{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. _data-format:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data format and SED data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The data are treated using two classes from the module :mod:`.data_loader`\n",
    "\n",
    "- :class:`jetset.data_loader.Data` class \n",
    "- :class:`jetset.data_loader.ObsData` class \n",
    "\n",
    "The class :class:`jetset.data_loader.Data` is in charge of storing the data, giving access to the I/O functionalities, and provides an interface  to the astropy Table class (see the  `astropy table <https://docs.astropy.org/en/stable/table/>`_. documentation, for further information)\n",
    "\n",
    "The class :class:`jetset.data_loader.ObsData` uses the information stored in :class:`jetset.data_loader.Data`, and can perform several operations \n",
    "This is the class to use for model fitting and in general in Jetset\n",
    " \n",
    " - rebinning (grouping) of the data\n",
    " - selection of time ranges\n",
    " - selection of datasets\n",
    " - transformation from linear to logarithmic representation\n",
    " - handling of errors and systematics\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. note::\n",
    "    old version of data file in non `ecsv` astropy table format, can be easily converted by importing them using the method described \n",
    "    in :ref:`import` and saving them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tested on jetset 1.2.2\n"
     ]
    }
   ],
   "source": [
    "import jetset\n",
    "print('tested on jetset',jetset.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data format for Data object\n",
    "\n",
    "\n",
    "The SED data are internally stored as astropy tables, but it is very easy to import from\n",
    "\n",
    " 1. ascii files\n",
    " 2. numpy array in general\n",
    " \n",
    "once that is clear the data format. The easiest way to understand the data format is to build an empty table to have a look at the structure of the table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jetset.data_loader import Data\n",
    "data=Data(n_rows=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can easily access the astropy table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=10</i>\n",
       "<table id=\"table140719485388544\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>x</th><th>dx</th><th>y</th><th>dy</th><th>T_start</th><th>T_stop</th><th>UL</th><th>data_set</th></tr></thead>\n",
       "<thead><tr><th>Hz</th><th>Hz</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th>MJD</th><th>MJD</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>bytes16</th></tr></thead>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=10>\n",
       "   x       dx         y             dy      T_start  T_stop   UL  data_set\n",
       "   Hz      Hz   erg / (cm2 s) erg / (cm2 s)   MJD     MJD                 \n",
       "float64 float64    float64       float64    float64 float64  bool bytes16 \n",
       "------- ------- ------------- ------------- ------- ------- ----- --------\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0\n",
       "    0.0     0.0           0.0           0.0     0.0     0.0 False      0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``x`` column is reserved to frequencies (mandatory)\n",
    "- ``y`` columm is reserved to fluxes  (mandatory)\n",
    "- ``dx`` columm is reserved to the error on the frequency,or bin width\n",
    "- ``dy`` columm is reserved to the  error on the fluxes\n",
    "- ``UL`` columm is reserved to the flag for Upper Limit \n",
    "- ``T_start`` and ``T_stop`` are used to identify the time range to select data using the  class `ObsData`\n",
    "- ``data_set``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;x&apos; dtype=&apos;float64&apos; unit=&apos;Hz&apos; length=10&gt;\n",
       "<table>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "<tr><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Column name='x' dtype='float64' unit='Hz' length=10>\n",
       "0.0\n",
       "0.0\n",
       "0.0\n",
       "0.0\n",
       "0.0\n",
       "0.0\n",
       "0.0\n",
       "0.0\n",
       "0.0\n",
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.table['x']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "columns with units are implemented using the `Units` module of astropy (https://docs.astropy.org/en/stable/units/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and we can easily access the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('z', 0),\n",
       "             ('UL_CL', 0.95),\n",
       "             ('restframe', 'obs'),\n",
       "             ('data_scale', 'lin-lin'),\n",
       "             ('obj_name', 'new-src')])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ``z``: the redshift of the object\n",
    "- ``UL_CL``: the CL for the UL\n",
    "- ``restframe``:  possible values``obs`` or ``src``, indicating if the data are observed flux, or luminosities, respectively\n",
    "- ``data_scale``: possible values``lin-lin`` or ``log-log``,  indicating if the data are in linear or logarithmic scale, respectively\n",
    "- ``obj_name``: the name of the object\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. note::\n",
    "   starting from version 1.1.0 ``src`` to ``obs`` transformation is available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading from astropy table\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can use the default SEDs distributed with the package to get familiar with data handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/orion/anaconda3/envs/jetset/lib/python3.8/site-packages/jetset/test_data/SEDs_data/SED_3C345.ecsv',\n",
       " '/Users/orion/anaconda3/envs/jetset/lib/python3.8/site-packages/jetset/test_data/SEDs_data/SED_MW_Mrk421_EBL_DEABS.ecsv',\n",
       " '/Users/orion/anaconda3/envs/jetset/lib/python3.8/site-packages/jetset/test_data/SEDs_data/SED_MW_Mrk501_EBL_ABS.ecsv',\n",
       " '/Users/orion/anaconda3/envs/jetset/lib/python3.8/site-packages/jetset/test_data/SEDs_data/SED_MW_Mrk501_EBL_DEABS.ecsv']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from jetset.test_data_helper import  test_SEDs\n",
    "test_SEDs"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "As you can see there are three 3 files. We use in this example the file for Mrk 421, and we use class:`jetset.data_loader.Data` class  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jetset.data_loader import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=Data.from_file(data_table=test_SEDs[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<thead><tr><th>Hz</th><th>Hz</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th>MJD</th><th>MJD</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>str13</th></tr></thead>\n",
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       "<tr><td>9.603433e+25</td><td>0.0</td><td>4.662219e-11</td><td>5.097912e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>1.522041e+26</td><td>0.0</td><td>5.221583e-11</td><td>4.89063e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>2.41227e+26</td><td>0.0</td><td>3.66834e-11</td><td>4.682033e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>3.823193e+26</td><td>0.0</td><td>2.247871e-11</td><td>4.343216e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>6.059363e+26</td><td>0.0</td><td>1.972081e-11</td><td>4.407365e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>9.603433e+26</td><td>0.0</td><td>7.994215e-12</td><td>3.469109e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "</table>"
      ],
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       "<Table length=110>\n",
       "     x          dx         y             dy      ...  T_stop   UL     data_set  \n",
       "     Hz         Hz   erg / (cm2 s) erg / (cm2 s) ...   MJD                      \n",
       "  float64    float64    float64       float64    ... float64  bool     str13    \n",
       "------------ ------- ------------- ------------- ... ------- ----- -------------\n",
       "2299540000.0     0.0    1.3409e-14      3.91e-16 ...     0.0 False campaing-2009\n",
       "2639697000.0     0.0  1.793088e-14  3.231099e-26 ...     0.0 False campaing-2009\n",
       "4799040000.0     0.0    2.3136e-14       2.4e-16 ...     0.0 False campaing-2009\n",
       "4805039000.0     0.0  1.773414e-14  1.773414e-15 ...     0.0 False campaing-2009\n",
       "4843552000.0     0.0   2.77614e-14  2.615339e-26 ...     0.0 False campaing-2009\n",
       "7698460000.0     0.0     3.696e-14      4.62e-16 ...     0.0 False campaing-2009\n",
       "8267346000.0     0.0  2.836267e-14  2.836267e-15 ...     0.0 False campaing-2009\n",
       "8331867000.0     0.0   3.98963e-14  3.627671e-26 ...     0.0 False campaing-2009\n",
       "8388659000.0     0.0   3.16345e-14  1.931495e-15 ...     0.0 False campaing-2009\n",
       "         ...     ...           ...           ... ...     ...   ...           ...\n",
       "2.417992e+25     0.0  9.754259e-11  3.560456e-11 ...     0.0 False campaing-2009\n",
       "3.823193e+25     0.0  8.199207e-11  7.050657e-12 ...     0.0 False campaing-2009\n",
       "6.059363e+25     0.0  5.614334e-11  5.793969e-12 ...     0.0 False campaing-2009\n",
       "6.073707e+25     0.0   1.14705e-10  6.573696e-11 ...     0.0 False campaing-2009\n",
       "9.603433e+25     0.0  4.662219e-11  5.097912e-12 ...     0.0 False campaing-2009\n",
       "1.522041e+26     0.0  5.221583e-11   4.89063e-12 ...     0.0 False campaing-2009\n",
       " 2.41227e+26     0.0   3.66834e-11  4.682033e-12 ...     0.0 False campaing-2009\n",
       "3.823193e+26     0.0  2.247871e-11  4.343216e-12 ...     0.0 False campaing-2009\n",
       "6.059363e+26     0.0  1.972081e-11  4.407365e-12 ...     0.0 False campaing-2009\n",
       "9.603433e+26     0.0  7.994215e-12  3.469109e-12 ...     0.0 False campaing-2009"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('z', 0.0308),\n",
       "             ('restframe', 'obs'),\n",
       "             ('data_scale', 'lin-lin'),\n",
       "             ('obj_name', 'J1104+3812,Mrk421')])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.metadata"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "this is an extract of the  astropy table saved in the format ``ascii.ecsv`` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "# %ECSV 0.9\n",
    "# ---\n",
    "# datatype:\n",
    "# - {name: x, unit: Hz, datatype: float64}\n",
    "# - {name: dx, unit: Hz, datatype: float64}\n",
    "# - {name: y, unit: erg / (cm2 s), datatype: float64}\n",
    "# - {name: dy, unit: erg / (cm2 s), datatype: float64}\n",
    "# - {name: T_start, unit: MJD, datatype: float64}\n",
    "# - {name: T_stop, unit: MJD, datatype: float64}\n",
    "# - {name: UL, datatype: bool}\n",
    "# - {name: data_set, datatype: string}\n",
    "# meta: !!omap\n",
    "# - {z: 0.0308}\n",
    "# - {restframe: obs}\n",
    "# - {data_scale: lin-lin}\n",
    "# - {obj_name: 'J1104+3812,Mrk421'}\n",
