD3qmzLmoMnabnELlEeC/wDJUF782soVcHo8qpJTbpZRXtyg6D/hQSnktcFY3idVhjpT70Oh+eotXz0fAM0KIM+jGiT0hRBLwEM0Tjh+HJhstwE2hat0l5y3Ayag+0f2AlUKIJ1HnH54DtgEPA37UN4KjmSjXUGCJEOJuoLj7RDooou5DCDESOF4IsVpK+UU3yqXRw+kVNn4Njc6gVTBYrZTyMyHErN5mEz9S7kOj+zjqXvs1jj6OFNfQI+U+NLofbcSvoaGhcZShjfg1NDQ0jjI0xa+hoaFxlNFbvHo0ugAhhAV4HNV/PQHYAGySUv6jWwXT0NDoUjQbvwZCiEJgGPAZcDPwDPAasBTVVbUUmADcDbiAOwABbJNSPnH4JdbQ0DgUtBG/xt7YLKW8SwgxGzU/0XxgOpAONIX+hnefeBoaGgeLpvg19kZD6L9HStkghPCiBoMpwJtSyp+7TzQNDY1DQVP8Gh3lGeCvQohSoFFKeX93C/T/7Z15eFTV+fg/78xkBxIg7MiqgIICgkoEISpaK4pV61artVX0V7WtW92q1X5bi0JbtXWpotalLrVurdatWFMR44KKZRVFwioISdiyzsx9f3+cO8lkMluSVs1y5gAAIABJREFU2ULu53nyzM29557zzsyZ957znve8r4ODQ9twbPwODg4dRkQuAL4LVAIr7aB3se55TlW/m2zZHFrjjPgdHBwSxZ8D4SPseFUzMPkhrsIEkZsBfAVYqnobgIgUA/OAX6jq12mSu8vh+PE7ODgkijki8h7GO8yP0S9ZmOCBAK/b6THH2f/3xrgTX+0o/dTijPgdHBwSxQLgP8ADQA9VPUVEfgDk29dr7FexX/diHhB9gepUCtrVcUb8Dg4OCUNVazH7P4aIyC+A46IUb8CkBb1RRMakQj4Hg7O46+Dg4NDFcEb8Dg4ODl0MR/E7ODg4dDEcxe/g4ODQxXAUv4ODg0MXw1H8Dg4ODl2MmIpfRIaJiNp/9SKyUUSeFJHhqRDQIbMI6Q+Bv53plsshtTj9oHPTlg1cnwJ/BI4GzgeOEZHxqvpNUiRzyHQ+xWy1B2hMpyAOacXpB52Qtij+Lar6KPCoiDQAc4BLgF8nQzCHjGc7sNA+9qZTEIe04vSDTkh7bfyv2a/jEyWIQ6fjeMyPfjvwjzTL4pA+nH7QCWlvrJ5ArA1n22/X5QPgJvvYibPSdUlYP7Ajek7GpPT8a0cFc4hMexX/t+zXDmVhEpHvALMwQZruVdU3O1KfQ0rZoaoLYxdz2MeJux+IyAhMGs9CVf2uiBQA92HWBspU9UkR+S9wafLEdYC2mXoGisgFIvIXjH1/K/BgRxpX1ZdUdQ5wAXBWR+pySDkDReTsoL+sdAvkkNmo6leqemHQqdOA52wdMFtEXMDP6aBecYhNW0b8EzHhVr8BngJuUtVtCZLjJuDe4BP5+flqWVbT/0VFRRQWFiaouchYloXLlf7tDe2RY82aNTtUtU+SRAplIvB00P89gaS48xUXF+uwYcOSUTUANTU1FBQUJK3+VLf18ccfp7IfdITBwDL72I+ZDfQEjgReDS2cCp2Qyt9/KtqKqBNUNW1/mLWCO4CZodcmTZqk6eDtt99OS7uhtEcOYImm8ftM1l+y+0Iqv/NktPXeukr97cI1+t66SlXN/H6AGeUDnAecZB8/E+u+AQMG6JgxY/Smm25K7AcYRGfvC6FE6gvpTsTyE0x2nkIR2V9V/5xmeRwykF27dnHxxRdz8sknc/LJJ6dbnIygvKKKsrWVFOdn8dMn3sab35ucLDcLLylJt2gREZHewG3ARBG5AbMv6B57UfflWPcXFBRw1FFHcfjhhydZ0n2ftCp+Vf0j5st3cIhIYWEhDz7omH0DlFdUMfOBchp9FmxagX/DMnTKWTT6LMrWVqZbvIioaiUm8UowP4z3fqcfJI70G7MdOgUiMkJErhSRa0XkhHTL05UpW1tJo8/Ct/Yj/Fu/xH3k2XhcQrbHRenI3ukWL2kEZn4vvxxzcuAQg3SbehwyHBGZDBwLrAHuVlVLRA4WkWuBbar6WLJlcEw9LSkd2RvXmkW46mvJnXIqd80ey45aL6Uje1MyrFe6xXPoBDiK3yEW61X1juATqroMWCYiKfEccab4LVn+7+f5xcxRZB00Iy5lLyL9gBwAVd2QChmTgdMPEoej+B2ioqrb23PNIbGUV1Tx2Ecb+d8rT3DajMn88idnx3WfiDwCVAI+zE77G5MoZlJxZn6Jo0OKX0QeB55W1ddiFnbotIhIdoRLqqpOYK4kU15RxdH3v0fDu0/D4HF88nk3plZUxWvWWa6qf0i2jKnAGfEnjo6O+C8CzhaRZ4By4CFVrem4WA4Zxs2Y0aKEnN8J/D7ZjXf1kd5/1nxDQ9mjMGoq0m9/vH6lbG1lvIr/FBEZCdQAqOq1yZQ1mXT1fpBIOqr4ewMjgN2YEA4PA/HNQR06Dap6czrb78ojvYaGBpY9+0eyD5mJt3AQAFluaYv3zvk0B1MMfXB3KrpyP0g0HVX812CCq30FICIbOy6Sg0PXZUF5Bc8v28rpB/fne4f04frrr+eOm6/jZ9qdx5dsAuD8yYPb4r1zCGaHrIUJsbE+GXI7dC46qvjXBin9K1T1rrbcHBqtr4OyOCQZ24unB7DBse0nngXlFVzyvAld88b/1vHU717lmft+R79+/RgK7XXVPFlVzwQQkfvpxDHzHVNP4mi34heR+UCJiAzDTCH3B9qk+O2HxoUi8lx75XBIPiJyMXAUUIex6w8SkV3AfFVdl1bh9iGeX7YVAK2phvK/4brgCvr169fRavNEZIh9nJpIdEnCMfUkjo6M+O8BlgKLMDbErQmRyGbTpk2MHj266f+TTjopJU/5vXv3UlZWlvR2OoscNktUtcUvzo6lPjBN8uyTnH5wf974aBl8/E+Y8UPOPuKARFR7KyYmFsD/JaJCh85PRxT/qcAgTPpFwSj/hHkMDB48mCVLliSqurgpKyujtLQ05e1mqhwAqvqJiFwVcAsUkdGq+jnwRSLq78qZlwLB1kpH9mZqYS3H7f0ALrqWMybux5ySYR2qW0SmAoVAmX1qFPBlB+pLqqlPRKYDl6qq4yCSZGIqfhERO7xnKH8DIvl3x0VotD5VnduR+hwSj4gUAXcCo0WkHpN17ULaEFzLybwUngXlFVz24nL8liLbvuB411pe++sDuN3uRDXRy/4L0K5UqYky9cXZD45sj4wObSOeEf91wO2hJ1X1axH5AfAM8CjwOWZaGTcRovU5pBAR6aeq20RkBrBJVdcGX1fVncAPReRbwA6Ml8gLbWkjzFpOIPPSyyLyNxF5GpN56c5w9yfb7JdKs1qgrZpGP3u272XuIfDlymVU1K/m2FO+y+tvlVGQnRjFb3++l6rqfWAcMNpZVUJMfbH6AfBkO+VzaCPxKP5pduzsXQCBTmQzDjgGeNx+deh8XCUiC4AzMdP480ILiEiuqr5h//txyPn6drTZpsxL3bt3Z8aMGUnz5kilWS3Q1ty3vuDmpavxf/EB7N6GHHoW//4Mfn3CEG4oTYhtP5wDxkja6IABSTX1tegHIjIeOEpElqrq66GF165dS1FREQUFBXTr1i0p637pGASkg3gU//yg49CpYg9MvtwfACclSCaH1DIAo+zvBC6JUOZ2EbGAlZip/nD770lgcTva3IT50S8FXKr663bU0ekor6hi654Grn9lBWVrq9BVZeD1IofOBsDjatPGrHgIdsCwgDanSk2EqS8Kof3gM2BWpMLFxcUcffTRSXXnTMcgIB3Eo/gHAxNU9ecichnwTuCCqjYpChH5aRLkc0gAIvIzYJyqzhGRm0MU7cNAX1X9UkTeD3e/ql4hIn2BsRib8et2hM542+9Q5qV9wY0vkDzl/8b6mLf0K/Sz1yG3G3JwKWCG4xMGJjZ/rKquF5FbMWG1Aw4YP2pjHR029QXoaD9wSBzxKP4STIJ1gGHBF0TkD0A/TEIXBb6XSOEcEsZIILCrunvItamq+lv7+BDg+Qh1/Bb4qarW2t4XcdPRzEv7wsadsrWV1Hstk/P0oxeheAgyfBIuO4iCpbBk405mPlDOwktKEhlXP/C5FwBz2lNBokx9He0HDokjHsXvAxCRQqB/yLWvVfWqhEvlkGgUs5FnHEELciLyd+AgEZlgn4rmoncL8LCI+DBT83eilE0onX3EX15RxRurt2GpxZsvPgMDJiKDDwLgmhkjWLplDwvXbMeCpvSJCVT8gZViH+3fd5FoU1+76Oz9IJOIR/E/ClwF/BmYF3JtmohAc+S/++iEBPtS1zT6mfvWF/taNqPfY1wlzwNuCJxU1TNEZKqqxvPj/TXGc2sE8GxSpIxAZx7xX//KCn7336/w+/3w7hOMO2UaC6v3p0eOh/knHcickmGUV1SxaJ1Jp5iE9In3Yh78DcBLwRdEZJqqvisiNwMrVDWsCaejpr5E0Zn7QaYRj+KfjvHXDfdFB8f5bpePcLoJ2F7rvRYetzD3YD83L11NTparacod/GDopA+DQ1T1egAR+TbNZh+AA0TkUuzIjaoayVx3rarusN347saE5E4JnXWkt6C8gnllX6G+Rlj0OIz/NoOGDoBqOHr/4qYNWiXDerHwkpJk9bHLgNmYxd3QWdpJ9gxuGzCT6Lb7dpv6EkVn7QeZSDzJ1t8DjheRp8LE1BkMnKSq/wUOTrh0Saa8oopfvbmGBq+FAl6/ophfSL3X4sp/rKD03neZfu9ibnptNTMfKKe8oipiXXPf+iLi9VhytPfeOJkRdHxUyLXRqnquqn4vitJHVXfYrzVE9v5JCp0xyfaC8gpueG012lgL/30EJp2C9GoOq3zt0SNblC8Z1osbjj0gGQOLBRgTnt8+DmY4JpzDC5gZQTQCpr4ngMMSLWQ8dMZ+kKnEM+KfiYm7XwF8EHIt4sJvPITbudfWOtpLYNekzwo/UVHgw407W5xr8La2v5ZXVDH/7S95eeU2LIXcoJlCPARmHIFpfoIX9gL0EZFjMW9rQMi1LBE5jGZz3cpIlYhIsaruUFV/ogWMRmcb6QWibGrdblj8JBz5PSS/kHMnDmRQjx2U/fiwVM4c16rq8wBBazkBLgNy7JncwzHqSZupL0Bn6weZTDwjfg/NNsK6kGvRFn7jIbBzbw5mOpoSyiuquDyK0o+UrUJcwobqOhaUVzD3rS9YUF7BMX8u56UV2/Cr/SHZD4dAO7FG8mVrjW3Xr80Le0ngp5g4LaOB0N2be4ATgTOAWKGxH0m8aLHpbCO955dtRfdWwuKn4Kjz6V1czAOnH8wT506if/eclCl9e/H+OyLyloi8Revf2KnAnfau2WlR6pkE/FxVbwV+jBn9p5zO1g8ymXhG/C8ApcBU4FDgzaBrj9K88HtHO9oP3cHZRDK36W/d08BvD2k5aH1/B0wphs93w7wJFgBPVcCIbuZ8E/Xr2L0WPtkBJX3gN+PMvQFK+sBo/3r+9eZ61myv4dPtkLsVqtYUUJDd/HHXNPrY0+DnAJcwb4JF+XZz7yj/esrKNid6V98QTLCuHOBn2FEaRWQ48Hean3Wx1mnSksGps430hko1fPg8zPghkpXD3G+P6XDAtfagqmfEKHJIUKz+aDt6xwLni4gXsxksLXt2Ols/yGTiUfwnAm8D9wcHaxORE+3Dv9mvAzFufm2hxc694AuDBw1iSXnY/UQdpnxDNRc+/BF1XqvF+ec3weA85aGvmvXbpzvN+XC8uBkEwW9/LG6BkSOG0UAWG3bW8dDSOvxq7r/48N7c/51xTe3Pevgj6r0WuVku7po1jol9vEwZ0YuSIT0BKFv8LqVHTo3/TWVnRbt6FWYhPtRd83jMTC1Y4UcL3dspF/BTyV/fWMwjDz2EzPgRLo+Ha2aMSIvSBxCReYSYYAOK3qbQjtGktAzm1gJVfRx4XEQ8GGePm0WkQFWdTZudlHgU/yLMKFFF5H5VDWjjPsApmCTrjZhFw1ZxVmLwApF27ingT44puWRQDxZeMImLXlrBqu21La4d0VvZtEmatjlGQ4Crpw5hV4MPFA4d2IMrXv2cep9FtltwCU0moEeWbOL88QMoGVJE2ZfGvKMY886OvY3cMGO4qTT4Pbfp/UdV/MtVdXnoSVV9QER6AefYb/eZON5yyslUN75Qb6/333+fR554Cmva+SAuUCjM61AA245yHTBZVT+KcP0KzHcfOI5FT0zqxl+mIwNbpvaDzkg8iv/HGNc9wSzEvg+gqo+JyIGqOh9ARNocUtD2EImwc0+TovjLN+6irKKa0mE9mT60qJXin1JsRujxDG39Cn96fyMLfzCRkv0Kmbuogka/rdD9ytCiHCp2GmcJrwWPf7qZkkHdKR1aSLbbRaPfItvtonRoYev3qgl9/0eLSClmnUZDRn13Ag8FHf8gSj03RLmWNDJxih+6KD93bAO1m9Zw9qXXUPaCecZaQHF+1AdyUlFVFZGjgVaKX0TOBIKNmGdjft+tyJQMbIF+YO3cQcNHC3EXFuM+YDz2XiKHNhBvIpaAHgz9hFVEHsGM+L9OmFSBFn1WzGJtoXzTLmb+9TPqfRZul3DVEYPJcQsN/vZbMBr8Fr96+ytumT6M0v0KcYkx/SiwYWeIh5yl4LMoGdCdhd8fT9n6nZQOLaJkQPfW7zWx7/8c4EBV/UhEBodc26OqiwBEJOribrhZQ1fl8SWbTAgGoH7lYu6vaOCRub+gbG0lLjFftUtgR23aUxPPtj26dtPyob+WlkHbov0IMiYDm9bVUPfCfeBtxOfxkFWzk+yJpXHdW7/6fzR8uYrcsRPJGT4qatkd22pYsdlF395ZHDi0o6nJm1FVvCs/wNq+Gbyh0VNSRzzv6H6MN4dgov01oao3iEgP+3h3QiVL7IgXgLJ11TTYJhafpdz5wSbOHNObJ1c2r84GL9QCUU0+gvmBL/yqmrcrdrJ/z9wWnkIW5sevany3zx/bt+k9lQzoRsmAbqZg2PeZ0Pd/F2a0/xFm1H5Z0LX/2fszFHgrWiXJzsDUWVhQXsGCD9ajgK74D4ryxahjmflAOXfNHkuOx5WsXbjt4RJVXQEgIk17bVT1YxG5MRCnSUR+RYQwHMnOwBbA3hhWAmxR1SfClbGqA97jCj4v/g1fQBTFr6pYlVtoWLuaqqceQv0+6pe/T9HpF5BzwPiw93x6wzye7n0u/qw8XFLLlF3vMOO4objGHUK3XDfZnubxr/r9VH24BM320HP8eNyeyCpVvQ00Ln8P7yf/BW8DVvHBWDu3s3f1aqpfepLsPsX0Of9S3L37Uf33R1n/1D/JGTSYA+bOJauwR9g66zdtoGbZZ+SPGUve8BER2w4lHsW/AViBUQzrW72ZRCv85ooTPuIvHdQdl0uwbOXstZSX1kTfNKXAsO7Z7Ncjhwa/8tHWvShmJXpEUS5rd9ZjAZalrKoM9XY1FczevxcnDi+irGIn+C1KBoX/Els1nLj3vweoto9bBNVS1QdF5H/mUEP3aQCZM9XPBMorqrj0xeVYCrr0VSgoQg44Est2x91R603mLtw2ISL5wDkichtmnHIJcLl9La44TUFhmcd0NCxzrAxcwCBVvUNErotUh6t3f3C5we0GceEeMS5qm94P38D3+RLU56XXhDHsrKjmyxeWM3pYOXVfLGfLo0/i7tadITf/Fu+qj2msrkM+e41ZUs7rpXdSn1fMGs9wBvYYChtr8KuQ7XFz4H65FGTDigt/ROPGdfi9fnJnfouDbrgeT5ZZ19lTZ7GqopZ8q5oe29fgfemvZPfpRY8J41j//H/wz57M8qUb8P9nIfnr1tGwYQM0/p6ex81i+a/vw9/gQz5fT+PlVzDhCeNJ7dv0JdbeXTQOGI1W72D9Vf+PgM/N8JtuIW9AX1x99kOycqJ/F+GzKrb4sh6wv5xs4OpU5cOcPH6CLnnt3wmvd8H/tnHpW+uIZN2ZP8Hi50vDb29w24u1Ac4d05snV8f2u3cLuF2C16/kelzcVTqUHfU+Sgf3oGRg+Ole2YrPKB0bfkQSloF9EJGPVXVy6CURuRHjjbEcsFT12qBr92BGbtnAGFW9MMz9h6rqJyHnCoCBqprQUV84DjjgAE1mHPa2xEWf+9YX3PjqKvjoBeg7Ahk2ETBaNZ7Ne6mIwR7oByLyF2AC8ClmAvq+qj4UVC7eOE3YYZm3YyK4Vqpqu53pReQ5W/GfB+wMysD1sarOE5HrVLWVe3j//v21sNAOXW1ZnDTrRE7+zqlR27Iqv6blnF1oqK7Bu7cBV5YHl1txZbshKxtXTg4Nnixya3bhrfOiIvglC83Ow9OnGOy1BPH7cFk+PLXVqNeLWoq/wYsqeIYOw+V24/F4qKm3yNHmwaC/uhqrthZ3fh6W109DfgG5BQU0SB45VRuhocGWVPDVm3oBXFkeCkaPQuv2ojVmnK3ioj6/GKn6BqnbC4AnPxdPQT6IID16AcLRRx8dVifEM+JfYSdIQESafuR2dp9g278GK5QOkwRTD8CcscV8sm0PDyzb0era9IHd6JNXiwuLcGNtvzabflzA9lovLghbNvQ+yw4H0eC3uOw/61CFbLew8LQxzSafVjd2/P2LWfn6N8aDyhVmZ+7nqvonu+xvwtWRqql+JDJpcXf68J5I+VPosEnIwDGAMefNOWIo508enFGxnFT1hyIyWFU32Yv7G0OKFNsKtzvQqKrfCVePiFwLLASuBWqBmxIkYug+nvdF5Hpgc7jCbcnE5vdb+PwW1sv3o7vMb91f34i3rpEt737FxoWrUD/gggFT+1LQvwc5gwayevLxlNR/BWpR+b+1rC6chhZ4yFu7nJxzfgiWn/7/nEe+bw9uj5A1+iAkv4Cdn3zON2++S97pZ1Dw7Vn06NaN2k0V5LkEdbnB7aGuvpIdjz5B3tBB1O6oY/NxJzA8N5d1+5UwsaeXyvnzqd1WS27vHPx+qF63C1QZcc3P2K+0lNq/341VudW8F3c2a4ecR2Nuf/LnX45keRh4XAkF3fqDJ4uscUfgLg5dzmsmHsV/poicZJd1i8iz9gLRPSHlEuvjrYAvOe6c54/qxUPLd7QYvbsF5pYMpGHPBuaM7cYDK1o/GLLsiYDPMjb700cUsmjLHhr9igiMKsxhdXVDiweBYBS8onj99qzBMg+LRr9StmEXJX3yWgupmpD3H/DsUNXQyKoBzrJtvx5glIjMC5kRJGyq31kJuG0WZcPT98zjgnPO5ImN2fgtxe0S7jt1XNp89ePgJ2JSa55B69SaJ2BCLc/F7PWIRC9gmKqeLSIjMTt3ExGvKTQD1zskINz33tpGKr42ls3u42fjevYh1v7lFVCl34T+FPTKJbtbNg27GsGCzYu+ofvgGoadUAC+Ruq215DfrztFo/ajpK+X+l55fOo+nWxLyXrzWSrfeIdKoHB4X/qOPxR3bg49J49jz+qv8K5Yjp5wIvWVX5NPDYILtXz4VGj4fA3i8eDzwd6vNiJuYWu/SezHBkRhw78342vws98x/ckfPYpBPz2d7EFD6D7WhPB2DxiOtbMS9XsBoTG3kIK8fHrNPp284gLyc71g+UAVV0FR1M8opuJX1bBbue3sPkdgOlK+fbpN2X1itJw8P/4+edx31CAuXbQZv5qA5fcdNYiSPnmU7VYO7Z3TZNZxCUzrl8+BPXM5tE8eP3nXDEZUlXFFOSw8aSRlW/ZSOtCM2qf/48umR6AAxw3uxi2TTTSLsi17Kc51c8XiLdT7FY8IG3bVU755NyX9C0LefkLffyTPDoBzo90YkoEpMNVvVwamzkggppO3oR4WPY5MnMXHW3K479Sx7Kj1pt2OHwfRUmtWYhK0HI4J5xEJwYRgX6yqa0WkXR0zVRm4tlXuJWDB3q3Z7Hj0NdRrRN722VaGlQ6j++DuNOxqNtPu3VKLt8aL+vzkFOYaeQEXFt2sXRzg/pyCTbXsPHoaHHMUdQvuZe8XyynetRvy81FVXHm55Bw+BZfbTa7VgLjskaJlITu+odfUmQw49Vx2vvUa3YYN58shQzgodx00NvLFrX/BV+dHXMY64OnVh14zj2+uA8gumYV070VdVSWbiydQ1LOAkf1zyRr3E9Sy8K39FN1dhWf4wUheBCuCTUf9lOYAWzFx+qP5f7cdxQyNk8Sc0b0YV5RD2dc1lA4ooKRfAfgtarwWP313S9NswAXMPbw/Jf0KmLv0m6YNWX6Fss17uGFC36YR+9yl36Ahs4hbDm2+HngdV5TD419U85fPq3lodRWPr6lm4azhRoZgEvf+p2MWzjaKSItVH1VttWAfShKn+hlHYHRfnJ/FJ5t3s+CD9fjra01Y5SPOgO69qfeaRdwbjk1MUvQk8zDQR8On1nwQs2B/HvBApApU9TowKTxFpAQTsbfNpCoDV1aWK2qsUXG5KJx8IHsrl1O/1Q7EKKC53XBlZeHOzUK9PvwuD1mFhShKtr+WnC/LKchyUzdkHPmX/BTfrT/DlW9+syo5DLjwcvLHT8Tt8eDfW0TN2s9Qvx/qGuh1zMm47EXf/NEHArCurIzeBx9J5dvl7Hp/JeJ2kT8wD3HBnk8+Zs+SD+hxeEmz3G432eOnkY2JvxL6nrIOmBT3Z9RRxb8NyMVYLvp2sK4QkjfiD1BSnEtJsXm6B9ra0+inMcgl0yj4vZQU51LaL49sl1DvV7JdQmm/vBYylvbLa9oX4Ba458j+pv6Q91FSnEvZ5iz8aupvtLSpjSYSO+K/CzOyuxAzymrrND1ZU32gyY3v0lQ5DkSivKKKo+9/ryk8N4DW7ob3nrIjbBpvLCW9G7PiJWiTltg5F0I52zYB/iHMtVao6t2YXAxpId61noF9emBZu2j0+unbqwd95v8fq6+7FQSGnjaD4tNOxZ1r0efYlay8/Tkaq/Yw+rafM+ic09hYVkbBGWejqlQ9cjsAlh+6r1+C+H149tizBLeLAWeeSbeTfoiohbhbqlJXj970OPgo1O/FlZ0XcZOZiFB8zJEct+1DNv7pLnaVvd70u1efr/0fVgxiKn6Jnqj7Scyz9VrgP21pWEQOB64GNqrqNa0KdFDxlX9TR9m2Wkr75VPSN4wNPQLds1xku5QGW/lnuaC0r1HeJb1zWHj8fs319s5pIWOr633NgyGcLKV9c1s+RPqGPCASG7LCT7Mr7q7gCyJyHHCYqv5WRM5W1XBhGzo01Y/lxqeqT4rIkW19U4lm3ttraQxa+NE9O+DD52D6+Uh2ftN5ISM2ZsXDWsyMHMJvSZltP8h3AcRyzhCRPqq6PeFSxkm8IRs8bhfDBvZsPnHKifQ/5cQWZaz6WrS+lkP/+GM8g8eQPeTAFtdFhKrFH1PZUEffQ0chLoHcXOr3G4uI0H/wILofGHCAC+8F6PJkgSe+AYI7N4dBP7qQupVLady6hYKDx9PjiJLYN7aTeEb80RJ1z1bV22lHtD5V/dD217083PVNO7Yz+rSTmv4/6YQTOPnbJ4Yr2ooan8WaPV4TGVOhqiaLAk88EahB1ctz0zxUNhgzS+8cFw0NX1MW5A9RUgQNDbtanAsm+HpAlhyF97e2lOXZqS72+JTuHmnVxl5fI2UU81PUAAAT5klEQVQbv4pLZgAqt0S72gAcKCKXY+KtBHM6ELh5MmHi9XR0qq+qXwEXSnMin0A47oAbX9Q8DMmM1Aqwd+9e/vXmWxyVtZdptlf79q1beG/5q5z4kwvIys4m2HdLBEb7N1BWFtb5JGZbCYy6GouzCfG8o+XiadT1nTD8CpPCs9Pjys0nb9LxUcv0+d6P2PbIvXyz9Et6lh5D77N+SPcCY2RJRpiIrN7FHPiXp1GfD4myESwRxFN72ETdNtPsRZrAiCFSrI+DMZ4DwURdCB7Uszflj/w1DvFaM+/zPfxqxW78wIub4JaxBVw7Or7t0YurtzK1Z3tSC8SWxU38siyu3sqRRfFbz7KPPDTseRGZiMm7OhYzNLk/pEidXc6FSbgTkQRO9Vu48YnIeOAoEVmqqq+HFm6LG197KCsr4287evHg0lqzG/ebdbCqDI66kPdWubGCFusP26+IO08Z2+4F3VT48QdxDybA4jOYh38LU1o86zsAdpiPkcBA2yyH7YGTUlLt1ls080S6TS5BvV6y+iTYkh2FZCt9iE/xByfqvjHk2vyg44junGry9Z4Uel5EhkW+x6KxIcxO2DgoKVSyXdBoQbbL/B+urg+qvbxb6WNabw9H9DRTMrXCl20vAVnqY8gSSlvliBID8ieYBdnAQ3k0xoUvwL8wM7Z/YOz/qSDUje8zYFakwsn+wdc0+nnkow1G6W9eBRWfwvQfIOLCUrNID5DtcXVI6aca2/Oum6p+CCAipwWuiUihqu6KfHcLijDhnbvTHOY55Yo/HXiKQifI+wbxKP4emC9e7eNgpsYT6yMcIjIKs0g4VkQuDg0CpZbiq6sPf3MMJuXCixNzWFxtMbWni0m5fnx1Lc3SH+7yc+qnDU0Phxcn5nB4oRtVq93ttleWcCRQjjcwnhTfpXm6HxxzvwzYgUnSEnUvRnttvB1140t2ON6texrMgm7FJ/DNOjjynBZTeZdL+NFhQzJug1acLBSRFzDrKc8Hnb9RRKrC7ZANxQ7Ot1xEptix+dOCE5Y5ccQblvk6jGK4C/gehI310diWhlV1DVFsjKoW3vr2j7wn5sBE22LjDaM/39muNFpm1bPRgne2NzAxR1BXx9ptjyzhSKAcQ4CHVTWS3exZ4ENMrBa1jyPRLhtvqtz42kN5RRU767zomnKo3YUcfnqrMpalDOmZ1xmVPqr6LOFz5HowWbUm0bzz/sww5YJJ1YwwLJm0g7uzE4/i/xIT5KuWoC36qnpGW2J9tBW1LLNpJkkckSdkSRaWQpbAEXlevA2K5rjxNqTfYyOBchwFjBCR2YT/ga+wF+gjkm4bb6J/8AFf/V11jbywfBuHfvUW+DzIhPDOAx53RkTZTCiqerWYkOob7P/3xHHPqqQL5pAS4lH8xwAzMQuD3qCQDWBS9y0GEJFfqOptiRJMLQtfXWJH3sGMd8OTQ928X+thSr6P8W4/vjrQ7IKkthsviZJDVWeLyCxV/VeEIl4R+TdmVy6q+r0wZdJq403kFL+8oopj/lxOgx35VD95hamH9EIOMt6kLoGpw3rx7roqFPOkvGDyfp1ytB8Hp2FmhIjIRlWNmHYzQqymlOKYehJHPCEbThaRsfbxipDLwSsfCR0SqSqN9ckb8QMc7IKD7Z3NjXZT2j0v6e3GQ6LkEJErMGGUZzTV3dJfu7+qHhdVljTbeBM54n98ySaTk0HV+Oj3H8W4yQcjS2H22P5ce/RIgBbZtc6fHDnYVaYjIjcB4zBmPA15sPdU1Tl2ufkR7g/Eahqd7lhNjqknccSzgetuzOIfInKJtkywvEFEHsM4OYdGfewQaln4EuhdE3e7WpiWdpMox99oTsgbzvk4X0TOxsTxQVWj5U1Oi403ESO9gHnnlZVbUbVg8VMw8jBkwGjAJOc5fEhR08g+U2LqJ4C+UXZEF4nIDzAPhbADt5BYTTvoYrGa9lXiMfX4Art1RSQ0wuMfgEGYJB9tWtyNhVncTf3IWy1NS7tJlCMXuBXzXTfax8G8jVm470MMr5502Xg7OtILBFrzWYr6fSbuzrhjkeKhTWVcQgs7fsmwXp1d4WN7TRWKyDk077UJfrBfgjHXCq0X34PryVXVN+x/Pw45n9AfS6aE79jXiUfxe0TkFvs4NPbBXUCBql4oJmFLwuK3qGXhTYOtPV3tJlGOXwBXqmqV7VY5DzNVR0SmYtv2iZ5lMq023o6M+BeUV/D/XliGqkl9x6LH4NDZSFHLTXrXzBjR6RV9GIoxoVSyCfNgV5M+M9LaTzC3i0hgVr8TGG7/PYm9xheOeEJ1hN6jqu9kQviOfZ14bPw/i2LjjxgDpqOopekxuWh6TExJlGOPqlaBcasUkeBUmb0w6zQBE1ArxZ8JNt72jvhbKP2GGlj0BEw5E+nWrOBdAv2753D1SWMTKHFmoKqPicilgR319npPe+q5QkT6YnZ/9wJetzdlxrovaqgOEakBAslf3lTVp6LVl+zQHZDakBopDt/RgqiK3/bVDzg3ICKhroDRYsB0iI768be7XSs97SZRjv1FJOCnKBi3TNOG+QFGVQyd1cZbXlHF5S8uN0q/die89zRM/T6S1zJcxpwjhjKoMHre5c6KvWB7pL1DPvDd39XO6n4L/FRVawMuve2gRagOVX0JeCm4QLTwHckO3QGpDamR4vAdLYiq+FX1jMCx2Fo/pMgvgOMwnSo0BkxUROQiTAKIAcDNqrq0RduWhTeBO2jjRS1NS7tJlOM5zDQ/QNPuTVsxlMRSDKm08YajPaaesrWV+FXR3dvho+dh+gVItrFUumyjVk6W8dhpqNg3FT8mVs9SYBHGAWNr9OJRuQV4WER8dp3tcedtEaojXIFY4TscEkOsEf81qvo7EZmDCeG6MhCp0eZ7mBg8HoxXQNxR1dQkfX7IDiJ2MqYzBF+nsTFKNoUkYamVlnaTJYeqPhblckAxvEt0xdAuG2+iaI+pp3Rkb7J2bcH/8asw40LEk4UAF08xuXGDPXbKKpIidibwO8yM/RSa13Bi7c6NxK+Bz4ERhN8J3IqOhupwSB6xbPxD7NfDbX/+UHe+Eap6FoCI3EsExR8lOmcVJkDYL0Pv2anKpUH2r+NnzOBbqZgWde9O3YwZsctlmBztsRXa0RljRmhsr403neRWr6e0bilV517B0m01WJY2+eR3Bo+dd955h/vuu49nnnmmxbny8nIGDhxIr169WLJkCSNHjuT73/9+2Drs3fW/BP6MMcte3AGRrlXVHfYC7d3ARbFuyORQHV2dWIp/gG3b3RahfA97Bd4DICIHqWorf/5w0TlFJAuzwn+XqraKbN8duG3q1OYTPh/1CxfGELfjZM+cSWMK2km0HKc8GTWkfSJIhI03JSxatIjHXvgX/x10It6v9+Jxu/jREZ0ryNr06dN5772WaQ/ef/99rrvuOu644w7OO+88ZsyYwX33hY2EHswBGHdrP9DueOOqusN+rRGRhHnvtQVnA1fiiKX4b8H4+QZiubwRcn03xsavwDeYCJARt32HcAcwCvixiLylqn8PvmipUmclL+duJDyQlnYzVY4gEmHjTTqvvvoqq1atYsSsH+F943OTO9lvJT3I2pVXXsk111xDcXExF110EU888UTTNVXll7/8JVOmTGHRokX079+fMWPGtLj/xRdfZOPGjYwdO5YPPviAG28MjYDeEsuymD9/PldeeWUs0R4EHsMo/oh5dduCqiY3J2oEnJANiSPW4u5Kgnbkquo/Qor8XVVXisj5wBeqWh5vw6p6VbTrFulRfAVpeuBkqhxBtNnGmyji/cE/++yzfPzlJoqOmE2f/CyyPa6msAvJDrJ22WWX8eCDDzJixAjOPrvl3qNPPvmE6dOns2jRIizLaqX0AU499VQuv/xyFi1axDPPPMNnn33GokWLmDBhAvX19cyaNYspU6Zw++23M2jQIG677Taqq6t57733OPHE8MHlRGQ4UImxs0OMDXoOXYeOpno5X0Sewiz0lQJxK/5YWKrUJTnZeth2IS3tZqocQbTZxpso4pniP/zww2za6+eePaNpfH012R4Xd80ey