getting_paperplots_inoneplace.ipynb

{
 "cells": [
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import interfacecosmology.halomassfunction as hmf\nfrom interfacecosmology.interfaces import FCPL\nimport interfacecosmology.psutils as psu\nfrom interfacecosmology.interfaces import FCPL\nimport interfacecosmology.massfunctions as mf\nimport interfacecosmology.hacc as hacc",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "from lsscosmo import halomassfunction as hmf\nfrom lsscosmo import psutils as psu\nfrom lsscosmo import hacc",
   "execution_count": 2,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import os",
   "execution_count": 3,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import numpy as np",
   "execution_count": 4,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import utils.plotutils as pu",
   "execution_count": 5,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import camb_utils.cambio as cio",
   "execution_count": 6,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "#import seaborn as sns\n#sns.set_style('whitegrid')\n#sns.set_context('paper')",
   "execution_count": 7,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import FormatStrFormatter\nimport matplotlib.ticker as ticker\nimport matplotlib.gridspec as gridspec\n#import massfunctionlib.massfn_lib as ml\nimport os",
   "execution_count": 8,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "datadir = '/Users/rbiswas/doc/projects/universalityofnuetrinomassfn/doc/data/'",
   "execution_count": 9,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import os",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import pandas as pd",
   "execution_count": 11,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "def dataframe(fname, datadir):\n    fname = os.path.join(datadir, fname)\n    data = np.loadtxt(fname)\n    with open(fname, 'r') as fh:\n        header = fh.readline()\n    #print(header)\n    cols = header[2:-1].split()\n    df = pd.DataFrame(data, columns=cols)\n    return df",
   "execution_count": 12,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "!pwd",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "text": "/Users/rbiswas/doc/projects/universalityofnuetrinomassfn/doc\r\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df = dataframe('M000_0.dat', datadir)\ndf.head()",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 14,
     "data": {
      "text/plain": "    MininvhMsun   dn/dlnM  dndlnM_fit(B),  dndlnM_fit(M),     massfnerr  \\\n0  3.484101e+12  0.001069        0.001073        0.001065  1.027312e-06   \n1  4.732952e+12  0.000808        0.000813        0.000807  8.935818e-07   \n2  6.423003e+12  0.000612        0.000615        0.000610  7.778197e-07   \n3  8.715419e+12  0.000460        0.000463        0.000459  6.745529e-07   \n4  1.183044e+13  0.000348        0.000347        0.000344  5.862792e-07   \n\n    massfnerror  \n0  1.026325e-06  \n1  8.925950e-07  \n2  7.768329e-07  \n3  6.735662e-07  \n4  5.852925e-07  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MininvhMsun</th>\n      <th>dn/dlnM</th>\n      <th>dndlnM_fit(B),</th>\n      <th>dndlnM_fit(M),</th>\n      <th>massfnerr</th>\n      <th>massfnerror</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.484101e+12</td>\n      <td>0.001069</td>\n      <td>0.001073</td>\n      <td>0.001065</td>\n      <td>1.027312e-06</td>\n      <td>1.026325e-06</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.732952e+12</td>\n      <td>0.000808</td>\n      <td>0.000813</td>\n      <td>0.000807</td>\n      <td>8.935818e-07</td>\n      <td>8.925950e-07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>6.423003e+12</td>\n      <td>0.000612</td>\n      <td>0.000615</td>\n      <td>0.000610</td>\n      <td>7.778197e-07</td>\n      <td>7.768329e-07</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.715419e+12</td>\n      <td>0.000460</td>\n      <td>0.000463</td>\n      <td>0.000459</td>\n      <td>6.745529e-07</td>\n      <td>6.735662e-07</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.183044e+13</td>\n      <td>0.000348</td>\n      <td>0.000347</td>\n      <td>0.000344</td>\n      <td>5.862792e-07</td>\n      <td>5.852925e-07</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "!pwd",
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "stream",
     "text": "/Users/rbiswas/doc/projects/universalityofnuetrinomassfn/doc\r\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "#fig, ax = plt.subplots(figsize=(6.4, 4.8))\n#fig = plt.figure(figsize=(8, 6))\nfig = plt.figure(figsize=(8, 6))\nax = plt.gca()\ndf = dataframe('M000_0.dat', datadir)\nax.errorbar(df.MininvhMsun, df['dn/dlnM'],\n            yerr=(df['massfnerr'],df['massfnerror']), fmt='ks',\n            label='z=0')\ndf = dataframe('M000_1.dat', datadir)\nax.errorbar(df.MininvhMsun, df['dn/dlnM'],\n            yerr=(df['massfnerr'],df['massfnerror']), fmt='ro',\n            label='z=1')\n\ndf = dataframe('M000_2.dat', datadir)\nax.errorbar(df.MininvhMsun, df['dn/dlnM'],\n            yerr=(df['massfnerr'],df['massfnerror']), fmt='bd',\n            label='z=2')\n\n\nax.grid(True, ls='dotted')\nax.set_xscale('log')\nax.set_yscale('log')\nax.set_xlim(10**12, 10.**15)\nax.set_ylim(10**(-10), 10**(-2))\nax.set_xlabel(r'Mass ($h^{-1} M_\\odot)$',fontsize=28)\nax.set_ylabel('$dn/d\\ln{(M)}$ ($h^{3} Mpc^{-3}$)',fontsize=28)\nxtl = ax.get_xticklabels()\nytl = ax.get_yticklabels()\nplt.setp(xtl, visible=True, fontsize=20)\nplt.setp(ytl, visible=True, fontsize=20)\nplt.legend(loc=\"best\",numpoints = 1)\nplt.tight_layout()\nfig.savefig('lcdm_mf.pdf')",
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 576x432 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.head()",
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 17,
     "data": {
      "text/plain": "    MininvhMsun   dn/dlnM  dndlnM_fit(B),  dndlnM_fit(M),     massfnerr  \\\n0  3.471196e+12  0.000420        0.000420             NaN  6.441439e-07   \n1  4.712025e+12  0.000269        0.000271             NaN  5.156973e-07   \n2  6.387624e+12  0.000170        0.000170             NaN  4.099448e-07   \n3  8.664869e+12  0.000104        0.000103             NaN  3.203773e-07   \n4  1.175200e+13  0.000061        0.000060             NaN  2.457607e-07   \n\n    massfnerror  \n0  6.431571e-07  \n1  5.147106e-07  \n2  4.089580e-07  \n3  3.193905e-07  \n4  2.447740e-07  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MininvhMsun</th>\n      <th>dn/dlnM</th>\n      <th>dndlnM_fit(B),</th>\n      <th>dndlnM_fit(M),</th>\n      <th>massfnerr</th>\n      <th>massfnerror</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.471196e+12</td>\n      <td>0.000420</td>\n      <td>0.000420</td>\n      <td>NaN</td>\n      <td>6.441439e-07</td>\n      <td>6.431571e-07</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.712025e+12</td>\n      <td>0.000269</td>\n      <td>0.000271</td>\n      <td>NaN</td>\n      <td>5.156973e-07</td>\n      <td>5.147106e-07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>6.387624e+12</td>\n      <td>0.000170</td>\n      <td>0.000170</td>\n      <td>NaN</td>\n      <td>4.099448e-07</td>\n      <td>4.089580e-07</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.664869e+12</td>\n      <td>0.000104</td>\n      <td>0.000103</td>\n      <td>NaN</td>\n      <td>3.203773e-07</td>\n      <td>3.193905e-07</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.175200e+13</td>\n      <td>0.000061</td>\n      <td>0.000060</td>\n      <td>NaN</td>\n      <td>2.457607e-07</td>\n      <td>2.447740e-07</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df['dndlnM_fit(M),']",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "text/plain": "0    NaN\n1    NaN\n2    NaN\n3    NaN\n4    NaN\n5    NaN\n6    NaN\n7    NaN\n8    NaN\n9    NaN\n10   NaN\n11   NaN\n12   NaN\n13   NaN\n14   NaN\n15   NaN\nName: dndlnM_fit(M),, dtype: float64"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.head()",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 19,
     "data": {