    "# schema: astropy-2.0\n",
    "x dx y dy T_start T_stop UL data_set\n",
    "2299540000.0 0.0 1.3409e-14 3.91e-16 0.0 0.0 False campaing-2009\n",
    "2639697000.0 0.0 1.793088e-14 3.231099e-26 0.0 0.0 False campaing-2009\n",
    "4799040000.0 0.0 2.3136e-14 2.4e-16 0.0 0.0 False campaing-2009\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving Data object to a file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.save_file('test.ecsv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the data can be loaded from the saved table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=Data.from_file('test.ecsv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=110</i>\n",
       "<table id=\"table140719502924000\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>x</th><th>dx</th><th>y</th><th>dy</th><th>T_start</th><th>T_stop</th><th>UL</th><th>data_set</th></tr></thead>\n",
       "<thead><tr><th>Hz</th><th>Hz</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th>MJD</th><th>MJD</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>str13</th></tr></thead>\n",
       "<tr><td>2299540000.0</td><td>0.0</td><td>1.3409e-14</td><td>3.91e-16</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>2639697000.0</td><td>0.0</td><td>1.793088e-14</td><td>3.231099e-26</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>4799040000.0</td><td>0.0</td><td>2.3136e-14</td><td>2.4e-16</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>4805039000.0</td><td>0.0</td><td>1.773414e-14</td><td>1.773414e-15</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>4843552000.0</td><td>0.0</td><td>2.77614e-14</td><td>2.615339e-26</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>7698460000.0</td><td>0.0</td><td>3.696e-14</td><td>4.62e-16</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>8267346000.0</td><td>0.0</td><td>2.836267e-14</td><td>2.836267e-15</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>8331867000.0</td><td>0.0</td><td>3.98963e-14</td><td>3.627671e-26</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>8388659000.0</td><td>0.0</td><td>3.16345e-14</td><td>1.931495e-15</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>2.417992e+25</td><td>0.0</td><td>9.754259e-11</td><td>3.560456e-11</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>3.823193e+25</td><td>0.0</td><td>8.199207e-11</td><td>7.050657e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>6.059363e+25</td><td>0.0</td><td>5.614334e-11</td><td>5.793969e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>6.073707e+25</td><td>0.0</td><td>1.14705e-10</td><td>6.573696e-11</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>9.603433e+25</td><td>0.0</td><td>4.662219e-11</td><td>5.097912e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>1.522041e+26</td><td>0.0</td><td>5.221583e-11</td><td>4.89063e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>2.41227e+26</td><td>0.0</td><td>3.66834e-11</td><td>4.682033e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>3.823193e+26</td><td>0.0</td><td>2.247871e-11</td><td>4.343216e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>6.059363e+26</td><td>0.0</td><td>1.972081e-11</td><td>4.407365e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "<tr><td>9.603433e+26</td><td>0.0</td><td>7.994215e-12</td><td>3.469109e-12</td><td>0.0</td><td>0.0</td><td>False</td><td>campaing-2009</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=110>\n",
       "     x          dx         y             dy      ...  T_stop   UL     data_set  \n",
       "     Hz         Hz   erg / (cm2 s) erg / (cm2 s) ...   MJD                      \n",
       "  float64    float64    float64       float64    ... float64  bool     str13    \n",
       "------------ ------- ------------- ------------- ... ------- ----- -------------\n",
       "2299540000.0     0.0    1.3409e-14      3.91e-16 ...     0.0 False campaing-2009\n",
       "2639697000.0     0.0  1.793088e-14  3.231099e-26 ...     0.0 False campaing-2009\n",
       "4799040000.0     0.0    2.3136e-14       2.4e-16 ...     0.0 False campaing-2009\n",
       "4805039000.0     0.0  1.773414e-14  1.773414e-15 ...     0.0 False campaing-2009\n",
       "4843552000.0     0.0   2.77614e-14  2.615339e-26 ...     0.0 False campaing-2009\n",
       "7698460000.0     0.0     3.696e-14      4.62e-16 ...     0.0 False campaing-2009\n",
       "8267346000.0     0.0  2.836267e-14  2.836267e-15 ...     0.0 False campaing-2009\n",
       "8331867000.0     0.0   3.98963e-14  3.627671e-26 ...     0.0 False campaing-2009\n",
       "8388659000.0     0.0   3.16345e-14  1.931495e-15 ...     0.0 False campaing-2009\n",
       "         ...     ...           ...           ... ...     ...   ...           ...\n",
       "2.417992e+25     0.0  9.754259e-11  3.560456e-11 ...     0.0 False campaing-2009\n",
       "3.823193e+25     0.0  8.199207e-11  7.050657e-12 ...     0.0 False campaing-2009\n",
       "6.059363e+25     0.0  5.614334e-11  5.793969e-12 ...     0.0 False campaing-2009\n",
       "6.073707e+25     0.0   1.14705e-10  6.573696e-11 ...     0.0 False campaing-2009\n",
       "9.603433e+25     0.0  4.662219e-11  5.097912e-12 ...     0.0 False campaing-2009\n",
       "1.522041e+26     0.0  5.221583e-11   4.89063e-12 ...     0.0 False campaing-2009\n",
       " 2.41227e+26     0.0   3.66834e-11  4.682033e-12 ...     0.0 False campaing-2009\n",
       "3.823193e+26     0.0  2.247871e-11  4.343216e-12 ...     0.0 False campaing-2009\n",
       "6.059363e+26     0.0  1.972081e-11  4.407365e-12 ...     0.0 False campaing-2009\n",
       "9.603433e+26     0.0  7.994215e-12  3.469109e-12 ...     0.0 False campaing-2009"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.table"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. _import:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Importing  from an arbitrary ascii file or numpy array to Data object\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume that your data are stored in an ASCII file named 'test-ascii.txt', with\n",
    "- ``x`` in the first column with frequency in ``Hz`` , \n",
    "- ``y`` in the second column with fluxes in erg ``cm-2 s-1``, \n",
    "- ``dy`` in the third column with the same units as ``y``\n",
    "- the data are  in ``log-log`` scale\n",
    "\n",
    "**of course the column number depends on the file that you are using, this is only an example**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jetset.data_loader import Data\n",
    "import numpy as np\n",
    "\n",
    "d=np.genfromtxt('test-ascii.txt')\n",
    "data=Data(n_rows=d.shape[0])\n",
    "data.set_field('x',d[:,0])\n",
    "data.set_field('y',d[:,1])\n",
    "data.set_field('dy',value=d[:,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "then you can set the meatdata as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.set_meta_data('z',1.02)\n",
    "data.set_meta_data('restframe','obs')\n",
    "data.set_meta_data('data_scale','log-log')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "of course this method applies if you have a generic 2-dim numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=20</i>\n",
       "<table id=\"table140719503204208\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>x</th><th>dx</th><th>y</th><th>dy</th><th>T_start</th><th>T_stop</th><th>UL</th><th>data_set</th></tr></thead>\n",
       "<thead><tr><th>Hz</th><th>Hz</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th>MJD</th><th>MJD</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>bytes16</th></tr></thead>\n",
       "<tr><td>24.161967</td><td>0.0</td><td>-12.497324</td><td>0.334376</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>25.161967</td><td>0.0</td><td>-12.512137</td><td>0.636293</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>23.161967</td><td>0.0</td><td>-12.444346</td><td>0.38048</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>23.684845</td><td>0.0</td><td>-12.257916</td><td>0.164397</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>22.684845</td><td>0.0</td><td>-12.000541</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>15.2900346113625</td><td>0.0</td><td>-13.3225755622988</td><td>0.127157926321555</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>15.1105897102992</td><td>0.0</td><td>-12.7495312032995</td><td>0.40748532657827</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>15.1105897102992</td><td>0.0</td><td>-12.8808101179388</td><td>0.410904985836407</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>14.6702458530741</td><td>0.0</td><td>-12.477274153289</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>14.5888317255942</td><td>0.0</td><td>-12.875874154363</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>10.644439</td><td>0.0</td><td>-12.515761</td><td>0.090508</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>10.477121</td><td>0.0</td><td>-12.665868</td><td>0.072712</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>10.2988530764097</td><td>0.0</td><td>-13.2322481107094</td><td>0.486627579841286</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>10.1760912590557</td><td>0.0</td><td>-13.3326058184618</td><td>0.935000421715186</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>9.93449845124357</td><td>0.0</td><td>-13.6367199590836</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>9.92427928606188</td><td>0.0</td><td>-13.5500291112572</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>9.68574173860226</td><td>0.0</td><td>-13.7561818857395</td><td>0.427742607163214</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>9.36172783601759</td><td>0.0</td><td>-14.121068671527</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>9.14612803567824</td><td>0.0</td><td>-14.5547242324656</td><td>0.682147390735292</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>7.86805636182304</td><td>0.0</td><td>-15.4827165132972</td><td>0.025977834481891</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=20>\n",
       "       x            dx           y         ...  T_stop   UL  data_set\n",
       "       Hz           Hz     erg / (cm2 s)   ...   MJD                 \n",
       "    float64      float64      float64      ... float64  bool bytes16 \n",
       "---------------- ------- ----------------- ... ------- ----- --------\n",
       "       24.161967     0.0        -12.497324 ...     0.0 False      0.0\n",
       "       25.161967     0.0        -12.512137 ...     0.0 False      0.0\n",
       "       23.161967     0.0        -12.444346 ...     0.0 False      0.0\n",
       "       23.684845     0.0        -12.257916 ...     0.0 False      0.0\n",
       "       22.684845     0.0        -12.000541 ...     0.0 False      0.0\n",
       "15.2900346113625     0.0 -13.3225755622988 ...     0.0 False      0.0\n",
       "15.1105897102992     0.0 -12.7495312032995 ...     0.0 False      0.0\n",
       "15.1105897102992     0.0 -12.8808101179388 ...     0.0 False      0.0\n",
       "14.6702458530741     0.0  -12.477274153289 ...     0.0 False      0.0\n",