45ab0rCLuy///5s2bKFVatWtbDLA4wcOZL58+czatQoli1bxqpVqzjwwJY5XlWVnTt34nK58Pl8jB8/nn/9q+XequnTpzN9epusbMcD/Wi5TyPeGXnG4Zh6EkdHFf9QzG7duUDMOWdbsID6NIx4VTUt7WaqHAEywcYbiLcTqsjvuusuRo4cyTfDxtC4aTV+hUafxY5aLzcce0DK5Dv22GP5+uuvcblaeioWFRU1PQzKyspaKX2Au+++m3POOYf+/ftz8803M29eaHSUdjEKs3P3nXjCLmc6jqkncXRU8f8Ksx17k4gk1D1L02TqSJeJKVPlCEc6bLzlFVUtImYuvKSEKUN7cttttzFt2jRKS0sprqhKqXknmMWLF/PCCy/w8MMPt+v+K65o3js3adKkRIn1R0xY9btFpBD4XFWjLx5kMM6IP3F0SPGr6uqg47IOSxNEuhRfuhaVM1UOABGJlNJX7XgvSadsbSWNPqtpNP+fNd/w8oO/47TTTmPy5MmACayWrqiaU6dOZWqwF1pmsBFYhTH39KN1rC2HLkry07m3k7Qp/jS1m6ly2NxMUOiOIHYCv+9IxbZraAmwRVWfiFSudGTvptF8lvhZ9uwf+eVVl3PQQQe1KNcZfPRTyGbg78Dtqrol3cI4ZA6Zq/hVqU/D4qamqd1MlQNAVW9u772xIjQCg1T1DhG5Lko1TaP5N5dvZPlz93L7rTcybNiw9orVVRgNzAAuF5E8oFJVf5NmmdqNY+NPHBmr+BVnxJ8JcnSUWBEaCYr9E4lAVEZVZfv27Zx11llUVFRQUVGREBn34YiMLvsvD5MyMz9VDScDx8afODJW8afLxv32O+9khMLNFDnsuCrdMcraF3T+MKBUVcOm7ItCiwiNwPsicj3GLBGW4KiM3/72t/F4Etttr776an7/+w5ZrDKyLUzAvTLM7viYoTkc4OWXX05ZxMxUthVK2Ah5qUBEjhKR+0XknyLyndDrgRFvqv/esRVuuv/aKkeysJO0v4sxF/xYRL4rItcC+e1Q+tAcoRHAparvqOrt0ez7gZHeySef3KT0X365tRNZ8Llwx8GvwcevvPJKVIFjtRWpjXBtxmornvZivTegEEBVL1DVRzuT0heRWSJyi4iED0AUQiK/G6DDfSHT+l0k0qb4VXWRqv4Y+AHGDtkCS5UGy0r5n0Ja2u2oHEn+rjap6l3A48ASVZ2nqv+N514R6S0if6Y5QuMLwOkicj9xRmgM2HYj/cDCnWvLDzAWiVYuHW0vDsVfFFdDSUZERojIwwEzn4gUiMhjIrJARM4Nd4890Pg9MDCeNjrbd5PqfhcJaR1iP/FEic45C7gcuEZV3w65Zw8tH0zbsZO+J5niFLUTi/bIMVRV+8Qu1rkQke20jiJaSOusb8Hnwh0HvxJ0nEX0zzpWW5HaCNdmrLbiaS/We+urqoVkCCLynL2wfx6wM2h952lCMnCJiAsTF+pOOwlQcD3hdIKXxH03u4j9u+ts/S6sTkiJjT9cdE6bv4jIXzEuZ2+H3NM9THmHLsi++DDrosTMwIXxAOsJHAkEJ4Z3dEICSdviroicBhyN8TSIO4GLg4NDpyWeDFy/TqlEXZSUmHocHBy6HkEZuI4DHsLOwAXUA++qatKTSDiEx1H8Dg4ODl2MtHn1ZDoiMl1EnoldMulyHCEi14rIBemWZV8n1d95W10XHVJHKvtCOvpBxm7gSjaxQgmo6pN2WslMkONUTIYzhw6Q6u88zvb+C1yaqDYd4iOVfSET+0GXHfGr6leqemHQqUAogTnA7EySQ1Wvx+yedegAqf7OY7Vnuy7+HJMe0SGFpLIvZGI/6LKKPwyDMWFsAfwiMh44SkROSLMcs0XkVszowCGxpPo7b9EeLV0XHdJLKvtC2vtBlzX1hKGFq5mqfobZYJZuOf4J/DMNcnQFUv2dh7bnuC5mDqnsC2nvB112xJ+IUAL7khxdgVR/1s53m7mk8rvJxH7guHM6ODg4dDG67IjfwcHBoaviKH4HBweHLoaj+B0cHBy6GI7id3BwcOhiOIrfwcHBoYvhKH4HBweHLoaj+B0cHBy6GPu04heRC0RkqX2cJSLrRSRcJrCOtvNcOu93iI7TDxwCOH3B0BVCNnxuR9nrB5QDiMhQ4GpAgLWY4Eg3YpJUf6aqC0TkXcwOu8mYnMBb7HvzQ8sCfUXk18Bo4EpgDiYI03IReUZVzxaRZcCjwCTgQuAw4GLgi0j1hsqAydH5G0ykzheBGuACzPdYrqpOJrPIOP3AIUCX7wv79Ijf5jngdOBbwJv2uUuBOqASOBhQzAdVBZxpl9mjqn/AJISeEVRfuLJ+Vb0ZmA+cH0GOTar6e0xHmwD8GLgI+HOUekNluAz4P1W9WlXfBa4CqjFJpyfG/5F0SZx+4BCgy/eFrjDir7NftwKWfewCnlDV/wGIyOnASlV9XEQCSd9r7FcvkBNU34lhymrQqwINgEdEBMiLUJ8F+OyykeoNvUeC3gNANnC3qlbH9Ul0bZx+4BCgy/eFrqD4Aa7FfPiBJ+89wG9F5GtgD/AEcLuIDADcMer6NExZj4j8BjgA88TtA/wEWIn5YsLxADAP0/ki1RvKfcCtttz/BO4A/iQi24AKVf1TDNm7Ok4/cAjQpfuCE6TNwcHBoYvRFWz8Dg4ODg5BOIrfwcHBoYvhKH4HBweHLoaj+B0cHBy6GI7id3BwcOhiOIrfwcHBoYvhKH4HBweHLsb/B9038+Nsz1zBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 396.85x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mimic_experimental(interaction=0, connectivity=1, N=80):\n",
    "    x = df_ts['David_stool_A'].values[150:, :]  # do not consider the traveling\n",
    "    experimental_abundance = np.sort(x[0, :])[::-1]\n",
    "    experimental_noise_color = noise_color(x.T)\n",