      "text/plain": "    MininvhMsun   dn/dlnM  dndlnM_fit(B),  dndlnM_fit(M),     massfnerr  \\\n0  3.471196e+12  0.000420        0.000420             NaN  6.441439e-07   \n1  4.712025e+12  0.000269        0.000271             NaN  5.156973e-07   \n2  6.387624e+12  0.000170        0.000170             NaN  4.099448e-07   \n3  8.664869e+12  0.000104        0.000103             NaN  3.203773e-07   \n4  1.175200e+13  0.000061        0.000060             NaN  2.457607e-07   \n\n    massfnerror  \n0  6.431571e-07  \n1  5.147106e-07  \n2  4.089580e-07  \n3  3.193905e-07  \n4  2.447740e-07  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>MininvhMsun</th>\n      <th>dn/dlnM</th>\n      <th>dndlnM_fit(B),</th>\n      <th>dndlnM_fit(M),</th>\n      <th>massfnerr</th>\n      <th>massfnerror</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.471196e+12</td>\n      <td>0.000420</td>\n      <td>0.000420</td>\n      <td>NaN</td>\n      <td>6.441439e-07</td>\n      <td>6.431571e-07</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>4.712025e+12</td>\n      <td>0.000269</td>\n      <td>0.000271</td>\n      <td>NaN</td>\n      <td>5.156973e-07</td>\n      <td>5.147106e-07</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>6.387624e+12</td>\n      <td>0.000170</td>\n      <td>0.000170</td>\n      <td>NaN</td>\n      <td>4.099448e-07</td>\n      <td>4.089580e-07</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>8.664869e+12</td>\n      <td>0.000104</td>\n      <td>0.000103</td>\n      <td>NaN</td>\n      <td>3.203773e-07</td>\n      <td>3.193905e-07</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.175200e+13</td>\n      <td>0.000061</td>\n      <td>0.000060</td>\n      <td>NaN</td>\n      <td>2.457607e-07</td>\n      <td>2.447740e-07</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "def plotratios(ax, df, text, showxticks=False, label=r'$\\Lambda CDM$', showlabel=False, showMice=True):\n    if showlabel:\n        ax.errorbar(df.MininvhMsun , df['dn/dlnM']/df['dndlnM_fit(B),'],\n                    yerr=(df.massfnerr/df['dndlnM_fit(B),'], df.massfnerror/df['dndlnM_fit(B),']),\n                    fmt='ks', label=label)\n        if showMice:\n            ax.plot(df.MininvhMsun, df['dndlnM_fit(M),']/df['dndlnM_fit(B),'], 'r-',lw =2.0, label='MICE fit')\n    else:\n        ax.errorbar(df.MininvhMsun , df['dn/dlnM']/df['dndlnM_fit(B),'],\n                    yerr=(df.massfnerr/df['dndlnM_fit(B),'], df.massfnerror/df['dndlnM_fit(B),']),\n                    fmt='ks')\n        if showMice:\n            ax.plot(df.MininvhMsun, df['dndlnM_fit(M),']/df['dndlnM_fit(B),'], 'r-', lw =2.0)\n\n    pu.drawxband(refval=1.0 , bandwidths=[-0.1, 0.1], ua=ax, xlims=(1.0e12, 1.0e15))\n    ax.text(1.2e12,1.05,text, fontsize=20)\n    ax.set_xlim(1.0e12, 1.0e15)\n    ax.set_xscale('log')\n    ax.set_ylim(0.75, 1.25)\n    ax.grid(True, ls='dotted')\n    xt = ax.get_xticklabels()\n    plt.setp(xt, visible= True, fontsize=20)\n    if not showxticks:\n        plt.setp(xt, visible= False)",
   "execution_count": 20,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "def threepanel():\n    import matplotlib.gridspec as gridspec\n    fig = plt.figure(figsize=(8, 6))\n    gs  = gridspec.GridSpec(3,1, height_ratios=(0.33, 0.33, 0.33))\n    ax0 = plt.subplot(gs[0])\n    ax1 = plt.subplot(gs[1])\n    ax2 = plt.subplot(gs[2])\n\n\n    return gs, fig, ax0 , ax1, ax2",
   "execution_count": 21,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "gs, fig, ax0, ax1, ax2 = threepanel()\n\ndf = dataframe('M000_0.dat', datadir)\nplt.sca(ax0)\nplotratios(ax0, df, 'z=0', showlabel=False, label=r'$\\Lambda CDM$')\n\ndf = dataframe('M000_1.dat', datadir)\nplt.sca(ax1)\nplotratios(ax1, df, 'z=1')\n\ndf = dataframe('M000_2.dat', datadir)\nplt.sca(ax2)\nplotratios(ax2, df, 'z=2', showxticks=True, showlabel=True)\nax2.set_xlabel(r'Mass ($h^{-1}M_\\odot$)', fontsize=28)\nax1.set_ylabel(\"massfn/fit\", fontsize=28)\nax2.legend(loc='lower left', numpoints=1, prop={'size': 12})\ngs.tight_layout(fig)\ngs.update(hspace=0, wspace=0)\nfig.savefig('lcdm_ratio.pdf')",
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 576x432 with 3 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "gs, fig, ax0, ax1, ax2 = threepanel()\n\nlabel=r'$\\nu\\Lambda CDM$'\nsourcedlabel=r'Fit, $\\delta_{\\nu}\\neq0$'\ndf = dataframe('M000n1_499_smoothnunosourced_0', datadir)\nplt.sca(ax0)\nplotratios(ax0, df, 'z=0', showlabel=False, label=label, showMice=False)\n\ndf_fit = dataframe('M000n1_499_smoothnusourced_0', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\n    \ndf = dataframe('M000n1_247_smoothnunosourced_1', datadir)\nplt.sca(ax1)\nplotratios(ax1, df, 'z=1', showMice=False)\ndf_fit = dataframe('M000n1_247_smoothnusourced_1', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\nax1.plot(x.MininvhMsun, x['dndlnM_fit(B),_fit']/x['dndlnM_fit(B),'], color='r', lw=2)\n\n\n\n\ndf = dataframe('M000n1_163_smoothnunosourced_2', datadir)\nplt.sca(ax2)\nplotratios(ax2, df, 'z=2', showxticks=True, showlabel=True, showMice=False, label=label)\ndf_fit = dataframe('M000n1_163_smoothnusourced_2', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\nax2.plot(x.MininvhMsun, x['dndlnM_fit(B),_fit']/x['dndlnM_fit(B),'], color='r', lw=2, label=sourcedlabel)\n\n\nax2.set_xlabel(r'Mass ($h^{-1}M_\\odot$)', fontsize=28)\nax1.set_ylabel(\"massfn/fit\", fontsize=28)\nax2.legend(loc='lower left', numpoints=1, prop={'size': 12})\ngs.tight_layout(fig)\ngs.update(hspace=0, wspace=0)\nfig.savefig('nulcdm_ratio.pdf')",
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 576x432 with 3 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "gs, fig, ax0, ax1, ax2 = threepanel()\n\nlabel=r'$\\nu DDE$'\nsourcedlabel=r'Fit, $\\delta_{\\nu}\\neq0$'\ndf = dataframe('M011_499_smoothnunosourced_0', datadir)\nplt.sca(ax0)\nplotratios(ax0, df, 'z=0', showlabel=False, label=label, showMice=False)\n\ndf_fit = dataframe('M011_499_smoothnusourced_0', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\n    \ndf = dataframe('M011_247_smoothnunosourced_1', datadir)\nplt.sca(ax1)\nplotratios(ax1, df, 'z=1', showMice=False)\ndf_fit = dataframe('M011_247_smoothnusourced_1', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\nax1.plot(x.MininvhMsun, x['dndlnM_fit(B),_fit']/x['dndlnM_fit(B),'], color='r', lw=2)\n\n\n\n\ndf = dataframe('M011_163_smoothnunosourced_2', datadir)\nplt.sca(ax2)\nplotratios(ax2, df, 'z=2', showxticks=True, showlabel=True, showMice=False, label=label)\ndf_fit = dataframe('M011_163_smoothnusourced_2', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\nax2.plot(x.MininvhMsun, x['dndlnM_fit(B),_fit']/x['dndlnM_fit(B),'], color='r', lw=2, label=sourcedlabel)\n\n\nax2.set_xlabel(r'Mass ($h^{-1}M_\\odot$)', fontsize=28)\nax1.set_ylabel(\"massfn/fit\", fontsize=28)\nax2.legend(loc='lower left', numpoints=1, prop={'size': 12})\ngs.tight_layout(fig)\ngs.update(hspace=0, wspace=0)\nfig.savefig('nudde_ratio.pdf')",
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 576x432 with 3 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "gs, fig, ax0, ax1, ax2 = threepanel()\n\nlabel=r'$\\nu\\Lambda$CDM'\nsourcedlabel=r'Fit, $\\delta_{\\nu}\\neq0$'\ndf = dataframe('M000n1_499_smoothnunosourced_0', datadir)\nplt.sca(ax0)\nplotratios(ax0, df, 'z=0', showlabel=False, label=label, showMice=False)\n\ndf_fit = dataframe('M000n1_499_smoothnusourced_0', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\n    \ndf = dataframe('M000n1_247_smoothnunosourced_1', datadir)\nplt.sca(ax1)\nplotratios(ax1, df, 'z=1', showMice=False)\ndf_fit = dataframe('M000n1_247_smoothnusourced_1', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\nax1.plot(x.MininvhMsun, x['dndlnM_fit(B),_fit']/x['dndlnM_fit(B),'], color='r', lw=2)\n\n\n\n\ndf = dataframe('M000n1_163_smoothnunosourced_2', datadir)\nplt.sca(ax2)\nplotratios(ax2, df, 'z=2', showxticks=True, showlabel=True, showMice=False, label=label)\ndf_fit = dataframe('M000n1_163_smoothnusourced_2', datadir)\nx = df.join(df_fit, rsuffix='_fit')\nx.head()\nax2.plot(x.MininvhMsun, x['dndlnM_fit(B),_fit']/x['dndlnM_fit(B),'], color='r', lw=2, label=sourcedlabel)\n\n\nax2.set_xlabel(r'Mass ($h^{-1}M_\\odot$)', fontsize=28)\nax1.set_ylabel(\"massfn/fit\", fontsize=28)\nax2.legend(loc='lower left', numpoints=1, prop={'size': 12})\ngs.tight_layout(fig)\ngs.update(hspace=0, wspace=0)\nfig.savefig('nulcdm_ratio.pdf')",
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 576x432 with 3 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "# Suppression\ndirname = '/Users/rbiswas/src/lsscosmo/examples'\nfname = os.path.join(dirname, 'M000M000n1comparisons.csv')\ndf = pd.read_csv(fname)",
   "execution_count": 27,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.head()",
   "execution_count": 28,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 28,
     "data": {