       "14.5888317255942     0.0  -12.875874154363 ...     0.0 False      0.0\n",
       "       10.644439     0.0        -12.515761 ...     0.0 False      0.0\n",
       "       10.477121     0.0        -12.665868 ...     0.0 False      0.0\n",
       "10.2988530764097     0.0 -13.2322481107094 ...     0.0 False      0.0\n",
       "10.1760912590557     0.0 -13.3326058184618 ...     0.0 False      0.0\n",
       "9.93449845124357     0.0 -13.6367199590836 ...     0.0 False      0.0\n",
       "9.92427928606188     0.0 -13.5500291112572 ...     0.0 False      0.0\n",
       "9.68574173860226     0.0 -13.7561818857395 ...     0.0 False      0.0\n",
       "9.36172783601759     0.0  -14.121068671527 ...     0.0 False      0.0\n",
       "9.14612803567824     0.0 -14.5547242324656 ...     0.0 False      0.0\n",
       "7.86805636182304     0.0 -15.4827165132972 ...     0.0 False      0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing to Data object from a generic astropy table mapping columns \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to use a ``TABLE`` with arbitrary column names, you can use an import dictionary, mapping the input name to the target.\n",
    "E.g. assume that you column in the input table column named ``freq`` that should target the ``x`` column, and another named \n",
    "``freq err`` associated to ``dx`` you can simply pass the dictionary to the ``from_file`` method:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "data=Data.from_file(data_table='your-file',import_dictionary={'freq':'x','freq err':'dx'})\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing from the ASI ssdc sedtool to  Data object\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To import data from a data file downloaded from the asi ssdc sedtool: https://tools.ssdc.asi.it/SED/  "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "we can use the importing tool in the :class:`jetset.data_loader.Data`. We just need to have the file downloaded from the asi ssdc sedtool, and to know the redshift of the object, the scale we selected (lin-lin, or log-log).\n",
    "Assume that we downloaded the data for Mrk421, in observed fluxes and linear scale, and the data are saved in the file 'MRK421_asdc.txt', we only have to do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jetset.data_loader import Data\n",
    "data=Data.from_asdc(asdc_sed_file='MRK421_asdc.txt',obj_name='Mrk421',restframe='obs',data_scale='lin-lin',z=0.038)\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. note::\n",
    "   starting from version 1.1.0 ``src`` to ``obs`` transformation is available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<thead><tr><th>x</th><th>dx</th><th>y</th><th>dy</th><th>T_start</th><th>T_stop</th><th>UL</th><th>data_set</th></tr></thead>\n",
       "<thead><tr><th>Hz</th><th>Hz</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th>MJD</th><th>MJD</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>bytes16</th></tr></thead>\n",
       "<tr><td>1.395e+17</td><td>2.077e+16</td><td>1.3665e-10</td><td>7.8618e-12</td><td>50569.13745</td><td>50569.61257</td><td>False</td><td>0.0</td></tr>\n",
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       "<tr><td>6.255e+17</td><td>9.316e+16</td><td>8.8421e-11</td><td>2.2462e-12</td><td>50569.13745</td><td>50569.61257</td><td>False</td><td>0.0</td></tr>\n",
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       "<tr><td>1.539e+18</td><td>2.292e+17</td><td>4.52e-11</td><td>2.9633e-12</td><td>50569.13745</td><td>50569.61257</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
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       "<tr><td>408000000.0</td><td>0.0</td><td>4.692e-15</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>2700000000.0</td><td>0.0</td><td>2.079e-14</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>10700000000.0</td><td>0.0</td><td>8.453e-14</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>5000000000.0</td><td>0.0</td><td>3.625e-14</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>8460000000.0</td><td>0.0</td><td>5.3433e-14</td><td>3.384e-17</td><td>47941.5</td><td>47941.5</td><td>False</td><td>0.0</td></tr>\n",
       "<tr><td>8400000000.0</td><td>0.0</td><td>5.3054e-14</td><td>0.0</td><td>0.0</td><td>0.0</td><td>False</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=3550>\n",
       "      x           dx          y       ...    T_stop     UL  data_set\n",
       "      Hz          Hz    erg / (cm2 s) ...     MJD                   \n",
       "   float64     float64     float64    ...   float64    bool bytes16 \n",
       "------------- --------- ------------- ... ----------- ----- --------\n",
       "    1.395e+17 2.077e+16    1.3665e-10 ... 50569.61257 False      0.0\n",
       "    1.883e+17 2.805e+16    1.3231e-10 ... 50569.61257 False      0.0\n",
       "    2.542e+17 3.786e+16    1.2801e-10 ... 50569.61257 False      0.0\n",
       "    3.432e+17 5.111e+16    1.1696e-10 ... 50569.61257 False      0.0\n",
       "    4.633e+17 6.901e+16    1.0488e-10 ... 50569.61257 False      0.0\n",
       "    6.255e+17 9.316e+16    8.8421e-11 ... 50569.61257 False      0.0\n",
       "    8.444e+17 1.258e+17    7.2995e-11 ... 50569.61257 False      0.0\n",
       "     1.14e+18 1.698e+17    5.7982e-11 ... 50569.61257 False      0.0\n",
       "    1.539e+18 2.292e+17      4.52e-11 ... 50569.61257 False      0.0\n",
       "          ...       ...           ... ...         ...   ...      ...\n",
       " 4850000000.0       0.0    2.9604e-14 ...         0.0 False      0.0\n",
       " 1400000000.0       0.0    5.0638e-16 ...     49443.5 False      0.0\n",
       " 1400000000.0       0.0      1.68e-17 ...     49443.5 False      0.0\n",
       " 1400000000.0       0.0    8.0331e-15 ...     49443.5 False      0.0\n",
       "  408000000.0       0.0     4.692e-15 ...         0.0 False      0.0\n",
       " 2700000000.0       0.0     2.079e-14 ...         0.0 False      0.0\n",
       "10700000000.0       0.0     8.453e-14 ...         0.0 False      0.0\n",
       " 5000000000.0       0.0     3.625e-14 ...         0.0 False      0.0\n",
       " 8460000000.0       0.0    5.3433e-14 ...     47941.5 False      0.0\n",
       " 8400000000.0       0.0    5.3054e-14 ...         0.0 False      0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.table"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    ".. note::\n",
    "   When importing data from the ``src`` frame, the Data constructor will not convert units, but will assume that input units are erg/s. If this is not the case an error message will be displayed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the SED the ObsData object\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Once we have a data table built with the class:`jetset.data_loader.Data`, following  one of the method described above, you can create  SED data using the  :class:`jetset.data_loader.ObsData` class.\n",
    "In the example we use one of the test SEDs provided by the package:"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We start to loading  the SED of  Mrk 421, and we pass to ``ObsData`` directly the path to the file, because this is already in the format that we need and that we have discussed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jetset.data_loader import Data\n",
    "from jetset.data_loader import ObsData\n",
    "from jetset.test_data_helper import  test_SEDs\n",
    "\n",
    "data_table=Data.from_file(test_SEDs[1])\n",
    "sed_data=ObsData(data_table=data_table)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "if you want to use a cosmology model different from the default one please read the :ref:`cosmology` section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see all the meta-data have been properly sourced from the SED file header. You also get information on \n",
    "the length of the data, before and after elimination of duplicated entries, and upper limits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=110</i>\n",
       "<table id=\"table140719503201136\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>nu_data</th><th>dnu_data</th><th>nuFnu_data</th><th>dnuFnu_data</th><th>nu_data_log</th><th>dnu_data_log</th><th>nuFnu_data_log</th><th>dnuFnu_data_log</th><th>dnuFnu_fake</th><th>dnuFnu_fake_log</th><th>UL</th><th>zero_error</th><th>T_start</th><th>T_stop</th><th>data_set</th></tr></thead>\n",
       "<thead><tr><th>Hz</th><th>Hz</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th>Hz</th><th>Hz</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th>erg / (cm2 s)</th><th></th><th></th><th></th><th>MJD</th><th>MJD</th><th></th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>bool</th><th>bool</th><th>float64</th><th>float64</th><th>str13</th></tr></thead>\n",
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       "<tr><td>4799040000.0</td><td>0.0</td><td>2.3136e-14</td><td>2.4e-16</td><td>9.681154369792159</td><td>0.0</td><td>-13.635711724385564</td><td>0.0045051294803241885</td><td>4.627200000000001e-15</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>4805039000.0</td><td>0.0</td><td>1.773414e-14</td><td>1.773414e-15</td><td>9.68169691696108</td><td>0.0</td><td>-13.751189867373059</td><td>0.04342944819032518</td><td>3.546828e-15</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>4843552000.0</td><td>0.0</td><td>2.77614e-14</td><td>2.615339e-26</td><td>9.68516396664987</td><td>0.0</td><td>-13.556558636309997</td><td>4.091390549490907e-13</td><td>5.55228e-15</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>7698460000.0</td><td>0.0</td><td>3.696e-14</td><td>4.62e-16</td><td>9.886403857589054</td><td>0.0</td><td>-13.43226803745193</td><td>0.005428681023790648</td><td>7.392e-15</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>8267346000.0</td><td>0.0</td><td>2.836267e-14</td><td>2.836267e-15</td><td>9.917366113839973</td><td>0.0</td><td>-13.547252888027566</td><td>0.043429448190325175</td><td>5.672534000000001e-15</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>8331867000.0</td><td>0.0</td><td>3.98963e-14</td><td>3.627671e-26</td><td>9.920742328771254</td><td>0.0</td><td>-13.399067379102538</td><td>3.948931348171262e-13</td><td>7.97926e-15</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>8388659000.0</td><td>0.0</td><td>3.16345e-14</td><td>1.931495e-15</td><td>9.92369254063231</td><td>0.0</td><td>-13.499839025404517</td><td>0.026516544289422034</td><td>6.3268999999999995e-15</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>2.417992e+25</td><td>0.0</td><td>9.754259e-11</td><td>3.560456e-11</td><td>25.38345485965064</td><td>0.0</td><td>-10.010805716985434</td><td>0.15852422965797036</td><td>1.9508518000000003e-11</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>3.823193e+25</td><td>0.0</td><td>8.199207e-11</td><td>7.050657e-12</td><td>25.582426222350527</td><td>0.0</td><td>-10.086228149101405</td><td>0.03734582416192853</td><td>1.6398414000000002e-11</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>6.059363e+25</