{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Analysis of experimental data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T07:37:13.153013Z",
     "start_time": "2020-02-21T07:37:12.297999Z"
    }
   },
   "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": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T07:37:14.518888Z",
     "start_time": "2020-02-21T07:37:13.225272Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "from noise_analysis import noise_color\n",
    "from noise_properties_plotting import noise_cmap_ww, noise_lim\n",
    "from scipy.stats import kstest, lognorm\n",
    "from matplotlib import colorbar\n",
    "\n",
    "#from neutral_covariance_test import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T19:01:19.938718Z",
     "start_time": "2020-02-18T19:01:19.906736Z"
    }
   },
   "source": [
    "## Settings figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T07:37:14.736921Z",
     "start_time": "2020-02-21T07:37:14.588504Z"
    }
   },
   "outputs": [],
   "source": [
    "from elife_settings import set_elife_settings, ELIFE\n",
    "\n",
    "set_elife_settings()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load experimental data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the plankton data of Martin Platero, the stool data of David and the timeseries of Caporaso."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T07:37:22.125862Z",
     "start_time": "2020-02-21T07:37:20.008161Z"
    }
   },
   "outputs": [],
   "source": [
    "# Load all dataframes\n",
    "\n",
    "# MartinPlatero plankton data\n",
    "\n",
    "df_ts = {}\n",
    "\n",
    "path = 'Data/MartinPlatero/'\n",
    "files = ['41467_2017_2571_MOESM4_ESM_MartinPlatero_Plankton_Bacteria.csv',\n",
    "         '41467_2017_2571_MOESM5_ESM_MartinPlatero_Plankton_Eukarya.csv']\n",
    "keys = ['plankton_bacteria', 'plankton_eukarya']\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",
    "         'Data/Faust/28_timeseries/28_timeseries.txt']\n",
    "keys = ['David_stool_A', 'David_stool_B']\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",
    "areas = ['feces', 'L_palm', 'R_palm', 'tongue']\n",
    "gender = ['F4', 'M3']\n",
    "\n",
    "for area in areas:\n",
    "    for gender_i in gender:\n",
    "        for taxlevel in range(2,7):\n",
    "            file = 'Data/Caporaso/' + gender_i + '_' + area + '_L%d' % taxlevel + '.txt'\n",
    "            key = 'Caporaso_' + gender_i + '_' + area + '_L%d' % taxlevel \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": {
    "ExecuteTime": {
     "end_time": "2020-02-18T19:14:41.386909Z",
     "start_time": "2020-02-18T19:14:41.355027Z"
    }
   },
   "source": [
    "## Code to generate figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T07:37:23.995299Z",
     "start_time": "2020-02-21T07:37:23.951686Z"
    }
   },
   "outputs": [],
   "source": [
    "def small_setup(composition=None):\n",
    "    fig = plt.figure(figsize=(6.5,6)) #, tight_layout=True)\n",
    "    nrows = 2\n",
    "    ncols = 2\n",
    "    \n",
    "    if composition == 'disdx':\n",
    "        gs = gridspec.GridSpec(nrows,ncols,wspace=0.1,hspace=0.1, right=0.8)\n",
    "        gs_cbar = gridspec.GridSpec(1, 1, left=0.87)\n",
    "        gs_tot = gridspec.GridSpec(1,1,top=0.9,bottom=0.08,left=0.07,right=0.8)\n",
    "    else:\n",
    "        gs = gridspec.GridSpec(nrows,ncols,wspace=0.1,hspace=0.1)\n",
    "        gs_cbar = None\n",
    "        gs_tot = gridspec.GridSpec(1,1,top=0.9,bottom=0.08,left=0.05,right=0.9)\n",
    "\n",
    "    keys = ['plankton_bacteria', 'David_stool_A', \n",
    "            'Caporaso_F4_L_palm_L6', 'Caporaso_F4_tongue_L6']\n",
    "\n",
    "    titles = ['Plankton bacteria', 'David Stool A', \n",
    "              'Caporaso palm', 'Caporaso tongue']\n",
    "    \n",
    "    return fig, nrows, ncols, gs, gs_cbar, gs_tot, keys, titles\n",
    "\n",
    "def large_setup(composition=None):\n",
    "    fig = plt.figure(figsize=(ELIFE.TEXTWIDTH,2.5)) #, tight_layout=True)\n",
    "    \n",
    "    nrows = 3\n",
    "    ncols = 4\n",
    "    \n",
    "    if composition == 'disdx':\n",
    "        gs = gridspec.GridSpec(nrows,ncols,wspace=0.1,hspace=0.12,top=0.98,bottom=0.12,left=0.08,right=0.85)\n",
    "        gs_cbar = gridspec.GridSpec(1, 1, top=0.98,bottom=0.1,left=0.88, right=0.9)\n",
    "    else:\n",
    "        gs = gridspec.GridSpec(nrows,ncols,wspace=0.1,hspace=0.1,top=0.98,bottom=0.15,left=0.12,right=0.98)\n",
    "        gs_cbar = None\n",
    "        gs_tot = gridspec.GridSpec(1,1,top=0.98,bottom=0.06,left=0.05,right=0.98)\n",
    "    \n",
    "    if composition == 'disdx':\n",
    "        gs_tot = gridspec.GridSpec(1,1,top=0.98,bottom=0.06,left=0.03,right=0.85)\n",
    "    elif composition == 'nc':\n",
    "        gs_tot = gridspec.GridSpec(1,1,top=0.98,bottom=0.07,left=0.07,right=0.98)\n",
    "    elif composition == 'dx':\n",
    "        gs_tot = gridspec.GridSpec(1,1,top=0.98,bottom=0.08,left=0.07,right=0.98)\n",
    "        \n",
    "    keys = ['David_stool_A', 'David_stool_B',\n",
    "            'plankton_bacteria', 'plankton_eukarya',\n",
    "            'Caporaso_F4_feces_L6', 'Caporaso_M3_feces_L6',\n",
    "            'Caporaso_F4_L_palm_L6', 'Caporaso_M3_L_palm_L6',\n",
    "            'Caporaso_F4_R_palm_L6', 'Caporaso_M3_R_palm_L6',\n",
    "            'Caporaso_F4_tongue_L6', 'Caporaso_M3_tongue_L6']\n",
    "\n",
    "    titles = ['Stool A', 'Stool B', \n",
    "              'Plankton bacteria', 'Plankton eukarya',\n",
    "              'Female feces', 'Male feces',\n",
    "                'Female left palm', 'Male left palm',\n",
    "                'Female right palm', 'Male right palm',\n",
    "                'Female tongue', 'Male tongue']\n",
    "    \n",
    "    return fig, nrows, ncols, gs, gs_cbar, gs_tot, keys, titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T07:56:19.325767Z",
     "start_time": "2020-02-21T07:56:19.262975Z"
    }
   },
   "outputs": [],
   "source": [
    "def compare_experimental_data(setup, composition=[], labels = 'in'):\n",
    "    fig, nrows, ncols, gs, gs_cbar, gs_tot, keys, titles = setup\n",
    "    \n",
    "    for i, gsi, key, title in zip(np.arange(len(keys)), gs, keys, titles):\n",
    "        # make axes and write lable\n",
    "        if i == 0:\n",
    "            ax = fig.add_subplot(gsi)\n",
    "        else:\n",
    "            ax = fig.add_subplot(gsi, sharex=ax, sharey=ax)\n",
    "        if labels == 'in':\n",
    "            ax.text(0.95 if composition == 'ra' else 0.05, 0.95, \n",
    "                    title, transform=ax.transAxes,\n",
    "                  va='top', ha='right' if composition ==  'ra' else 'left' )\n",
    "        elif labels == 'out':\n",
    "            ax.set_title(title)\n",
    "\n",
    "        df = df_ts[key]\n",
    "\n",
    "        mean = df.mean()\n",
    "        mean.drop('time', inplace=True)\n",
    "        \n",
    "        if composition == 'disdx' or composition == 'disdx2':\n",
    "            \n",
    "            def fit_ratio(x):\n",
    "                # ratios of succesive time points without infinity and 0\n",
    "                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 = lognorm.fit(x, floc=0)  # Gives the paramters of the fit\n",
    "                    stat, pval = 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",
    "\n",
    "            dx_ratio = pd.DataFrame(index=df.columns, columns=['s', 'loc', 'scale', 'kstest-statistic', 'kstest-pvalue'])\n",
    "            dx_ratio.drop('time', axis='rows', inplace=True)\n",
    "\n",
    "            for idx in dx_ratio.index:\n",
    "                a, b, c, stat, pval = fit_ratio(df[idx].values)  # b = 0, c = 1\n",
    "                dx_ratio['s'].loc[idx] = a\n",
    "                dx_ratio['loc'].loc[idx] = b\n",
    "                dx_ratio['scale'].loc[idx] = c\n",
    "                dx_ratio['kstest-statistic'].loc[idx] = stat\n",
    "                dx_ratio['kstest-pvalue'].loc[idx] = pval\n",
    "            \n",
    "            if composition == 'disdx':\n",
    "                scat = ax.scatter(mean, dx_ratio['s'].values, \n",
    "                       c=dx_ratio['kstest-pvalue'].values, vmin=0, vmax=1, cmap='coolwarm', s=3) #, label=param)\n",
    "            elif composition == 'disdx2':\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",
    "                    ss_selfint = np.full_like(x, np.nan)\n",
    "                    y = np.zeros_like(x)\n",
    "                    \n",
    "                    y[~np.isnan(x)] = amplitude/(x[~np.isnan(x)]-offset) - 1\n",
    "                    \n",
    "                    ss_selfint[y>0] = 10**( -1/x0 * np.log(y[y>0]) + k)\n",
    "                    return ss_selfint \n",
    "\n",
    "                ns = noise_color(df_ts[key])['slope_linear']\n",
    "                \n",
    "                selfints = find_ss_selfint(ns.values) / mean.values #.flatten()\n",
    "\n",
    "                scat = ax.scatter(mean * selfints, dx_ratio['s'].values, \n",
    "                       c=dx_ratio['kstest-pvalue'].values, vmin=0, vmax=1, cmap='coolwarm', s=3) #, label=param)\n",
    "        \n",
    "        \n",
    "            #ax.set_xlim([0.1*np.nanmin(mean.values), 10*np.nanmax(mean.values)])\n",
    "            #ax.set_ylim([0.5*np.nanmin(dx_ratio['s'].values), 5*np.nanmax(dx_ratio['s'].values)])\n",
    "\n",
    "            ax.set_yscale('log')\n",
    "            ax.set_xscale('log')\n",
    "        \n",
    "        if composition == 'nc':\n",
    "            vmin = 0.1*mean.min()\n",
    "            vmax = 10*mean.max()\n",
    "\n",
    "            ns = noise_color(df_ts[key])['slope_linear']\n",
    "            ax.scatter(mean, ns, 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=(1e-3, 2e5, -3, 2),\n",
    "                         aspect='auto', alpha=0.75)\n",
    "\n",
    "            ax.set_xscale('log')\n",
    "        \n",
    "        if composition == 'dx':\n",
    "            dx = pd.DataFrame(index=df.columns, columns=['abs_dx'])\n",
    "            dx.drop('time', axis='rows', inplace=True)\n",
    "\n",
    "            for idx in dx.index:\n",