      "text/plain": "   ApproxErrorHigh  ApproxErrorLow   M000_MF     M000_mass  M000n1_InterpMF  \\\n0         0.001259        0.001258  0.001069  3.484101e+12         0.000964   \n1         0.001446        0.001445  0.000808  4.732952e+12         0.000728   \n2         0.001651        0.001649  0.000612  6.423003e+12         0.000547   \n3         0.001901        0.001898  0.000460  8.715419e+12         0.000410   \n4         0.002165        0.002162  0.000348  1.183044e+13         0.000305   \n\n   M000n1_MF   M000n1_mass  directRatio  interpolatedRatio  numClusters1  \\\n0   0.000964  3.482870e+12     0.902171           0.901880     1082834.0   \n1   0.000729  4.728572e+12     0.901622           0.900838      819157.0   \n2   0.000547  6.421064e+12     0.892921           0.892667      620573.0   \n3   0.000410  8.716214e+12     0.890588           0.890664      466646.0   \n4   0.000305  1.183010e+13     0.878332           0.878307      352417.0   \n\n   numClusters2  stepNum    z  \n0      976911.0      499  0.0  \n1      738571.0      499  0.0  \n2      554116.0      499  0.0  \n3      415581.0      499  0.0  \n4      309540.0      499  0.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ApproxErrorHigh</th>\n      <th>ApproxErrorLow</th>\n      <th>M000_MF</th>\n      <th>M000_mass</th>\n      <th>M000n1_InterpMF</th>\n      <th>M000n1_MF</th>\n      <th>M000n1_mass</th>\n      <th>directRatio</th>\n      <th>interpolatedRatio</th>\n      <th>numClusters1</th>\n      <th>numClusters2</th>\n      <th>stepNum</th>\n      <th>z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.001259</td>\n      <td>0.001258</td>\n      <td>0.001069</td>\n      <td>3.484101e+12</td>\n      <td>0.000964</td>\n      <td>0.000964</td>\n      <td>3.482870e+12</td>\n      <td>0.902171</td>\n      <td>0.901880</td>\n      <td>1082834.0</td>\n      <td>976911.0</td>\n      <td>499</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.001446</td>\n      <td>0.001445</td>\n      <td>0.000808</td>\n      <td>4.732952e+12</td>\n      <td>0.000728</td>\n      <td>0.000729</td>\n      <td>4.728572e+12</td>\n      <td>0.901622</td>\n      <td>0.900838</td>\n      <td>819157.0</td>\n      <td>738571.0</td>\n      <td>499</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.001651</td>\n      <td>0.001649</td>\n      <td>0.000612</td>\n      <td>6.423003e+12</td>\n      <td>0.000547</td>\n      <td>0.000547</td>\n      <td>6.421064e+12</td>\n      <td>0.892921</td>\n      <td>0.892667</td>\n      <td>620573.0</td>\n      <td>554116.0</td>\n      <td>499</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.001901</td>\n      <td>0.001898</td>\n      <td>0.000460</td>\n      <td>8.715419e+12</td>\n      <td>0.000410</td>\n      <td>0.000410</td>\n      <td>8.716214e+12</td>\n      <td>0.890588</td>\n      <td>0.890664</td>\n      <td>466646.0</td>\n      <td>415581.0</td>\n      <td>499</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.002165</td>\n      <td>0.002162</td>\n      <td>0.000348</td>\n      <td>1.183044e+13</td>\n      <td>0.000305</td>\n      <td>0.000305</td>\n      <td>1.183010e+13</td>\n      <td>0.878332</td>\n      <td>0.878307</td>\n      <td>352417.0</td>\n      <td>309540.0</td>\n      <td>499</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "def fix_panel(ax, showxticks=False, text='z=0'):\n    #pu.drawxband(refval=1.0 , bandwidths=[-0.1, 0.1], ua=ax,\n    #             xlims=(1.0e12, 1.0e15))\n    ax.text(1.2e12, 0.85,text, fontsize=20)\n    ax.set_xlim(1.0e12, 1.0e15)\n    ax.set_xscale('log')\n    ax.set_ylim(0.699, 1.001)\n    ax.grid(True, ls='dotted')\n    ax.yaxis.set_major_locator(plt.FixedLocator([0.8, 0.9, 1.0]))\n    \n    #plt.setp(xt, visible=False, fontsize=20)\n    #plt.setp(xt[::2], visible=False, fontsize=20)\n    if not showxticks:\n        ax.xaxis.set_major_locator(plt.NullLocator())\n    return #xt",
   "execution_count": 29,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "gs, fig, ax0, ax1, ax2 = threepanel()\n\nx = df.query('stepNum == 499')\nax0.errorbar(x.M000_mass, x.interpolatedRatio,\n             yerr=(x.ApproxErrorLow, x.ApproxErrorHigh), fmt='ks')\nfix_panel(ax0)\n\nx = df.query('stepNum == 247')\nax1.errorbar(x.M000_mass, x.interpolatedRatio,\n             yerr=(x.ApproxErrorLow, x.ApproxErrorHigh), fmt='ks')\nfix_panel(ax1, text='z=1')\n\n\nx = df.query('stepNum == 163')\nax2.errorbar(x.M000_mass, x.interpolatedRatio,\n             yerr=(x.ApproxErrorLow, x.ApproxErrorHigh), fmt='ks')\nfix_panel(ax2, text='z=2', showxticks=True)\n\nax2.set_xlabel(r'Mass ($h^{-1}M_\\odot$)', fontsize=28)\nax1.set_ylabel(r\"$\\nu\\Lambda CDM/\\Lambda CDM$\", fontsize=28)\n#ax2.legend(loc='lower left', numpoints=1, prop={'size': 7.5})\ngs.tight_layout(fig)\ngs.update(hspace=0, wspace=0)\n#fig.savefig('suppression.pdf')",
   "execution_count": 30,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 576x432 with 3 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "example_data = '/Users/rbiswas/data/datastar'\ndatadir = os.path.join(example_data,'simulations/')",
   "execution_count": 31,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "M000simtransfer = os.path.join(datadir,  \"MiraU/Grid/M000/CAMB/cmbM000.tf\")\n\nM000indatfile = os.path.join(datadir, \"MiraU/Grid/M000/L2100/HACC001/run/indat.params\")\nM000simtransfer = os.path.join(datadir,  \"MiraU/Grid/M000/CAMB/cmbM000.tf\")\nM000 = hacc.haccsim(M000indatfile, name = \"M000\")",
   "execution_count": 32,
   "outputs": [
    {
     "output_type": "stream",
     "text": "printing omeganu **************\n0.0\nFrom indat  0.0\nFrom cosmo  0.0\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "M000n1indatfile = \"/Users/rbiswas/data/datastar/simulations/Neutrinos/M000n1/L2100/HACC000/run/indat.params\"\nM000n1Simtransfer='/Users/rbiswas/data/datastar/simulations/Neutrinos/M000n1/CAMB/cmbM001n.tf'\nM000n1 = hacc.haccsim(M000n1indatfile, name = \"M000n1\")",
   "execution_count": 33,
   "outputs": [
    {
     "output_type": "stream",
     "text": "printing omeganu **************\n0.01983733\nFrom indat  0.01983733\nFrom cosmo  0.01983733\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "M000ps = psu.powerspectrum(koverh=None,\n                           pstype=\"matter\",\n                           sigma8type=\"matter\",\n                           asciifile=M000simtransfer,\n                           cosmo=M000.cosmo)",
   "execution_count": 34,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "M000n1cbmps = psu.powerspectrum(koverh=None,\n                               pstype=\"cbmatter\",\n                               sigma8type=\"matter\",\n                               asciifile=M000n1Simtransfer,\n                               cosmo=M000n1.cosmo)",
   "execution_count": 35,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "massininvh = np.logspace(12, 15)\nM000h = M000.cosmo.h\nM000n1h = M000n1.cosmo.h\n# M011h = M011.cosmo.h\nstep=499\nMF_M000_0 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,\n                      z=M000.steptoredshift(step))/ M000h**3\nMF_M000n1_0 = hmf.dndlnM0(massininvh/M000n1h, ps=M000n1cbmps, cosmo=M000n1.cosmo,\n                      z=M000n1.steptoredshift(step))/ M000n1h**3\n# MF_M011_0 = hmf.dndlnM0(massininvh/M011h, ps=M000n1cbmps, cosmo=M011.cosmo,\n#                        z=M011.steptoredshift(step))/ M011h**3\n\n\nstep=247\nMF_M000_1 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,\n                      z=M000.steptoredshift(step))/ M000h**3\nMF_M000n1_1 = hmf.dndlnM0(massininvh/M000n1h, ps=M000n1cbmps, cosmo=M000n1.cosmo,\n                      z=M000n1.steptoredshift(step))/ M000n1h**3\n# MF_M011_1 = hmf.dndlnM0(massininvh/M011h, ps=M000n1cbmps, cosmo=M011.cosmo,\n#                        z=M011.steptoredshift(step))/ M011h**3\n\nstep=163\nMF_M000_2 = hmf.dndlnM0(massininvh/M000h, ps=M000ps, cosmo=M000.cosmo,\n                      z=M000.steptoredshift(step))/ M000h**3\nMF_M000n1_2 = hmf.dndlnM0(massininvh/M000n1h, ps=M000n1cbmps, cosmo=M000n1.cosmo,\n                      z=M000n1.steptoredshift(step))/ M000n1h**3\n# MF_M011_2 = hmf.dndlnM0(massininvh/M011h, ps=M000n1cbmps, cosmo=M011.cosmo,\n#                        z=M011.steptoredshift(step))/ M011h**3",
   "execution_count": 36,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "ax0.plot(massininvh, MF_M000n1_0 / MF_M000_0, 'k', lw=2)\nax1.plot(massininvh, MF_M000n1_1 / MF_M000_1, 'k', lw=2)\nax2.plot(massininvh, MF_M000n1_2 / MF_M000_2, 'k', lw=2)",
   "execution_count": 37,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 37,
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x1154f9b10>]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "fig.savefig('suppression.pdf')\nfig",
   "execution_count": 38,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 38,
     "data": {