td><td>0.0</td><td>5.614334e-11</td><td>5.793969e-12</td><td>25.78242697068017</td><td>0.0</td><td>-10.250701754501332</td><td>0.044819007294872405</td><td>1.1228668000000001e-11</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>6.073707e+25</td><td>0.0</td><td>1.14705e-10</td><td>6.573696e-11</td><td>25.78345383740898</td><td>0.0</td><td>-9.94041765075539</td><td>0.24889236724724106</td><td>2.2941000000000003e-11</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>9.603433e+25</td><td>0.0</td><td>4.662219e-11</td><td>5.097912e-12</td><td>25.982426510793527</td><td>0.0</td><td>-10.33140733007377</td><td>0.04748801055523926</td><td>9.324438000000001e-12</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>1.522041e+26</td><td>0.0</td><td>5.221583e-11</td><td>4.89063e-12</td><td>26.1824263514056</td><td>0.0</td><td>-10.282197814249994</td><td>0.04067681433064456</td><td>1.0443166e-11</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>2.41227e+26</td><td>0.0</td><td>3.66834e-11</td><td>4.682033e-12</td><td>26.38242591580127</td><td>0.0</td><td>-10.43553041856344</td><td>0.05543055158433863</td><td>7.33668e-12</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>3.823193e+26</td><td>0.0</td><td>2.247871e-11</td><td>4.343216e-12</td><td>26.582426222350527</td><td>0.0</td><td>-10.648228615520983</td><td>0.08391205467368516</td><td>4.495742000000001e-12</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>6.059363e+26</td><td>0.0</td><td>1.972081e-11</td><td>4.407365e-12</td><td>26.78242697068017</td><td>0.0</td><td>-10.705075251093293</td><td>0.09705961870904517</td><td>3.944162e-12</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "<tr><td>9.603433e+26</td><td>0.0</td><td>7.994215e-12</td><td>3.469109e-12</td><td>26.982426510793527</td><td>0.0</td><td>-11.097224175808465</td><td>0.18846314438889974</td><td>1.598843e-12</td><td>0.2</td><td>False</td><td>False</td><td>0.0</td><td>0.0</td><td>campaing-2009</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=110>\n",
       "  nu_data    dnu_data   nuFnu_data  ... T_start  T_stop    data_set  \n",
       "     Hz         Hz    erg / (cm2 s) ...   MJD     MJD                \n",
       "  float64    float64     float64    ... float64 float64     str13    \n",
       "------------ -------- ------------- ... ------- ------- -------------\n",
       "2299540000.0      0.0    1.3409e-14 ...     0.0     0.0 campaing-2009\n",
       "2639697000.0      0.0  1.793088e-14 ...     0.0     0.0 campaing-2009\n",
       "4799040000.0      0.0    2.3136e-14 ...     0.0     0.0 campaing-2009\n",
       "4805039000.0      0.0  1.773414e-14 ...     0.0     0.0 campaing-2009\n",
       "4843552000.0      0.0   2.77614e-14 ...     0.0     0.0 campaing-2009\n",
       "7698460000.0      0.0     3.696e-14 ...     0.0     0.0 campaing-2009\n",
       "8267346000.0      0.0  2.836267e-14 ...     0.0     0.0 campaing-2009\n",
       "8331867000.0      0.0   3.98963e-14 ...     0.0     0.0 campaing-2009\n",
       "8388659000.0      0.0   3.16345e-14 ...     0.0     0.0 campaing-2009\n",
       "         ...      ...           ... ...     ...     ...           ...\n",
       "2.417992e+25      0.0  9.754259e-11 ...     0.0     0.0 campaing-2009\n",
       "3.823193e+25      0.0  8.199207e-11 ...     0.0     0.0 campaing-2009\n",
       "6.059363e+25      0.0  5.614334e-11 ...     0.0     0.0 campaing-2009\n",
       "6.073707e+25      0.0   1.14705e-10 ...     0.0     0.0 campaing-2009\n",
       "9.603433e+25      0.0  4.662219e-11 ...     0.0     0.0 campaing-2009\n",
       "1.522041e+26      0.0  5.221583e-11 ...     0.0     0.0 campaing-2009\n",
       " 2.41227e+26      0.0   3.66834e-11 ...     0.0     0.0 campaing-2009\n",
       "3.823193e+26      0.0  2.247871e-11 ...     0.0     0.0 campaing-2009\n",
       "6.059363e+26      0.0  1.972081e-11 ...     0.0     0.0 campaing-2009\n",
       "9.603433e+26      0.0  7.994215e-12 ...     0.0     0.0 campaing-2009"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sed_data.table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'z': 0.0308,\n",
       " 'obj_name': 'J1104+3812,Mrk421',\n",
       " 'restframe': 'obs',\n",
       " 'data_scale': 'lin-lin',\n",
       " 'UL_CL': 0.95}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sed_data.metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting ObsData\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We can now plot our SED using the :class:`BlazarSEDFit.plot_sedfit.Plot` class \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from jetset.plot_sedfit import PlotSED\n",
    "myPlot=PlotSED(sed_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or you can create the object to plot on the fly in this way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myPlot=sed_data.plot_sed()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can rescale your plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myPlot=sed_data.plot_sed()\n",
    "myPlot.setlim(x_min=1E7,x_max=1E28,y_min=1E-15,y_max=1E-9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plotting in the ``src`` restframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "myPlot=sed_data.plot_sed(frame='src')\n",
    "myPlot.setlim(x_min=1E7,x_max=1E28,y_min=1E40,y_max=1E46)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**to have interactive plot in jupyter**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "if you want to to have interacitve plot in a jupyter notebook use:\n",
    "\n",
    ".. code-block:: no\n",
    "\n",
    "    %matplotlib notebook\n",
    "\n",
    "to have interactive plot in jupyter lab use:\n",
    "\n",
    ".. code-block:: no\n",
    " \n",
    "    %matplotlib widget\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping data\n",
    "\n",
    "As you can see, due to the overlapping of different instruments and to different time snapshots, \n",
    "some points have multiple values. Although this is not a problem for the fit process, you might \n",
    "want to rebin (group) your data. This can be obtained with the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "\n",
      "***  binning data  ***\n",
      "---> N bins= 89\n",
      "---> bin_widht= 0.2\n",
      "================================================================================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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1eZVMC2lQYLnGBV+MJN1V5ymszuUbtPt/03X+1DkqLqto9ucCAABwqEi8Y8zMJprZMknzEx0LACSLHJXVm1wvCPdTudf+c1TuQS0M99PgwPKo540JvKabd9yqf027XsW7iuIePwAAQFOQeMeYu09z9/6ShiQ6FgBIFiUFZ9ebXD8eOkeF3lXlHqwuL/Quejx0TtSk3F3Kt806PbhYY3Y+oW1ThpN8AwCApEbiDQCIuwEXTtaGQPeoyXWJ2mj0vjv1QGis5ugkPdv+2/r3qX/Ut0cdp78Gvqw1NZLykJtcUtBcUqTLevfwRi19+t5EPTUAAICDYlYaAEDc5bbLk66do4VP36vszQu0u/MgvdPtYp20vkySZGZqXfBDnTisQMNrTJg26Yw++uNrBcp77zF137VY3X2Degc217p2poWUvXlBsz4fAACAQ0HiDQBoFrnt8nTqJXdXbw+X9L2DnZOVoQlnnyid/YCKyyr0r6nX6chdT9Ya913uQZV2Y3QPAABIXnQ1BwCkhNysDH358p/s12V9Q6CbBoy5McHRAQAA1I8WbwBAyqjbZb20y2ANGPvDSDkAAECSIvEGAKSUul3WD0VxWYVmzF2tBYVFGpyfp/HDCpSbxZ9CAAAQX3zaAAC0SMW7irT06XuVs3m+SroM0VH/d72+/vh7WrOtVBVh19yVW/XsO+s1c9Jwkm8AABBXjPEGALQ4xdvWa9uU4RpU+KCO37tQgwofVPHUkdqybasqwpGlyCrCro+3lOjKJxequKwiwREDAICWjMQbANDiLL3/AnUPb6ie/TzTwjrSN+lb9qJytEdXB5/TY5k/19XB5/TOynU6f+ockm8AABA39K0DALQ4OSqrteSYFFnv+5TAMo0JzlG+bVKmhTU8sFRjgnM0Zsudevi1j3Xd2f0SFDEAAGjJaPEGALQ4JQVnq9xr/4mrWoKsKumWIi3h+bZZ3wm+qEdeX02rNwAAiAsSbwBAizPgwsn7rfdd6F0UkKK2hA8KLFdpeUgPv/ZxAqIFAAAtHYl3jJnZRDNbJml+omMBgHSV2y5PHa+do4X5EzRHJ2lKxViN3nen5oX7R20JXxiOdDH/7asfa/OuvYkIGQAAtGAk3jHm7tPcvb+kIYmOBQDSWdV63++O/J0e9jEqURs9HjpHhd51v5bwx0PnRLbDri898D+6nAMAgJgi8QYAtGjjhxWoV8dsZQRMJWqj0fvu1JSKsXoldEJ1S3iJ2lQfv72kXDPmrk5gxAAAoKVhVnMAQIuWm5WhmZOGa8bc1Xp60Tqt2SZND42WQvWf8/SidRo/rEC5WfyZBAAATUeLNwCgxcvNytCkM/qqZ8cchQ9+uFZvK9bIn7/CeG+ghSrdV6H8H/1T+T/6p0r3MbQEQPyReAMA0sbg/DxlBKze/Tnao6uDz+mxzF/oa2V/05d/8S+SbwAA0GT0oQMApI3xwwr07DvrVbi1RCGvvS9He/Rcq1uq1/keHliqMT5HY+8P6t//71y6nQMAgEajxRsAkDaqxnufelTH/faNC/67OumWIut959tmfaXsBSZbAwAATULiDQBIK7lZGQoG9v/zNzjwUXXSXSXTQhoUWK45K7c2V3gAAKAFIvEGgCRRvKtI8x6brCX3fEHzHpus4l1FiQ6pxYo21nthuJ/KvfafxXIPamG4nxZ/soO1vQEAQKOReANAAhWXVWjqf5bpuw/+V5/eN0yDCh/U8XsXalDhg9o2ZTjJd5zUXNtbkoImzWx1nj6xrir3oKRI0l3oXfR46BztKQ/r4dc+TmTIAAAghZF4A0CCFJdV6Pypc3T/yyvU75OndKRvrDW+uHt4o5Y+fW+Co2yZqsZ6X3NmXw3v01HtszO1sSxD/7f3Tk2puECvhE7QlIqxGr3vTpWojSTpkddX0+oNAAAahSlaASBB/vjKO/rS9ic1MHOFetnmqOOLszcvSFB0LV/V2t6S9Oaq7QqFpRK10fTQGCm0//Gl5SE9/NrHuu7sfs0cKQAASHUk3gDQ3PaVSGXFOm/RJeqcsV6ZFlbIpbBLNYcdl3tQpd2GJC7ONLGgsEgVYT/4gZJ+++rH+ubne6lLu9ZxjgoAALQkdDUHgOZ2V3fpV0era/m66lbuoEkmKeSRzLvcg9oQ6KYBY25MYKDpIdpEa1bPseVh1xfvf40u50ASKd1Xofwf/VP5P/qnSvfx3gSQnEi8ASABKtwUtNqtrGbSJ9ZV77YerIX5E9Tx2jnKbZeXoAjTR92J1jICpvyO2WqTGYx6fFFpuR5+5cPmDBEAAKQ4upoDQDMrvn6t/vKbyRpX9uda47pDylC3Ud9V/um0cjenqonWZsxdrTdXbassNXVp31pvrtpefVyO9mhc8EUNDizXikXHSmfcK2W1TUzQAAAgpdDiDQDNbMaCT/Wb4i+o0GsvXbWzTQ9lDb0ywdGlp9ysDI0fVqCNO/fqzVXbNWflVi1YXTvpfq7VLbo24+86PbhYl5T/RRUPjpLKdicwagCNVVJjuMhDr61i+AiAuEv7xNvMnjWzIjP7e5R955nZcjNbYWaXJSI+AKmjuKxCU19ZoXG/n6+pr6yo94PcgsIi7Qy31uh9d2pKxdjqpatu6vgALagJNGPuahVuK6meaC1UPRLANS74ovJtU63l3nz7apW98dvEBAug0YrLKnTxQ29Wb0+dvULnT51D8g0grtI+8Zb0a0nfqVtoZhmS7pN0hqSTJf0/M+vQzLEBSBHVa3L/d4Ve+2iL7v9v/R/kqibziixdNVqXlv8/PexjNOCoHgmIHFUWFBYpFN6/PKCwBgeWR1nuLaz1i2c3U3QAYmXG3NVas72kejsUltZsK9WMuasTGBWAli7tE293ny0pWl/BIZLed/f17r5b0r8kndOswQFIGTPmrtaabaXVraUVYa/3g1y0ybx6dczWuKH5zRky6og2u3lGwHRKfgctCPdTudf+k1nuQc3c3pNWMiDFRPuSrSLsWlhYlJiAAKSFpE68zWykmT1vZhvMzM1sdJRjrjaz1Wa218wWmdmIGN2+u6T1NbbXSToiRtcG0MJEWwu6vg9yVZN5XXNmX5129OG65sy+mjlpuHKzmO8yker7QuT+bwzSP1qdt9+Y/ELvot+Xn0UrGZBiBufnKVjnE3BGwDS4gI6NAOIn2T/l5UhaLOkxSU/X3WlmF0u6X9LVkuZKmiBplpn1d/e1lccskpQV5dpnu/uGA9w72jKuHqUMQJorLqtQKLx/H+UDfZDLzcrQpDP6xjs0HIKas5svLCzS8T3aS2b64d+X6LzBR+vCOXfqm/aiBgWWa2G4nx4PnaMStdGbq7ZRl0AC1Z0o7fKRvQ/4Reb4YQV65u31WrU10t2cXkcAmkNSJ97uPkvSLEkyi5YH6zpJj7r7I5Xb15jZOZKukjS58hoDG3n79ardwt1D0luNvBaAFFRcVqEZc1drQWGRBufnafywgv0+zFWN7S7cWlKr3CRlBgMqqwiruKyC1uwUUfWFSFW9Vg0fmLtyq9pmtdP0PaOlUO1zlm3cTR0DCRJtorQXlmw4YC+i3KwMPTXh8xr8s5clSRNP73PQZB0Amiplf8OYWStJAyXdU2fXS5KGxuAW8yUdZ2ZHSNol6cuS7jhAPFmq3bLeVpJCoZBCoVD0k9AsQqGQwuEw9ZAEUqkuissqdMFv52nNthJVhKW5K7fo2XfW65mrTq314ezBV1dq9ZYS1W3vdkl7ykOaPnuF/rlkw37nJVoq1UUiPDZnlQq3lVSPA60Iu3btLVebzID2lNeu7V17yvXYnFW6+rSjGnUv6iJ5UBfJo6F18dicVVEnSjvYe7J1xmcNOpcN76U2GUa9R8FrAsRO8nwKPHSdJAUlba5TvllS14ZexMxeVGTW8hwzWydpjLsvcPcKM7te0mxFxsLf6+7bDnCpyZJuq1u4atUqtWvXrqHhIA7C4bC2b9+ulStXKhBI6mkNWrxUqos/LylS4daS6iWlKsJS4dYS3ffC2/r68XmSpNLysB55vXC/pLumaOclg1Sqi0R4bdmG/SZfCrmUYfuPOKoIu15btk5nHXGg/wn1oy6SB3WRPBpaF9Heqw15T+6t8QXaxys/VutM6juaXbt2JToEoMVI5cS7St1PQRalrP6T3eudqdzd/yHpHw281N2KLD9Wpa2kdb1791ZeXvJ82E5HoVBIK1euVJ8+fRQMBhMdTlpLpbpYPXdhjXWcI0IuFRYH1LdvZDzv9Fc/VlnFwX/dhFxauUvV5yWDVKqLRBi1PqB3N65QRY3P7RkB0/FH5umt1dtrlUtS6w1vqsefH1CbH62QWuUc0r2oi+RBXSSPhtbFqPUBvbNxRa3kOyNgGtW/h/r2rb/Fu3RfhaTIxIhH9TlK2a1awkfi2CsqYqZ3IFZS+bfMVkVG2tVt3e6s/VvB487dyySVVW1XjUkPBoP88U4CgUCAukgSqVIXQwo66I2Pt9WaqTwjYBpS0LE69oVrdhz0W74c7dG44Isatu4jVfzvXGUNvUrKahvHyBsuVeoiES4Z3lvPvbuheox31Zj9AT3ytGFnmdZuK6n1xcw8P1bn77tTMytcuW0O/fWkLpIHdZE8DloX+0p0yf9G6Dm/Ux+ruySrnijtkuG9D1iHwaDX+Jn6rg+vCxA7Kduvxt33SVok