    "                dx['abs_dx'].loc[idx] = np.nanmean(abs(df[idx].values[1:] - df[idx].values[:-1]))\n",
    "\n",
    "            ax.scatter(mean, dx['abs_dx'].values, s=3)\n",
    "\n",
    "            p_lin = np.polyfit(np.log10(mean.astype(np.float64)), np.log10(dx.astype(np.float64)), 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",
    "            \n",
    "            ax.text(0.95, 0.05, r'y $\\propto$ x$^{%.2f}$' % p_lin[0], transform=ax.transAxes,\n",
    "              va='bottom', ha='right')\n",
    "        \n",
    "            ax.set_yscale('log')\n",
    "            ax.set_xscale('log')\n",
    "        \n",
    "        if composition == 'ra':\n",
    "            timepoints = [1, 30, 60] if key != 'David_stool_A' else [1, 50, 100, 108, 300]\n",
    "\n",
    "            for j in timepoints:\n",
    "                d = np.copy(df_ts[key].iloc[j])\n",
    "                d /= np.sum(d)\n",
    "                ax.plot(range(1,len(d)+1), np.sort(d)[::-1], label='Day %d' % j)\n",
    "\n",
    "                ax.set_ylim([1e-8,1])\n",
    "                ax.legend(loc=3)\n",
    "                ax.set_xscale('log')\n",
    "                ax.set_yscale('log')                \n",
    "        \n",
    "        ax.grid()\n",
    "\n",
    "        if i%ncols != 0:\n",
    "            ax.tick_params(axis=\"both\", left=True, labelleft=False)\n",
    "        if i < (nrows-1)*ncols:\n",
    "            ax.tick_params(axis=\"both\", bottom=True, labelbottom=False)\n",
    "\n",
    "    if composition == 'disdx' or composition == 'disdx2':\n",
    "        ax_cbar = fig.add_subplot(gs_cbar[0])\n",
    "        fig.colorbar(scat, cax=ax_cbar)\n",
    "        ax_cbar.set_ylabel('P-value lognormal fit')\n",
    "    \n",
    "    # add labels (common for all subplots)\n",
    "    ax = fig.add_subplot(gs_tot[0], frameon=False)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    \n",
    "    if composition == 'ra':\n",
    "        ax.set_xlabel('Rank', ha='right', x=1)\n",
    "    elif composition == 'disdx2':\n",
    "        ax.set_xlabel(r'Mean abundance $\\times$ associated self-interaction', ha='right', x=1) \n",
    "    else:\n",
    "        ax.set_xlabel('Mean abundance', ha='right', x=1) \n",
    "    \n",
    "    if composition == 'nc':\n",
    "        ax.set_ylabel('Slope power spectral density')\n",
    "    elif composition == 'disdx' or composition == 'disdx2':\n",
    "        ax.set_ylabel('Width distribution of ' + r'$x(t+\\delta t) / x(t)$')\n",
    "    elif composition == 'dx':\n",
    "        ax.set_ylabel(r'$\\langle \\vert x(t + \\delta t) - x(t) \\vert \\rangle$')\n",
    "    elif composition == 'ra':\n",
    "        ax.set_ylabel('Abundance') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rank abundance curve\n",
    "\n",
    "The rank abundance is heavy tailed. This means that there are few abundant species and many rare species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:24:46.103265Z",
     "start_time": "2020-02-18T20:24:45.590578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x129.6 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# system dependence\n",
    "\n",
    "fig = plt.figure(figsize=(ELIFE.TEXTWIDTH,1.8), tight_layout=True)\n",
    "gs = gridspec.GridSpec(1,2,width_ratios=[1.5,1])\n",
    "\n",
    "ax = fig.add_subplot(gs[0])\n",
    "\n",
    "NUM_COLORS = 10\n",
    "\n",
    "# Static data\n",
    "\n",
    "path = 'Data/Arumugam/'\n",
    "files = ['MetaHIT_41SangerSamples.genus.csv', #'MetaHIT_41SangerSamples.phylum.csv',\n",
    "          'MetaHIT_85IlluminaSamples.genus.csv', 'Turnbaugh_154Pyroseq16S.genus.csv']\n",
    "titles = ['Sanger', #'Sanger phylum', \n",
    "          'Illumina', 'Pyroseq']\n",
    "\n",
    "for file, title in zip(files, titles):\n",
    "    i = 0\n",
    "    if title == 'Sanger':\n",
    "        i = 4\n",
    "        \n",
    "    d = pd.read_csv(path + file, index_col=0).values[1:,i]\n",
    "    d /= np.sum(d)\n",
    "    ax.plot(range(1,len(d)+1), np.sort(d)[::-1], label=title)\n",
    " \n",
    "keys = ['plankton_bacteria', 'plankton_eukarya', 'David_stool_A', 'David_stool_B', \n",
    "        'Caporaso_F4_feces_L6', 'Caporaso_F4_L_palm_L6', 'Caporaso_F4_tongue_L6']\n",
    "\n",
    "titles = ['Plankton bacteria', 'Plankton eukarya', 'Stool A', 'Stool B', \n",
    "          'Female feces', 'Female palm', 'Female tongue']\n",
    "\n",
    "for key, title in zip(keys, titles):\n",
    "    d = np.copy(df_ts[key].values[0,1:])\n",
    "    d /= np.sum(d)\n",
    "    ax.plot(range(1,len(d)+1), np.sort(d)[::-1], label=title)\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "ax.set_xlim([1,1000])\n",
    "ax.grid()\n",
    "ax.set_xlabel('Rank', ha='right', x=1)\n",
    "ax.set_ylabel('Relative abundance')\n",
    "\n",
    "ax_legend = fig.add_subplot(gs[1])\n",
    "ax_legend.axis('off')\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "ax_legend.legend(handles, labels, handlelength=1, ncol=2)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The curve can not be exactly matched with a lognorm or powerlaw.\n",
    "TODO: check fit of these curves for histograms of abundances!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:26:17.889205Z",
     "start_time": "2020-02-18T20:26:17.841627Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "from fractions import gcd\n",
    "\n",
    "\n",
    "def keys_titles_experimental():\n",
    "    keys = ['plankton_bacteria', 'plankton_eukarya',\n",
    "            'David_stool_A', 'David_stool_B']\n",
    "\n",
    "    titles = ['Plankton bacteria', 'Plankton eukarya',\n",
    "              'David Stool A', 'David Stool B']\n",
    "\n",
    "    areas = ['feces', 'L_palm', 'R_palm', 'tongue']\n",
    "    gender = ['F4', 'M3']\n",
    "\n",
    "    for area in areas:\n",
    "        for gender_i in gender:\n",
    "            for taxlevel in range(2, 7):\n",
    "                keys += ['Caporaso_' + gender_i +\n",
    "                         '_' + area + '_L%d' % taxlevel]\n",
    "                titles += [gender_i + ' ' + area + ' L%d' % taxlevel]\n",
    "    return keys, titles\n",
    "\n",
    "\n",
    "def calculate_neutrality(keys):\n",
    "    neutrality = pd.DataFrame(index=keys, columns=['KL', 'NCT'])\n",
    "\n",
    "    for key in keys:\n",
    "        if True:  # np.isnan(neutrality['NCT'].loc[key]):\n",
    "            if np.isnan(neutrality['KL'].loc[key]):\n",
    "                print(df_ts[key].columns)\n",
    "                if key.startswith('Caporaso'):\n",
    "                    neutrality['KL'].loc[key] = KullbackLeibler(\n",
    "                        df_ts[key].drop(df_ts[key].columns[-1], axis='columns'))\n",
    "                else:\n",
    "                    neutrality['KL'].loc[key] = KullbackLeibler(\n",
    "                        df_ts[key])  # , verbose=True)\n",
    "                norm_ts = df_ts[key].values[:, 1:]\n",
    "                norm_ts /= norm_ts.sum(axis=1, keepdims=True)\n",
    "                neutrality['NCT'].loc[key] = neutral_covariance_test(\n",
    "                    norm_ts, ntests=500, method='Kolmogorov', seed=56)\n",
    "\n",
    "    neutrality.to_csv('experimental_neutrality.csv')\n",
    "\n",
    "\n",
    "def calculate_timestep_slope(keys):\n",
    "    timestep = pd.DataFrame(index=keys, columns=['slope'])\n",
    "\n",
    "    for key in keys:\n",
    "        x = df_ts[key]\n",
    "        x = x.loc[:, (x != 0).any(axis=0)]\n",
    "\n",
    "        mean = x.mean()\n",
    "        mean.drop('time', inplace=True)\n",
    "\n",
    "        dx = (x.values[1:, 1:] - x.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",
    "        timestep['slope'].loc[key] = p_lin[0]\n",
    "\n",
    "    timestep.to_csv('experimental_timestep_slope.csv')\n",
    "\n",
    "\n",
    "def lognormal(x, mu, sigma, scale):\n",
    "    return scale/(x*sigma*np.sqrt(2*np.pi))*np.exp(-0.5*((np.log(x)-mu)/sigma)**2)\n",
    "\n",
    "\n",
    "def powerlaw(x, index, amp):\n",
    "    return amp * x ** index\n",
    "\n",
    "\n",
    "def logseries(x, p):\n",
    "    r = np.full_like(x, np.nan)\n",
    "    if p == 0:\n",
    "        return r\n",
    "    r[x > 0] = -1/(np.log(1-p)) * p**x[x > 0]/x[x > 0]\n",
    "    return r\n",
    "\n",
    "\n",
    "def rank_abundance_fit_parameters(keys):\n",
    "    ra = pd.DataFrame(index=keys, columns=['rss_lognormal', 'mu_lognormal', 'sigma_lognormal', 'scale_lognormal',\n",
    "                                           'rss_powerlaw', 'index_powerlaw', 'amp_powerlaw',\n",
    "                                           'index_powerlaw_linfit', 'amp_powerlaw_linfit',\n",
    "                                           'rss_logseries', 'p_logseries'])\n",
    "\n",
    "    for key in keys:\n",
    "        y = df_ts[key].values[0, 1:]\n",
    "        y = y[y > 0]\n",
    "        y = np.sort(y)[::-1]\n",
    "\n",
    "        x = np.arange(1, len(y)+1)\n",
    "\n",
    "        popt, pcov = curve_fit(lognormal, x, y, p0=[0.01, 1, 10])\n",
    "        mu, sigma, scale = popt\n",
    "\n",
    "        ra['rss_lognormal'].loc[key] = sum(\n",
    "            (lognormal(x, mu, sigma, scale) - y)**2)\n",
    "        ra['mu_lognormal'].loc[key] = mu\n",
    "        ra['sigma_lognormal'].loc[key] = sigma\n",
    "        ra['scale_lognormal'].loc[key] = scale\n",
    "\n",
    "        p_lin = np.polyfit(np.log10(x), np.log10(y), deg=1, cov=False)\n",
    "\n",
    "        index0 = p_lin[0]\n",
    "        amp0 = 10.0**p_lin[1]\n",
    "\n",
    "        popt, pcov = curve_fit(powerlaw, x, y, p0=[index0, amp0])\n",
    "        index, amp = popt\n",
    "\n",
    "        #indexErr = np.sqrt( covar[1][1] )\n",
    "        #ampErr = np.sqrt( covar[0][0] ) * amp\n",
    "\n",
    "        ra['index_powerlaw'].loc[key] = index\n",
    "        ra['amp_powerlaw'].loc[key] = amp\n",
    "        ra['rss_powerlaw'].loc[key] = sum((powerlaw(x, index, amp) - y)**2)\n",
    "        ra['index_powerlaw_linfit'].loc[key] = index0\n",
    "        ra['amp_powerlaw_linfit'].loc[key] = amp0\n",
    "\n",
    "        p, perr = curve_fit(logseries, x, y, p0=0.5)\n",
    "\n",
    "        ra['rss_logseries'].loc[key] = sum((logseries(x, p) - y)**2)\n",