      "image/png": 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rHTbbzMrK8thzhJjPhoaGqKqqoqysjNLSUiOVlJTQ0dHh9B4/Pz/S0tLIysoiMzOTzMxMsrKyyMjIIDg4WFZzFmIekgDHTQtls00hxOTs6/yMDXpKS0uprq52eo9SirVr1066/5c7/2+UliEhZk4CHDdJgCPE4tfb20tZWRklJSWUlJRQXFxMSUkJlZWVDA8PT3rvv/3bv5GVlcX69evJysoiNDTU5ed6o2VIgiaxVEiA4yYJcIRYugYHBzGZTNPq4k1MTGT9+vVs2LCBDRs2sH79ejIzMwkODh6X1xsBjnSniYVo27ZtALzzzjsu3yObbQohxAwFBASQmZk5aZ4f/ehHxlT2srIyY3f3AwcOGHl8fHxISUkxAh/7UQgx9yTAEUIsWZOt5vz973/f+Dw8PIzJZKKoqIiioiKOHj1KUVGRMc29oqJi2ot6CiG8SwIcIcSS5erYFT8/P2PRwSuvvNK4PjAwQHl5uUPQU1RUhMlkmrCsc845hw0bNrBx40ays7PJzs4mIiLC7XcRQjiSAEcIIWYoMDDQCFJGi46OprW11ek9H3zwAR988IHDtfj4eLKzs41VobOzs8nIyPDqIqJCLHYS4AghhIdNtMt5a2srhYWFRiooKODo0aPU1dVRV1fnML7H39+frKwsI+iJiIhwut5PdHS0195jpmTGl5gPJMARQohZsnr1arZv38727duNayMjI5hMJgoKCoygp6CgALPZTH5+Pvn5+Q5lREdHs2nTJjZt2sTGjRvZtGkTQ0ND86q1Z6IAb6LrQniDTBMXQoh5qLu7m6KiIiPgyc/Pp6CggO7u7nF5AwICyMrKYtOmTeTk5BgB0MqVK+eg5jKlXZwyl9PEJcARQogFwmKxUFNTYwQ79haeiQY1x8fHjwt6UlJSvL45rwQ4wk4CnDkgAY4QYrGwt/bk5eWRn59PXl4ehYWF9PX1jcsbGhrKxo0bycnJMdKGDRucLlg4U94OcGbyl6aYmjd+rrLQnxBCiBkLCwvjrLPO4qyzzjKujYyMUFlZabTy5OXlkZeXR2Njo9OZXGNFRUVNOBNsrowdvGwPpGTwsnBGAhwhhFiEfH19jbV7rrrqKuN6W1ubQ8Czd+9ep/e3tbURFxdntPLk5uaSk5NDUlLSlF1cky2g6A4ZvCymQ7qohBBiCZusO8mZ5cuXG+N67EHP+vXrCQgI8FINT5GxPd4lXVRzQCl1CfAY4As8o7V+cMz3CcDzwApbnu9qrQ+MK0gIIYTLysvLycvL48iRI8axubmZ9957j/fee8/IZ1+zxx7w5ObmsmnTJsLDw+ew9mIuzYfuxHnfgqOU8gXKgQuBeuATYKfWunhUnqeAI1rrXymlsoADWuu1k5UrLThCCDH9VpHm5mby8/M5cuSIEfhUVFQ4zZucnOzQ0pObm8uaNWum3Wo007rOBwtpQLQn6+rO72opteBsBSq11mYApdQ+4AtA8ag8GlhuOw8HGme1hkIIsUTExMQQExPDxRdfbFzr6emhoKDAoaWnsLAQs9mM2Wzmj3/8o5F31apVDgFPTk4O6enp+Pr6zsXriEXMu4sheEYsUDfqc73t2mg/BK5TStUDB4BvOStIKXWrUuqgUupgU1MTx44do6mpiYaGBjo6OjCZTPT391NcXIzFYuHw4cMAHDp0CIDDhw9jsVgoLi6mv78fk8lER0cHDQ0N2Murrq6mp6eH0tJShoeHjVVI7WXYj4WFhQwMDFBRUUFXVxe1tbW0trbS2tpKbW0tXV1dVFRUMDAwQGFhodMy8vPzGR4eprS0lJ6eHqqrq+Wd5J3kneSdpvVOq1atwhn7IoGuvNOxY8fYuHEj27dv51e/+hXPPvssPT097Nu3j+eff56dO3eybds2wsLCOHbsGG+88QYPPfQQ11xzDVlZWYSFhXH66afz5S9/mR/96Ee89957fPzxx+PeaaJBylFRUfP29zQyMkJ/f/+C+LNnX0TSE3/2JjPVO3nKQuii+iJwsdb6Ftvn64GtWutvjcpzB9Z3+ZlS6izgWWCD1toyUbnSRSWEELNLa01NTc24cT11dXXj8vr4+JCRkeGwXk9ubi5RUVFzUPPpW0j7cXmjrvOhi2rKAEcp9U9Yx7fUu/uwmbAFLD/UWl9s+3w3gNb6gVF5jgKXaK3rbJ/NwKe01hMu4iABjhBCzA/Hjx83gh37FPaSkhJGRkbG5V2zZo2xMrP9mJqa6nYXl6fHynhzvNBCqOt8CHBcGYPzJ0ArpTqAI0Ce7XgEKNXebwL6BEhTSiUBDcCXgGvG5KkFtgN7lFKZQBDQ5uV6CSGE8IDIyMhxm5CePHmSo0ePOrT25Ofn09jYSGNjo8PO6yEhIWRnZxvbUdg3Ig0LC5vy2fNhts9i5K21kKbDlRac0d08YzOfBAo5FfTkAQVa636PVlKpzwGPYp0C/mut9f1KqfuAg1rr/baZU08DobY6fkdr/dfJypQWHCGEWFgsFgtms9lhS4r8/HynXVwAKSkpRrBjT2MXKvRWS8tSb8GxWwjr4GhgABgCRofEwcAZtmRnUUqV4xj0HNFaH59pJW1r2hwYc+3/jjovBs6ZaflCCCHmPx8fH1JTU0lNTWXHjh3G9fb2doctKfLz8zl69CgmkwmTyeQwiys0NJTs7Gwj4BGLlystOINYAyENvAY8CQQCOaPSGie3ji24EVvXltb6Hveq7T5pwRFCiMVrcHCQ0tJSCgoKHFJTU5PLZZw8eZLAwMAZPd8brSLeGri8lFtwsoCfAlcAl2BdcO9prAN/22yVWQXkcirgyQXSsHYp2cXa0mXAnAc4QgghFq+AgACnrTRtbW0UFhaSn59PQUEBe/bsmbCM0NBQ0tPTyc7OZsOGDWRnZ7N+/XqSkpKmHNTsjTEo3tqLaz6Ml/EGl6eJK6XOBX4GbMHaOtMDPAA8orUecJI/CNjIqYAnB8gGgrXWc76ik7TgCCGEmKz1QinltAUjMDCQdevWsX79erKysoyUkpKCn59ju8F8WR3YFUt2Lyqt9XvAVqXUtcD9QILt+DWl1N1a6xfH5D8JfGxL9korIN3dSgshhBCeMFnrhdlspri4mKKiIgoLCykqKqK4uJj6+npjzM9oAQEBZGRkkJWVxbp161i3bh3d3d2EhITM1uuIUaa9VYPWeq9S6r+BO4C7gHjgN0qp3cC3bYHQRPdqoGymlRVCCCE8yT52ZaKWhi1btrBli2NjQmdnJyUlJRQXF3P06FGKi4spLi6mtraWwsJCY3Xf0RISEli3bh0ZGRnGMSMjg9jYWIdZXcJz3FrJWCkVBdwH3IJ1vI3Gum7OXVrrCo/U0Euki0oIIYQndXd3G4FPWVkZZWVllJaWUllZydDQkNN7goKCSE5ONmaHpaamkpKSQmpqKgkJCQ5dXtJFNT1ubbZpG2T8NaXUE8BDwKVYN8K8TCn1JHCv1rrd3UoKIYQQ811YWBhbt25l69atDteHh4epqqpyCHrs562trUYL0Fh+fn4kJSWRkpJCYmIioaGh9PT0jMu30AcDe4tH96JSSl0APIx1cLEGOoEfa61/7rGHeIi04AghhJhr3d3dmEwmKisrjaM91ddPvkOSv78/8fHxrF27lsTERBISElizZg1r1qzhtNNOY82aNaxevdrlbSykBWcSWus3lVKbsXZb3QWsAH4MzLsARwghhJhrYWFhxmaiY/X391NVVYXJZKKmpobq6mpqamp4/fXXOXnyJENDQ5jNZsxm84Tl+/j4EB0dbQQ8a9asITo6msjISIe0cuVKhoaGxs0CW8jcehOlVCKwfkzKxLrCsRBCCCFmKDg42JiCPpq9VeTAgQPU1tZSU1NDTU0NtbW1NDU10djYaBzb2tpoamqiqamJw4cPT/lMHx8foqKiWLlyJREREYSEhLBs2TKH49jzwMBAh/FBo8/tg7ife+45hoaG6O3tpaenxyGNveYpLgU4Sql4xgcyWcDouW9jRz+1Y92n6pD71RRCCCHEaCEhIcZ09IkMDg7S0tLiEPg0NzfT3t7O8ePHjWT/3NXVxbFjxzh27JhH63rzzTd7tDxXTBngKKU6sW5iaVwak2UY69TvgtFJa93gqUoKIYQQwmo641kCAgKIj48nPj7epfxDQ0N0dHRw/PhxTpw4QV9fH319ffT29jocR58PDJxa63f0uN6xY3x9fX0JCwtj2bJlhIaGGmn052XLlrF582aX328yrrTgjN5cs4UxgQxQrLV2Pv9NCCGEEAuGv78/q1evZvXq1XNdFbdNZzfxPqAIa1CTZ0slWmuLl+omhBBCCDEjrgY4ClgGnG9LdgNKqSJOBTx5QL7WutejtRRCCCGEmAZXApwbOLVh5iZg5ajvgoDNtmSnlVJmHIOePK11o0dqLIQQQggxhSkDHK31C8AL9s9KqQROBTz2XcITRt2igFQgBbhy1H3HgHzgiNb6Lk9UXgghhBDCmZlstlkL1AL77deUUitwDHhygXVY96eyiwIuALZjXQRQCCGEEMIrPLJkodb6BPC2LQGglAoEsnEMerJxXDtHCCGEEMLjvLYms9Z6ADhoSwAo6/KG6d56phBCCCEEuBHgKKUigNVAANAGtGmtRya7R1tX/Smb6TOFEEIIIVwxrQDHNtbmTqyDh9PGfG1RSh0B/gq8rLWWrbqFEEIIMSd8XM2olDobqAC+izW4UWOSL9bp4ncDHyml3lRKfdbjNRZCCCGEmIKrm21mAm9gXffGvhdVOVAHDAIRWGdJJY/6/jzgPKXUI8BdU3VfCSGEEEJ4iqtdVM8AwbbzXwP3aq3rxmaydWGdA/wLcDnWFqLbgQ1Kqc/Jtg5CCCGEmA1TdlEppc4AzsK6H9X9WutbnAU3YJ0urrX+i9b6CqxTwwuxtuhcCDzuuWoLIYQQQkzMlTE4O2zHWuAeVwvWWhcBZ2AddKyArymltk23gkIIIYQQ0+VKgHMG1tabfdPtYtJaD2INkIptl+6eXvWEEEIIIabPlQDHPh38k5k8QGvdA3wHayvOBUopWehPCCGEEF7lSoATbjvOeDdwrfUBoMj28byZliOEEEII4QpXApxQ27HbzWe9irUV59zp3KSU+rVSqlUpVTTB90op9bhSqlIpVaCUOt3NegohhBBigXN5oT+s43Dc8ZHtmDXN+/YAl0zy/aVYu9HSgFuBX027ZkIIIYRYVKYT4LirzXZcOZ2btNZ/B9onyfIF4L+01YfACqXUaTOsoxBCCCEWgensRaWmzjKpDtsxws1yxorFuqKyXb3tWtPYjEqpW7G28hASEkJOTg7W/T/Bx8eH4eFh/P39GRwcJCgoiL6+PkJCQsYdT548SUBAAENDQ/j5+WGxWOzlY7FY8PPzY3BwkMDAQE6ePElwcPC4Mvr7+wkKCmJwcBA/Pz9GRkbw8bHGmxaLBV9fX4aHhwkICJiyjIGBAQICAhgeHsbHx0feSd5J3kneSd5pEb9TTU0NFouFzMxMt9+ptrZ2XFlz/XsCPDLUZDoBziGlVDGQb0t5QL7WumPy2wz2Lq7QSXNNn7PAy2l3mtb6KeApgC1btuiDB2U/UCGEEAvLtm3bAHjnnXfmVVmeopQ67IlyXA1wFBAAbLKl0RVpwBbs2JPWusITlXNRPRA/6nMcbsz4EkIIIcTC50qAcwPWbRdysQY3Y8fQxGHtErrMfkEp1Yd1mwajpQf3BylPZD/wTaXUPuBMoFNrPa57SgghhBBLx5QBjtb6BeAF+2elVAKnAp5c23nCmNuWYQ02znS3gkqp3wLbgFVKqXqs20X42+r2JHAA+BxQCfQBN7n7TCGEEGK+iYmJoaWlxfislHWERnR0NM3NzXNVrXlrOmNwANBa12Ldl2q//ZptF/HRAU8usA7wdbeCWuudU3yvgW+4+xwhhBBiPhsd3LhyfambdoDjjNb6BPC2LQGglAoEsnEMerKBEE88UwghhBBiIh4JcJzRWg8AB20JsK46DMheVEIIIYTwKpcW+lNKxSildiilkt15mG0xvjJ3yhBCCCGEmIqrKxk/ArwEVCilvurF+gghhBBCuG3KAEcplQJcbfv4W9vMJSGEEELMoujo6GldX+pcGYNzne3YC3x7ug9QSp0D/DNwCPiHbRaWEEIIIabBPhV8Pq6LuPa5AAAgAElEQVQ+PB+50kX1WayL9P1Raz2TuWgfAGcDvwFencH9QgghhBDT4kqAk2k7zig4sa1Tcw/W7R7WKaVyZ1KOEEIIIYSrXAlw7Lt/z7hrSWv9V8A+e+qSmZYjhBBCCOEKVwKcXtvxpJvPOoC1FedsN8sRQgghhJiUKwGOfdxNrJvP+sB2XOdmOUIIIYQQk3IlwKm2Hc9181n2Hb5Xu1mOEEIIIcSkXAlw7F1L1ymlgtx4Vp/tGOxGGUIIIYQQU3IlwHkZsAAxWGdDzdQq27HLjTKEEEIIIaY0ZYCjta4Hfou1FedOpdTNM3zWWbZj/QzvF0IIIYRwiat7Ud0ONNvyP62U+olSKsDVhyilgoFbsC4Y+I9p11IIIYQQYhpcCnC01seAy4FOrC05/wYUKaVuUEpNut2DUioQ6yrG8bZLL868ukIIIYQQU3NlLyoAtNaHlVKfAf4HSAZSgOeAnyul/gS8BxQBbcAIcBrWmVdfs+XXwOta6w+cFC+EEEII4TEuBzgAWutCpdRG4BHgX2yXVwI32tJEFGACbph+FYUQQgghpsfVMTgGrXWf1vorwCas3U3DWAOYydJ+4FO2ri4hhBBCCK+aVgvOaFrrQqxr43wDOB84D1iLdSE/hbWr6hPgT1rrPPerKoQQQgjhmhkHOHZa606s43L+x/3qCCGEEEK4b9pdVDOllApWSl2rlPrrbD1TCCGEEEuT2y04U7HNvNoF7ABCvf08IYQQQgivBDhKqWSsM6ZuABLtl21H7Y1nCiGEEELYeSzAUUotB67C2lpztv3yqCylwF5koT8hhBBCeJlbAY5SSgEXYQ1qvgDYdxu3BzaNwD5gr9b6iDvPEkIIIcTitn//fo+VNaMARym1HmtQcy3WXcbhVFDTBfwBa2vN21pr6ZISQgghxKTq6urYtWuXx8pzOcBRSkUC12ANbHLtl23HQeAA1qDmz1rrAY/VUAghhBCLmsVi4aabbuLEiRMeK3PKaeJKqSuUUv+DtbvpUeB0TgU27wK3AjFa63/WWv/BG8GNUuoSpVSZUqpSKfVdJ98nKqXeUkoVKKXeUUrFeboOQgghhPCOJ554grfeeotVq1Z5rExX1sH5I/B5wB9rYFMA3AUkaq3P01o/o7X2XMg1hlLKF/gFcCmQBexUSmWNyfYw8F9a643AfcAD3qqPEEIIITynuLiYu+66C4Cnn37aY+VOZ6G/YeAeYKvW+iGtdb3HajG5rUCl1tqstR7EOmj5C2PyZAFv2c7fdvK9EEIIIeaZwcFBrrvuOgYGBrj55pu54oorPFa2qwGOAnyBHwItSqmnlVLneawWk4sF6kZ9rrddGy0fuNJ2/n+AMNuYISGEEELMU/feey9HjhwhKSmJRx991KNluxLgZAM/A5qxBjrhwM3Am0qpeqXUQ0qp3MkKcJNycm3szKx/Az6rlDoCfBZowNri5FiQUrcqpQ4qpQ42NTVx7NgxmpqaaGhooKOjA5PJRH9/P8XFxVgsFg4fPgzAoUOHADh8+DAWi4Xi4mL6+/sxmUx0dHTQ0NCAvbzq6mp6enooLS1leHiY/Px8hzLsx8LCQgYGBqioqKCrq4va2lpaW1tpbW2ltraWrq4uKioqGBgYoLCw0GkZ+fn5DA8PU1paSk9PD9XV1fJO8k7yTvJO8k6L/J0Aenp6PPZO3d3dc/JOv/vd73jwwQdRSrFnzx4qKirwJOXqLG6llA/O17yxF1AGvAD8Vmtd5bEKKnUW8EOt9cW2z3cDaK2djrNRSoUCpVrrSQcab9myRR88eNBT1RRCCCFmxbZt2wB455135lVZ09Hd3U1OTg5ms5nvfve7PPDAqb/SlVKHtNZb3H2Gy2NwtNYWrfVrWuudWNe++SrwAdYWFgWsA34EVCqlPlBKfV0p5Ynh0J8AaUqpJKVUAPAlwGElIKXUKlsABnA38GsPPFcIIYQQXnDHHXdgNpvJycnh3nvv9cozZrSbuNa6S2v9lNb600AacD9Qw6lg50zgCaBRKXXAtot4yAyfNQx8E3gdKAF+p7U+qpS6Tyn1eVu2bUCZUqociLbVRwghhBDzzP79+3nmmWcIDAzkhRdeICAgwCvPcXsvKq21CfgB8AOl1DasXVhXYt053A+42Jb6lVJ/Al7UWv9lms84gHUhwdHX/u+o8/8G/tuN1xBCCCGEl7W2tnLLLbcA8OCDD7J+/XqvPWtGLTgT0Vq/o7W+CWsX1i7gb7avFBCCtXvpT5585mL38ccfc/fdd3PppZcSExODUoq4OFnHUAghxMKiteaWW26hra2N888/n3/913/16vM8tpv4aFrrPuA3wG+UUvHADcD1QLo3nreYvfjiizz22GP4+/uTmZlJS0vLXFdJCCGEmLZnn32WV155hfDwcPbs2YOPj0fbWMaZMsBRSl2EddPMoZk8QGtdh3VMzP22GVE3zKQcT6urq+OnP/0psbGxrFmzxjiGhobOddUc3HjjjezatYv169cTEBCAdQN3IYQQYuEwmUzs3r0bgF/+8pfEx8d7/ZmutOC8BvQopf6KdfbSX7TWx2fyMK31/wL/O5N7Pa21tdVYGnq05cuXOwQ8sbGxRoqLiyM2Npazzz6bmpqaCcvetWsXe/bs8Ug9c3JyPFKOEEIIMRf6+/u55ppr6O3t5eqrr2bnzp2z8lxXApwRrAOG/xnrKsEWpdSHwCvAfq11qRfr5zVxcXFcddVVNDY20tDQYBy7urro6uqitHTi11JKERYWRlhYGMuXLzdSZWUl1dXVDA8PMzg46LWR4UIIIcRCYLFY2LVrFx9//DHx8fH88pe/nLWeCFcCnEisG11+HrgEiADOAc4GHlBKmbG27LwCvKe1HvFSXT0qOjqan/