6aw6u86S9EbzRxRhZhPNbJkiY8QBpKiGrLUdbd3nmnK0R8+1ukXXZvxdw/SuMl67R3r4NKlsd5yjR1NVzXB+9WlHKTszKFNkzP4jr6+S3GvNVp+jPZoQfEG3ZjyhpX/7GfULNKNc26uZrW5RhiIzm088vU+tidWKdxVp3mOTteSeL2jeY5NVvIsWXACJkdQt3maWK6lPjaICMztR0vbK5cLuk/SkmS2UNE/SFZJ6SnqwuWOt4u7TJE0zs3aSdiYqDgBNU3dpqUFRZjUfP6xAz76zXlu3bdU37UUNDnyoBeFjqpeZGhd8Ufm2SZkW6QMZVEjatlJ66yFp5A2JempooNysDLXKCKgsFKoex18Rdn1StKe6p0PVlytV9Vyx9n3p4ZelK15Nmp4NQEuXa3uVqZAqlKkJo3pHuo3vK1Hx7h3aNvUsDQpvVKaFVV74tjZM+Yd07RwFWvP+BNC8kjrxljRIkcnNqlSNoX5c0nh3f8rMOkq6VVI3Se9J+rK7r2neMAG0RAdbazvXyjRzfF+V/e57ardnnTItrOGB9zQmOEej992hwYHl1Ul3LWvf3L8MSWlBYVHUiZtMkV4Q46z2lysZCvPlCtBcyoqrf7wi+LweCZ2n0n0VkcT7ru5aGjpGgwIbq9+fmRZW9/BGLfzFF3Vq8EO10e+1R60TFT2ANJPUibe7v6rIZGkHOma6pOnNEhAAqMb63v/5qy4PvqBTgutqfbDL12aNC76kBeF+Gh5YWjv5DmRIPU9NUOQ4VIPz8zRn5Zb9Jm76yolH6IUlG3TKjg/4cgVobvtKIkn3Y1+qLpqUMVNfCb4p23eaVJlM56hsv/dnpoWU/dmUPADQbFJ2jHeyYow30ELtK5Fub6/i27ro/N+8rvv/u0Kv+YmqsIyoH+wGBZbr8dA5KvSuKvfI5DQVCkodekunXJGIZ4BGGD+sQPkdc/Yb63/5yN6aOWm4WhUMVUWdP6XOlytAfN3VXfrV0dL2j6uLMi2snrZZ7z37CxWXVUg3bVBJwdkq99rvz3IPqlRZzR0xAJB4x5q7T3P3/pKGJDoWALE3o+JsrdleWj3b+fzwMVE/2C0M91OJ2mj0vjs1pWKsXg2foAX5V0qXv8LY3xRSNdb/mjP76rSjD9c1Z/atnrgpNytDAy6crPXWvfrLlXIPaq13VfGJlyY4ciD9ZFpYvmaezp86R8WepQEXTtaGQLda788NgW4aEFid4EgBpCMSbwA4BAvCx9Raw7luq3a5B1XoXfR46BwFTCpRGz3sY3Rn+zs04Ot3kHSnoKqx/jMuHaJJZ/StNcHejIVb9X9lP9GUirF6JXSCplSM1VfK7tCMhVsTGDGQHEqLd0q3t5dubx/5OVZu2iCN+pEUqL3UVbkHNT/UT2u2lWrG3NXKbZennCtmaWG4rxaHCvRWz++q46SXlGt0NQfQ/JJ6jDcAJJvBgQ8110+sTr6rWrXHBV/UoMByLQz30+Ohc9SpQ0edf9IRWvLJjqgzoqNlWFBYpF3hNpqu0VLos/KFhSxZBMRNqxxp6CTpvb+rfOuqyIzlNb70rJBXvwc7deulTne+9dm5+0oSFDSAdMenQABoiMrZc6/MeF6ts9rqN8Vf0M5wZAKfErXR9NDo6sSrV4c2+ucPRpBop4HB+Xmau3JLrV4QGQGrtc43gDjIaiuN/5cW3vsVlamV5tdYypH3IIBkRFfzGGNyNaAF2r25evbcDHN9t/wvmt3+JzomL/rho0/qQdKdJsYPK1CvGpOvmaTMYEBlFeHIBE9AGivdvUPzQsdoSahAbz/1UxXvOnhPkKru6cE7O2jfnoO0TmflakCgUHdWfFvTQ+dXJ929OmZr3ND82DwJAIgREu8YY3I1IHUV7yrSvMcma8k9X9C8xybr061bNfWVFfrbz7+r0LZV1ceZh9RhzxrdfPjrUa/j7s0VMhKsavK1q087StmZQZmkPeUhTZ+9MjLBE8k30tG+EhVvW6+Sh7+kQYGPdHxwtU5Z+4i2TRneoOT7UOTaXs1sdYsyFHmvXTGyoHoCRABIJvxWApB+9pVId3VXsbfWjOGzteCTYg3q7PrKoks0KLwxMl6w8G2t+fVzerjiTj0Q3KWg7Z9MZ6x/S9KI/cqXrIvhJEJIerlZGWqVEVBZKKSqHucVYa+e4GnSGX0TGh/Q7O7qrqWhYzQosLF6ucVMC6t7eKMWPn2vTr3k7pjeLtf2KlMhVShTlw5nPg0AyYnfTADSUrG31vn77tSa2atVEZaO+/gZdc+o/SGxlzbrW/aiFoT7aXhgaa31uss9qP/t7bPfdRlbmJ4WFBYpVHs5d1WEnUnWkLZyVFbrd6YkZVpI2ZsXxPW+2a34aAsgOdHVHEBamlFxttZ4l+pJsQYFVkT9kDgosPyAS4ZJUuXwXsYWprHB+XkK1vmLypcwSFs3bVBJwdkq99pvinIPqrRbDEfitcqRbt8p3b5TH9wzVoX3nHvwxLtyokxJuiL4vHK0R6X7GBICIP5IvGOMydWA1LAgfIwqanT6WRDuF/VD4sJwv+olw6ZUXFC9VvPofXeqRG0kSb06ZOu0ow/XNWf2ZWxhmho/rEC9OuRUb/MlDNJaqxwNuHCyNgS61frCckOgmwaMuTFxcdWYKFOSJmXM1MxWt+gPr73HfAwA4o7EO8aYXA1IDYMDH1ZPxiMpaqv2GnXRH/2LkqSyQBv9JXSGLi+/XtNDo6uT7oyA6cJBR2rGpUM06Yy+JN1pqHRfhY677UWt2vrZDMwTT+/DlzBIa7nt8pRzxSwtDPfV4lCB3ur5XXW8do5y29WzHESlmjOhb3r99yretSN2Qf3qaGn7x9WbmRZWL9usinkPMRkigLjjEwGA9FNWrMsz/qnBgeV63U/QYxXnqCyQrR/k/ko3dfqf2n66UKVdBuuo/7tely8p0sLCIg06MlcXvf4lfaP8x1pjR6giTKsmIkr4sA5Eld32MJ0a/FCSVHrxzcrObV//wftKVLx7R+VM6JH5Nj63bY02/Hq2gtfOPWjC3liZFtLJtlwPNnAyxK0b12jF9K8qR2XalX+WTrzoprjFBqBlIfEGkD72lUTG9z32JWVZSKcEl2uQVuobrefp+SF/0DdHHafcrC/XOmXSGZ0+O3fuDs1sdYtmDJ+thZ8Ua1B+nsYPYwbddFZcVqGLH3pzv/LfvLJCT84rVP+uOfr8mgc1PuMl5f54VWRMKpCCSot3KvuXPSM/37D2wEl0Y9Q7E/qm2M2EPupH0uu/kMKh6qKqIUUHnQwxyhcD5Wsf0YYp/5Ia0JIPAHxaBJA+7uq+X1FQIXWv+EQTWv9XyjrxoJfItb2aNKoXCRQkSTPmrtaa7SX7lYdd2l5arjmrduhNXahnwyM0s6xCua0SECQQA6W7d2hx6JhIS+9TP21QS292bvvI5GeSshtwj7jPhD50kvTe3xXa+rGC5rUmyjzoZIjNvEQagJaHMd4AIElr92+1BA4m2jJiNeVoj64IvqBbM57Q0r/9TCrb3XzBAbGwr0TF29ZXtvR+pOODq3XK2ke0bcpwFe+K4XJ5zTETelZbafy/VKGA3godoykVF2j0vjtVFshu0LChRC2RBqBlIPEGkD5u2hDpahgI1i4PZEg9Tz3wuTWWraG1G1WiLSNWJUd79FyrW3Rtxt81KrhUg9f+Tnr4NJJvpJa7umvp/Reoe3j/lt6lT98bu/s010zoWbnKspCODRTq4dB5KlEbXTGyoNZkiFs3rtG8W07RkltO1JxHb4x8wdBcS6QBaLFIvGOM5cSAJNYqJ9LVMK/gs7JAhtSht3TKFYmLCymr7jJin3GNC76ofNtUnaxkKCxtWym99VDzBgk0UXO19NadCX12x6/rsO//Ly7jp3NtrzIVGetdvSLFgVr39+5LziXSAKQMEu8YYzkxIMlVdjWsNuIG6fJXIuXAIcrNytBTEz6/X3kblWlw4MP9khVJDGtAamnmlt6qmdBPCK5W1xGXKrfdYTG/R5UP7viiCu85V9mtKqc8OkjrfmOXSAMAicnVAKSjrNzqH4sHXa0ZczdpQeEHGsws5WiEnCj/X/YoSwvCx2h44L1aybcHMmQHG9YAJJOqLuBTXlD38CZlWqhFt/QerHW/U7de6nTnW4kIDUCKo8UbQNoq9tY6/6G3df9/V+i1j7bo/v+u0PlT56iYdZnRZKbHQ+eo0LvW6pa6PauHik+8NMGxAYcmbVp6GccNII5IvAGkrRkVZ2vN9lJVhF2SVBF2rdlWqhlzVyc4MqSS7FYZKrznXBXec65GHX14dXmJ2mj0vjs1pWKsXgmdoCkVY3X6zlt0/u8W8+UOEibqxGENULML+MkX39zykm6p+SZ4A5CW6E8JIG0tCB+jCq9dVhF2LSyM4RI5SCuD8/M0Z+WW6iXGStRG00PnSyGrPqa08sudSWf0TVCUSFefTRwWGcNcvvYRbZjyL6kBrdeHuiZ3qsptl6e9V8zSwulfVbbKtDv/rAatWQ4AB0OLN4C0NTjwoTLq/BbMCJgGF3RITEBIedFnObdaW3y5g0RplmXBWoBO3Xrp1Dvf0gl3vqvh3/1Fg5Lu0uKd0u3tpdvbR34GgDpIvAGkrfEZL6lXh2xlBCKJUUbA1KtjtsYNzU9sYEhZdWc5P6VXOwVVu1s5X+4gUZprWTAAwP7oah5jZjZR0kTxpQaQvFrlSLfvVK6kmWUVmjF3tRYWFmkQs5ojBg5v21qF95wrSdqye68G/+zl6n18uYNEKik4W+Vrflcr+WbisNgo3b1Di0PHKEdl2vXUT+meDmA/fLqMMXefJmmambWTRF8jIMnlZmUw1hbN5vIRBZp4Rl++3EFCpNOyYM1mX4mKd+9o9Nh5AOmDVlkAAOKguKxCFz/0Zq2yF9/fnKBogMjEYR2vnauF+RO0uPVgLcyfkHTLglVN4ha6Zbtatak7X0ISuqs7Y+cBNAhfuQMAEAcz5q7Wmu0ltcrWbmdGcyRWbrs8nXrJ3YkOo0Vh7DyAhqDFGwCAOFhQWFS9rFgVZjQHWpibNkTGznvtj9SMnQdQF4k3AABxMDg/T0GWqwNatlY5kbHzgW4q96AkMXYeQFQk3gAAxEHdNb2Z0RxomXLb5SnnillaGO6rxaECvdXzu0k3dh5A4jHGGwCAOKha07tqObGJp/fR5SN7M6M50AJ16tZLne58K9FhAEhi/PUHACBOcmok2RNG9VZ2K/7sAgCQjvgEAAAAkGDFu4q09Ol7lbN5vkq6DNGAsT88aFflrRvXaMX0rypHZdqVf5ZOvOgmujcDQJIi8Y4xM5soaaIYPw8AaS+7VYYK7zk30WEgme0rUfHuHdo29SwNqlwLurzwbW2Y8g+pvnHCleeUPPwlDQpUnrP2EW2Y8q/6zwEAJBTJYYy5+zR37y+JNSQAAMCB3dVdS++/QN0rk25JyrSwuoc3aunT98buHABAQtHiDQAAkEA5KqtOoKtkWkjZmxfE9BxIapUj3b4z0VEASEO0eAMAACTKTRtUUnC2yr32R7JyD6q0Wz2d5xpzDgAgoUi8AQAAEqVVjgZcOFkbAt1U7kFJkQR6Q6CbBoy5MXbnIGFKi3dKt7eXbm8f+RlAWqKrOQAAQALltsuTrp2rhU/fq+zNC1TaZfBBZzVvzDkAgMQh8QYAAEiw3HZ5OvWSu+N+DgAgMehqDgAAEEN0LUZNpbt3aF7oGC0JFejtp36q4l1FkXL+nwBphcQbAAAghupLtJBm9pWoeNv6yvXWP9LxwdU6Ze0j2jZlOP8ngDRE4g0AABALJFqoifXWAdRA4g0AQDOie2kLRqKFOlhvHUAVEm8AAIAYIdFCtYOst86QBCC9kHgDAADEwkESLaSZA6y33mfUtxiSAKQZEm8AAIBYOECiNWDMjQkODomQ2y5POVfM0sJwXy0OFeitnt9Vx2vnaOWjlzAkAUgzrOMdY2Y2UdJE8aUGAABpJ7ddnvZeMUsLp39V2SrT7vyzdOJFNym3XV6iQ0OCZLc9TKcGP5QklV58s7Jz2zMkAUhDJIcx5u7T3L2/JPqUAQD2s/+4zh2JDgkxVpVonRBcrZMvvpmkG/thSAKQfki8AQBoJtGWmtrx6xHaW7o70aEhhrJz20u375Ru3xn5GaiDIQlA+iHxBgCgmURfamqTts57MsGRAYiXaF/E1Df2m94RQMvFGG8AAJpJfeM6O+xYmqCIACRKtLHfAFouWrwBAGgm9Y3r3H7Y8QmKCAAANAcSbwAAmkl94zo7nfqtBEcGAADiicQbAIBmEm1c52Hf/59aZ7dNdGgAACCOGOMNAEAzqjuuM6tNrrR5S4KjAgAA8UTiDQAAADSz6tnOJWUnOBYA8UdXcwAAAAAA4ojEGwAAAACAOCLxBgAAAAAgjki8AQAAAACIIxJvAAAAAADiiFnNAQBoRnVnMg6FQokNCAAAxB0t3gAAAAAAxBGJNwAAAAAAcZT2ibeZPWtmRWb290PZBwAAAABAQ6R94i3p15K+04h9AAAAQMyVFu+Ubm8v3d4+8jOAlJf2ibe7z5a0+1D3AQAAAADQEEmdeJvZSDN73sw2mJmb2egox1xtZqvNbK+ZLTKzEQkIFQAAAACAqJJ9ObEcSYslPSbp6bo7zexiSfdLulrSXEkTJM0ys/7uvrbymEWSsqJc+2x33xCnuAEAAAAAkJTkibe7z5I0S5LMLNoh10l61N0fqdy+xszOkXSVpMmV1xjYDKHKzLJUO8FvK0XWZ2WN1sQKhUIKh8PUQxKgLpIHdZE8qIvkQV0kj3Svi5rPO5GfJdP19QfiIakT7wMxs1aSBkq6p86ulyQNbf6INFnSbXULV61apXbt2iUgHFQJh8Pavn27Vq5cqUAgqUdXtHjURfKgLpIHdZE8qIvkke51sW9PiY6v/Hn1qtVq1SYnIXHs2rUrIfcFWqKUTbwldZIUlLS5TvlmSV0behEze1HSyZJyzGydpDHuvuBg+6K4W9J9NbbbSlrXu3dv5eXlNTQcxEEoFNLKlSvVp08fBYPBRIeT1qiL5EFdJA/qInlQF8kj3eui5kzmBb0LlJ3bPiFxFBUVJeS+QEuUyol3Fa+zbVHK6j/Z/ZzG7ItybJmksuogKrvGB4PBtPyDkWwCgQB1kSSoi+RBXSQP6iJ5UBfJI53rouZzTuRrkI6vPRAvqdx3Z6ukkPZv3e6s/VvBAQAAAABIiJRNvN19n6RFks6qs+ssSW80f0QRZjbRzJZJmp+oGAAAAAAAySOpE28zyzWzE83sxMqigsrtnpXb90m6zMwuNbPPmdkUST0lPZiIeCXJ3ae5e39JQxIVAwAAAFJX6e4dmhc6RktCBXr7qZ+qeBdjrYFUl+xjvAdJml1ju2rysscljXf3p8yso6RbJXWT9J6kL7v7muYNEwAAAGiifSUq3r1DJQ9/SYMCG5VpYZWvfUQbpvxLunaOctsxYS+QqpI68Xb3VxWZLO1Ax0yXNL1ZAgIAAADi5a7uWho6pjrplqRMC6t7eKMWPn2vTr3k7gQHCKCxkrqreSpijDcAAAAaK0dl1Ul3lUwLKXtzfSvaAkgFJN4xxhhvAAAANMpNG1RScLbKvfZH9HIPqrQbHy2BVEbiDQAAACSDVjkacOFkbQh0U7lH1tAu96A2BLppwJgbExwcgKYg8QYAAACSRG67POVcMUsLw321OFSgt3p+Vx2ZWA1IeTGZXM3MMiV1lZQtaYu7b4/FdQEAAIB0k932MJ0a/FCSVHrxzcrObZ/giAA0VaNbvCvX2J5gZq9K2impUNIySVvMbI2Z/c7MBscmzNTB5GoAAAAAgJoalXib2bWKJNqXS3pF0gWSTpTUT9Kpkn6iSGv6f8zs32bWNxbBpgImVwMAAAAA1NTYruZDJZ3u7kvr2T9f0u/N7EpJ35U0StKKRt4LAAAAAICU1ajE290vauBxZZKmN+YeAAAAAAC0BDGb1dzMBsbqWgAAAAAAtBSxXE7s2RheK2UxuRoAAAAAoKZD6mpuZn+tb5ekDk0PJ/W5+zRJ08ysnSKzvQMAAAAA0tihjvE+U9K3JRXXKTdJI2MSEQAAAAAALcihJt6vSip299fq7jCzd2ISEQAAAAAALcghJd7ufsEB9n2x6eEAAAAAANCyxHJyNQAAAAAAUEeTEm8z6xqrQFoKZjUHAAAAANTU1Bbvl2ISRQvi7tPcvb+kIYmOBQAAAACQeE1NvC0mUQAAAAAA0EI1NfH2mEQBAAAAAEALdajLiQEAAACIo+zc9tLtOyM/JzgWALHBrOYAAAAAAMRRUxPvfTGJAgAAAACAFqpJibe7D4pVIAAAAAAAtERN7mpuZm3MLLvGdi8zu8bMzm7qtVMR63gDAAAAAGqKxRjvmZK+I0lmdpiktyRdL2mmmV0Vg+unFNbxBgAAAADUFIvE+2RJr1f+fKGkzZJ6KZKMfz8G1wcAAAAAIGXFIvHOlrS78uezJT3j7mFJbyqSgAMAAAAAkLZikXivlDTazI6UdI6klyrLO0vaFYPrAwAAAACQsmKReN8h6ZeSCiW95e7zKsvPlvRODK4PAAAAAEDKymjqBdz972Y2R1I3SYtr7HpZ0rNNvT4AAAAAAKmsyYm3JLn7Jkmb6pSxnBYAAAAAIO3Foqs5AAAAAACoB4k3AAAAAABxROIdY2Y20cyWSaKrPQAAAACAxDvW3H2au/eXNCTRsQAAAAAAEo/EGwAAAACAOCLxBgAAAAAgjki8AQAAAACIIxJvAAAAAADi6JATbzNrY2ZHRCk/NjYhAQAAAADQchxS4m1mF0r6SNK/zGyJmZ1SY/eTMY0MAAAAAIAW4FBbvG+WdLK7nyDpUkm/N7NvVO6zmEYGAAAAAEALkHGIx2e6+xZJcveFZjZS0jNm1keSxzw6AAAAAABS3KG2eH9qZsdXbbj7NklnSfqcpOPrPQsAAAAAgDR1qIn3tyV9WrPA3fe5+9cljYpZVAAAAAAAtBCHlHi7+zp331S1bWZda+ybG8vAAAAAAABoCZq6jvdLMYkCAAAAAIAWqqmJNzOZAwAAAABwAIc6q3ldzGReh5lNlDRRTf9SAwAAAEh6ixYtaiupm/j8i/QRlrRx4MCBuxt6QlMTb9Th7tMkTTOzdpJ2JjoeAAAAIB4WLVoUkHRTMBj8jpllit6wSB/u7uWLFi16QtJdAwcODB/sBBJvAAAAAI1xU2Zm5lVdu3bdl5OTU2pm9IZFWnB3Kykpyd60adNV5eXlkvTTg53T1MR7XxPPBwAAAJBiFi1a1C4YDH6na9eu+zp37rwt0fEAzS0nJ2ePpI4bNmz4zqJFix44WLfzJo3DcPdBTTkfAAAAQErqamaZOTk5pYkOBEiUyp4emYrMcXBATIAAAAAA4FAFJBndy5HOKv//mxqQV8ck8Taz1mY2xMzOM7Ov1HzE4voAAAAAWqbiveWB/B/9c2D+j/45sHhvOQ2DqOWII44YcMcdd3ROdBxN1eT/2Gb2RUlrJb0p6R+SnqvxeLap1wcAAACAWBk7dmz+mWeeeZQkzZo1K/eMM87o07lz5+PNbOCTTz55WN3jH3/88cOGDx/eNy8v7wQzG/jGG2+0qXvMnj17bNy4cUfm5eWd0KZNm5POOOOMPh9//HFmtPvv2bPHjjnmmP71XauhXnzxxdyTTz75mMMOO+zE1q1bn1xQUHDsT37yk/0S1DvuuKNzfn7+ca1btz65a9eux3/3u989srS0tHoG+oO9BmVlZXbVVVcdcfTRR/dv06bNSZ07dz5+zJgx+YWFhVGfX5Xrrruuu5kNHDFiRN+6+26++eYuZjZwyJAh/Rr7/KOZPHlyVzMbeOmllx55qPH/8pe/7DRkyJB+ubm5J5nZwK1btwZjGVssvlGaKulvkrq5e6DOI6bBAgAAAECs7N69O3DccceV/uIXv1hb3zHFxcWBz3/+88W33HLL+vqOueyyy47897//nffYY4+t+u9///thaWlp4Nxzz+1bUVGx37FXX311jy5duhx0kuoXXnih7RFHHDGgvv25ubmhCRMmfPrf//73w8WLF7934403brznnnuO+OUvf9mp6pjf/va3HX72s5/1+NGPfrRh8eLF702fPr3w+eefz/ve977Xo6GvQXFxcWDx4sXZP/zhDze++eaby/785z9/vGrVqtbnnXden4M9h8MPP7z8rbfealv3S4g//elPnbp163bA12Dv3r2HtDzda6+9lv3EE08cfvTRR+9pTPylpaWBM888c+f3v//9jYdy34aKxXJinSXd5+6bY3AtAAAAAGlkV43u5VP+81GX75959Ob2bTIPui5yLHz1q1/d9dWvfnWXJI0fPz7qMRMnTtwuScuXL291/fXX77d/27Ztwb/+9a+dpk+fvnr06NG7Jekvf/nL6qOOOur4mTNnths7duyuqmP/+te/tnvttdfa/e1vf/t40KBB7ZsS+7Bhw/YMGzasOsns16/f9ueeey5vzpw5bW+44YatkjRv3rzck08+ufjKK6/cXnnMvlmzZm1/++23cxr6GnTs2DH0xhtvrKhRVPbrX/967Wmnnfa5FStWtOrbt2+9CXSHDh0qjjvuuNKHH364489//vNNkvSf//wnp6ioKOPcc88t+uijj6pb/MeOHZu/c+fO4ODBg0seffTRzpmZmb5+/fqlda/5wAMPdLz55puPfOKJJ1aNGTNmlyTt3LkzMG7cuN7Tpk0rvOuuu7o3Jv5bb731UynyhUd9z6cpYtHi/XdJp8XgOgAAAABasOK95YGajw07SjMufHDeMVX7H3ujsNu5D7z+uQ07SjNqHpfImA9m7ty52RUVFTZ69OjqBDs/P7+8T58+e+bMmZNbVfbJJ59kfO9738ufMWPG6tzc3Jh/sTB37tw2b7/9du7IkSOrl7UaPnz47vfeey979uzZ2ZK0bNmyVi+//HL7c845Z2dT7lVUVBQ0M3Xs2HH/Jv06xo8fv/Uvf/lLdSv8I4880mnMmDHbW7Vqtd/EfPPmzWv34Ycftv7Xv/710XPPPbei7v5bb721y2233Xbkc889t6Iq6ZakSy65pOcXvvCFnVVffMQy/liJRYv3JEl/M7MRkpZKKq+5091/HYN7AAAAAEhxx93+0kkH2h922bode