    "        ra['p_logseries'].loc[key] = p\n",
    "\n",
    "    return ra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:26:21.160874Z",
     "start_time": "2020-02-18T20:26:20.770567Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "key = 'plankton_eukarya' #'David_stool_A'\n",
    "title = 'Plankton eukarya'\n",
    "\n",
    "params = rank_abundance_fit_parameters([key])\n",
    "\n",
    "abundance = np.copy(df_ts[key].values[0, 1:])\n",
    "abundance = abundance[abundance > 0]\n",
    "abundance = np.sort(abundance)[::-1]\n",
    "\n",
    "rank = np.arange(1,len(abundance)+1)\n",
    "\n",
    "fig = plt.figure(figsize=(2.5,2), tight_layout=True)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.text(0.95, 0.95, title, transform=ax.transAxes,\n",
    "              va='top', ha='right')\n",
    "\n",
    "ax.plot(rank, abundance, label='Abundance')\n",
    "\n",
    "x = np.logspace(0,np.log10(len(abundance)),500)\n",
    "\n",
    "ax.plot(x, lognormal(x, params['mu_lognormal'].loc[key], \n",
    "                     params['sigma_lognormal'].loc[key], \n",
    "                     params['scale_lognormal'].loc[key]), label='Lognorm')\n",
    "\n",
    "ax.plot(x, params['amp_powerlaw'].loc[key]*(x**params['index_powerlaw'].loc[key]), \n",
    "        label='Powerlaw (fit in linear axes)')\n",
    "\n",
    "ax.plot(x, params['amp_powerlaw_linfit'].loc[key]*(x**params['index_powerlaw_linfit'].loc[key]), \n",
    "        label='Powerlaw (fit in log-log axes)')\n",
    "\n",
    "#plt.plot(x, logseries(x, 0.3), label='logseries')\n",
    "\n",
    "ax.legend(loc=3)\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylim([1e-2,1e5])\n",
    "\n",
    "ax.set_xlabel('Rank', ha='right', x=1)\n",
    "ax.set_ylabel('Abundance')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rank abundance curve is mostly stable over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:26:27.022013Z",
     "start_time": "2020-02-18T20:26:25.118473Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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maRQ4SkkvLiC9pKDy987cdJanFZJeUkBGcQHZZdE4fG+jnV8ww5qEkqyDX7f+wasL19AtuIwO/iUYy9JxFh7FHNkHn5Y3YwrrLjdTCI9mNhvo2y2Kvt2iANibVcj3O9L54Y80XKnHGOgop0tOMZZf9qGzGjEmBGFu7o/RZECvUxh0Ogw6hZdeh9XLB0vMQLrFDCTN3JKM1Z/zaHhLbij3wlyUhrl8J+bSbEylWfjk/0lgy+H4trsXvdnPzbUgapMkplqglMLXaMbXaCbOFlRjWU3TWJN5kPn7NvPskV20axLO0C4DSEuzctOqFBJDfbm5SxOC81fjs+YdDDn3Y4gaiDX+RnzDOuFlkAEzhWeLDbRyf69m3N+rGUVlDhb/mcX83RnkhprwLXIQv+YQsYv2km7Ss9lqYLe3jjJNo6TcRVG5kxPDdyqtBU8cTCPTWMykACteXv4YDX7o9dEovYscc2eaHSzgyh0juSamPc26PIjeemmM1SdqJompnimluCw4hsuCY6okqUWpu2jXMYxYr2Z8tzuD8vI4issfokwVEbN7Je23PUWQK4UMQvG3+RMRFExYYDB6kw2jvRneTa+SvlTC41i8DFyTGFJ5A9EJmqZRlJLHFZvSyN+TjU+MH7YWgRisRgwWI3qLEYO3gaKUObT7aAKuEQtIK3JyNL+U1LwSjuaXcrS0lH0FGUwvi+aVXbsI2fk4wU4Tx/TdsRkjCTP507KJjfZhNtqH24jy85ZT5JcISUxudGqSWpt5iP/s28Tv+gMUUw5GwAK7/A38xGDQNCwKnKVOio6VUXa4hAhzPt29V3Dj8n8SHN4eW8JIzJF95VEEwqMppbBG2bFG2dFcGvl7synYn4OjqBxnUTmOonIcxQ5waTh0j+P811fYdC78Ld609bVh9AvE4GtHH9YUnVccOq+B5GulbCtaxuq0Bez0bs5mfTC/ZWoUpzopWu3CWGYjsrgNeqUnN6+Qv/umMKJjBDqdJCtPI4nJQyil6BYcTbfg6DOWcWku8stLySkrJru04mfdkUwWHtzHjOIAAvY5uWH3ewzjfgz2OEIiWmELboXRrznm8J5yoVh4JKVT2JoHYGsecMYymqbhyMum8M8NFO/dSMmBPyjZkYEhuCWmyM4YwtrhZ7TT7nA34vNaY9anooo/wtaqJbb24zHYYpi5YyX/2b+Jz/rdzspf1/Lr4VzeXLGPade2pmfTM7+3qH+SmC4hOqXD7uWN3cubGJ+Kef3CmzOxS3dcmoulR/fy3o7V3JHZAbvDiNpdQMT2HfS2rCDB+Tj2poNp1mkMgX5RGOWISlxClFIY7QH4dR6AX+cBQMUzpEoPbabwj58pWPURjtxUDPYwbNZQygsTKC26hcO/mklb/TmWoCPc2qErkSFNuPK7N5hgac60a/uxL6uIkZ9s4KHLY7mpQ/WdgUX9k8TUQOiUjn5hzekX1pwiRxm78zL5My+TrcfS+OHwEd7P74hrfwqWvZPJ0Nto4R/PY10HYWxgD4oUjYfS6TDHdMAc04HAqx5Fczpw5mfiyM/AmZ+BI/coRX/+TP7uwxTmtSJvfx7RePMuUaRpTt77/TNKg0yMjrTyybJl/LzHh2HdYml6vHO9fHlzH0lMDZDF4EX7gHDaB4RzQ1Og0/+WaZrGro3/5Ze1b/PCtxv5wxBMUv4eHmnXje7RiSidNAlxaVJ6Awa/UAx+oZXz7D1GEAY48tIp2b+B8qwDlGXsx7D6S1p6R5DqTKSwqB/3mK1k73CQu2kvnwRv4deQo/yt15UMimjpvh1qxOS/UCOjlKJlx+uJ7zCM2w8s4refprMiYy8PLVxJtk6PhhGj3k4Xv3D+kpBA++AwlN6MzssXnXeQ9KkSlySDrQk+7QZXTm8PHkiM2gvfv4R364OYmoXjisijMO1PwgoG03Nre37bu5y/B/+HoLAmWLzN+Bi86BwYyb0JPdHJ56BOSWJqpJRSWJsOxNDCyDNJSTwDOJ0uUg+u5c+d37AgbRt3LdtOHjoitBLitXzaqkza6IqIDojDHt0DS9hl+Ia2R6eXx2eLS4zS4Z80FlvXG0h582a8wxIIuvovaC4noYeXUZq+lVZ7LQzY1RTtQBl6nQ6dQUemMYNHN33K34ZeR7C3j7v3osGSxCQq6fU6IptdRmSzy0g6Pq/M4WBTVhorj6bw29FDzM4+TFZuJgG/76PN2jV0c+ylqcvJRr+7KQ/sRocIO4NbBuNvkTsAhefTW/2JfuQbDs8aTXn2YZrc+E+8o5LwjkrCrzM0peIpA6//vIE1u3Yyyfcbrsm4kkXTfyZxaFvaJ54+fJm4eJKYRI28DAa6hkTQNSSCB9tfBlRcp9pXcIzVGQdZk3GQz1N3UZizmtjc38nP1vPp0qbYgnvTKsSHB3vH4muWZiY8lzJ4EXHP/5H278dIfX8cYWPeqdIP0M/byN+TLyO3uBMfr+tB4Ib7yDVH8dt/S9g5fxu7Q8sI7xtFUngcTX0CpBNvLbgk/2Mopa4BugB7NE37P3fH09gopYg9/miQW2M7AlBaVsza7V+yOieLbWonGWWbiDxUxOx3nfRrmYAtqifNEq90c+RCVE/pdITcOo3Mr6dw+K1biRj/fyhD1aN+u7eRCZe3QOu9kPR9yzm88WNK0zZhzBzL9s+PkRT6M6UGjYH+XbgtvgM9YwLkS9kFcnutKaVigScBu6Zpw5VSVuAtoAxYomna3FPX0TTtW6XUUuC++o1WnInJy5ve7W+lN/AokJKfyac7lzP7z828eqSYyAOf0WPppyQEdSMqNApvnyACQxJICLVLz3vhEZRSBF/3FMd+msG+53rg3+8e7L1GofPyPq1cSOzlhMRejqs0j6IDP9FmiYNrjiryAzbxqHkLr24q5Pmfgikqc2I3G7mxfTjXJDQhJkCGDTsXSvOQfixKqfnHE9MoIEfTtAVKqc+AT4Ghx4st1DTtE1Vxa9gzwHRN03JO3k5oaKhmt9srp5OTkxkyZEjldEFBAT4+ctHyhPqoD03T2FeWyxfpq9hVXsyokjTaOdLxc6aw1nUZdv94zIGdCPW1YHRzknJH++jXr996TdO6XMw2pN2fn7PVhyrJxXvnAkwHV4DLAXojLi9fHP7NKGp7K5rZfto6ugIN854s9MeK2WzUyLdlEBxtwGxO5JcjGusyHWSVaLQL0DOihRcBJs+5s8/T2r3bj5iqEQlsOf7aqWnal8CXp5R5EvAHegLfVVk5MpJ169adceNLliwhKSmp1oK91NVXffQD7mQoBwqOMW75f9hjsTEoLJYOBzdQdnQj3gf/y/dlSfi3HMbEq3ph0LvnQ3uptg9p9+fnnOpj8HWVL13lpbiKcynctojM754hKPlv2C+7qdrVXA4XEQd3sv6XzTg3emH22s0DfjuwND2CreejfJsRzeu/7efFaxJpGWz1iBuFPK19eGJiSqEiOW0Eqv3vpGna8/Uakag1MT4B/Hjl3Sw9uocVafuZWlDMqPYjuNp3FH87tpkdax9n2RvZOG0JRPZ/kVZN490dshDojCZ0xibYe4zAt+O1HHz1GpTBC1vnoaeXNegIjU3gmtgEckuK+cfi7/A54EfX/X4Y/zhIgu0Ik/392LpgLc+WmzDYzPSNC+Ke7jFyTeo4t9eCUioQmAJ0VEo9AbwOzDh+g8MCtwYn6oRSiqSw5iSFNWd8qx58fmALj+7aysGCQkZ0f57mllDSdy8n98tRFNtNNO9xP76tbnZ32EIAoDP7EPXgVxx67VoKty0iYMD9mMITqi1rN3vzylU3kFKYw+68TJal7sK5fzt3FuUR5aXRNas5XrFOlmpd6PPWCrpG+fHuje3reY88j9sTk6ZpWcD4U2aPcUcsov4Fmq3c3bI7d7fsTomjnPd3r2Ft9j6yfP1ZG3Mjvvk+dPvpP4QunIVJ+REe3Jr+XXvjF9EZvVlGhBbuobf6EfPEEgo2f0/avyfiLMwm5NapWJr3qLZ8pNWPSKsfvUOacdmhHdx3w2OEePtSdCSVlAWL6bFxPX3sAcw+sJ8j6XGEN2nc1wPdnpiEOMFsMHJfQq/K6SJHGR//uZ7UombsL0hnx7FUDuQdw/TzV7RiFt7hyYzr3JMYH38iLHa89NKcRf1ROh2+Ha7Bt8M1lGXsJ+Wtm7FfdguW+N6YIhLRmaynrWPU6XmpSzJXLZzF3zsMYmhMG+LvGYGrNI/0pXO5ZYMi5Y3PyPQvokmiCYNvE2yte+HlF+yGPXQf+SQLj2UxeHFPq9O/ge7JPcZv6+ezcddXPJWTTpOwIPblZ9E+IJzLQ2MZGt2GQPPp/xSEqCtewU2J+esicn6dTfbidyg98gdaWTHmmI749bsHS9xllWUHRsTTLfherv95Dr+k7uah1n2I9Q0kdNC9hA6CzYezmffxQobtN+BLIYcX/YCtRSDBPXtiDrFi8G74Q4BJYhKXnDh7ALH9xnJtuJ0dS17ki503MuPWB9hdmsb6zENct+h9SpwOQi2+tPUP49bYjrQLkGftiLql9/Yl8MqHKqc1l5OiXSs49sOrpOUcwbdDMoFXPowyeGH38uanwffwzaHt3LZ0Ljc2bc9DrfuglKJdhD95Nw/gn7/uJbfEwYh+3nTf9Qn7/rsPnG2wRgQQ2rcplnBfN+5t3ZLEJC5JSumwtbqZTuE98Fr4NCvmXMVlN82hb5skHmmThKZpHCnKY+Oxw4xbMY+B4S35R+er3B22aESUTo+1VR+srfpQnpNK5tf/IGfZB/j3uweoeIbatdGtGRzRkr+s+i93LPuUx9v2p7V/KL2bBdK7WSAHs4uYtymVZ7xuooVtNVfkTuA/Oc8z+D+F2Fxgi/MntG8MXnazm/e2dkliEpc0gy2aDsM/5OiP73J0wXCix/8OVNz5F2G1E2G1c3VkAknfv8XsXau5MqIlkVY/N0ctGhujXxhNbvgH+1/sh733aHRGU+UyL72BmT2H823Kdu777XPa+ocxueOVBJmtRPtbeCwp7njJLhTubUbkxo/5R9FD/H6kiKu3ljBoRwaR/t40v709elPD+JfuOV2PhbgIffvfRVqZlWffeoa5a3ZQXO6sXKaUYs7lt3CstIhrF73P/b99zvz9m9iZm05WSaEboxaNid7qT8AVE9g3uSs5yz+qskwpRXJUIr8MvpfuTWK4efFHHCrIOW0blmaDCQ5vzQuucfzc9kP+OjSdVR2DeT0rn+/+tYpda1IoySiqr12qM5KYRIPgbdRz5YiZjGpRRLPfx/DAzHdJy/3fB7SZbyAT2/Zj7ZCHuD6mHdtz0pj8+0L6f/82Xx7YSomj3I3Ri8bCP2kszf7+G4XbFnHglSsp2PoTjryMyuV6nY7b4jrzSJu+DFk0m2OlVZOMUjr8uz9F5O2bsbUbh/PAAsYbZzKx3w5obuDD73ay+q01vPfDTpwuzxhu7kI0jOM+IQCvoDbEDnyF8MMrsKx6j/Xvv4vvFe9zebuOlWX0Oh1XhLfgivAWABwoOMYbfyzn+Y0LuToygSvwPtPmhagVOpOViHs+ovTIDrJ+nE7mV8+j9w0iYvzcygFjr4lKxKlp9Pzmde5P6MXtzbtgP2UwWXNEL0whXSjY+Sn+pXl0LptIhxZlHEvrQPyqPixYs4/QQa3p2i0S/SU2ULIkJtHgmCN60eGGXvjv+pU9397K00uHEdv5Dkb3aHnas3JifAKY2u1aNE3jliUf81XqIa5ZV8j4Vj2I8ZEOvKLumMJbET7mHQCyF7/LwVevwRzVDmtCP3w7Xce10a0ZGB7PxLULSPruLdoGhDGl09VE+fzvGqkymPBtPRoAe6cHAAjLT6EwZQUHF71FwfdD+fGbJmS3jKDP1fFEBV4a3SgkMYkGKya+D6FRq0hY/TL7N9zAW2s70LXrEDp1vR6DserAmUopPut3O1//vJBjdn9GLf2EOFsQs3vfhE7JGW9Rt/z73Y25aWdcpYVkLpiCMprxaXsl3gYjM3pcT7GjnBXp+xiyaDY9mzRlQmIvTDoDsb6Bp33ZMvhGYk+4mTbxwyhN/50jP0zh6IHrmf1aJk0HN+eWbtGYjfozROIZ5BMnGjSTtx9hSS/Q/d6NXH7ZYA5u+YLVM1ry7LR7eOTrbeQWV722ZNN7MbpFV5ZefT96pfGHaa8AACAASURBVOOGXz5kSeqfeMrjYUTD5d2sM9ZWfWhy04tkL32v6jKDkQHh8axM/gs9mjTln5t+4a7ln/HSll/OuD2l98IcdhnN7viCZm2WcVMLHSE/7mP6kj11vSsXTRKTaBSU3ki77qMYfs88ej6wk7ui9nBFyYckv/45R3KLTy+vFO/2Gs6DiZfz1o6VdPrqVX45stsNkYvGxjumI2gu9j3XHUdeepVlFoMXo5p35uO+I/h58L38ln6Aefs21rg9pTPg320c1qKRNA1NpXDtPo+/MUISk2h0lN6L8OsX0DPal3fDZ7Jqdl/mzJ9BbnFZlXI6pSMprDnz+t3OgoF38de13zB3zwZcmstNkYvGIuovnxN49eOkz3/qjGX0Oh0f9bmVlzcvZvau1TVuzxzencjR2/GLXM11JZl8+fH9uMo9t6uEJCbRKOkM3vh3f5qEUUsYeOu7RBT8ypq327F92384Vlh6WvlIqx9fD7yT71O2M/jHWfyRc9QNUYvGxLfTdRTvXY2r9Mz9kuxe3nw14E7+s28TK9P217g9vdmf0CH/wtseRnbWYQ5v/qyWI649kphEo+cb1oGBo+fRZfQvNNO28fPsZNKy0k8rF26x8399R/JY2ySGLZrDk+u/w+FyVrNFIS6e0unw6z2arB+m1VguwmrnX92HMnrZpyw7urfmbSqFOTiQoIib2Lm75lOA7iSJSYjj/APCMbd+Bnvclez5sDMl+dUfFQ2KaMmmoY9SWF7GFT/MlM65os4EDJhA/oavKD92uMZyLe1NWDDwLp7f9NNZt9mkeyThB8Lw3d6ztsKsdZKYhDiZUgy65jG2hT/Mbx8MYs3qL6stZjYYea37UAaGxzN62b/lrj1RJ5TBSNCQv5H5zT/PWralvQlGpef5jQtrLGeLD8R8UzyoHMpyS2or1FoliUmIatx508NYe0/HtfIBpsx+mdSj+6st92T7AZS7nMzcsbJ+AxSNhm+noRTvXoGz8PSx80715YAx/N+fG1iXeajGcqGBwWDcRs42z7zTVBKTENXQ6xTdOl1B6xv/TR/7AQ5+0pOUg5tPK6eU4qM+t/LSlsW8vX2FGyIVDZ3S6bD1GEnemnlnLWvU6Xmn13AmrllQY7kgqxcrDGFk/74RV7nnXSeVxCREDXwje3L58DfJ7vgvsj7vxyf/nYF2yu3iVqOJ9dc+zAe71/KWJCdRB+zdbyF78Uyyl8zCVX76XaMnSwprjklvoPNXrzL8lw+rLaPTKRYaW1GW62LPx6d/4XI3SUxCnIPB/W4k+tZlGPd8yNYv7kBzVu3zFGi28uOVdzNzx0pm/LHcTVGKhsoYEEnw9c9TsOk7Cv/4+azlf7jybtZf9wj78rMoczqqLVOgs+MfO5vygrJql7uTJCYhzpF/aCIxw7/jh1057P/vjacvN1lYfNV9PLH+W5wu6YQrapdvh2vwS7qbou2Lz3mdYTFtCfrk79V2a/CzWnGUl5w21p4nkMQkxHno1jSYwCveZOehfeRteue05YFmKz2aNGV1xkE3RCcaOkt8b4r3rObPSQlkfD3lrOWf6jCQqyMT2JOfddoyu8WrYmgiz8tLkpiEOF93dovmY/trbFz6BllLHjlt+X2terHg0DY3RCYaOr23L02f/JWYvy6i+M/fzmmd1v4hjF8xn7yyqreG+5oMuDy0m4MkJiEuwNw7+/OieRYrt6xn61tNKTmyqnLZFeHN+ergVhlTT9QZg384juyaO92e8FibfpgNRv7ISasy32Y2eOxgrpKYhLhAn4/pRsdRPzDp2ASOfnMTZZlbAfA1mom0+rEsbZ+bIxQNlVIKZbKS+vFfSP1oAuU5qWcs620w0icklkOn9IOymw045YhJiIbF26gn0s8b76gkDkY/QNqC4ZSm/w7AwPB41mXU3MlRiIsRed9n2LvfiiP3KCX719dc1urHsrS9bMs+SnpxPgA2s5EihwIPTE6SmIS4SCM7R3D7ug4cDh9DzqrncJUX0sY/lJyy05/zJERtMQZEYGnRE2urpNOe23Sqnk1iOFZaxOTff2TYz3MA6B7tT3oRaEhiEqLBGdomjBeubsUqw