3sZw7XtNZ0dHQYwY498Kmvrzc+NzQ00NraSnd3N93d3TQ2No4re+/evezdu5eVK1cSGBjI8uXLCQ8PNwKh8PBwwsPDCQsLG9cHuXv3blasWOHVdxdCCCFmw913383vf/97li9fzl/+8hdWrlw5a8+eMsDRWncBLwEv2Xb2Phf4J1tKBVKA3bbUqZR6FWuw86rWutNbFfcGpRQrV65k5cqVbNiwYcJ8AwMDxnLTDQ0NfPLJJzz++OMAZGVlcezYMRobG2lvbwegqanJ5TporcnNzSUhIYGEhAQiIyNl3I0QQogF58knn+SnP/0pfn5+/OEPfyA7O3tWnz+tWVS21pl3bOnbSql1WFt2/gk4C1iBdSr4l4BhpdQ/sLXuaK3Nnqv23AoMDGTt2rWsXbuWpqYm7rzzTpRSvPHGG5x77rkADA8P09zcTF1dHfX19dTV1RnntbW11NXV0dzczNitMu677z6HzyEhIUawk5iYCEBfXx/vvvsuCQkJxMXF4e/vPzsvLoQQQrjg1Vdf5Rvf+AYA//mf/8kFF1ww63Vwa5q4bfxNKfBT2+7dl2ENdi4CwoDzsK4y/HOlVAnWlp1XtNYfuPPc+aK3t5fLL7+curo69u7dawQ3AH5+fsTFxU26Zs3g4CANDQ1G8GMPfOzHmpoaOjs7KS0tdRgT1NHRYewf4uPjQ2xsLImJiaxdu5bExESH84SEBIKCgiaogRBCCOFZeXl5XHXVVVgsFr7//e9z8803z0k9PLYOjm1m1X8B/6WU8sca3HweuBxIALKATOA7SqljwF+AJ7TWRzxVh9lksVjYuXMnhw8f5v7773c6KjwvL4+XX355WuX+x3/8h8MYnM7OTmpra4309a9/neDgYHJzc6mpqaGxsdEIkP7xj384LTMmJsZocUpKSnI4JiQkEBgYOL2Xn0BMTIzTdXqio6Npbm72yDOEEELMX/X19Vx22WX09PRwzTXXjOuVmE0u7ybu1kOU2sSpcTtbsK5srIF7tdZz8vbu7ib+r//6rzzxxBPcfPPNPPvss07z7Nmzh5tuumla5VZVVbF27doJv1dKERsbS319PWBtBaqvr6empobq6upxx7q6OkZGJh73rZRizZo1RtBjT8nJySQlJREbG4uvr69LdZ9srJA7f84kcBJCLFTe2K17vu4m3tXVxbnnnktBQQHnnnsub7zxxoz+Ae2p3cS9spLxWFrrfCAf+LFSKoZTwU7fbDzf0x599FGeeOIJtm/fzpNPPjlhvhtvvJEbb7zRq3UJCAggOTmZ5ORkp9+PjIzQ0NBAdXW1kaqqqoxjXV2dMVj6/fffH3e/v78/iYmJxjPswY89zcaMr4lWb5ZVnYUQYn4YGhriqquuoqCggIyMDF5++WWP9Q7M1KwEOKNprZuBp21pwWlububb3/42Simys7O5//7xG5fn5ORwxRVXeOR5paWlPPjggw7XOjo6HAKnhx9+mFWrVjm939fX1xik/JnPfGbc90NDQ9TX1xsBjz2ZzWbMZjMtLS1UVlZSWVnptPyVK1eSnJxMSkrKzF9SCCHEgqW15utf/zqvv/46UVFRHDhwYFang0/Ela0a/gk4orWun4X6zHsnT57EYrEA1pYcZ3bt2uWxAKe5uZnnn3/e4VpfX5/DtR/+8IcTBjhT8ff3N7qlzjvvvHHf9/b2GsGP2WymqqoKk8lkfG5vb6e9vZ2puvtuu+02UlJSSE1NJSUlhaSkpDldCFG6vYQQwjN+8pOf8MwzzxAUFMT+/fsn7FGYba604PwJ0EqpDuAIkGc7HgFK9WwM4plH1q5d69Z4kunatm3brD5vrGXLlrF+/XqnW9prrWlpacFkMmE2m7nhhom3GbOvE2Tn4+NDfHw8qampDiktLY3k5GSCg4M9/i6jSbeXEEK4b9++fdx9990opXjhhRf41Kc+NddVMrjaRaWAlcD5tmR3UilVyKmgJw8o0Fr3e7SWYl5SShETE0NMTAznnHMOd955p9MAYcWKFfz7v/87lZWVmEwmKisrqa2tpaamhpqaGt56661x5cbFxRkBjxBCiPnn+eef58tf/jIADz30EFdeeeUc18iRqwGOBgaAIazr29gFA2fYkp1FKVWOY9BzZKYbdIqFYzpdO4ODg1RXV2MymaioqDDG+VRUVBiDn+vq6nj77bcnLCM4OJhHH32U9PR00tLSWLt2rSx6KIQQs+CRRx7hjjvuAOD73/++cT6fuBLgDNvyBQJvA0/aznNGpTWj8vtiXe9mHdYVjQFQSjVi69rSWt/jicqLhSsgIID09HTS09O59NJLHb4bGhqipqbGCHjsR3vwY5/23t/fz+23327c5+fnR1JSklFuRkaGcTzttNNkywshhHCT1pof/OAHxgSbRx55hN27d89xrZxzJcDJAn4KXIF1s80Lsc6A+qHWug1AKbUKyOVUwJMLpGENduxibekyQAIcMSF/f39jTM4ll1zi8N3Q0BBVVVVUVFRQXl5uHMvLy6mrqzMCob/85S8O94WGhjoEPt4ig5eFEIvVyMgI3/rWt/jVr36Fr68vzz77LLt27Zrrak3Ilc02K4F/VkqdC/wM60J9XwWuU0o9ADyitT4GvGFLACilgoCNnAp4coBsrN1aQsyIv7+/EahcdtllDt/19fVhMpmMgKe8vJyysjLKy8s5fvw4hw8f5vDhw5OWHxYWxuuvv05GRgYJCQnjdnufigxeFkIsRoODg9xwww289NJLBAYG8rvf/Y7Pf/7zc12tSbm8Do7W+j1gq1LqWuB+rNsv3A98TSl1t9b6xTH5TwIf2xIAytpHkO6JigsxVkhICNnZ2U53rD1+/LhDwFNeXk5paSkVFRUMDg4a+bq7u41Wo6CgIKPFJzMzk3Xr1rFu3ToyMjIICQmZtfeSViEhvMsbqw0vJr29vezYsYPXXnuNsLAw9u/fb/zM5rNpL/Sntd6rlPpv4A7gLiAe+I1SajfwbVsgNNG9GiibaWWFmKnIyEjOOusszjrrLIfrIyMj1NTUUFZWRmlpqcOxubmZgoICCgoKxpWXmJjIunXrHAKfzMxMr9RdWoWEEHOlo6ODyy+/nA8++IBVq1bx2muvsXnz5rmulktmtJKx1noAeEAp9QxwH3AL1q6rd5RSfwLu0lpXeK6aQniHr6+vse3E2MHOnZ2dDgFPSUmJ0epjn+L++uuvz1HN3SctQ0KIyTQ1NXHxxRdTWFhIfHw8b7zxhlfHMHqaW1s12AYZf00p9QTwEHAp8AXgMqXUk1g302x3v5pCzL7w8HC2bt3K1q1bHa4PDQ1hNpspLS01gh77eVdX14Tlbd68mczMTDIzM8nKyiIrK4uUlBT8/GZ9xxRAWoaEWKhmoyvNbDZz4YUXYjabWbduHX/961+Jj4/3+nM9ySP/Z9VaF2MNai4AHsY6uPibwPVKqR9rrX/uiecIMR/4+/uTkZFBRkYGX/jCF4zrWmuio6Npa2tzep+zQc72srKyshwCn7S0tDnfqE4IsTTt37+fm266ifb2drZs2cKBAweIioqa62pNm0f/6ai1flMptRlrt9VdwArgx4AEOGLRU0rR2trq9LuOjg5KSkooKSmhuLjYONbU1FBUVERRUZFDfl9fX1JTU8nKypqNqgshBAMDA9x111089thjAHzuc5/jt7/9LcuXL5/jms2MWwGOUioRWD8mZSJTwYVwEBERwdlnn83ZZ5/tcL2np4fS0lKKi4sdktlspqysjLKyicfkL1u2jH379pGVlUVGRsa8afGRsT1CLDwmk4mrr76aQ4cO4efnx4MPPsjtt98+7aUy5hOXAhylVDzjA5ksYPRc2bHLxLYDhcAh96spxOIUGhrKli1b2LJli8P1/v5+ysrKOHr0KMXFxcbRZDIZu9n39vayc+dOwNrik5aWZmyMak/p6emzvn2FjO0RYmFpbW0lNzeX7u5u1q5dy759+zjzzDPnulpumzLAUUp1AqGjL43JMox16nfB6KS1bvBUJYVYaoKDg8nJySEnJ8fhuj3wGR30HD16FJPJZAx2/sMf/mDk9/PzIz09nQ0bNrB+/Xo2bNjAhg0bSElJITo6esKWFiHE4tfX10d5eTlNTU0A7Nixg6effpoVK1bMcc08w5UWnNGba7YwJpABirXWQ16omxBijMkCn9LSUo4ePeqQzGaz0e01WmBgIJmZmVx44YVG0LN+/foZrd4shFh4iouLueqqq2hqakIpxS9+8Qu++tWvLqo9+6azm3gfUIQ1qMmzpRKttcVLdTMopS4BHsO6t9UzWusHx3yfADyPdVCzL/BdrfUBb9dLiPkiODiY3NxccnNzHa739vZSUlLC0aNHKSoqMgKf2tpa8vLyyMvLc8gfGhrq0NJjT9HR0XP6Pz4Z1yOEZ2itee655/jmN79Jf38/wcHBZGVl8bWvfW2uq+ZxrgY4ClgGnG9LdgNKqSJOBTx5QL7WutdTFVRK+QK/wLrJZz3wiVJqv21qut33gd9prX+llMoCDgBrPVUHIRaqZcuWOR3j09nZSXFxsRH02GdytbS08NFHH/HRRx855I+MjBwX9Kxfv56IiIhZeQ8Z1yOE+2pqarjjjjv44x//CMANN9yA2WzG19d3ijsXJlcCnBs4tWHmJmDlqO+CgM22ZKeVUmYcg548rXXjDOu4FajUWpsBlFL7sC4mODrA0YB9Hls4MNNnCbEkhIeHO9264tixY+OCnsLCQo4fP867777Lu+++65A/NjbWCHiys7PZsGEDq1evdjpdXsb2CDE3+vv7eeihh3jwwQfp7+9n2bJl/OIXv2DXrl0LYk+pGdNaTyth3WTz88A9wMtANWBxkkbGpBbgr8BPpvm8HVi7peyfrwf+35g8p2GdsVUPdACbJyjrVuAgcHDNmjW6ra1NNzY26vr6et3e3q4rKyt1X1+fPnr0qB4ZGdGHDh3SWmt98OBBrbXWhw4d0iMjI/ro0aO6r69PV1ZW6vb2dl1fX68bGxt1W1ubrqqq0t3d3bqkpEQPDQ3pvLw8hzLsx4KCAn3y5EldXl6uOzs7dU1NjW5padEtLS26pqZGd3Z26vLycn3y5EldUFDgtIy8vDw9NDSkS0pKdHd3t66qqpJ3knfy+DtZLBZ94MAB/dprr+nbbrtN79q1S2dlZeng4GCN9R8XDkkppVNSUvRFF12kv/vd7+qf/exnuqioSH/00Uczfidnz7En+T3JO7n7Tlu3btXnnHOOV97p05/+tN66deuc/J6OHz+un3rqKR0XF2f897Jjxw79t7/9zXinz372s/r000+fV78n4KCeZmziLClrWe5RSq3A2sKTy6nWnnVYx8OMpbXWLreHKaW+CFystb7F9vl6YKvW+luj8twBKK31z5RSZwHPAhv0JOODtmzZog8ePOhqNYQQY4yMjFBVVeXQ0lNUVERZWRkjIyPj8gcEBLBu3TqjpceeEhMTpxzfM9n3nvh/mFjavLmb+FztVH706FFuu+023nrrLQA2btzIE088wWc+85l5Ub/JKKUOaa23TJ1zcp7aquEE8LYtAaCUCgSycQx6snFcO8cV9Vh3LLeLY3wX1JeBS2x1+V+lVBCwCnC+rKwQwm321ZZTU1O54oorjOsDAwOUl5c7BD1FRUVUVVU53Z09LCxs3MDm7OxsVq9e7fV3kMHLVvPxL7nZMPb3bw+kF/Lv/8SJE9x777088cQTjIyMEBERwY9//GNuvfXWOdv3bq547W21dcfxg7YEgLL+6UmfZlGfAGlKqSSgAfgScM2YPLXAdmCPUioT69gg5xsCCSG8KjAwkOzsbLKzs42FCMG6avPosT32AKilpYUPP/yQDz/80KGcqKgoI+BZvny5041M3R3XI4OXl7bF9Pu3WCw899xz3H333bS1taGU4qtf/So//vGPiYyMnOvqzYkZBzhKqQhgNRCANZho01qPb5cexda3NvHa887vGVZKfRN4HWuX16+11keVUvdh7afbD3wbeFopdTvWfsYbtbRbCzGvhIaGcuaZZ45bIbWtrc0Y2Dy6xaetrY23336bt99+2yF/fHy8w8KFhw4dIjMzk5CQ6TYOC7HwDQwM8OKLL/Lwww8b6119+tOf5vHHHx+3bMRSM60xOLaxNncCVwJpY762AEewDiR+WWs9rwe4yBgcIeYvrTX19fXjurmKi4sZGBgYl18pRXJyskPgs379+in36FrqY3uWehedN3//3v7ZdnZ28tRTT/Hoo4/S2GgdtREfH89PfvITvvSlL7m8btV87J701BgclwMcpdTZwJ84NU3c2U9vdGFvAz/SWr/rJN+ckwBHiIVneHgYs9k8bip7eXk5w8PD4/JPtEdXWloaAQEBXvkLbiEFDUs9wPPm+3ur7MbGRh577DGefPJJo9t2w4YNfOc73+FLX/rStPeeW/IBjm1cy0GsY1vsv7VyoA4YBCKAKCB51Pf2gh8B7pqq+2q2SYAjxOIxODhoDGy2d3XZ9+hy9v84+x5dY7ewGG2mfwktpKBhIdXVGxZSgFNcXMzDDz/MCy+8wNCQdXekbdu28Z3vfIdLLrlkxiuNL+YAx9UxOM8AwbbzXwP3aq3rnFRqBXAO8C/A5YAPcDuwQSn1ucmmbQshxEwFBAQYA5JHm2iPrqqqqkmDm8DAQH7wgx+QlZVFVlYWGRkZBAUFefs1xCyb7xvODg8P8+abb/LLX/6SV155BQAfHx+++MUvcuedd3LGGWfMcQ3ntylbcJRSZwAfYW2R+Q+t9Q9cKlipDcBerFPDNfArrfU33auu50gLjhBLV29vrxH42DcjLS4uxmw2O/3XtY+PD0lJSWRmZhpp3bp1ZGZmjtt5eSG1iiykunqTN1oxZvqz1Vrz8ccfs3fvXl566SVjVfCgoCBuuukm7rjjDlJTUz1Wz6XegrPDdqzFunqxS7TWRbbgaD9wEfA1pdR/a63fmXYthRDCg5YtW8bmzZvZvHmzw/X+/n7KysrGBT6VlZWYTCZMJhN//vOfHe6JiYkxgp3MzMzZfI15bz7+5TlflZWV8eKLL7J3715MJpNxPT09neuuu46vfOUrs7I21GLiSoBzBtYWmH3T7WLSWg8qpXYAHwKZwN3AO9OtpBBCzIbg4GBycnLIyclxuD4wMEBFRQUlJSWUlpZSUlJCSUkJZWVlNDc309zc7NJf4gcPHiQjI4OwsLAZ19GTQcN876JZyFz52TY1NbFv3z727t3LoUOHjOsxMTHs3LmTa6+9ltNPP33G42uWOlcCHPt08E9m8gCtdY9S6jvAn4ELlFLpWuvymZQlhBBzITAw0OkYH4vFQm1trUPgs2fPHmMQ6Fj2MROnnXYa6enppKWlOaSUlBSCg4Od3uuNVXft90lLi+c5+9kODg7y4Ycf8oMf/IA333yTjz/+GIvF2m4QFhbGlVdeybXXXst5553n9R2+F+MqzmO5MganC1gGnKO1/nDSzJOXUwCsB76utf7PmZbjKTIGRwjhTSdOnKCsrGxcqqiocLqWj11cXNy4oCc5OXlcq9Jo83W8zEIJnLxVT601W7dupaOjg4yMDN599116e3uN7/39/fnc5z7Htddey+WXXz5hcOsN83n81WyOwQnF2kXV7eazXgU2AOcCcx7gCCGEN61YscLpys0jIyPU1tZSUVHhkCorKzGbzdTX11NfXz9uBefJ1NbWEhsb6/V/9bvKm60D8zloslgsVFRU8OGHH/Lmm2/y5ptvGu9rH1eTlZXFhRdeyAUXXMBnP/tZt7orxeSms1WDuyHdR7ZjlpvlCCHEguXr60tSUhJJSUlcdNFFDt8NDw9TU1PjEPiYzWbMZjMlJSUTlpmYmIivry9xcXEkJiaSmJhIQkKCw3lCQsKsbWexmPZ4msjg4CDFxcUcOXKEw4cPc+TIEfLy8hxaaMC6hEFERAQPPfQQ27dvZ82aNXNU46VnNrcWtW9+uXLSXEIIsUT5+fmRkpJCSkoKl1xyicN3k3UpxMTE0NzcTE1NDTU1NRPmi4qKIi4ujtjYWNasWUNsbKyR7J9Xrlwpg1pHGRoaora2FrPZTHl5OUeOHOHIkSMUFRUxODg4Ln9cXBybN9w1TFcAACAASURBVG/mvPPO44ILLuDrX/86Simuv/76Oaj90jadAMfdP/EdtmOEm+UIIYQYpampiZMnT1JXV0dNTQ21tbVGsGM/r6uro62tjba2No4cOTJhWUFBQaxZs4bo6GhWr149LkVFRRnnkZGR+PnN5r+TPW9kZITBwUH6+/t58cUXMZvNVFVVGce6ujpjIPBYaWlp5Obmcvrpp5Obm0tubi5RUVEOeSRYnDvT+ZN5SClVDOTbUh6Qr7XumPw2g72LK3QazxRCCMHU046DgoKMgcnOWCwWmpqaaGhooLGxkYaGBqfnnZ2dRrfYVJRShIeHs2LFCoc0mbfeeovg4GAjhYSEOHz28fFxKSjQWjMwMEB/fz8nT56kv79/3HlfXx/t7e0cO3ZswtTR0WEMqr322mudvmN8fDzJyckkJyezadMmTj/9dDZt2sTy5cunrOd8tRSWCHA1wFFAALDJlk59oVQDtmDHnrTWFZ6spBBCLHXuDs718fExuqMm09vbS0NDA62trbS2ttLW1macj03Hjx/nxIkTnDhxwuV6XHDBBS7nVUqhlDKCHqWUMQXf19fXI7N9lFL4+/sTGBjIpZdeSlJSEsnJycYxISGBgIAAt58z3yyFJQJcCXBuAHKAXKzBzdgxNHFALHCZ/YJSqg8oZFRLD+4PUhZCCOFly5YtIz09nfT09CnzjoyM0NXVZQQ5J06coKOjg5tuusnY6Xq0gIAAzjnnHPr6+oxWlrFpdNCitUZrPWEXUUBAAMHBwQQFBRktQKPPg4ODiYyMZNWqVROmiIgItm/fDsDvfve7Gf7UxHw0ZYCjtX4BeMH+WSmVwKmAJ9d2njDmtmXAmbYkhBDi/7d35+FR1lfDx78nCSEJyI5E9l0UqIBLFRfwUdyrLbVWtAqodXvbvtaltdaKG4VqH5eKT0VbRX3cq1ap+EqLS91ala0RRBIIWSAQEtlCdua8f9wzw2SfzJL7zsz5XNdcs93LuZkfmTO/NQGlpqbSu3dvevdu2LVy5syZQOS1A4HEJvTm8/lQVWbMmBE8pleGxRtvanfvMFUtxFmX6s3Aa/5VxEMTnsnAOMBKnzHGmHYJNEeFajy3TqBzcyLNvGtiKybd31V1N/Ce/waAiHTFWUk8NOmZCHTMRAzGGGMSRjzn1knE/icmjvPgqGoN8IX/BoA4KXnbDbvGGGOMMVEIK8ERkWzgJGCVqrY9drAF6vQe+zrS/Y0xxnQeVjNi3JQS5nYPAi8BuSJybRzjMcYYY4yJWpsJjoiMAn7of/qCqj4W35CMMcYYY6ITTg3Oj/z3+4Gb2nsCETlRRP5bRC7xDzE3xhhj2qWlGXYTaeZdE1vhJDjTcCbpe01VI+mu/gkwFXgWeDuC/Y0xxiS57du3o6pMmzaNadOmBefHsSHipiXhJDhH+O8jSk78HYvn4Sz3ME5EJkdyHGOMMcaYcIWT4ASmqCyM9CSqupyDo6fOivQ4xhhjjDHhCCfB2e+/r47yXMtwanGmRnkcY4wxxphWhZPgBPrdtL4Ebds+8d+Pi/I4xhhjjDGtCifB2eK/PznKc5X47w+N8jjGGGOMMa0KJ8EJNC39SEQyojhXpf8+M4pjGGOMSWLvv/++zZBswhJOgvNXwAdk44yGilQ///3e9uwkIk+KSKmIfNnC+yIifxCRPBH5j4hMiSJGY4wxxiSANhMcVS0GXsCpxblFRK6I8Fwn+O+L27nfElofeXU2MMZ/uxr4Y7sjM8YYY0xCCXc18Z8Dp+HU4jwhIocDv1HV2nB2FpFM4CqcCQM/ak+AqvpPERneyiYXAM/459v5l4j0EpHDVLWklX2MMcaYuLPmNPeEleCoapmInAesAHoCNwPfE5F7gedVtb6lfUWkK84sxkNwEpzno466oUFAUcjzYv9rTRIcEbkap5aHzMxMJk2ahJMXQUpKCvX19XTp0oXa2loyMjKorKwkKyuryX11dTXp6enU1dWRlpaGz+cLHB+fz0daWhq1tbV07dqV6upqMjMzmxyjqqqKjIwMamtrSUtL48CBA6SkOBVqPp+P1NRU6uvrSU9Pb/MYNTU1pKenU19fT0pKil1TJ7umAwcOICIJdU2J+Dkl+jVVVVXRrVu3mF7TV199RUpKCkOGDInZNQWOOWzYsJh9Tl9//TUiwqhRo+L6ORUVFbFz504yMzM58sgjPVX2jjnmGM/8fwJi0tUk3BocVHWViJwCvA6MBEYBTwEPiMgbwIfAl8BO4ABwGM7Iq+v82yvwjqp+0szhoyHNhdvCNTwOPA5wzDHH6BdffBHjUIxpP5/PF/zjYYxb4lEOp0+fDsS2FqOzHLOx3bt3M2LECACWLVsWPKdpSkRWxeI47SrNqpoDfAt4AiexEKAPMAf4M/BvYDNQAPwLuB8nuRH/65fHIuhGinFqhwIGA9va2qm6Otp5C42JjQ0bNrgdgjFWDuPswQcfZPfu3Zx66qmW3HSQdqfrqlqpqtcAR+E0N9VzMNlp6fYmcLyqlsUo7lBvApf7R1MdD+wJp/9Nenp6HEIxpv0Cv+qMcZOVw/j55ptvePDBBwG4++67XY4meYTdRNWYvzbnRyLyf4D/Ak4FhuNM5Cc4TVWfA2+o6ppIzyMiLwDTgX4iUowzVL2LP4bHcObpOQfIw5lrZ244x62rq4s0JGNiatu2bcG2f2PcYuUwfn7/+9+zb98+zjjjDE466SS3w0kaESc4Aaq6B6dfzuvRh9Ps8We18b4C/6e9x01Li/rSjYmJPn36uB2CMVYO42Tnzp384Q9/AOCuu+5yOZrk0mE9G0UkU0QuFZHlHXXO1gR6lhvjtsrKyrY3MibOrBzGx/3338/+/fs555xzOP74490OJ6nEvRrDP/JqNnAh0D3e5zOms7ERVMYLrBzG3vbt21m0aBFgfW/cEJcER0RG4oyYuhwYFnjZf9/sEO6OJtLc6HJjOl6XLl3cDsEYK4dx8Lvf/Y6qqiouuOACjj76aLfDSToxS9lFpIeIXCUiHwK5wG9wOh0HRlJt8L82OlbnjIY1URmvqKiocDsEY6wcxtjWrVv54x+dlYOs7407oqrBEaca5AycJqgLgMBq44HqkW3Ai8Bzqro6mnPFmnUyNl7Rr1+/tjcyJs6sHMbWggULqKmp4cILL+Soo45yO5ykFFENjoiMF5H7cCbZWwb8EMjESWz24cxwfDowRFVv9lpyA1BbG9YyWsbEXXFxe9efNSb2rBzGTmFhIU888QQiwrx589wOJ2mFXY0hIn2BS3BqayYHXvbf1+IkOs8Bf1PVmlgGGQ9du3Z1OwRjABg92hOttibJWTmMnfnz51NbW8vFF1/MhAkT3A4nabVZgyMi3xWR13Gamx7CWQQrkNh8gLN4ZbaqzlTVVztDcgO2VIPxjnXr1rkdgjFWDmMkPz+fJ598kpSUFKu9cVk4NTiv4Yx8CiQ1/8GpqXlBVTttnWZmZqbbIRgDYO3zxhOsHMbGr3/9a+rr67nssssYN26c2+Ektfb0wanHWSbhOFW9vzMnN2CTWhnvWLlypdshGGPlMAaWL1/OCy+8QEZGho2c8oBwExwBUoE7gR0i8oSInBq3qDpAVlaW2yEYA2DzYxhPsHIYnaqqKq677joA5s2bZ4uXekA4Cc5E4L+B7TiJTk/gCuAfIlIsIveLyOTWDuBFVoNjvMJ+ORsvsHIYnfnz57N582YmTJjATTfd5HY4hjASHFVdp6q3AENwVu1+CajBSXYGAjcCX4jIehG5TUQ6RdpqNTjGK+yXs/ECK4eRW79+Pffddx8AixcvtlmhPSLsPjiq6lPV/+df3TsbuBb4hIMzFY8D7gHyROQTEbleRDw7c1RVVZXbIRgDQE5OjtshGGPlMEI+n49rrrmGuro6rr76aqZOnep2SMYvoon+VHWvqj6uqicBY4D5QAEHk51vA48A20RkmX8VcU9VmWRkZLS9kTEdYOzYsW6HYIyVwwg99dRTfPTRRxx66KEsXLjQ7XBMiKjXolLVTar6G1UdAfwX8DSwHyfRSQPOBJ4BSkXkORE5N9pzxoLNZGy8orCw0O0QjLFyGIHS0lJuueUWAB588EF69+7tckQmVMwW2wRQ1fdVdS5OE9Zs4F3/WwJkARcDb8TynJGytaiMVwwYMMDtEIyxchiBm2++mV27djFjxgxmzZrldjimkZgmOAGqWqmqz6rq6Tgriv8G2MjBJizXHThwwO0QjAFg9+7dbodgjJXDdlqxYgXPPvssGRkZ/PGPf8RZe9p4SVwSnFCqWqSq81V1HHAi8Hi8zxmOlJS4X7oxYbH+YMYLrByGr7q6Ojjnze23386oUaNcjsg0J6J2GnFS1TOAU3HmyekPVACfAX9R1S+a209VPwU+jSxUY4wxxn0LFiwgNzeXI444ItgHx3hPpB1R8nCanqBhk9M04BYReQ+4XlU3RhFbXPl8PrdDMAawhV+NN1g5DM+GDRtYsGAB4Mx5k56e7nJEpiWRttOMwElstgDLgb/h1MzU+l8/FVgjIj+KQYxxkZqa6nYIxgDQq1cvt0MwxsphGFSVa6+9lrq6Oq688kpOPvlkt0MyrYg0wXkKmKCqo1T1LFU9X1VPBAYAt+E0V2UAT4vIbTGKNabq6+vdDsEYAHbs2OF2CMZYOQzD008/zQcffED//v2DMxcb74p0or8rVXV9M6/vVdWFOP1yArMc3yMiC6ILM/asWtF4xdChQ90OwRgrh20oKyvj5ptvBuCBBx6gT58+Lkdk2hKvYeKFOM1UT+IkOb8Qkfvjca5IWXuz8YqNGz3bVc0kESuHLVNVrrnmGsrLyznttNO49NJL3Q7JhCFuY6VVtU5VrwJ+gZPk3Cgi98brfO2VmZnpdgjGADBx4kS3QzDGymErHn74YV577TV69OjB4sWLbc6bTqIj5sH5PfB/cZKcX4nIDfE+ZzgqKyvdDsEYAFauXOl2CMZYOWzBv/71r+BQ8KeeesrmvOlEYr5egYikAgOBwcAg//1gYDvOEg6/Bx6K9XnbKyvLU2t/miR29NFHux2CMVYOm1FeXs5FF11EfX09N9xwAzNnznQ7JNMOkU70N42DiUtoEjMYOJTWl2PwRN2e1eAYr1i5cqV9uRjXWTlsyOfzcdlll1FUVMTxxx/P7373O7dDMu0UaQ3Oe4A283pbyUslUNzek4nIWcDDQCrwJ/9IrdD3h+F0aO4PfAP8SFVbPY/V4BivsC8V4wVWDhtasGABb7/9Nn369OGll16ykbedUDRNVKHJjAJlwNaQW3Hj56q6p90ncZq8HgVm+I/5uYi82WiY+u+BZ1T1aRH5L2ABcFlrx62qqmpvKMbExdq1aznqqKPcDsMkOSuHB7333nvccccdAPzv//6vDaHvpCJNcG6mUQKjqnUxi6qh44A8Vd0MICIvAhcAoQnOkcDP/Y/fA/7a1kFtYTnjFePHj3c7BNNJTJ8+HYD3338/5se2cugoKSlh1qxZ+Hw+fv3rX3P22We7HZKJUKQT/T2gqi+r6sequiWOyQ04fXyKQp4X+18LtRb4vv/x94BDRKRv4wOJyNUi8oWIfLFt2zbKysooKSlh69at7Nq1i02bNlFVVcX69evx+XysWrUKODi6YNWqVfh8PtavX09VVRWbNm1i165dbN26lZKSEsrKytiyZQsVFRVs2LCB+vp61q5d2+AYgfucnBxqamrIzc1l7969FBYWUlpaSmlpKYWFhezdu5fc3FxqamrIyclp9hhr166lvr6eDRs2UFFRwZYtW+yaOuE15eTkJNw1JeLn5IVrqquro66uLi7X9Omnn8b8mvbt2xfzz6miogJVjennVFVVRX19PQUFBcycOZMdO3ZwwgkncMstt1jZc+GaYkVUm+tK4x0i8gPgTP+cOojIZcBxqvrTkG0GAotw1sj6J06yM761JrEpU6ZorP8xjYlERUUF3bt3dzsM0wnEswYnHuUwHvHG85gnnHACCxcuJDs7m9WrV5OdnR2zc5jwichKVT0m2uPEfJh4HBQDQ0KeDwa2hW6gqtuAmQAi0h34flv9fWwtKuMVZWVlluAY1yV7OSwvL2fhwoWkpKTw4osvWnKTAOI+0V8MfA6MEZERIpIOXAy8GbqBiPQTkcC1/ApnRFWrUlI6w6WbZJDMXyrGO5K5HFZXV7NhwwYA5s+fz7Rp01yOyMSC57/lVbUe+AnwDvAV8LKqrhORu0XkfP9m04GvRWQjzorm88M4bpwiNqZ96uri2YXNmPAkazmsra1l/fr11NfXc9555/GLX/zC7ZBMjHSGJipUdRmwrNFrd4Q8/gvwl46Oy5hY8Pl8bodgTFKWQ5/Px49//GP27dtH165defrpp612P4Ek7SfZGQpxXV0dr7/+OldeeSUTJkygR48eZGVlMXHiRO64447gCAXTudmkk8YLkq0cqio33ngjzzzzDCkpKYwfP54+ffq4HZaJIe9/y8dJZ+hkvGnTJmbOnMlLL73EiBEjuO6665g7dy5VVVXcc889HHPMMZSVlbkdponSN99843YIxiRdObz33nt5+OGH6dKlC+PHj+eQQw5xOyQTY52iiSoeunTp4nYIbTrkkEN49NFHmT17Nt26dQu+Xltby8yZM3nrrbe46667eOSRR1yM0kRr4MCBbodgTFKVw0cffZQ77riDlJQUnn/+eRYtWuR2SCYOkrYGp7a2NuJ9hw8fjoi0eJszZ05MYhw0aBDXX399g+QGID09ndtuuw2Iz3wYpmPl5+e7HYIxSVMOn3vuOX7yk58A8Pjjj3PhhRe6HJGJl6StwYlmqYYbbriB3bt3N3l96dKlrFq1qkPasgM1UGlpSfsRJoxx48a5HYIxSVEO//a3vzF79mwA7r//fq688kqXIzLxlLTfjpWVlRHve8MNNzR57e9//zvz589n9OjR3H333QCsWbOGv/61zWWxmhy7V69ebW735JPOVD9nnXVWu45vvGfNmjVMmTLF7TBMkkv0cvjPf/6TH/zgBxw4cIBbb72Vm2++2e2QTJwlbYITy1qWL7/8kgsvvJCePXuybNky+vXrBzh/MO666652HWvOnDltJjhvvvkmixcvZvDgwTZnQwJI5C8V03kkcjlcvXo13/nOd6iurubqq6/mt7/9rdshmQ6QtH1woqnBCVVSUsK5555LTU0Nr7/+OmPGjAm+N2fOHFS1Xbfhw4e3er5PPvmESy65hG7duvHqq6/Su3fvmFyHcU9gcTpj3JSo5XDjxo2ceeaZ7N27l4suuoj/+Z//QUTcDst0AKvBicL+/fs577zzKCoq4rnnnuPkk0+OQWQt+/TTTzn77LNJSUnh7bff5rjjjovr+UzHOProo90OwZiELIdFRUXMmDGDnTt3cuaZZ/Lss8+SmprqdlimgyRtghNtDY7P52PWrFmsWrWK+fPnM2vWrCbbxLIPzocffsi5555LSkoK77zzDscff3zEsRtvWbVqVUI3D5joZWdns2PHjuDzQA3EgAED2L59e0zOkWjlsLS0lDPOOIPCwkJOOOEEXn31VdLT090Oy3SgpE1woq3BueGGG1i6dClXXHFFcMh2Y7Hqg/Puu+9y/vnnk56ezjvvvMOxxx4bcdzGeyZNmuR2CMbjQpObcF6PRCKVw02bNnHWWWeRl5fHxIkTeeutt5pMt2ESX9L2wamuro5434ceeohHHnmE0047jccee6zF7WLRB2f58uWcd955ZGRksGLFCktuElBgFWNj3BTLcpidnY2I8MEHH/DBBx8E5wjLzs6O2Tla8tlnn3HCCSeQl5fH5MmTWb58ufVVTFJJW4MTaVXl9u3buemmmxARJk6cyPz5TRcunzRpEt/97nejDZGvv/6aCy64gOrqas455xzeeOMN3njjjSbb3XnnnVGfy7hnxIgRbodgTEzLYUfUODVn6dKl/PCHP6SqqoozzzyTV155xZZgSGJJm+DU1dVFtF91dXVw1d2HHnqo2W1mz54dkwSnpKQkWNP06quv8uqrrza7nSU4ndu2bdsYNWqU22GYJNfZy+HixYu5/vrr8fl8zJ07l8WLF3eKJXlM/CRtghPpDMDDhw9HVWMcTfOmT5/eYecy7rEVjI0XdNZyqKrcfvvtwblt5s2bx7x582wouEnePjiBWhhj3BarOZlM4howYEC7Xm+P6dOnM3369E5ZDmtra5k9eza//e1vSU1N5U9/+hN33nmnJTcGSOIaHGO8IiUlaX9nmDAFhoJPnz4diM8iu52tHO7Zs4fvf//7rFixgm7duvHKK69w9tlnux2W8ZCkTXAswzdeYf0EjBfEshwOGDCg2Q7FsahxAqipqeGUU07hP//5DwMGDOCtt95KyIkKTXSSNsEpKSlhyZIljBw5khEjRjBo0KBO9wvGJIaKiorg+mXGuCWW5TCeNU779u1j3bp11NTUcPjhh/P222/bSETTrKRNcEpLS5k7d27weXp6OsOGDQsmPKH3I0eODGuFb2MiYcmN8QKvl0Ofz8cDDzzA6tWrUVVOPPFE3njjDfr27et2aMajkjbB6devHzNmzCA/P5/NmzdTWlpKbm4uubm5zW7fu3fvBglP6G3o0KHWzGAiVlxczLhx49wOwyQ5L5fD7du3M3v2bJYvXw7AwIED+cc//kFGRobLkRkvS9oEZ+jQoTz//PPB5/v37yc/Pz9427x5M5s3bw4+3rVrF7t27WLVqlVNjpWSksKQIUMYNWpUg9vIkSMZNWoUPXv27MhLM53M6NGj3Q7BGM+Ww2XLljFnzhx27txJ3759yc7Opl+/fpbcmDYlbYLTeKmGbt26MWHCBCZMmNBkW1WltLS0QeITeisuLqagoICCggLefffdJvv37du3SfITuB122GHW4TnJrVu3jqOOOsrtMEyS81o5rK6u5tZbb+Xhhx8G4LTTTuOZZ57hkksucTky01kkbYKTmZkZ9rYiwoABAxgwYECzq3jX1NRQUFDApk2b2LRpE5s3b27wuLy8nPLycj777LNm4wjU9IwePTqY+IwePZphw4ZFPCGh6Ty89KVikpeXyuFXX33FrFmzWLt2LWlpacyfP58HHniAQYMGBbeJx4rqJrEk7bdnLCe16tq1K2PHjmXs2LFN3lNVtm/fHkx4Gt/KyspYt24d69ata7JvWloaw4YNY/To0U1uI0aMoGvXrjG7BuOelStX2hBX4zovlENV5YknnuCGG26gqqqKUaNG8cILL3Dsscfyy1/+stl94r2+lem8kjbBycrK6pDziAiHHXYYhx12GCeddFKT9/fs2dMk6cnLy2PTpk0UFRUFX3vnnXeaHHfIkCGMHj2aMWPGBBOfMWPGMHLkyHbVUBl3uf2lYgy4Xw5LS0u5/vrrg2vuXX755SxatMgWyzQRS9oExyvTkvfs2ZMpU6YwZcqUJu9VV1eTn59PXl5ek1tBQQGFhYUUFhY22+9n8ODBTRKfQBNYRyV3Jjxe+OVsjFvlsK6ujkcffZR58+axd+9eDjnkEB577DHra2OilrQJTmf4ks/IyOCII47giCOOaPJeXV0dW7ZsaZD05ObmkpeXR35+PsXFxRQXF/Pee+812Xfw4MHBpCeQ+IwZM4ZRo0ZZzY8LLLkxXuBGOVyxYgU/+9nPWL9+PQBnn302ixYtYuTIkR0ei0k8SZvgVFVVuR1CVLp06RJMUBqrr6+noKAgmPQEEp+8vLzgqK/i4uJmZxcN1PyMHTs2ePxAs1dbfX6ys7NbnJ7dOgG2LCcnh4kTJ7odhklyHVkOCwoKuOmmm4LNUaNGjeKhhx7ivPPO65Dzm+TQKRIcETkLeBhIBf6kqgsbvT8UeBro5d/mVlVd1toxE3kOhbS0tOBorDPPPLPBe/X19RQWFgaTntAEKDT5aVzzk5KSwtChQ5tNfoYPH06XLl1a7OwXbSfARE+cmuucbkxH64hyWFVVxX333cfChQuprq4mKyuL22+/nRtvvLHNH1DxXt/KJB7PJzgikgo8CswAioHPReRNVV0fstntwMuq+kcRORJYBgxv7bi1tbVxitjb0tLSgjMwN5f8FBQUBJOewG3jxo1s2bIlePv73//e5JjxXAsmXomTVxQWFjZbE2dMY/FYRTwgnuVQVXn99de58cYbKSgoAGDWrFncd999DB48OKxjdMSK6iaxeD7BAY4D8lR1M4CIvAhcAIQmOAr08D/uCWxr66A2v0xToTU/Z511VoP3amtryc/Pb5L45ObmBmuEWvO9730vWOMTqAFyc5JDL9UK2S9Q4wXxKod79+7ljDPO4B//+AcA3/rWt3jkkUc45ZRT4nI+YwI6w7f8IKAo5Hkx8O1G29wJLBeRnwLdgNPbOuiBAwdiFV9SSE9P5/DDD+fwww9v8l5VVRWbNm1qtf3+r3/9a5PXunXr1iDhCb2P9wJ6XqoV2r17Nz169Gh7Q2PiKJblUFVZsWIFa9euZffu3YCznt+9997L1VdfbT8wTYdIcTuAMDT3E18bPZ8FLFHVwcA5wLMi0uTaRORqEflCRL4oKyujrKyMkpIStm7dyq5du9i0aRNVVVWsX78en88XXHdq5cqVAKxatQqfz8f69euDX+q7du1i69atlJSUUFZWxpYtW6ioqGDDhg3U19ezdu3aBscI3Ofk5FBTU0Nubi579+6lsLCQ0tJSSktLKSwsZO/eveTm5lJTU0NOTk6zx1i7di319fVs2LCBiooKtmzZ4so1rV+/vtklLkItWrSI22+/nQsuuIBvf/vb9OzZk/3797NmzRpefvll5s+fz+zZs5k6dSr9+vWjV69eHHvsscycObPV40Z6TW3pyM+prq7Oyp5dk2vXVFVVhc/nY8eOHVFf0/r163n11VeZOHEiM2bMYPfu3aSmpjJ37lw2btzIySefzIEDB6K6poqKClQ1pp9TVVUV9fX1nv6cErHstXRNsSKqjXMFbxGRE4A7VfVM//NfAajqgpBt1gFnqWqR//lm0etAWwAAGnRJREFU4HhVLW3puJMmTdI1a9bENfZk095mn/Ly8gZNXaFNXxUVFWGdc+HChcFan/YMc2+taSya/xORNH2VlpZy6KGHRnxOY6IR6NPy8ssvR1wO6+vrefHFF1mwYEFwyHe/fv3o3r07gwYN4qOPPopVuHHpg2P9erxFRFaq6jHRHqcz1BN+DowRkRHAVuBioPEMUIXAacASETkCyAB2tnZQn88Xh1CTW3v7rvTt25e+ffs2Wd9LVdmxY0cw8fnZz37W4sSMt956a/CxiDB48OAmo7wCw9zT09Pbf1HtFEnTV+OFX41xQyTlsLq6miVLlnDfffeRn58PwJAhQ7j55pu56qqrOOecc2IdpjFh83yCo6r1IvIT4B2cIeBPquo6Ebkb+EJV3wRuAp4QkZ/jNF/N0TZ+hqempsY7dBMhESE7O5vs7GxOOeUUrrzyyuB7Pp+PoqKiJjU/GzduJD8/n6KiIoqKilixYkWDY6akpDB8+PAW5w5yU69evVp930sdok3iaqschvr666956qmnWLJkSbBsjh07lltvvZVLL720Q35MGNMWzyc4AP45bZY1eu2OkMfrgRPbc8z6+vrYBGc6VEpKCsOGDWPYsGGcfnrDvuSB2Z2bG+ZeWFjI5s2b2bx5c5N1vUJlZWWxaNGi4OzOHbGi+44dO1rt3OmlDtEmcbVVDvft28fLL7/MU089xccffxx8fdKkSdx2223MnDnTfjgaT+kUCU482C+MxNPa7M41NTVs3ry5SfKTl5dHUVFRsN9NZWUlP/3pT4P7Beb4aW5F9+HDh8ekHA0dOjTqYxgTrebKoary4Ycf8uSTT/LKK68Em4q7d+/ORRddxBVXXMHUqVNdm+7BmNYkbYJj/R6SS9euXVtc16uqqiqY/ITO7pybm0txcXHwcWOB2qTQpCcSGzdutKUajOtCy2FxcTFPP/00S5YsIS8vL7jNKaecwty5c7nwwgvp3r27W6EaE5akTXBsUUkTkJmZyfjx4xk/fnyT9wLDLfPy8oL3oSu65+fnk5+f32R258a6d+/OCy+8EJxIsU+fPsFfvZbcGLepKgcOHOCee+5h6dKlfP7558H3Bg0axOzZs5kzZ47n+q8Z05qkTXBaGpVjTKjMzEwmTJjQ7Dw/NTU1TVZ0DyRBW7Zsoa6uLrhtRUUFl1xycPBfz549g0tmZGVlMXXq1ODzoUOHdkgTajw6L1uH6M6jurqa9957j9zcXMrLy5k8eXLwvYyMDL7zne9wxRVXMGPGDOtbYzqlpE1wsrKy3A7BdHJdu3ZtcXbnAwcOUFRUxKZNm5rc8vLy2LNnD6tXr2b16tUAPPvss8F9U1JSGDx4MCNHjmTEiBF069aN/fv3NzlHtFPrx6PzsnWI9rbt27ezbNkyli5d2uzs4uCMptq6dav9jTSdXtImOFaDY+IpNTWV4cOHM3z4cE477bQG76kq5eXlwVFdH374YbAT9ObNmykqKqKwsJDCwsJmJx7r0qULQ4cOZcSIEfz4xz9m+PDhwZFlw4YNY+DAgTYVvge4PXmcqrJx40Y+/vhjPvroIz7++GM2btzY5n67d++25MYkhKT9K2j/gY1bRIR+/frRr18/jjvuOC6++OIG79fW1gaHtW/ZsoX8/PwG9zt27AjWBjUnNTWVQYMGBROeoUOHBh8PHjyYIUOG2NpXCai2tpaVK1c2SGgaL0uSlZXFtGnTOP/887nuuutcitSYjpG0CU5VVZXbIRgDOGu2HHXUUcHn6enprY7KqqyspKCgoEHSU1BQQGFhIQUFBZSUlARrgD788MNmj9FWgrN792569uzpueG/bteKeMWuXbvIycnhyy+/JCcnh5ycHFauXNlkdGh2djYnnngiJ510EieeeCKTJk2iS5cuAJbgmISXtAlORkaG2yEYA9Ds6K3WZGVltTjkHZzOz0VFRQ2SnsDj4uJiioqK2Lt3b6vn6N27N5mZmQwcOLDFW3Z2NgMGDKBXr16eS4QSRWVlJV999VUwkQncb9u2rdntjzzyyAYJzciRI+2zMUkraROcmpoat0MwBoC8vDzGjRsXs+N17dq11RogVWXXrl2MHTuW8vLyJu+npqaSmZlJRUVFq01hAenp6Rx66KEMGDCA9PR0amtrm2zTt29fCgoK6Nu3L926dWv3l27j0VmB/Tv76Kz9+/cHa+MCNXGBx1u2bKG0tPn1ggNTG0ycOJEJEyYwceJEpkyZQt++fTv4CozxrqRNcGwmY+MVgwcP7tDziQh9+vRp0j+jsX379lFSUsK2bduavZWUlLBjxw727dtHcXExxcXFLR6rvLyc4cOHA87/vcBCq/369Qs+7tOnDz169KBnz54Nbj169Og0o7MOHDjA7t272blzJ7t376a+vp7HH3+cnTt3Bm9lZWXs3LmT4uJidu5sdU1g0tLSGDNmTINEZsKECYwcOZKUlJSoYh0wYECLQ/qNSQRJm+DYWlTGK8rKyjw5K+whhxzCIYccwtixY1vdrqqqitLSUnbs2NHkVlpaSmlpKeXl5cFbdXU1JSUllJSUxCTOyZMnk5WVRWZmZoP7rl27kpaWRmpqKmlpacFb6PPU1FREhPr6eg4cOMCBAwdafFxdXc3+/fupqKhg//79zT5ubob0a665psXY09PTGTZsWHAkXGDkXeD5YYcdFrc5aAI1X4F+TUuWLAkmocYkgqRNcKL99WNMrHgxuWmPzMzM4CitcFRWVjZIeAK3b775hr1797Jnzx727NnT4PGXX37Z4vHWrFkTq0uJiV69etG/f3927NhBly5d+O53v0v//v3p378//fr1Cz4eNGgQAwYM8Mzfos5eDo1pLGkTnMDiisa4LXTG42SQlZVFVlYWQ4YMCXuf1vrsrFy5kqqqKiorK4P3lZWV1NTUBGthAjUxzT1W1QY1O6mpqU0ep6amkpGRQbdu3ejevTvdunUL3kKfBzpmh65d9uc//xnwfn+hZCuHJvElbYJjjFf4fD63Q+jUpkyZ4nYIDXSW/kKNWTk0icYbdaMu8Eq1sDE26WTbWur4ah1iY8fKoUk0Sfstb52MjVd88803bofgedu3b0dVmTZtGtOmTUNVUVVPN/l0NlYOTaJJ2iaqwGyexrht4MCBbofQaST7DMbxZOXQJJqkrcFpbjIyY9yQn5/vdgjGWDk0CSdpExxbqsF4RSxnMTbu66z9hawcmkSTtAlOZWWl2yEYA3hvHhcTnc7aX8jKoUk0SZvg2IgB4xVeG+ZskpOVQ5NokjbBsRoc4xUrV650O4SkNn369OByBcnMyqFJNEmb4FgNjvGKo48+2u0QjLFyaBJO0iY4VoNjvGLVqlVuh2CMlUOTcJJ2HhyrwTFeMWnSJLdDSErZ2dkNlk8IrHfl9TWj4sXKoUk0SVuDU11d7XYIxgCwYcMGt0NISvFeM+r999/vVBMTWjk0iSZpE5z09HS3QzAGgBEjRrgdgjFWDk3CSdoEp66uzu0QjAFg27ZtbodgjJVDk3CSNsFJS0va7kfGY/r06eN2CMZYOTQJJ2kTHJ/P53YIxgA2os94g5VDk2iSNsExxitSUuy/oRs665pR8WLl0CSapG2nCQwJNcZtXbp0cTuEpBQYCh6YxbgzjXiKByuHJtGIqrodgytEZB/wtdtxRKknsKeTnzPa40Wyf3v2CWfbtrZp6/1+QFmY8XhVR5fFeJwvmmN6oRyGs11r71s59MY5k+FvYlvbHK6qh4QZT8sCK90m2w34wu0YYnANj3f2c0Z7vEj2b88+4Wzb1jZhvG9l0QPni+aYXiiH4WzX2vtWDr1xzmT4m9jWNrEqi9bo2rktTYBzRnu8SPZvzz7hbNvWNm58Th2to68xHueL5pheKIfhbJfoZdH+JnqjLMZqm6gkcxPVF6p6jNtxGGNl0XiBlUPjFbEqi8lcg/O42wEY42dl0XiBlUPjFTEpi0lbg2OMMcaYxJXMNTjGGGOMSVCW4BhjjDEm4ViCY4wxxpiEYwmOMcYYYxJO0ic4IjJSRP4sIn8Jee27IvKEiLwhIme4GZ9JDi2UwyNE5DER+YuIXOdmfCZ5NFcW/a93E5GVInKeW7GZ5NLC38XpIvKh/2/j9Nb2T8gER0SeFJFSEfmy0etnicjXIpInIrcCqOpmVb0ydDtV/auq/hiYA/ywwwI3CSUG5fArVb0WuAiw+UlMxKIti36/BF7uiHhN4opBWVSgAsgAils7V0ImOMAS4KzQF0QkFXgUOBs4EpglIke2cZzb/fsYE4klRFkOReR84CNgRfzCNElgCVGURRE5HVgP7IhvmCYJLCG6v4sfqurZOAn3Xa2dKCETHFX9J/BNo5ePA/L8GWEt8CJwQXP7i+N3wNuquiq+0ZpEFW059B/jTVWdClwav0hNootBWTwVOB64BPixiCTkd4eJv2jLoqr6/A93AV1bO1cyFdJBQFHI82JgkIj0FZHHgMki8iv/ez8FTgcuFJFrOzhOk9jCLof+tuY/iMhiYJkLsZrEFnZZVNVfq+oNwPPAEyFfMsbEQnv+Ls70/018FljU2kHT4hWtB0kzr6mqlgPXNnrxD8AfOiQqk2zaUw7fB97vgJhMcgq7LIa8uSSuEZlk1Z6/i68Br4Vz0GSqwSkGhoQ8HwxscykWk7ysHBqvsLJovCIuZTGZEpzPgTEiMkJE0oGLgTddjskkHyuHxiusLBqviEtZTMgER0ReAD4FDheRYhG5UlXrgZ8A7wBfAS+r6jo34zSJzcqh8Qori8YrOrIs2mrixhhjjEk4CVmDY4wxxpjkZgmOMcYYYxKOJTjGGGOMSTiW4BhjjDEm4ViCY4wxxpiEYwmOMcYYYxKOJTjGGGOMSTiW4BhjjDEm4ViCY4wxERCRH4nIYhFZKSI1IqIi8iO34zLGOJJpNXFjjImle4FhwE5gBw0XCzTGuMxqcIwxJjJXAcNU9VDgSbeDMcY0ZAmOMcYTRCRVRHL8TT1XtrJdiojs9293f0fGGEpV/6GqhW6d3y0i8hv/v/0Kt2MxpjWW4BgTJRGZ7v+DH7hViUjPMPfd2Gjfa+Mdr4ddB0wAtgDPtLLd4UCW//HaOMcUFyJyWaPPfZ+ISBj7jRGR2kb7XtARMYf4A7Ab+C8R+V4Hn9uYsFmCY0zsZQAXtrWRiEwFxsQ/HO8TkSzgdv/T+apa18rmk0Ied8oEh4bXANAdGBrGfr8DujR6bXVMIgqTqu4BHvY/vVdE7HvEeJIVTGNiq9p/f3kY2wa2qYpTLJ3JtcAAoAx4uo1tA8lBLfBVPIOKo8n++8qQ18a3toOInAwEakxq/Pe7XGom+wPOv/+RhJHMG+MGS3CMia03/Pcni8jwljYSka7ARY32SUoikgr81P/05TZqb+BggrNOVeujOG9xo6aetm5zIj1XM47y3/8LZwQWtJLg+Juv/tv/9P/hJBfQwbU3Aar6jT8OgP/rRgzGtMWGiRsTWx8Ax+MMH/4RzlDi5pwP9Mb5Jf4ycHGHROdNpwPD/Y//N4ztA8lBtM1TLwN92rF9XpTnA0BEhoWcdw1wAJhB6zU4lwDH+rd9DDgrZH+3PIdTjqeKyBGq2llr00yCsgTHmNhSnD/8twGX0XKCE2ie+huwq62DisgE4LvAyThfhP2BOqAE+AT4o6r+q41jZOPUlJwJjAa6+c+9E9gALAdeU9WdsdivHX7ov9+mqp+2cQ0DcZqywP/l7m+6+THOv81AYA/wd+B2Vc1v6ViqemOE8UYrtP/NGpwy02KCIyIZwG/9T/8EpIe87UoNjt9SnJqkdJwE7DcuxmJME5bgGBN7z+AkOGNF5Nuq+u/QN0WkPwd/gbc2Wiiw/XTgvWbeSsdJOEYDl4vIQlX9VQvHOBEnmerV6K3+/tuRwExAcGoIotqvnU713/+71a0cocnB1yLyFDCnmdguAU4Xkcmqui3CuOJlcsjjNTj/dgBHioioqjba/uc4HZArgHnAz0Lecy3BUdUqEVkDHAecgyU4xmMswTEmxlT1axH5DOcP/2U0/eK+FOf/XhnwNnBiG4dMA/YDbwHv4tSa7AUOxfnV/zOcJrFbRWSjqj4VurOIpAMv4SQpFcBiYAVQ6j/2MODbODVEUe/XHiIymIPNU5+FsUtognM7cAJOs9YrwFacROBWnH/7Q4EbgZsjjS9OAglODU4n6VT/8yycf4tgrZOIHIpzPQALVXWHiAT+DapxyoKb/o3zbz1ZRHqo6l6X4zEmyBIcY+LjGZw//BeLyM8bdZwNNE+9oKp1YUx/sgYYrKq7m3nvHRFZhFPLMgOYJyLPqOqBkG1OAgb5H1+iqksbHePfwMsicjMNa2oi3a89poY8XhXG9qEJzlHADFV9N+S1lSLyLpCLU5PTVvIYMRG5CuffKDSuq0XkdP/jj1T1T83sGtj2S1WtF5H1QD3O3+PxhCQ4wF1AD6AYeKDR/jmNPmc3rPTfC07i9oGLsRjTgI2iMiY+XsTpI9MXp/oeABEZz8Ff8G02TwGoalkLyU3g/VrgFv/TYTSdYyU75HGLX0DqCO0PFOl+7TE45PGOFrc6KPTaLmuU3ATi2cPBL97uEcYVjpOA2f5boOPzySGvndR4BxHpzcH5btb4460Fvva/Nj5k2yNx+hYB3OZvEuqP088ouL/LSkMej3QtCmOaYQmOMXGgquXAMv/Ty0Lemu2//0pVv4jk2CLSVUSGisiRIjLB3wE5tBroqEa7hPZBmduOU0W6X3v0D3n8TWsbikh3nP5GACtU9a+tbB5IbMqjiK1VqjpHVaWV25xmdmvc/ybgP/770I7G9+M0X63i4Oiy0P3d7GAcEPqZZbe4lTEusATHmPgJ1NCcJyK9/TO+Xup/7dn2HEhEuonIr0RkLU5/nAJgHZDjv4V+2fVrtPvHHBzi/JCIfC4ivxaRk/0jdFoS6X7t0TfkcYu1VH5HcTCRW9LGtoEZor9udauOF1aC42/mCtT83RTS8bjxCCy3hSY43VyLwphmWB8cY+LnbzhfAH1wJvXbgtO84CO8+V4A8E8Y+C4wIsxdMkOf+Pv5fAenI+4E4Bj/DaBGRD4Bngee8TeXRLVfO4WOGMoA9rWybeiX+z9b2khEBnBwKPl/WtrOJYFrUBrO4xOI8wgRSQN+73/+pqq+38z+PrxxbaFlra0JGo