1oPvWf2CTXLC+85d1F8I2u8DRs2ZGZmZvrhhx8eqlneqVOn8s2bN2dKUjgc1je/+c2CcePGbRk5cmTp8uXLW8Xq/l26dDm+qKgoIxQK2XXXXbfhuuuu21q174orrijasmVL5tlnn32MuysUCtk3v/nNLXfdddemxt6vtLTUbr755h7/93//t71Dhw4H/QLh4osv3nnttdf2mjVrVu6wYcNK//nPf3Z4+eWXP3zooYc61T22TZs24T//+c9rWrduvV9SPnHixCP+9re/dXzppZeWDxkypLql/+GHH8577733st99990P4hF/rMQi8f6GpHMk7VGk5bvmi+SSSLwBAAAApBV3lypzo7vuuqtzcXFx8K677jrg+OHs7OzqLybC4bDt27fPapYNGjSo+H//+1+tluDXXnvtw127dgVff/31nDvvvLNHnz59yiZMmLBdinSbnjJlSref//zna4cPH17y4YcfZv2///f/jrzxxhu7/eIXvzjkscxlZWX2la98pXc4HNZjjz22piHnZGVl+QUXXLDt0Ucf7bRixYrd+fn5e0855ZQ9Dz300H7HHn300aXRku7p06d33bNnT2DevHnL+vfvX921feXKlZmTJ0/u+cILL3yUnZ190KXtGhN/rMQi8f6ppFsl3ePuzfaNAQAAAIDU8t7tZ79Tc3vKfz7q8tgbhd3CruqJtIIB+fhT8zdee9bRKTGHVPfu3cvLy8tty5YtwZqt3tu2bcs85ZRTSiTp1Vdfbbt48eKc1q1bD6x57siRI/t/5Stf2fbMM88UStL8+fOXVe17/fXXc26//fYeL7/88vKqspycnP3yrWOOOWafJA0ZMmTP5s2bM+++++7uVYn3bbfd1n3s2LHbqlrBhwwZsqe4uDhw/fXX97rnnns2BoMNnwu7rKzMzjvvvN6ffPJJ1muvvbb8UFqLJ0yYsHXkyJGf+/DDD9t861vf2lrfcdnZ2VGvOXjw4N2vvvpq+z/84Q8darbWz5s3L2f79u0ZI0aM6F9VFgqFtHDhwtwnnnii8969exdlZGQ0Of5YiEXi3UrSUyTdAAAAAA4kt3XtSdO+f+bRm198f3OHdTv2tJYiSXf39m3Kvn/m0ZvrHpushg0bVpqRkeEzZ85sd9lllxVJ0po1azJXrlzZZvjw4esk6be//e0nO3bsqJ4V/ZNPPmk1duzYvo888sjHI0aMKKkqP+6448qqfi4sLGyVkZHhNcsOxt1VXl5e/SXG3r17A4FAoFZLcDAYlLtbZYt8g1QlrYWFha1fe+215V27dg0d/KzPDBo0aG+fPn32fPTRR9mXXXbZ9kM5V5IGDx5c8oMf/ODT0aNHHx0MBv3OO+/cLEnnnXfervnz579f89hLLrmkoE+fPnt//OMfb6ybdDc2/liIReL9uKSLJd0Vg2sBAAAASBPt22SG/3rl55dXjekef2r+xuac1Xznzp2B999/P6tqe9WqVa3eeOONNocffnioarbrzZs3Bz/++ONWn3zySStJev/991tLUo8ePcp79uxZ0bFjx9BXv/rVrTfffPORhx9+eEWnTp0qbrjhhiP79u275/zzz98lSXVn/m7Xrl1Yko4++uiyo446qtYcWQ119913H96rV699AwYM2CtJr7zySu6DDz7Y5ZJLLvm06phzzjln58MPP9zlpJNOKh05cmTJBx980PpnP/tZ9y984Qs7qpLSg70G5eXl+vKXv9z7/fffz3n22WdXVFRUaO3atRmS1Llz51C0ruHRzJkz56N9+/ZZp06dGpX0nnnmmSXPPPPMR2PGjDk6IyPDb7vttk/z8vLCgwcP3lvzuOzs7HCHDh0qqsobGv/atWsz1q1bl/nRRx9lSdLChQvbtGvXLnTUUUft69KlS5MT9Vgk3kFJPzSzcyQt0f6Tq10Xg3sAAAAAaIHa1WjZvvas+Ld0h8NhZWRkuBTpzv1///d/R1ftu/3224+8/fbbdcEFF2x7+umnCyXpqaeeOuwHP/hBftUxV1xxRW9Juvbaazfed999GyTpd7/73SdXXXWVjxs37qiysjI79dRTdz/55JMrqpLbOD0Pu+2223qsW7euVUZGhh955JFlN9988/obbrhhS9UxP//5zzeYmf/0pz894tNPP22Vl5dXfuaZZ+6cMmVKdev7wV6DVatWtXrllVcOk6ShQ4f2rxnD888//9F55523W4osB/bJJ59kzZ8/f7miqPqyoSnOPvvskr///e8rxo4d2zcYDOrmm2/+9GDnNDT++++/v/OUKVO6Ve370pe+1E+SHnjggcLvf//725oa+yF1MYh6AbPZB9jt7n5Gk26QosysnaSd27dvV15eXqLDSWuhUEgrVqxQ3759dSjjWBB71EXyoC6SB3WRPKiL5EFdJIeioiJ16NBBktq7+66a+xYtWnRMRkbGv/v27VucnZ29N/oVGqZ4b3mgarbz924/+514J94jRozoW1BQUPbEE0+sjed90s2QIUP6DR8+fHfVlxHpoLS0tPWKFStyKyoqvjhw4MAPD3Rsk7+CcffTm3qNRDKzZxWZjf1ld7+wRvmRkp6U1FlShaQ73f1vCQkSAAAAQJNs2bIl+PLLL+fOnz+/7RVXXLHl4GegoYqKigJr167Nuu222/ZbexsR8ev7kDp+Len3ksbVKa+QdI27v2tmnSW9bWb/cveS/a4AAAAAoFFyW2eGm2Od7m984xv5S5Ysybniiis2f/Ob39wR7/ulk7y8vPCmTZuWJDqOZNbkxNvMJkva7O6/r1N+qaTD3f3nTb1HPLn7bDM7LUr5RkkbK3/+1My2S+ogicQbAAAASDH/+c9/Pk50DEhfgRhcY4KkaP3Z35d0ZVMubGYjzex5M9tgZm5mo6Mcc7WZrTazvWa2yMxGNOWe9cQxSFLA3T+J9bUBAAAAAC1bLLqad1Vly3AdWyR1i1J+KHIkLZb0mKSn6+40s4sl3S/paklzFfkSYJaZ9Xf3tZXHLJKUVfdcSWe7+0EH/ptZR0lPSLqskc8BAAAAAJDGYpF4fyJpmKTVdcqHSWrSjHbuPkvSLEkys2iHXCfpUXd/pHL7msplza6SNLnyGgMbe38zy5L0rKS73f2NBhxbM8FvK0Vm5QyFmn19dtQQCoUUDoephyRAXSQP6iJ5UBfJg7pIHtRFcuD1B2InFon3I5LuN7NMSa9Uln1B0r2SfhWD60dlZq0kDZR0T51dL0kaGoPrm6QZkl5x9ycbcMpkSbfVLVy1apXatWvX1HDQBOFwWNu3b9fKlSsVCMRidAUai7pIHtRF8qAukgd1kTyoi+Swa9eugx8EoEFikXjfq8ikY9Mltaos2yvp5+5+dwyuX59OkoKSNtcp36xI9/cGMbMXJZ0sKcfM1kka4+4LFGmxv1jSkhpjy7/t7kvrudTdku6rsd1W0rrevXuzjneChUIhrVy5Un369GEt0ASjLpIHdZE8qIvkQV0kD+oiORQVFSU6BKDFiMU63i7p/5nZnZI+J2mPpBXuXtbUazc0hDrbFqWs/pPdz6mnfI4OYfK5yudb/ZyrusYHg0H+YCSBQCBAXSQJ6iJ5UBfJg7pIHtRF8qAuEq/ZXvuy3QHd3eMkSdLkde8oq224eW4MNJ+Y9d1x92J3X+Du7zVT0r1VUkj7t2531v6t4AAAAADQKEOGDOl36aWXHnmgY4444ogBd9xxR+fmiulAzGzgk08+eVii48BnYtHVPCHcfV/ljOVnKTIBWpWzJM1MTFSSmU2UNFEx/FIDAAAAQHJbsGDBB23b0lqP6JI68TazXEl9ahQVmNmJkrZXLhd2n6QnzWyhpHmSrpDUU9KDzR1rFXefJmmambWTtDNRcQAAAAA4uL1791rr1q0bPFS1Pt27d6+IRTxomZK9VXaQpHcqH1Ik0X5H0h2S5O5PSbpG0q2S3pU0UtKX3X1NcwcKAAAAoBH27vwsJ5l9Vxft2RHXHGXIkCH9vvOd7/S87LLLeuTl5Z0wYsSIoxctWtR61KhRfbKzs0/q2LHjCaNHjy7YuHFjrUbKiooK+853vtOzbdu2Jx522GEnfv/73+8eDn/WwF23q7mZDbzvvvs6nXXWWUe1adPmpF69eh33xz/+sX3V/hdeeKGtmQ2cOXNm2+OOO+5zbdq0Oemkk046ZvHixTWXKNaf/vSn9scee+znsrKyTu7Ro8eA66+/vlt5eXn1/qVLl2YNGjSoX1ZW1slHHXXUsc8++yxLKiWhpE683f1Vd7coj/E1jpnu7vnunuXuA939fwkMGQAAAEB9ynYHaj12rsvQ7885pnr/Ww9204MjPqed6zJqHRdjTz/9dMeMjAy9+uqrH959993rzjzzzH4DBgzYM3fu3A/+8Y9/fLRly5aMMWPG9I5yjs+ZM+eDu+++e+3vfve7LlOmTOl0oPvce++93S+88MKiBQsWLDvjjDN2XnHFFb03b95ca9a6W2+99Yif//znn8yZM+eDYDDol1xySUGNe7abMGFCwZVXXvnpO++8895vfvObNX/5y186/ehHP+omRVYAGDt27FHBYNBnz579wdSpU9f8+Mc/PiKWrxVio8ldzc3svnp2uSLLiq2UNNPdtzf1XqmAMd4AAABAPapmL6+Ph00717bWlGNPqFV++85FsQyjZ8+eZQ8++OA6Sbrmmms6HnvssaVTp05dX7X/iSeeKOzTp8/xS5YsyTr++OPLJKlr1677HnnkkU8CgYBOOOGEsqVLl7aZPn16l+uvv35rffe5+OKLt06YMGG7JD3wwAPrH3/88c6vv/56zoUXXli9SPodd9yx/txzzy2WpBtvvHHT1772tT6lpaWWnZ3td999d7dJkyZt+t73vrdNkvr3779v48aN62+//fYev/rVrzbOnDmz3apVq9osX758yVFHHVUuSSUlJesvuuiivrF8vdB0sRjjfZIi62AHJS1XZDmvvorMOP6hpKsl/crMhrv7shjcL6kxxhsAAABIbieccEJJ1c/vvvtu9ltvvdU2Ozt7vy8FPvzww+rE++STTy4JBD5rWxs6dGjJww8/3KWiokIZGdHTqhNOOGFP1c/t2rUL5+TkhDZt2pRZ85jBgwdXH9OjR499krR+/frMvn377nv//fez33vvvZwHHnigW9Ux4XDYysrKbPfu3YH333+/ddeuXfdVJd2SdPrpp5cISScWifdMSdslXeLuuySpMul8VNIcSb+T9CdJUyRFXTMbAAAAQBqYvO6dWtuz7+qitx7sJg9bdVkg6Bpy5UadPjluSwRnZ2dXD84Oh8N2xhln7PzVr361ru5xPXv2LK9bdigyMzP3m7St5rhwSWrVqlX1MWZW6xh3txtuuGH91772tR11r5OdnR12339OODNr8kRxiL1YJN43SjqrKumWJHffZWa3S3rJ3R8wszskvRSDewEAAABIVVl1ltsa9f8264MXOmjn2taSIkl3ux5lGvXDzfsdGycnnHBC6QsvvHBYv379yjIzM+s97u23386puT1v3rycXr16ldXX2h0L/fv3L/3oo49aH3fccWXR9h933HF7N23a1KqwsDAzPz+/XJJmz56dG7eA0GixGIfcXlK0heIPl1Q1o94OSa1icK+kZ2YTzWyZpPmJjgUAAABIam0OC+vSWcurt4dM2KgJ//tAbQ5rtvWwr7/++k937tyZ8ZWvfKX37Nmzs5ctW9bqmWeeaXfRRRflV1R8tkLYpk2bWl122WU9Fi9enPXQQw91eOyxxzpfeeWVcWuVl6Qf//jHG5555pmO1113XfeFCxe2fvvtt1v/7ne/y/v+97/fXZLOP//8Xfn5+Xu/8Y1vFMybN6/Nv//979zbbruNydWSUCwS75mSfm9mY8ysh5kdYWZjFOlq/lzlMUMkfRSDeyU9d5/m7v0Vec4AAAAADqR1+8+S7NNv2tycSbck5efnl7/22msfhkIhO//8848eOHDgsTfccMOR7dq1C9Uc033BBRds27NnT2D48OGf++EPf9jzkksu+fRAE6vFwtixY3c99dRTK1999dV2I0aM+NyoUaOO+c1vftOlV69e+yQpGAzqmWeeWVlWVmajRo363JVXXpn/k5/8ZP3BrovmF4t+ERMUGb/9lxrXq5D0uKRrK7c/lHRZDO4FAAAAAI02f/785XXLBgwYUPbSSy993MBz1kY7Zv369Utrbrv7fjOx7969+92qn88777zddY8ZOnTonrplY8eO3TV27Nhdqsfxxx9ftmjRolrPKdq9kVhNTrzdvVjS5WZ2raTeisxq/nFledUx7zb1PgAAAAAApKImJd5mlqnIpGkT3P0jSUtiEhUAAACA9JDVNhzrdbqBZNOkxNvdy83sOElMWQ/EyNaNa7Ri+leVozLtyj9LJ150k3Lb5SU6LAAAAACNFIvJ1Z6Q9N0YXKdFYFZzNNq+EhVvW6+Sh7+kQYGPdHxwtU5Z+4i2TRmu4l1FiY4OAAAAQCPFYnK1VpIuM7OzJC2UVFJzp7tfF4N7pAx3nyZpmpm1k7Qz0fEghdzVXUtDx2hQYKMyLTKZZ6aF1T28UQufvlenXnJ3ggMEAAAA0BixSLyPk/R25c9H19lHF3TgEOSorDrprpJpIWVvXpCgiAAAAKIKS3J3t0QHAiRK5f9/V+T9cECxmNX89KZeA4Ckmzao5A8/Ufma39VKvss9qNJuLAsPAACSyiZ3Ly8pKcnOycnZk+hggEQoKSnJdvdySRsPdmwsWrxlZiMUWc+7t6SL3H29mX1b0mp3nxOLewAtXqscDbhwsjZMeUHdw5uUaSGVe1AbAt00YMyNiY4OAACg2sCBA3ctWrToiU2bNl0lqWNOTk6pmdHbFWnB3a2kpCR706ZNrUKh0KMDBw7cfbBzmpx4m9lYSU9K+qOkkyVlVe5qK+kmSV9u6j2AdJHbLk+6dq4WPn2vsjcvUGmXwRow9ofMag4AAJLRXeXl5dqwYcN3zCxbEt3OkS7c3ctDodCjku5qyAmxaPG+WdKV7v6EmX2tRvkbkm6NwfVTiplNlDRRsZkxHmkot10eE6kBAICkN3DgwLCkny5atOgBSd3E51+kj7CkjQ1p6a4Si8S7n6T/RSnfJemwGFw/pTCrOQAAANJJZfLR4AQESEex+FZqo6Q+UcqHS1oVg+sDAAAAAJCyYpF4PyTpATM7RZGp1Lub2Tcl/VLS9BhcHwAAAACAlBWL5cTuNbP2kmZLaq1It/MySb9096lNvT4AAAAAAKksJsuJufuPzexnkvor0oq+zN2LY3FtAAAAAABSWUwSb0ly91JJC2N1PQAAAAAAWoJGjfE2s56HePwRjbkPAAAAAACprrGTqy0ws9+Z2ZD6DjCz9mZ2uZm9J+mCRt4n5ZjZRDNbJml+omMBAAAAACReY7uaf07STZL+bWblinQx3yBpr6Q8RcZ6H1tZfqO7z4pBrCmBdbwBAAAAADU1qsXb3be7+w2Suku6StJHkjpJ6lt5yB8lDXT3YemUdAMAAAAAUFeTJldz972Snql8AAAAAACAOho7xhsAAAAAADQAiTcAAAAAAHFE4g0AAAAAQByReAMAAAAAEEck3gAAAAAAxBGJNwAAAAAAcUTiHWNmNtHMlkman+hYAAAAAACJR+IdY+4+zd37SxqS6FgAAAAAAIlH4g0AAAAAQByReAMAAAAAEEck3gAAAAAAxBGJNwAAAAAAcUTiDQAAAABAHMUt8TazLmbWJl7XBwAAAAAgFWTE46Jm9rakIkndzWyWpMnuXhaPewEAAAAAkMzi1eI9xN2/IKm/pPWSXjSzw+J0LwAAAAAAklZcEm93r6j8MVfS85LmSnolHvcCAAAAACCZxaur+QpJHSQVS9ooaZOkt+JxLwAAAAAAklmjE28zy3X34np2f97dtzX22gAAAAAAtBRN6WpeZGadou0g6QYAAAAAIKIpiXew5vlmNtfMujQ9pNRmZhPNbJmk+YmOBQAAAACQeLGcXO14STkxvF5Kcvdp7t5f0pBExwIAAAAASLx4LScGAAAAAADU9MT7G2Z2spllVm57UwMCAAAAAKAlacpyYnMk/URSW0nlldf6mZm9LultSYvdfW/TQwQAAAAAIHU1OvF295GSZGZHSzq58jFQ0s8kHSapwsw+dPfjYxAnAAAAAAApqSkt3lVWSuoiaZ+k+9x9k5kVSBok6aQYXB8AAAAAgJQVi8T7U0mtJJVJOszM/iDpe+7+N0l/i8H1AQAAAABIWbGY1fwid2/n7odLGiwpX9J8M+sag2sDzaZ0X4Xyf/RP5f/onyrdV1FrX3FZhaa+skLjfj9fU19ZoeKyinquAgAAAAC1NbnF291n1/j5XTM7Q9K9kuaY2Qh339jUewCJVFxWoa/8Zo5WbS2RJM1ZuUXPvrNeMycNV25WLDqNAAAAAGjJ4rGOd46kRyV9JOmlOFwfaFYz5q7Wmu0l1duhsLRmW6lmzF2dwKgAAAAApIomN9eZ2WOSjpDUo/Lf3KpdklhODClvQWGRQuHaZRVh18LCosQEBAAAACClxKKfbBdJayS9IWl9zYe7b43B9YGEGpyfpzkrt9RKvjMCpsEFHRIXFAAAAICUEYsx3l+ORSBAsho/rEDPvrNea7aVqiLsygiYenXM1rih+YkODQAAAEAKYGYo4CByszI0c9JwzZi7WgsLizQoP0/jhxUwsRoAAACABiFzABogNytDk87om+gwAAAAAKQgEm/gEG3ZvVeDf/ayJOkHX+iry0f2pvUbAAAAQL3isZwYkJJKyiqqf37otVUqrrFdpbisQhc/9Gb19tTZK3T+1DlRjwUAAAAAicRbZvasmRWZ2d/rlLc1swVm9q6ZLTWzyxMVI+KvvoR68669mvrKCo37/Xw99NK7evePN+u2Xbfq6uBzytEe1vQGAAAAcFD0j5V+Len3ksbVKS+VNMrdS80sW9J7ZvaMu29r9ggRdzPmrtaa7SXV26GwVLi1RF964H/atadCWeFS3VJ4i3rZJmUGwhpqSzUmOEej992pknAb1vQGAAAAUK+0b/F299mSdkcpD7l7aeVma0lBSdacsaH5LCgsqrVOtySFXNpeUq6KsGtc8MVI0m2RgzItrHzbrHHBF1nTGwAAAMABJXXibWYjzex5M9tgZm5mo6Mcc7WZrTazvWa2yMxGxPD+h5nZYknrJN3r7ltjdW0kl8H5eQoe4N0wOLC8OumukmkhDQkuZ01vAAAAAAeU1Im3pBxJiyVNirbTzC6WdL+kn0k6SdLrkmaZWc8axywys/eiPLof7ObuvsPdT5BUIOkbZtal6U8JyWj8sAL16pBTvZ0RMHXIzqxOxheE+6nca79dyj2ojPxhmjlpOLOaAwAAAKhXUmcL7j5L0ixJMovay/s6SY+6+yOV29eY2TmSrpI0ufIaA2MQx2YzWyJppKS/RTvGzLIkZdUoaitJoVBIoVCoqSGgCUKhkMLh8AHroU2G6U+XDdbn73lVknTVab319cFH6tu/X6A120r1eOgcXRCco162WZkKyQMZysjrrVO/9v+kDKOOG6ghdYHmQV0kD+oieVAXyYO6SA68/kDsJHXifSBm1krSQEn31Nn1kqShMbh+F0l73H2XmbVTJOn+7QFOmSzptrqFq1atUrt27ZoaDpogHA5r+/btWrlypQKB+jt57C3/rCv5F7qFtWvzJ/rF2Z0184Oden9zaz3VaarGZ7yo3K1L9NC6I/X4+nP0x483qHVmsnccSR4NrQvEH3WRPKiL5EFdJA/qIjns2rUr0SEALUbKJt6SOiky4dnmOuWbJXVt6EXM7EVJJ0vKMbN1ksa4+wJJPSQ9apGmdpM01d2XHOBSd0u6r8Z2W0nrevfurby8vIaGgzgIhUJauXKl+vTpo2AwWO9xpfsqJEWWBTuqz1HKbhV5e5zQv+ZRp2nL7r2aXtky/vLGgC4bXkBX8wZqaF0g/qiL5EFdJA/qInlQF8mhqIhVW4BYaQnZgtfZtihl9Z/sfk495YsknXgI1ymTVFYdRGXX+GAwyB+MJBAIBA5aF8Gg1/g5+rHFZRX6xiMLqrenv/qx/rV0E+O8D0FD6gLNg7pIHtRF8qAukgd1kXi89kDspHLfna2SQtq/dbuz9m8FB2Ii2nrfa7aVasbc1QmMCgAAAEAyS9nE2933SVok6aw6u86S9EbzRxRhZhPNbJmk+YmKAfETbb3virBrYSFdsQAAAABEl9SJt5nlmtmJZnZiZVFB5XbVcmH3SbrMzC41s8+Z2RRJPSU9mIh4Jcndp7l7f0lDEhUDmiZHe/TC1Ou15oEvqmz2vVLZ7up90db7zgiYBhd0aOYoAQAAAKSKZB+UOkjS7BrbVZOXPS5pvLs/ZWYdJd0qqZuk9yR92d3XNG+YaAlKdu9Urkr0bKvblL9zkzItrPLX3lJo6VMKTnhVymqr8cMK9Mzb67Vqa6S7eUbA1KtjtsYNzU9o7AAAAACSV1In3u7+qiKTpR3omOmSpjdLQGjRnrrvGo0PtlK+RZJuScpUWKFtH0tvPSSNvEG5WRn6x/eGa8bc1VpYWKRB+XkaP4xZzQEAAADUj2whxsxsoqSJSvJu/NjfgvAxGp/xYnXSXSVoLq19s3o7NytDk87o29zhAQAAAEhRJIcxxhjv1HXiqPO1IHy0yr322yKkDKnnqdXbW3bvVf6P/qn8H/1TU/7zkYrLKpo7VAAAAAAphMQbqDRmUL4eD31Rhd5V5R5Zt7Lcg/IOBdIpV0iKrON98UOftX5Pnb1C50+dQ/INAAAAoF4k3kClP7y5RiVqo9H77tSUirF6JXSCHghdqEePeUTKaiuJdbwBAAAAHDrGeAOKtGT/4a21kqQStdH00GgpFNl32rp9mlB5HOt4AwAAADhUtHjHmJlNNLNlkuYnOhZEV1xWoamvrNC438/X1FdWqLisQjPmrlZZeXi/YwOmWmt0s443AAAAgENFi3eMufs0SdPMrJ2knYmOB7UVl1Xo/KlztGZbqSrCrrkrt+rZd9arW/s28ijHt84I1lqjm3W8AQAAABwqWryRVmbMXV2ddEuRbuJrtpVK8v1ask3S5SN711qjOzcrQ09N+Hz19sTT+2jmpOGs4w0AAACgXmQLSCsLCouqk+4qVdtH5mWrcFuppM9asi8f2Xu/axzetrUK7zk3/sECAAAAaBFIvJFWBufnae7KrbWS74yA6dSjOmnc0HzNmLtaCwuLNCg/T+OHFdCSDQAAAKDJyCpizMwmSpoouvEnpfHDCvT02+u1ddtWfTvwooYElmtFmwH6+sBTlZuVoUln9E10iAAAAABaGBLvGGNytSS3r0RtQiV6JvMW5dsmZVpYw/YtVWDGm9KEV6vX6wYAAACAWKFVFmllxj2TdMauZ6qTbknKVFja9rH01kMJjg4AAABAS0TijbSyIHyMBgZWVCfdVYLm0to3ExQVAAAAgJaMxBtpZfAZY7TI+6nca//XDylD6nlqgqICAAAA0JKReCOtjB95tP7bdrQKvavKPShJKveg1llXFZ94aYKjAwAAANASMbkaWrTisgo9NmeVXlu2QaPWB/TVwT21aW+mRu+7U+OCL2pQYLkWhvvpj/5FXb5wqyadkZfokAEAAAC0MCTeMcZyYsmjuKxC50+do8KtJQq5tHD9Ck1/9WPtKQ9LaqPpodFS6LPjFxYWJSpUAAAAAC0YiXeMsZxY8vjjK+/oS9uf1MCMFVoQ7qfHQ+eopLxNvccPLujQjNEBAAAASBck3mh59pVIZcU6b9El6pyxXpkW1vDAUo0JztHofXeqRPsn3x1yWmnc0PzmjxUAAABAi0d3aLQ8d3WXfnW0upav+2ytbgsr3zZrXPDF/Q7Py87UrB+MUG4W30MBAAAAiD0yDbRYQfNa25kW0qDAcgXD0ql9OirDAhqUn6fxwwpIugEAAADEDdkGWpzCCSv0t6mTdU3GM9Ut3lJk2bC3/Rjld8rRg98aRLINAAAAoFmQeaBF2bxrr876zUK1Cn9Jo4NvKF+blWkhVSioTzO7q+2wq/XoyX103G2RLuc/+EJfXT6yN0k4AAAAgLhhjDdalB/85R2Vh10laqPR++7UlIqxeiV0gv6S9VV1vW6OvjnqOF06Y2H18VNnr9D5U+eouKwigVEDAAAAaMlo5osx1vFOnOKyCs1fvb16u6TGWt3t9gb09ay2mvHaKq3ZXlJ9TCgsrdlWqhlzV2vSGX0TEDUAAACAlo7kMMbcfZq795c0JNGxpJsZc1cr7NH39emYJUlaUFikULj2voqwa2FhUZyjAwAAAJCuSLzRYiyoJ3k2SdcP7yxJGpyfp2Cd//UZAdPggg5xjg4AAABAuiLxRosxOD9PGQHbr/yKEQXqmB0ZVTF+WIHyO+ZUH5cRMPXqmK1xQ/ObM1QAAAAAaYTEGy3G+GEF6tUxu1ZSfdThObr69KOqj8nNytDMScN1zZl9ddrRh+uaM/tq5qThzGoOAAAAIG7INtBiVCXVD7/2sZ5fvEEu6dzju0c9jonUAAAAADQXWrzR4rywZKNWbytV4bZSTZu9Qhf8dp5Ky8MHPxEAAAAA4oDEGy3KjLmroy4XNvODnQmMCgAAAEA6I/FGi1LfcmHvb96TmIAAAAAApD3GeCPlFJdVaMbc1VpQWKShPVppfOZLylr/ltTzVA3tcabmrFSt5DsjYDquS5vEBQwAAAAgrZF4x5iZTZQ0UfQmiIvisgqdP3WO1mwrVVa4VLcU3qJAYJOksLTqVX23/Z/0W/1YO9S6+hwz6aw+bRMXNAAAAIC0RnIYY+4+zd37SxqS6FhaohlzV2vNtlJVhF3jgi+ql21Spiqbt8MVsu2r9M3Av2udEw67Xlq5OwHRAgAAAACJN1LMgsIiVYRdkjQ4sFyZVntAd9BcA215rbKQizHeAAAAABKGxBspZXB+njICJklaEO6ncq/9XzikDC3yfrXKGOMNAAAAIJFIvJFSxg8rUK+O2coImB4PnaM13lXlCkZ2BjKkjgV6tf2Y6uMzAqZeHbP1lc+1T1DEAAAAANIdk6shpeRmZWjmpOGaMXe1FhYW6eUef9b4zJek9fOlnp9X8JQJekptqvcPys/Ttz/fUxvXrk506AAAAADSFIk3UpZLKs/IUfnQ65SV9dl/5VxJk87oW70dCoWaPzgAAAAAqETijZRSczmxirBr7sqtevad9Zo5abhys/jvDAAAACD5MMYbKWXG3NUq3FZSPbN5Rdi1ZlupZsylKzkAAACA5ETijZSyoLBIodoriKki7FpYWJSYgAAAAADgIEi8kVJqLidWJSNgGlzQIUERAQAAAMCBkXgjpdRcTkz6bLmwcUPzExsYAAAAANSD2aiQUuouJzYoP0/jhxUwsRoAAACApEW2gpSTm5VRa7kwAAAAAEhmdDWPMTObaGbLJM1PdCwAAAAAgMSjxTvG3H2apGlm1k7SzkTHk1LKdqvsjd9q05JXND98jLYfO17fHHXcft3It+zeq8E/e1mS9IMv9NXlI3vT1RwAAABA0qLFG4m3r0TavVmhh05T4LW71atonkbveFxfmPN1fe03L6m4rKL60OKyCl380JvV21Nnr9D5U+fUOgYAAAAAkgmJNxLvru7Sr46Wtn2sTEUW6c60sHrZZp224znNmLu6+tAZc1drzfaS6u1QWFqzrbTWMQAAAACQTEi8kTSC5rW2My2kk+1DLSwsqi5bUFikULj2eRVhr3UMAAAAACQTEm8k3k0bpFE/UkjBWsXlHtTbfowGF3SoLhucn6dgnf+1GQGrdQwAAAAAJBMSbyReqxxp6CSpY2+VVybf5R7UGu+iVw8brXFD86sPHT+sQL065FRvZwRMvTpm1zoGAAAAAJIJU0EjOWS1VfCK2ap447cqXDJbC8P9tO3Y8fpLnVnNc7My9NSEz1fPaj7x9D7Mag4AAAAgqZGtIHlktVXW6T9U/uk/VP4BDju8bWsV3nNuc0UFAAAAAE1CV3MAAAAAAOKIxBsAAAAAgDiiqzmSRnFZhWbMXa0FhUUanJ+n8cMKGLsNAAAAIOWR1SApFJdV6Pypc7RmW6kqwq65K7fq2XfWa+ak4STfAAAAAFIaXc2RFGbMXa3CbSWqCLskqSLsWrOtVDPmrk5wZAAAAADQNCTeSAoLCosUCtcuqwi7FhYWJSYgAAAAAIgREm8khcH5ecoIWK2yjIBpcEGHBEUEAAAAALFB4o2kMH5YgXp1zK5OvjMCpl4dszVuaH5iAwMAAACAJmLWKiSF3KwMzZw0XDPmrtbCwiINYlZzAAAAAC1E2mc1ZvaspNMkvezuF0bZny3pA0l/c/cbmjm8tJKblaFJZ/RNdBgAAAAAEFN0NZd+Lek7B9j/Y0lvNVMsAAAAAIAWJu0Tb3efLWl3tH1m1lfSMZL+1axBAQAAAABajKROvM1spJk9b2YbzMzNbHSUY642s9VmttfMFpnZiBiG8EtJk2N4PQAAAABAmknqxFtSjqTFkiZF22lmF0u6X9LPJJ0k6XVJs8ysZ41jFpnZe1Ee3Q90YzM7X9JH7v5RjJ4LAAAAACANJfXkau4+S9IsSTKzaIdcJ+lRd3+kcvsaMztH0lWqbKl294GNvP3nJX3NzC6SlCsp08x2ufsd0Q42syxJWTWK2kpSKBRSKBRqZAiIhVAopHA4TD0kAeoieVAXyYO6SB7URfKgLpIDrz8QO0mdeB+ImbWSNFDSPXV2vSRpaFOv7+6TVZm8m9l4ScfVl3RXmizptrqFq1atUrt27ZoaDpogHA5r+/btWrlypQKBZO/k0bJRF8mDukge1EXyoC6SB3WRHHbt2pXoEIAWI2UTb0mdJAUlba5TvllS14ZexMxelHSypBwzWydpjLsvaEQ8d0u6r8Z2W0nrevfurby8vEZcDrESCoW0cuVK9enTR8FgMNHhpDXqInlQF8mDukge1EXyoC6SQ1FRUaJDAFqMVE68q3idbYtSVv/J7uc04JgZDTimTFJZdRCVXeODwSB/MJJAIBCgLpIEdZE8qIvkQV0kD+oieVAXicdrD8ROKvfd2SoppP1btztr/1ZwAAAAAAASImUTb3ffJ2mRpLPq7DpL0hvNH1GEmU00s2WS5icqBgAAAABA8kjqruZmliupT42iAjM7UdJ2d1+ryJjqJ81soaR5kq6Q1FPSg80daxV3nyZpmpm1k7QzUXEAAAAAAJJDUifekgZJml1ju2rysscljXf3p8yso6RbJXWT9J6kL7v7muYNEwAAAACA6JI68Xb3VxWZLO1Ax0yXNL1ZAgIAAAAA4BCl7BjvZMUYbwAAAABATSTeMebu09y9v6QhiY4FAAAAAJB4JN4AAAAAAMQRiTcAAAAAAHFE4g0AAAAAQByReMcYk6sBAAAAAGoi8Y4xJlcDAAAAANRE4g0AAAAAQByReAMAAAAAEEck3gAAAAAAxBGJd4wxuRoAAAAAoCYS7xhjcjUAAAAAQE0k3gAAAAAAxBGJNwAAAAAAcUTiDQAAAABAHJF4AwAAAAAQRyTeMcas5gAAAACAmki8Y4xZzQEAAAAANZF4AwAAAAAQRyTeAAAAAADEEYk3AAAAAABxROINAAAAAEAckXgDAAAAABBHJN4AAAAAAMQRiXeMsY43AAAAAKAmEu8YYx1vAAAAAEBNJN4AAAAAAMQRiTcAAAAAAHFE4g0AAAAAQByReAMAAAAAEEck3gAAAAAAxBGJNwAAAAAAcUTiDQAAAABAHJF4AwAAAAAQRyTeMWZmE81smaT5iY4FAAAAAJB4JN4x5u7T3L2/pCGJjgUAAAAAkHgk3gAAAAAAxBGJNwAAAAAAcUTiDQAAAABAHJF4AwAAAAAQRyTeAAAAAADEEYk3AAAAAABxROINAAAAAEAckXgDAAAAABBHJN4AAAAAAMQRiTcAAAAAAHFE4g0AAAAAQByReAMAAAAAEEck3jFmZhPNbJmk+YmOBQAAAACQeCTeMebu09y9v6QhiY4FAAAAAJB4JN4AAAAAAMQRiTcAAAAAAHFE4g0AAAAAQByReAMAAAAAEEck3gAAAAAAxBGJNwAAAAAAcUTiDQAAAABAHJF4AwAAAAAQRyTeAAAAAADEEYk3AAAAAABxROINAAAAAEAckXgDAAAAABBHJN4AAAAAAMQRiTcAAAAAAHFE4g0AAAAAQByReAMAAAAAEEck3gAAAAAAxFHaJ95m9qyZFZnZ36PsqzCzdysfjyQiPgAAAABAastIdABJ4NeSfi9pXJR9O9z9xOYNBwAAAADQkqR9i7e7z5a0O9FxAAAAAABapqROvM1spJk9b2YbzMzNbHSUY642s9VmttfMFpnZiBiG0K7ymnPMbFQMrwsAAAAASBPJ3tU8R9JiSY9JerruTjO7WNL9kq6WNFfSBEmzzKy/u6+tPGaRpKwo1z7b3Tcc5P757r7BzI6T9E8zG+Duuxr9bAAAAAAAaSepE293nyVpliSZWbRDrpP0qLtXTXx2jZmdI+kqSZMrrzGwCfffUPnve2a2TNLRkhZGO9bMslQ7wW8rSTt27Gjs7REjoVBIu3btUlFRkYLBYKLDSWvURfKgLpIHdZE8qIvkQV0kBz7HArGT1In3gZhZK0kDJd1TZ9dLkobG4Pp5kkrdvczMekjqL2nVAU6ZLOm2uoW9e/duaigAAABAInWQRK9PoAlSNvGW1ElSUNLmOuWbJXVt6EXM7EVJJ0vKMbN1ksa4+wJJn5P0kJmFJbmkH7j79gNc6m5J99XYbitpnaQeYvK2RKMukgd1kTyoi+RBXSQP6iJ5UBfJoaoeDvQZGEADpHLiXcXrbFuUsvpPdj+nnvI3JA04hOuUSSqrDuKzrvG7GReeWNRF8qAukgd1kTyoi+RBXSQP6iI51DPUE0AjJPWs5gexVVJI+7dud9b+reAAAAAAACREyibe7r5P0iJJZ9XZdZakN5o/IgAAAAAA9pfUXc3NLFdSnxpFBWZ2oqTtlcuF3SfpSTNbKGmepCsk9ZT0YHPHGkWZpJ+oRvdzJAx1kTyoi+RBXSQP6iJ5UBfJg7pIDtQDECPm3uDh0M3OzE6TNDvKrsfdfXzlMVdL+qGkbpLek3Stu/+vmUIEAAAAAOCAkjrxBgAAAAAg1aXsGG8AAAAAAFIBiTcAAAAAAHFE4g0AAAAAQByReDczM+tnZu/WeOwxs9GJjiudmNmzZlZkZn+vU36emS03sxVmdlmi4ksn0erCzNqa2YLK98dSM7s8kTGmiwO8LwrMbLaZLausj5xExZguDlAXN5jZ+2b2npl9K1HxpQszO9LMXq38v7/EzC46UDni50CvuZlV1PhM9Ugi40wHB6mLayt/Ry0zs1+bmSUyViDZMLlaAlUul1YoqZe7lyQ4nLRhZqdLypU0zt0vrCzLkLRM0umSdkl6W9Ip7r49YYGmgXrqIigpy91LzSxbkdUKBrv7tgSG2uJFq4vK8tck3ezur5tZB0m73L0iUXGmg3reFwMkPS5paOVhL0s61913JCTINGBm3SR1cfd3zayzIn8X+klqF62cv+PxU19duHuJmW11904JDjFtHOB9kS3pTUnHSiqX9D9JN7j7vIQFCyQZWrwT6yuSXuaPdfNy99mSdtcpHiLpfXdf7+67Jf1L0jnNHlyaiVYX7h5y99LKzdaSgpL41jzOotWFmR0rqdzdX688ZjtJd/zV8zvqc5LecPe97r5X0ruSvtjcsaUTd9/o7u9W/vyppO2SOtRXnqg40wGvefI4SF1kKPJ3O7Py8WkiYgSSFYn3ITKzkWb2vJltMDOP1k3czK42s9VmttfMFpnZiHou91VJT8U14BYmxq9/Td0lra+xvU7SETEKu0WKY13IzA4zs8WK1MO97r41xuG3KHGsi76Sis3sH2b2tpndFPPgW5g41sV7kk6vfG8cJukM8TvqgGJZF2Y2SFLA3T9pSDlqi3NdtKs8fo6ZjYrj02gR4lUX7r5F0i8lrZW0QdJ/3f3jeD4XINWQeB+6HEmLJU2KttPMLpZ0v6SfSTpJ0uuSZplZzzrHtZM0TJGWVTRcTF7/aKdGKWMcxoHFqy7k7jvc/QRJBZK+YWZdYhV0CxWvusiUNELSREmnSjrLzM6KUcwtVVzqwt2XSfq1pFckPStpgSR6HxxYrP5ed5T0hKQrGlKOqOJZF/nuPlDSlZKeqPx8hfrFpS7MLE/SeZLyFflScKiZjYzLMwBSlbvzaORDkcRsdJ2ytyT9tk7ZB5LurlP2bUl/SPRzSOVHE1//0yT9vcb2UEnP1th+QNI3Ev0cU+URy7qIcu3fSroo0c8xVR4xfl+cKunfNbZvlHRjop9jqjzi/L54RJEx3gl/nqnwaGxdSMpSZKzqt+scF7WcR/PXRZ1zZkkalOjnmCqPWNaFpIskTauxfaOkHyb6OfLgkUwPWrxjyMxaSRoo6aU6u17SZxPiVKGbeYwd4utf13xJx5nZEWbWVtKXJb0Y+yjTQ1Pqwsy6VLVYVP47UtLyeMSZDpr4vlggqYuZ5ZlZQJG6+CD2UaaHJtaFLDKRkcysnyLzUvA7qpEaUhdmZpJmSHrF3Z+scW7UcjROE+siz8yyKn/uIam/pFXNEHaL1JS6kPSJIq3crS0ySepp4m83UEtGogNoYTopMhHU5jrlmyV1rdows/aKfGga23yhpYWGvv4vSjpZUo6ZrZM0xt0XmNn1kmYrMgTjXmcW7aZodF1ICkt6tPKPu0ma6u5LmiXqlqmp74ubFGnZMEkvufsLzRN2i9SkupD0XOX47hJJlzgT3TVFQ+pimKSLJS2pMQ7225LaRyt396XxDLgFa0pdtJX0kJmFFWm9/YGzGklTNLou3P1NM/uXpHcU+Tv+sqR/xD1iIIWQeMdH3bHBVrPM3XdKYsxq/Bzs9Y86W7m7/0P8kYi1RtWFpBPjFVAaa+z7YpYi3TcRO42ti4O2iuOQ1VsX7j5H9c+FQ4/B2GtsXQyIZ1BpqlF14e4/lvTj+IYGpC7+cMTWVkkh1Wi5qNRZ+397iNjj9U8e1EXyoC6SB3WRPKiL5EFdJA/qAogjEu8Ycvd9khZJqjvr71mS3mj+iNILr3/yoC6SB3WRPKiL5EFdJA/qInlQF0B80dX8EJlZrqQ+NYoKzOxESdvdfa2k+yQ9aWYLJc1TZJmFnpIebO5YWyJe/+RBXSQP6iJ5UBfJg7pIHtRF8qAugARK9LTqqfZQZJZGj/KYUeOYqyUVSipT5JvDkYmOu6U8eP2T50FdJM+DukieB3WRPA/qInke1EXyPKgLHjwS9zD3uvMnAAAAAACAWGGMNwAAAAAAcUTiDQAAAABAHJF4AwAAAAAQRyTeAAAAAADEEYk3AAAAAABxROINAAAAAEAckXgDAAAAABBHJN4AAAAAAMQRiTcAAAAAAHFE4g0AAAAAQByReAMAAAAAEEck3gCAlGFmHc3sUzPLj/F1/25m18XymgAAAFVIvAEAqWSypOfdvbCqwMxmmNlzdQ80s9PMzM3ssAZc9w5JPzazdrEKFAAAoAqJNwAgJZhZG0nflfRIrK/t7kskFUr6ZqyvDQAAQOINAGgWZjbLzB6vsX2GmW0zs4wGXuJLkircfV4j7p1f2fpd9/FqjcP+Ienrh3ptAACAgyHxBgA0l/WSjqixPVtSlqRhDTx/pKSFjbz3J5K61XicJGmbpP/VOGa+pCFmltXIewAAAETV0FYGAACaar2k4VUb7u5mtlfS4Q08P1/Shnr2nWdmxXXKgjXuFZK0SZLMrLWk5yTNk3R7nfiyJHWVtKaBMQEAABwUiTcAoLnUavE2sxMl5Ul6o4Hnt5G0t559syVdVafsFEl/iHLso5LaSjrL3cM1yvdU/pvdwHgAAAAahMQbANBc1kvKrZw5vFjSFEl/dPf6WrHr2qpIoh5NibuvrFlgZj3qHmRmN0v6oqQh7r67zu4Olf9uaWA8AAAADULiDQBoLusr/+0h6RJFxlqffwjnvyPpW429uZmNlXSrpC+5+8dRDjlO0jp339rYewAAAETD5GoAgOZSlXj/StJYSV9w912HcP6Lko41s/pavetlZsdJekLSzyW9b2ZdKx8dahw2QtJLh3ptAACAgyHxBgA0l62SyiT1kjTS3dcf5Pha3H2pIrOaf7UR9x6kyNjtmyVtrPF4RqqecG2MpN814toAAAAHZO6e6BgAAGgQM/uypF9KOq7OxGhNve5ESee7+9mxuiYAAEAVxngDAFKGu//LzPoqMjv6JzG8dLmk78XwegAAANVo8QYAAAAAII4Y4w0AAAAAQByReAMAAAAAEEck3gAAAAAAxBGJNwAAAAAAcUTiDQAAAABAHJF4AwAAAAAQRyTeAAAAAADEEYk3AAAAAABxROINAAAAAEAckXgDAAAAABBHJN4AAAAAAMQRiTcAAAAAAHFE4g0AAAAAQByReAMAAAAAEEck3gAAAAAAxBGJNwAAAAAAcUTiHYWZTTazBWa228w+NbPnzKxfouMCAAAAAKQeEu/oRkmaJunzks6SlCHpJTPLSWhUAAAAAICUY+6e6BiSnpkdLulTSaPc/X+JjgcAAAAAkDoyEh1Aimhf+e/2+g4wsyxJWXWKOxzoHAAAACDJtZW0wWmtA5qEFu+DMDOTNFNSnruPOMBxt///9u49xtK7vA/497HXu/baXoNxuBjfUElpjaOYsCEKBBtSYRIHcREmqDTEoFBanKRNwVQy0JggRbQWITcTUgVkLqmUuIirhcEtWXMRBnMHA6IF7OAL4BgXr2+7vuzTP87scnaYmZ2ZnXfOOTufj/TTzPmd57zvc84z5z3zzHuZJBevV14AALBOTurumyedBMwyjfcBVNVbkvxGkl/p7puWiJu/x/vYJDfdeOON2bZt28BZAgDA2tq5c2dOPvnkJDmuu3dOOh+YZQ41X0JV/WWSZyc5a6mmO0m6e3eS3WOPTZJs27ZN4w0AALCBabwXMHd4+V8meV6Sp3X39RNOCQAAgBml8V7YW5K8KMlzktxZVY+cm7+ju++dXFoAAADMGv/He2GvyOhK5lcn+f7YeOEEcwIAAGAG2eO9gO6uSecAAADAocEebwAAABiQxhsAAAAGpPEGAACAAWm8AQAAYEAabwAAABiQxhsAAAAGpPEGAACAAWm8AQAAYEAabwAAABiQxhsAAAAGpPEGAACAAWm8AQAAYEAabwAAABiQxhsAAAAGpPEGAACAAWm8AQAAYEAabwAAABiQxhsAAAAGpPEGAACAAWm8F1FVZ1XVh6rqlqrqqnrupHMCAABg9mi8F3d0kq8k+b1JJwIAAMDs2jTpBKZVd1+Z5MokqaoJZwMAAMCs0nivkarakmTL2NSxk8oFAACA6eFQ87VzUZI7xsZNk00HAACAaaDxXjtvTHLc2DhpsukAAAAwDRxqvka6e3eS3XtvOy8cAACAxB5vAAAAGJQ93ouoqmOSPHZs6jFVdWaS27v7e5PJCgAAgFmj8V7c9iQ7xm6/ee7rO5O8ZN2zAQAAYCZpvBfR3VcncaI2AAAAB8U53gAAADAgjTcAAAAMSOMNAAAAA9J4AwAAwIA03gAAADAgjTcAAAAMSOMNAAAAA9J4AwAAwIA03gAAADAgjTcAAAAMSOMNAAAAA9J4AwAAwIA03gAAADAgjTcAAAAMSOMNAAAAA9J4AwAAwICmvvGuqqOqauvY7VOr6g+q6pxJ5gUAAADLMfWNd5IPJPntJKmqhyT5bJJXJflAVb1ignkBAADAAc1C4/0LST459/15SX6Y5NSMmvH/MKmkAAAAYDlmofHemuTOue/PSfLe7t6T5DMZNeCDqaoLqur6qtpVVV+oqqcOuT4AAAAOPbPQeH87yXOr6uQkz0xy1dz8w5PsHGqlVfXCJH+W5I+TPCGjve5XVtUpQ60TAACAQ88sNN5vSPKmJDckuba7r5mbPyfJlwZc7yuTvL2739bd3+zuP0hyYxLnlQMAALBsmyadwIF093uq6lNJHpXkK2N3fSzJ+4ZYZ1VtTvLEJP913l1XJXnySpZ199135/DDD/+p+cMPPzxHHnnkfnGLOeyww3LUUUetKvaee+5Jdy8YW1XZunXrqmLvvffe7NmzZ9E8jj766FXF7tq1Kw8++OCaxG7dujVVlSTZvXt3HnjggTWJPeqoo3LYYaO/Wd133325//771yT2yCOP3PezspLY+++/P/fdd9+isVu2bMmmTZtWHPvAAw9k9+7di8Zu3rw5RxxxxIpjH3zwwezatWvR2COOOCKbN29eceyePXty