    "\n",
    "    def find_ss_selfint(x):\n",
    "        amplitude = 2.10E+00\n",
    "        x0 = 2.87E+00\n",
    "        k = 1.14E+00\n",
    "        offset = -1.77E+00\n",
    "\n",
    "        return 10**(-1/x0 * np.log(amplitude/(x-offset) - 1) + k)\n",
    "\n",
    "    params = {}\n",
    "\n",
    "    steadystate = (experimental_abundance[:N]).reshape([N, 1])\n",
    "\n",
    "    selfints = - \\\n",
    "        find_ss_selfint(\n",
    "            experimental_noise_color['slope_linear'].values[:N]) / steadystate.flatten()\n",
    "\n",
    "    # interaction\n",
    "    if interaction == 0:\n",
    "        omega = np.zeros([N, N])\n",
    "    else:\n",
    "        omega = np.random.normal(0, interaction, [N, N])\n",
    "        omega *= np.random.choice([0, 1], [N, N],\n",
    "                                  p=[1-connectivity, connectivity])\n",
    "    np.fill_diagonal(omega, selfints)\n",
    "\n",
    "    params['interaction_matrix'] = omega\n",
    "\n",
    "    # no immigration\n",
    "    params['immigration_rate'] = np.zeros([N, 1])\n",
    "\n",
    "    # different growthrates determined by the steady state\n",
    "    params['growth_rate'] = - (omega).dot(steadystate)\n",
    "\n",
    "    params['initial_condition'] = np.copy(\n",
    "        steadystate) * np.random.normal(1, 0.1, steadystate.shape)\n",
    "\n",
    "    params['noise'] = 2.5\n",
    "\n",
    "    params['noise_linear'] = 2.5\n",
    "    params['noise_sqrt'] = 0  # 0.005*steadystate #*np.sqrt(steadystate)\n",
    "    \n",
    "    np.save('test-params2.npy', params)\n",
    "    \n",
    "    ts = Timeseries(params, noise_implementation=NOISE.LANGEVIN_LINEAR_SQRT,\n",
    "                    dt=0.01, tskip=19, T=50.0, seed=int(time.time())).timeseries\n",
    "    ts.time = np.arange(1, len(ts)+1)\n",
    "\n",
    "    return ts\n",
    "\n",
    "\n",
    "def figure_characteristics_timeseries(ts):\n",
    "    fig = plt.figure(figsize=(ELIFE.TEXTWIDTH, 3))\n",
    "\n",
    "    gs1 = gridspec.GridSpec(1, 3, width_ratios=[\n",
    "                            2.5, 2.5, 1], wspace=0.5, hspace=0.3, left=0.1, right=0.95, top=0.95, bottom=0.62)\n",
    "    gs2 = gridspec.GridSpec(1, 3, wspace=0.7, hspace=0.4,\n",
    "                            left=0.1, right=0.95, top=0.45, bottom=0.12)\n",
    "\n",
    "    # timeseries\n",
    "    ax = fig.add_subplot(gs1[0])\n",
    "    ax.text(-0.2, 1.1, 'A', transform=ax.transAxes, fontsize=10,\n",
    "            fontweight='bold', va='top', ha='right')\n",
    "    ax.grid()\n",
    "\n",
    "    PlotTimeseriesComparison([ts], composition=['ts'], vertical=False, fig=ax)\n",
    "\n",
    "    ax = fig.add_subplot(gs1[1])\n",
    "    ax.text(-0.2, 1.1, 'B', transform=ax.transAxes, fontsize=10,\n",
    "            fontweight='bold', va='top', ha='right')\n",
    "    ax.grid()\n",
    "    # , ffig = 'figures/interaction_rescaled_model.png')\n",
    "    PlotTimeseriesComparison([ts], composition=['ra'], fig=ax)\n",
    "    ax.set_ylim([1e-2, 1e5])\n",
    "\n",
    "    ax = fig.add_subplot(gs1[-1], frameon=False)\n",