3pUFaDY0yc+L/0X/I/vZyDnYvfV9WidhzqWZzkRoEngTOAIUBGoDmEgyNxoGFzVSCWDTg1IOcBT3CwZqMrzjDtJ4AcERkdi/3aIbTWpq1J9wJf7t+0sTyB15pxQgVi26yqoclcIFnJBO7G+TevB37Rwv4bVXV/e08uIt8SkctF5FoROdff7BeN0M+srRo4YzqUJTjGxFegmWoq8P1Gr7VJRMZxsLPqb1X1SlX9u6oWq2pNyKZtzsirqj5VfUtVr1bVcTi1HD8CPvRvMpaDCVnU+4WpIORx7za2DSQ4bSUtgSRA8UYtBxCcsG+c/2mD5iVVLeZgc88v/feLVTXYxCYimRxsemtX85SInCgiq3FqjZ4G/ohTw1gqIvNFpPECnuEK/czcWBPLmBZZgmNMHPlnF97of9oVZ3HFV9txiNBOp60lEce08l6zVLVUVZ8DpgHv+F+e0lZtTKT7tWBdyOOxLW3kX69qgv9puAlOrqpWRBBTvEzkYE1bcwlKjv8+BWc25jsbvf+tkP3DrpkSkYtwJopMwWkq7YdTU3QU8DhOLdHyCPtVHR7yeF2LWxnjAktwjIm/JTiTutXgLCbZni/d0H5yrXXivDaCuABnmDdOH5+Axp2UY7pfI6twmmLAWWupJUfg9NGB8BMcL3TCDdVSB+OAlRwsJ/NVtazR++3uYCwiE3FqDN8CjlPVV1S1XFWrVfU/qnoDcC7OhImLwryOUN/233+DM/eQMZ5hCY4xcaaqC1Q1w39r73Dr0C+NOc1tICLXARe0dAD/qKcxrbyfApwWCBenM3TE+7WHvx9KYA2t41rZNKwvdxE5BBjV1nYuafUaVPWmkHJyfzP7R9K36B6c+YUubdSkGXre5ThLi1whIkeEedyAwGf2j2aWmDDGVTaKyhhvWw18idM8c41/orhncRbZHIzTF+ZCnCHdLc3aexrwGxH5CGdunrU4E7R1xZmc7UoOrgf1uqpuj3K/9noNp5/RZBHpo6rNzYcTSA6qaH3o9yQOdrL2agfjMn+fm/YK/BtsC2dhU3+fnbOAu1S1MuT1M3HmH/q7qgYmV3wMmI+z7EZYq4L7k6HApIOvh3UFxnQgS3CM8TBVVRG5DKcpqDdOH4qLGm2WA/yAhhPzNZYCnOK/teQD4KoY7dceLwD3AV1wrmNxM9sEvtz/08byBG01A7nCX9s10f90bWvbhrF/uInbYJxktPG/w104TUun4p89WlUrRSSXgxMphuMS//1e4I127GdMh7AmKmM8TlXX4HzBP4Yz6qgOp8/DZzgLSR6nqiWtHOI+nEnjHgA+9R+jGqevRyHOr+8fAqc2WnIh0v3ae33bcWpx4OBEiI0FZmduK2kJJDg7oqhRioexHOxDFUniNRZnMc727O/z34f7QzYVaM/aVoEE50lVrWrHfsZ0CLFmU2OM20TkaOALnL48R4QOjzaR8a8GXw48oqq3hbzeE6e2bE9gEVgR6YNTm3OLqj4UxrFPxalVrAPGquqW2F+BMdGxGhxjjOtUdSXwJk7/md+4HE5C8E80+SpwrX9258Dre/wLuIbOPHwbTo3Pa4Qn8Bn92ZIb41VWg2OM8QQRORynP1EKMN5qcaInIkNw+uxsBs5trnOyfxTeo8A9qjovjGOejLNUxl6c2ptwVoE3psNZgmOM8QwRuRhntt9/quq7bW1v2iYiU4GlOP1rHsFpWqrAmaRvLs5IqyeBq8IZ6i0i5wNTgC9U9W/xituYaFmCY4wxCc6/YOtC4HtAeshbeTg1N2EvH2JMZ2EJjjHGJAn/RIhH4izVUKyqeS6HZEzcWIJjjDHGmIRjo6iMMcYYk3AswTHGGGNMwrEExxhjjDEJxxIcY4wxxiQcS3CMMcYYk3AswTHGGGNMwrEExxhjjDEJ5/8DiGSuL+qhehgAAAAASUVORK5CYII=\n",
      "text/plain": "<Figure size 576x432 with 3 Axes>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "## HaloMass Function Fits"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "from haloPropertyAnalysis.comparisons import Sim, CompareSims\nfrom lsscosmo import halomassfunction as hmf\nfrom lsscosmo import psutils as psu\nfrom lsscosmo import hacc\nfrom utils import plotutils as pu",
   "execution_count": 38,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "#fig, ax = plt.subplots(figsize=(6.4, 4.8))\n#fig = plt.figure(figsize=(8, 6))\nfig = plt.figure(figsize=(8, 6))\nax = plt.gca()\ndf = dataframe('M000_0.dat', datadir)\nax.errorbar(df.MininvhMsun, df['dn/dlnM'],\n            yerr=(df['massfnerr'],df['massfnerror']), fmt='ks',\n            label='z=0')\ndf = dataframe('M000_1.dat', datadir)\nax.errorbar(df.MininvhMsun, df['dn/dlnM'],\n            yerr=(df['massfnerr'],df['massfnerror']), fmt='ro',\n            label='z=1')\n\ndf = dataframe('M000_2.dat', datadir)\nax.errorbar(df.MininvhMsun, df['dn/dlnM'],\n            yerr=(df['massfnerr'],df['massfnerror']), fmt='bd',\n            label='z=2')\n\n\nax.grid(True, ls='dotted')\nax.set_xscale('log')\nax.set_yscale('log')\nax.set_xlim(10**12, 10.**15)\nax.set_ylim(10**(-10), 10**(-2))\nax.set_xlabel(r'Mass ($h^{-1} M_\\odot)$',fontsize=28)\nax.set_ylabel('$dn/d\\ln{(M)}$ ($h^{3} Mpc^{-3}$)',fontsize=28)\nxtl = ax.get_xticklabels()\nytl = ax.get_yticklabels()\nplt.setp(xtl, visible=True, fontsize=20)\nplt.setp(ytl, visible=True, fontsize=20)\nplt.legend(loc=\"best\",numpoints = 1)\nplt.tight_layout()\nfig.savefig('comparison.pdf')",
   "execution_count": 39,
   "outputs": [
    {
     "output_type": "error",
     "ename": "IOError",
     "evalue": "/Users/rbiswas/data/datastar/simulations/M000_0.dat not found.",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-9ad90dca034e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M000_0.dat'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatadir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m ax.errorbar(df.MininvhMsun, df['dn/dlnM'],\n\u001b[1;32m      7\u001b[0m             \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'massfnerr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'massfnerror'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ks'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-f3866bcdf26f>\u001b[0m in \u001b[0;36mdataframe\u001b[0;34m(fname, datadir)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatadir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdatadir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadtxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mheader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rbiswas/soft/miniconda2/lib/python2.7/site-packages/numpy/lib/npyio.pyc\u001b[0m in \u001b[0;36mloadtxt\u001b[0;34m(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding)\u001b[0m\n\u001b[1;32m    924\u001b[0m             \u001b[0mfname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    925\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_is_string_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 926\u001b[0;31m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_datasource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    927\u001b[0m             \u001b[0mfencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'encoding'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'latin1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    928\u001b[0m             \u001b[0mfh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rbiswas/soft/miniconda2/lib/python2.7/site-packages/numpy/lib/_datasource.pyc\u001b[0m in \u001b[0;36mopen\u001b[0;34m(path, mode, destpath, encoding, newline)\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m     \u001b[0mds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataSource\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdestpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnewline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/rbiswas/soft/miniconda2/lib/python2.7/site-packages/numpy/lib/_datasource.pyc\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, path, mode, encoding, newline)\u001b[0m\n\u001b[1;32m    616\u001b[0m                                       encoding=encoding, newline=newline)\n\u001b[1;32m    617\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 618\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s not found.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    620\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: /Users/rbiswas/data/datastar/simulations/M000_0.dat not found."
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 576x432 with 1 Axes>"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "hmf.dndlnM()",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "x['dndlnM_fit(B),_fit']/x['dndlnM_fit(B),']",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "x.head()",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "\ndef dataframe_M00n1(fname, datadir):\n    fname = os.path.join(datadir, fname)\n    data = np.loadtxt(fname)\n    with open(fname, 'r') as fh:\n        header = fh.readline()\n    #print(header)\n    cols = header[2:-1].split()\n    df = pd.DataFrame(data, columns=cols)\n    return df",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df = dataframe('M000n1_499_smoothnunosourced_0' ,datadir)",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "df.head()",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import matplotlib as mpl",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "mpl.matplotlib_fname()",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "!less /Users/rbiswas/soft/miniconda2/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "!lessax.set_xlabel(r'Mass ($h^{-1} M_\\odot)$',fontsize =28)\nax.set_ylabel(r'$dn/d\\ln{(M)}$ ($h^{3} Mpc^{-3}$)',fontsize =28)\nxtl = ax.get_xticklabels()\nytl = ax.get_yticklabels()\nplt.setp(xtl, visible=True, fontsize =20)\nplt.setp(ytl, visible=True, fontsize =20)\nplt.legend(loc=\"best\",numpoints = 1)\nplt.tight_layout()",
   "execution_count": null,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "plt.figure()",
   "execution_count": null,
   "outputs": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python2",
   "display_name": "Python 2",
   "language": "python"
  },
  "language_info": {
   "mimetype": "text/x-python",
   "nbconvert_exporter": "python",
   "name": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15",
   "file_extension": ".py",
   "codemirror_mode": {
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    "name": "ipython"
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  }
 },
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}