7733rknspk2bsmXLliRJd+eee+5Zk9iVvO9tIxaOtY2wjbCNWHmsbcTqYm0jlv5ZAVaou2diJHlsRoeaHzV3uwZc14lJOsmT582/Jsm3FnnMliTbxsaj55ax4Dj33HN73NatWxeNPfvss/eLPeGEExaN3b59+36xp5566qKxp59++n6xp59++qKxp5566n6x27dvXzT2hBNO2C/27LPPXjR269at+8Wee+65i8aOflx/4rzzzlsy9q677toXe/755y8Ze+utt+6LveCCC5aMvf766/fFXnjhhUvGXnfddftiL7744iVjr7322n2xl1xyyZKxO3bs2Bd76aWXLhl7xRVX7Iu97LLLloy9/PLL98VefvnlS8Zedtll+2KvuOKKJWMvvfTSfbE7duxYMvaSSy7ZF3vttdcuGXvxxRfvi73uuuuWjL3wwgv3xV5//fVLxl5wwQX7Ym+99dYlY88///x9sXfdddeSseedd95+P8NLxdpGjIZtxE+GbcRo2EaMhm3EaNhG/GQMtY2YG9t6CvoBw5jlMfWHmlfVw6rqY0n+T5IPZ7TnO0neVlV/MvDqe346C8ztdVGSO8bGTQPmBQAAwIyo7sX6yOlQVe/K6EJqL0vyzSQ/393frapzkvxpdz9+gHVuTnJPkhd09/vG5v88yZndffYCj9mS0V7vvY5NctMtt9ySbdu2/dQ6HCK2cKxDxBxG6jDSlcfaRqwu1jZixDZi5bG2ESO2EauLnaVtxM6dO3PiiScmyXHdPdhFjWEjmIXG+wdJntndX6mqO/OTxvsxSb7W3ccMtN7PJvlCd18wNveNJB/o7ouW8fhtSe644447Fmy8AQBgmu3cuTPHHXdcovGGgzb1F1dLcnRGe5/nOyHJ4n8+P3hvTvLuqvp8kmuSvDzJKUn+esB1AgAAcIiZhcb7E0l+O8l/mbvdVXVYklcn2THUSrv776vqYUn+MKPzyq9Lcm53/+NQ6wQAAODQMwuN94VJPl5V25NsTnJJkscnOT7JU4ZccXf/VZK/GnIdAAAAHNqm+qrmVXVERo3vs5Ncm+R/ZXTo+XuTPKG7vzPB9AAAAOCApnqPd3ffX1VnJPlRd1886XwAAABgpaZ6j/ecdyX5nUknAQAAAKsx1Xu852xO8rKqekaSzyfZ759PdvcrJ5IVAAAALMMsNN5nJPni3Pf/fN590/1PyAEAANjwpr7x7u6nTzoHAAAAWK1ZOMcbAAAAZpbGGwAAAAak8QYAAIABabwBAABgQBpvAAAAGJDGGwAAAAak8QYAAIABabwBAABgQBpvAAAAGJDGGwAAAAak8QYAAIABabwBAABgQBpvAAAAGJDGewFV9dqq+nRV3VNVP550PgAAAMwujffCNif5n0neOulEAAAAmG2bJp3ANOrui5Okql4y4VQAAACYcfZ4AwAAwIDs8V4jVbUlyZaxqWMnlQsAAADTY8Ps8a6q11dVH2BsP4hVXJTkjrFx05okDgAAwEzbSHu8L03ydweIueEglv/GJG8eu31sNN8AAAAb3oZpvLv7tiS3Dbj83Ul2771dVUOtCgAAgBmyYRrvlaiqU5Icn+SUJIdX1Zlzd327u++aWGIAAADMHI33wt6Q5Pyx21+a+/r0JFevezYAAADMrA1zcbWV6O6XdHctMK6edG4AAADMFo03AAAADEjjDQAAAAPSeAMAAMCANN4AAAAwII03AAAADEjjDQAAAAPSeAMAAMCANN4AAAAwII03AAAADEjjDQAAAAPSeAMAAMCANN4AAAAwII03AAAADEjjDQAAAAPSeAMAAMCANN4AAAAwII03AAAADEjjDQAAAAPSeAMAAMCANN4AAAAwII33PFV1WlW9vaqur6p7q+o7VfVHVbV50rkBAAAwezZNOoEp9C8y+oPEv0vy7SRnJPmbJEcnuXCCeQEAADCDNN7zdPdHknxkbOq7VfW4JK+IxhsAAIAV0ngvz3FJbl8qoKq2JNkyNnVskuzcuXPAtAAAYBh+j4W1o/E+gKr6Z0l+P8mrDhB6UZKL50+efPLJQ6QFAADr5fgkunA4CNXdk85hXVTV67NAYzzPL3b358cec2KSjyf5eHe/7ADLX2iP901JTkpy52pyZs2oxfRQi+mhFtNDLaaHWkwPtZgOe+twXHdrvOEgbKQ93pcm+bsDxNyw95u5pntHkmuSvPxAC+/u3Ul2jz1+77d32lBNllpMD7WYHmoxPdRieqjF9FCL6TBWB+AgbZjGu7tvS3LbcmKr6tEZNd1fSPLS7t4zZG4AAAAcujZM471cc3u6r07yvYyuYv4ze//a190/mFxmAAAAzCKN9087J8lj58ZN8+5byfE2u5P8UcYOP2di1GJ6qMX0UIvpoRbTQy2mh1pMB3WANbJhLq4GAAAAk3DYpBMAAACAQ5nGGwAAAAak8QYAAIABabwBAABgQBrvdVZVj6uqL4+Ne6vquZPOayOpqvdV1f+rqvfMm39WVX2rqv5vVb1sUvltJAvVoqqOrarPzb0/vlZV/3aSOW4US7wvHlNVO6rqG3P1OHpSOW4US9Tiwqr6elVdV1W/Nan8NoqqOrmqrp772f9qVb1gqXmGs9RrXlUPjP1O9bZJ5rkRHKAW/2luG/WNqvqL2vv/eIEkrmo+UVV1TJIbkpza3XdPOJ0No6qenuSYJOd393lzc5uSfCPJ05PsTPLFJL/U3bdPLNENYJFaHJ5kS3ffU1Vbk1yX5Be7+0cTTPWQt1At5uY/nuR13f3Jqjo+yc7ufmBSeW4Ei7wvfi7JO5M8eS7sY0l+o7t/PJEkN4CqelSSR3T3l6vq4Rl9LjwuybaF5n2OD2exWnT33VV1W3efMOEUN4wl3hdbk3wmyeOT3J/kE0ku7O5rJpYsTBl7vCfr2Uk+5sN6fXX3jiR3zpt+UpKvd/fN3X1nkg8neea6J7fBLFSL7n6wu++Zu3lkksOT+Kv5wBaqRVU9Psn93f3JuZjbNd3DW2Qb9S+TfLq7d3X3riRfTvJr653bRtLd3+/uL899f2uS25Mcv9j8pPLcCLzm0+MAtdiU0ef2EXPj1knkCNNK471CVXVWVX2oqm6pql7oMPGquqCqrq+qXVX1hap66iKL+80kfz9owoeYNX79x52Y5Oax2zclefQapX1IGrAWqaqHVNVXMqrDJd192xqnf0gZsBY/m+SuqvpgVX2xql6z5skfYgasxXVJnj733nhIkl+NbdSS1rIWVbU9yWHdfeNy5tnfwLXYNhf/qao6e8CncUgYqhbd/U9J3pTke0luSfK/u/s7Qz4XmDUa75U7OslXkvzeQndW1QuT/FmSP07yhCSfTHJlVZ0yL25bkqdktGeV5VuT13+hhy4w5zyMpQ1Vi3T3j7v755M8JsmLquoRa5X0IWqoWhyR5KlJfjfJLyd5RlU9Y41yPlQNUovu/kaSv0jyD0nel+RzSRx9sLS1+rx+WJJ3JXn5cuZZ0JC1OK27n5jk3yd519zvVyxukFpU1UOTPCvJaRn9UfDJVXXWIM8AZlV3G6scGTVmz50399kkb503980kb5w39+Ikfzvp5zDL4yBf/6clec/Y7Scned/Y7T9P8qJJP8dZGWtZiwWW/dYkL5j0c5yVscbvi19O8pGx269O8upJP8dZGQO/L96W0TneE3+eszBWW4skWzI6V/XF8+IWnDfWvxbzHnNlku2Tfo6zMtayFklekOQtY7dfneQ/T/o5GsY0DXu811BVbU7yxCRXzbvrqvzkgjh7Ocx8ja3w9Z/v2iRnVNWjq+rYJOcm+ejaZ7kxHEwtquoRe/dYzH09K8m3hshzIzjI98Xnkjyiqh5aVYdlVItvrn2WG8NB1iI1upBRqupxGV2XwjZqlZZTi6qqJO9I8g/d/e6xxy44z+ocZC0eWlVb5r4/KcnpSb67Dmkfkg6mFkluzGgv95E1ukjq0+KzG/azadIJHGJOyOhCUD+cN//DJI/ce6Oqjsvol6bnr19qG8JyX/+PJvmFJEdX1U1Jntfdn6uqVyXZkdEpGJe0q2gfjFXXIsmeJG+f+3CvJJd291fXJetD08G+L16T0Z6NSnJVd1+xPmkfkg6qFkneP3d+991JXtoudHcwllOLpyR5YZKvjp0H++Ikxy00391fGzLhQ9jB1OLYJP+9qvZktPf2P7b/RnIwVl2L7v5MVX04yZcy+hz/WJIPDp4xzBCN9zDmnxtc43PdfUcS56wO50Cv/4JXK+/uD8aHxFpbVS2SnDlUQhvYat8XV2Z0+CZrZ7W1OOBecVZs0Vp096ey+LVwHDG49lZbi58bMqkNalW16O7XJnntsKnB7PLBsbZuS/JgxvZczHl4fvqvh6w9r//0UIvpoRbTQy2mh1pMD7WYHmoBA9J4r6Huvi/JF5LMv+rvM5J8ev0z2li8/tNDLaaHWkwPtZgeajE91GJ6qAUMy6HmK1RVxyR57NjUY6rqzCS3d/f3krw5ybur6vNJrsno3yyckuSv1zvXQ5HXf3qoxfRQi+mhFtNDLaaHWkwPtYAJmvRl1WdtZHSVxl5gvGMs5oIkNyTZndFfDs+adN6HyvD6T89Qi+kZajE9Qy2mZ6jF9Ay1mJ6hFoYxuVHd86+fAAAAAKwV53gDAADAgDTeAAAAMCCNNwAAAAxI4w0AAAAD0ngDAADAgDTeAAAAMCCNNwAAAAxI4w0AAAAD0ngDAADAgDTeAAAAMCCNNwAAAAxI4w3AzKiqh1XVrVV12hov9z1V9cq1XCYAwF4abwBmyUVJPtTdN+ydqKp3VNX75wdW1dOqqqvqIctY7huSvLaqtq1VogAAe2m8AZgJVXVUkt9J8ra1XnZ3fzXJDUn+zVovGwBA4w3AuqiqK6vqnWO3f7WqflRVm5a5iF9P8kB3X7OKdZ82t/d7/rh6LOyDSf71SpcNAHAgGm8A1svNSR49dntHki1JnrLMx5+V5POrXPeNSR41Np6Q5EdJPjEWc22SJ1XVllWuAwBgQcvdywAAB+vmJL+y90Z3d1XtSvIzy3z8aUluWeS+Z1XVXfPmDh9b14NJfpAkVXVkkvcnuSbJ6+fltyXJI5P84zJzAgA4II03AOtlvz3eVXVmkocm+fQyH39Ukl2L3LcjySvmzf1Skr9dIPbtSY5N8ozu3jM2f+/c163LzAcAYFk03gCsl5uTHDN35fC7kvxpkv/R3YvtxZ7vtowa9YXc3d3fHp+oqpPmB1XV65L8WpIndfed8+4+fu7rPy0zHwCAZdF4A7Bebp77elKSl2Z0rvVzVvD4LyX5rdWuvKqen+QPk/x6d39ngZAzktzU3betdh0AAAtxcTUA1svexvtPkjw/yb/q7p0rePxHkzy+qhbb672oqjojybuS/LckX6+qR86N48fCnprkqpUuGwDgQDTeAKyX25LsTnJqkrO6++YDxO+nu7+W0VXNf3MV696e0bnbr0vy/bHx3mTfBdeel+RvVrFsAIAlVXdPOgcAWJaqOjfJm5KcMe/CaAe73N9N8pzuPmetlgkAsJdzvAGYGd394ar62Yyujn7jGi76/iS/v4bLAwDYxx5vAAAAGJBzvAEAAGBAGm8AAAAYkMYbAAAABqTxBgAAgAFpvAEAAGBAGm8AAAAYkMYbAAAABqTxBgAAgAFpvAEAAGBAGm8AAAAY0P8HweukT/1Oj9YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "myPlot=sed_data.plot_sed()\n",
    "sed_data.group_data(bin_width=0.2)\n",
    "myPlot.add_data_plot(sed_data,label='rebinned')\n",
    "myPlot.setlim(x_min=1E7,x_max=1E28,y_min=1E-15,y_max=1E-9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Handling errors and systematics\n",
    "\n",
    "Another important issue when dealing with fitting of data, is the proper handling of errors.\n",
    "Typically one might need to add systematics for different reasons:\n",
    " \n",
    " - data are not really simultaneous, and you want to add systematics to take this into account\n",
    " - data (typically IR up to UV), might have very small errors compared to those at higher energies.\n",
    "   This might bias the minimizer to accommodate the parameters  in order to fit 'better' the low\n",
    "   frequencies branch.\n",
    "  \n",
    "For these reasons the package offer the possibility to add systematics \n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sed_data.add_systematics(0.2,[10.**6,10.**29])\n",
    "myPlot=sed_data.plot_sed()\n",
    "myPlot.setlim(x_min=1E7,x_max=1E28,y_min=1E-15,y_max=1E-9)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "with this command we add 20% systematics for data between :math:`10^{6}<\\nu<10^{29}` Hz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering data sets \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we use the `show_data_sets()`  method to have know wich data sets are defined in our table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current datasets\n",
      "dataset 0.0\n"
     ]
    }
   ],
   "source": [
    "sed_data.show_data_sets()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we use `show_dataset=True` to have the legend of all the datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=Data.from_file(test_SEDs[0])\n",
    "sed_data=ObsData(data_table=data)\n",
    "%matplotlib inline\n",
    "p=sed_data.plot_sed(show_dataset=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current datasets\n",
      "dataset -1\n",
      "dataset 0\n",
      "dataset 1\n",
      "dataset 2\n"
     ]
    }
   ],
   "source": [
    "sed_data.show_data_sets()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we filter out the data set `-1` using the `filter_data_set()` method. Please not with `exclude=True` we exclude dataset in `filters`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> excluding  data_set/s ['-1']\n",
      "filter -1 192\n",
      "current datasets\n",
      "dataset 0\n",
      "dataset 1\n",
      "dataset 2\n",
      "---> data sets left after filtering None\n",
      "---> data len after filtering=192\n",
      "current datasets\n",
      "dataset 0\n",
      "dataset 1\n",
      "dataset 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sed_data.filter_data_set(filters='-1',exclude=True)\n",
    "sed_data.show_data_sets()\n",
    "p=sed_data.plot_sed(show_dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can pass more datasets, comma separated "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> excluding  data_set/s ['-1', '0']\n",
      "filter -1 192\n",
      "filter 0 57\n",
      "current datasets\n",
      "dataset 1\n",
      "dataset 2\n",
      "---> data sets left after filtering None\n",
      "---> data len after filtering=57\n",
      "current datasets\n",
      "dataset 1\n",
      "dataset 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sed_data.filter_data_set(filters='-1,0',exclude=True)\n",
    "sed_data.show_data_sets()\n",
    "p=sed_data.plot_sed(show_dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can also use `filter_data_set` to exclude *only* the datasets in `filters` with `exclude=False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> excluding  data_set/s ['-1']\n",
      "filter -1 192\n",
      "current datasets\n",
      "dataset 0\n",
      "dataset 1\n",
      "dataset 2\n",
      "---> data sets left after filtering None\n",
      "---> data len after filtering=192\n",
      "current datasets\n",
      "dataset 0\n",
      "dataset 1\n",
      "dataset 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sed_data.filter_data_set(filters='-1',exclude=True)\n",
    "sed_data.show_data_sets()\n",
    "p=sed_data.plot_sed(show_dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can revert `sed_data` to the original state with the `reset_data()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "sed_data.reset_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current datasets\n",
      "dataset -1\n",
      "dataset 0\n",
      "dataset 1\n",
      "dataset 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sed_data.show_data_sets()\n",
    "p=sed_data.plot_sed(show_dataset=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving sed_data and loading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can save and relaod you sed_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "sed_data.save('3C454_data.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "sed_data=ObsData.load('3C454_data.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p=sed_data.plot_sed(show_dataset=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernel_info": {
   "name": "python3"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  },
  "nteract": {
   "version": "0.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}