2AAyrP+IMzbxvcpO9wcmWgMDPYQnLlpNZaJswXxf31H8mnSbTi0ilvHO0fawQNvFQdJTELUijahNj7enMOMQ+1I/+EOmu7/j7tDEo2E3tYER17NiekEg05fecODTueZSQkkMQlRK9qF21j/cF++K+tP0ICZOHI8c3BM0fAYbCFnPZV3sioHSZ53Fg8Ag7sDuFBKqSeAfZqm/dvdsQhxQqnDxZQ1esZlL6TYP4Y9eZnE2YLcHZZowAx+YeStnc+B/MzKea6SPIKu+zu+7a92Y2QXzu2JSSkVCzwJ2DVNG66UsgJvAWXAEk3T5lazTh9gC+BTr8EKcRbzRnXhka+3McbkR3JUIr+k/imJSdQpvcVOyzcz4aQx8XJXf0ZpypZqE1O5y8XcPRsw6w0c0lkJLq1+LD13cnti0jRtL3CXUmr+8VnXA/M1TVuglPpMKVUIDD2+bKGmaZ8AXQA/wAbIEZPwGImhvjQLtMDRfbTWcjhS6u3ukEQjoPe2VZ32CcBVkl9t2ec6DWZrdio5ZcX84NuKuAOl+KKvjzDPmdsTUzUiqTgaAnBqmvYlUOUxopqmvaqUagp0P3XllJQUWrZsWTmdnJzMkCFDKqcLCgpYsmRJrQd9qZL6qKo26iM7rYTV5tGY9qzklTI/uh+r+w+9tPvz09Drw7R3G/rCNLZWs482oCcG8pwmlmvluJwOCgqKPao+PDExpVCRnDZSw80ZmqbtB/afOj8yMpJ169adceNLliwhKSnpYmNsMKQ+qqqN+igKSWPOf9fzTj8fYjPs9VK/0u7PT0Ovj1zvNMozfQiqYR+PlRZhODQfg8GI1cfkUfXh9rvylFKBSqmZQMfjNzR8AdyglHobqPkxjEJ4oKsTQjB72yjL3IZWzeMGhBA1c/sRk6ZpWcD4U2aPcUcsQtSWQ6opLsdCjhZmUlBeio/R5O6QhLhkuP2ISYiGqNzgj1fTa2jtbSKtuPqL0EKI6kliEqIOmA16HBjx1yvK5HSeEOdFEpMQdcBk0LElvRQvXJQ6Pa+fiBCeTBKTEHVgfI8YPv49HZ3LKUdMQpwnSUxC1IEhrUMJ8PHBUJQiR0xCnCdJTELUkV26dpQXZ572SGshRM0kMQlRR4qVD21Mepyay92hCHFJkcQkRB1SaHjuU2+E8EySmIQQQngUSUxCCCE8iiQmIYQQHkUSkxBCCI8iiUkIIYRHkcQkhBDCo0hiEkII4VEkMQkhhPAokpiEEEJ4FElMQgghPIokJiGEEB5FEpMQQgiPIolJCCGER5HEJIQQwqNIYhJCCOFRDO4OQAhRe9LS0jh27Nhp8+12O9u3b3dDRGdnNBoJCgrCz8/P3aEIDyGJSYgG5NixY8THx6PX66vMz8/Px9fX101RnZmmaZSUlLB//35JTKKSnMoTooE5NSl5MqUU3t7e7g5DeBhJTEII5syZQ3JyMg8++CB33nkne/fuPa/116xZw80338xjjz1WRxGKxkQSkxACgPHjx/Ovf/2LV155hcmTJ1NUVMRTTz3FhAkTmDVrFqmpqTz88MMATJs2jVWrVlWu261bN1566SV3hS4aGLnGJEQDdNWsVWQWllVOO51O9Ho9QVYvvh/XvcZ1AwMDKS8vRymFw+EgICCAefPmMW7cOEpLS8nOzmbt2rU8+uijdb0bopGSxCREA3Rq8jmfmx+ysrLw8vLiu+++IzExkdtvv51+/foBMG7cOG699VZGjBhR6zELcYIkJiEEADNnzuSnn34iLy+PyZMno2kakyZNIjU1FafTCUDHjh0pLCzk5ptvrrLurl27ePbZZ9m2bRvvvvsud999tzt2QTQQkpiEEIwePZrRo0efNn/evHkAPP744wBMmjSJW265BZPJVKVcfHw8c+fOrfM4ReNwSSYmpVRr4Fpgo6Zp37s7HiEaixdffNHdIYhGwO135SmlYpVSs5VS849PW5VSHyqlZimlRp5htWFAIeCqt0CFEELUC7cnJk3T9mqadtdJs64H5muaNg64Vik1VCk15/jPiSuuTYDZQI/6jlcIIUTd8sRTeZHAluOvnZqmfQl8eUqZz4C/AemnrpySkkLLli0rp5OTkxkyZEjldEFBAUuWLKnlkC9dUh9V1WZ95OQUUaov5fDu3SxJK6+VbZ7JiXb/5ptv4nQ6sdvtVYb4cTqd5Ofnn3H9uXPn8uWXX9KsWTMKCgqYOHEizZo1O+f3nzt3Ll988QVRUVGMGzeO1q1b88wzz1BUVITFYuHZZ5+tcf2SkpJ6bYcNvd2b9m5DX5jG1hr2Mc9ZhsPpwOEop6Cg3KPqwxMTUwoVyWkjZzii0zRtBbCiumWRkZGsW7fujBtfsmQJSUlJFx9lAyH1UVVt1off9pWYzCbiW7QgKaFXrWzzTE60++3bt5OQkHDa8rPdLm42m5kwYQLJyclkZWXx8MMPM3PmTF544QVycnJo3749ycnJvPzyy0yfPp1p06bRq1cvunevuC3dYrFgs9nQ6XTExsaSnZ2NUoqZM2cyceJEcnJyiIqKqvH9O3bsePEVcY4aervP9U6jPNOHoBr28VhpEYZD8zEYjFh9TB5VH25PTEqpQGAK0FEp9QTwOjBDKXUNsMCtwQlxiTr632ScxZmV0y6XkzydHr13EKHDvqlx3QvpYHvbbbdx++23s3nzZl588UVuvPHGykQUHR1NSkpKjYlJiJO5PTFpmpYFjD9l9hh3xCJEQ3Fq8qnrDrY6XcXJjSZNmlBQUEBERAQpKSkAHDp0iKFDh17sLolGxO2JSQjhGS6mg+27777Lhg0byMrK4plnniE6Ohqj0cgjjzyCyWSSoyVxXiQxCSEuuoNtdSM9/POf/6z9QEWjIIlJCHHOpIOtqA8NNjFdCo+YDggIICQkxN1hCCGER2mwicnTHzHtdDrZtWuXJCYhhDiF20d+qEue/IhpT45NCCHcqUEnpgshj5gWjVFdtPtp06YxYcIE7rnnHjRN48iRI4wcOZLbb7+dxYsX1/YuiAZEElM15BHTojGqzXZfVlbGhg0bmDFjBm3btmX58uXMnj2bSZMmMWfOHGbNmlXv+ycuHQ32GtMJ8ohp0RhdtXAWWSWFldNOlxO9Tk+g2cr3g8bVuG5ttPusrCyCg4MBiImJISUlpXL0hxOdcYU4kwafmOQR06IxOjX51He7DwwMJDOzYkikgwcP0q5dOyIjI0lJScFms13AHonGpMEnpgshj5gWjVFtt/tOnTrx4IMPUlpayn333UdcXByTJk3CYDAwduxYd+yiuERIYjqFPGJaNEZ10e4feeSRKtPh4eF89NFHtRi1aKgkMV0g6QEvGiNp96I+yFVIIYQQHkUSkxBCCI8ip/JOMWfOHObPn09cXBz5+fk89dRTxMbGnvP6y5Yt45NPPuHw4cPceeedDB06lGnTprFv3z7Ky8uZOXMmSqk63AMhzt/Ftvs1a9Ywbdo0oqKimDp1KsBp7X7Dhg0899xz2Gw2BgwYwB133FFXuyMucXLEVI2L6Wh4+eWX8/bbb/Phhx+ydOnSajsaCuGJ6rqD7e+//859993HBx98wNKlS+t9/8Slo8EfMdX3I6YBPvjgA2bMmMHUqVOr7WgoRF07MHUwzvz/tXun00WGXofeN4iYx36ocd266mB7xRVXMHLkSJ5//nmeeeaZ2tlR0SA1+MRU34+YBhgzZgy33XYbN954I/PmzTuto6EQde3U5OMJHWynTZvGv//9b6Kjo7nxxhsZOHDgBeyZaAwafGK6EBfT0fCLL75g8eLFFBUVcdttt+Hl5XVaR0MhPFFdd7AtLy/nr3/9K76+vnTt2tUduyguEZKYTnGxHQ2vv/56rr/++irzTu1oKISnqY8Otv3796d///61GLVoqCQxXSDpaCgaI2n3oj7IXXlCCCE8iiQmIYQQHkUSkxBCCI8i15hOcbE94AsKCnjmmWcoKytj0KBBDBkyREZ+EB6vPkY8SU1NZeLEiej1esaMGVN5C7oQp5IjpmpcTA/4WbNm4XA40Ol0REVFycgP4pJR1yOeyKPVxblq8EdM9f2I6Z07dzJs2DD69+/PqFGjmD59uoz8IOrd7vd/x1FUXjntdDrR6/UYLEZa3NmxxnXrasQTebS6OFcNPjHV9yOmIyMj8ff3x2g0omlatT3ghahrpyYfTxjxRB6tLs5Vg09MF+JiesCPHTuWxx9/nNmzZ3PTTTfJyA/iklHXI57Io9XFuTprYlJKGTRNc9RHMJ7gYnvAh4aG8uGHH1aZJyM/CE9XHyOeyKPVxbmqMTEppZ4HmgG3KaWma5r2cP2E5fmkB7xojKTdi/pwtquQvsCu46/LaypYn5RSw5RSDymlnq6p3InTDyfLycmps7jOR3WxucOCBQvcHYJHaQj14cnt/lSaplFcXFzv79sQ/s61ydPq42yn8jQgTCmVDITWRQBKqVjgScCuadpwpZQVeAsoA5Zomja3mtWcQByw+0zbTUtLq3xdVFSExWIB4PDhwxQUFNTeDpyy/fMpHxAQUO3yBQsWMGTIkHNedi7zTp4++fU333zDtGnTzjn2c1FT/BdS/lKvj81btkBCr1rd5plcCu3+VEajkbCwsEv+73yptfsffvjfo1E8oT6q0DTtjD+ADRgP3Av41lT2Yn+A+cd/jwKGHH/9GTAUmHP8Z8Tx+eOP/37q1O107txZ0zRNGzdunHbCya/j4+O12nby9mujfE3Lq1t2LvOkPuq/Pvq9tUJ75cNkrddT99VYDlinXeTnR9p99fMaa33krPq3lvHNizXWx+33jtN6fzBO+/aZRW6pj5ravapYXj2l1O2apn10/PWtmqZ9emHp7+yUUvO1iiOmJ4DvNU3bqJT6RNO00+5LVUrdQcW1rzxN0149ZVk+Faco9VQcWWVQcRoy93iRICCT2mU/afu1Ub6m5dUtO5d5J0+f/Frqw/31EaNpWvDFvIG0+zPO86S/8/mWb+j1ccZ2f7ZTeW1Pet0OqLPEdJIUIBLYyBmugWma9mF1848vO7fOGkI0INLuRUNytsRkU0rdRcW1puoviFwkpVQgMAXoePxo6XVghlLqGsCzrsgJIYSoc2c7lacHBh2fXKhpmmfcSiaEEKLBOltiGgAMB0yApmnanfUVmBBCiMbpbKfyhgKP4UF9mIQQQjRsZ0tMhwFvwFUPsQghhBBnPZX3ARU3PijkVJ4QQoh6UGNiAlBKBQMWKhLTwXqJSgghRKN1tkFcXwG6UzH0Twvg8voISgghRON1tkFcdVSMV3cn8GU9xCOEEKKRO9vND3sBvVJqNhU3QQghhBB16qzXmACUUv5AjnYuhYUQQoiLcMYjJqXUf4AIKvowlVExIF+3eorrjJRSfYAewBFN0z52dzxCCCFq1xmvMWmadiPwi6ZpfTVNGwh8Uh8BKaVilVKzlVLzj09blVIfKqVmKaVGAt01TXsJCK+PeIQQQtSvs11jaq6U6kvFEVNCPcSDpml7gbtOJCbgeiqe1bRAKfUZsL6m9S0Wi+Zy/a8/sJ+fH3a7vXLa5XKh053tno/GQ+qjKnfUx65duzIv9rEX0u7Pj9RHVZ7W7s+WmP4C3Hz89ZO1GtW5iwS2HH/tBFYppSZRMSrFaRITE1m3bt0ZN7ZkyRKSkpJqO8ZLltRHVe6oD6XUgYvdhrT78yP1UZWntfuzpcgQwAcIBO6rzaDOw4nnMwHoNE37VdO0F+X6khBCNExnO2J6BHiVehzEVZ7PJIQQjdvZEtNWTdO21kskx2malgWMP2X2mPqMQQghhPucLTH1U0olAaVUjJV3U92HJIQQojGrMTFpmjakvgIRQggh4OyDuH5KxWMvfIBoTdM61EtUQgghGq2zHTHdeuK1Uuqhug9HNHZlZWU8+uijaJpGeXk5I0aMoG/fvhe8vbrunzF8+HDmz59/9oJutPFwLu9uL6VFx2Ii7DLkpbt4UtuePHkyw4cPp02bNhf8/nXpbEdMJ24RNwJd6j4c0djNmjWLq6++mquuugqo+DB/++23LF26lPT0dF599VW+/vprFi9eTGJiInq9nscee4wHHngAg8FAeXk5b7zxBsnJyfTs2ZOuXbuyc+dO9u7di06nY+rUqTzxxBOUlJQQFRXFI488UmXdG264oTKWyZMnk5eXh6+vL3FxcQwfPpwXXniBnJwc2rdvz7hx4yrL9u3blz59+rBz506SkpJYu3YtAwYMYOTIkfVeh6dqE+pLc5uOGz9aT3yQlUf6xtEu3ObusBodd7ftN954ozKW5cuXk56ezogRI9i2bRvff/89s2fP5rXXXuONN94gPz8fg8FAq1atGDNmDHfffTfBwcH89ttvvP7660ydOpUZM2ZgMBgYP3487733Hk8++SSlpaU4nU5ee+019Hr9BdfV2dLtNmArsAoYd5ayHq80/XcCD7xE/raPcOQfcnc4ohrbtm2ja9euldNeXl7o9XpcLhfl5eUsWrQIgIEDB/L444+zbt06Nm/ejL+/P6+++ipBQUFs3boVl8vFX//6VwYOHIjT6cTb25vffvuN9PR0MjIy6NmzJ2PHjmXr1q1V1t23b1+VeK6//nqeffZZvvnmG5RSOBwOAgICmDdvXpVyJpOJ559/nl69ehEZGcn777/PV199VfcVdg4Meh39I4ysmNCL0V2jePL77Vw1axU/7cpAxmWuP+5u21u3/u8G6969e3PffffRu3dvfvzxRx566CHGjh3Lp59+CsBNN93ESy+9xMKFC9myZQsRERFMmTKFhITqBwD66aef2L9/P35+fhQUFHD4cLXjH5yzsyUmA3A/8BDQ86LeyQN4BbcnP/h6nMUZZC66l8Nzu5H5ywMU/vlfnCXZ7g5PAK1bt2b9+v+NOlVWVsbbb7/N1KlTGTRoEEVFRQA4HA4AysvLUUqhlAKo/G2xWDAYDGRlZbFp0yamTJlCfHw8RUVFvP322wQHB3PLLbegadpp657s5Pf57rvvSExM5Lnnnqucf4LNVnEEYjKZsNlsKKU4eYggd1qTcZAHjqzg+l/mMPfoL7Rtn0W7NrlMWbuMhLc+59lla9mdk0lBeakkqjrkSW375FOA1ZWxWq0AaJpW7XZMJhMOh4PCwkKg4rRir169mDx5Mh988AHR0dEXVVdnu138NuBWQAGzgJ8v6t3cTCkdZZYW+HVJgi6PojlKKTm6mpKDP5O74XVwOTBH9sE7uj+m8J7oDHI+vr6NGzeORx55hAULFuB0OrnllltITExkypQpbN++nQEDBgCwcOFCNm/eTLdu3Wjbti3vvPMOEydOpLi4uMp5cz8/P4qKipg6dSq7du0C4Mknn8TlchEbG3vaun369KkSz7x58/jss88YNmwYHTt2ZNKkSaSmpuJ0OuuvUi5St+BoXg7tTmK3TmSWFJJRWkhmSSHhNm/25eby9cFNvPbHUgJ8FTaLQq8UOqXwN1kINlsJNvsQZLISZK74CTZbCfH2Jd4WXG0yF9Vzd9s+ed2uXbvyyiuvcOedd3LFFVfw+uuvY7VamT59OjNmzKgSd7t27ZgxYwZPPvkkmzZtwsfHh2HDhvH0008TEREBwJVXXsn48eOZOHEiOTk5vPHGG5jN5guuqzM+j0kpZQGeAN6l4s688ZqmPXXB71RPunTpol3omGGu0jxKDv9K8cHFlKSuROdlxzsqCe/oK/Bq0gmlu/Bzpp7qUhwzbM6cOQQFBZGcnFzr2z65PurrArFSar2maRd1Dfdi2j1AYamDD9YeYvaag/RvHsSE3jH4WKhIZCUFZJYUkVFSQFZpIRklhRwqzCGlMIc74y9jZGxHrEbTxYRf7zy13ddl267J2epj9uzZ7N69m5KSEl577bVaec+a2n1NR0xvUpGQngW8gIb3X/kUOpMNS2wyltiKRuEoTKXk0GLyNr9LWfoGDPZmeEf1xxzdH6N/S/m26CajR4+ul/eZPHlyvbyPJ7CaDEzo3Yx7ezblv1tSuf2TjcT4W3g0KY4+ESHVrpNRUsDsXavp/e0MBoTHc19CT5r5BtZz5A1LfbXt83XXXXfV6/vVlJjuBYYDQ4B4YFS9RORBDNYwfFqNwKfVCDRNw5HzJ8WHFpO98hnKc3ZjCu6AOTIJvSUIpfMCnRGl90LpjKA3onTG468r5im9F8rgjc5odfeuCVEtvU4xvH04N7QLY8X+Yzy3cCeFZU4e7hPL4FZNqnwZCzb7MKndFTzWJokvD25l3PJ5WI0mJiT0ZkB4C/niJi5YTYlpB/AUMAKYUd9j5tWF1LwSlhwpJyy9gOZBVvS6c//gKKUw+rfA6N8CW7u70VxOyjI2UXL4Vxx5e9GcZWguBzjL0FzlFT/OMjj+W3OVg7McR/4BAvpMxdJscB3uqRD/U5K6htCd93H4sE/FjDMlDKUDFErpQOmIVTretCqKTRqHlpSy4EcXkXYLoTYzOp2+8ouWMniTZPCmn8XMNqcXs9fsYVKpi5FBdm5tEoyvlxVlMKN0/9/efcdHUed/HH99t2ez2U3vCSWB0ENvigRBVJqICIh4ogJy590pNmynnt7ZznKWUxQ5xFM8yg8RUFHwAAUVpAuKSJOWQBJITzbZ3fn9kYiEGiDJ7IbP8/HII9nZmclnvnzDe2fmOzOmY7+Dqt+hjnutjDaM9kiMQVEoi1OC7SJ2pmC6FrgZ6A/EKqWsmqa566esuuHTNA4U+3h08TZ+zinGbjHSNs5J+3gn7eNdtI0LwW4523iQSspgxBrTEWtMx3OqwVtymKx5A7BGd8AYfOpDJELUJltcV7LSXqfFGc4hVJ5r1kDzgVb5XcNX9dpHE00ju7CUt9f8wqc/ZnJN62iahZkJUuUEGcqxqgpsqpwoynk0tJxiTzFzj2TTf+tOegWbuDXURIpFHVufq17zRQAAIABJREFUdux3VX0BvooSfKU5eEuz8ZUXVNahFEZbJM4Of8Te5Or6aTChu9P+L6xp2gZgg1LKSuUhvfervgesSIeZQU0VV/ZOx2o0UeT28n1WAZsOFvDv7/ayJbOQMo+XtCgH6fEu2idUBlZMSO2d2DXaowm79Cmyl0wg5poPqz4xCqGvyr2Tqj2ZX6edME+M1cnDA2KY1M/DnE2ZbCsso6zCR2mFlzJP5Xd31feyCh+lHi+uCg9Lcg/yftYerGYY1qgdj3TtSaLDRU1omg9v4X5yl99FyZ7FhPd6RkbLXgTOuntQtZf0ftVXQNuen81T2Rt4/rPtuL2eyk9tv7KAramZCKOZbJ+Rjwtg1iGNo1/5KCuHUIuN5FAHKWEhpEW6SA1zEmKx4DTbaBsWh/Ecbg1ib9yf0r1LKdj4L1wd/lQHWypE3bFbTNzcJemcl/ti9wH+tuZrWs78F5F2K7ekdeIPbbsQaTv9OVelDJicyUQP/j8Kv3+bzDmXE9n3dazRHS5kE4Sfq9lxqwaibXgcz8Z2O+WwSE3TcHs9FHnKKfa4KfaUU1xRTpGnnKIKNwcLS9iWk8fOo4Ws2p9FdkkpyuDFYYdSYz794lpwT7tLaB0RXaNawns+SebcftgSL8MalV7LWyqE/+nbJIG+Ta6n3ONjxqafeWXTGl7c9DVJTge/b92VQY3ScJit2I1mrEZTtXNMSimc7cZjS7yMnCXjsacMwdXpbjni0EBdVMF0JkopbCYzNpOZSGo2aq7Y7eH7rELW7Mtlwb4t9NoxHZ+m0drWjKsSWtElIYL28U5inSdfaKZMViL7v032Z2OJG/4FBrO9tjdJCL9kMRkY3ymN8Z3SyCoo4+XVP/L0l+t5OugbYpwmbBYo93mrH9EAbEYzTrON0Ohx2PZu5dKdo7hp0MuYguN02hJRVySYLkCw1UT3RmF0bxTGn0kF4Of8HF7avIpp+z9iQXY01q/iKSoIIs5pJT3eRXq8k/Q4J82jgrGEp+FsO54jX95HZN9/6bw1QtS/WKeNp6/owFP92rN2Xz7Tv9vL1z8dZVCrGMZ2SSI18rcPiWWeCvIrysgrLyWvvDeTvvoP0f83mj69H8Le6Aodt0LUNgmmWtbMFcnrva7B6xvMZwd+4t8/ryGrpIA+iemkmBz8fLiERT8cYnt2EQalaBnVnnHueagVM0jpMJKYEKsMkxUXHaUUXZJD6ZIcSlmFl/lbsvjTh99TVuHjps6JXNsmljC7BZvJTExQCADvXfF7hi+18P7aV4nZt5ywHo+jjGadt0TUBgmmOmI0GBiQ1JIBSS05XFrIezvX8cyuubQMjaF356bcEZpMakgkmXleftj7NKnrb2DStjC2FbsIMhuxm40EmQ3HfraZjdgtRi5pHMbAljHYzA3+RhziImUzGxnVIYFRHRLYn1fKf9btZ9C0NQSZjQxsFc2QVrGkRAbTNCSCxzsNZPJPEbxjySFr3lWE9ngMW0Iv+XAX4CSY6kF0UAh3t8lgUuverM3Zx9qcfczctZ6tR7PIrygjNiiEFi3uoMehmdwz7FkaBUdiwkyZx0dJuZfSCi+lFT4K3R6W/JzNk0t+pmOiixs7JpCREnlOFwoLEUgSQ4N4sG8zHuzbjIP5ZSz68RB3fbSFzAI37eKdGA2KvW4DVx9OYIBxIiPXTSdkxT0ENxuGo+VNmEIS9d4EcR4kmOqRUoouUcl0ifrtlvCappFVWsiWo1l8V7KXV1dNZa8lniKPG02rvEVMuLXyjs7RNgcpTSJ4qX0CRYUW5m46yN0LttKzcTjdk8Po1iiU5pEODBJUogGKd9mY0L0RE7o3otjt4afsIjTgNm8Sf97wPs6EnoxffRtXpQZzh30T2Z+Pw1eagyEoAnNoKvaUIdgbX6n3ZogakGDSmVKKOLuTOLuTfrFPkDnvSsI6PowtqQ9KGfD4vBxxl3C4rIjDpUXsKMxlwb6t/JB3iGxzEa4WVvaoYDb94uPprV7ySryEmK081v0SxrRtrvfmCVEngq0mOiaGHns9P+p3DFoyjWmjRvLZ5hIGLEtlynWzaRfvxFuaS0XeDo589QAogwyUCAASTH5EGc1E9f83R1c+yJEvJ2OwR2GN7UJwTBdaxnalTVgzLqdZtWXyy0vZW5RHqbei8stTwU+5efx5zf/x8g/RzB84nIQaXmUvRKCKt7v4z2WjuX3VHPonpPGvYV24e8FW8korSHDZaBIRTO8mr9B15TiMtnC9yxVnIcHkZ8yuxkQPrHy8sac4i/JDaynLWkPBpjfwlRzGEtMRe5MBBCX3w2Bx4LIE0Ta8+i1arkqEO9p05aaPP6flrFcY36ojD3ToTZTNoccmCVEvWofFsnzAH3hhywru2PAfXr/+OtqFxXOwoIyducXM35LF9Jx7eHbRbRgT7tW7XHEGEkx+zBQci+m450NpPi/uQ2sp2f0xed89h9EaRlDTAdgbX4U5NKX6skYDHwy5ii93dWLMJ5/w762vEGS00sLalI7OFOJDgokMtlBwxEPv4x6dLEQgMxmMTG53OYOTWzFp9QJyy4pJDA6luSuSTm3iubFLBu9+4WPAj38hN2gTznYTMIc1O/uKRb2SYAogymDEFtcNW1w36PkEnoK9lOz+lNwV9+At3Ic1rjtByX2xJfXBaAsD4LKmUfxyx+8oLvey9lAms3ZvZH7WYtK0RDp5WrNun4fp//yS0R0SublLIpHBgfUkUiFOpVVoLJ9dOQGf5mN/cT7bC7JZl7Ofd7avoTi0nE+K7uHpMjMdl92FtzQbY1AURmcSEb1fkOel+QEJpgBmcibjTL8dZ/rtaN4K3FmrKd27lLy1z2ON7kjYJU9gtIWjlMJhNZGRnERGchIe3wA+2ruVV3/4irKYIronNWf10Tzee2cbMeYIhqfHMbRNrISUCHgGZSDZEUayI4x+8c2Z3O5yCivKGDrvDcbvsLDx5nnE2E34ynI5+u2TlO3/Uh6v4QfkDogNhDKasSVcSliPx4kftQpbwqVkzb2Cwq0z0Kqed/Mrk8HIdY3bsXzAHUyKbMeIlHYMSIsntMkvtGqTS26Jm6HTv+Oqt77lfz/n6LRFQtSNELONB6PTSYgrY+i8hSiDEaM9GnuTAZTtX6F3eQIJpgZJKYWjxSjirl9Gec4WMuf2o3jHR2jeipPmjTEF0Ts2hbHNurDkqtuJCraxqORzbulvZvKVCTy8+Ec+++mwDlshRN0xKQPLBt/GDu9OHlixCk3TsMVfQtnBb/QuTSDB1KAZrE4iev+DyL5vUHbwaw580I3cL++nPGfLKec3GYw8mN6XaZeOpKjCzSs7llKW9B0T//cJszbvrXrKqRANQ7DZyvIh43h79zL6vrWSbzM9oPnwlRfqXdpFLyDPMSmlBgKdgZ2apr2ndz3+zhKeRsRlz6J5/0bJnk85+vVjeEuzcbQYjbEiCl95EcpoAYMZpRTNXVE0d0VxZ+vLOOou4dUt3/DH9e/x+2/N9I1LY1hqc4amphBklhtmisDWMiKCR7v0ZvuRPJ74fDt32lsQevBruUOEznQPJqVUU+BhwKVp2nClVDDwOlAOLNc07aQn52qa9rFSagXwh/qtNrApo5nglCEEpwzBW3KYom0zidj7PIc+eh3NW47PU4zZ2RhbQi9MIUkoqwu7NZQHmiTySNuurDtSyEvr1/Lomv8xbtUsIommd1g7mjgimdA9mQSXPPJaBJ7ft+zJJYte5b8jezJ7fluKln/I8Jv7yyUUOtI9mDRN2wXcppSaWzVpGDBX07SFSqlZSqliYGjVe59rmjZTVT628j7gJR1KbhCM9mhcHe9iQ0F7WlU90VfTNDz5uyg7sIqKvJ34yvPxuQvwledTkfsDLTvfy/tXj0Iphdfn44Ofv+f5LcvIcofzzrvfcW2bBBqHOhiemkZiiFPfDRSihswGI092uppHNnzKuyPGsHrG1dw2exNvDW+HyShnO/Sg/OW8gVJqbtUe04PAp5qmbVRKzdQ0bfQp5v0LEAUs1jTtk+Pfi42N1Vyu327BM2jQIAYPHnzsdVFREQ6H3AHhVzVtD+UtJvzAGyhvCUcTJuK1VD5CXtM0vik9zM7SIjYcqaDI62G/ysFVHk5XlUL/BButwwLnER169I8+ffqs0zSt84WsQ/r9uTlVe/wz53u2uo/QuGwHHu1SKvISebSDHYux4e85+Vu/132P6RT2A4nARk4zOEPTtCdPt3BiYiJr16497cqXL19ORtUegjjX9hhIyS9LOLrqLwQlZWBPuQZrbBf6GKp3I6/Pxwubv+Lt7avZUhBMYRY4zVZCzDZSwh1c2jiSBEcIVye2wGH2r2ulArV/SL8/N6dqjwwy0DSN9Uvv4f9MGjMzt/DArlRW3HQNITZ//K+y9vhb/9C9tZVSEcDfgQ5Ve0uvAK9VDXBYqGtx4iT2RlcQlJhBye6PKdz6DrnLJwEaQUmXE9JuPGZXU4wGA/e37824ll04UJzP0fIS9hcWcaCwiM1Z+bywYidGSzl/CV3C7a26kBDsontUI5IdYXpvnrjIKaVo1WIIsV/ex22tJ3LjrixavjuVtTfcTGyIXe/yLhq6B5OmabnAxBMm36JHLaJmlNFMcOpQglMrT/1p3nJKdi3k8KJRRGS8hC3hEgDCrXbCrVV/zLG/La9pGocK3Yz470reXHmQ5OgsXjSsQjN4yIhL4eZmnUmwu3BZZDCFqH9BSRnEXvsxBZteZ75xEw+n3ErKrOfoFh/L6NQOjGveTe8SGzzdg0kEPmW0ENzsOqxxPTi08DrMzsaYnI0IaTsOc2jqyfMrRazTxpcT+pFXWsH8LVms2nOEn3PySU2x85d1izniLsFkMDCiSXt6xTQl2RGK3WTRYevExchojyasx+NkzunLm907MzimE899uY1X3V9xW7OuMmKvjsmQE1FrTI544q7/H2GX/A1rXA+yl0wgZ9mdeMvyTrtMaJCZsV2SmHp9Ok/0b83rnxXjzE7nhvCBPN91CGXeCh7bsJhLFr3K+zvXk1tWXI9bJC52wWmjKN72X4a2ief2Ls3Yf9jIfUvXkFd68l1URO2RPSZRqwymIAyhKZhDU7CnDKHox/fImtuXoMZXEtToCqxx3TGYTn2I7rKUCL67sxcbDxbw2srdLPnJyz8Gd+JPrXqR5y7lsQ2f8dKWFYRabFyRkEb36Eakh8XjstjkE6yoE8HNh3Pow0G4Ok3i5i5JlAdfyswffuaJJQ5eHNJa7/IaLAkmUWeUUoS0uglH8xGU7FlMyc4FHFn5ML8OlnC0vBFLRKtqy5iMBjonhTJ9VHtW7Mzluhnf8f6NHUmLcvBy96H4NB+HS4v4InMHc3Zv4skNn1PocXN943SuSEgjPTxen40VDZLRFoYxJIHynO+xRLbl+pQ2TNv5DV9sz8bn0zAY5ANRXZBgEnVOmawEp15DcOo1APg8ZZTu/pgjK+7DFNoUV+d7MQZFYTD/NupJKUVGaiTTRrTnTx9u4VChm6tbRDOwZQy9moZzY0pHbkzpCEBmSQGfH/iJ+75biMfn5caUTgxKakVMUIgu2ysaFkeL0RR+P43wjJcItQbhsthIijOxZl8e3RvJSNK6IMEk6p3BZCO42XXYU4dRuOkNjiy/G29ZLlpFCWhezJFtibriLZTRQvsEF0tu74HH62P+liymrdnLpAVbsJqMXNM6lpHt42kc7uTmZl24uVkXDpUWMm37agYteZumIRGkh8cTHRTC1QktSAh2nb04IU5gbzKAkl0fk/XhQCJ6P8/Q5NasMx1lzqaDEkx1RIJJ6EYphbP9H3C2r37Lw/wNr3Jo0Qgiej9/bFSfyWhgeHo8w9MrD9UdKnSz8IcsJszZRKHbw0vXtKF7ozBigkJ4KL0fD7S7nB0FuWw+epA9hUfot3gKN6Z0pGd0Y3rHpmA0yLgfUTPKaCGq/1Tch9aR/dmtXNflAd4s3Ilvpx1NayXnN+uABJPwO64Of8IS0YqcJRPRvG6UKQhjUASWqPaYw9OwNxlATIiVcd0aMa5bI/YcKeH2uZuwm418MKYTNrMRgzIcu0s6wMQWPZm2fTUv//AV3xz+hQfTL8egJJxEzVljOhE77BMOfzyaQb5kDkZm8sQCM48OuVTCqZZJMAm/FJTcl6Dkvvg8peAtx1OcSXn2ZtyZ35K3+imM9mgcLW7A6IinUWJvPpvQgze+3kOfN76mzOMjxGqieZSDf17TGofVhMNs5c7WlzEhrQcPrv2Y7gtf4XepnRmU1IrGIeF6b64IEEZbOLHXfsykw5vJWD6XiKynuX9aYwak9cPZOIOYkCASQ+XC8AslwST8msEUBKYgLFYXlvAWkDYCgIr8XRRt+y+lB77iyJeTsacM4ebYDoy/JR2jI4Hici/zt2TR+/VVADitZjJSI7indwr/7D6UXYW5LN6/jdtWzqLI4+bJjlfTPyFNz00VAUIZjETEduDtvuFsydnPlI2f4fhpJpE7NzOzsB/tE1yM75ZMq5gQTAYlI/fOgwSTCEhmV1PCuj0EgM9dQMnuTyg7sJKCja/jKckkqt+bjOnUhTGdEgE4UlLO9DX76DvlGzw+HzEOK8PaJfJuz45oRjeT1nzEm9u+IaTATen+GK5KaCGHZ8QZdYtqRLeoRgxP6cjVi9+ga963vHnjBHYeNPDcsh3sOVpKWYWXKcPb0TVZBkmcCwkmEfAMVieOFqOgxSgAynO3krviXuKGfXpsnnC7hXsyUrgnIwWA7dlFzNl0kAlzNnMgv4wOCe1o2xTKyn7i3R3rmLT6I25K6cy9bTOwGuXPRJyeyxLEFwP+xAeL9/HQyhnkGhy8PuA60sPj2ZVbzB/nfY9BKf7cqwlXNI+SDzw1IGd/RYNjiWiN2dmYg7N6k710IkdW/YUTnzvWPMrBw/2a8/G4bqy5sxd3XNKEFVsqmLY2hNaerqwdPIkKzUuvj19j/i9bdNoSESiCTGZu6vVn3nR/wTu9RjFu5SymbV9NcpiNT8Z355mBLZm9KZNe/1rFoUK33uX6PQkm0SBF9nuD6IEzcba7HW9xFpmzMyje8SHesqMnzWsxVd5t4qNbu/LqJXb25ZUy6t2N3NuqL+/1vpGH1n3M3D2bdNgKEUjMriYY7dEkFe9kyZUT2VmQS49Fr7Bo3w+0iXPy9oh0RrZPYM6mg3qX6vckmESDZXIkYI3uQOQVbxI94H1K9ywhc1Yv8te9dNpl7CbFlOva0bd5JO1fXMFH6/P5T89beHT9Ym5aMZOj7pJ63AIRaFyd7iZ//YuEWoN4qvMAFvS7jSc3fs6+osobGV/TOoZFPxzSuUr/J8EkGjylDJhCEons9zoJN22keOcCcpfdhbfk8GnmV0y6LIWt92Xg0+D2D7bRxd2PHlGNaD//BUYse5ef87PreStEILDGdgZNI/fL+/G5C4izO3moXT+e+f4LAJLD7OSVVlBQJncnPxMJJnFRUQYTcdd9jjm8BQc/uISjXz+G+/CGk85BAVhNRiZfnsraSZeRHBbM6k12Fve9g7GpXcj49HX2Fp18WFCI6EGzsES0InNOH46u/jt9CteyIffAsf4yrF0c07/bp3OV/k2CSVx0lNGMM30i8Td8jTmiFbnL7uLQ/CG4D2887TJ/7Z9G90ZhXPP2Rt5bUcL0S0fRY9ErLNi7tR4rF4FAKQMhrccSO3wp5tBUPEd/4vbC5fz1s6fwufO5o2djpq/Zx6IfDlFW4dW7XL8kwSQuWkZ7FI60kcSNWI6zwx/J+XwcEXtfIH/dS2je8mrzGgyK3/dszLb7+xARbGH1j4rPr5zIm9u+If3D5/H6fDpthfBXRlsYjrSRhPd6mlH9/8a2CgO9PnyKQV+8xV39Ks813TJr4yn31i92EkzioqeUwt74SuJHraLE1QNP0UEOvNeJI18/esqAevKqFvxytJS+L2/kiVbD6RvfjNtWztKpehEIrBGtWH7d48yoWMb4oHw2FW1kyvB2mA2KFTtz9S7P70gwCVFFmayUunoS0fsfxN+4Bk/+bg5/cuNJ4RQaZObtEeksvb0Hg6etQctKYX3uAd7dsVanykUgMJuDSB4wnf5OG5/vWInHU85NnZOYtyVL79L8jgSTEKdgMAURdeU7mENTOPBBdw6814n89S+jab8dsmsT5+TH+/uQX+oh9kgXntu8jGc3/0/HqoW/s0S0JrLTJC51hjB7xbNkpESwfEcOS7bLKM/jSTAJcRrKYCS81zMkjllP3PXLKN75EUdXPYLm++2EtSvIzLSR6fRrmsDhza15dvMy3F6PjlWLQPDQ5ZN4Yd9+Di79Pf9u+gF3zV2D1yfnmn4lwSREDRisTmIGz8N9eCP5a5+r9p5Sivv7pPLi4HY43LG89/MGnaoUgaKRK5qR7YbQNy+OI84IxoYtl1sVHUeCSYgaMtpCieo/lfz1L+Mp3HfSaKrRHRJINMazaMdunSoUgeT+jgOZetkY5lrb0Mf7IYUfDyZ/w2t6l+UXJJiEOAcmRwLhvZ4hc25/SvcsrvaewaCY2L4NC7LXyPBxUSO9Y5vyXW4mnzSbzbbmL1K49d9oPjkULMEkxDkKaT2W8Ev/Ru7yuzk453KKts859l6vpAQiiGKO3PRV1IBBGegV25QSSxl7SoOxxV9KwYZXKNm1qPJr9+Jq5zQvFhJMQpwHe+owEsasJazH4xRvn3tsSHlyWBCOwiZ8duAncsuKda5SBII+cakc8GayL68UV6dJ+MqLcB9aj/vQevLXvUjpL0v0LrHeSTAJcR6UUhjMwVijO+ItzqR03zIAjAZFqBbG9tx8Hlr3ic5VikDQOzaF7wt+4fu8gxyxRBLW49FjX87023EfXqd3ifVOgkmIC2CwOHCkjaJ096donspRVW9f14WdW+PYkJOpc3UiEIRb7Qxv0o5V+ZvpMWc6q3YfOfaeJboT5YckmIQQ5yioyQDch9dTnrMZgI6JoYxu24TdhXKrGVEzj3Tox7fDx6GZy5h/3J0gTM5GeEuyOTCzG5lz++tYYf2SYBLiApldjbEl9sZb+lsQNQ4LxqzZ5AadosbaxLoIthjYmVN0bJpSivhRX5EwejW+isKLZsRewAaTUupBpdQovesQAsAS0ZIjX03m0KKRAFyeGklJhYcf8uRppaLmkhwudhfknfI9oz0Gb8nF0Z90DyalVFOl1DSl1Nyq18FKqRlKqalKqRtPs8xlwPf1WqgQZ+BoMZrE323CU7AHgNTIYNz5Lg6XFZ15QSGOk+qMoITiU+5pmxyJeAoP6FBV/TPpXYCmabuA234NJmAYMFfTtIVKqVlKqWJgaNV7n2uaNhPoDIQCTuC/9V60EGdhMxsJt9korJDbzIiaS3VGsiB4C2/+sIYQm/nYdAVcZo8nqGg/0FW3+uqL8pdj4EqpuZqmDVdKPQh8qmnaRqXUTE3TRp9m/sZAd03TqgVTbGys5nK5jr0eNGgQgwcPPva6qKgIh8NRF5sQkKQ9qrvQ9ojbNp6iiAEURl3LiA0/MiE1hH4hiWdcpk+fPus0Tet83r8U6ffnyl/b45CnlKd27CWzxIfZqI5NL7Bmc1VJPq08+XziGcAtzc20CreAwVIrv1eP9jhTv9d9j+kU9gOJwEbOcKhR07Q9wJ4TpycmJrJ27emfi7N8+XIyMjIutMYGQ9qjugttj7K098j+7BY6Xf8yzh/3E5fShIxWl9Regach/f7c+HN7DL9cw+2pfreHyWsXMtAVTJstTzEw/1ksBQbCcnaTOPZHjLbQC/6d/tYeugeTUioC+DvQoWpv6RXgNaXUQGChrsUJcY5scd0wWF1oPi9BRjNHysr0LkkEGKNBYbdU/6/ZZDQQHJFK4sjlzPlyJ1HBVvrt/T34KnSqsm7pHkyapuUCE0+YfIsetQhRG5TRiuZ1YzdaOeou1bscIQKO7qPyhGholNGG5nXjMFvIL5fBD0KcKwkmIWpZ5R5TGTaDhcPFJXqXI0TAkWASopYpcxCFm98kyelgWfaPFMuQcSHOiQSTELUsss+rlO5ZQvvIGGIsYeS6Za9JiHMhwSRELTPYwgCwmU04jcE6VyNE4JFgEqKOWIwKr59cwC5EIJFgEqKOWE0GfBJMQpwzCSYh6ojZaKDY7T37jEKIaiSYhKgjTcPtuL0+vcsQIuBIMAlRR+wWI0alzj6jEKIaCSYhhBB+RYJJCCGEX5FgEkII4VckmIQQQvgVCSYhhBB+RYJJCCGEX5FgEkII4VckmIQQQvgVCSYhhBB+RYJJCCGEX5FgEkII4VckmIQQQvgVCSYhhBB+RYJJCCGEX5FgEkII4VckmIQQQvgVCSYhhBB+RYJJCCGEX5FgEkII4VckmIQQQvgVCSYhhBB+RYJJCCGEXwnIYFJKtVZKPaiUulrvWoQQQtQu3YNJKdVUKTVNKTW36nWwUmqGUmqqUurG0yx2LVAM+OqtUCGEEPVC92DSNG2Xpmm3HTdpGDBX07TxwBCl1FCl1DtVX6Or5okGpgE96rteIYQQdcukdwGnkAh8X/WzV9O0+cD8E+aZBTwEHD5x4f3795OWlnbs9aBBgxg8ePCx10VFRSxfvryWSw5c0h7V1Up7aF5iCwtY/+1q3OVuvvn2W3aZgmqlvtORfn9uAq099ufuZ8NRD17bXnbsKifHoujozmX716vwmUIveP3+1h7+GEz7qQynjZxmj07TtFXAqlO9l5iYyNq1a0+78uXLl5ORkXHhVTYQ0h7V1UZ7aD4PmVlOunfvhnXBZnp0706yI6x2CjwN6ffnJtDa46PV+XRo1JZesU3ZYNhJVLCV8L0RpPW8BKM96oLX72/tofuhPKVUhFJqCtBBKfUgMA+4Tin1BrBQ3+qEEELUN933mDRNywUmnjD5Fj1qEUIIoT/d95iEEEKI40kwCSGE8Cu6H8qrK3l5eWRmZp403eVy8eOPP+pQ0cni4uIIDb3wETVC/Er6vWgIGmww5eTk0LjSpNJpAAAIcElEQVRxY4KCqg/TLSwsJCQkRKeqflNaWsqBAwfkD1TUKun3oiFosIfyKioqsNlsepdxWjabjYqKCr3LEA2M9HvREDTYYAJQSuldwmn5c20isPlz3/Ln2oT/aNDBdD7eeecdBg0axJ133smtt97Krl27zmn5NWvWMHLkSO699946qlCI2if9XvgTCaZTmDhxIi+//DL/+Mc/ePzxxykpKeGRRx7hj3/8I1OnTiUzM5NJkyYB8MILL/Dtt98eW7Zr1648++yzepUuxHmTfi/8RYMd/PCrq6d+S05x+bHXXq8Xo9FIZLCFT8d3P+OyERERVFRUoJTC4/EQHh7O7NmzGT9+PG63m6NHj/Ldd99xzz331PVmCHFOpN+LQNbgg+nEP8JzGZ2Um5uLxWLhk08+oVWrVvzud7+jT58+AIwfP54bbriB0aNHn2UtQtQ/6fcikDX4YDofU6ZMYcmSJRQUFPD444+jaRoPPPAAmZmZeL1eADp06EBxcTEjR46stuz27dv561//ytatW3nrrbeYMGGCHpsgxDmTfi/8hQTTCcaOHcvYsWNPmj579mwAJk+eDMADDzzAqFGjsFqt1eZr3rw577//fp3XKURtkn4v/IkE03l65pln9C5BiHon/V7UBxmVJ4QQwq9IMAkhhPArcijvBO+88w5z584lJSWFwsJCHnnkEZo2bVrj5b/66itmzpzJgQMHuPXWWxk6dCgvvPACu3fvpqKigilTpsjV78LvSL8X/kT2mE7hQi407NWrF2+88QYzZsxgxYoVlJeXs379el577TXatm3LypUr9dosIc5I+r3wFw1+jynrw0F4S3OOvfb5vBQYjBiDIom9dtEZlz3fCw2nT5/Oa6+9xvPPP09ubi5RUVEANGrUiP3799f+RgpxAun3IpA1+GA68Y+wPi40vOWWWxgzZgzXX389s2fPJien8j+IvXv30q5duwvcIiHOTvq9CGQNPpjOx4VcaDhv3jyWLVtGSUkJY8aMwWKx0LFjR+68807cbjd/+MMf9NgkIc5K+r3wFxJMJ7jQCw2HDRvGsGHDqk27++6766ZYIWqJ9HvhTySYzpNcaCguRtLvRX2QUXlCCCH8igSTEEIIvyLBJIQQwq/IOaYTXOgV8EVFRTz22GOUl5fTv39/Bg8eLFfAC78n/V74E9ljOoULuQJ+6tSpeDweDAYDSUlJcgW8CBjS74W/aPB7TFd/PpXcsuJjr70+L0aDkQhbMJ/2H3/GZc/nCviffvqJa6+9lssvv5ybbrqJl156Sa6AF/VO+r0IZA0+mE78I6zrK+ATExMJCwvDbDajaRoRERFyBbyod9LvRSBr8MF0Pi7kCvhx48YxefJkpk2bxogRI+QKeBEwpN8LfyHBdIILvQI+NjaWGTNmVJsmV8ALfyf9XviTi27wQ15eXq2s55lnnuGOO+6olXXpaeHChXqX4FcaantIv6+uof47ny9/a4+ADCal1LVKqbuUUn853TwlJSXHDj8c/0eZn59f6/Wc6x99Xl7esdpO5Uyd5FTv1WTa8a+P/3nRojM/AuF8nGsnP9v8gd4eS5curfV1no70+4uj32/YsKFG8wdSexxP90N5SqmmwMOAS9O04UqpYOB1oBxYrmna+6dYzAukAD+fbr1r1qzBbrcDlSdzIyIiAMjKysJoNNbqNhy//nOZPzw8/JTvL1y4kMGDB9f4vZpMO/71mdZfG851/WebP9DbY+nSpdw69Po6W//xpN833H6voR37ef2G9dCvcuqZ1hdI7XE8pWna2eeqB0qpuVXBdBOQp2naQqXULOADYGjVbJ9rmjZTKTVR07QpSqlHNE372wnrKaRyT9BIZYBlAxXArx8ZI4EcapfruPXXxvxnev9U79Vk2vGvj/9Z2kP/9mikaVrUhfwC6fenneZP/87nOn9Db4/T9nvd95hOIRH4vupnr6Zp84H5J8xTqpR6HCg4cWFN02o2JlaIBkT6vWhI/DGY9lMZThs5zTkwTdNmnGq6EEKIwKf74AelVIRSagrQQSn1IDAPuE4p9QbgX0NFhBBC1Dm/OcckhBBCgB/sMQkhhBDHu2iDSSl1mVJqctUoQMGxNvmv3nX4C6XUQKXUY0qpMXrXUluk359M+n11/tDv/XHwQ6042/VRQIKmac8qpSbrWGa9qsk1Y0qpnroWWY9q2B4rgIC50Zv0+5NJv68uEPp9g91j0jRtl6Zptx03aRgwV9O08cAQncrSlbRJdWdrD6WUAbgPeEuXAs+D/BufTNqkukDo9w02mE4hEdhX9bMX+FYp9QBwUL+SdFetTZRS6UAvpdRVOtakpxP7yMNAGBDIn6al359M+n11ftfvG+yhvFOodn2UpmlfAl/qW5LuTmyTTcBAfUvS1Ynt8aTO9dQG6fcnk35fnd/1+wa7xyTXR51M2qS6htgeDXGbLpS0SXWB0B5yHZMQQgi/0mD3mIQQQgQmCSYhhBB+RYJJCCGEX5FgEkII4VckmIQQQvgVCSYhhBB+RYJJCCGEX5FgEkII4VckmIQQQvgVCSYhhBB+RYJJCCGEX5FgEkIIcU6UUmOVUouUUjNq+tBJpdTcmq7/YnrshRBCiNozRdO0RUqp/yqlBgK9gWjgbiofwNgb2AX4NE37O4BSKhJ4DnhY07TM061Y9piEEEKcj/FKqa+BRVQ+YNAAmIF+Ve8vrnq2U5uq1xHAS8A9ZwolkD0mIYQQ52cq8D/gTcCpado1SqmbAXvV+8VV31XV9yIqAywaOHqmFcsekxBCiPOiaVoJsAZIVko9DFxxhtndwETgIaVUizOtVx4UKIQQwq/IHpMQQgi/IsEkhBDCr0gwCSGE8CsSTEIIIfyKBJMQQgi/IsEkhBDCr0gwCSGE8Cv/DxNcO0qUIiYFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 468x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_experimental_data(small_setup('ra'), composition='ra', labels = 'in')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the noise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The noise color is independent of the mean abundance. The noise colors are often in the pink to white region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:28:38.726631Z",
     "start_time": "2020-02-18T20:28:15.844302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x180 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_experimental_data(large_setup('nc'), composition='nc', labels = 'in')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit the ratios of abundances between successive time points with lognormal curves. The fits are mostly good (high p-values). The widths of the distributions are large (order 1) and independent of the mean abundance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:36:29.662691Z",
     "start_time": "2020-02-18T20:36:27.118792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x180 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(ELIFE.TEXTWIDTH,2.5)) #, tight_layout=True)\n",
    "\n",
    "nrow = 3\n",
    "ncol = 4\n",
    "\n",
    "gs = gridspec.GridSpec(nrow,ncol,wspace=0.1,hspace=0.1, left=0.08, bottom=0.12, right=0.85)\n",
    "gs_tot = gridspec.GridSpec(1,1, bottom=0.07, left=0.05, right=0.85)\n",
    "gs_cbar = gridspec.GridSpec(1, 1, left=0.88, bottom= 0.12, right=0.9)\n",
    "\n",
    "gs = np.array([[gs[i,j] for j in range(ncol)] for i in range(nrow)])\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",
    "def ratio(x):\n",
    "    x = x[:-1]/x[1:]\n",
    "    x = x[np.isfinite(x)]\n",
    "    return x\n",
    "\n",
    "def fit_ratio(x):\n",
    "    # ratios of succesive time points\n",
    "    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 = lognorm.fit(x, floc=0)  # Gives the paramters of the fit\n",
    "        stat, pval = 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",
    "        #a, b = stats.norm.fit(x) #, floc=0)  # Gives the paramters of the fit\n",
    "        #c = 0\n",
    "        #stat, pval = kstest(x, 'norm', args=((a, b))) # get pvalue for kolmogorov-smirnov test \n",
    "        \n",
    "        return a, b, c, stat, pval\n",
    "    else:\n",
    "        return (np.nan, np.nan, np.nan, np.nan, np.nan)\n",
    "\n",
    "cmap = plt.cm.get_cmap('coolwarm')\n",
    "\n",
    "\n",
    "for i, key, title in zip(np.arange(len(keys)), keys, titles):\n",
    "    df = df_ts[key]\n",
    "\n",
    "    # plot different mean abundances, first sort the species\n",
    "    sorted_idces = df.mean().sort_values().index.tolist()[::-1]\n",
    "    sorted_idces.remove('time')\n",
    "\n",
    "    mean = df.mean()\n",
    "    \n",
    "    for j, gsi, num in zip(np.arange(nrow), gs[:,i], [1, 20, 50]):\n",
    "        if i == 0 and j == 0:\n",
    "            ax = fig.add_subplot(gsi)\n",
    "        else:\n",
    "            ax = fig.add_subplot(gsi, sharey=ax)\n",
    "        \n",
    "        if j == 0:\n",
    "            ax.set_title(title)\n",
    "\n",
    "        idx = sorted_idces[num]\n",
    "        \n",
    "        x = df[idx].values\n",
    "        \n",
    "        x_transf = [x1/x2 for x1, x2 in zip(x[:-1], x[1:]) if x1 != 0 and x2 != 0]\n",
    "        \n",
    "        a, b, c, stat, pval = fit_ratio(x)\n",
    "\n",
    "        bound = 1.95\n",
    "\n",
    "        x_fit = np.logspace(-bound,bound,100)\n",
    "        pdf_fitted = lognorm.pdf(x_fit,a,b,c) #Gives the PDF\n",
    "        #pdf_fitted = stats.norm.pdf(x_fit,a,b) #Gives the PDF\n",
    "\n",
    "        ax.hist(x_transf, density=True, color='lightgrey', alpha=0.8,\n",
    "                bins = np.logspace(-bound,bound,20))\n",
    "        ax.plot(x_fit, pdf_fitted, color=cmap(pval), linewidth=1.5)\n",
    "        ax.grid()\n",
    "\n",
    "        ax.set_xscale('log')\n",
    "        ax.set_xlim([10**(-bound),10**bound])\n",
    "\n",
    "        if i > 0:\n",
    "            ax.tick_params(axis=\"both\", left=True, labelleft=False)\n",
    "        if j < nrow - 1:\n",
    "            ax.tick_params(axis=\"both\", bottom=True, labelbottom=False)\n",
    "\n",
    "        label = 'species %d' % num + '\\n \\n s = %.2f' % a\n",
    "\n",
    "        ax.text(0.95, 0.95, label, transform=ax.transAxes,\n",
    "              va='top', ha='right')\n",
    "            \n",
    "            \n",
    "ax.set_ylim([0, 1.3])\n",
    "\n",
    "ax_tot = fig.add_subplot(gs_tot[0], frameon=False)\n",
    "ax_tot.set_ylabel('Relative count')\n",
    "ax_tot.set_xlabel('Ratios of abundances at successive time points', ha='right', x=1)\n",
    "ax_tot.set_xticks([])\n",
    "ax_tot.set_yticks([])\n",
    "\n",
    "ax_cbar = fig.add_subplot(gs_cbar[0])\n",
    "colorbar.ColorbarBase(ax_cbar, cmap=plt.cm.coolwarm,\n",
    "                            orientation='vertical')\n",
    "\n",
    "ax_cbar.set_ylabel('P-value lognormal fit')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:37:19.082932Z",
     "start_time": "2020-02-18T20:37:12.844280Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x180 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_experimental_data(large_setup('disdx'), composition='disdx', labels = 'in')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-21T07:56:49.639540Z",
     "start_time": "2020-02-21T07:56:23.539447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x180 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_experimental_data(large_setup('disdx'), composition='disdx2', labels = 'in')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean absolute difference between successive time steps describe a monomial (linear in log-logscale) with the mean abundance. This hints at a linear source of the noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T19:37:58.558651Z",
     "start_time": "2020-02-18T19:37:55.628093Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7HK8acC4fQghEi9eLHFflVlEQ5RwMxJ1pj2c4UWOzuFdzRmw8QVq2Vo0tGIhpXRrpZycBamW+olgsyjkIIQIAfyBRSrnc3PYozI819dFaC0Pa1mHfhRus+iOBt1uqsQVF8Zg62E89YDzgmhcm1AlYCGQB4UAtKeV0IcQYU9qlUDxuLOvdSjkFxUMxdwzpfkCylHKzEOK/QJSUcoYQYoyUcvr99z0usWKNYYOKw2tcGyxJX6VtxS67ptLW3N1KPsDxvM+5QIQQYiyQUOzFFTRWrCXYoLQ1Lo9LDGlz2PC4lV1TpW9u53AZnYOIAWyklHuBveY1SaFQKBQ2pkxMCOEphFgEtBRCfAasA3oKIf4DbDalLQqFQqF4MCZtOUgpbwDD7jv8jiltUCgUCsWjMXe3kqKE7N27l0OHDlGzZk369esHwA8//MClS5do2bIltra2REZG4ufnR9++fc1srfVRnL7r168nLi6OO3fuEBwczFdffUXdunV56623zGytdaHKrvF4lLZJSUn6z926dSvVs03araQoOxEREYwZM4bExET9sfj4eD7//HM2bdpEly5dGDlyZKHzipJTnL62tracP38eV1dX9u7dS7Nmaqv1sqDKrvF4lLYFP5cW5RyMxIgRI0hISCAzM1Pv0fORUhIcHMzWrVtZunQpZ86cKXL/+vXrmTdvHlFRUUydOrXYNDp16sS0adPw8vJCq9Xy9ddf89577xklP5aGIfRdu3Ytu3fvfqC+iYmJzJ8/n9u3bxMZGcmRI0eIiIgwSn4sCVNo+7iWXVNrW/BzaVHOwUh88MEHLFmyhNWrVxfphoiOjiYgIIDDhw+j1Wp56qmnitzfo0cPzp49y+bNmxkzZgxt27Zl2rRp1KxZkw0bNpCdnY2UEltbW7p168aUKVO4desWBw8eNFUWzYoh9L18+TKLFi16oL4ajYZJkyZRuXJlPvnkE959913atm1rqiyaDVNo+7iWXVNrW/BzaVFjDkaifv36JCYmcvr0aVavXl3onJ+fH19//TUNGzZk//79+Pv706hR4Zi8UkqSk5MRQpCTk0NAQAABAQGFrunQoQMdOnQAwN/f37gZsjAMoW9qaioeHh4P1HfAgAGFvvv6+uLr62uU/FgSptD2cS27ptY2/3tZUM7BiLz44otcuXIFG5vCDTQ3N7ciBeN+5s6dy9tvv83ly5cJDg5mxowZxjTVKimvvp06daJz585K32JQ2hoPq9FWSmk1P61bt5amZM+ePWW+d//+/fKNN96Qt2/fNpsNBQEiZQXRVkrD6GsobaW0LH2VtsbF3O8FU2mrWg5Gol27drRr187cZlRYlL7GQ2lrPKxJWzUgrVAoFIoiKOfwEDZvNv+OHpZggzGwhHxZgg3GwBLyZQk2GAtz581U6Svn8BC2bNlibhMswgZjYAn5sgQbjIEl5MsSbDAW5s6bqdJXzkGhUCgURTBLsJ+yIoS4DsSZMMmqQJIJ0zOmDXWklA9cJqm0LTeWpK/S1riYW1+TaGtVzkGhUCgUpkF1KykUCoWiCMo5KBQKhaIIyjkoFAqFoghWtUK6atWq0pQbn929excnJyeTpWdMG6KiopIeNqintC0flqSv0ta4mFtfk2n7oH01ivsB7EtzvaF/rGkPFUuzgQq0P425bcjNzS1yzJL0tWZti8OStJXS/PqaSttHdisJITRCiL5CiDnAFCHEXCHE10KI1wziuhQG5cKFC4wZM4b09HRzm1LhuH79OsHBwezatcvcpigURqckYw4uwBkp5cdSyk+llB8B44Bc45r2aJYuXcrrr7/OsGHD2LBhQ7meFRsby6hRox56zerVq3nnnXeIiooqV1rGICUlhcmTJ7Nx40a++OILNBpNuZ63dOlSWrRoAUB2djZ16tQpdmWmtetWEjIzM5kzZw4LFy7E84VeDD4sCI0o37R6U5fdgQMHkpqaWuT4+fPn6datG7/88gu9evUqddrh4eEsWLCg1PcZE0OW3cdJt/t55JiDlPKqEGKYEOL1+04JIURbKeWXRrKtRLz//vsEBQUBEBcXx8yZM5FS4ufnR/fu3Rk4cCDPPfcc6enp1KhRg8OHDzNt2jS0Wi0rVqzg6tWrDB48mBo1agCQk5PD+PHjyczMJD4+nvbt22Nra0tqaiqLFi2icePGeHl5MWbMGDIzM8nNzWXOnDnMmjWL+Ph43NzcCAkJYcqUKVy/fp07d+4wZ84cRo4ciYuLC02aNOGdd94xWP5zcnL0AcWHDx+Ot7c3oRFxhISdJfjlhgxpW6fMz37yySc5ePAgV69e1QdkOXny5CN1y9fkYbrla2su3UqClJI1a9Zw+PBhhg4dSoMGDagdEsbllAxCws6WS1swXtk9f/48a9eu1f8NChIWFsbWrVtJT0+nZ8+e7Ny5k5ycHLy9vTl16hSTJk1i1KhRODs7A7pdRN944w1OnjzJpEmTiI2NZfPmzfz9999MmDBB/9zw8HBmz56Nn58f9vb2XL16lW+++YYff/yxTCEqy4shym5BCupWv359tmzZYhTdNBoNkZGRZtOtICWdrbQU+L/7fvKPmZWFCxcybNgwjh49ysKFC9FoNHh6enL8+HEAGjZsyIwZM7h8+TLDhw9nzJgx/Prrr1SqVImMjAy8vb1Zvny5/nlhYWHExsbi5uZGeno6CQkJADg7OxMYGMj777/PyZMn9dekpqaSkJDA8ePHmT9/PiEhIZw+fZq9e/fi5uaGg4MDp0+f5tatW3Ts2JG333673HkOjYijdkgYH83+P0aPHo2/vz8hISF4e3sDEBJ2Vv8CKw+9evVi7dq17Nixg86dOwOUSLd8TeDBuuVra0rdSsORI0f46KOP8PLy4ptvvqFBgwYABL/cEB9XR4JfbljuNIxVdp2dnQv9DQoyb9483NzcqFGjBkeOHCEoKIjXX3+dgIAAGjduzKRJk/QvOACNRsPHH3/M+++/z7Jly3BwcCArK4vKlSuzbt26Qs9u06YNs2bNIjo6mgEDBtC7d2/2799fbp3KgiHKbkEK6nbmzBmj6TZx4kSz6laQEs1WklLGAQghvNB1M8VLKbMNbYwQog3QAbgmpVxaknsK1r5++ukn+vXrR/PmzQFds9HFxQXQFQwXFxccHBzIzMxk7ty5jB49GiklEydO1D9Pq9XSrl07PvzwQ8LDw6ldu3aRNAteA3Dnzh2EEIXON2nShEmTJumPLVu2jD179jBw4MBHRnt6FJ+v2Mnfh7aystGzXFs2q1DaoHuB5bccykN+11T16tX1UatKotuDuF9bd3d3k+pWEuLj4/n2229p0qQJc+bMKRKta0jbOuVuMeRjrLLbvHlzAgMDi01Tq9UyYcIE7Ox0//rh4eH6c/eXI9DVqkHXPSOEYPr06axatYqDBw+yZ8+eQtfm2+vp6YkQQm9vaEQcH63YS8bVOPp2f51lvVuVQa3SYYyym69bQc3AeLqVhCWHYpm861y5ewmKo0TOQQjxHtAeSAeSgVpCiBTgaynlxZImJoSoB4wHXKWUvYQQTsBCIAsIl1KuEEL0AK6VMh8ADB8+nHHjxlGjRg2qVKlSJAZwQTp27Mj06dP1te18XnnlFYYNG8bo0aM5ffo0bdu2xdHR8YHXJCcnM3/+fBo3bsyIESPw8PAgODgYGxsbPvnkE9LT0xk3bhxfffUVGo2Ghg3L/sK+du0a8+fP50WNI78HDeXzVxsXWzAN+QKbMWMGQgiWLVsGlEy3fE0eptvp06dZs2aNSXS7n/xut1mt7m0dM3/3SYKnzeTFJ71ZNv1zk09VNGTZPXjwICtWrCj2b/Dhhx8yePBgPDw8eOaZZ6hZs6b+nLe3N2PHjmXChAn6WrBWq2X8+PGcO3eO2bNnU7lyZSZOnMjdu3dxd3d/ZL7Czlzlx++mIdNvQ+turPojwSTOAQxbdgvqVrlyZV566SX9OWPo9ihCI+IYvSyMlMOboe2bjNmSY3DnUNIprK2KOeYENCjJ/cXcuybvdz+ga97n/xY4/+/i7ntcpqwtORQrn/hyp1xyKFb+smWH9AneIp/pO1K6dOovZ+84WqZnoqYD6nniy51SjNwk563cJHNycuR3330nq7R/U/Lej/KJL3eW6ZmWpK8hte3Zs2eZbRgbuk6KlkGS/nOlGLlJipGbZL8VUaV+liVpK2XJ9C2rbiVh27ZtUhPYX9JxsOTjtVKM3CQ9Jmwv07Mepm2JN94TQnwipZyV9/lJKeWfZXVIQog1Utdy+AzYLqWMEUKsBFYDrYArUsrF999XvXp16erqqv8eFBRE165dy2rGI0lNTS3Uj2gqjl+5Q1auFnsbwcXo/fxx+hzPd3oFT+8aONja0KxGlVI/s2PHjlFSymcedP5x0RYg6W4WV25ncvNcDMeOxtDw2fZU9amLAGq7a6jq5FDqZ1qSvubUNvZmGvFXbxCzaxOOXrVo+XwHbGxssLUR+Lg6Wr22YD59pZT89ttvnD59mmYdu+Ds6gFgNG0f2a0khHADZgNPCiEygGPAIMAQU0cuAz5ADGAjpdwEbHrQxT4+PkRGRhog2ZIRHh7+wL5bY3IuIo4JSzaQeWIPQ19tw95G7xKfoSHnoiT45YYEGrr5SMXVtv/KaH6K1g0w9m1Vi/b1PPlo5XbSo39lcIcmrPcdAElCvwGy9pvORrHDlPqaq9xqtVo6dfkEeesKXw78B3MSqrI/ztYo/eEFqahlt+CswzaVU/j+++/p2bMnL774Iucc6xpkRuLDKMlU1mTgHSHEK+j+hZoD6x5+V/EIITyBKUDLvFbDPGCBEKILUHHjCpaCS5cucX7Df7BLkdxu/w61GwIxcCk5neyvjVcbqoiERsTpHQPA8v2nWT5/M1R2hfb9eaqlLcTcG7NpU9vNHGZaPf1XRrNy537qXzmIg/dzZDV5kcrOWqZ1aWRUp1DRCQk7y6WrSYz6bBnT32zHzJkz9QPihhxXfBAlHZB2lFLuyPsadd/xjJImJqW8AQy777BpJ69bKPN/O8WYyd+QIyVOz3aja886eS82LQBvt6xlXgOtkPypvDInG479Cul3oFVXhEY3O8TORiIARzvBnO7N1IusFIRGxDFszTG0WWkQuRGc3Pir+Vv8542WhISdpY67pJfSs8z0+ymSSwe2QnIiDbr1Y9gw01cMS7rx3jQhhBY4hW62Ut28nxXAASPZ9liQm5vL8uXL+TR0GxlNX0dU8SRFC+Hnb9C3VS2E9hJ9W9Uy2QwPa6f/ymhW/ZHA2y1rMeGlBoyb9yMOf5/h77qB2Hn6kCshf5TNRghyv1GtsdLQf2U0K6IT0EoJZw/A1b+g9T8QTu70blVLX6O9f7qnouQ0G7mEE3s2Q+OOiKYvcvSWeewo6TqHj4UQ1YAmgAfwq5TyuFEtq4Dcv3J5z549TFm0nBMerbFpPwCRo2sluDna6a8JD7/NJ4HKMZSEgt1IK7eH4+4Rx89D/sEr258BCTlSN+6w9fQ1pJTUcLF9xBMVcK/cBvp58lN0AvJmAhzdBn5tEAED9depCkzZyK/QPO0OZ3es4k7latBxMELo1meYq9egNFt2TwU+lFKmCSECjGVQRSZ/5fLoZWF8MvJXaj7VgrM+3RFC4G5vi6eTg9EH7yoaBVsK4edvIO8kwR9bqdOgIbNmzeKH/12mkt0J0rJ1jjf8/A1uhLyq+6xqtyUiv9wuP3wBojeDnT0EDETY2uuvUeM1ZSM0Io7lkfFwfCdRd29Bq27YODrrW7dtaruZzemWxjlMBL4XQuSgm1201zgmVRwKvriW9W7FJ89V49PJM0ixd4Zne3POzp784VA1eFc68muzCSkZSGDl4b+oefE3uJUObf8f2V7u2NraEhJ2lrRsLe4ae5wcbA2y7cXjRGhEHDfTspAXIiH+qG7MxqUaoGvhTg9qrMptKSjYClv1RwI5l07A2UPQ7GWEl6/+Oq0FdHeWxjmEAH8C9YCfjWNOxWLVHwnkSlgZGUfuH9vYEvknOc1eR1TWDYja24BW6pqN6h+s5IRGxPH+2mPkStDYStKO/kbuzctcavE6oqFus7LUzBxCI+IKbSWiNH40+WMKAt1UCHn7OkRvgieaIwIH6a9z19jrW2CKkpE/iC+B5b8f0+nq7ZfXhd58KbgAACAASURBVHRv1lyfVpYx+aQ0zuFTKWVS3pYXc4HBRrLJarl/TKG1jyuHw8OolnyW35p15k7rp6lsb0N6tlbNkCkjBdctiEvHqJ1ygjNVn0U0fbHQdckZOYSEnSU++GWlcSnI11abmw0x2yAnG9r1RdhX0l+jsRNM69LIXCZaJfWm7CL2VjoyX9fsTPB/C+Gg2wNKY4HvgxI7ByllUt7vu0KIocYzyXopuBvqpXOnOLJ8Efi25vozffV94qoGW3oKOt1Vf+QNiB77lcq+Tfi7dT9E5r3QIrYCnnnCjYSUDNWFVApCI+L493rdHBN56Tici4AWryE8fApd16a2G4c+bG8OE60S/3n7OByfDICMjYaL0fD0awiPe60DS52NWKoY0kKIqlLKJCml2QP9WBqhEXHczcrFMSOF64dWMNPNGxnwDsLGllypGwiND37Z3GZaHQW7kCZtjKTxhZ2cSJZUChxAGna429ggyEWiujrKSpWxW7ibI5F3b0HURqhWtKsDYHGv5qpiUwpCI+I4HJ+MTPkb/tgKPk0hcJBeVyc7wZ1pQWa28sGUyjkAPwDdjGGItRIaEcfYrae5eTsVjm5D5GQjW/XAw80VTwdbAv089S0GRenIdww5OdlwbCeJaSnc9f8HfXo2ZOvpazhKqe/eMMQW5Y8rqVk5cHwn3E2GNv8PUamy/pxqKZSe0Ig4Rmw8wd20dPhjCwibQl1z1qJpaZ1D0f2hH1Py+76llHBmL1y7AC1ex8bVGyGgS6NqFtlUtAbyHe6ttCzk+cNw+SQ064zwfILb3Bvo93F11NdkVY22HPy5H2o8iahWr9BhS+3usGS8greTlJYNf0VAwmlo2QXhem+LcCc7YRWOAUrvHEq2hWsFptAUyoTTun+sJ19ANOoA5G12kdeNpCg9oRFxDF1zDHn1PJz8Deo9U2iWjJujHUGNvVVrzIDkl918fN01XBj/0gOuVhSHvtzevAxHt0PdZxCB7xa6xtqcrWo5lBB9bTY9G5lyFRGzDarVg46DEMIGexvI0cJztdVgaGnJd7i1XB2JOHVe1xR3qwmB7yJsCq9ivjn5NTNZWXHxddcQeytdOYVy8N7KCN0CQYfKxS4QtJbWQkFK6xw+M4oVVkBI2FluJqfoXlx29vB8b30foo+roxpsLgchYWe5lJTMpbCtILXQ5g2Eg67fu2+rWvrplZXtSxryXFEalEMwANdjoelLCGfPQoetrbVQkFI5BynlCWMZYokk3c3CfvRmcnJz4cRuuH0VWgZh4+ROnwJ79KhWQumJvZmG/ejNtK7lws3InXDlArTognDxwsFWUM25kn7ab/t6nmrAWWHRiFqNC3231tZCQUo7ldULcAHipZTZhjYmb88mfyBRSrnc0M8vLZdTMsi5GAMXI6HJS4jmnS1ysYo1ciMtm5z4UxzefQgaBSAC7m3XNb9HYX1NsXe9QlEe2tR243B8coVwCvmUNJ7De0B7IB3dlt21hBApwNdSyoslTUwIUQ8YD7jmhQl1AhYCWUA4UEtKOV0IMaZ02TAc+fshvV4ti5TfloFjcwi8N+c7K1eqF1UZyZ/il3btMgFXtoPtk/r59LZ5o1lqKxGFNVJRHEJBStpyiJRSLil4IO/FXrM0iUkpLwCDhBBr8g79E1gjpdwshPgvBQIJmZr8lYwy7Tb8sZktzu583W8Ae4/pNsd7rrYbkZeSVdCdMtJ/ZTTLD/6pG7Nx0PBqvz7sPeWoP1+lkp0abFYoLAghZclmpwohPpFSzsr7/KSU8s8yJyrEmryWw2fAdilljBBiJbAIeB5IKK5byViBxJPuZnH+2m0O/baD9LupvNC5C14ebnhX0nI5TVDDpVKZgncbAkMFMzdXkPbYm2lcv53OofCd3Em+RbuXu1DF1Y3aThB/Vzf9zaYcAdLLgyEDxZtL3+IwZL4swQZL0hbMr6+ptH1ky0EI4QbMBp4SQmQAx4BBGCa852XAB90W4DZSyr08ZCtwQwcS778ymuVRl+GvQ5BwBpq/gqhXi7C/wMe1Esva29PjH4EGS68smCqYuaG19Z+3j4i4W3AhEi4dg6YvI+rXZvdFXf/sVz65vNEj0GDplQVTaQuG1/dhmDJflmCDKbUF8+trqvQf6RyklMnAO0KIV4DrQHNgXVkSE0J4AlOAlnmthnnAAiFEF2BzWZ5ZFvSrm/8+B6fCof5z+gUrbQquU8go8XCKogBewdtJijsHJ3dB3daIwEHYoFsgmD9gpwLtKBSWTUkHpD8FdgGfAmnAhLIkJqW8AQy777AhWiAlxit4O0nX8jbC8vAptNDq/jnJ4eHKOZQGr+DtJF2/BjFbwcUbOtzT9j9q0zaFwqoo6YC0B+ArpXxLCOGHLiqcVW3bXWXsFlLT03VOQUpo+//0e6lbw26Te/fu5dChQ9SsWZN+/foBsH79euLi4rhz5w61atXi0qVLtGzZkm7dTLs3ov+8fUScv6rbpz43G57tqd+8zU7Atz2Vvo8zxWn7ww8/6PX08/Nj06ZNtGjRgtdeU5MSSsOjtO3WrRtfffUVdevW5a233irVs0u65FQALwghvKWU5wGr2bK7/8poxMiNpB7dDYf+C08FINr0wtZBg/abrmi/6WrxLy6AiIgIxowZQ2Jiov6Yra0t58+fx9XVlfj4eD7//HM2bdpkMpvytY3YsQEOroSGzyPa6nb11NgJFvdqTtbXSt/HneK0Lajn+vXrcXJywsZGrYAvLY/Sdu/evTRr1qxMzy7RX0NKOUZK+QnwlhBiNXCmTKmZEK/g7diM2szyjTtgz/fg4YMIGIBw8UJjJ/hPr+ZGTX/EiBEkJCSQmZmp9+j5SCkJDg5m69atLF26lDNnisq5fv165s2bR1RUFFOnTi02jcTERObPn8/t27fp1KkT06ZNw8vLyyj5uR+v4O0s3xSm09a1OqLDO/rdJxf3as7daUFGdQqG0Hft2rXs3r3bIvU1J6bQtqCe165dY9CgQRw6dMgo+bEkTK1tZGQkR44cISIiotS2lnb7jLnoQoRaNDajNiNvJcLRX6FGQ/1CK1MG1/jggw9YsmQJ9erVK9Kci46OJiAggH379uHr68tTTz1V5P4ePXowfPhwTpw4we7duzlw4ADTpk2jVq1abNiwgS5duqDRaJg0aRIuLi5IKbG1tTV6l0e9Kbu4ePGirgupeoMiQWFM1UVnCH2XLVvGokWLWL16tcXoawmYQtuCemq1WqZOnUq1atVMlUWzYWpt/f39iY2NNb5zEEJ4SSmvlzoVE2I3fBUyb6EVL/RF2DlgJyDra+PNey6O+vXrk5iYyOnTp1m9enWhc35+fnz99dc0bNiQ/fv34+/vT6NGhWPySilJTk5GCEFOTg4BAQEEFNhiAmDAgAGFvnfoUHjrZUNjN3wVuTFbwd4R2vXRbzzoqbFD42Bn0hCohtA3NTUVDw8Pi9HXUjCFth06dCikZ7t27YyXIQvCHNr6+vri6+tbaltLuyvrF8D7pU7FBIgR6+FEGKTegJZBiMpugG7//+lBjR9xt3F48cUXuXLlSpG+VDc3tyIF437mzp3L22+/zeXLlwkODmbGjBnGNPWh9F1+hBXL/g9Sk3Sb4zm568+ZczC/vPp26tSJzp07m11fS0RpazysRduSTmX1AfyAmnmb45G3YM3sSCnZtm0b7F2qixZWtbb+nDk3wTpw4ADr1q3j+++/L9P9H3/8MaBb8DJ0qHkmhkkpses5CW1cjG47Yi9f/Tlzz/AyhL7h4eG0bt2a1q1bG9g660ZpazysSduSthzcAF+gSt5veMhKZlMhBi7Qxb71bYXoOFh/3MfV0aRdHMXRrl07q28qNxk+B60QhYKig/kdA1QMfS0Vpa3xsCZtS+Qc8uI4nBBCtJVSLjOyTSXHzkG/0Koibplrbs5o6iPq3vtuzYFLFApF6SjtmMM8o1hRRoRbDf1n5RAMT77DBeUYFIrHjVKtOpFSnjaWIZbI5s0m2+7JIm049GF7/UJBQzuGx11bY2IJ+bIEG4yFufNmqvStekmip8au0G9Ds2XLFqM819psMAaWkC9LsMEYWEK+LMEGY2HuvJkq/RK/VQ0Zz8FQXA9R+7AoFAqFMXhksJ+C8RyA5eTFc5BSmnQ31TxbrgNxJkyyKpBkwvSMaUMdKeUD935Q2pYbS9JXaWtczK2vSbQtUSQ4IYQGCMgzqDlwXUq5RQjhKKXMMJCRCoVCobAQSuoc5qCL1XIKSEG31qEusEJKecCYBioUCoXC9JQmhnQ1oAm62A5/5q19UCgUCkUFpDSzlaYCh6WUa9E5CIVCoVBUUErTcqgFfAPkADFSypnGNKw4qlatKsuyu2BZuXv3Lk5OTiZLz5g2REVFJT1sUE9pWz4sSV+lrXExt74m01ZKWaIf4AdgErAMeKKk9xnyp3Xr1tKU7Nmzx6TpGdMGIFIqbY1mgyXpq7Q1LubW11TalqZb6VMp5STgX+hiSJudpUuX8vrrrzNs2DA2bNhQrmfFxsYyatSoEl2r1WoZPXr0A226f5FKr169Sm1PeHg4CxYsKPV9hmLp0qW0aNECgOzsbOrUqVPs4puKqpuUkjGL11I7JIzQCMPPkjR12R04cCCpqanlSsdaMGTZfZx0u58SL4KTUibl/b4rhDDPHtLF8P777xMUpIvuFhcXx8yZM5FS4ufnR/fu3Rk4cCDPPfcc6enp1KhRg8OHDzNt2jS0Wi0rVqzg6tWrDB48mBo1dPs05eTkMH78eDIzM4mPj6d9+/bY2toCuoLi6+tLs2bNdNHQgNmzZxMfH09KSgqtWrXC2dmZn3/+mV9//RVvb2969OjBqVOnmDRpEqNGjcLZ2RnQ7c74xhtvcPLkSSZNmkRsbCybN2/m77//ZsKECfr8xcTEMHv2bPz8/LC3t0ej0RAZGcmPP/5o9JCVTz75JAcPHuTq1av4+/sDcPLkyUfqlpuby5w5cx6q2y+//MLGjRuNplt4eHiZdAuNiOOjH3eQ/r8tVHqyLVm1mhESdtYou9Aaq+yeP3+etWvX6v8GqampxMTEMHXqVHr37s3atWu5desWKSkpLF68mPfee48GDRqQkJBA9+7dCQgIYNCgQdStW5ft27fz+++/M3DgQNasWcOZM2dYvXo1I0aMYOLEiUgpqVKlCpMnTza4PuXBEGX3Qbr9+eefPP/88xVSt4KUqOUghJgohPg8/wcYn3/MyPY9koULFzJs2DCOHj3KwoUL0Wg0eHp6cvz4cQAaNmzIjBkzuHz5MsOHD2fMmDH8+uuvVKpUiYyMDLy9vVm+fLn+eWFhYcTGxuLm5kZ6ejoJCQmF0hsyZAg9e/bUf9+/fz+zZ8/mtdfurdZ+5ZVXWLBgASdOnKBp06Y0btyYSZMm6V9wABqNho8//pj333+fZcuW4eDgQFZWFpUrV2bdunWF0mzTpg2zZs0iOjqaiRMn0rt3b/bv329QHYujV69erF27lh07dtC5c2eAEumWmpr6SN2OHz9ucbqlpaUxOvhL0s8egQ4DyarVDDdHXYQ7Y2Cssuvs7Fzob+Ds7EyLFi0YN24ctWvXJjY2ljlz5hAYGMjOnTsBGDx4MNOnT2ft2rWEhYXRuXNnJk+eTPXq1Yu1feXKlaSnp+Pu7s6FCxfIysoyikZlxRBl90G6Pf300xatW2hEHJ7Bv+I+fmu5Wr2PbDkIISoBPwHZgChwyhkwe3urYO3rp59+ol+/fjRv3hzQNRtdXFwAXcFwcXHBwcGBzMxM5s6dy+jRo5FSMnHivV4yrVZLu3bt+PDDDwkPD6d27dqF0nN1dS30PT/OQcF4B/nXFHcun5ycHEDX7BVCMH36dFatWsXBgwfZs2dPoWvz8+Dp6YkQQp8HY6PRaACoXr26PmpVSXQrDkvXbevWrfz222+Mff9dJh9JJi1bC4BzJTujxa4wVtlt3rw5gYGBhdK6P+oYFNbXyckJOzs7MjMzkVIW+Rvk33/37l19Wl26dLHYmNqGKrvWqNuoZbu4/b+t8OQLhIQ5lLn8lqRbyQP4AN0iuP3olm4/DdyWUo4vU6oPQAjRBugAXJNSLi3t/cOHD2fcuHHUqFGDKlWqFIkBXJCOHTsyffp0vL29Cx1/5ZVXGDZsGKNHj+b06dO0bdsWR0fHBz7nhRdeYMSIEdy4cYNOnToVe423tzdjx45lwoQJ+lqwVqtl/PjxnDt3jtmzZ1O5cmUmTpzI3bt3cXd3L/Y55mDGjBkIIVi2TBfGoyS6JScnM3/+/Ifq1qxZM4vQLS4ujnnz5tGpUydmztRNwBvbQ1f7Cgk7a7RWw/0YsuwePHiQFStWFPob+Pv78+mnnzJs2DDq1KnDyJEjuXnzJosXL2bNmjWFntO5c2cGDx7MuXPnSExMxNHRkVdeeYVx48aRnZ2Nk5MTffv2Zfjw4ezbt4+srCzmzp1reFHKiSHKbnG6nTp1ii+++MLidLt27RrffvstOeevQrs+2Ng5lK/8Pmik+v4fwBbdi/vpkt5TzDPqAd8Da/K+OwH/B4QCffKOTQMGFne/Jc5K2L17t/zss8/kgAEDZFJSUomf3bNnT4PZUBKwsBkfM2fONKpuD6PfiihpO2KtfGvgYPnVV1/JtLS0cj/TkvQ1RJn55ptv5KhRo+SXX35ZbhvS09Plmx9/LmuN3yiXHIot9bMsSVspH65veXUrTfpLDsXKJ77cKef/dkr2Gj5OunTqL2f/GqM/XhKtH6Ztidc5GBIhxBopZS8hRD8gWUq5WQjxXynlm3nn/y2lnH//fdWrV5cFuyeCgoLo2rWr0exMTU0t1N9tDgxlQ8eOHaOklM886PzjoG3szTRupGVz7uQxTkYf4Z9dX6Nl08YGebYl6WsJ5fZWym3iUyWnIw9w7txftO30Ch7VquNga0OzGlVK9SxL0hbMr++N5NvEpUpyc3OJ3L+H638n4v/iq3hUrVZqfR+mrXECIZQcH+B43udcIUQ3oBVwpdiLfXyIjIw0lW2Eh4cX6bs1BaERcXy54zSfv9KIBlw0iQ0VXdvQiDje2xkOURuhegNoOoTubtJkNphSX3OV23xCI+KI2b2ChVsjEE+2g6faE3PLHqe7tgS/3JBAA4/hVPSyez8zl29g1NpIuHwCGndEPNmZ8MtgmwALezY3mL7mdg6X0TmIGMBGSrkJ2GRek8xLjzmb2fDT99C4IyFhtixrb29uk6ya0Ig4Pl5/lLSo7ZB+G/zfRDhUBsDJwapjXVkkk3/ew+cz5tKnbQPoOJjKDrZ4VNb1fRtrYP9x4vDhw+z471JsNK14rv9Izl6/S0Z2LpXsbJge1NigGpvUOQghPIEpQEshxGfoYlIvEEJ0ASpuXMFH0H9lND8dvoDdsa3kaIF2fRH2lXSDSRkXzW2e1REaEcfYrae5nZFNTsIZOLMXmnVGePnqr1ncqzkNlLYGIykpiQULFjD3f9eQL/SnxTO2bD1jb/AX1uPK5F/CmTJrATbe9fhx6DB2dn3V6Gma1DlIKW8Aw+47bPKgQZZCaEQcX+78k4SocGTCabJbBeHkWZ3MHC1vt6zFkLZ1CA9XL7DSMnzdMbLu3tZ1IbnXgo6DEULXSnBztNO/sJS2paPgDK4hbevoyu+2E9ie2EH830k4PhtErz7dCD9/gzrukpuTO5vbZKtn5rYovpwxm+xKVcho8zbCzoGrqdkmSdvc3UqPLf1XRrM87DAc245jwzZkBb6Lo51g9j+aqppWOej3UyRZx3ZByt/QujtCc29wrk1tNw592N6M1lk3IWFnuZySQUjYWaSUDJvyLfLvv+DpVxH1apIBhJ+/QXzwy4SHh5vbXKtm/m+nGDn5G7KzsqBFFzzc3dGgm11aw8XWJDYo52BC8rs70lPvkH5kA1RyhoCBzHuztXII5cB/3j4Oxycjr12EE2HQKBDR9CX9ee03xpu58jgR/HJDQsLO8la1FEZ+MgJZpy0iMAB7G8jRgqOdMNm6kIrA/S0x0C3yXLp0KeOX7SL7qc4IF91WL9O6NNJfYyrHq5yDCch3CjfTsuDM75AUBy27Ipw90NgJ5RjKSL0pu4i9lY7MTIOoDeDkAYGDEDb3alZ9W9Uyo4UVi45Vs/kzZQetGjzDNzNnMW77n0gp1bhCGSnYEhvcpjYbN25k7969VGnVGfsOA3HIzCZHC31a1TKLvso5GJnQiDiGrjmGvHYBTuyCJ19ANApEY2ejn2GgKD1Vxm4hNVsLf+6DaxehdTeE070V0n1b1WJZ71ZmtLDicOPGDQaOCWF/YhZTxnzEWx2eBGDo83XNbJl1E+jnyao/EnC5+Rcuz/+HIb17MWvWLGqHhHErPQMfV0fig182m33KORiY/KZioJ8n645f4e7tFIjeCFW89LVajZ0Nd6d1MbepVkloRBzD1hxDe/MyHN0O9f0RAQH688opGI7MzExeGDKBqL8u4/hMEBmNXZi2N57385yDomQU130UGhHHT7sOI4/t5KS3Hzw/gPnxNjSKiNN335m7i045BwOT31RcHhkPJ3bDnWvQqhtC46K/Zk73pma00HqpMnYLqekZEL0J7CtBwDsI23tF2NddoxxDGSn4AhvcpjbDpixk1fZwUp98EZ4PIBPwcXU0+wvLGinYfQTw6X8PkByxESq7wQv9EHa6tUy5UndtfPDLFtFNp5yDAQmNiONySgYy8Qyc2QdNOiGa35vOp7ETzOnezCL+8NZE/5XR/BSdgDx/BC4d1zlbl3sxGXzdNVwY/9JDnqB4FPkvsA+//YWhH+zF4annyfLvT2V7G/3UauV4y0Z+S6BdLQ3DPp2IVpurm0nneG8Ljja13UhIybAo56ucg4HwCt5OUtJ1Xa3Ws3be3Hrdtr0F59YrSs/y8BidrnVaIAIH6Y/bAtlqJlK5CY2I4/bfl7D93xYyvOpB4CCyhY2+paDKbfkY2Lomn02bw+otl+Hp1wpVbCy5wqicQxkJjYhjxMYTpGVrkbk5cPRXyEyF595AVKqsv25xr+YW+Ye3Kmzt9KvG8/HU2HE95LWH3KQoCTdv3uSjMRNIt6kEz/dB2DkAuhkyqqVQPqSUbNiwgfGha7nh1QZRX7c1fZvabkReSrb41phyDqWkoFMAkHFH4cIRaP4qwvOJQtcqx2AYCs5CArVuobyERsQxZuNRUo5sRZt2G1p2QVTW7WpqK3Sbt6lyWz4OHDjAxHnfc8ThSVIbv6mPkmYrsJqFmMo5lIL8aakA8vY1iN4CtZ6CwMFFopb1NdPc5IrI4l7Ni8z2UJSNJYdi+ddX/0Gb+Cc0fwXh4QPoQjy6qu7PcqHf9eD4Duo3bcHFJ/6BtkAk5vwuJGtBOYdSMGLjCWROFvyxFaQW2vUp1NUhgEWqtWBwhrStozQtA/dPoXzti6X8unEdNPAvNHZjJ+Bb1VooF1euXGH5vOng7AHt+3PeVjcDyZqdrnIOpeDuxRPw1yFo0QXhVjiAuJpfr7Ak8teDSOC973by3r+3Q7W60HEQQtjg664h9la62m+qnNy5c4c6vUZwKy0Tnu2BqOQEgL0NeFex7gF9i3IOQogAwB9IlFIuN7c9RfCuh/ApvKJZOQWFJRISdhZtRir8sQUcNPB8b30rV2Nno6b+lpHQiDjSEm/z+uiNiJO7cMtJ4VbdTogqnoCupeBowTOQSoOp4znUA8YDrnlhQp2AhUAWEA7UklJOF0KMMaVdJaWqizM30nP039WAs8JSKNiFJK6mcCl8O6Ql5w02uwG67iPnSnZqy5Yy0n9lNMujLtMv+wDpB/+Epi+TXrW2frC5oq23MXU8hwvAICHEmrxD/wTW5MeQBqJMaU9pUVMnFZZKSNhZLiWn897URQTknILaryI8dYPNbo523Jysym5ZCY2I49/rj5MZdwLOHcT51TaIwEG4OdrxZDVnq5iWWhaElNL0iQqxJq/l8BmwXUoZI4RYCSwCngcSiutWetwCiRvSBhWk3bg2mEvf2Jtp3EjLJu6vs/xxaC9NW7ch8JlmXE7TnRdAbXcNVZ0cyp1Waago2t5My+ZqwiUifg+jjt+TNH/ueZ5wEiSkQ6taro9+iBEwlbbmHnO4P4b0XmDvgy5+3AKJm9IGpa1xMYa+/VdGs3zPOYjZDl6+0HQwB7JsqJ+m5ajNE2atyVqztvrtWlJvQMw2cPaEZgMQtvZwFGa21FLFrwWBZupSNpW2Koa0QmFl1Juyi4tXrusGm+0dCw02+7praO1jx8jAitXFYSr85+0j4lwixGwFBDzbE1GpcqEpqQ0yLprNMZgSFUNaobAS6k3ZxcWk23A8DO7ehBZdsHFyx7bAQLMuNna4uU21OryCt5OUmq7TNvUmtHgN4aybgXT/3miPS+xxc3crKUrI3r17OXToEDVr1qRfv34A/PDDD1y6dImWLVvSrVs3vvrqK+rWrctbb71lZmutj0fp6+fnx6ZNm2jRogWvvWb6wV3nMZu5e+YQJJyCZp0Rnk9YzeyY4rRdv349cXFx3Llzh6ZNm+o/BwcHm9Q2r+DtJKVlw9kDcOUsNHsJ4VkbsI79ux6l7T//+c8yl1ubR1+isAQiIiIYM2YMiYmJ+mPx8fF8/vnnbNq0ib1799KsmfUszbc0HqXv+vXrcXJywsbGdP8y/VdGYzNqM6LPTO7uCoVKlRGBgxCeT9C3VS2rcAxQvLa2tracP38eV1fXQp9Nhf+8fdiM2kzS2RgI/w6c3BGB7+odw+JezS3eMcCjtS1PuVXOwUiMGDGChIQEMjMz9R49HyklwcHBbN26laVLl3LmzJki969fv5558+YRFRXF1KlTi02jU6dOTJs2DS8vLyIjIzly5AgRERFGyY+lYQh9165dy+7du0uk77Vr1xg0aBCHDh0ySn7ux3/ePpbviUb+/iPcStBFEaz9NGD8hZem0DYxMZH58+dz+/btQp9NgePozUREH0OGfw/pt3V7o/k0wVNjh/abrmi/6Wq09Uum1rY8/EeuJQAABkNJREFU5VZ1KxmJDz74gCVLllCvXr0i3TzR0dEEBASwb98+fH19eeqpp4rc36NHD4YPH86JEyfYvXs3Bw4cYNq0adSqVYsNGzbQpUsXpJTY2trSrVs3/P39iY2NfWycgyH0XbZsGYsWLWL16tWP1Fer1TJ16lSqVatm9LyJ4at0g812lfSDzQ4CqrmYZjsGU2ir0WiYNGkSLi4uhT4bkypjt5B6KwmObtOF7W0/QB9J0FTbiJha2zfffLPM5VY5ByNRv359EhMTOX36NKtXry50zs/Pj6+//pqGDRuyf/9+/P39adSoUaFrpJQkJycjhCAnJ4eAgAACCsRKBujQoQMdOnTQf/f19cXX19doebIkDKFvamoqHh4eJda3Xbt2RsuPzajNurggx3fqBkRbdtFvVW7qlfim0HbAgAFGz0dBfIM3knpgDQhbeK4nwuFezBVTbgFvDm3LWm6VczAiL774IleuXCnS3+fm5lakYNzP3Llzefvtt7l8+TLBwcHMmDHDmKZaJeXVt1OnTnTu3Nns+kopkeciIOEkNHtZ3+8N5tv6vaJoW/eL7cTu25Q3A+l1hLOH/py59kWzFm2VczASBw4cYN26dXz//fdluv/jjz8GdAtehg4dakjTKgSG0Dc8PJzWrVvTunVrA1tXckTfWXAqHPyeLbSNNpjv5VURtNVqtbj1Gsed+D/zZnf5FDpvrn3RrElb5RyMRLt27YzaDfG4Y+36xsXFsXDhQriRCYHvImxsLSbCnbVrC+AXsoM7jp6IwHcLHTe3xtakrXIOCoWJEf9eDcd26ro5mlR69A2KUhN3JwdRUzegaw3rFSwR5RwUChMjKjnBsz0A89dkKyr5wYysZaGgJaLWOTyEzZvNv92TJdhgDCwhX5ZggzGwhHyZ24YL419C+01XozgGc+fNVOkr5/AQtmzZYm4TLMIGY2AJ+TKXDfkLrYzVanictTUF5s6bqdJXzkGhUCgURTBLsJ+yIoS4DsSZMMmqQJIJ0zOmDXWklF4POqm0LTeWpK/S1riYW1+TaGtVzkGhUCgUpkF1KykUCoWiCMo5KBQKhaIIyjkoFAqFoghqEVwJEEIEAP5AopRyuZlsaAN0AK5JKZeawwZjoLQ1Hkpb41LR9VUtB0AIUU8I8b0QYk3edychxP8JIUKFEH2AtlLK6UBNc9kgpTwMeDziMRaH0tZ4KG2Ny+Our3IOgJTygpSy4JaY/wTWSCmHAN0sxQYp5VigiinsMRRKW+OhtDUuj7u+qlupeHyA43mfc4EIIcRYIMFcNgghugGtgCsmtMEYKG2Nh9LWuDxW+irnUDyX0f0RYgAbKeVeYK+ZbdgEbDKxDcZAaWs8lLbG5bHSV3UrAUIITyHEIqClEOIzYB3QU/z/du7dNYooiuP492iiaGWjYmUlVoKiNv4DgtgFxEoCPkDFQiMWgiAaxAcKEvHZiBG0CAiWNjaBlD4JiAgK4hNNEUIQNcdiJpDs3Y02a7b4fppZljN3z53i/mZY5kZcA/7LLled0EM7dMK8OqGHduiEeXVCD+3SCXObzx58Q1qSVPDJQZJUMBwkSQXDQZJUMBwkSQXDQZJUMBwkqRYRvRHxtP7cHRHvImJ7G35naD7P/xe+BCdJs72KiC3ASmAEICJWA31AAG+Am8BxYBnwLDNvRcQw1XsIm4CjmfmhPndpYy2wIiJOA2uBw8Beqm0xXkbE/czcGREvgNvARmA3sBnYB7xuNW5jD0A30A98AR4AE0Av1do/kpl3W10EnxwkabYhoAfYCjyqvzsATALfgHVAUi2w34Eddc14Zl4C7lHtlDqtWe3vzDwBXAB2tejjfWZepAqo9cB+YA9wfY5xG3s4CJzKzL7MHAaOAGPAV2DDXBfBJwdJmm2yPn4CpurPC4DBzHwOEBE9wGhm3omIx3XNRH38CSyeMd62JrU545jAD6ArIgJY0mK8KeBXXdtq3MZzYsYcABYBlzNz7G8XwXCQpNIxqkV7+q7+CnAmIj4C48AgcDYiVgEL/zLWkya1XRHRD6yhuptfDhwCRqkW9GZuAOepQqvVuI2uAifrvh8C54CBiPgMvM3MgVZNu32GJKngfw6SpILhIEkqGA6SpILhIEkqGA6SpILhIEkqGA6SpMIfKSrlY3mJF+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 396.85x180 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_experimental_data(large_setup('dx'), composition='dx', labels = 'in')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:10:14.304906Z",
     "start_time": "2020-02-18T20:10:14.272869Z"
    }
   },
   "source": [
    "## Neutrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:15:57.301361Z",
     "start_time": "2020-02-18T20:15:57.266683Z"
    }
   },
   "source": [
    "Most of the experimental data is neutral for both neutrality measures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-02-18T20:15:34.648807Z",
     "start_time": "2020-02-18T20:15:34.071100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396.85x129.6 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neutrality = pd.read_csv('results/experimental/neutrality.csv', index_col=0)\n",
    "\n",
    "keys = ['David_stool_A', 'David_stool_B',\n",
    "        'plankton_bacteria', 'plankton_eukarya',\n",
    "        'Caporaso_F4_feces_L6', 'Caporaso_M3_feces_L6',\n",
    "        'Caporaso_F4_L_palm_L6', 'Caporaso_M3_L_palm_L6',\n",
    "        'Caporaso_F4_R_palm_L6', 'Caporaso_M3_R_palm_L6',\n",
    "        'Caporaso_F4_tongue_L6', 'Caporaso_M3_tongue_L6']\n",
    "\n",
    "titles = ['Stool A', 'Stool B', \n",
    "          'Plankton \\n bacteria', 'Plankton \\n eukarya',\n",
    "          'Female \\n feces', 'Male \\n feces',\n",
    "            'Female \\n left palm', 'Male \\n left palm',\n",
    "            'Female \\n right palm', 'Male \\n right palm',\n",
    "            'Female \\n tongue', 'Male \\n tongue']\n",
    "\n",
    "neutrality = neutrality.loc[keys]\n",
    "\n",
    "fig = plt.figure(figsize=(ELIFE.TEXTWIDTH,1.8)) #, tight_layout=True)\n",
    "\n",
    "gs1 = gridspec.GridSpec(2,1,hspace=0, left = 0.15, bottom=0.75, top=0.95, right=0.95)\n",
    "gs2 = gridspec.GridSpec(1,2,wspace=0.2, left = 0.15, top=0.2, bottom=0.1, right=0.95)\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",
    "# KL\n",
    "\n",
    "KL = np.log10(neutrality['KL'].values.astype(np.float64))\n",
    "KL = KL.reshape([1, len(KL)])\n",
    "KL[np.isinf(KL)] = 3.0\n",
    "mat_KL = ax_KL.matshow(KL, origin='lower', cmap='Blues_r', aspect='auto', vmin=-1, vmax=3)\n",
    "\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",
    "\n",
    "\n",
    "# NCT \n",
    "\n",
    "NCT = np.log10(neutrality['NCT'].values.astype(np.float64))\n",
    "NCT = NCT.reshape([1, len(NCT)])\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(NCT, 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_xticks(range(len(NCT[0])))\n",
    "ax_NCT.set_xticklabels(titles)\n",
    "\n",
    "ax_NCT.set_yticks([0])\n",
    "ax_NCT.set_yticklabels([r'log$_{10}$($p_{NCT}$)'],)\n",
    "\n",
    "# Set ticks on both sides of axes on\n",
    "ax_NCT.tick_params(axis=\"both\", bottom=True, top=False, \n",
    "                   labelbottom=True, labeltop=False, left=True, labelleft=True)\n",
    "ax_clb_NCT.set_title(r'log$_{10}$($p_{NCT}$)')\n",
    "\n",
    "# Rotate and align bottom ticklabels\n",
    "plt.setp([tick.label1 for tick in ax_NCT.xaxis.get_major_ticks()], rotation=90,\n",
    "         ha=\"right\", va=\"center\", rotation_mode=\"anchor\")\n",
    "plt.setp([tick.label1 for tick in ax_NCT.yaxis.get_major_ticks()], rotation=0,\n",
    "        ha=\"right\", va=\"center\")\n",
    "\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_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",
    "plt.show()"
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": null,
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