    "    ax.tick_params(left=False, labelleft=False,\n",
    "                   bottom=False, labelbottom=False)\n",
    "    ax.text(-0.5, 1.1, 'C', transform=ax.transAxes, fontsize=10,\n",
    "            fontweight='bold', va='top', ha='right')\n",
    "\n",
    "    sub_gs = gs1[0, -1].subgridspec(4, 1,\n",
    "                                    height_ratios=[1.5, 1, 1, 1.5], hspace=0.3)\n",
    "    ax_KL = fig.add_subplot(sub_gs[1])\n",
    "    ax_NCT = fig.add_subplot(sub_gs[2])\n",
    "    # , ffig = 'figures/interaction_rescaled_model.png')\n",
    "    PlotTimeseriesComparison([ts], composition=['nn'], fig=[ax_KL, ax_NCT])\n",
    "\n",
    "    # characteristics\n",
    "\n",
    "    for i, (char, letter) in enumerate(zip(['nc', 'dx', 'disdx'], ['D', 'E', 'F'])):\n",
    "        ax = fig.add_subplot(gs2[i])\n",
    "        ax.text(-0.3, 1.1, letter, transform=ax.transAxes,\n",
    "                fontsize=10, fontweight='bold', va='top', ha='right')\n",
    "\n",
    "        ax.grid()\n",
    "        # , ffig = 'figures/interaction_rescaled_model.png')\n",
    "        PlotTimeseriesComparison([ts], composition=[char], fig=ax)\n",
    "        if char == 'disdx':\n",
    "            ax.set_ylim([1e-2, 1e2])\n",
    "            ax.set_ylabel('Width distribution \\n of ratios \\n' +\n",
    "                          r'$x(t + \\delta t) / x(t)$')\n",
    "        elif char == 'dx':\n",
    "            ax.set_ylabel('Difference \\n time points \\n' +\n",
    "                          r'$\\left< \\mid x(t+\\delta t) - x(t) \\mid \\right>$')\n",
    "\n",
    "    # fig.align_labels()\n",
    "\n",
    "#KL = np.zeros(100)\n",
    "#NCT = np.zeros(100)\n",
    "\n",
    "if True:\n",
    "    ts = mimic_experimental(interaction=0)\n",
    "    figure_characteristics_timeseries(ts)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T10:08:08.431930Z",
     "start_time": "2020-02-20T10:08:08.398038Z"
    }
   },
   "outputs": [],
   "source": [
    "def check_neutrality_100_timeseries():\n",
    "    for i in range(100):\n",
    "        print(i)\n",
    "        ts = mimic_experimental()\n",
    "        KL[i] = KullbackLeibler_neutrality(ts)\n",
    "        norm_ts = ts.values[:, 1:].copy()\n",
    "        norm_ts /= norm_ts.sum(axis=1, keepdims=True)\n",
    "        NCT[i] = neutral_covariance_test(norm_ts, ntests=500, method='Kolmogorov', seed=56)\n",
    "\n",
    "    print(\"KL\", KL)\n",
    "    print(\"NCT\", NCT)\n",
    "\n",
    "# check_neutrality_100_timeseries()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduce noise characteristics in presence of noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-20T10:31:19.615169Z",
     "start_time": "2020-02-20T10:31:09.212659Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 396.85x216 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = mimic_experimental(interaction=0.02, connectivity=0.1, N=50)\n",
    "figure_characteristics_timeseries(ts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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